diff --git "a/1347.jsonl" "b/1347.jsonl" new file mode 100644--- /dev/null +++ "b/1347.jsonl" @@ -0,0 +1,461 @@ +{"seq_id": "642371914", "text": "#!/usr/bin/env python\n'''\nCreated on Jun 8, 2013\n\n@author: mmartin\n'''\n\nfrom gi.repository import Gtk\nfrom CDMIConstants.constants import(\n ABOUT_DIALOG,\n ABOUT_UI\n)\n\n\nclass CDMIAbout(object):\n\n def __init__(self, session):\n '''\n Display the About dialog\n '''\n self.session = session\n builder = Gtk.Builder()\n builder.add_from_file(ABOUT_UI % self.session.ppath)\n about = builder.get_object(ABOUT_DIALOG)\n about.run()\n about.hide()\n", "sub_path": "scripts/cdmi_explorer/CDMIAbout/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "gi.repository.Gtk.Builder", "line_number": 22, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 22, "usage_type": "name"}, {"api_name": "CDMIConstants.constants.ABOUT_UI", "line_number": 23, "usage_type": "name"}, {"api_name": "CDMIConstants.constants.ABOUT_DIALOG", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "320057176", "text": "\n# coding: utf-8\n\n# In[1]:\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pickle\n\n\n# In[2]:\n\nclass NeuralNetwork(object):\n def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n # Set number of nodes in input, hidden and output layers.\n self.input_nodes = input_nodes\n self.hidden_nodes = hidden_nodes\n self.output_nodes = output_nodes\n\n # Initialize weights\n self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, \n (self.input_nodes, self.hidden_nodes))\n\n self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, \n (self.hidden_nodes, self.output_nodes))\n self.lr = learning_rate\n \n self.activation_function = lambda x : 1/(1 + np.exp(-x)) # Replace 0 with your sigmoid calculation.\n \n \n \n def train(self, features, targets):\n\n n_records = features.shape[0]\n delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n for X, y in zip(features, targets):\n hidden_inputs = np.dot(X,self.weights_input_to_hidden) # signals into hidden layer\n hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n \n final_inputs = np.dot(hidden_outputs,self.weights_hidden_to_output) # signals into final output layer\n final_outputs = final_inputs # signals from final output layer'this\n \n #### Implement the backward pass here ####\n ### Backward pass ###\n\n error = y - final_outputs # Output layer error is the difference between desired target and actual output.\n\n \n output_error_term = error * 1\n\n hidden_error = np.dot(self.weights_hidden_to_output, error)\n hidden_error_term = hidden_error * hidden_outputs * (1- hidden_outputs)\n\n delta_weights_i_h += hidden_error_term * X[:,None]\n \n # Weight step (hidden to output)\n hidden_outputs = hidden_outputs[:,None]\n delta_weights_h_o += output_error_term * hidden_outputs\n #print('delta hidden to out: ' + str(delta_weights_h_o))\n self.weights_hidden_to_output += self.lr * delta_weights_h_o/n_records # update hidden-to-output weights with gradient descent step\n self.weights_input_to_hidden += self.lr * delta_weights_i_h/n_records # update input-to-hidden weights with gradient descent step\n \n def run(self, features):\n\n hidden_inputs = np.dot(features,self.weights_input_to_hidden) # signals into hidden layer\n hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n \n final_inputs = np.dot(hidden_outputs,self.weights_hidden_to_output) # signals into final output layer\n final_outputs = (final_inputs) # signals from final output layer \n \n return final_outputs\n\n\n# In[3]:\n\nlearning_rate = 0.00\nhidden_nodes = 3200\noutput_nodes = 1\n\nN_i = 6\nnetwork = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n\n\n# In[4]:\n\nimport json\n\nweights_in = []\nwith open('weight_in_no_grades', 'rb') as f:\n weights_in = pickle.load(f)\n \nweights_out = []\nwith open('weight_out_no_grades', 'rb') as f:\n weights_out = pickle.load(f)\nscaled_features = {} \nwith open('variables.json', 'r') as f:\n try:\n scaled_features = json.load(f)\n # if the file is empty the ValueError will be thrown\n except ValueError:\n scaled_features = {}\n \nnetwork.weights_input_to_hidden = weights_in\nnetwork.weights_hidden_to_output = weights_out\n\n\n# In[5]:\n\n\n\nbasisweight = float(input('Enter a basisweight: '))\ncaliper = float(input('Enter a caliper: '))\ncull = float(input('Enter a cull low: '))\nmoisture = float(input('Enter a moisture: '))\nstfi = float(input('Enter a stfi: '))\ntsi = float(input('Enter a tsi: '))\nbasismean, basisstd = scaled_features['basisweight']\ncalipermean, caliperstd = scaled_features['caliper']\ncullmean, cullstd = scaled_features['cull']\nmoisturemean, moisturestd = scaled_features['moisture']\nstfimean, stfistd = scaled_features['stfi']\ntsimean, tsistd = scaled_features['tsi']\ninbasis = (basisweight - basismean)/basisstd\nincaliper = (caliper - calipermean)/caliperstd\nincull = (cull - cullmean)/cullstd\ninmoisture = (moisture - moisturemean)/moisturestd\ninstfi = (stfi - stfimean)/stfistd\nintsi = (tsi - tsimean)/tsistd\nrow = [intsi, instfi, incaliper, inmoisture, inbasis, incull]\ncolumns = ['tsi','stfi', 'caliper', 'moisture', 'basisweight', 'cull']\ndf = pd.DataFrame(columns=columns)\n'''\ndf['tsi'] = intsi\ndf['stfi'] = instfi\ndf['caliper'] = incaliper\ndf['moisture'] = inmoisture\ndf['basisweight'] = inbasis\ndf['cull'] = incull '''\ndf.loc[1] = row\nmean, std = scaled_features['rct']\nprediction = network.run(df.loc[1]).T*std+mean\nprint('Predicted Rct :' + str(prediction))\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n", "sub_path": "T1+Predictor.py", "file_name": "T1+Predictor.py", "file_ext": "py", "file_size_in_byte": 5109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.random.normal", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 70, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 92, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 96, "usage_type": "call"}, {"api_name": "json.load", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "192601663", "text": "# Copyright (c) 2020 - present Vitor Oriel \n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom .BaseReport import BaseReport\nfrom .reports import *\nfrom ..utils.utils import stringfyList\nfrom ..utils.file_utils import getReports\n\nfrom importlib import import_module\nfrom typing import Dict, Type\n\nclass Report:\n \"\"\"Class that handles with the report operations\"\"\"\n @staticmethod\n def getAvailableReports() -> Dict[str, Type[BaseReport]]:\n \"\"\"Gets the available report formats\n\n @returns Dict[str, Type[BaseReport]]: The dict that contains the available reports\n \"\"\"\n def classCreator(name: str) -> Type[BaseReport]:\n \"\"\"Creates the class type\n\n @type name: str\n @param name: The class name\n @returns Type[BaseReport]: The base report type\n \"\"\"\n Report = import_module(\n f\"fuzzingtool.reports.reports.{name}\",\n package=name\n )\n return getattr(Report, name)\n \n availableReports = {}\n for report in getReports():\n Report = classCreator(report)\n availableReports[Report.__alias__] = Report\n return availableReports\n\n @staticmethod\n def build(name: str) -> BaseReport:\n \"\"\"Build the report\n\n @type name: str\n @param name: The name of the report file\n @returns BaseReport: The report object\n \"\"\"\n if '.' in name:\n reportName, reportType = name.rsplit('.', 1)\n else:\n reportType = name\n reportName = ''\n reportType = reportType.lower()\n availableReports = Report.getAvailableReports()\n try:\n return availableReports[reportType](reportName)\n except:\n raise Exception(f\"Unsupported report format for {reportType}! Accepts: \"+\n stringfyList(list(availableReports.keys())))", "sub_path": "src/fuzzingtool/reports/Report.py", "file_name": "Report.py", "file_ext": "py", "file_size_in_byte": 2963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "importlib.import_module", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.Type", "line_number": 37, "usage_type": "name"}, {"api_name": "BaseReport.BaseReport", "line_number": 37, "usage_type": "name"}, {"api_name": "utils.file_utils.getReports", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 32, "usage_type": "name"}, {"api_name": "BaseReport.BaseReport", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.utils.stringfyList", "line_number": 75, "usage_type": "call"}, {"api_name": "BaseReport.BaseReport", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "623275931", "text": "# vim: set fileencoding=utf-8 :\nfrom nose.tools import *\nimport unittest\nimport os\nimport sys\nfrom datetime import datetime\n\nfrom helpers import create_osm_file, osmobj, HandlerTestBase, check_repr\n\nimport osmium as o\n\nclass TestLength(HandlerTestBase, unittest.TestCase):\n data = \"\"\"\\\n r2 Mn3@\n r4\n r45 Mw1@fo,r45@4,r45@5\n \"\"\"\n\n class Handler(o.SimpleHandler):\n expected_length = { 2 : 1, 4 : 0, 45 : 3 }\n\n def relation(self, r):\n assert_equals(self.expected_length[r.id], len(r.members))\n\nclass TestMembers(HandlerTestBase, unittest.TestCase):\n data = u\"\"\"r34 Mn23@,n12@foo,w5@.,r34359737784@(ü)\"\"\"\n\n class Handler(o.SimpleHandler):\n\n def relation(self, r):\n m = list(r.members)\n eq_(4, len(m))\n eq_(23, m[0].ref)\n eq_('n', m[0].type)\n eq_('', m[0].role)\n eq_(12, m[1].ref)\n eq_('n', m[1].type)\n eq_('foo', m[1].role)\n eq_(5, m[2].ref)\n eq_('w', m[2].type)\n eq_('.', m[2].role)\n eq_(34359737784, m[3].ref)\n eq_('r', m[3].type)\n eq_(u'(ü)', m[3].role)\n assert_true(check_repr(m))\n", "sub_path": "test/test_memberlist.py", "file_name": "test_memberlist.py", "file_ext": "py", "file_size_in_byte": 1232, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "helpers.HandlerTestBase", "line_number": 12, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "osmium.SimpleHandler", "line_number": 19, "usage_type": "attribute"}, {"api_name": "helpers.HandlerTestBase", "line_number": 25, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 25, "usage_type": "attribute"}, {"api_name": "osmium.SimpleHandler", "line_number": 28, "usage_type": "attribute"}, {"api_name": "helpers.check_repr", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "634695682", "text": "import tensorflow as tf\nimport itertools\nimport os\nimport sklearn as sk\nimport pandas as pd\n\nBOT = 1\nHUMAN = 0\nclass DNNReg:\n\n train_features = None\n label = \"Class\"\n def run(self, train,typ):\n\n self.train_features = list(train)\n feature_cols = []\n for k in self.train_features:\n if k[0] == 'B':\n feature_cols.append(\n tf.feature_column.indicator_column(tf.feature_column.categorical_column_with_identity(key=k, num_buckets=2)))\n else:\n feature_cols.append(tf.feature_column.numeric_column(key=k))\n direc = os.path.dirname(os.path.abspath(__file__))\n regressor = tf.estimator.DNNClassifier(feature_columns=feature_cols,activation_fn=tf.nn.relu, hidden_units=[10, 5],model_dir=direc+'\\\\models\\\\' + typ, n_classes=2)\n y = regressor.predict(input_fn=lambda: self.input_fn(train,pred=True))\n predictions = list(itertools.islice(y, train.shape[0]))\n return predictions[0]['probabilities'][BOT]\n def input_fn(self, data_set, pred=False):\n\n if pred == False:\n feature_cols = {k: tf.constant(data_set[k].values) for k in self.train_features}\n labels = tf.constant(data_set[self.label].values)\n\n return feature_cols, labels\n\n if pred == True:\n feature_cols = {k: tf.constant(data_set[k].values) for k in self.train_features}\n\n return feature_cols", "sub_path": "PYTHON/DNNReg.py", "file_name": "DNNReg.py", "file_ext": "py", "file_size_in_byte": 1445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tensorflow.feature_column.indicator_column", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.feature_column.categorical_column_with_identity", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.feature_column.numeric_column", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.estimator.DNNClassifier", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "itertools.islice", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "198176396", "text": "import pathlib\nimport yaml\n\nBASE_DIR = pathlib.Path(__file__).parent\nconfig_path = pathlib.Path.joinpath(BASE_DIR, 'config', 'config.yaml')\n\n\ndef get_config(path):\n with open(path) as f:\n config = yaml.load(f)\n return config\n\n\nconfig = get_config(config_path)", "sub_path": "settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "pathlib.Path.joinpath", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "509859590", "text": "from collections import namedtuple\nfrom functools import reduce\nfrom time import time\n\n\nclass TimeItResults(namedtuple(\"TimeItResults\", [\"min_time\", \"max_time\", \"avg_time\"])):\n def __new__(cls, min_time=0, max_time=0, avg_time=0):\n return super(TimeItResults, cls).__new__(cls, min_time=min_time, max_time=max_time, avg_time=avg_time)\n\n\ndef time_function(func, args=None, kwargs=None, times_to_run=1):\n args = () if args is None else args\n kwargs = {} if kwargs is None else kwargs\n\n results = []\n\n for i in range(0, times_to_run):\n start = time()\n func(*args, **kwargs)\n results.append(time()-start)\n\n return TimeItResults(min_time=min(results), max_time=max(results), avg_time=reduce(lambda x, y: x + y, results) / len(results))", "sub_path": "pytools/util/timing.py", "file_name": "timing.py", "file_ext": "py", "file_size_in_byte": 776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "651621170", "text": "# -*- coding: utf-8 -*-\n# @Author : Li Zihao\n# @Time : 2020/5/21 21:19\n# @File : urls.py\n\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom JmeterPlatform import views\nfrom JmeterPlatform.allviews import task_view as tv\n\nurlpatterns = [\n path('admin', admin.site.urls),\n path('uploadfiles', views.upload_files, name='uploadfiles'),\n path('filelist', views.file_list, name='filelist'),\n path('downloadfile', views.download_file, name='downloadfile'),\n path('deletefile', views.delete_file, name='deletefile'),\n path('addtask', views.add_task),\n path('jsonrp', views.json_response),\n path('myscript', views.my_script),\n]", "sub_path": "mydemos/DjangoProject/JmeterPlatform/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.upload_files", "line_number": 14, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.file_list", "line_number": 15, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.download_file", "line_number": 16, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.delete_file", "line_number": 17, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.add_task", "line_number": 18, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.json_response", "line_number": 19, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "JmeterPlatform.views.my_script", "line_number": 20, "usage_type": "attribute"}, {"api_name": "JmeterPlatform.views", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "157990655", "text": "#!usr/bin/env python3\n\nfrom datetime import date\nfrom typing import AnyStr, Dict, List, Optional\n\nfrom termcolor import colored, cprint\nfrom xlsxwriter import Workbook\n\n\ndef export_subnets(\n subnets: List[Dict],\n workbook_name: Optional[AnyStr] = \"New-Schema.xlsx\",\n):\n \"\"\"Exports an Excel file of subnetting data\n\n Parameters\n ----------\n subnets : List[Dict]\n List of subnets went througth subnetting\n workbook_name : Optional[AnyStr], optional\n Name of Workbook to create, by default \"New-Schema.xlsx\"\n\n Raises\n ------\n SystemExit\n TypeError, KeyError\n \"\"\"\n\n wb_name, ext = workbook_name.split(\".\")\n excel_fname = f\"{wb_name}_{date.today()}.{ext}\"\n\n # Create an Excel file\n with Workbook(filename=excel_fname) as workbook:\n # Create a sheet within the Excel file\n worksheet = workbook.add_worksheet(name=\"Subnetting Results\")\n # Filters\n worksheet.autofilter(\"A1:L1\")\n # Freeze top row and 2 most left columns\n worksheet.freeze_panes(1, 2)\n\n # Header line in Excel sheet\n header_line = {\n \"A1\": \"VLAN ID\",\n \"B1\": \"VLAN Name\",\n \"C1\": \"CIDR Notation\",\n \"D1\": \"Network Address\",\n \"E1\": \"Prefix Length\",\n \"F1\": \"Broadcast Address\",\n \"G1\": \"Addresses Range\",\n \"H1\": \"IP Helper Address\",\n \"I1\": \"Gateway\",\n \"J1\": \"Subnet Mask\",\n \"K1\": \"Wildcard Mask\",\n \"L1\": \"Max. No. of Usable Hosts\",\n }\n\n # Header line format\n h_frmt = workbook.add_format(\n properties={\n \"bold\": True,\n \"border\": True,\n \"align\": \"center\",\n \"valign\": \"vcenter\",\n }\n )\n\n # Create a header line row\n for cell, value in header_line.items():\n worksheet.write_string(cell, value, cell_format=h_frmt)\n\n # Generic cell format\n c_frmt = workbook.add_format(\n properties={\"border\": True, \"align\": \"center\", \"valign\": \"vcenter\"}\n )\n\n # Format cell containing number\n num_frmt = workbook.add_format(\n properties={\n \"border\": True,\n \"align\": \"center\",\n \"valign\": \"vcenter\",\n \"num_format\": \"#,##0\",\n }\n )\n\n # Initial values for row and column\n row, col = 1, 0\n\n try:\n # Place subnetting data according to header line above\n for subnet in subnets:\n worksheet.write(row, col + 0, \"\", c_frmt) # A\n worksheet.write(row, col + 1, \"\", c_frmt) # B\n worksheet.write(row, col + 2, subnet[\"cidr\"], c_frmt) # C\n worksheet.write(row, col + 3, subnet[\"net_addr\"], c_frmt) # D\n worksheet.write(row, col + 4, f'/{subnet[\"prefix_len\"]}', c_frmt) # E\n worksheet.write(row, col + 5, subnet[\"broadcast_addr\"], c_frmt) # F\n worksheet.write(row, col + 6, subnet[\"range\"], c_frmt) # G\n worksheet.write(row, col + 7, \"\", c_frmt) # H\n worksheet.write(row, col + 8, subnet[\"gateway\"], c_frmt) # I\n worksheet.write(row, col + 9, subnet[\"netmask\"], c_frmt) # J\n worksheet.write(row, col + 10, subnet[\"wildcard\"], c_frmt) # K\n worksheet.write_number(\n row, col + 11, subnet[\"num_hosts\"], num_frmt\n ) # L\n # Jump to next row\n row += 1\n\n except (TypeError, KeyError) as e:\n raise SystemExit(colored(text=f\"export_subnets.py: {e}\", color=\"red\"))\n cprint(text=f\"\\nPlease check {excel_fname} in the PWD.\\n\", color=\"green\")\n", "sub_path": "export_subnets.py", "file_name": "export_subnets.py", "file_ext": "py", "file_size_in_byte": 3788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 30, "usage_type": "name"}, {"api_name": "xlsxwriter.Workbook", "line_number": 33, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 110, "usage_type": "call"}, {"api_name": "termcolor.cprint", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "136500835", "text": "#https://towardsdatascience.com/detecting-facial-features-using-deep-learning-2e23c8660a7a\r\n#prototype facital recognition as a boolean of face present/not present\r\n#https://gist.github.com/EncodeTS/6bbe8cb8bebad7a672f0d872561782d9 #keras implementation of face categorization/recognition\r\n#https://github.com/TadasBaltrusaitis/OpenFace.....not quit yet sure on this one, expression and eye tracking...also only installation instructions for linux\r\n#promising for implementation....https://github.com/informramiz/opencv-face-recognition-python/blob/master/OpenCV-Face-Recognition-Python.py\r\n#https://github.com/opencv/opencv/tree/master/data/haarcascades\r\n#http://truelogic.org/wordpress/2015/12/25/easy-face-detection-using-opencv-and-python/\r\n\r\n#import supporting libraries\r\nimport numpy as np\r\nimport cv2\r\nimport copy\r\n\r\n#load all haarcascade classifiers into independent objects\r\nface_default_cascade = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_frontalface_default.xml')\r\nface_tree_cascade = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_frontalface_alt_tree.xml')\r\nface_alt1_cascade = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_frontalface_alt.xml')\r\nface_alt2_cascade = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_frontalface_alt2.xml')\r\nface_profile_cascade = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_profileface.xml')\r\nface_eyes = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_eye.xml')\r\nface_eyes_glasses = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_eye_tree_eyeglasses.xml')\r\nface_eyes_left = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_lefteye_2splits.xml')\r\nface_eyes_right = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_righteye_2splits.xml')\r\nbody_full = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_fullbody.xml')\r\nbody_upper = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_upperbody.xml')\r\nbody_lower = cv2.CascadeClassifier('D:/carseatAPP/python/cv2files/haarcascade_lowerbody.xml')\r\n\r\n\r\nthis_path = \"D:/carseatAPP/trainingPics/train/baby/\"\r\nthis_name = \"download4.jpg\"\r\nthis_full_path = this_path + this_name\r\nwrite_path = \"D:/carseatAPP/trainingPics/output1/\"\r\nthis_full_output = write_path + this_name\r\n\r\n#def detectbaby(imagePath, haar_classifier):\r\n\r\n\r\n\r\nimg = cv2.imread(this_full_path)\r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\nimg2 = copy.copy(img)\r\ngray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\r\n\r\nfaces = face_default_cascade.detectMultiScale(gray, 1.25, 6)\r\nfor (x,y,w,h) in faces:\r\n cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\r\n roi_gray = gray[y:y+h, x:x+w]\r\n roi_color = img[y:y+h, x:x+w]\r\n eyes = face_eyes.detectMultiScale(roi_gray)\r\n for (ex,ey,ew,eh) in eyes:\r\n cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\r\n \r\ncv2.imwrite(this_full_output, img)\r\ncv2.startWindowThread()\r\ncv2.namedWindow(\"face\")\r\n#cv2.imshow(\"face\", grayscale_image[y:y+w, x:x+h]);cv2.waitKey(0); cv2.destroyAllWindows()\r\ncv2.imshow(\"face\", img);cv2.waitKey(0); cv2.destroyAllWindows()\r\n\r\nfaces2 = face_profile_cascade.detectMultiScale(gray2, 1.25, 6)\r\nfor (x,y,w,h) in faces:\r\n cv2.rectangle(img2,(x,y),(x+w,y+h),(255,0,0),2)\r\n roi_gray = gray[y:y+h, x:x+w]\r\n roi_color = img[y:y+h, x:x+w]\r\n eyes = eye_cascade.detectMultiScale(roi_gray)\r\n for (ex,ey,ew,eh) in eyes:\r\n cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\r\n \r\ncv2.imwrite(\"/home/amit/Downloads/facedetectprofile.png\", img2)\r\n\r\n\r\n\r\n#MERGE THE TWO LISTS HERE\r\n\r\n\r\n\r\n\r\ndetectbaby(this_path, face_default_cascade)\r\n\r\n", "sub_path": "opencv_boundingbox2.py", "file_name": "opencv_boundingbox2.py", "file_ext": "py", "file_size_in_byte": 3707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 40, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.startWindowThread", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "322662043", "text": "import networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef adjacent_edges(nodes, halfk):\n n = len(nodes)\n for i, u in enumerate(nodes):\n for j in range(i + 1, i + halfk + 1):\n v = nodes[j % n]\n yield u, v\n\ndef make_ring_lattice(n, k):\n G = nx.Graph()\n nodes = range(n)\n G.add_nodes_from(nodes)\n G.add_edges_from(adjacent_edges(nodes, k//2))\n return G\n\ndef draw_graph(G):\n nx.draw_circular(G, \n node_color = 'y', \n node_size = 300, \n with_labels = True)\n plt.show()\n\ndef flip(p):\n return np.random.random() < p\n\ndef rewire(G, p):\n nodes = set(G)\n for u, v in G.edges():\n if flip(p):\n choices = nodes - {u} - set(G[u])\n new_v = np.random.choice(list(choices))\n G.remove_edge(u, v)\n G.add_edge(u, new_v)\n\ndef make_ws_graph(n, k, p):\n ws = make_ring_lattice(n, k)\n rewire(ws, p)\n return ws\n\ndef all_pairs(nodes):\n for i, j in enumerate(nodes):\n for v, w in enumerate(nodes):\n if i > v:\n yield j, w\n\ndef node_clustering(G, u):\n neighbors = G[u]\n k = len(neighbors)\n if k < 2:\n return np.nan\n \n possible = k * (k - 1) / 2\n exist = 0\n for v, w in all_pairs(neighbors):\n if G.has_edge(v, w):\n exist += 1 \n return exist / possible\n\ndef clustering_coefficient(G):\n cu = [node_clustering(G, node) for node in G]\n return np.nanmean(cu)\n\ndef path_lengths(G):\n length_iter = nx.shortest_path_length(G)\n for source, dist_map in length_iter:\n for dest, dist in dist_map.items():\n yield dist\n\ndef characteristic_path_length(G):\n return np.mean(list(path_lengths(G)))\n\ndef run_one_graph(n, k, p):\n ws = make_ws_graph(n, k, p)\n mpl = characteristic_path_length(ws)\n cc = clustering_coefficient(ws)\n # print(mpl, cc)\n return mpl, cc\n\ndef run_experiment(ps, n = 100, k = 4, iters = 20):\n res = []\n for p in ps:\n t = [run_one_graph(n, k, p) for _ in range(iters)]\n means = np.array(t).mean(axis = 0)\n res.append(means)\n return np.array(res)\n\nps = np.logspace(-4, 0, 15)\nres = run_experiment(ps)\nL, C = np.transpose(res)\nL /= L[0]\nC /= C[0]\n\nplt.plot(ps, C, 's-', linewidth=1, label='C(p)/C(0)')\nplt.plot(ps, L, 'o-', linewidth=1, label='L(p)/L(0)')\nplt.xscale('log')\nplt.legend()\nplt.show()\n", "sub_path": "my_code/chapter3-small-world-graphs.py", "file_name": "chapter3-small-world-graphs.py", "file_ext": "py", "file_size_in_byte": 2397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "networkx.Graph", "line_number": 13, "usage_type": "call"}, {"api_name": "networkx.draw_circular", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.nanmean", "line_number": 64, "usage_type": "call"}, {"api_name": "networkx.shortest_path_length", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "562336448", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sqlite3\nfrom rapidfuzz import process\nimport unicodedata\nimport re\nimport discord.ext.commands\nfrom discord.ext import commands\n\n# Connect to the local sqlite database `rocbot.sqlite` and generate a list of \n# ship names from the `ship` table\ndef get_ships():\n # connect to the sqlite database\n conn = sqlite3.connect('rocbot.sqlite')\n # Return a list of items instead of 1 item tuples \n conn.row_factory = lambda cursor, row: row[0]\n # make an sqlite connection object\n c = conn.cursor()\n # creates a variable and assigns the list of ship names to it\n ship_list = c.execute('''SELECT name FROM ship''').fetchall()\n # close the databse connection\n conn.close()\n # return a list of ship names\n return ship_list\n\n# return the ship name from name_list which is a list of ship names \n# extracted from the databases table called ship\ndef ship_search(find_this):\n # using the class initiated list ship_list find one ship name that \n # matches the given string as close as possible\n found_this = process.extractOne(find_this, get_ships())\n # rapidfuzz returns the name and the ratio so strip the ratio and keep\n # the ship name\n ship_name = found_this[0]\n # return the ship name as a string\n return ship_name\n\n# Connect to the local sqlite database `rocbot.sqlite` and generate a list of \n# invader names from the invaders table\ndef get_invaders():\n # connect to the sqlite database\n conn = sqlite3.connect('rocbot.sqlite')\n # Return a list of items instead of 1 item tuples \n conn.row_factory = lambda cursor, row: row[0]\n # make an sqlite connection object\n c = conn.cursor()\n # creates a variable and assigns the list of ship names to it\n invader_list = c.execute('''SELECT name FROM invaders''').fetchall()\n # close the databse connection\n conn.close()\n # return a list of ship names\n return invader_list\n\ndef invader_search(find_this):\n if find_this != None:\n # using the class initiated list ship_list find one ship name that \n # matches the given string as close as possible\n found_this = process.extractOne(find_this, get_invaders())\n # rapidfuzz returns the name and the ratio so strip the ratio and keep\n # the ship name\n invader_name = found_this[0]\n # return the ship name as a string\n return invader_name\n else:\n pass\n\n# strip all non lete\ndef sanitise_input(input_string):\n # \\W+ matches any non-word character (equal to [^a-zA-Z0-9_])\n # + Quantifier — Matches between one and unlimited times, as many times as \n # possible, giving back as needed (greedy)\n words_only = re.sub(r'\\W+','', str(input_string))\n return unicodedata.normalize('NFKD', words_only).encode('ascii', 'ignore').decode('utf8')\n\ndef customemoji(self, find_this):\n find_sanitised = sanitise_input(find_this.lower())\n return discord.utils.get(self.bot.emojis, name = find_sanitised)\n\ndef embed_pagination(description):\n paginator = commands.Paginator(prefix='', suffix='', max_size=2000)\n for ship_line in description:\n paginator.add_line(ship_line)\n return paginator.pages\n\ndef shortcut_obj(arg1):\n # connect to the sqlite database\n conn = sqlite3.connect('rocbot.sqlite')\n # return a class sqlite3.row object which requires a tuple input query\n conn.row_factory = sqlite3.Row\n # make an sqlite connection object\n c = conn.cursor()\n # using a defined view shortcut collect all table info \n c.execute('select * from shortcut where shortcut =?', (arg1,))\n # return the shortcut object including the required elemnts\n # using shortc instead of sc so not to be confused with \n # sub command abbrehviations \n shortc_obj = c.fetchall()\n # close the databse connection\n conn.close()\n # return the sqlite3.cursor object\n return shortc_obj\n\ndef sql_dmg_brackets():\n # connect to the sqlite database\n conn = sqlite3.connect('rocbot.sqlite')\n # Return a list of items instead of 1 item tuples \n conn.row_factory = lambda cursor, row: row[0]\n # make an sqlite connection object\n c = conn.cursor()\n # creates a variable and assigns the list of ship names to it\n dmg_obj = c.execute('''SELECT amount FROM ship_damage''').fetchall()\n # close the databse connection\n conn.close()\n # return a list of ship names\n return dmg_obj\n\ndef dmg_bracket_list():\n dmg_list = []\n for i in sql_dmg_brackets():\n dmg_list.append(i)\n return dmg_list\n\n\n\n\n33234234234\n\ndef sql_arg_list():\n # connect to the sqlite database\n conn = sqlite3.connect('rocbot.sqlite')\n # Return a list of items instead of 1 item tuples \n conn.row_factory = lambda cursor, row: row[0]\n # make an sqlite connection object\n c = conn.cursor()\n # creates a variable and assigns the list of ship names to it\n dmg_obj = c.execute('''SELECT name FROM shortcut''').fetchall()\n # close the databse connection\n conn.close()\n # return a list of ship names\n return dmg_obj\n\ndef arg_parse_list():\n dmg_list = []\n for i in sql_arg_list():\n dmg_list.append(i)\n return dmg_list\n\ndef argument_parser(sc, arg1):\n clean_arg1 = sanitise_input(arg1)\n if sc == 'dmg':\n dmg_bracket = process.extractOne(clean_arg1, dmg_bracket_list())\n return dmg_bracket[0]\n elif sc == 'rand':\n try:\n int(arg1)\n except ValueError:\n return 10\n except TypeError:\n return 10\n else:\n return arg1\n else:\n if len(clean_arg1) <= 4:\n shortcut = shortcut_obj(clean_arg1.lower())\n if len(shortcut) > 0:\n return shortcut[0]['name']\n else:\n arg_found = process.extractOne(clean_arg1, arg_parse_list())\n return arg_found[0]\n\ndef get_em_colour(arg1):\n embed_colours = {\"Shield Breaker\": 0x3a77f9, \"High Impact\": 0xee4529, \"Armor Piercing\": 0xffb820}\n return embed_colours[arg1]\n \n", "sub_path": "res/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 6039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sqlite3.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "rapidfuzz.process.extractOne", "line_number": 32, "usage_type": "call"}, {"api_name": "rapidfuzz.process", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 43, "usage_type": "call"}, {"api_name": "rapidfuzz.process.extractOne", "line_number": 59, "usage_type": "call"}, {"api_name": "rapidfuzz.process", "line_number": 59, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 73, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 74, "usage_type": "call"}, {"api_name": "discord.ext.commands.utils.get", "line_number": 78, "usage_type": "call"}, {"api_name": "discord.ext.commands.utils", "line_number": 78, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 78, "usage_type": "name"}, {"api_name": "discord.ext.commands.Paginator", "line_number": 81, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 106, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 131, "usage_type": "call"}, {"api_name": "rapidfuzz.process.extractOne", "line_number": 152, "usage_type": "call"}, {"api_name": "rapidfuzz.process", "line_number": 152, "usage_type": "name"}, {"api_name": "rapidfuzz.process.extractOne", "line_number": 169, "usage_type": "call"}, {"api_name": "rapidfuzz.process", "line_number": 169, "usage_type": "name"}]} +{"seq_id": "639205255", "text": "#!/usr/local/bin/python\n\nimport os\nimport struct\nimport numpy as np\nimport math\nfrom math import sqrt\n\nnp.set_printoptions (precision = 4, suppress = True, linewidth = np.nan, threshold = np.nan)\n\ndef sigmoid (x):\n x0 = 10.0\n return 2.0 * (1.0 / (1.0 + np.exp (- x / x0)) - 0.5)\n\ndef MNISTread (dataset = \"training\", path = \".\"):\n if dataset is \"training\":\n fname_img = os.path.join (path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join (path, 'train-labels-idx1-ubyte')\n elif dataset is \"testing\":\n fname_img = os.path.join (path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join (path, 't10k-labels-idx1-ubyte')\n else: raise ValueError (\"Dataset must be 'testing' or 'training'\")\n with open (fname_lbl, 'rb') as flbl:\n magic, num = struct.unpack (\">II\", flbl.read (8))\n lbl = np.fromfile (flbl, dtype = np.int8)\n with open (fname_img, 'rb') as fimg:\n magic, num, rows, cols = struct.unpack (\">IIII\", fimg.read (16))\n img = np.fromfile (fimg, dtype = np.uint8).reshape (len (lbl), rows, cols)\n get_img = lambda idx: (lbl[idx], img[idx])\n for i in range (0, len (lbl)): yield get_img (i)\n\ndef DigitDisplay (digit):\n from matplotlib import pyplot\n import matplotlib as mpl\n fig = pyplot.figure ()\n ax = fig.add_subplot (1, 1, 1)\n imgplot = ax.imshow (digit, cmap = mpl.cm.Greys)\n imgplot.set_interpolation ('nearest')\n ax.xaxis.set_ticks_position ('top')\n ax.yaxis.set_ticks_position ('left')\n pyplot.show ()\n\ndef Vector2Image (v, rows, columns):\n v = v - v.min ()\n v *= 255 / v.max ()\n return np.uint8 (v.reshape (rows, columns))\n\ndef Matrix2Image (M):\n M = M - M.min ()\n M *= 255 / M.max ()\n return np.uint8 (M)\n\ndef GetSize (type, path):\n images = MNISTread (type, path)\n label, digit = next (images)\n rows, columns = digit.shape\n return rows, columns\n\ndef GetImages (type, path):\n images = MNISTread (type, path)\n return images\n\ndef Train (TS, D, N, NT, ETA, E, TOL):\n DN = D * N\n WEIGHT = np.zeros ((DN, DN))\n STATE = np.zeros ((DN))\n for t in range (0, NT):\n LABEL, DIGIT = next (TS)\n print (\"Train input: \", t + 1, \"of \", NT, \" Label: \", LABEL)\n IN = np.float64 (DIGIT.reshape (N))\n IN -= IN.mean ()\n IN /= np.linalg.norm (IN)\n INPUT = np.zeros ((DN))\n INPUT = np.concatenate ( (INPUT[ : LABEL * N], IN, INPUT[(LABEL + 1) * N :]) )\n STATE += np.array (INPUT)\n for n in range (0, DN):\n STATE[n] = sigmoid (STATE[n])\n for e in range (0, E):\n STATE += STATE.dot (WEIGHT)\n for n in range (0, DN):\n STATE[n] = sigmoid (STATE[n])\n DELTA = np.zeros ((DN, DN))\n DELTA = ETA * STATE * STATE.T\n WEIGHT = WEIGHT + DELTA\n if abs (DELTA).max () < TOL and abs (STATE).max () < TOL: break\n return WEIGHT, STATE\n\ndef Test (TS, W, R, C, D, N, NT, DISPLAY):\n DN = D * N;\n STATE = np.zeros ((DN))\n for t in range (0, NT):\n LABEL, DIGIT = next (TS)\n print (\"Test input: \", t + 1, \"of \", NT, \" Label: \", LABEL)\n IN = np.float64 (DIGIT.reshape (N))\n IN -= IN.mean ()\n IN /= np.linalg.norm (IN)\n INPUT = np.zeros ((DN))\n INPUT = np.concatenate ( (INPUT[ : LABEL * N], IN, INPUT[(LABEL + 1) * N :]) )\n RESULT = W.dot (INPUT)\n if (DISPLAY):\n INIMAGE = Vector2Image (INPUT, 2 * R, 5 * C)\n DigitDisplay (INIMAGE)\n RESIMAGE = Vector2Image (RESULT, 2 * R, 5 * C)\n DigitDisplay (RESIMAGE)\n\n\n# main\ndisplay = 1\ntolerance = 1e-15\neta = 0.0001\ndigits = 10\nepochs = 100\ntrains = 1000\ntests = 10\n\nrows, columns = GetSize (\"training\", \"../MNIST\")\nneurons = rows * columns\ntrainSet = GetImages (\"training\", \"../MNIST\")\nweight, state = Train (trainSet, digits, neurons, trains, eta, epochs, tolerance)\ntestSet = GetImages (\"testing\", \"../MNIST\")\nTest (testSet, weight, rows, columns, digits, neurons, tests, display)\n\n\n\n\n\n\n", "sub_path": "Promising/Abramo/nnC.py", "file_name": "nnC.py", "file_ext": "py", "file_size_in_byte": 3861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.set_printoptions", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 25, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 28, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.cm", "line_number": 37, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "636380366", "text": "from numpy import zeros, array, linspace\nimport matplotlib.pyplot as plt\n\n\ndef f(x):\n return -x ** 2 + 6.0 * x - 5.0\n\n\nn = 7\nxs = zeros(n)\nx0 = -2.0\nx1 = 3.0\nit = list(range(0, 7))\n\nfor k in range(n):\n x2 = x1 - f(x1) * ((x1 - x0) / (f(x1) - f(x0)))\n x0 = x1\n x1 = x2\n xs[k] = x2\n\n# printing output\nprint('%5s %8s' % ('k', 'x',))\nfor k in range(n):\n print('%5d %9.4f' % (k + 1, xs[k],))\n\nplt.plot(it, xs, 'ko-')\nplt.xlabel('iteration')\nplt.ylabel('x')\nplt.show()\n", "sub_path": "src/21-Secant-1.py", "file_name": "21-Secant-1.py", "file_ext": "py", "file_size_in_byte": 481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "76114680", "text": "from django import template\nimport re\n\nregister = template.Library()\n\n@register.filter(name='addswearwords')\ndef addswearwords(value):\n if isinstance(value, str):\n return str(value) + f' охуительно пиздец заебал'\n else:\n raise ValueError(f'Нельзя добавить бранные слова (текст) в тип {type(value)}')\n\n\n# Добавляю только три шаблона матерных слов. Расширять можно до бесконечности.\nSWEARWORDS = [\n r'\\w*ху[йяие]\\w*',\n r'\\w*ебал\\w*',\n r'\\w*пизд\\w*',\n]\n\n@register.filter(name='censor')\ndef censor(value):\n if isinstance(value, str):\n censored_text = ''\n for one_word in value.split():\n for swearword in SWEARWORDS:\n if re.search(swearword, one_word.strip('.,;:?!-').lower()):\n censored_text += '!censored! '\n break\n else:\n censored_text += one_word + ' '\n return str(censored_text)\n else:\n raise ValueError('Цензор умеет работать только со строками.')\n", "sub_path": "newapp/templatetags/custom_filters.py", "file_name": "custom_filters.py", "file_ext": "py", "file_size_in_byte": 1185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.template.Library", "line_number": 4, "usage_type": "call"}, {"api_name": "django.template", "line_number": 4, "usage_type": "name"}, {"api_name": "re.search", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "64636910", "text": "import xlrd\n\nbook = xlrd.open_workbook('SOWC 2014 Stat Tables_Table 9.xlsx')\n\nfor sheet in book.sheets():\n print(sheet.name)\nsheet = book.sheet_names()[1]\n\nprint(sheet)\n\nfor i in range(sheet.nrows):\n print(i)\n", "sub_path": "scrapy_file/parse_excel.py", "file_name": "parse_excel.py", "file_ext": "py", "file_size_in_byte": 215, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "xlrd.open_workbook", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "438714586", "text": "import torch\nimport os, glob, json\nimport numpy as np\n\nfrom tqdm import tqdm\n\nfrom datetime import timedelta\n\nfrom tensorboardX import SummaryWriter\nfrom utils.nqubit_setting import get_args \n\nfrom energy_env.EnvSetting import OneHotEnv, DoubleOneHotEnv, NoOneHotEnv, OneHotActionEnv #Env\n\nfrom agents.SACAgent import SACAgent # Agent\n\n\nif __name__ == '__main__':\n \n ########################### args & json & log_dir & writer ###################################\n args = get_args()\n sac_dict = vars(args)\n\n # log dir & summary writer\n current_dir = './results'\n train_log_dir = '/EnvSetting' + str(args.nbit) + '/sac'\n exp_name = '{}'.format(args.env_id) + '/seed{0}'.format(args.seed)\n log_dir = current_dir + train_log_dir + exp_name \n\n try:\n os.makedirs(log_dir)\n except OSError:\n files = glob.glob(os.path.join(log_dir, 'events.out.tfevents*'))\\\n + glob.glob(os.path.join(log_dir, '*.dump')) \\\n + glob.glob(os.path.join(log_dir, '*.json'))\n for f in files:\n os.remove(f)\n \n writer = SummaryWriter(log_dir)\n \n with open(os.path.join(log_dir, 'params.json'), 'w') as f:\n f.write(json.dumps(sac_dict, ensure_ascii=False, indent=4, separators=(',',':')))\n\n ############################## Device & Env & RNG & Agent #####################\n # Device\n device = torch.device(\"cuda:{}\".format(args.GPU))\n\n # Env\n if args.env_id == 'OneHotEnv':\n env = OneHotEnv(args.nbit, args.episode_length, args.measure_every_n_steps, args.reward_scale)\n elif args.env_id == 'DoubleOneHotEnv':\n env = DoubleOneHotEnv(args.nbit, args.episode_length, args.measure_every_n_steps, args.reward_scale)\n elif args.env_id == 'OneHotActionEnv':\n env = OneHotActionEnv(args.nbit, args.episode_length, args.measure_every_n_steps, args.reward_scale)\n elif args.env_id == 'NoOneHotEnv':\n env = NoOneHotEnv(args.nbit, args.episode_length, args.measure_every_n_steps, args.reward_scale)\n\n # RNG\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n env.action_space.seed(args.seed)\n\n # Agent\n agent = SACAgent(args, env, log_dir, device)\n\n\n ############################# Main Training Loop ############################\n totalstep = 0\n best_b = None\n best_threshold = -2.0\n\n # Training Loop\n for episode in tqdm(range(args.num_episodes)): # int(1e6)\n obs = env.reset() # (9, )\n episode_reward = [] # record_energy\n\n for step in tqdm(range(args.episode_length)): # (0, 1, 2)\n totalstep += 1\n \n # This if-else is used to increase initial exploration \n if totalstep > args.random_steps: # 900\n # required extra exploration strategy\n action = agent.get_action(obs, deterministic = False)\n else:\n action = env.action_space.sample()\n\n # Excute\n prev_obs = obs\n obs, reward, done, info = env.step(action)\n\n episode_reward.append(reward)\n \n \n\n # store ( sometimes is wrote into agent.update )\n agent.buffer.store(prev_obs, action, reward, obs, done)\n\n # when to update & how often we update\n if (totalstep > args.learn_start_steps) and (totalstep % args.update_freq_steps==0):\n #value_loss, policy_loss, log_prob_mag, q_value_mag, alpha = agent.update(args.update_freq_per_step, totalstep)\n value_loss, policy_loss, log_prob_mag, q_value_mag = agent.update(args.update_freq_per_step, totalstep)\n \n writer.add_scalar('value_loss', value_loss, totalstep)\n writer.add_scalar('policy_loss', policy_loss, totalstep)\n writer.add_scalar('log_prob', log_prob_mag, totalstep)\n writer.add_scalar('q_value_prob', q_value_mag, totalstep)\n # logwriter.add_scalar('alpha', alpha, totalstep)\n \n \n # log_state & action\n #if totalstep % args.log_state_action_steps == 0:\n \n # writer.add_scalars('state_value', {'s0':obs[-6], 's1':obs[-5], 's2':obs[-4], 's3':obs[-3], 's4':obs[-2], 's5':obs[-1]}, totalstep)\n # writer.add_scalars('log_action', {'a0':action[0], 'a1':action[1], 'a2':action[2], 'a3':action[3], 'a4':action[4], 'a5':action[5]}, totalstep)\n\n # record threshold \n if (totalstep % args.measure_every_n_steps == 0):\n writer.add_scalar('step-threshold', info['threshold'], totalstep)\n if info['threshold'] > best_threshold:\n best_threshold = info['threshold']\n best_b = info['solution']\n #writer.add_scalar('reward', info['reward'], totalstep)\n #writer.add_scalar('extra_reward', info['extra_reward'], totalstep)\n \n if info and (info['threshold'] >= -1.05):\n pass\n '''\n if satisfied_flag == 0:\n satisfied_flag = episode\n elif ((episode - satisfied_flag)== 1): \n convergence_buffer.append(info['threshold'])\n satisfied_flag == episode\n else:\n satisfied_flag == episode\n \n \n if len(convergence_buffer) == 100:\n mean = np.mean(np.array(convergence_buffer))\n if (mean >= -1.005):\n #torch.save(agent.model.state_dict(), os.path.join(log_dir, 'sac_model.dump'))\n '''\n \n\n '''\n\n avg_reward = 0.\n test_episodes = 5\n for _ in range(test_episodes):\n obs, done, ep_rew = test_env.reset(), False, 0.0\n for i in range(args.max_episode_steps): # 3\n action = agent.get_action(obs, deterministic = True)\n next_obs, reward, done, info = env.step(action)\n ep_rew += reward\n obs = next_obs\n\n avg_reward += ep_rew\n \n avg_reward /= test_episodes\n\n '''\n \n #torch.save(agent.model.state_dict(), os.path.join(log_dir, 'sac_model.dump'))\n #writer.add_scalar('test_episode_reward', avg_reward, totalstep)\n \n\n \n \n \n measure_state = info['solution']\n\n writer.add_scalar('episode_threshold',info['threshold'], episode)\n writer.add_scalars('soluiton', {'s0':measure_state[0], 's1':measure_state[1], 's2':measure_state[2], 's3':measure_state[3],'s4':measure_state[4],'s5':measure_state[5]}, episode)\n writer.add_scalar('episode_reward', np.mean(np.array(episode_reward)), episode)\n\n if (episode % 1000 == 0):\n with open(os.path.join(log_dir, 'solution.txt'), 'a') as f:\n f.write('best_threshold:{0}, bset_solution:{1}'.format(best_threshold, best_b))\n\n torch.save(agent.model.state_dict(), os.path.join(log_dir, 'sac_model.dump'))\n \n \n\n env.close()\n \n writer.close()\n\n \n \n # def main(args, log_dir):\n #pass\n\n# if __name__ == '__main__':\n# for arg in args:\n# log_dir = functionOfArg(arg)\n# main(log_dir, arg)\n# how to evaluate the hyperparameters properly\n\n\n\n \n\n\n \n\n\n\n\n\n\n", "sub_path": "nqubit/Train_SAC_setting.py", "file_name": "Train_SAC_setting.py", "file_ext": "py", "file_size_in_byte": 7563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "utils.nqubit_setting.get_args", "line_number": 20, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 45, "usage_type": "call"}, {"api_name": "energy_env.EnvSetting.OneHotEnv", "line_number": 49, "usage_type": "call"}, {"api_name": "energy_env.EnvSetting.DoubleOneHotEnv", "line_number": 51, "usage_type": "call"}, {"api_name": "energy_env.EnvSetting.OneHotActionEnv", "line_number": 53, "usage_type": "call"}, {"api_name": "energy_env.EnvSetting.NoOneHotEnv", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 59, "usage_type": "call"}, {"api_name": "agents.SACAgent.SACAgent", "line_number": 63, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 72, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}]} +{"seq_id": "555116732", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\n\nimport os\nos.chdir(\"/Users/tomoyuki/python_workspace/NLP/VNC\")\nimport MeCab\nimport sqlite3\nfrom contextlib import closing\nimport pandas as pd\nimport pandas.io.sql as psql\nimport numpy as np\nfrom copy import deepcopy\nimport re\nimport jaconv\nimport traceback\nfrom tqdm import tqdm\n\nfrom funcs import char_handler\n\nchar_handler.removeBracket\nchar_handler.checkCharacterType\nchar_handler.removeCharInString\n\n## DBに接続\ndbname = \"EDICT.sqlite3\"\nconn = sqlite3.connect(dbname)\n\nselectColumns(conn, \"code_1332\")\nselectRecords(conn, \"code_1332\")\n\npsql.read_sql(\"SELECT * FROM articles;\", con)\n\n# itemsテーブルのカラム名取得\ncolumn_list = []\ncur = conn.cursor()\nselect_sql = \"pragma table_info(edict);\" \nfor row in cur.execute(select_sql):\n print_txt = \"\"\n for i in range(len(row)):\n print_txt = f\"{print_txt}{row[i]} | \"\n print(print_txt)\n column_list.append(row[1])\ncur.close()\n\n\n\n## 一部分をpandas化\ndf_dict = pd.DataFrame(data=None, columns=column_list)\n\n#id_list = list(range(0,180000,500))\n\nselect_sql = \"select * from edict\" \ncur = conn.cursor()\ni=0\n\nfor row in tqdm(list(cur.execute(select_sql))):\n# if i in id_list:\n # DataFarmeに値(行)を追加していく\n tmp_se = pd.Series(row, index=df_dict.columns )\n df_dict = df_dict.append( tmp_se, ignore_index=True )\n \n i+=1\n \ncur.close()\n\n\n# 保存\ndf_dict = df_dict.drop(0, axis=0)\ndf_dict.to_csv(\"df_dict_all.csv\", encoding=\"utf-8\",index=False)\n\n\n\n\n\n## \n##\n#string=\"くぁwせdrfヲヲヲエええ?$#ーーうえ\"\n#char_handler.removeCharInString(string ,ctype=\"hira\")\n##\n##\n### カタカナ2ひらがな\n#char_handler.removeCharInString(jaconv.kata2hira(string),ctype=\"hira\")\n#char_handler.removeCharInString(jaconv.hira2kata(string),ctype=\"kata\")\n\n\n## df_dict読み込み\ndf_dict = pd.read_csv(\"df_dict.csv\")\n\n\n## 1.日本語文字が複数のもの(;)はレコードを分ける\n# カラム名初期化\ncolumns = list(df_dict.columns)\ncolumns.insert(2, \"jpn_hira\")\ncolumns.insert(3, \"jpn_kata\")\n\n# df初期化\ndf_dict_clen1 = pd.DataFrame(data=None, columns=columns)\ndf_dict_clen1_remove = pd.DataFrame(data=None, columns=columns[0:3])\n\n\n\nrow_id = 182\n\n\n\nfor row_id in range(df_dict.shape[0]):\n # 一行取得\n tmp_row = deepcopy(df_dict.iloc[row_id,:])\n \n # 英単語中の()文字を削除\n remove_dict = {}\n tmp_row_index = list(tmp_row.index)\n for i in range(2,tmp_row.shape[0]):\n tmp_str,remove_dict[tmp_row_index[i]] = char_handler.removeBracket(string=tmp_row[tmp_row_index[i]])\n \n # 英単語\n if type(tmp_str) is str:\n tmp_str_split = tmp_str.split(\" \")\n while \"\" in tmp_str_split :\n tmp_str_split.remove(\"\")\n tmp_row[tmp_row_index[i]] = \" \".join(tmp_str_split)\n \n else:\n tmp_row[tmp_row_index[i]] = tmp_str\n \n \n \n # 日本語を取得\n jpn = tmp_row[\"jpn\"]\n \n # 漢字と読み仮名に分割(空欄で分割)\n jpn_org = jpn.split(\" \")[0]\n jpn_readings = jpn.split(\" \")[1]\n \n \n ## 漢字をクレンジング\n # ()文字を削除\n jpn_org, remove_org = char_handler.removeBracket(jpn_org)\n \n \n ## 読み仮名をクレンジング\n # 外側の[]削除\n jpn_readings = jpn_readings[1:-1]\n \n # ()文字を削除\n jpn_readings, remove_readings = char_handler.removeBracket(string=jpn_readings)\n \n # \";\"で分割\n jpn_readings_list = []\n for part_jpn_readings in jpn_readings.split(\";\"):\n # カタカナ2ひらがな\n part_jpn_readings = jaconv.kata2hira(part_jpn_readings)\n \n # ひらがな以外の文字を削除\n part_jpn_readings = char_handler.removeCharInString(part_jpn_readings ,ctype=\"hira\")\n \n # すでに対象文字(part_jpn_readings)がnew_jpn_readingsに含まれていない場合，追加\n if part_jpn_readings not in jpn_readings_list:\n jpn_readings_list.append(part_jpn_readings)\n \n \n \n ## 漢字リスト(jpn_org.split(\";\"))x読み仮名リスト(jpn_readings_list)でレコード追加\n for part_jpn in jpn_org.split(\";\"):\n \n for part_jpn_readings in jpn_readings_list:\n \n # 読み仮名(part_jpn_readings)がない場合は，part_jpnをひらがなに直し，挿入\n if part_jpn_readings==\"\":\n part_jpn_readings = jaconv.kata2hira(part_jpn)\n \n # 追加するレコードを初期化\n new_row = deepcopy(tmp_row)\n \n # jpnをpart_jpnに変換\n new_row[\"jpn\"] = part_jpn\n \n # 読み仮名(ひらがな)を追加\n new_row[\"jpn_hira\"] = part_jpn_readings\n \n # 読み仮名(カタカナ)を追加\n new_row[\"jpn_kata\"] = jaconv.hira2kata(part_jpn_readings)\n \n # 追加\n df_dict_clen1 = df_dict_clen1.append(new_row, ignore_index=True )\n \n # 除外した文字列を格納\n remove_dict[\"id\"] = new_row[\"id\"]\n remove_dict[\"jpn\"] = remove_org\n remove_dict[\"jpn_hira\"] = remove_readings\n \n df_dict_clen1_remove = df_dict_clen1_remove.append(pd.Series(remove_dict), ignore_index=True )\n\n## 保存\ndf_dict_clen1.to_csv(\"df_dict_clen1.csv\", encoding=\"utf-8\",index=False)\ndf_dict_clen1_remove.to_csv(\"df_dict_clen1_remove.csv\", encoding=\"utf-8\",index=False)\n\n\n\n\n\n", "sub_path": "python/NLP/VNC/db_cleansing.py", "file_name": "db_cleansing.py", "file_ext": "py", "file_size_in_byte": 5570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.chdir", "line_number": 7, "usage_type": "call"}, {"api_name": "funcs.char_handler.removeBracket", "line_number": 22, "usage_type": "attribute"}, {"api_name": "funcs.char_handler", "line_number": 22, "usage_type": "name"}, {"api_name": "funcs.char_handler.checkCharacterType", "line_number": 23, "usage_type": "attribute"}, {"api_name": "funcs.char_handler", "line_number": 23, "usage_type": "name"}, {"api_name": "funcs.char_handler.removeCharInString", "line_number": 24, "usage_type": "attribute"}, {"api_name": "funcs.char_handler", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.io.sql.read_sql", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.io.sql", "line_number": 33, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 50, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 100, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 110, "usage_type": "call"}, {"api_name": "funcs.char_handler.removeBracket", "line_number": 116, "usage_type": "call"}, {"api_name": "funcs.char_handler", "line_number": 116, "usage_type": "name"}, {"api_name": "funcs.char_handler.removeBracket", "line_number": 140, "usage_type": "call"}, {"api_name": "funcs.char_handler", "line_number": 140, "usage_type": "name"}, {"api_name": "funcs.char_handler.removeBracket", "line_number": 148, "usage_type": "call"}, {"api_name": "funcs.char_handler", "line_number": 148, "usage_type": "name"}, {"api_name": "jaconv.kata2hira", "line_number": 154, "usage_type": "call"}, {"api_name": "funcs.char_handler.removeCharInString", "line_number": 157, "usage_type": "call"}, {"api_name": "funcs.char_handler", "line_number": 157, "usage_type": "name"}, {"api_name": "jaconv.kata2hira", "line_number": 172, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 175, "usage_type": "call"}, {"api_name": "jaconv.hira2kata", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 194, "usage_type": "call"}]} +{"seq_id": "584837142", "text": "from datetime import datetime\n\nimport scrapy\nfrom scrapy.selector import Selector\n\nfrom smzdm.items import SmzdmItem\n\n\nclass PhoneSpider(scrapy.Spider):\n name = 'phone'\n allowed_domains = ['smzdm.com']\n start_urls = ['https://www.smzdm.com/fenlei/zhinengshouji/h5c4s0f0t0p1/#feed-main/']\n\n # 抓取首页数据\n def start_requests(self):\n url = 'https://www.smzdm.com/fenlei/zhinengshouji/h5c4s0f0t0p1/#feed-main/'\n yield scrapy.Request(url=url, callback=self.scrape_index)\n\n # 获取首页前十产品链接\n def scrape_index(self, response):\n atags = Selector(response=response).xpath('//h5[@class=\"feed-block-title\"]/a/@href').getall()[:10]\n for atag in atags:\n yield scrapy.Request(url=atag, callback=self.parse_detail)\n\n # 获取用户昵称及评论，实现自动翻页\n def parse_detail(self, response):\n item = SmzdmItem()\n li_box = Selector(response=response).xpath('//div[@id=\"commentTabBlockNew\"]/ul/li[@class=\"comment_list\"]')\n for li in li_box:\n nickname = li.xpath('./div[2]/div/a/span/text()').get()\n comment = li.xpath(\n './div[2]/div[@class=\"comment_conWrap\"]/div/p/span/text()').get().strip()\n comment_time = li.xpath('./div[2]/div/div/meta/@content').get()\n comment_time = comment_time if comment_time else datetime.now().strftime('%Y-%m-%d')\n item['nickname'] = nickname\n item['comment'] = comment\n item['comment_time'] = comment_time\n yield item\n next_comment_url = Selector(response=response).xpath('//li[@class=\"pagedown\"]/a/@href').get()\n if next_comment_url is not None:\n yield scrapy.Request(url=next_comment_url, callback=self.parse_detail)\n\n # def parse(self, response):\n # pass\n", "sub_path": "week12/smzdm/smzdm/spiders/phone.py", "file_name": "phone.py", "file_ext": "py", "file_size_in_byte": 1833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 21, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 23, "usage_type": "call"}, {"api_name": "smzdm.items.SmzdmItem", "line_number": 27, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "scrapy.selector.Selector", "line_number": 39, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "508313855", "text": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport os\nimport wget\nfrom config import cfg as cfg_\n\n\nclass SSD(nn.Module):\n def __init__(self):\n super().__init__()\n # vgg16中conv1_1到conv4_3再加上一个pool两个conv\n self.vgg = vgg(vgg_base['300'],vgg_pretrain=True)\n # vgg300的特征缩放配置文件\n self.extras = add_extras(extras_base['300'])\n self.l2_norm = L2Norm(512, scale=20)\n self.cls_blocks,self.reg_blocks = cls_reg_blocks()\n\n def forward(self, x):\n # :x [10, 3, 300, 300] 输入图片\n # :return: [10, 8732, 18] [10, 8732, 4] SSD网络预测的修正系数与分类概率\n # target_labels (batch_size, num_anchors): 所有框的真实类别\n # target_locs (batch_size, num_anchors, 4): 所有框真实的位置\n features = []\n # vgg16的前23层\n for i in range(23):\n x = self.vgg[i](x)\n s = self.l2_norm(x) # x为现vgg网络最后一个maxpool之前的特征图 torch.Size([10, 512, 38, 38]) 也是后续第一个特征图\n features.append(s)\n # vgg16尾部魔改的部分\n for i in range(23, len(self.vgg)):\n x = self.vgg[i](x)\n features.append(x) # s.shape [10, 1024, 19, 19]\n # 特征缩放的部分\n for k, v in enumerate(self.extras):\n x = F.relu(v(x), inplace=True)\n if k % 2 == 1:\n features.append(x)\n # features 最后是整个SSD中出现的6个特征图\n # [10, 512, 38, 38] [10, 1024, 19, 19] [10, 512, 10, 10] [10, 256, 5, 5] [10, 256, 3, 3] [10, 256, 1, 1]\n # 对输入的特征图中每个特征点进行分类及回归(不同特征图特征点对应的输出数是不一样的,以检测框数量为准)\n pred_cls = []\n pred_locs = []\n batch_size = features[0].shape[0]\n # 六个特征图与其对应的分类与定位卷积\n for feature, cls_block, reg_block in zip(features, self.cls_blocks, self.reg_blocks):\n pred_cls.append(cls_block(feature).permute(0, 2, 3, 1))\n pred_locs.append(reg_block(feature).permute(0, 2, 3, 1))\n # 将六个特征图每个特征点上的不同anchor预测得出的各类置信度合并到一起\n # [batch_size, num_anchors*num_classes]) -> [batch_size, num_anchors, num_classes]\n pred_cls = torch.cat([c.reshape(batch_size, -1) for c in pred_cls], dim=1).view(batch_size, -1, cfg_.num_classes)\n # 将六个特征图每个特征点上的不同anchor预测得出的各个修正系数合并到一起\n # [batch_size, num_anchors*4] -> [batch_size, num_anchors, 4]\n pred_locs = torch.cat([l.reshape(batch_size, -1) for l in pred_locs], dim=1).view(batch_size, -1, 4)\n return pred_locs, pred_cls\n\n\nvgg_base = { # vgg中第23层↓\n '300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M', 512, 512, 512],\n # '512': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M', 512, 512, 512],\n}\n\n\ndef vgg(cfg, vgg_pretrain=True):\n # 创建经过魔改的vgg特征提取层 :原生vgg16去fc层+一个pool两个conv\n layers = []\n in_channels = 3\n for v in cfg:\n if v == 'M':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n elif v == 'C':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]\n else:\n conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n # layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n # 采用没有bn的vgg\n layers += [conv2d, nn.ReLU(inplace=True)]\n in_channels = v\n pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)\n conv7 = nn.Conv2d(1024, 1024, kernel_size=1)\n layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]\n vgg_layers = nn.ModuleList(layers)\n # 是否加载已经训练好的模型\n if vgg_pretrain:\n # 加载已经训练好的vgg模型,不包括extras_base层,除非你从头开始训练.否则,这个模型可以不用下载\n url = 'https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth'\n # 下载路径\n weight_path = cfg_.vgg16_reducedfc\n if not os.path.exists(weight_path):\n print('模型不存在,下载中')\n wget.download(url=url, out=weight_path)\n print('下载完成')\n print(' --- load weight finish ---')\n vgg_layers.load_state_dict(torch.load(weight_path))\n return vgg_layers\n\n\nextras_base = {\n '300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],\n # '512': [256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256],\n}\n\n\ndef add_extras(cfg):\n layers = [] # 额外添加的特征缩放层\n in_channels = 1024 # 是每次conv的输入通道数\n flag = False\n for k, v in enumerate(cfg):\n if in_channels != 'S':\n # 这里进行是否等于'S'的判断作用在于想在特征图19*19 -> 10*10以及10*10 -> 5*5时添加padding以使特征图尺寸能顺利减半\n if v == 'S':\n layers += [nn.Conv2d(in_channels, cfg[k + 1], kernel_size=(1, 3)[flag], stride=2, padding=1)]\n else:\n layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]\n flag = not flag\n in_channels = v\n # if size == 512: 如果是SSD512的话后面还需要添加两个conv\n # layers.append(nn.Conv2d(in_channels, 128, kernel_size=1, stride=1))\n # layers.append(nn.Conv2d(128, 256, kernel_size=4, stride=1, padding=1))\n return nn.ModuleList(layers)\n\n\ndef cls_reg_blocks():\n # 针对不同的特征图创建不同的定位和分类卷积,然后初始化其中的权重\n cls_blocks = nn.ModuleList()\n reg_blocks = nn.ModuleList()\n # 创建针对不同特征图的定位和分类卷积\n for anchors_per_feature, c_out in zip([4, 6, 6, 6, 4, 4], [512, 1024, 512, 256, 256, 256]):\n cls_blocks.append(nn.Conv2d(c_out, anchors_per_feature * cfg_.num_classes, kernel_size=3, stride=1, padding=1))\n reg_blocks.append(nn.Conv2d(c_out, anchors_per_feature * 4, kernel_size=3, stride=1, padding=1))\n # 参数初始化\n for ms in (cls_blocks,reg_blocks):\n for m in ms:\n nn.init.xavier_uniform_(m.weight)\n nn.init.zeros_(m.bias)\n return cls_blocks,reg_blocks\n\n\nclass L2Norm(nn.Module):\n def __init__(self, n_channels, scale):\n # 对于conv4_3后的特征图进行L2归一化,和普通的bn不同,它只针对与channels上的归一化,可以加快网络收敛\n # 详情参考 https://zhuanlan.zhihu.com/p/39399799\n super(L2Norm, self).__init__()\n self.gamma = scale or None\n self.eps = 1e-10\n self.weight = nn.Parameter(torch.Tensor(n_channels))\n nn.init.constant_(self.weight, self.gamma)\n\n def forward(self, x):\n norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps\n x = torch.div(x, norm)\n out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x\n return out\n", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 7287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 51, "usage_type": "call"}, {"api_name": "config.cfg.num_classes", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "config.cfg.vgg16_reducedfc", "line_number": 89, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 89, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "config.cfg.num_classes", "line_number": 130, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.init.zeros_", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn.init.constant_", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.div", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "456417418", "text": "import json\nimport pymysql\nimport urllib\nfrom decimal import *\nfrom datetime import datetime\nfrom Condition import Condition\nfrom JSONBuilder import JSONBuilder\n\n#This lambda functions receives a scenario Id and then connects to several different sources to then evaluate the\n#points and data associated with that scenario\ndef lambda_handler(event, context):\n \n scenarioId = event['id']\n \n ListOfSnapshots = []\n \n conn = pymysql.connect(host='test1.ce8cn9mhhgds.us-east-1.rds.amazonaws.com', user='Wallen', passwd='MyRDSdb1', db='whattodo')\n myCursor = conn.cursor()\n \n myCursor.execute(\"\"\"SELECT ConditionFiveName, ConditionFourName, ConditionThreeName, ConditionTwoName, ConditionOneName\n FROM Scenario WHERE Id = %s\"\"\", (scenarioId))\n tplConditions = myCursor.fetchall()\n lstConditions = list(tplConditions[0])\n \n \n myCursor.execute(\"\"\"SELECT ConditionFiveType, ConditionFourType, ConditionThreeType, ConditionTwoType, ConditionOneType\n FROM Scenario WHERE Id = %s\"\"\", (scenarioId))\n tplConTypes = myCursor.fetchall()\n \n myCursor.execute(\"\"\"SELECT ConditionFivePreference, ConditionFourPreference, ConditionThreePreference, ConditionTwoPreference, ConditionOnePreference\n FROM Scenario WHERE Id = %s\"\"\", (scenarioId))\n tplPref = myCursor.fetchall()\n \n \n myCursor.execute(\"\"\"SELECT ConditionFiveRank, ConditionFourRank, ConditionThreeRank, ConditionTwoRank, ConditionOneRank\n FROM Scenario WHERE Id = %s\"\"\", (scenarioId))\n tplConRanks = myCursor.fetchall()\n \n \n lstCurrentStatus = []\n lstConEvalTypes = []\n \n \n w = 0\n for i in tplConditions[0]:\n if (i == None):\n continue\n \n else: \n myCursor.execute(\"\"\"SELECT EvaluationType FROM \"\"\" + tplConTypes[0][w] + \"\"\" WHERE `Name` = %s\"\"\", (i))\n myEvalItems = myCursor.fetchall()\n \n #print(\"myitems: \" + str(myItems))\n \n lstConEvalTypes.append(myEvalItems[0][0])\n \n w = w + 1\n \n \"\"\"\n print('Names:')\n print(tplConditions)\n print('---------')\n print('Preferences:')\n print(tplPref)\n print('---------')\n print('Types:')\n print(tplConTypes)\n print('---------')\n print('Rank')\n print(tplConRanks)\n print('---------')\n \"\"\"\n x = 0\n while x < 40:\n \n SnapList = []\n z = 0\n for i in tplConditions[0]:\n if (i == None):\n z = z + 1\n continue\n \n else:\n myCursor.execute(\"\"\"SELECT CurrentStatus FROM \"\"\" + tplConTypes[0][z] + \"\"\" WHERE `Name` = %s AND TimeGroupId = %s\"\"\", (i, x))\n myCurrentStatuses = myCursor.fetchall()\n \n myCursor.execute(\"\"\"SELECT TimeFrame FROM \"\"\" + tplConTypes[0][z] + \"\"\" WHERE `Name` = %s AND TimeGroupId = %s\"\"\", (i, x))\n myTimeFrames = myCursor.fetchall()\n \n myCursor.execute(\"\"\"SELECT ConditionDateTime FROM \"\"\" + tplConTypes[0][z] + \"\"\" WHERE `Name` = %s AND TimeGroupId = %s\"\"\", (i, x))\n myConditionDateTimes = myCursor.fetchall()\n \n #print('inner loop for each condition: ' + str(z))\n \n #print(tplConditions[0][z])\n #print(tplConTypes[0][z])\n #print(tplPref[0][z])\n #print('Current Status')\n #print(myCurrentStatuses[0][0])\n #print(lstConEvalTypes[z])\n #print(tplConRanks[0][z])\n #print(myTimeFrames[0][0])\n \n CurrentCondition = Condition(tplConditions[0][z], tplConTypes[0][z], tplPref[0][z],\n myCurrentStatuses[0][0], lstConEvalTypes[z], tplConRanks[0][z],\n myTimeFrames[0][0], myConditionDateTimes[0][0])\n \"\"\"\n print('------')\n print(CurrentCondition.Name)\n print(CurrentCondition.Type)\n print(CurrentCondition.Preference)\n print(CurrentCondition.CurrentStatus)\n print(CurrentCondition.Rank)\n print(CurrentCondition.TimeFrame)\n print(CurrentCondition.EvalType)\n print(CurrentCondition.ConditionDateTime)\n \"\"\"\n \n SnapList.append(CurrentCondition)\n \n z = z + 1\n \n \n ListOfSnapshots.append(SnapList)\n x = x + 1\n \n JSONInit = {}\n keyValueList = []\n \n \n #print(ListOfSnapshots)\n #print(ListOfSnapshots[0][1].Preference) #The List of list of condition objects is being correctly generated\n \n \n #print(\"List of Snapshots[4][0].Evaluate(): \")\n #print(ListOfSnapshots[4][0])\n #print(ListOfSnapshots[23][2].Evaluate())\n #print(ListOfSnapshots[4][3].Evaluate())\n \n h = 0\n for c in ListOfSnapshots: \n \n #print(h)\n \n #print('type of object in c')\n #print(c)\n #print(type(c[0].Name))\n \n PointSum = 0\n \n Values = {}\n Preferences = {}\n Ranks = {}\n \n \n d = 0\n while (d < len(c)):\n #print(\"Does this look to be lining up: \")\n #print(\"Preference: \" + c[d].Preference)\n #print(\"Current Status: \" + c[d].CurrentStatus)\n \n namelist = len(c)\n \n \n Values.update({c[d].Name : c[d].CurrentStatus})\n Preferences.update({c[d].Name : c[d].Preference})\n Ranks.update({c[d].Name : c[d].Rank})\n #print(Values)\n \n \n \n currentpoints = c[d].Evaluate()\n #print(\"current points: \" + str(currentpoints))\n \n PointSum = PointSum + currentpoints\n d = d + 1\n \n \n #print('PointSum = ' + str(PointSum))\n \n #print(c[4])\n \n #points = c[h].Evaluate()\n #print('points: ' + str(points))\n \n #TODO: APPEND ALL CURRENT VALUES TO THE END OF VALUES\n \n keyValueList.append({\"TimeGroupId\": h,\"Scenario Snap-Shot Points\": PointSum, \"TimeFrame\": ListOfSnapshots[h][0].TimeFrame, \"Condition Time\":\n str(ListOfSnapshots[h][0].ConditionDateTime), \"Status Values\": Values, \"Status Preferences\" : Preferences, \"Status Ranks\" : Ranks})\n \n h = h + 1\n \n \n print(keyValueList)\n \n return(keyValueList)\n #print(ListOfSnapshots)\n\n\"\"\"\nevent = {\n \"id\": 1\n}\ncontext = 'c'\n\nx = lambda_handler(event, context)\n\"\"\"", "sub_path": "WhatDoCompleteEvalScenario.py", "file_name": "WhatDoCompleteEvalScenario.py", "file_ext": "py", "file_size_in_byte": 6847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pymysql.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "Condition.Condition", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "144636166", "text": "# Copyright (c) Tianyu Wang. All Rights Reserved.\nfrom typing import Dict, List\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom detectron2.layers import ShapeSpec\nfrom detectron2.utils.registry import Registry\n\nfrom detectron2.modeling.anchor_generator import build_anchor_generator\nfrom detectron2.modeling.box_regression import Box2BoxTransform\nfrom detectron2.modeling.matcher import Matcher\nfrom detectron2.modeling import PROPOSAL_GENERATOR_REGISTRY, RPN_HEAD_REGISTRY\nfrom detectron2.modeling.proposal_generator.rpn_outputs import RPNOutputs, find_top_rpn_proposals\nfrom detectron2.modeling.proposal_generator.rpn import StandardRPNHead, RPN\nfrom detectron2.structures import BoxMode,Boxes\n\n\"\"\"\nRegistry for LISA RPN heads, which take CNN feature maps and perform\nobjectness classification and bounding box regression for anchors.\n\"\"\"\n\n# NOTE: `cfg.MODEL.RPN.HEAD_NAME` should be \"LISARPNHead\".\n\ndef build_rpn_head(cfg, input_shape,shadow_object_part=False):\n \"\"\"\n Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.\n \"\"\"\n name = cfg.MODEL.RPN.HEAD_NAME\n return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape,shadow_object_part)\n\n\n@RPN_HEAD_REGISTRY.register()\nclass LISARPNHead(StandardRPNHead):\n def __init__(self, cfg, input_shape: List[ShapeSpec], shadow_object_part= False):\n super(LISARPNHead, self).__init__(cfg,input_shape)\n self.shadow_object_part = shadow_object_part\n if self.shadow_object_part:\n in_channels = [s.channels for s in input_shape]\n assert len(set(in_channels)) == 1, \"Each level must have the same channel!\"\n in_channels = in_channels[0]\n self.conv = nn.Conv2d(in_channels , in_channels, kernel_size=3, stride=1, padding=1)\n for l in [self.conv]:\n nn.init.normal_(l.weight, std=0.01)\n nn.init.constant_(l.bias, 0)\n\n def forward(self, features):\n \"\"\"\n Args:\n features (list[Tensor]): list of feature maps\n \"\"\"\n \n pred_objectness_logits = []\n pred_anchor_deltas = []\n if self.shadow_object_part == False:\n pre_features = []\n for i,x in enumerate(features):\n\n t = F.relu(self.conv(x))\n\n # if self.shadow_object_part == False:\n # pre_features.append(t)\n \n pred_objectness_logits.append(self.objectness_logits(t))\n pred_anchor_deltas.append(self.anchor_deltas(t))\n \n if self.shadow_object_part == False:\n return pred_objectness_logits, pred_anchor_deltas, None\n else:\n return pred_objectness_logits, pred_anchor_deltas\n\n\ndef build_proposal_generator(cfg, input_shape, **args):\n \"\"\"\n Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.\n The name can be \"PrecomputedProposals\" to use no proposal generator.\n \"\"\"\n name = cfg.MODEL.PROPOSAL_GENERATOR.NAME\n if name == \"PrecomputedProposals\":\n return None\n\n return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape,**args)\n \n@PROPOSAL_GENERATOR_REGISTRY.register()\nclass LISARPN(RPN):\n\n def __init__(self, cfg, input_shape: Dict[str, ShapeSpec], shadow_object_part= False):\n super(LISARPN, self).__init__(cfg, input_shape)\n self.shadow_object_part = shadow_object_part\n if self.shadow_object_part:\n self.rpn_head = build_rpn_head(cfg, [input_shape[f] for f in self.in_features], self.shadow_object_part)\n \n def forward(self, images, features, gt_instances=None, pre_proposals=None):\n gt_boxes = [x.gt_boxes for x in gt_instances] if gt_instances is not None else None\n del gt_instances\n\n if self.shadow_object_part == False:\n features = [features[f] for f in self.in_features]\n pred_objectness_logits, pred_anchor_deltas, pre_features = self.rpn_head(features)\n anchors = self.anchor_generator(features)\n else:\n features = [features[f] for f in self.in_features]\n pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)\n anchors = self.anchor_generator(features)\n assert len(anchors[0]) == len(pre_proposals), \"number of pre_proposals {} and pre_anchors {} should be same.\".format(len(anchors[0]),len(pre_proposals))\n\n outputs = RPNOutputs(\n self.box2box_transform,\n self.anchor_matcher,\n self.batch_size_per_image,\n self.positive_fraction,\n images,\n pred_objectness_logits,\n pred_anchor_deltas,\n anchors,\n self.boundary_threshold,\n gt_boxes,\n self.smooth_l1_beta,\n )\n \n if self.training:\n if self.shadow_object_part == False:\n losses = {k+'_rela': v * self.loss_weight for k, v in outputs.losses().items()}\n else:\n losses = {k: v * self.loss_weight for k, v in outputs.losses().items()}\n else:\n losses = {}\n\n with torch.no_grad():\n\n \n pre_proposals = outputs.predict_proposals()\n # Find the top proposals by applying NMS and removing boxes that\n # are too small. The proposals are treated as fixed for approximate\n # joint training with roi heads. This approach ignores the derivative\n # w.r.t. the proposal boxes’ coordinates that are also network\n # responses, so is approximate.\n proposals = find_top_rpn_proposals(\n pre_proposals,\n outputs.predict_objectness_logits(),\n images,\n self.nms_thresh,\n self.pre_nms_topk[self.training],\n self.post_nms_topk[self.training],\n self.min_box_side_len,\n self.training,\n )\n # For RPN-only models, the proposals are the final output and we return them in\n # high-to-low confidence order.\n # For end-to-end models, the RPN proposals are an intermediate state\n # and this sorting is actually not needed. But the cost is negligible.\n inds = [p.objectness_logits.sort(descending=True)[1] for p in proposals]\n proposals = [p[ind] for p, ind in zip(proposals, inds)]\n if self.shadow_object_part == False:\n return proposals, losses, pre_features, pre_proposals\n else:\n return proposals, losses\n \n\n\n", "sub_path": "build/lib.linux-x86_64-3.6/detectron2/modeling/proposal_generator/LISA_rpn.py", "file_name": "LISA_rpn.py", "file_ext": "py", "file_size_in_byte": 6512, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "detectron2.modeling.RPN_HEAD_REGISTRY.get", "line_number": 30, "usage_type": "call"}, {"api_name": "detectron2.modeling.RPN_HEAD_REGISTRY", "line_number": 30, "usage_type": "name"}, {"api_name": "detectron2.modeling.proposal_generator.rpn.StandardRPNHead", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}, {"api_name": "detectron2.layers.ShapeSpec", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 59, "usage_type": "name"}, {"api_name": "detectron2.modeling.RPN_HEAD_REGISTRY.register", "line_number": 33, "usage_type": "call"}, {"api_name": "detectron2.modeling.RPN_HEAD_REGISTRY", "line_number": 33, "usage_type": "name"}, {"api_name": "detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY.get", "line_number": 82, "usage_type": "call"}, {"api_name": "detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY", "line_number": 82, "usage_type": "name"}, {"api_name": "detectron2.modeling.proposal_generator.rpn.RPN", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 87, "usage_type": "name"}, {"api_name": "detectron2.layers.ShapeSpec", "line_number": 87, "usage_type": "name"}, {"api_name": "detectron2.modeling.proposal_generator.rpn_outputs.RPNOutputs", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 129, "usage_type": "call"}, {"api_name": "detectron2.modeling.proposal_generator.rpn_outputs.find_top_rpn_proposals", "line_number": 138, "usage_type": "call"}, {"api_name": "detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY.register", "line_number": 84, "usage_type": "call"}, {"api_name": "detectron2.modeling.PROPOSAL_GENERATOR_REGISTRY", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "148111123", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# restPlugin - HTML preview of reSt formatted text in gedit\n#\n# Copyright (C) 2007 - Christophe Kibleur\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2, or (at your option)\n# 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., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n\nfrom gi.repository import Gedit\nfrom gi.repository import GObject\n\nimport os\nimport io\nfrom gi.repository import Gtk\nfrom gi.repository import WebKit\n\nfrom gettext import gettext as _\nfrom makeTable import toRSTtable\n## pygments support\n#import RegisterPygment\n## docutils\nfrom docutils.core import publish_parts\n\n## I'm not satisfied with that\nrestpluginDir = os.path.dirname(os.path.abspath(__file__))\ncss = os.path.join(restpluginDir, 'restmain.css')\nstyles = open(css, 'r')\n\nSTART_HTML = \"\"\"\n\n\n \n \n \n\n\"\"\" % (styles.read())\n\nstyles.close()\n\nEND_HTML = \"\"\"\n\n\"\"\"\n\n# Menu item example, insert a new item in the Tools menu\nui_str = \"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \n HTML\" action=\"--> HTML\"/>\n \n \n LaTeX\" action=\"--> LaTeX\"/>\n \n \n OpenOffice\" action=\"--> OpenOffice\"/>\n \n \n \n \n\n\"\"\"\n\n\nclass restPlugin(GObject.Object, Gedit.WindowActivatable):\n\n window = GObject.property(type=Gedit.Window)\n\n def __init__(self):\n GObject.Object.__init__(self)\n\n def do_activate(self):\n ## TODO : Maybe have to check the filetype ?\n\n # Store data in the window object\n windowdata = dict()\n self.window.reStPreviewData = windowdata\n\n scrolled_window = Gtk.ScrolledWindow()\n scrolled_window.set_property(\"hscrollbar-policy\",\n Gtk.PolicyType.AUTOMATIC)\n scrolled_window.set_property(\"vscrollbar-policy\",\n Gtk.PolicyType.AUTOMATIC)\n scrolled_window.set_property(\"shadow-type\",\n Gtk.ShadowType.IN)\n\n html_view = WebKit.WebView()\n html_view.load_string(\"%s\\n

reStructuredText viewer

\\n%s\" %\n (START_HTML, END_HTML), 'text/html',\n 'utf8', '')\n\n #scrolled_window.set_hadjustment(html_view.get_hadjustment())\n #scrolled_window.set_vadjustment(html_view.get_vadjustment())\n scrolled_window.add(html_view)\n scrolled_window.show_all()\n\n bottom = self.window.get_bottom_panel()\n image = Gtk.Image()\n image.set_from_icon_name(\"gnome-mime-text-html\", Gtk.IconSize.MENU)\n bottom.add(scrolled_window)\n windowdata[\"bottom_panel\"] = scrolled_window\n windowdata[\"html_doc\"] = html_view\n\n manager = self.window.get_ui_manager()\n\n ## Added later\n #separator = Gtk.SeparatorMenuItem()\n self._action_group = Gtk.ActionGroup(\"reStPluginActions\")\n self._action_group.add_actions([(\"preview\", None, _(\"reSt preview\"),\n \"R\",\n _(\"reSt preview\"),\n self.on_update_preview),\n (\"table\", None, _(\"Create Table\"),\n None, _(\"Create a reSt table\"),\n self.on_create_table),\n (\"sourcecode\", None, _(\"Paste Code\"),\n None, _(\"Paste sourcecode\"),\n self.on_paste_code),\n (\"--> HTML\", None, _(\"--> HTML\"),\n None, _(\"transform to HTML\"),\n self.on_html),\n (\"--> LaTeX\", None, _(\"--> LaTeX\"),\n None, _(\"transform to LaTeX\"),\n self.on_latex),\n (\"--> OpenOffice\", None,\n _(\"--> OpenOffice\"),\n None, _(\"transform to OpenOffice\"),\n self.on_openoffice),\n ])\n\n # Insert the action group\n manager.insert_action_group(self._action_group, -1)\n\n # Merge the UI\n self._ui_id = manager.add_ui_from_string(ui_str)\n\n def do_deactivate(self):\n # Retreive the data of the window object\n windowdata = self.window.reStPreviewData\n\n # Remove the menu action\n if 'ui_id' in windowdata:\n manager = self.window.get_ui_manager()\n manager.remove_ui(windowdata[\"ui_id\"])\n manager.remove_action_group(windowdata[\"action_group\"])\n\n # Remove the bottom panel\n bottom = self.window.get_bottom_panel()\n print('keys = %s' %\n [x.get_name() for x in bottom.get_children()])\n bottom.remove(windowdata[\"bottom_panel\"])\n\n def getSelection(self):\n view = self.window.get_active_view()\n if not view:\n return\n\n doc = view.get_buffer()\n\n start = doc.get_start_iter()\n end = doc.get_end_iter()\n\n if doc.get_selection_bounds():\n start = doc.get_iter_at_mark(doc.get_insert())\n end = doc.get_iter_at_mark(doc.get_selection_bound())\n\n text = doc.get_text(start, end) # noqa\n\n # Menu activate handlers\n def on_update_preview(self, window):\n # Retreive the data of the window object\n windowdata = self.window.reStPreviewData\n\n view = self.window.get_active_view()\n if not view:\n return\n\n doc = view.get_buffer()\n\n start = doc.get_start_iter()\n end = doc.get_end_iter()\n\n if doc.get_selection_bounds():\n start = doc.get_iter_at_mark(doc.get_insert())\n end = doc.get_iter_at_mark(doc.get_selection_bound())\n\n text = doc.get_text(start, end, False)\n html = publish_parts(text, writer_name=\"html\")[\"html_body\"]\n\n# ## Sortie\n# sortie = '\\n'.join([START_HTML, html, END_HTML])\n# fs = io.open('sortie.html', 'w', encoding='utf8')\n# fs.write(sortie)\n# fs.close()\n\n p = windowdata[\"bottom_panel\"].get_placement()\n\n html_doc = windowdata[\"html_doc\"]\n html_doc.load_string(\"%s\\n%s\\n%s\" %\n (START_HTML, html, END_HTML),\n 'text/html', 'utf8', '')\n\n windowdata[\"bottom_panel\"].set_placement(p)\n\n def on_latex(self, action):\n doc = self.window.get_active_document()\n filename = doc.get_uri_for_display()[:-4]\n pd = restpluginDir\n os.popen2('python %s/to_tex.py \"%s.rst\" \"%s.tex\"' %\n (pd, filename, filename))\n\n def on_html(self, action):\n doc = self.window.get_active_document()\n filename = doc.get_uri_for_display()[:-4]\n pd = restpluginDir\n os.popen2('python %s/to_html.py --stylesheet=%s/restmain.css '\n '\"%s.rst\" \"%s.html\"' %\n (pd, pd, filename, filename))\n\n def on_openoffice(self, action):\n doc = self.window.get_active_document()\n filename = doc.get_uri_for_display()[:-4]\n pd = restpluginDir\n os.popen2('python %s/to_odt.py --add-syntax-highlighting '\n '--stylesheet=%s/default.odt \"%s.rst\" \"%s.odt\"' %\n (pd, pd, filename, filename))\n\n def on_paste_code(self, action):\n doc = self.window.get_active_document()\n\n if not doc:\n return\n\n lines = Gtk.clipboard_get().wait_for_text().split('\\n')\n to_copy = \"\\n\".join([line for line in lines[1:]])\n doc.insert_at_cursor('..sourcecode:: ChoosenLanguage\\n\\n %s\\n' %\n lines[0])\n doc.insert_at_cursor(to_copy + '\\n\\n')\n\n def on_create_table(self, action):\n view = self.window.get_active_view()\n\n if not view:\n return\n\n indent = view.get_indent() # noqa\n\n doc = view.get_buffer()\n #print 'language=',doc.get_language()\n\n start = doc.get_start_iter()\n end = doc.get_end_iter()\n\n if doc.get_selection_bounds():\n start = doc.get_iter_at_mark(doc.get_insert())\n end = doc.get_iter_at_mark(doc.get_selection_bound())\n\n text = doc.get_text(start, end)\n doc.delete(start, end)\n\n lines = text.split(\"\\n\")\n labels = lines[0].split(',')\n rows = [row.strip().split(',') for row in lines[1:]]\n\n doc.insert_at_cursor(toRSTtable([labels] + rows))\n", "sub_path": "reStPlugin/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 10154, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "gi.repository.GObject.Object", "line_number": 90, "usage_type": "attribute"}, {"api_name": "gi.repository.GObject", "line_number": 90, "usage_type": "name"}, {"api_name": "gi.repository.Gedit.WindowActivatable", "line_number": 90, "usage_type": "attribute"}, {"api_name": "gi.repository.Gedit", "line_number": 90, "usage_type": "name"}, {"api_name": "gi.repository.GObject.property", "line_number": 92, "usage_type": "call"}, {"api_name": "gi.repository.GObject", "line_number": 92, "usage_type": "name"}, {"api_name": "gi.repository.Gedit.Window", "line_number": 92, "usage_type": "attribute"}, {"api_name": "gi.repository.Gedit", "line_number": 92, "usage_type": "name"}, {"api_name": "gi.repository.GObject.Object.__init__", "line_number": 95, "usage_type": "call"}, {"api_name": "gi.repository.GObject.Object", "line_number": 95, "usage_type": "attribute"}, {"api_name": "gi.repository.GObject", "line_number": 95, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ScrolledWindow", "line_number": 104, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 104, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.PolicyType", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 106, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.PolicyType", "line_number": 108, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 108, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ShadowType", "line_number": 110, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 110, "usage_type": "name"}, {"api_name": "gi.repository.WebKit.WebView", "line_number": 112, "usage_type": "call"}, {"api_name": "gi.repository.WebKit", "line_number": 112, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Image", "line_number": 123, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 123, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.IconSize", "line_number": 124, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 124, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ActionGroup", "line_number": 133, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 133, "usage_type": "name"}, {"api_name": "gettext.gettext", "line_number": 134, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 136, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 138, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 139, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 141, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 142, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 144, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 145, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 147, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 148, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 151, "usage_type": "call"}, {"api_name": "gettext.gettext", "line_number": 152, "usage_type": "call"}, {"api_name": "docutils.core.publish_parts", "line_number": 213, "usage_type": "call"}, {"api_name": "os.popen2", "line_number": 234, "usage_type": "call"}, {"api_name": "os.popen2", "line_number": 241, "usage_type": "call"}, {"api_name": "os.popen2", "line_number": 249, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.clipboard_get", "line_number": 259, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 259, "usage_type": "name"}, {"api_name": "makeTable.toRSTtable", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "487918238", "text": "import pytz\n\nfrom django.utils import timezone\nfrom django.utils.deprecation import MiddlewareMixin\n\n\nclass TimezoneMiddleware(MiddlewareMixin):\n \"\"\"\n Промежуточное программное обеспечение для активации пользовательского часового пояса, если он был сохранен в сеансе.\n \"\"\"\n\n def process_request(self, request):\n tzname = request.META.get('HTTP_TIME_ZONE')\n if tzname:\n timezone.activate(pytz.timezone(tzname))\n else:\n timezone.deactivate()\n", "sub_path": "common/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.utils.deprecation.MiddlewareMixin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.utils.timezone.activate", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 15, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.timezone.deactivate", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "54214219", "text": "# following https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html\n\n# import the necessary packages\nfrom sklearn.preprocessing import LabelEncoder\n# from sklearn.cross_validation import train_test_split\nfrom sklearn.model_selection import train_test_split\n\nfrom keras.models import Sequential\nfrom keras.layers import Activation\nfrom keras.optimizers import SGD\nfrom keras.layers import Dense\nfrom keras.utils import np_utils\n\nfrom keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\n\n# VGG16\nfrom keras.applications.vgg16 import VGG16\nfrom keras.models import Model\nfrom keras.layers import Input\nfrom keras import backend as K\nfrom keras import optimizers\n\nimport numpy as np\nimport argparse\nimport cv2\nimport os, os.path\n\nimport matplotlib.pyplot as plt\n\n# ---- load data ----\n\n# path to training images\ntrain_path = 'train'\n\n# path to validation images\nvalidate_path = 'validate'\n\n# images to be resized to (image_dim) x (image_dim)\nimage_dim = 128\n\nx_train = []\ny_train = []\nx_valid = []\ny_valid = []\n\n# load training data\nfor filename in next(os.walk(train_path))[2]:\n # full path is path to filename + '/' + filename\n image = cv2.imread(''.join([train_path, '/', filename]))\n # append resized image\n x_train.append(cv2.resize(image, (image_dim, image_dim)))\n # filenames are of the form {class}.{image_num}.jpg\n label = filename.split(os.path.sep)[-1].split(\".\")[0]\n # record label\n y_train.append(label)\n\n# load validation data\nfor filename in next(os.walk(validate_path))[2]:\n # full path is path to filename + '/' + filename\n image = cv2.imread(''.join([validate_path, '/', filename]))\n # append resized image\n x_valid.append(cv2.resize(image, (image_dim, image_dim)))\n # filenames are of the form {class}.{image_num}.jpg\n label = filename.split(os.path.sep)[-1].split(\".\")[0]\n # record label\n y_valid.append(label)\n\n\n# change labels from strings to integers, e.g 'cat' -> 0, 'dog' -> 1\nle = LabelEncoder()\ny_train = le.fit_transform(y_train) \ny_valid = le.fit_transform(y_valid) \n\n\n# convert data to NumPy array of floats\nx_train = np.array(x_train, np.float32)\nx_valid = np.array(x_valid, np.float32)\n\n\n\n# ---- define data generator ----\ndatagen = ImageDataGenerator() # VGG16 already rescales input images, no need for further rescaling\n\ndatagen.fit(x_train)\n\n\n\n\n# ---- define the model ----\n# VGG16\nbase_model = VGG16(input_shape=(image_dim, image_dim, 3), include_top=False, weights='imagenet')\n# base_model = VGG16(input_shape=(image_dim, image_dim, 3), include_top=False)\nx = base_model.output\nx = Flatten()(x)\nd1 = Dense(64, activation='relu')(x)\nd1 = Dropout(0.5)(d1)\npredictions = Dense(1, activation='sigmoid')(d1)\nmodel = Model(inputs=base_model.input, outputs=predictions) # final model\n\nopt = optimizers.SGD(lr=0.0001)\n\nmodel.compile(loss='binary_crossentropy',\n optimizer=opt,\n metrics=['accuracy'])\n\n\nmodel.summary()\n\n\n# ---- train the model ----\nbatch_size = 32\nnum_epochs = 10\n\nhistory = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n steps_per_epoch=len(x_train) / batch_size, epochs=num_epochs,\n validation_data=datagen.flow(x_valid, y_valid, batch_size=batch_size),\n validation_steps = len(x_valid) / batch_size)\n\n\n\n# ---- save the model and the weights ----\nmodel.save('saved_model/vgg16_catsdogs.h5')\nmodel.save_weights('saved_weight/vgg16_catsdogs_weights.h5')\nprint('model saved')\n\n\n\n# ---- display history ----\n# list all data in history\nprint(history.history.keys())\n# summarize history for accuracy\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.savefig('graph/train_test_accuracy_vgg16_augmentation.png')\nplt.clf() # clear figure\n\n# summarize history for loss (binary cross-entropy)\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.ylabel('binary cross-entropy')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.savefig('graph/train_test_loss_vgg16_augmentation.png')\nplt.clf()", "sub_path": "vgg16_transfer_imagenet/vgg16_transferlearning.py", "file_name": "vgg16_transferlearning.py", "file_ext": "py", "file_size_in_byte": 4407, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.walk", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 79, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.applications.vgg16.VGG16", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}]} +{"seq_id": "636529997", "text": "# Data Definition Module\n\nfrom .sql import Sql\n\n# Data Types for SQLite\nINT = \"INTEGER\"\nNULL = \"NULL\"\nFLOAT = \"FLOAT\"\nVARCHAR = \"TEXT\"\nRAW = \"BLOB\"\n\n\n# Column Class, set Columns Attributes\nclass Column:\n\n # Attributes\n name = None\n type = INT\n primary = False\n unique = False\n null = True\n\n # Constructor, Populate the object\n def __init__(self, name, type=INT, primary=False, unique=False, null=True):\n self.name = name\n self.type = type\n self.primary = primary\n self.unique = unique\n self.null = null\n\n\n# Connect to DataBase and Create an Object\ndef connect(database=\":memory:\"):\n return Define(database)\n\n\n# Definition Class, creates and manage tables inside the Database\nclass Define(Sql):\n\n # Show tables\n def tables(self):\n result = self.query(\"SELECT name FROM sqlite_master WHERE type = 'table'\")\n tables = []\n for item in result:\n tables.append(item[0])\n return tables\n\n # Create a new table\n def create(self, name, *columns):\n sql = \"CREATE TABLE IF NOT EXISTS %s ( \" % name\n for item in columns:\n sql += self.__columnToSql(item)\n sql += \",\"\n\n sql = sql[:-1] + \")\"\n self.run(sql)\n\n # Create a temporary table\n def __createTemp(self, name, *columns):\n from random import randrange\n temp = name + \"Temp\" + str(randrange(1, 1024))\n self.create(temp, *columns)\n return temp\n\n # Drop a table\n def drop(self, table):\n sql = \"DROP TABLE IF EXISTS %s\" % table\n self.run(sql)\n\n # Rename a Table\n def rename(self, table, name):\n sql = \"ALTER TABLE %s RENAME TO %s\" % (table, name)\n self.run(sql)\n\n # Column Manipulation\n\n # Receive a column and transform to SQL code\n def __columnToSql(self, column):\n sql = \"%s %s\" % (column.name, column.type)\n if column.primary:\n sql += \" PRIMARY KEY\"\n if column.unique:\n sql += \" UNIQUE\"\n if column.null is False:\n sql += \" NOT NULL\"\n return sql\n\n # Get Columns from Table\n def getColumns(self, table):\n sql = \"table_info(%s)\" % table\n result = self.pragma(sql)\n columns = []\n for item in result:\n column = Column(item[1])\n column.type = item[2]\n column.null = item[3] is 0\n column.primary = item[5] is 1\n columns.append(column)\n return(columns)\n\n # Add new columns to a table\n def addColumns(self, table, *columns):\n for item in columns:\n item.primary = False\n item.unique = False\n sql = \"ALTER TABLE %s ADD COLUMN \" % table\n sql += self.__columnToSql(item)\n self.run(sql)\n\n # Drop a column from the table\n def dropColumn(self, table, name):\n columns = self.getColumns(table)\n for item in columns:\n if item.name == name:\n columns.remove(item)\n tempTable = self.__createTemp(table, *columns)\n sql = \"INSERT INTO %s SELECT \" % tempTable\n for item in columns:\n sql += \"%s,\" % item.name\n sql = sql[:-1] + \" FROM %s\" % table\n self.run(sql)\n self.drop(table)\n self.rename(tempTable, table)\n\n # Rename a column from the table\n def renameColumn(self, table, column, name):\n columns = self.getColumns(table)\n for item in columns:\n if item.name == column:\n item.name = name\n tempTable = self.__createTemp(table, *columns)\n sql = \"INSERT INTO %s SELECT * FROM %s\" % (tempTable, table)\n self.run(sql)\n self.drop(table)\n self.rename(tempTable, table)\n", "sub_path": "sqlight/define.py", "file_name": "define.py", "file_ext": "py", "file_size_in_byte": 3737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sql.Sql", "line_number": 38, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "301401408", "text": "from flask import Flask\nfrom flask_pymongo import PyMongo\n\napp = Flask(__name__)\napp.config['MONGO_URI'] = \"mongodb://localhost:27017/DummyDB\"\nmongo = PyMongo(app)\n\n\n@app.route(\"/\")\ndef main():\n return \"it's working !\"\n\n\n@app.route(\"/install\")\ndef install():\n collections = [\"Users\", \"Products\", \"Bills\"]\n for coll in collections:\n mongo.db.create_collection(coll)\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", debug=True, use_reloader=True, threaded=True)\n", "sub_path": "assistant_app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "191190266", "text": "import collections\nimport copy\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom absl import app\nfrom torch.utils.data import DataLoader, Dataset\n\nPADDING_TOKEN = 0\nCKPT_VOCABULARY_SIZE = 82\nCKPT_EMBEDDING_DIM = 256\nCKPT_HIDDEN_SIZE = 128\n\n\nclass VisualizeInternalGates(nn.Module):\n\n def __init__(self):\n super().__init__()\n vocabulary_size = CKPT_VOCABULARY_SIZE\n embedding_dim = CKPT_EMBEDDING_DIM\n hidden_size = CKPT_HIDDEN_SIZE\n\n self.embedding = nn.Embedding(num_embeddings=vocabulary_size,\n embedding_dim=embedding_dim,\n padding_idx=PADDING_TOKEN)\n self.rnn_model = VisualizeGRUCell(input_size=embedding_dim,\n hidden_size=hidden_size)\n self.classifier = nn.Linear(hidden_size, vocabulary_size)\n return\n\n def forward(self, batch_reviews):\n data = self.embedding(batch_reviews)\n\n state = None\n batch_size, total_steps, _ = data.shape\n internals = []\n for step in range(total_steps):\n next_h, gate_signals = self.rnn_model(data[:, step, :], state)\n internals.append(gate_signals)\n state = next_h\n\n logits = self.classifier(state)\n\n internals = list(zip(*internals))\n outputs = {\n 'update_signals': internals[0],\n 'reset_signals': internals[1],\n 'cell_state_candidates': internals[2],\n }\n return logits, outputs\n\n\nclass VisualizeGRUCell(nn.Module):\n\n def __init__(self, input_size, hidden_size):\n super().__init__()\n\n self.input_size = input_size\n self.hidden_size = hidden_size\n\n self.W_z = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))\n self.W_r = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))\n self.W = nn.Parameter(torch.Tensor(hidden_size, hidden_size + input_size))\n\n self.reset_parameters()\n\n def forward(self, x, prev_state):\n if prev_state is None:\n batch = x.shape[0]\n prev_h = torch.zeros((batch, self.hidden_size), device=x.device)\n else:\n prev_h = prev_state\n\n concat_hx = torch.cat((prev_h, x), dim=1)\n z = torch.sigmoid(F.linear(concat_hx, self.W_z))\n r = torch.sigmoid(F.linear(concat_hx, self.W_r))\n h_tilde = torch.tanh(F.linear(torch.cat((r * prev_h, x), dim=1), self.W))\n next_h = (1 - z) * prev_h + z * h_tilde\n return next_h, (z, r, h_tilde)\n\n def reset_parameters(self):\n sqrt_k = (1. / self.hidden_size)**0.5\n with torch.no_grad():\n for param in self.parameters():\n param.uniform_(-sqrt_k, sqrt_k)\n return\n\n def extra_repr(self):\n return 'input_size={}, hidden_size={}'.format(self.input_size,\n self.hidden_size)\n\n\nclass VisualizeWarAndPeaceDataset(Dataset):\n\n def __init__(self, vocabulary):\n self.vocabulary = vocabulary\n\n # Hardcode the parameters to match the provided checkpoint\n txt_path = 'data/war_and_peace_visualize.txt'\n\n with open(txt_path, 'rb') as fp:\n raw_text = fp.read().strip().decode(encoding='utf-8')\n\n self.data = raw_text.split('\\n')\n\n self.char2index = {x: i for (i, x) in enumerate(self.vocabulary)}\n self.index2char = {i: x for (i, x) in enumerate(self.vocabulary)}\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n return np.array([self.char2index[x] for x in self.data[index]]), -1\n\n def convert_to_chars(self, sequence):\n if isinstance(sequence, torch.Tensor):\n sequence = sequence.squeeze(0).detach().numpy().tolist()\n return [self.index2char[x] for x in sequence]\n\n\ndef visualize_internals(sequence_id,\n sequence,\n gate_name,\n states,\n saving_dir='visualize/'):\n states = torch.cat(states, dim=0).detach().numpy().T\n hidden_size, time_stamps = states.shape\n fig, ax = plt.subplots(figsize=(time_stamps / 5, hidden_size / 5))\n\n if gate_name in ['update_signals', 'reset_signals']:\n vmin = 0\n elif gate_name == 'cell_state_candidates':\n vmin = -1\n else:\n raise ValueError\n\n sns.heatmap(states,\n cbar=False,\n square=True,\n linewidth=0.05,\n xticklabels=sequence,\n yticklabels=False,\n vmin=vmin,\n vmax=1,\n cmap='bwr',\n ax=ax)\n\n plt.xlabel('Sequence')\n plt.ylabel('Hidden Cells')\n\n ax.xaxis.set_ticks_position('top')\n\n plt.tight_layout()\n os.makedirs(saving_dir, exist_ok=True)\n plt.savefig(\n os.path.join(saving_dir,\n 'S%02d_' % sequence_id + gate_name.lower() + '.png'))\n return\n\n\ndef war_and_peace_visualizer():\n\n #domain\n model_domain = VisualizeInternalGates()\n model_domain.load_state_dict(torch.load('data/war_and_peace_model_checkpoint.pt'))\n print(model_domain.features)\n # for i in range(5):\n # kernels_domain = model_domain.features[arr[i]].weight.detach()\n # name = \"conv2D_\"+str(i)+\"_domain\"\n # visualize_kernels(name, kernels_domain)\n\n return\n\n\ndef main(unused_argvs):\n war_and_peace_visualizer()\n\n\nif __name__ == '__main__':\n app.run(main)\n", "sub_path": "rnn_practice/visualization.py", "file_name": "visualization.py", "file_ext": "py", "file_size_in_byte": 5229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 170, "usage_type": "call"}, {"api_name": "absl.app.run", "line_number": 185, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 185, "usage_type": "name"}]} +{"seq_id": "38474439", "text": "\n\"\"\"\npy.test module for unit testing the resample_spec step.\n\"\"\"\n\nimport os\nimport time\nimport pytest\nimport logging\nfrom glob import glob\nfrom astropy.io import fits\nfrom jwst.resample import ResampleSpecStep\n\nfrom nirspec_pipe_testing_tool.utils import change_filter_opaque2science\nfrom . import resample_utils\nfrom .. import core_utils\nfrom .. import TESTSDIR\n\n\n\n# HEADER\n__author__ = \"M. A. Pena-Guerrero\"\n__version__ = \"1.2\"\n\n# HISTORY\n# Nov 2017 - Version 1.0: initial version completed\n# Mar 2019 - Version 1.1: separated completion from other tests\n# Apr 2019 - Version 1.2: implemented logging capability\n\n\n# Set up the fixtures needed for all of the tests, i.e. open up all of the FITS files\n\n# Default names of pipeline input and output files\n@pytest.fixture(scope=\"module\")\ndef set_inandout_filenames(request, config):\n step = \"resample_spec\"\n step_info = core_utils.set_inandout_filenames(step, config)\n step_input_filename, step_output_filename, in_file_suffix, out_file_suffix, True_steps_suffix_map = step_info\n return step, step_input_filename, step_output_filename, in_file_suffix, out_file_suffix, True_steps_suffix_map\n\n\n# fixture to read the output file header\n@pytest.fixture(scope=\"module\")\ndef output_hdul(set_inandout_filenames, config):\n set_inandout_filenames_info = core_utils.read_info4outputhdul(config, set_inandout_filenames)\n step, txt_name, step_input_file, step_output_file, run_calwebb_spec2, outstep_file_suffix = set_inandout_filenames_info\n run_pipe_step = config.getboolean(\"run_pipe_steps\", step)\n # determine which tests are to be run\n resample_spec_completion_tests = config.getboolean(\"run_pytest\", \"_\".join((step, \"completion\", \"tests\")))\n #resample_spec_reffile_tests = config.getboolean(\"run_pytest\", \"_\".join((step, \"reffile\", \"tests\")))\n #resample_spec_validation_tests = config.getboolean(\"run_pytest\", \"_\".join((step, \"validation\", \"tests\")))\n run_pytests = [resample_spec_completion_tests]#, resample_spec_reffile_tests, resample_spec_validation_tests]\n\n end_time = '0.0'\n # Only run step if data is not IFU or BOTS\n mode_used = config.get(\"calwebb_spec2_input_file\", \"mode_used\").lower()\n output_directory = config.get(\"calwebb_spec2_input_file\", \"output_directory\")\n initial_input_file = config.get(\"calwebb_spec2_input_file\", \"input_file\")\n initial_input_file = os.path.join(output_directory, initial_input_file)\n detector = fits.getval(initial_input_file, \"DETECTOR\", 0)\n if not os.path.isfile(initial_input_file):\n pytest.skip(\"Skipping \"+step+\" because the initial input file given in PTT_config.cfg does not exist.\")\n\n if mode_used != \"bots\" and mode_used != \"ifu\":\n # if run_calwebb_spec2 is True calwebb_spec2 will be called, else individual steps will be ran\n step_completed = False\n\n # check if the filter is to be changed\n change_filter_opaque = config.getboolean(\"calwebb_spec2_input_file\", \"change_filter_opaque\")\n if change_filter_opaque:\n is_filter_opaque, step_input_filename = change_filter_opaque2science.change_filter_opaque(step_input_file, step=step)\n if is_filter_opaque:\n filter_opaque_msg = \"With FILTER=OPAQUE, the calwebb_spec2 will run up to the extract_2d step. Resample pytest now set to Skip.\"\n print(filter_opaque_msg)\n core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)\n pytest.skip(\"Skipping \"+step+\" because the input file does not exist.\")\n\n if run_calwebb_spec2:\n hdul = core_utils.read_hdrfits(step_output_file, info=False, show_hdr=False)\n return hdul, step_output_file, run_pytests\n else:\n\n if run_pipe_step:\n # Create the logfile for PTT, but erase the previous one if it exists\n PTTcalspec2_log = os.path.join(output_directory, 'PTT_calspec2_'+detector+'_'+step+'.log')\n if os.path.isfile(PTTcalspec2_log):\n os.remove(PTTcalspec2_log)\n print(\"Information outputed to screen from PTT will be logged in file: \", PTTcalspec2_log)\n for handler in logging.root.handlers[:]:\n logging.root.removeHandler(handler)\n logging.basicConfig(filename=PTTcalspec2_log, level=logging.INFO)\n # print pipeline version\n import jwst\n pipeline_version = \"\\n *** Using jwst pipeline version: \"+jwst.__version__+\" *** \\n\"\n print(pipeline_version)\n logging.info(pipeline_version)\n if change_filter_opaque:\n logging.info(filter_opaque_msg)\n\n if os.path.isfile(step_input_file):\n\n msg = \" *** Step \"+step+\" set to True\"\n print(msg)\n logging.info(msg)\n stp = ResampleSpecStep()\n\n # check that previous pipeline steps were run up to this point\n core_utils.check_completed_steps(step, step_input_file)\n\n # get the right configuration files to run the step\n local_pipe_cfg_path = config.get(\"calwebb_spec2_input_file\", \"local_pipe_cfg_path\")\n # start the timer to compute the step running time\n start_time = time.time()\n if local_pipe_cfg_path == \"pipe_source_tree_code\":\n result = stp.call(step_input_file)\n else:\n result = stp.call(step_input_file, config_file=local_pipe_cfg_path+'/resample_spec.cfg')\n result.save(step_output_file)\n # end the timer to compute the step running time\n end_time = repr(time.time() - start_time) # this is in seconds\n msg = \"Step \"+step+\" took \"+end_time+\" seconds to finish\"\n print(msg)\n logging.info(msg)\n step_completed = True\n hdul = core_utils.read_hdrfits(step_output_file, info=False, show_hdr=False)\n\n # rename and move the pipeline log file\n try:\n calspec2_pilelog = \"calspec2_pipeline_\"+step+\"_\"+detector+\".log\"\n pytest_workdir = TESTSDIR\n logfile = glob(pytest_workdir+\"/pipeline.log\")[0]\n os.rename(logfile, os.path.join(output_directory, calspec2_pilelog))\n except:\n IndexError\n\n # add the running time for this step\n core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)\n return hdul, step_output_file, run_pytests\n\n else:\n msg = \" The input file does not exist. Skipping step.\"\n print(msg)\n logging.info(msg)\n core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)\n pytest.skip(\"Skipping \"+step+\" because the input file does not exist.\")\n\n else:\n msg = \"Skipping running pipeline step \"+step\n print(msg)\n logging.info(msg)\n end_time = core_utils.get_stp_run_time_from_screenfile(step, detector, output_directory)\n if os.path.isfile(step_output_file):\n hdul = core_utils.read_hdrfits(step_output_file, info=False, show_hdr=False)\n step_completed = True\n # add the running time for this step\n core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)\n return hdul, step_output_file, run_pytests\n else:\n step_completed = False\n # add the running time for this step\n core_utils.add_completed_steps(txt_name, step, outstep_file_suffix, step_completed, end_time)\n pytest.skip(\"Test skipped because input file \"+step_output_file+\" does not exist.\")\n\n else:\n pytest.skip(\"Skipping \"+step+\" because data is either IFU or BOTS.\")\n\n\n# Unit tests\n\ndef test_s_resample_exists(output_hdul):\n # want to run this pytest?\n # output_hdul[2] = resample_spec_completion_tests, resample_spec_reffile_tests, resample_spec_validation_tests\n run_pytests = output_hdul[2][0]\n if not run_pytests:\n msg = \"Skipping completion pytest: option to run Pytest is set to False in PTT_config.cfg file.\\n\"\n print(msg)\n logging.info(msg)\n pytest.skip(msg)\n else:\n msg = \"\\n * Running completion pytest...\\n\"\n print(msg)\n logging.info(msg)\n assert resample_utils.s_resamp_exists(output_hdul[0]), \"The keyword S_RESAMP was not added to the header --> Resample step was not completed.\"\n\n", "sub_path": "nirspec_pipe_testing_tool/calwebb_spec2_pytests/K_resample/test_resample.py", "file_name": "test_resample.py", "file_ext": "py", "file_size_in_byte": 9023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pytest.fixture", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getval", "line_number": 60, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 62, "usage_type": "call"}, {"api_name": "nirspec_pipe_testing_tool.utils.change_filter_opaque2science.change_filter_opaque", "line_number": 71, "usage_type": "call"}, {"api_name": "nirspec_pipe_testing_tool.utils.change_filter_opaque2science", "line_number": 71, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 89, "usage_type": "attribute"}, {"api_name": "logging.root.removeHandler", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 90, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 91, "usage_type": "attribute"}, {"api_name": "jwst.__version__", "line_number": 94, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 104, "usage_type": "call"}, {"api_name": "jwst.resample.ResampleSpecStep", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 123, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 131, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 143, "usage_type": "call"}, {"api_name": "pytest.skip", "line_number": 145, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 162, "usage_type": "call"}, {"api_name": "pytest.skip", "line_number": 165, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 177, "usage_type": "call"}, {"api_name": "pytest.skip", "line_number": 178, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "330368058", "text": "\"\"\" Further selection of dt.cc\n\"\"\"\nimport numpy as np\nfrom dataset_cc import read_fsta, read_fpha_dict, calc_dist_km\nimport config\n\ncfg = config.Config()\n# i/o paths\nfdt_in = 'input/dt_all.cc'\nfdt_out = open('input/dt.cc','w')\nfpha = 'input/phase.temp'\nevent_dict = read_fpha_dict(fpha)\nfsta = cfg.fsta\nsta_dict = read_fsta(fsta)\n# thres for linking event pairs\ncc_thres = cfg.cc_thres[1]\nloc_dev_thres = cfg.loc_dev_thres[1]\ndep_dev_thres = cfg.dep_dev_thres[1]\ndist_thres = cfg.dist_thres[1]\ndt_thres = cfg.dt_thres[1]\nnum_sta_thres = cfg.num_sta_thres[1]\n\n\n# read dt.cc\nprint('reading %s'%fdt_in)\ndt_list = []\nf=open(fdt_in); lines=f.readlines(); f.close()\nfor i,line in enumerate(lines):\n if i%1e6==0: print('done/total %s/%s | %s pairs selected'%(i,len(lines),len(dt_list)))\n codes = line.split()\n if line[0]=='#':\n to_add = True\n data_id, temp_id = codes[1:3]\n if data_id not in event_dict or temp_id not in event_dict: \n to_add = False; continue\n data_lat, data_lon, data_dep = event_dict[data_id][0]\n temp_lat, temp_lon, temp_dep = event_dict[temp_id][0]\n # 1. select loc dev\n loc_dev = calc_dist_km([data_lat,temp_lat], [data_lon,temp_lon])\n dep_dev = abs(data_dep - temp_dep)\n if not (loc_devdist_thres: continue\n # select by CC\n dt, wht = [float(code) for code in codes[1:3]]\n cc = wht**2\n pha = codes[-1]\n if ccdt_thres[0]: continue\n if pha=='S' and abs(dt)>dt_thres[1]: continue\n dt_list[-1][-1].append([sta, line])\n\n# write dt.cc\nprint('write input/dt.cc')\nfor [[data_id, temp_id], head_line, pha_dt_list] in dt_list:\n sta_list = np.unique([sta for [sta, _] in pha_dt_list])\n if len(sta_list)%(thisidta)s ''',\n {'script':0,'thisidta':row['idta']}):\n endidta = row3['endidta']\n if not endidta:\n endidta = sys.maxint - 1\n #reinject\n for row4 in botslib.query('''SELECT idta\n FROM ta\n WHERE idta<%(endidta)s\n AND idta>%(rootidta)s \n AND status=%(status)s \n AND statust=%(statust)s\n AND tochannel=%(tochannel)s ''',\n {'statust':OK,'status':RAWOUT,'rootidta':rootidta,'endidta':endidta,'tochannel':row['tochannel']}):\n retransmit = True\n ta_outgoing = botslib.OldTransaction(row4['idta'])\n ta_outgoing_copy = ta_outgoing.copyta(status=RAWOUT,statust=OK)\n ta_outgoing.update(statust=DONE)\n return retransmit\n\n\n@botslib.log_session\ndef prepareautomaticrecommunication():\n ''' reinjects all files for which communication failed (status = RAWOUT)\n '''\n retransmit = False #indicate retransmit\n #bots keeps track of last time automaticretrycommunication was done; reason is mainly performance\n startidta = max(botslib.keeptrackoflastretry('bots__automaticretrycommunication',botslib.getlastrun()),botslib.get_idta_last_error())\n #reinject\n for row4 in botslib.query('''SELECT idta\n FROM ta\n WHERE idta>%(startidta)s\n AND status=%(status)s \n AND statust=%(statust)s ''',\n {'statust':OK,'status':RAWOUT,'startidta':startidta}):\n retransmit = True\n ta_outgoing = botslib.OldTransaction(row4['idta'])\n ta_outgoing_copy = ta_outgoing.copyta(status=RAWOUT,statust=OK)\n ta_outgoing.update(statust=DONE)\n return retransmit\n\n\n@botslib.log_session\ndef prepareretry():\n ''' reinjects all files for which communication failed (status = RAWOUT)\n '''\n retransmit = False #indicate retransmit\n #bots keeps track of last time retry was done; reason is mainly performance\n startidta = max(botslib.keeptrackoflastretry('bots__retry',botslib.getlastrun()),botslib.get_idta_last_error())\n #reinject\n for row4 in botslib.query('''SELECT idta,status\n FROM ta\n WHERE idta>%(startidta)s\n AND statust=%(statust)s ''',\n {'statust':OK,'startidta':startidta}):\n retransmit = True\n ta_outgoing = botslib.OldTransaction(row4['idta'])\n ta_outgoing_copy = ta_outgoing.copyta(status=row4['status'],statust=OK)\n ta_outgoing.update(statust=DONE)\n return retransmit\n\n\n@botslib.log_session\ndef routedispatcher(routestorun,type=None):\n ''' run all route(s). '''\n if type == '--retransmit':\n if not prepareretransmit():\n return 0\n elif type == '--retrycommunication':\n if not preparerecommunication():\n return 0\n elif type == '--automaticretrycommunication':\n if not prepareautomaticrecommunication():\n return 0\n elif type == '--retry':\n if not prepareretry():\n return 0\n stuff2evaluate = botslib.getlastrun()\n botslib.set_minta4query()\n for route in routestorun:\n foundroute=False\n for routedict in botslib.query('''SELECT idroute ,\n fromchannel_id as fromchannel,\n tochannel_id as tochannel,\n fromeditype,\n frommessagetype,\n alt,\n frompartner_id as frompartner,\n topartner_id as topartner,\n toeditype,\n tomessagetype,\n seq,\n frompartner_tochannel_id,\n topartner_tochannel_id,\n testindicator,\n translateind,\n defer\n FROM routes\n WHERE idroute=%(idroute)s\n AND active=%(active)s\n ORDER BY seq''',\n {'idroute':route,'active':True}):\n botsglobal.logger.info(_(u'running route %(idroute)s %(seq)s'),{'idroute':routedict['idroute'],'seq':routedict['seq']})\n botslib.setrouteid(routedict['idroute'])\n foundroute=True\n router(routedict)\n botslib.setrouteid('')\n botsglobal.logger.debug(u'finished route %s %s',routedict['idroute'],routedict['seq'])\n if not foundroute:\n botsglobal.logger.warning(_(u'there is no (active) route \"%s\".'),route)\n return stuff2evaluate\n\n\n@botslib.log_session\ndef router(routedict):\n ''' communication.run one route. variants:\n - a route can be just script; \n - a route can do only incoming\n - a route can do only outgoing\n - a route can do both incoming and outgoing\n - at several points functions from a route script are called - if function is in route script\n '''\n #is there a user route script?\n try:\n botsglobal.logger.debug(u'(try) to read user routescript route \"%s\".',routedict['idroute'])\n userscript,scriptname = botslib.botsimport('routescripts',routedict['idroute'])\n except ImportError: #other errors, eg syntax errors are just passed\n userscript = scriptname = None\n \n #if user route script has function 'main': communication.run 'main' (and do nothing else)\n if botslib.tryrunscript(userscript,scriptname,'main',routedict=routedict):\n return #so: if function ' main' : communication.run only the routescript, nothing else.\n if not (userscript or routedict['fromchannel'] or routedict['tochannel'] or routedict['translateind']): \n raise botslib.ScriptError(_(u'Route \"$route\" is empty: no script, not enough parameters.'),route=routedict['idroute'])\n\n \n botslib.tryrunscript(userscript,scriptname,'start',routedict=routedict)\n \n #communication.run incoming channel\n if routedict['fromchannel']: #do incoming part of route: in-communication; set ready for translation; translate\n botslib.tryrunscript(userscript,scriptname,'preincommunication',routedict=routedict)\n communication.run(idchannel=routedict['fromchannel'],idroute=routedict['idroute']) #communication.run incommunication\n #add attributes from route to the received files\n where={'status':FILEIN,'fromchannel':routedict['fromchannel'],'idroute':routedict['idroute']}\n change={'editype':routedict['fromeditype'],'messagetype':routedict['frommessagetype'],'frompartner':routedict['frompartner'],'topartner':routedict['topartner'],'alt':routedict['alt']}\n botslib.updateinfo(change=change,where=where)\n \n #all received files have status FILEIN\n botslib.tryrunscript(userscript,scriptname,'postincommunication',routedict=routedict)\n \n #communication.run translation\n if routedict['translateind']:\n botslib.tryrunscript(userscript,scriptname,'pretranslation',routedict=routedict)\n if botslib.addinfo(change={'status':MAILBAG},where={'status':FILEIN,'idroute':routedict['idroute'],'editype':'mailbag'}):\n transform.splitmailbag(idroute=routedict['idroute'])\n botslib.addinfo(change={'status':TRANSLATE},where={'status':FILEIN,'idroute':routedict['idroute']})\n transform.translate(idroute=routedict['idroute'])\n botslib.tryrunscript(userscript,scriptname,'posttranslation',routedict=routedict)\n \n #merge messags & communication.run outgoing channel\n if routedict['tochannel']: #do outgoing part of route\n botslib.tryrunscript(userscript,scriptname,'premerge',routedict=routedict)\n envelope.mergemessages(idroute=routedict['idroute'])\n botslib.tryrunscript(userscript,scriptname,'postmerge',routedict=routedict)\n \n #communication.run outgoing channel\n #build for query: towhere (dict) and wherestring \n towhere=dict(status=MERGED,\n idroute=routedict['idroute'],\n editype=routedict['toeditype'],\n messagetype=routedict['tomessagetype'],\n testindicator=routedict['testindicator'])\n towhere=dict([(key, value) for (key, value) in towhere.iteritems() if value]) #remove nul-values from dict\n wherestring = ' AND '.join([key+'=%('+key+')s' for key in towhere])\n if routedict['frompartner_tochannel_id']: #use frompartner_tochannel in where-clause of query (partner/group dependent outchannel\n towhere['frompartner_tochannel_id']=routedict['frompartner_tochannel_id']\n wherestring += ''' AND (frompartner=%(frompartner_tochannel_id)s \n OR frompartner in (SELECT from_partner_id \n FROM partnergroup\n WHERE to_partner_id =%(frompartner_tochannel_id)s ))'''\n if routedict['topartner_tochannel_id']: #use topartner_tochannel in where-clause of query (partner/group dependent outchannel\n towhere['topartner_tochannel_id']=routedict['topartner_tochannel_id']\n wherestring += ''' AND (topartner=%(topartner_tochannel_id)s \n OR topartner in (SELECT from_partner_id \n FROM partnergroup\n WHERE to_partner_id=%(topartner_tochannel_id)s ))'''\n toset={'tochannel':routedict['tochannel'],'status':FILEOUT}\n botslib.addinfocore(change=toset,where=towhere,wherestring=wherestring)\n \n if not routedict['defer']: #do outgoing part of route\n botslib.tryrunscript(userscript,scriptname,'preoutcommunication',routedict=routedict)\n communication.run(idchannel=routedict['tochannel'],idroute=routedict['idroute']) #communication.run outcommunication\n botslib.tryrunscript(userscript,scriptname,'postoutcommunication',routedict=routedict)\n \n botslib.tryrunscript(userscript,scriptname,'end',routedict=routedict)\n", "sub_path": "bots/router.py", "file_name": "router.py", "file_ext": "py", "file_size_in_byte": 14840, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "botslib.query", "line_number": 16, "usage_type": "call"}, {"api_name": "botslib.change", "line_number": 21, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 26, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 32, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 36, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 43, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 45, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 11, "usage_type": "attribute"}, {"api_name": "botslib.query", "line_number": 55, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 61, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 64, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.maxint", "line_number": 78, "usage_type": "attribute"}, {"api_name": "botslib.query", "line_number": 80, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 89, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 51, "usage_type": "attribute"}, {"api_name": "botslib.keeptrackoflastretry", "line_number": 101, "usage_type": "call"}, {"api_name": "botslib.getlastrun", "line_number": 101, "usage_type": "call"}, {"api_name": "botslib.get_idta_last_error", "line_number": 101, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 103, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 110, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 95, "usage_type": "attribute"}, {"api_name": "botslib.keeptrackoflastretry", "line_number": 122, "usage_type": "call"}, {"api_name": "botslib.getlastrun", "line_number": 122, "usage_type": "call"}, {"api_name": "botslib.get_idta_last_error", "line_number": 122, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 124, "usage_type": "call"}, {"api_name": "botslib.OldTransaction", "line_number": 130, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 116, "usage_type": "attribute"}, {"api_name": "botslib.getlastrun", "line_number": 151, "usage_type": "call"}, {"api_name": "botslib.set_minta4query", "line_number": 152, "usage_type": "call"}, {"api_name": "botslib.query", "line_number": 155, "usage_type": "call"}, {"api_name": "botsglobal.logger.info", "line_number": 176, "usage_type": "call"}, {"api_name": "botsglobal.logger", "line_number": 176, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext", "line_number": 176, "usage_type": "call"}, {"api_name": "botslib.setrouteid", "line_number": 177, "usage_type": "call"}, {"api_name": "botslib.setrouteid", "line_number": 180, "usage_type": "call"}, {"api_name": "botsglobal.logger.debug", "line_number": 181, "usage_type": "call"}, {"api_name": "botsglobal.logger", "line_number": 181, "usage_type": "attribute"}, {"api_name": "botsglobal.logger.warning", "line_number": 183, "usage_type": "call"}, {"api_name": "botsglobal.logger", "line_number": 183, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext", "line_number": 183, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 136, "usage_type": "attribute"}, {"api_name": "botsglobal.logger.debug", "line_number": 198, "usage_type": "call"}, {"api_name": "botsglobal.logger", "line_number": 198, "usage_type": "attribute"}, {"api_name": "botslib.botsimport", "line_number": 199, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 204, "usage_type": "call"}, {"api_name": "botslib.ScriptError", "line_number": 207, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 207, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 210, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 214, "usage_type": "call"}, {"api_name": "communication.run", "line_number": 215, "usage_type": "call"}, {"api_name": "botslib.updateinfo", "line_number": 219, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 222, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 226, "usage_type": "call"}, {"api_name": "botslib.addinfo", "line_number": 227, "usage_type": "call"}, {"api_name": "transform.splitmailbag", "line_number": 228, "usage_type": "call"}, {"api_name": "botslib.addinfo", "line_number": 229, "usage_type": "call"}, {"api_name": "transform.translate", "line_number": 230, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 231, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 235, "usage_type": "call"}, {"api_name": "envelope.mergemessages", "line_number": 236, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 237, "usage_type": "call"}, {"api_name": "botslib.addinfocore", "line_number": 261, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 264, "usage_type": "call"}, {"api_name": "communication.run", "line_number": 265, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 266, "usage_type": "call"}, {"api_name": "botslib.tryrunscript", "line_number": 268, "usage_type": "call"}, {"api_name": "botslib.log_session", "line_number": 187, "usage_type": "attribute"}]} +{"seq_id": "13822299", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n\ndef computeCost(X,y,t):\n m = X.shape[0]\n J = 1/(2*m) * sum(sum(np.square(X@t-y)))\n return J\n\ndef gradientDescent(X,y,t,a,i):\n m = X.shape[0]\n if t.all == (t - a/m * X.T@(X@t-y)).all:\n return t\n else:\n for i in range(i):\n t = t - a/m * X.T@(X@t-y)\n i += 1\n return t\n\"\"\"\nPLOT DATA\n\"\"\"\nex1data1 = pd.read_csv('ex1data1.txt',names=['population','profit'])\nplt.scatter(ex1data1.population, ex1data1.profit, marker=\"x\",color=\"blue\")\nplt.xlabel(\"Population in 10 000\")\nplt.ylabel(\"Profit in 10 000 USD\")\nplt.savefig('ex1chart1.png')\n\n\n\"\"\"\nPREPARE MATRIX AND VECTORS\n\"\"\"\nex1X1 = np.c_[np.ones(len(ex1data1)),ex1data1.population]\nex1y1 = np.array(ex1data1.profit,ndmin=2).T\ntheta = np.zeros((ex1X1.shape[1],1))\niterations = 1500\nalpha = 0.01\nprint(computeCost(ex1X1,ex1y1,theta))\nprint(gradientDescent(ex1X1,ex1y1,theta,alpha,iterations))\nprint(computeCost(ex1X1,ex1y1,gradientDescent(ex1X1,ex1y1,theta,alpha,iterations)))\n\nx = np.linspace(5,22.5,100)\ny = gradientDescent(ex1X1,ex1y1,theta,alpha,iterations)[0] + gradientDescent(ex1X1,ex1y1,theta,alpha,iterations)[1]*x\nplt.plot(x,y,\"red\")\nplt.savefig('ex1chart2.png')\n", "sub_path": "linear_regression.py", "file_name": "linear_regression.py", "file_ext": "py", "file_size_in_byte": 1291, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.square", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.c_", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "572570389", "text": "import numpy as np\nfrom scipy import signal, misc\nimport matplotlib.pyplot as plt\n\nimg = plt.imread(\"./test.jpg\")\n# a=np.array([[1,1,1],[1,1,1],[1,1,1]])\nb = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])\n# print(a)\n# print(b)\n# imgr=img[:,:,0]\n# imgg=img[:,:,1]\n# imgb=img[:,:,2]\n# plt.imshow(imgr,\"Reds\")\n# plt.imshow(imgg,\"Greens\")\n# plt.imshow(imgb,\"Blues\")\nimgr = np.array(\n [\n [100, 100, 100, 0, 0, 0],\n [100, 100, 100, 0, 0, 0],\n [100, 100, 100, 0, 0, 0],\n [100, 100, 100, 0, 0, 0],\n [100, 100, 100, 0, 0, 0],\n [100, 100, 100, 0, 0, 0],\n ]\n)\nplt.subplot(2, 1, 1)\nplt.imshow(imgr, \"gray\")\nprint(b)\nprint(imgr)\ni = signal.convolve2d(imgr, b, \"valid\")\nprint(i)\nplt.subplot(2, 1, 2)\nplt.imshow(i, \"gray\")\nplt.show()\n", "sub_path": "statistical/cv.py", "file_name": "cv.py", "file_ext": "py", "file_size_in_byte": 767, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "matplotlib.pyplot.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "scipy.signal.convolve2d", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "99593878", "text": "import sys\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nfrom sklearn.model_selection import train_test_split\r\nfrom constant import *\r\nimport preprocess\r\n\r\n\r\ndef main(pklname):\r\n #---------------------------------------------------------------\r\n # load data\r\n #---------------------------------------------------------------\r\n tweets, labels, vocab_size = preprocess.load_data_with_labels(pklname)\r\n x_train, x_test, y_train, y_test = train_test_split(tweets, labels, train_size=0.8)\r\n\r\n #---------------------------------------------------------------\r\n # buid model\r\n #---------------------------------------------------------------\r\n embedding_dim = 64\r\n model = keras.Sequential([\r\n layers.Embedding(vocab_size, embedding_dim, input_length=MAX_LENGTH_OF_TWEETS),\r\n layers.Dense(16, activation=\"relu\"),\r\n layers.GlobalAveragePooling1D(),\r\n layers.Dense(1, activation=\"sigmoid\")\r\n ])\r\n\r\n #---------------------------------------------------------------\r\n # compile and train model\r\n #---------------------------------------------------------------\r\n model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\r\n print(model.summary())\r\n\r\n batch_size = 1024\r\n epochs = 15\r\n history = model.fit(x_train,\r\n y_train,\r\n validation_data=(x_test, y_test),\r\n batch_size=batch_size,\r\n epochs=epochs)\r\n\r\n #---------------------------------------------------------------\r\n # save model and parameters\r\n #---------------------------------------------------------------\r\n model_json_str = model.to_json()\r\n open(MODEL_FILE_PATH, \"w\").write(model_json_str)\r\n model.save_weights(PARAMS_PATH)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n args = sys.argv\r\n pklname = args[1]\r\n main(pklname)", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1937, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "preprocess.load_data_with_labels", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 23, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.GlobalAveragePooling1D", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 24, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 25, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}]} +{"seq_id": "71161585", "text": "import discord\nimport asyncio\nfrom Classes.access_class import acFunctionality\nfrom Classes.overwatch import oWFunctionality\nfrom Classes.todolists import todoFunctionality\n\n# Initialise discord client\nclient = discord.Client()\nserver = discord.Server\n\n# Globals\naccess_channel = None\noWChannel = None\ntestChannel = None\nprogChannel = None\nrole_bot = None\noWBot = None\nworkTracker = None\n\n@client.event\nasync def on_ready():\n print('Logged in as')\n print(client.user.name)\n print(client.user.id)\n print('------')\n\n await client.change_presence(game=discord.Game(name = 'Say !help'))\n \n # Global variables\n global access_channel\n global oWChannel\n global testChannel\n global progChannel\n global role_bot\n global oWBot\n global workTracker\n access_channel = client.get_channel('251117025659846666')\n oWChannel = client.get_channel('243882648114692096')\n testChannel = client.get_channel('244871141989154816')\n progChannel = client.get_channel('244669160666169344')\n role_bot = acFunctionality(client, access_channel)\n oWBot = oWFunctionality(client, oWChannel)\n workTracker = todoFunctionality(client, progChannel)\n\n # Refresh the channel messages\n await role_bot.refresh()\n await oWBot.refresh()\n await workTracker.refresh()\n\n@client.event\nasync def on_message(message):\n if message.channel == access_channel:\n await role_bot.channel_commands(message)\n\n if message.channel == oWChannel:\n await oWBot.channel_commands(message)\n\n if message.channel == progChannel:\n await workTracker.channel_commands(message)\n\n # This is a global command and works from every channel\n if message.content.startswith('!help'):\n with open('help.txt', 'r') as f:\n await client.send_message(message.author, f.read())\n return\n\nclient.run('TOKEN_HERE')\n", "sub_path": "basic_bot.py", "file_name": "basic_bot.py", "file_ext": "py", "file_size_in_byte": 1862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "discord.Client", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.Server", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.Game", "line_number": 27, "usage_type": "call"}, {"api_name": "Classes.access_class.acFunctionality", "line_number": 41, "usage_type": "call"}, {"api_name": "Classes.overwatch.oWFunctionality", "line_number": 42, "usage_type": "call"}, {"api_name": "Classes.todolists.todoFunctionality", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "468400970", "text": "__author__ = 'lslacker'\n# -*- coding: utf-8 -*-\nimport argparse\nfrom mssqlwrapper import DB, TempTable\nimport logging\nfrom reader import ExcelReader\nimport datetime\nfrom itertools import repeat\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_investment_id(db, investment_code):\n\n return db.get_one_value('''\n select stockID\n from vewEquities\n where stockCode=?\n ''', investment_code) if get_investment_id else None\n\n\ndef delists(db, investment_id, investment_code, investment_status_id):\n investment_ids = investment_id.split(',') if investment_id else repeat(None)\n investment_codes = investment_code.split(',') if investment_code else repeat(None)\n params = (zip(investment_ids, investment_codes, repeat(investment_status_id)))\n return any([delist(db, *param) for param in params])\n\n\ndef delist(db, investment_id, investment_code, investment_status_id):\n investment_id = investment_id or get_investment_id(db, investment_code)\n\n data_dict = locals()\n del data_dict['db']\n logger.info('{}'.format(investment_code))\n del data_dict['investment_code']\n\n data_dict = ['@{k}={v!r}'.format(k=k.replace('_', ''), v=v) for k, v in data_dict.items()]\n\n proc_query = '''\n exec Lonsec.dbo.prcInvestmentVariablesPut {params}\n '''.format(params=','.join(data_dict))\n logger.info(proc_query)\n\n count = db.execute(proc_query)\n\n # count is always -1, does not make sense to return it???\n return count\n\n\n\ndef consoleUI():\n parser = argparse.ArgumentParser(description='Merge multiple csv files into excel file, each csv')\n parser.add_argument('--server', default=r'MEL-TST-001\\WEBSQL', help='Database Server')\n parser.add_argument('--database', default=r'Lonsec', help='Database Name')\n parser.add_argument('-v', '--verbose', action='count', default=0)\n parser.add_argument('--investment-status-id', help='Investment Status ID. Default: 3 (closed)', type=int, default=3)\n parser.add_argument('--dry-run', help='An excel file (normally from Jen Lee)', action='store_true')\n\n group = parser.add_mutually_exclusive_group()\n group.add_argument('--investment-code', help='Investment Code aka Stock Code')\n group.add_argument('--investment-id', help='Investment ID aka Stock ID')\n\n a = parser.parse_args()\n\n if a.verbose > 1:\n logging.basicConfig(level=logging.INFO)\n\n connection_string1 = r'Driver={{SQL Server Native Client 11.0}};Server={server};Database={database};' \\\n 'Trusted_Connection=yes;'.format(server=a.server, database=a.database)\n\n db = DB.from_connection_string(connection_string1)\n if a.verbose > 1:\n db.debug = True\n\n logger.info(delists(db, a.investment_id, a.investment_code, a.investment_status_id))\n\n if not a.dry_run:\n logger.info('Commit changes')\n db.commit()\n else:\n logger.info('All changes did not commit')\n\nif __name__ == '__main__':\n consoleUI()\n", "sub_path": "stock_delist.py", "file_name": "stock_delist.py", "file_ext": "py", "file_size_in_byte": 2949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 23, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 24, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 25, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 66, "usage_type": "attribute"}, {"api_name": "mssqlwrapper.DB.from_connection_string", "line_number": 71, "usage_type": "call"}, {"api_name": "mssqlwrapper.DB", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "395566441", "text": "from django.core import urlresolvers\nfrom django.contrib.sitemaps import Sitemap\n\nclass GeoRSSSitemap(Sitemap):\n \"\"\"\n A minimal hook to produce sitemaps for GeoRSS feeds.\n \"\"\"\n def __init__(self, feed_dict, slug_dict=None):\n \"\"\"\n This sitemap object initializes on a feed dictionary (as would be passed\n to `django.contrib.gis.views.feed`) and a slug dictionary.\n If the slug dictionary is not defined, then it's assumed the keys provide\n the URL parameter to the feed. However, if you have a complex feed (e.g.,\n you override `get_object`, then you'll need to provide a slug dictionary.\n The slug dictionary should have the same keys as the feed dictionary, but\n each value in the slug dictionary should be a sequence of slugs that may\n be used for valid feeds. For example, let's say we have a feed that\n returns objects for a specific ZIP code in our feed dictionary:\n\n feed_dict = {'zipcode' : ZipFeed}\n\n Then we would use a slug dictionary with a list of the zip code slugs\n corresponding to feeds you want listed in the sitemap:\n\n slug_dict = {'zipcode' : ['77002', '77054']}\n \"\"\"\n # Setting up.\n self.feed_dict = feed_dict\n self.locations = []\n if slug_dict is None:\n slug_dict = {}\n # Getting the feed locations.\n for section in feed_dict.keys():\n if slug_dict.get(section, False):\n for slug in slug_dict[section]:\n self.locations.append('%s/%s' % (section, slug))\n else:\n self.locations.append(section)\n\n def get_urls(self, page=1, site=None):\n \"\"\"\n This method is overrridden so the appropriate `geo_format` attribute\n is placed on each URL element.\n \"\"\"\n urls = Sitemap.get_urls(self, page=page, site=site)\n for url in urls:\n url['geo_format'] = 'georss'\n return urls\n\n def items(self):\n return self.locations\n\n def location(self, obj):\n return urlresolvers.reverse('django.contrib.gis.views.feed', args=(obj,))\n", "sub_path": "django/contrib/gis/sitemaps/georss.py", "file_name": "georss.py", "file_ext": "py", "file_size_in_byte": 2157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.contrib.sitemaps.Sitemap", "line_number": 4, "usage_type": "name"}, {"api_name": "django.contrib.sitemaps.Sitemap.get_urls", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.sitemaps.Sitemap", "line_number": 45, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "django.core.urlresolvers", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "136514820", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 8 02:25:07 2020\n\n@author: ikaro_beraldo\n\"\"\"\n\nimport math as ma\nimport numpy as np\nimport sys\nfrom scipy import signal\nfrom scipy.io import loadmat\nfrom mne import filter\n\n#Breaks the vetor into a multiarray data of n blocks with n samples each\ndef break_in_blocks(lfp_data,fs, block_length):\n #Check if the LFP data is a single dimension array\n if lfp_data.ndim == 1:\n sys.exit(\"Error: the LFP data must be a single dimension array\")\n \n #Number of blocks\n n_block = ma.floor(lfp_data.size / (fs*block_length))\n #NUmber of samples of each block\n n_samples = fs*block_length\n \n #Excluded the remaining samples\n lfp_data = np.delete(lfp_data,np.arange(block_length*n_block*fs,len(lfp_data)))\n \n #Operation to break the blocks\n blocked_array = lfp_data.reshape((n_block,n_samples))\n \n #Return the blocked_array\n return blocked_array\n\n#Calculate the root mean square of a vector or matrix\ndef root_mean_square(data,dimension):\n #If dimension = 1, the RMS will be calculate along the columns; If 2, the\n #RMS will be calculated along the rows\n if (dimension == 1): #If along columns, get the number of columns\n iterations = np.array(data.shape)[1] #number of iterations\n output_rms = np.zeros((1,iterations)) #pre-alocate the output \n #Loop through every vector (row or column)\n for ite in np.arange(iterations):\n output_rms[ite] = np.sqrt(np.mean(np.square(data[:,ite]))) #calculate RMS\n\n if (dimension == 2): #If along rows, get the number of rows\n iterations = np.array(data.shape)[0] #number of iterations\n output_rms = np.zeros((iterations,1)) #pre-alocate the output \n #Loop through every vector (row or column)\n for ite in np.arange(iterations): \n output_rms[ite] = np.sqrt(np.mean(np.square(data[ite,:]))) #calculate RMS\n \n return output_rms\n\n#Welch for multiple segments\ndef welch_multiple_vectors(data,fs,fft_number):\n #fft_number = nfft of welch calculation (Length of the FFT used)\n #fs = sampling frequency of data\n #data = multiple segments data (the calculation will be performed on each row)\n\n #Pre-alocate the PSD matrix\n columns = int(fft_number/2 + 1) #number of frequency components based on nfft\n rows = data.shape[0] #number of data segments\n Pxx = np.zeros((rows,columns)) #Create the pxx matrix for PSD data\n \n #iteration for each segment (row)\n for seg in np.arange(rows):\n #calculation of PSD for each segment\n f, Pxx[seg,:] = signal.welch(data[seg,:],fs,nfft = fft_number)\n \n return f, Pxx\n \n \n \n\n\n\n", "sub_path": "Basic_functions.py", "file_name": "Basic_functions.py", "file_ext": "py", "file_size_in_byte": 2708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.signal.welch", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "224364531", "text": "# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\" Set of interfaces that allow interaction with BIDS data. Currently\navailable interfaces are:\n\nBIDSDataGrabber: Query data from BIDS dataset using pybids grabbids.\n\n\n Change directory to provide relative paths for doctests\n >>> import os\n >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )\n >>> datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))\n >>> os.chdir(datadir)\n\"\"\"\nfrom os.path import join, dirname\nimport json\nfrom .. import logging\nfrom .base import (traits,\n DynamicTraitedSpec,\n Directory,\n BaseInterface,\n isdefined,\n Str,\n Undefined)\n\nhave_pybids = True\ntry:\n from bids import grabbids as gb\nexcept ImportError:\n have_pybids = False\n\nLOGGER = logging.getLogger('workflows')\n\n\nclass BIDSDataGrabberInputSpec(DynamicTraitedSpec):\n base_dir = Directory(exists=True,\n desc='Path to BIDS Directory.',\n mandatory=True)\n output_query = traits.Dict(key_trait=Str,\n value_trait=traits.Dict,\n desc='Queries for outfield outputs')\n raise_on_empty = traits.Bool(True, usedefault=True,\n desc='Generate exception if list is empty '\n 'for a given field')\n return_type = traits.Enum('file', 'namedtuple', usedefault=True)\n\n\nclass BIDSDataGrabber(BaseInterface):\n\n \"\"\" BIDS datagrabber module that wraps around pybids to allow arbitrary\n querying of BIDS datasets.\n\n Examples\n --------\n\n By default, the BIDSDataGrabber fetches anatomical and functional images\n from a project, and makes BIDS entities (e.g. subject) available for\n filtering outputs.\n\n >>> bg = BIDSDataGrabber()\n >>> bg.inputs.base_dir = 'ds005/'\n >>> bg.inputs.subject = '01'\n >>> results = bg.run() # doctest: +SKIP\n\n\n Dynamically created, user-defined output fields can also be defined to\n return different types of outputs from the same project. All outputs\n are filtered on common entities, which can be explicitly defined as\n infields.\n\n >>> bg = BIDSDataGrabber(infields = ['subject'], outfields = ['dwi'])\n >>> bg.inputs.base_dir = 'ds005/'\n >>> bg.inputs.subject = '01'\n >>> bg.inputs.output_query['dwi'] = dict(modality='dwi')\n >>> results = bg.run() # doctest: +SKIP\n\n \"\"\"\n input_spec = BIDSDataGrabberInputSpec\n output_spec = DynamicTraitedSpec\n _always_run = True\n\n def __init__(self, infields=None, **kwargs):\n \"\"\"\n Parameters\n ----------\n infields : list of str\n Indicates the input fields to be dynamically created\n\n outfields: list of str\n Indicates output fields to be dynamically created.\n If no matching items, returns Undefined.\n \"\"\"\n super(BIDSDataGrabber, self).__init__(**kwargs)\n\n if not isdefined(self.inputs.output_query):\n self.inputs.output_query = {\"func\": {\"modality\": \"func\"},\n \"anat\": {\"modality\": \"anat\"}}\n\n # If infields is empty, use all BIDS entities\n if not infields is None and have_pybids:\n bids_config = join(dirname(gb.__file__), 'config', 'bids.json')\n bids_config = json.load(open(bids_config, 'r'))\n infields = [i['name'] for i in bids_config['entities']]\n\n self._infields = infields or []\n\n # used for mandatory inputs check\n undefined_traits = {}\n for key in self._infields:\n self.inputs.add_trait(key, traits.Any)\n undefined_traits[key] = kwargs[key] if key in kwargs else Undefined\n\n self.inputs.trait_set(trait_change_notify=False, **undefined_traits)\n\n def _run_interface(self, runtime):\n if not have_pybids:\n raise ImportError(\n \"The BIDSEventsGrabber interface requires pybids.\"\n \" Please make sure it is installed.\")\n return runtime\n\n def _list_outputs(self):\n layout = gb.BIDSLayout(self.inputs.base_dir)\n\n # If infield is not given nm input value, silently ignore\n filters = {}\n for key in self._infields:\n value = getattr(self.inputs, key)\n if isdefined(value):\n filters[key] = value\n\n outputs = {}\n for key, query in self.inputs.output_query.items():\n args = query.copy()\n args.update(filters)\n filelist = layout.get(return_type=self.inputs.return_type, **args)\n if len(filelist) == 0:\n msg = 'Output key: %s returned no files' % key\n if self.inputs.raise_on_empty:\n raise IOError(msg)\n else:\n LOGGER.warning(msg)\n filelist = Undefined\n\n outputs[key] = filelist\n return outputs\n", "sub_path": "nipype/interfaces/bids_utils.py", "file_name": "bids_utils.py", "file_ext": "py", "file_size_in_byte": 5108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "base.DynamicTraitedSpec", "line_number": 36, "usage_type": "name"}, {"api_name": "base.Directory", "line_number": 37, "usage_type": "call"}, {"api_name": "base.traits.Dict", "line_number": 40, "usage_type": "call"}, {"api_name": "base.traits", "line_number": 40, "usage_type": "name"}, {"api_name": "base.Str", "line_number": 40, "usage_type": "name"}, {"api_name": "base.traits.Dict", "line_number": 41, "usage_type": "attribute"}, {"api_name": "base.traits", "line_number": 41, "usage_type": "name"}, {"api_name": "base.traits.Bool", "line_number": 43, "usage_type": "call"}, {"api_name": "base.traits", "line_number": 43, "usage_type": "name"}, {"api_name": "base.traits.Enum", "line_number": 46, "usage_type": "call"}, {"api_name": "base.traits", "line_number": 46, "usage_type": "name"}, {"api_name": "base.BaseInterface", "line_number": 49, "usage_type": "name"}, {"api_name": "base.DynamicTraitedSpec", "line_number": 80, "usage_type": "name"}, {"api_name": "base.isdefined", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 102, "usage_type": "call"}, {"api_name": "bids.grabbids.__file__", "line_number": 102, "usage_type": "attribute"}, {"api_name": "bids.grabbids", "line_number": 102, "usage_type": "name"}, {"api_name": "json.load", "line_number": 103, "usage_type": "call"}, {"api_name": "base.traits.Any", "line_number": 111, "usage_type": "attribute"}, {"api_name": "base.traits", "line_number": 111, "usage_type": "name"}, {"api_name": "base.Undefined", "line_number": 112, "usage_type": "name"}, {"api_name": "bids.grabbids.BIDSLayout", "line_number": 124, "usage_type": "call"}, {"api_name": "bids.grabbids", "line_number": 124, "usage_type": "name"}, {"api_name": "base.isdefined", "line_number": 130, "usage_type": "call"}, {"api_name": "base.Undefined", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "103665051", "text": "import torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\nimport os\nimport torch\nimport numpy as np\nimport pickle\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader\nimport copy\nimport torchvision\nfrom PIL import Image\nfrom torchvision import transforms\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nimport torch.nn.functional as F\nimport sys\n\n################################################################################################################# \n## This model is training a VGG with transfer learning with sparse l1 norm on last layer \n####### imageDataRGB.p are the rgb images from bold5000 sorted\n####### dataForCNN.p are the average neural firing for the neurons for each stimuli 227 neurons 4899 stimuli####\n### Alpha hyperparameter must be played around with for best performance. Once this is trained, we do inception ########\n###################################################################\n\n\n'''\nGPU_ID = int(sys.argv[1])\n \nif GPU_ID !=-1:\n os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_ID)\nelse:\n os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'\n\n'''\n\n\n\n\nclass loader(Dataset):\n def __init__(self, text_mode = False):\n self.target = pickle.load(open('dataForCNNNorm1.p' , 'rb' ))\n self.data = pickle.load(open('imageDataRGB.p', 'rb'), encoding = 'bytes')\n self.transformData = transforms.Compose([transforms.ToPILImage(), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))])\n #self.transformData = transforms.Compose([transforms.ToTensor()]) \n self.transformLabel = transforms.Compose([transforms.ToTensor()])\n\n def __getitem__(self, index):\n data = self.data[index]\n target = self.target[index]\n pf0 = self.transformData(data)\n target = torch.from_numpy(target)\n return pf0, target\n\n def __len__ (self):\n return len(self.target)\n\ntest_data = loader(text_mode = False)\n\ntest_loader = DataLoader(test_data, batch_size = 1, shuffle = False, num_workers = 4, drop_last=False)\n\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\n__all__ = [\n 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',\n 'vgg19_bn', 'vgg19',\n]\n\n\nmodel_urls = {\n 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',\n 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',\n 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',\n 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',\n 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',\n 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',\n 'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',\n 'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',\n}\n\n\n\n\nclass VGG(nn.Module):\n\n def __init__(self, features, num_classes=1000, init_weights=True):\n super(VGG, self).__init__()\n self.features = features\n self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\n self.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, num_classes),\n )\n if init_weights:\n self._initialize_weights()\n\n def forward(self, x, embedding = False):\n #print(\"hello\")\n x = self.features(x)\n x = self.avgpool(x)\n x = x.view(x.size(0), -1)\n #print(\"hi hi\")\n #assert False\n #print(x.shape) #torch.Size([5, 25088])\n \n #assert False\n if embedding == True:\n return x\n\n x = self.classifier(x)\n return x\n\n def _initialize_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.Linear):\n nn.init.normal_(m.weight, 0, 0.01)\n nn.init.constant_(m.bias, 0)\n\n\ndef make_layers(cfg, batch_norm=False):\n print(\"making layers\")\n layers = []\n in_channels = 3\n for v in cfg:\n if v == 'M':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n else:\n conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n if batch_norm:\n layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n else:\n layers += [conv2d, nn.ReLU(inplace=True)]\n in_channels = v\n return nn.Sequential(*layers)\n\n\ncfg = {\n 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n}\n\n\ndef vgg11(pretrained=False, **kwargs):\n \"\"\"VGG 11-layer model (configuration \"A\")\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['A']), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg11']))\n return model\n\n\ndef vgg11_bn(pretrained=False, **kwargs):\n \"\"\"VGG 11-layer model (configuration \"A\") with batch normalization\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))\n return model\n\n\ndef vgg13(pretrained=False, **kwargs):\n \"\"\"VGG 13-layer model (configuration \"B\")\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['B']), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg13']))\n return model\n\n\ndef vgg13_bn(pretrained=False, **kwargs):\n \"\"\"VGG 13-layer model (configuration \"B\") with batch normalization\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))\n return model\n\n\ndef vgg16(pretrained=False, **kwargs):\n \"\"\"VGG 16-layer model (configuration \"D\")\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['D']), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg16']))\n return model\n\n\ndef vgg16_bn(pretrained=False, **kwargs):\n \"\"\"VGG 16-layer model (configuration \"D\") with batch normalization\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n if pretrained:\n kwargs['init_weights'] = False\n model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))\n return model\n\n\nnet = vgg16(pretrained = True) #.to(device)\nprint(net)\nprint(\"this is the stoppage\")\nmoduleList = list(net.features.modules())\n\n\n\nnet.features = nn.Sequential(*moduleList[0][:24])\nfeatures = list(net.children())[:-7] # Remove last layer\nmyLayer = nn.Linear(25088, 227)\n\nfeatures.extend([myLayer]) # Add our layer with 4 outputs\nnet.classifier = nn.Sequential(*features)\n\n\nnewModules = list(net.modules())\n\nprint(net)\nnet.eval()\nnet.load_state_dict(torch.load(\"epoch_99_.001_23LayerNew\"))\n\n\nnet.cuda()\n\n\ncriterion = nn.MSELoss()\n\noptimizer = torch.optim.Adam(myLayer.parameters(), lr =.00001)\nscheduler = ReduceLROnPlateau(optimizer)\n\n\n\n####### Definining A Hook now\nclass Hook():\n def __init__(self, module, backward=False):\n if backward==False:\n self.hook = module.register_forward_hook(self.hook_fn)\n else:\n self.hook = module.register_backward_hook(self.hook_fn)\n def hook_fn(self, module, input, output):\n self.input = input\n self.output = output\n def close(self):\n self.hook.remove()\n\n\n\n\n'''\nif GPU_ID == -1:\n net = nn.DataParallel(net)\n#net.cuda()\n'''\n\n\n\n\ndef test_epoch(model, test_loader):\n model.eval()\n\n running_loss = 0.0\n running_corrects = 0.0\n total = 0.0\n correct_predictions = 0.0\n predictions = []\n targets = []\n #start_time = time.time()\n with torch.no_grad():\n for batch_idx, (data, target) in enumerate(test_loader):\n\n data = data.to(device)\n target = target.to(device)\n\n \n outputs = model(data)\n predictions.append(outputs)\n targets.append(target)\n\n\n return predictions, targets\n\n\n\n\n\n\n\n\npredictions, targets = test_epoch(net, test_loader)\n\nprint(predictions[0])\nprint(type(predictions[0]))\nprint(targets[0])\nprint(type(targets[0]))\n\n\nfinalPredictions = []\nfinalTargets = []\n\nfor item in predictions:\n finalPredictions.append(item.cpu().detach().numpy())\n\nfor item in targets:\n finalTargets.append(item.cpu().detach().numpy())\n\n\n\npickle.dump(finalPredictions, open(\"layer23PredictionsAll.p\" , \"wb\"))\npickle.dump(finalTargets, open(\"layer23TargetsAll.p\", \"wb\"))\n\n\nprint(\"done with all of it\")\n", "sub_path": "firstRun/vgg16MaxPool4Test.py", "file_name": "vgg16MaxPool4Test.py", "file_ext": "py", "file_size_in_byte": 10046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 38, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 124, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 126, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 128, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 129, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 193, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 232, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 248, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 263, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 303, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 343, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 344, "usage_type": "call"}]} +{"seq_id": "347899933", "text": "#!/usr/bin/env python3\n\nimport time\nimport openstack\nimport pymysql\nimport queue\nimport threading\nimport logging as log\nfrom pathlib import Path\nfrom datetime import datetime\n\n\nlog.basicConfig(format=\"%(asctime)s: %(message)s\", level=log.INFO, datefmt=\"%Y-%m-%d %H:%M:%S\")\nconn = openstack.connect()\n\n\ndef check_core_services(return_queue, retry=3):\n for i in range(retry):\n try:\n data = []\n services = conn.list_services()\n for s in services:\n log.debug(f'core_services: {s.id} {s.name} {s.type} {s.enabled}')\n data.append({'id': s.id, 'name': s.name, 'type': s.type, 'enabled': s.enabled})\n break\n except Exception as e:\n log.error(f'fail to list core_services({i}): {e}')\n return_queue.put({'core_services': data})\n\ndef check_hypervisors(return_queue, retry=3):\n for i in range(retry):\n try:\n data = []\n hypervisors = conn.list_hypervisors()\n for h in hypervisors:\n #print(type(h.cpu_info))\n #print(dir(h.cpu_info))\n log.debug((f'{h.id} {h.name} {h.status} {h.state} {h.vcpus} {h.vcpus_used} '\n f'{h.memory_size} {h.memory_used} {h.local_disk_size} {h.local_disk_used} '\n f'{h.running_vms}'))\n data.append({\n 'id': h.id,\n 'name': h.name,\n 'status': h.status,\n 'state': h.state,\n 'vcpus': h.vcpus,\n 'vcpus_used': h.vcpus_used,\n 'memory_size': h.memory_size,\n 'memory_used': h.memory_used,\n 'local_disk_size': h.local_disk_size,\n 'local_disk_used': h.local_disk_used,\n 'running_vms': h.running_vms\n })\n break\n except Exception as e:\n log.error(f'fail to list network hypervisors({i}): {e}')\n return_queue.put({'hypervisors': data})\n\ndef check_compute_services(return_queue, retry=3):\n for i in range(retry):\n try:\n data = []\n services = conn.compute.services()\n for s in services:\n log.debug(f'{s.id} {s.binary} {s.state} {s.host}')\n data.append({'id': s.id, 'name': s.binary, 'state': s.state, 'host': s.host})\n break\n except Exception as e:\n log.error(f'fail to list nova services({i}): {e}')\n return_queue.put({'compute_services': data})\n\ndef check_network_agents(return_queue, retry=3):\n for i in range(retry):\n try:\n data = []\n agents = conn.network.agents()\n for a in agents:\n log.debug((f'{a.id} {a.binary} {a.is_admin_state_up} {a.is_alive} '\n f'{a.host} {a.last_heartbeat_at} {a.started_at} {a.created_at}'))\n data.append({\n 'id': a.id,\n 'name': a.binary,\n 'state': a.is_admin_state_up,\n 'alive': a.is_alive,\n 'host': a.host,\n 'last_heartbeat_at': a.last_heartbeat_at,\n 'started_at': a.started_at,\n 'created_at': a.created_at\n })\n break\n except Exception as e:\n log.error(f'fail to list network agents({i}): {e}')\n return_queue.put({'network_agents': data})\n\n\ndef main(interval=3600, log_dir='./log'):\n region = conn._compute_region\n log.info(f'Start monitoring services, region={region}, interval={interval}')\n\n core_services_log_file = \"services.core-services.log\"\n compute_services_log_file = \"services.compute-services.log\"\n hypervisors_log_file = \"services.hypervisors.log\"\n network_agents_log_file = \"services.network-agents.log\"\n\n return_queue = queue.Queue()\n while True:\n #ts = datetime.now().timestamp()\n now = datetime.now()\n check_time = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n core_services_t = threading.Thread(target=check_core_services, args=(return_queue,))\n hypervisors_t = threading.Thread(target=check_hypervisors, args=(return_queue,))\n nova_services_t = threading.Thread(target=check_compute_services, args=(return_queue,))\n network_agents_t = threading.Thread(target=check_network_agents, args=(return_queue,))\n\n core_services_t.start()\n hypervisors_t.start()\n nova_services_t.start()\n network_agents_t.start()\n\n count_t = threading.active_count()\n log.debug(f'Active threads {count_t}')\n\n for i in range(4): # monitoring 4 services\n data = return_queue.get()\n if 'core_services' in data:\n log.debug(f\"Core Services:\\n{data['core_services']}\")\n target_log_file = core_services_log_file\n target_data = data['core_services']\n elif 'compute_services' in data:\n log.debug(f\"Compute Services:\\n{data['compute_services']}\")\n target_log_file = compute_services_log_file\n target_data = data['compute_services']\n elif 'hypervisors' in data:\n log.debug(f\"Hypervisors:\\n{data['hypervisors']}\")\n target_log_file = hypervisors_log_file\n target_data = data['hypervisors']\n elif 'network_agents' in data:\n log.debug(f\"Network Agents:\\n{data['network_agents']}\")\n target_log_file = network_agents_log_file\n target_data = data['network_agents']\n\n Path(log_dir).mkdir(parents=True, exist_ok=True)\n target_log_file = f'{log_dir}/{region}.{target_log_file}'\n with open(target_log_file, 'a') as f:\n for item in target_data:\n l_data = []\n for key, value in item.items():\n l_data.append(f\"{key}={value}\")\n s_data = f','.join(l_data)\n f.write(f'{check_time} {s_data}\\n')\n\n if threading.active_count() == 1:\n log.debug(f'Checking threads finished')\n\n time.sleep(interval)\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "scripts/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 6269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "openstack.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 69, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 108, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 111, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 112, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 113, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 114, "usage_type": "call"}, {"api_name": "threading.active_count", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 135, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 139, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 143, "usage_type": "call"}, {"api_name": "threading.active_count", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "123437269", "text": "# ##### 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 Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nbl_info = {\"name\": \"UV Equalize\",\n \"description\": \"Equalizes scale of UVs of selected objects to active object.\",\n \"author\": \"Jakub Uhlik\",\n \"version\": (0, 2, 3),\n \"blender\": (2, 70, 0),\n \"location\": \"View3d > Object > UV Equalize\",\n \"warning\": \"\",\n \"wiki_url\": \"\",\n \"tracker_url\": \"\",\n \"category\": \"UV\", }\n\n\n# - Use when tileable texture needs to be applied on all objects and its scale should be the same across them.\n# - Available in Object menu of 3d view while in object mode.\n# - To enable, more than two mesh objects must be selected, one must be active.\n\n\n# changelog:\n# 2015.01.05 better fix for bug fixed in previous version..\n# 2014.10.23 fixed bug which prevented script to work, operators are used for transforming uvs,\n# but when in image editor is loaded 'Render Result', UV will not be displayed\n# and therefore operators will not work.. it's one line fix, just set displayed\n# image to None..\n# 2014.10.22 auto deselect non mesh objects\n# 2014.10.13 complete rewrite, now it is pure math\n# 2014.10.12 fixed different uv names bug\n# 2014.06.16 uuid windows workaround\n# 2014.06.12 first release\n\n\nimport bpy\nimport bmesh\nfrom bpy.props import FloatProperty, BoolProperty\nfrom mathutils import Vector\nimport math\n\n\ndef equalize(operator, context, use_pack, rotate, margin, use_active, ):\n def activate_object(o):\n bpy.ops.object.select_all(action='DESELECT')\n sc = bpy.context.scene\n o.select = True\n sc.objects.active = o\n \n ao = context.scene.objects.active\n # obs = [ob for ob in context.scene.objects if ob.name != ao.name and ob.select]\n # make it easier to select all, exclude non-mesh objects from list\n obs = [ob for ob in context.scene.objects if ob.name != ao.name and ob.select and ob.type == 'MESH']\n \n # some checks\n for o in obs:\n if(o.type != 'MESH'):\n operator.report({'ERROR'}, \"Object {} is not a mesh.\".format(o.name))\n return False\n if(len(o.data.uv_layers) < 1):\n operator.report({'ERROR'}, \"Object {} has no uv map.\".format(o.name))\n return False\n \n cache = {}\n \n def calc_areas(o):\n # cache\n k = o.name\n try:\n mesh_area = cache[k][0]\n uv_area = cache[k][1]\n return mesh_area, uv_area\n except:\n pass\n # prepare\n bm = bmesh.new()\n # bm.from_mesh(o.data)\n # this way modifiers are taken into count, like mirror etc..\n me = o.to_mesh(context.scene, True, 'PREVIEW', )\n bm.from_mesh(me)\n #\n bm.transform(o.matrix_world)\n bmesh.ops.triangulate(bm, faces=bm.faces)\n # mesh\n mesh_area = sum([f.calc_area() for f in bm.faces])\n # uv\n uv_layer = bm.loops.layers.uv.active\n tas = []\n for f in bm.faces:\n locs = []\n for l in f.loops:\n x, y = l[uv_layer].uv\n locs.append((x, y, ))\n a = Vector((locs[0][0], locs[0][1], 0.0))\n b = Vector((locs[1][0], locs[1][1], 0.0))\n c = Vector((locs[2][0], locs[2][1], 0.0))\n ab = b - a\n ac = c - a\n cr = ab.cross(ac)\n a = cr.length * 0.5\n tas.append(a)\n uv_area = sum(tas)\n # cleanup\n bm.free()\n # also remove temp mesh\n bpy.data.meshes.remove(me)\n # cache\n cache[k] = (mesh_area, uv_area, )\n return mesh_area, uv_area\n \n if(not use_active):\n obs.append(ao)\n oms = []\n ouvs = []\n for o in obs:\n om, ouv = calc_areas(o)\n oms.append(om)\n ouvs.append(ouv)\n aom = sum(oms) / len(oms)\n aouv = sum(ouvs) / len(ouvs)\n else:\n aom, aouv = calc_areas(ao)\n \n for o in obs:\n activate_object(o)\n # store image assignments\n pi = []\n uv = o.data.uv_textures.active\n for p in uv.data:\n pi.append(p.image)\n \n # average and pack islands\n if(use_pack):\n bpy.ops.object.mode_set(mode='EDIT')\n bpy.ops.mesh.select_all(action='SELECT')\n bpy.ops.uv.select_all(action='SELECT')\n bpy.ops.uv.average_islands_scale()\n bpy.ops.uv.pack_islands(rotate=rotate, margin=margin, )\n bpy.ops.object.mode_set(mode='OBJECT')\n # transform uv\n bpy.ops.object.mode_set(mode='EDIT')\n if(not use_pack):\n bpy.ops.mesh.select_all(action='SELECT')\n bpy.ops.uv.select_all(action='SELECT')\n \n original_type = bpy.context.area.type\n bpy.context.area.type = \"IMAGE_EDITOR\"\n # reset image inside editor, it might be Render Result and in this case,\n # UV operators will not work because UVs will not be displayed..\n bpy.context.area.spaces[0].image = None\n \n om, ouv = calc_areas(o)\n x = (aouv / aom) * om\n v = x / ouv\n v = math.sqrt(v)\n \n bpy.ops.transform.resize(value=(v, v, v), )\n bpy.context.area.type = original_type\n \n bpy.ops.object.mode_set(mode='OBJECT')\n \n # restore image assignments\n uv = o.data.uv_textures.active\n for i, p in enumerate(uv.data):\n p.image = pi[i]\n \n # activate the one which was not changed\n activate_object(ao)\n # reselect objects for convenience\n for o in obs:\n o.select = True\n \n return True\n\n\nclass UVEqualize(bpy.types.Operator):\n bl_idname = \"uv.uv_equalize\"\n bl_label = \"UV Equalize\"\n bl_description = \"Equalizes scale of UVs of selected objects to active object.\"\n bl_options = {'REGISTER', 'UNDO'}\n \n use_active = BoolProperty(name=\"Use Active\",\n description=\"Use active object as scale specimen. Otherwise will be used object with largest polygons after packing. This object will be packed to fit bounds.\",\n default=True, )\n use_pack = BoolProperty(name=\"Average Scale and Pack Islands\",\n description=\"Average island scale and pack\",\n default=False, )\n rotate = BoolProperty(name=\"Pack Islands Rotate\",\n description=\"Rotate islands for best fit\",\n default=True, )\n margin = FloatProperty(name=\"Pack Islands Margin\",\n description=\"Space between islands\",\n min=0.0,\n max=1.0,\n default=0.001, )\n \n @classmethod\n def poll(cls, context):\n ao = context.active_object\n so = bpy.context.selected_objects\n return (ao and ao.type == 'MESH' and len(so) > 1 and context.mode == 'OBJECT')\n \n def execute(self, context):\n r = equalize(self, context, self.use_pack, self.rotate, self.margin, self.use_active, )\n if(r is False):\n return {'CANCELLED'}\n return {'FINISHED'}\n \n def draw(self, context):\n l = self.layout\n \n r = l.row()\n r.prop(self, \"use_active\")\n \n r = l.row()\n r.prop(self, \"use_pack\")\n r = l.row()\n r.prop(self, \"rotate\")\n r.enabled = self.use_pack\n r = l.row()\n r.prop(self, \"margin\")\n r.enabled = self.use_pack\n\n\ndef menu_func(self, context):\n l = self.layout\n l.separator()\n l.operator(UVEqualize.bl_idname, text=UVEqualize.bl_label)\n\n\ndef register():\n bpy.utils.register_module(__name__)\n bpy.types.VIEW3D_MT_object.append(menu_func)\n\n\ndef unregister():\n bpy.utils.unregister_module(__name__)\n bpy.types.VIEW3D_MT_object.remove(menu_func)\n\n\nif __name__ == \"__main__\":\n register()\n", "sub_path": "scripts/addons_extern/uv_equalize.py", "file_name": "uv_equalize.py", "file_ext": "py", "file_size_in_byte": 8683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "bpy.ops.object.select_all", "line_number": 58, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 59, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 89, "usage_type": "call"}, {"api_name": "bmesh.ops.triangulate", "line_number": 96, "usage_type": "call"}, {"api_name": "bmesh.ops", "line_number": 96, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 107, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 108, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 109, "usage_type": "call"}, {"api_name": "bpy.data.meshes.remove", "line_number": 119, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 119, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 147, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 147, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.select_all", "line_number": 148, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 148, "usage_type": "attribute"}, {"api_name": "bpy.ops.uv.select_all", "line_number": 149, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 149, "usage_type": "attribute"}, {"api_name": "bpy.ops.uv.average_islands_scale", "line_number": 150, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 150, "usage_type": "attribute"}, {"api_name": "bpy.ops.uv.pack_islands", "line_number": 151, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 151, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 152, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 152, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 154, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 154, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.select_all", "line_number": 156, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 156, "usage_type": "attribute"}, {"api_name": "bpy.ops.uv.select_all", "line_number": 157, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 157, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 159, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 160, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 163, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 168, "usage_type": "call"}, {"api_name": "bpy.ops.transform.resize", "line_number": 170, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 170, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 171, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 173, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 173, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 189, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 195, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 198, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 201, "usage_type": "call"}, {"api_name": "bpy.props.FloatProperty", "line_number": 204, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 213, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_module", "line_number": 245, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 245, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.append", "line_number": 246, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 246, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_module", "line_number": 250, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 250, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.remove", "line_number": 251, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 251, "usage_type": "attribute"}]} +{"seq_id": "429249499", "text": "import math\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\n# Factorised NoisyLinear layer with bias\nclass NoisyLinear(nn.Module):\n \"\"\"NosiyLinear called Nosiy Networks has been studied in DeepMind.\n\n A deep reinforcement learning agent with parametric noise added to its weights,\n and show that th induced stochasticity of the agent's policy can be used to aid efficient exploration.\n\n \"\"\"\n def __init__(self, in_features, out_features, std_init=0.5):\n \"\"\"This module extends torch.nn.Linear\n Args:\n in_features: the number of input feature\n out_features: the number of output feature\n std_init: parameter for NoisyLinear\n \"\"\"\n super(NoisyLinear, self).__init__()\n self.in_features = in_features\n self.out_features = out_features\n self.std_init = std_init\n self.weight_mu = nn.Parameter(torch.empty(out_features, in_features))\n self.weight_sigma = nn.Parameter(torch.empty(out_features, in_features))\n self.register_buffer('weight_epsilon', torch.empty(out_features, in_features))\n self.bias_mu = nn.Parameter(torch.empty(out_features))\n self.bias_sigma = nn.Parameter(torch.empty(out_features))\n self.register_buffer('bias_epsilon', torch.empty(out_features))\n self.reset_parameters()\n self.reset_noise()\n\n def reset_parameters(self):\n \"\"\"This method for reset layer parameter.\n\n Notes:\n For factorised noisy networks, each element mu_i,j was initialised by a sample\n from an independent uniform distribuntions MU[-1/root(p),+1/root(p)] and\n each element sigma_i,j was initialised to a contant sigma_0/root(p).\n in paper, hyperparameter sigma_0 is set to 0.5\n std_init=0.5\n\n \"\"\"\n mu_range = 1 / math.sqrt(self.in_features)\n self.weight_mu.data.uniform_(-mu_range, mu_range)\n self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.in_features))\n self.bias_mu.data.uniform_(-mu_range, mu_range)\n self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.out_features))\n\n def _scale_noise(self, size):\n \"\"\"This method for scale noise by the number of input/output features.\n\n Args:\n size: size is int for setting scale\n\n Returns:\n scaled noise\n\n \"\"\"\n x = torch.randn(size)\n return x.sign().mul_(x.abs().sqrt_())\n\n def reset_noise(self):\n \"\"\"This method make initialized noise.\n\n The Noise depends on the number of input/output featuers.\n\n \"\"\"\n epsilon_in = self._scale_noise(self.in_features)\n epsilon_out = self._scale_noise(self.out_features)\n self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in))\n self.bias_epsilon.copy_(epsilon_out)\n\n def forward(self, input):\n \"\"\"This method is override nn.Linear's forward\n\n Args:\n input: Input data\n\n Returns:\n Return is nn.Linear's output. but use noisy parameter.\n\n \"\"\"\n if self.training:\n return F.linear(input, self.weight_mu + self.weight_sigma * self.weight_epsilon,\n self.bias_mu + self.bias_sigma * self.bias_epsilon)\n else:\n return F.linear(input, self.weight_mu, self.bias_mu)\n\n\nclass DQN(nn.Module):\n \"\"\"This is DQN where 'C51', 'Duelling', 'NoisyNetwork'\n \n \"\"\"\n def __init__(self, args, action_space):\n super().__init__()\n self.atoms = args.atoms\n self.action_space = action_space\n\n self.conv1 = nn.Conv2d(args.history_length, 32, 8, stride=4, padding=1)\n self.conv2 = nn.Conv2d(32, 64, 4, stride=2)\n self.conv3 = nn.Conv2d(64, 64, 3)\n self.fc_h_v = NoisyLinear(3136, args.hidden_size, std_init=args.noisy_std)\n self.fc_h_a = NoisyLinear(3136, args.hidden_size, std_init=args.noisy_std)\n self.fc_z_v = NoisyLinear(args.hidden_size, self.atoms, std_init=args.noisy_std)\n self.fc_z_a = NoisyLinear(args.hidden_size, action_space * self.atoms, std_init=args.noisy_std)\n\n def forward(self, x, log=False):\n x = F.relu(self.conv1(x))\n x = F.relu(self.conv2(x))\n x = F.relu(self.conv3(x))\n x = x.view(-1, 3136)\n v = self.fc_z_v(F.relu(self.fc_h_v(x))) # Value stream\n a = self.fc_z_a(F.relu(self.fc_h_a(x))) # Advantage stream\n v, a = v.view(-1, 1, self.atoms), a.view(-1, self.action_space, self.atoms)\n q = v + a - a.mean(1, keepdim=True) # Combine streams\n if log: # Use log softmax for numerical stability\n q = F.log_softmax(q, dim=2) # Log probabilities with action over second dimension\n else:\n q = F.softmax(q, dim=2) # Probabilities with action over second dimension\n return q\n\n def reset_noise(self):\n for name, module in self.named_children():\n if 'fc' in name:\n module.reset_noise()\n", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 5004, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 31, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.functional.linear", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 122, "usage_type": "name"}]} +{"seq_id": "20042666", "text": "from Crypto.PublicKey import RSA\nfrom Crypto.Cipher import PKCS1_OAEP\nfrom Crypto.Util.number import long_to_bytes\nimport base64\nimport random\n\nfrom secret import flag\n\ndef long_to_b64(payload):\n return base64.b64encode(long_to_bytes(payload)).decode()\n\ndef main():\n k = RSA.generate(2048)\n oaep_k = PKCS1_OAEP.new(k)\n\n failed = False\n exited = False\n\n print('🔑', long_to_b64(k.n))\n\n for i in range(1, 100+1):\n\n c0 = random.getrandbits(1)\n\n print(f'🤯 {i}')\n\n while True:\n params = input('🤖 ').split(' ')\n action = params.pop(0)\n\n if action == '📦':\n if c0 == 0:\n ciphertext = random.randint(0, k.n-1)\n else:\n ciphertext = int.from_bytes(oaep_k.encrypt(flag), 'big')\n print('🤫', long_to_b64(ciphertext))\n elif action == '🔓':\n c1 = int(params[0])\n if c0 != c1:\n failed = True\n else:\n print('👌')\n break\n elif action == '🏃':\n exited = True\n break\n if failed or exited: break\n\n if exited:\n print('🤨')\n elif failed:\n print('👋')\n else:\n print('🏁', flag.decode())\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "20210116-firebird-internal-ctf/obvious-transfer/env/chall/chall.py", "file_name": "chall.py", "file_ext": "py", "file_size_in_byte": 1374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "base64.b64encode", "line_number": 10, "usage_type": "call"}, {"api_name": "Crypto.Util.number.long_to_bytes", "line_number": 10, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.generate", "line_number": 13, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 13, "usage_type": "name"}, {"api_name": "Crypto.Cipher.PKCS1_OAEP.new", "line_number": 14, "usage_type": "call"}, {"api_name": "Crypto.Cipher.PKCS1_OAEP", "line_number": 14, "usage_type": "name"}, {"api_name": "random.getrandbits", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "secret.flag", "line_number": 35, "usage_type": "argument"}, {"api_name": "secret.flag.decode", "line_number": 54, "usage_type": "call"}, {"api_name": "secret.flag", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "539588525", "text": "import math\nimport os.path\nimport simplejson\n\ndef calculate_idfs(filename, force_recalc=False):\n if not force_recalc:\n if os.path.isfile(filename):\n with open(filename) as f:\n idfs_ascii = simplejson.load(f)\n\n idfs = {}\n for w, c in idfs_ascii.iteritems():\n idfs[w.encode('utf-8')] = c\n \n return idfs\n\n\n with open(filename) as f:\n lines = 0\n dfs = {}\n idfs = {}\n\n for line in f:\n lines += 1\n words_on_this_line = []\n if lines % 100000 == 0:\n print(str(lines))\n for word in line.strip().split(' ')[2:]:\n if word == '':\n continue\n\n if word in words_on_this_line:\n continue\n\n if word in dfs:\n dfs[word] += 1\n else:\n dfs[word] = 1\n\n words_on_this_line.append(word)\n\n for word, count in dfs.iteritems():\n idfs[word] = math.log(float(lines) / count)\n\n return idfs\n", "sub_path": "calculate_idfs.py", "file_name": "calculate_idfs.py", "file_ext": "py", "file_size_in_byte": 1120, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.path.isfile", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "simplejson.load", "line_number": 9, "usage_type": "call"}, {"api_name": "math.log", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "226184578", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*\n\nfrom django.db import transaction\nfrom django.http import HttpResponseRedirect\n\nfrom core.common.utils import force_post\nfrom invites.models import Invite\nfrom places.models import UserPlaceSettings\n\n@force_post\n@transaction.commit_on_success\ndef resolve_invite(req, invite_id, accepted):\n try:\n invite = Invite.objects.get(id=invite_id)\n if not accepted:\n invite.accepted = accepted\n invite.save()\n return HttpResponseRedirect('/')\n #endif\n\n _ups = None\n # User wanted to get to place\n # OR Admin invited user to place\n if invite.from_user is None and UserPlaceSettings.is_admin(req.user.id, invite.place.id):\n _ups = UserPlaceSettings()\n _ups.user = invite.to_user\n _ups.place = invite.place\n _ups.save()\n invite.accepted = True\n invite.save()\n return HttpResponseRedirect('/')\n elif invite.to_user == req.user:\n _ups = UserPlaceSettings()\n _ups.user = invite.to_user\n _ups.place = invite.place\n _ups.save()\n invite.accepted = True\n invite.save()\n return HttpResponseRedirect('/place/%s' % _ups.place.id)\n #endif \n\n # Add user to place\n except Invite.DoesNotExist:\n return HttpResponseRedirect('/')\n #endtry\n#enddef\n", "sub_path": "src/invites/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "invites.models.Invite.objects.get", "line_number": 15, "usage_type": "call"}, {"api_name": "invites.models.Invite.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "invites.models.Invite", "line_number": 15, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 19, "usage_type": "call"}, {"api_name": "places.models.UserPlaceSettings.is_admin", "line_number": 25, "usage_type": "call"}, {"api_name": "places.models.UserPlaceSettings", "line_number": 25, "usage_type": "name"}, {"api_name": "places.models.UserPlaceSettings", "line_number": 26, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 32, "usage_type": "call"}, {"api_name": "places.models.UserPlaceSettings", "line_number": 34, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 40, "usage_type": "call"}, {"api_name": "invites.models.Invite.DoesNotExist", "line_number": 44, "usage_type": "attribute"}, {"api_name": "invites.models.Invite", "line_number": 44, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 45, "usage_type": "call"}, {"api_name": "core.common.utils.force_post", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "443195265", "text": "import os\nimport time\nimport random\n\nimport numpy as np\nimport pandas as pd\n\nimport torch\n\nfrom pathlib import Path\n\nfrom ESRNN.utils.config import ModelConfig\nfrom ESRNN.utils.losses import DisaggregatedPinballLoss\nfrom ESRNN.utils.data import Iterator\n\nfrom ESRNN.ESRNN import ESRNN\n\nfrom ESRNN.utils_evaluation import owa\n\nclass ESRNNensemble(object):\n \"\"\" Exponential Smoothing Recursive Neural Network Ensemble.\n n_models=1\n n_top=1\n \"\"\"\n def __init__(self, n_models=1, n_top=1, max_epochs=15, batch_size=1, batch_size_test=128,\n freq_of_test=-1, learning_rate=1e-3, lr_scheduler_step_size=9, lr_decay=0.9,\n per_series_lr_multip=1.0, gradient_eps=1e-8, gradient_clipping_threshold=20,\n rnn_weight_decay=0, noise_std=0.001, level_variability_penalty=80,\n testing_percentile=50, training_percentile=50, ensemble=False, cell_type='LSTM',\n state_hsize=40, dilations=[[1, 2], [4, 8]],\n add_nl_layer=False, seasonality=[4], input_size=4, output_size=8,\n frequency='D', max_periods=20, random_seed=1,\n device='cuda', root_dir='./'):\n super(ESRNNensemble, self).__init__()\n\n self.n_models = n_models\n self.n_top = n_top\n assert n_models>=2, \"Number of models for ensemble should be greater than 1\"\n assert n_top<=n_models, \"Number of top models should be smaller than models to ensemble\"\n self.big_float = 1e6\n self.mc = ModelConfig(max_epochs=max_epochs, batch_size=batch_size, batch_size_test=batch_size_test,\n freq_of_test=freq_of_test, learning_rate=learning_rate,\n lr_scheduler_step_size=lr_scheduler_step_size, lr_decay=lr_decay,\n per_series_lr_multip=per_series_lr_multip,\n gradient_eps=gradient_eps, gradient_clipping_threshold=gradient_clipping_threshold,\n rnn_weight_decay=rnn_weight_decay, noise_std=noise_std,\n level_variability_penalty=level_variability_penalty,\n testing_percentile=testing_percentile, training_percentile=training_percentile,\n ensemble=ensemble, cell_type=cell_type,\n state_hsize=state_hsize, dilations=dilations, add_nl_layer=add_nl_layer,\n seasonality=seasonality, input_size=input_size, output_size=output_size,\n frequency=frequency, max_periods=max_periods, random_seed=random_seed,\n device=device, root_dir=root_dir)\n self._fitted = False\n\n def fit(self, X_df, y_df, X_test_df=None, y_test_df=None, shuffle=True):\n # Transform long dfs to wide numpy\n assert type(X_df) == pd.core.frame.DataFrame\n assert type(y_df) == pd.core.frame.DataFrame\n assert all([(col in X_df) for col in ['unique_id', 'ds', 'x']])\n assert all([(col in y_df) for col in ['unique_id', 'ds', 'y']])\n\n # Storing dfs for OWA evaluation, initializing min_owa\n self.y_train_df = y_df\n self.X_test_df = X_test_df\n self.y_test_df = y_test_df\n self.min_owa = 4.0\n self.min_epoch = 0\n\n # Exogenous variables\n unique_categories = X_df['x'].unique()\n self.mc.category_to_idx = dict((word, index) for index, word in enumerate(unique_categories))\n self.mc.exogenous_size = len(unique_categories)\n\n self.unique_ids = X_df['unique_id'].unique()\n self.mc.n_series = len(self.unique_ids)\n\n # Set seeds\n torch.manual_seed(self.mc.random_seed)\n np.random.seed(self.mc.random_seed)\n\n # Initial series random assignment to models\n self.series_models_map = np.zeros((self.mc.n_series, self.n_models))\n n_initial_models = int(np.ceil(self.n_models/2))\n for i in range(self.mc.n_series):\n id_models = np.random.choice(self.n_models, n_initial_models)\n self.series_models_map[i,id_models] = 1\n\n self.esrnn_ensemble = []\n for _ in range(self.n_models):\n esrnn = ESRNN(max_epochs=self.mc.max_epochs, batch_size=self.mc.batch_size, batch_size_test=self.mc.batch_size_test,\n freq_of_test=-1, learning_rate=self.mc.learning_rate,\n lr_scheduler_step_size=self.mc.lr_scheduler_step_size, lr_decay=self.mc.lr_decay,\n per_series_lr_multip=self.mc.per_series_lr_multip,\n gradient_eps=self.mc.gradient_eps, gradient_clipping_threshold=self.mc.gradient_clipping_threshold,\n rnn_weight_decay=self.mc.rnn_weight_decay, noise_std=self.mc.noise_std,\n level_variability_penalty=self.mc.level_variability_penalty,\n testing_percentile=self.mc.testing_percentile,\n training_percentile=self.mc.training_percentile, ensemble=self.mc.ensemble,\n cell_type=self.mc.cell_type,\n state_hsize=self.mc.state_hsize, dilations=self.mc.dilations, add_nl_layer=self.mc.add_nl_layer,\n seasonality=self.mc.seasonality, input_size=self.mc.input_size, output_size=self.mc.output_size,\n frequency=self.mc.frequency, max_periods=self.mc.max_periods, random_seed=self.mc.random_seed,\n device=self.mc.device, root_dir=self.mc.root_dir)\n\n # To instantiate _ESRNN object within ESRNN class we need n_series\n esrnn.instantiate_esrnn(self.mc.exogenous_size, self.mc.n_series)\n esrnn._fitted = True\n self.esrnn_ensemble.append(esrnn)\n\n self.X, self.y = esrnn.long_to_wide(X_df, y_df)\n assert len(self.X)==len(self.y)\n assert self.X.shape[1]>=3\n\n # Train model\n self._fitted = True\n self.train()\n\n def train(self):\n # Initial performance matrix\n self.performance_matrix = np.ones((self.mc.n_series, self.n_models)) * self.big_float\n warm_start = False\n train_tau = self.mc.training_percentile/100\n criterion = DisaggregatedPinballLoss(train_tau)\n\n # Train epoch loop\n for epoch in range(self.mc.max_epochs):\n start = time.time()\n\n # Solve degenerate models\n for model_id in range(self.n_models):\n if np.sum(self.series_models_map[:,model_id])==0:\n print('Reassigning random series to model ', model_id)\n n_sample_series= int(self.mc.n_series/2)\n index_series = np.random.choice(self.mc.n_series, n_sample_series, replace=False)\n self.series_models_map[index_series, model_id] = 1\n\n # Model loop\n for model_id, esrnn in enumerate(self.esrnn_ensemble):\n # Train model with subset data\n dataloader = Iterator(mc = self.mc, X=self.X, y=self.y,\n weights=self.series_models_map[:, model_id])\n esrnn.train(dataloader, max_epochs=1, warm_start=warm_start, shuffle=True, verbose=False)\n\n # Compute model performance for each series\n dataloader = Iterator(mc=self.mc, X=self.X, y=self.y)\n per_series_evaluation = esrnn.per_series_evaluation(dataloader, criterion=criterion)\n self.performance_matrix[:, model_id] = per_series_evaluation\n\n # Reassign series to models\n self.series_models_map = np.zeros((self.mc.n_series, self.n_models))\n top_models = np.argpartition(self.performance_matrix, self.n_top)[:, :self.n_top]\n for i in range(self.mc.n_series):\n self.series_models_map[i, top_models[i,:]] = 1\n\n warm_start = True\n\n print(\"========= Epoch {} finished =========\".format(epoch))\n print(\"Training time: {}\".format(round(time.time()-start, 5)))\n self.train_loss = np.einsum('ij,ij->i',self.performance_matrix, self.series_models_map)/self.n_top\n self.train_loss = np.mean(self.train_loss)\n print(\"Training loss ({} prc): {:.5f}\".format(self.mc.training_percentile,\n self.train_loss))\n print('Models num series', np.sum(self.series_models_map, axis=0))\n\n if (epoch % self.mc.freq_of_test == 0) and (self.mc.freq_of_test > 0):\n if self.y_test_df is not None:\n self.evaluate_model_prediction(self.y_train_df, self.X_test_df,\n self.y_test_df, epoch=epoch)\n print('Train finished! \\n')\n\n def predict(self, X_df):\n \"\"\"\n Predictions for all stored time series\n Returns:\n Y_hat_panel : array-like (n_samples, 1).\n Predicted values for models in Family for ids in Panel.\n ds: Corresponding list of date stamps\n unique_id: Corresponding list of unique_id\n \"\"\"\n assert type(X_df) == pd.core.frame.DataFrame\n assert 'unique_id' in X_df\n assert self._fitted, \"Model not fitted yet\"\n\n dataloader = Iterator(mc=self.mc, X=self.X, y=self.y)\n\n output_size = self.mc.output_size\n n_unique_id = len(dataloader.sort_key['unique_id'])\n\n ensemble_y_hat = np.zeros((self.n_models, n_unique_id, output_size))\n\n for model_id, esrnn in enumerate(self.esrnn_ensemble):\n esrnn.esrnn.eval()\n\n # Predict ALL series\n count = 0\n for j in range(dataloader.n_batches):\n batch = dataloader.get_batch()\n batch_size = batch.y.shape[0]\n\n y_hat = esrnn.esrnn.predict(batch)\n\n y_hat = y_hat.data.cpu().numpy()\n\n ensemble_y_hat[model_id, count:count+batch_size, :] = y_hat\n count += batch_size\n\n # Weighted average of prediction for n_top best models per series\n # (n_models x n_unique_id x output_size) (n_unique_id x n_models)\n y_hat = np.einsum('ijk,ji->jk', ensemble_y_hat, self.series_models_map) / self.n_top\n y_hat = y_hat.flatten()\n\n panel_unique_id = pd.Series(dataloader.sort_key['unique_id']).repeat(output_size)\n panel_last_ds = pd.Series(dataloader.X[:, 2]).repeat(output_size)\n\n panel_delta = list(range(1, output_size+1)) * n_unique_id\n panel_delta = pd.to_timedelta(panel_delta, unit=self.mc.frequency)\n panel_ds = panel_last_ds + panel_delta\n\n assert len(panel_ds) == len(y_hat) == len(panel_unique_id)\n\n Y_hat_panel_dict = {'unique_id': panel_unique_id,\n 'ds': panel_ds,\n 'y_hat': y_hat}\n\n Y_hat_panel = pd.DataFrame.from_dict(Y_hat_panel_dict)\n\n if 'ds' in X_df:\n Y_hat_panel = X_df.merge(Y_hat_panel, on=['unique_id', 'ds'], how='left')\n else:\n Y_hat_panel = X_df.merge(Y_hat_panel, on=['unique_id'], how='left')\n\n return Y_hat_panel\n\n def evaluate_model_prediction(self, y_train_df, X_test_df, y_test_df, epoch=None):\n \"\"\"\n y_train_df: pandas df\n panel with columns unique_id, ds, y\n X_test_df: pandas df\n panel with columns unique_id, ds, x\n y_test_df: pandas df\n panel with columns unique_id, ds, y, y_hat_naive2\n model: python class\n python class with predict method\n \"\"\"\n assert self._fitted, \"Model not fitted yet\"\n\n y_panel = y_test_df.filter(['unique_id', 'ds', 'y'])\n y_naive2_panel = y_test_df.filter(['unique_id', 'ds', 'y_hat_naive2'])\n y_naive2_panel.rename(columns={'y_hat_naive2': 'y_hat'}, inplace=True)\n y_hat_panel = self.predict(X_test_df)\n y_insample = y_train_df.filter(['unique_id', 'ds', 'y'])\n\n model_owa, model_mase, model_smape = owa(y_panel, y_hat_panel,\n y_naive2_panel, y_insample,\n seasonality=self.mc.naive_seasonality)\n\n if self.min_owa > model_owa:\n self.min_owa = model_owa\n if epoch is not None:\n self.min_epoch = epoch\n\n print('OWA: {} '.format(np.round(model_owa, 3)))\n print('SMAPE: {} '.format(np.round(model_smape, 3)))\n print('MASE: {} '.format(np.round(model_mase, 3)))\n\n return model_owa, model_mase, model_smape\n", "sub_path": "ESRNN/ESRNNensemble.py", "file_name": "ESRNNensemble.py", "file_ext": "py", "file_size_in_byte": 11653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "ESRNN.utils.config.ModelConfig", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.core", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pandas.core", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ESRNN.ESRNN.ESRNN", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 121, "usage_type": "call"}, {"api_name": "ESRNN.utils.losses.DisaggregatedPinballLoss", "line_number": 124, "usage_type": "call"}, {"api_name": "time.time", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 135, "usage_type": "attribute"}, {"api_name": "ESRNN.utils.data.Iterator", "line_number": 141, "usage_type": "call"}, {"api_name": "ESRNN.utils.data.Iterator", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 164, "usage_type": "call"}, {"api_name": "pandas.core", "line_number": 181, "usage_type": "attribute"}, {"api_name": "ESRNN.utils.data.Iterator", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 214, "usage_type": "call"}, {"api_name": "pandas.to_timedelta", "line_number": 217, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 226, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 226, "usage_type": "attribute"}, {"api_name": "ESRNN.utils_evaluation.owa", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 265, "usage_type": "call"}]} +{"seq_id": "267390090", "text": "\"\"\"\nNeural network.\n@author: David Diaz Vico\n\"\"\"\n\nfrom .estimator import ClassifierBuilder, EstimatorBuilder, RegressorBuilder, delog\nfrom keras.callbacks import EarlyStopping, History, ModelCheckpoint\nfrom keras.layers import Dense, Dropout, Input\nfrom keras.layers import Convolution2D, Flatten, MaxPooling2D\nfrom keras.layers import LSTM\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras.models import Model\nfrom keras.optimizers import SGD\nfrom keras.regularizers import l1l2, activity_l1l2\nfrom keras.utils.np_utils import to_categorical, probas_to_classes\nimport numpy as np\n\n\ndef temporal_tensor(x, window):\n return np.array([x[i:i + window] for i in range(x.shape[0] - window + 1)])\n\n\nclass NeuralNetwork(Model):\n\n def fit(\n self,\n x,\n y,\n optimizer=None,\n x_test=None,\n y_test=None,\n validation_split=0.1,\n nb_epoch=100,\n patience=0.01,\n metrics=[]\n ):\n if optimizer is None:\n optimizer = SGD(lr=self.lr)\n Model.compile(\n self,\n optimizer=optimizer,\n loss={'output': self.loss},\n metrics=metrics\n )\n if self.window > 1:\n x = temporal_tensor(x, self.window)\n y = y[self.window - 1:]\n if x_test is not None:\n x_test = temporal_tensor(x_test, self.window)\n y_test = y_test[self.window - 1:]\n callbacks = [\n EarlyStopping(\n monitor='val_loss',\n patience=int(nb_epoch * patience)\n ),\n History(),\n ModelCheckpoint(\n filepath='weights_best_neural_network.hdf5',\n monitor='val_loss',\n save_best_only=True\n )\n ]\n if x_test is not None and y_test is not None:\n self.history = Model.fit(\n self,\n x={'input': x},\n y={'output': y},\n batch_size=self.batch_size,\n nb_epoch=nb_epoch,\n verbose=0,\n callbacks=callbacks,\n validation_data=({'input': x_test}, {'output': y_test})\n )\n else:\n self.history = Model.fit(\n self,\n x={'input': x},\n y={'output': y},\n batch_size=self.batch_size,\n nb_epoch=nb_epoch,\n verbose=0,\n callbacks=callbacks,\n validation_split=validation_split\n )\n\n self.load_weights('weights_best_neural_network.hdf5')\n return self\n\n def predict(self, x):\n if self.window > 1:\n x = temporal_tensor(\n np.vstack(([x[0] for i in range(self.window - 1)], x)),\n self.window\n )\n return Model.predict(self, x={'input': x})\n\n\nclass FeedForward(NeuralNetwork):\n\n def __init__(self, **kwargs):\n return self._init(**delog(kwargs))\n\n def _init(\n self,\n x,\n y,\n loss,\n act_out,\n init_out='glorot_uniform',\n n_convolutional=0,\n nb_filter=1,\n nb_row=1,\n nb_col=1,\n pool_h=1,\n pool_w=1,\n stride_h=1,\n stride_w=1,\n n_lstm=0,\n window=1,\n n_dense=1,\n act_hidden='relu',\n init_hidden='he_uniform',\n n_hidden=100,\n dropout_p=0.0,\n l1=0.0,\n l2=0.0,\n a_l1=0.0,\n a_l2=0.0,\n bias=True,\n normalize_output=False,\n lr=0.1,\n batch_size=128\n ):\n\n def convolution2d(x):\n return Convolution2D(\n nb_filter=nb_filter,\n nb_row=nb_row,\n nb_col=nb_col,\n init=init_hidden,\n activation=act_hidden,\n W_regularizer=l1l2(l1=l1, l2=l2),\n activity_regularizer=activity_l1l2(l1=a_l1, l2=a_l2),\n bias=bias\n )(x)\n\n def maxpooling(x):\n return MaxPooling2D(pool_size=pool_size, strides=strides)(x)\n\n def lstm(x):\n return LSTM(output_dim=n_hidden, return_sequences=l < n_lstm - 1)(x)\n\n def dense(x):\n return Dense(\n output_dim=n_hidden,\n init=init_hidden,\n activation=act_hidden,\n W_regularizer=l1l2(l1=l1, l2=l2),\n activity_regularizer=activity_l1l2(l1=a_l1, l2=a_l2),\n bias=bias\n )(x)\n\n self.loss = loss\n self.lr = lr\n self.batch_size = batch_size\n self.window = window if n_lstm > 0 else 1\n input_shape = x.shape[1:]\n output_shape = y.shape[1:]\n pool_size = (pool_h, pool_w)\n strides = (stride_h, stride_w)\n n_dense_hidden = n_dense - 1\n\n if n_lstm > 0 and self.window > 1:\n x = inputs = Input(\n shape=(self.window, ) + input_shape,\n name='input'\n )\n for c in range(n_convolutional):\n x = BatchNormalization(axis=1)(x)\n x = TimeDistributed(convolution2d)(x)\n x = TimeDistributed(maxpooling)(x)\n if n_convolutional > 0:\n x = TimeDistributed(Flatten())(x)\n for l in range(n_lstm):\n x = BatchNormalization()(x)\n x = lstm(x)\n x = Dropout(dropout_p)(x)\n else:\n x = inputs = Input(shape=input_shape, name='input')\n for c in range(n_convolutional):\n x = BatchNormalization(axis=1)(x)\n x = convolution2d(x)\n x = maxpooling(x)\n if n_convolutional > 0:\n x = Flatten()(x)\n for d in range(n_dense_hidden):\n# x = BatchNormalization()(x)\n x = dense(x)\n x = Dropout(dropout_p)(x)\n if normalize_output:\n x = Dense(\n output_dim=np.prod(output_shape),\n init=init_out,\n activation=act_out,\n W_regularizer=l1l2(l1=l1, l2=l2),\n )(x)\n outputs = BatchNormalization(name='output')(x)\n else:\n outputs = Dense(\n output_dim=np.prod(output_shape),\n init=init_out,\n activation=act_out,\n W_regularizer=l1l2(l1=l1, l2=l2),\n name='output'\n )(x)\n\n NeuralNetwork.__init__(self, input=inputs, output=outputs)\n\n\nclass FeedForwardBuilder(EstimatorBuilder):\n\n @staticmethod\n def space(x):\n search_space = {\n 'window': [1, 10],\n 'n_dense': [0, 3],\n 'n_hidden': [1, 100],\n 'dropout_p': [0.0, 1.0],\n 'log_l1': [-5.0, -1.0],\n 'log_l2': [-5.0, -1.0],\n 'log_a_l1': [-5.0, -1.0],\n 'log_a_l2': [-5.0, -1.0],\n 'log_lr': [-5.0, -1.0],\n 'batch_size': [1, 128]\n }\n if len(x.shape) > 2:\n search_space.update({\n 'nb_filter': [1, 16],\n 'nb_row': [1, 3],\n 'nb_col': [1, 3]\n })\n return search_space\n\n\nclass FeedForwardClassifier(FeedForward):\n\n def __init__(self, x, y, **kwargs):\n y = to_categorical(y=y.flatten().astype(int))\n FeedForward.__init__(\n self,\n x=x,\n y=y,\n loss='categorical_crossentropy',\n act_out='softmax',\n **kwargs\n )\n\n def fit(self, x, y, y_test=None, **kwargs):\n return FeedForward.fit(\n self,\n x=x,\n y=to_categorical(y=y.flatten().astype(int)),\n y_test=to_categorical(y=y_test.flatten().astype(int)) if y_test is not None else None,\n metrics=['categorical_accuracy'],\n **kwargs\n )\n\n def predict(self, x):\n return probas_to_classes(FeedForward.predict(self, x=x))\n\n def predict_proba(self, x):\n return FeedForward.predict(self, x=x)\n\n\nclass FeedForwardClassifierBuilder(FeedForwardBuilder, ClassifierBuilder):\n\n @staticmethod\n def build(**kwargs):\n return FeedForwardClassifier(**kwargs)\n\n\nclass FeedForwardRegressor(FeedForward):\n\n def __init__(self, x, y, **kwargs):\n FeedForward.__init__(\n self,\n x=x,\n y=y,\n loss='mean_absolute_error',\n act_out='linear',\n **kwargs\n )\n\n def fit(self, x, y, **kwargs):\n return FeedForward.fit(\n self,\n x=x,\n y=y,\n metrics=['mean_absolute_error', 'mean_squared_error'],\n **kwargs\n )\n\n def predict(self, x):\n predictions = FeedForward.predict(self, x=x)\n if predictions.shape[1] == 1:\n predictions = predictions.flatten()\n return predictions\n\n\nclass FeedForwardRegressorBuilder(FeedForwardBuilder, RegressorBuilder):\n\n @staticmethod\n def build(**kwargs):\n return FeedForwardRegressor(**kwargs)\n", "sub_path": "predictor/estimator/neuralnetwork.py", "file_name": "neuralnetwork.py", "file_ext": "py", "file_size_in_byte": 9125, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 24, "usage_type": "name"}, {"api_name": "keras.optimizers.SGD", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.models.Model.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 40, "usage_type": "name"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.callbacks.History", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.models.Model.fit", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 65, "usage_type": "name"}, {"api_name": "keras.models.Model.fit", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.models.Model.predict", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 96, "usage_type": "name"}, {"api_name": "estimator.delog", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.regularizers.l1l2", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.regularizers.activity_l1l2", "line_number": 144, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.regularizers.l1l2", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.regularizers.activity_l1l2", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.layers.wrappers.TimeDistributed", "line_number": 181, "usage_type": "call"}, {"api_name": "keras.layers.wrappers.TimeDistributed", "line_number": 182, "usage_type": "call"}, {"api_name": "keras.layers.wrappers.TimeDistributed", "line_number": 184, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 184, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 186, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 188, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 190, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 192, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 196, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 200, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 203, "usage_type": "call"}, {"api_name": "keras.regularizers.l1l2", "line_number": 206, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 208, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 211, "usage_type": "call"}, {"api_name": "keras.regularizers.l1l2", "line_number": 214, "usage_type": "call"}, {"api_name": "estimator.EstimatorBuilder", "line_number": 221, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 249, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 263, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 264, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.probas_to_classes", "line_number": 270, "usage_type": "call"}, {"api_name": "estimator.ClassifierBuilder", "line_number": 276, "usage_type": "name"}, {"api_name": "estimator.RegressorBuilder", "line_number": 311, "usage_type": "name"}]} +{"seq_id": "596765251", "text": "import argparse\nimport os.path\nfrom ftplib import FTP\nimport glob\nimport gzip\nimport shutil\nimport tarfile\nimport os\nimport requests\nimport logging\nimport uuid\n\n\nlogger = logging.getLogger(\"DWD Crawler (script)\")\nlogger.setLevel(logging.INFO)\n\nfile_handler = logging.FileHandler(\"dwd-crawler.log\")\nfile_handler.setLevel(logging.INFO)\nlogger.addHandler(file_handler)\n\nstream_handler = logging.StreamHandler()\nstream_handler.setLevel(logging.INFO)\nlogger.addHandler(stream_handler)\n\n\nhost_protocol = \"ftp://\"\nhost_url = \"ftp-cdc.dwd.de\"\nhost_directory = \"pub/CDC/grids_germany/hourly/radolan/historical/bin/\"\nlocal_directory = \"./\"\n\nminutely_host_protocol = \"https://\"\nminutely_host_url = \"opendata.dwd.de/climate_environment/CDC/grids_germany/5_minutes/radolan/reproc/2017_002/bin/\"\nminutely_year_begin = 2001\nminutely_year_end = 2018\nminutely_filename_prefix = \"YW2017.002_\"\nminutely_filename_end = \".tar\"\n\n\ndef daily_uncompress(archive_directory, target_directory, year=None):\n temp_dir_name = \"tmp_\" + str(uuid.uuid4())\n os.chdir(archive_directory)\n if not os.path.isdir(temp_dir_name):\n os.mkdir(temp_dir_name)\n\n if not year:\n archive_wildcard = minutely_filename_prefix + \"*.tar\"\n else:\n archive_wildcard = minutely_filename_prefix + str(year) + \"*.tar\"\n\n for file in glob.glob(archive_wildcard):\n uncompress_tarfile(archive_directory + '/' + file, \"./\" + temp_dir_name)\n\n # Move to tmp directory and uncompress archives to target\n os.chdir(temp_dir_name)\n logger.info(\"Uncompressing .tar.gz files in \" + os.getcwd())\n for file in glob.glob(\"*.tar.gz\"):\n uncompress_targzfile(file, target_directory)\n logger.info(\"Removing temp folder\")\n os.chdir(\"..\")\n shutil.rmtree(\"./\" + temp_dir_name)\n\n\ndef daily_download_years(target_directory):\n for year in range(minutely_year_begin, minutely_year_end + 1):\n daily_download_months(year, target_directory)\n\n\ndef daily_download_months(year, target_directory):\n for month in range(1, 13):\n daily_filename = minutely_filename_prefix + str(year) + str(month).zfill(2) + minutely_filename_end\n url_complete = minutely_host_protocol + minutely_host_url + str(year) + '/' + daily_filename\n if os.path.isfile(target_directory + daily_filename):\n logger.info(\"File already downloaded: \" + daily_filename)\n continue\n logger.info(\"Downloading: \" + url_complete)\n r = requests.get(url_complete, stream=True)\n r.raw.decode_content = True\n with open(target_directory + daily_filename, 'wb') as file:\n file.write(r.content)\n\n\ndef gunzip(file_path, output_path):\n logger.info(\"Uncompressing gz file: \" + file_path)\n with gzip.open(file_path, \"rb\") as compressed, open(output_path, \"wb\") as file_out:\n shutil.copyfileobj(compressed, file_out)\n\n\ndef uncompress_tarfile(tar_file_path, destination):\n if tarfile.is_tarfile(tar_file_path):\n logger.info(\"Uncompressing tar file: \" + tar_file_path)\n file = tarfile.open(tar_file_path, \"r|\")\n file.extractall(destination)\n else:\n logger.error(\"Error uncompressing tar file: \" + tar_file_path)\n\n\ndef uncompress_targzfile(tar_file_path, destination):\n if tarfile.is_tarfile(tar_file_path):\n logger.info(\"Uncompressing tar.gz file: \" + tar_file_path)\n file = tarfile.open(tar_file_path, \"r:gz\")\n file.extractall(destination)\n else:\n logger.error(\"Error uncompressing tar.gz file: \" + tar_file_path)\n\n\ndef uncompress_monthly_all(source_path, destination_path):\n os.chdir(source_path)\n for file in glob.glob(\"*.tar.gz\"):\n subdir = destination_path + '/' + file\n if not os.path.exists(subdir):\n os.makedirs(subdir)\n uncompress_targzfile(file, subdir)\n\n\ndef download_with_new_connection(ftp, filename):\n if os.path.isfile(filename):\n logger.info(\"File \" + filename + \" already downloaded!\")\n else:\n logger.info(\"Downloading: \" + ftp.pwd() + filename)\n with open(filename, 'wb') as f:\n ftp.retrbinary('RETR ' + filename, f.write)\n\n\ndef download_files(ftp, file_list):\n for file in file_list:\n if file[0]:\n download_with_new_connection(ftp, file[1])\n\n\ndef ftp_file(ftp, directory):\n dir_listing = []\n ftp.cwd(directory)\n ftp.dir(lambda x: dir_listing.append(x))\n return [(line[0].upper() != 'D', line.rsplit()[-1]) for line in dir_listing]\n\n\ndef ftp_dir(ftp, directory):\n dir_listing = []\n ftp.cwd(directory)\n ftp.dir(lambda x: dir_listing.append(x))\n return [(line[0].upper() == 'D', line.rsplit()[-1]) for line in dir_listing]\n\n\ndef ftp_dir_year(ftp, directory_file_list):\n for df in directory_file_list:\n if df[0]:\n current_directory = df[1]\n file_list = ftp_file(ftp, current_directory)\n download_files(ftp, file_list)\n ftp.cwd(\"..\")\n\n\ndef main(download_dir=\"./\", out_directory=\"./\", download=True, unpack=True, minutely=True, year=None):\n logger.info(\"Downloads are at: \" + download_dir)\n logger.info(\"Uncompressing to: \" + out_directory)\n\n logger.info(\"Doing: \")\n\n if download:\n if not minutely:\n logger.info(\"Downloading hourly files\")\n os.chdir(download_dir)\n ftp_session = FTP(host_url)\n ftp_session.login()\n ftp_dir_year(ftp_session, ftp_dir(ftp_session, host_directory))\n ftp_session.close()\n else:\n logger.info(\"Downloading minutely files\")\n if not year:\n daily_download_years(download_dir)\n else:\n num_year = int(year)\n if minutely_year_begin <= num_year <= minutely_year_end:\n daily_download_months(num_year, download_dir)\n else:\n logger.info(\"Year not available for download: \" + str(year))\n\n if unpack:\n if not minutely:\n print(\"Uncompressing hourly files\")\n uncompress_monthly_all(download_dir, out_directory)\n else:\n print(\"Uncompressing minutely files\")\n daily_uncompress(download_dir, out_directory, year)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Downloads and extracts radio data from DWD ftp server\")\n parser.add_argument(\"-z\", \"--downloadDir\",\n dest=\"down_directory\",\n help=\"Target directory for downloads.\")\n parser.add_argument(\"-o\", \"--outputDir\",\n dest=\"out_directory\",\n help=\"Target directory for binary files.\")\n parser.add_argument(\"-d\", \"--download-only\",\n dest=\"downloadOnly\",\n help=\"Download only, do not unpack.\",\n action=\"store_true\")\n parser.add_argument(\"-u\", \"--unpack-only\",\n dest=\"unpackOnly\",\n help=\"Only unpack, do not download.\",\n action=\"store_true\")\n parser.add_argument(\"-m\", \"--minutely\",\n dest=\"minutely\",\n help=\"Download files containing data for every 5 minutes, instead of hourly data\",\n action=\"store_true\")\n parser.add_argument(\"-y\", \"--year\",\n dest=\"year\",\n help=\"Specify the year to be downloaded. ONLY WORKS with option: -m\")\n\n logger.info(\"All Arguments initialized\")\n\n args = parser.parse_args()\n logger.info(\"Parsed arguments:\")\n logger.info(\"downloadOnly: \")\n logger.info(\"True\" if args.downloadOnly else \"False\")\n logger.info(\"unpackOnly: \")\n logger.info(\"True\" if args.unpackOnly else \"False\")\n logger.info(\"hourly files: \")\n logger.info(\"True\" if not args.minutely else \"False\")\n logger.info(\"5 minutely files: \")\n logger.info(\"True\" if args.minutely else \"False\")\n\n logger.info(\"Download YEAR: \" + \"ALL\" if not args.year else str(args.year))\n\n down_dir = \"./\" if args.down_directory is None else os.path.join(args.down_directory, '')\n out_dir = \"./\" if args.out_directory is None else os.path.join(args.out_directory, '')\n\n if args.downloadOnly and args.unpackOnly:\n logger.error(\"Contradicting arguments: downloadOnly AND unpackOnly\")\n logger.info(\"YOU wanted me to do nothing!!!\")\n logger.info(\"Exiting now - tschau!\")\n else:\n main(download_dir=down_dir,\n out_directory=out_dir,\n download=not args.unpackOnly,\n unpack=not args.downloadOnly,\n minutely=args.minutely,\n year=args.year)\n logger.info(\"Crawler finished!\")\n", "sub_path": "DWD_Crawler/DWD_Crawler.py", "file_name": "DWD_Crawler.py", "file_ext": "py", "file_size_in_byte": 8694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 40, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 43, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 50, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 55, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 56, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 59, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 76, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 84, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 85, "usage_type": "call"}, {"api_name": "tarfile.is_tarfile", "line_number": 89, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 91, "usage_type": "call"}, {"api_name": "tarfile.is_tarfile", "line_number": 98, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 100, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 107, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 162, "usage_type": "call"}, {"api_name": "ftplib.FTP", "line_number": 163, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path", "line_number": 226, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}]} +{"seq_id": "255241952", "text": "import kivy\nkivy.require('1.9.1') # replace with your current kivy version !\n\nfrom kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.tabbedpanel import TabbedPanel\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.floatlayout import FloatLayout\nfrom kivy.uix.label import Label\nfrom kivy.uix.popup import Popup\nfrom kivy.clock import Clock\nfrom kivy.properties import StringProperty, NumericProperty, ObjectProperty\nfrom kivy.config import Config\nfrom math import sin\n#from kivy.garden.graph import Graph, MeshLinePlot\nfrom kivy.uix.effectwidget import EffectWidget\nfrom kivy.uix.effectwidget import InvertEffect\nfrom kivy.uix.scatterlayout import ScatterLayout\nfrom kivy.uix.scatter import Scatter\nfrom main import MainScreen, RIOData\nfrom kivy.vector import Vector\nfrom kivy.uix.widget import Widget\nfrom kivy.graphics.transformation import Matrix\nfrom math import radians\nfrom kivy.uix.image import Image\nfrom kivy.properties import BooleanProperty\nfrom kivy.animation import Animation\nimport math\n\nfrom kivy.core.window import Window\n\nWindow.size = (1920,1080)\nWindow.maximize()\nWindow.clearcolor = (1, 1, 1, 1)\n#Window.fullscreen = True\n#Config.set('graphics', 'maxfps', '10')\n\nscale_global = 0.06\npositions = [(1411.5, 428), (1460.5, 428), (1509.5, 428), (1558.5, 428)]\n\nclass MyScatterLayout(ScatterLayout):\n double_click = BooleanProperty(False)\n\n def on_touch_up( self, touch ):\n x, y = touch.x, touch.y\n # if the touch isnt on the widget we do nothing, just try children\n if not touch.grab_current == self:\n touch.push()\n touch.apply_transform_2d(self.to_local)\n if super(Scatter, self).on_touch_up(touch):\n touch.pop()\n return True\n touch.pop()\n\n # remove it from our saved touches\n if touch in self._touches and touch.grab_state:\n touch.ungrab(self)\n del self._last_touch_pos[touch]\n self._touches.remove(touch)\n\n # stop propagating if its within our bounds\n #if self.collide_point(x, y):\n # return True\n if self.double_click:\n if self.collide_point(*touch.pos):\n if touch.is_double_tap:\n if self.scale > scale_global:\n scale = scale_global / self.scale\n anim = Animation(scale=scale_global ** (1/30), duration=.5, s=1/30, pos = positions[int(self.id)])\n anim.start(self)\n #self.apply_transform(Matrix().scale(scale, scale, 1))\n #self.pos = positions[int(self.id)]\n else:\n scale = 1 / self.scale\n anim = Animation(scale=scale ** (1/30), duration=1, s=1/30, pos = (533, 316))\n anim.start(self)\n #self.apply_transform(Matrix().scale(scale, scale, 1))\n #self.pos = (533, 316)\n\n return super(MyScatterLayout, self).on_touch_up(touch)\n\n '''def alarm_animation(self, state):\n anim = Animation(opacity=0, duration=.5) + Animation(\n opacity=1, duration=.5)\n if state:\n anim.repeat = True\n anim.start(self.main_screen.ids.alarm_indicator)\n else:\n anim.cancel_all(self.main_screen.ids.alarm_indicator)\n self.main_screen.ids.alarm_indicator.opacity = 0\n self.alarm_state = state'''\n\n def transform_with_touch(self, touch):\n # just do a simple one finger drag\n changed = False\n if len(self._touches) == self.translation_touches:\n # _last_touch_pos has last pos in correct parent space,\n # just like incoming touch\n dx = (touch.x - self._last_touch_pos[touch][0]) \\\n * self.do_translation_x\n dy = (touch.y - self._last_touch_pos[touch][1]) \\\n * self.do_translation_y\n dx = dx / self.translation_touches\n dy = dy / self.translation_touches\n self.apply_transform(Matrix().translate(dx, dy, 0))\n changed = True\n\n if len(self._touches) == 1:\n return changed\n\n # we have more than one touch... list of last known pos\n points = [Vector(self._last_touch_pos[t]) for t in self._touches\n if t is not touch]\n # add current touch last\n points.append(Vector(touch.pos))\n\n # we only want to transform if the touch is part of the two touches\n # farthest apart! So first we find anchor, the point to transform\n # around as another touch farthest away from current touch's pos\n anchor = max(points[:-1], key=lambda p: p.distance(touch.pos))\n\n # now we find the touch farthest away from anchor, if its not the\n # same as touch. Touch is not one of the two touches used to transform\n farthest = max(points, key=anchor.distance)\n if farthest is not points[-1]:\n return changed\n\n # ok, so we have touch, and anchor, so we can actually compute the\n # transformation\n old_line = Vector(*touch.ppos) - anchor\n new_line = Vector(*touch.pos) - anchor\n if not old_line.length(): # div by zero\n return changed\n\n angle = radians(new_line.angle(old_line)) * self.do_rotation\n self.apply_transform(Matrix().rotate(angle, 0, 0, 1), anchor=anchor)\n\n if self.do_scale:\n scale = new_line.length() / old_line.length()\n new_scale = scale * self.scale\n if new_scale < self.scale_min:\n scale = self.scale_min / self.scale\n elif new_scale > self.scale_max:\n scale = self.scale_max / self.scale\n self.apply_transform(Matrix().scale(scale, scale, scale),\n anchor=anchor)\n changed = True\n return changed\n\nclass MyFloatLayout(FloatLayout):\n pass\n\nclass MainApp(App):\n passcode = '1234'\n passcode_try = ''\n logged_in = NumericProperty(0)\n\n def build(self):\n self.rio_data = RIOData()\n main_layout = MyFloatLayout()\n image_scatter = MyScatterLayout(do_rotation=False)\n image_scatter.add_widget(Image(source='lab_layout.png', double_click = False))\n main_layout.add_widget(image_scatter)\n for i in range(4):\n tank_layout = BoxLayout(id = 'float_' + str(i), orientation='vertical', padding=25)\n tank_layout.add_widget(Label(text='Tank ' + str(i), size_hint=(1, .1), font_size='30sp', color=(0,0,0,1)))\n tank_layout.add_widget(MainScreen(size_hint=(1, .9)))\n tank_scatter = MyScatterLayout(id=str(i), do_rotation=False, size=(800, 528), size_hint=(None, None), scale=scale_global, pos=positions[i], double_click = True, on_scale=lambda scale: self.apply_transform(Matrix().scale(self.scale, self.scale, 1)))\n tank_scatter.add_widget(tank_layout)\n image_scatter.add_widget(tank_scatter)\n\n return main_layout\n\n\nMainApp().run()\n\n", "sub_path": "smart_tank_scatter.py", "file_name": "smart_tank_scatter.py", "file_ext": "py", "file_size_in_byte": 7085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "kivy.require", "line_number": 2, "usage_type": "call"}, {"api_name": "kivy.core.window.Window.size", "line_number": 32, "usage_type": "attribute"}, {"api_name": "kivy.core.window.Window", "line_number": 32, "usage_type": "name"}, {"api_name": "kivy.core.window.Window.maximize", "line_number": 33, "usage_type": "call"}, {"api_name": "kivy.core.window.Window", "line_number": 33, "usage_type": "name"}, {"api_name": "kivy.core.window.Window.clearcolor", "line_number": 34, "usage_type": "attribute"}, {"api_name": "kivy.core.window.Window", "line_number": 34, "usage_type": "name"}, {"api_name": "kivy.uix.scatterlayout.ScatterLayout", "line_number": 41, "usage_type": "name"}, {"api_name": "kivy.properties.BooleanProperty", "line_number": 42, "usage_type": "call"}, {"api_name": "kivy.uix.scatter.Scatter", "line_number": 50, "usage_type": "argument"}, {"api_name": "kivy.animation.Animation", "line_number": 69, "usage_type": "call"}, {"api_name": "kivy.animation.Animation", "line_number": 75, "usage_type": "call"}, {"api_name": "kivy.graphics.transformation.Matrix", "line_number": 105, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 112, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 115, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 130, "usage_type": "call"}, {"api_name": "kivy.vector.Vector", "line_number": 131, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 135, "usage_type": "call"}, {"api_name": "kivy.graphics.transformation.Matrix", "line_number": 136, "usage_type": "call"}, {"api_name": "kivy.graphics.transformation.Matrix", "line_number": 145, "usage_type": "call"}, {"api_name": "kivy.uix.floatlayout.FloatLayout", "line_number": 150, "usage_type": "name"}, {"api_name": "kivy.app.App", "line_number": 153, "usage_type": "name"}, {"api_name": "kivy.properties.NumericProperty", "line_number": 156, "usage_type": "call"}, {"api_name": "main.RIOData", "line_number": 159, "usage_type": "call"}, {"api_name": "kivy.uix.image.Image", "line_number": 162, "usage_type": "call"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 165, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 166, "usage_type": "call"}, {"api_name": "main.MainScreen", "line_number": 167, "usage_type": "call"}, {"api_name": "kivy.graphics.transformation.Matrix", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "405300926", "text": "#!/usr/bin/env python\nimport codecs\nimport json\n\nfrom tokenizer import Tokenizer\nfrom storage import Storage\n\nclass Grapher(object):\n def __init__(self,tokenizer=None,load=None):\n if load is not None:\n with open(load,'r') as store:\n self.graph = json.load(store) \n return\n \n self.feed(tokenizer)\n\n def feed(self,tokenizer):\n old_graph = self.graph\n self.graph = graph = {}\n prev = None\n count = 0\n for char in tokenizer.feed():\n if prev is not None:\n # backtrace\n self._connect(prev,char)\n prev = char\n \n # if changed?\n if old_graph is None:\n self.changed = True\n return\n\n for key,words in old_graph.items():\n new_words = self.graph.get(key,None)\n if new_words is None or len(new_words) != len(words):\n self.changed = True\n return \n # size equal,check detail\n mark = {}\n for word in words.keys():\n if word not in new_words:\n self.changed = True\n return \n self.changed = False\n return \n\n def _node(self,char):\n node = self.graph.get(char,None) \n if node is None:\n self.graph[char] = node = {}\n return node\n \n def _connect(self,prev,char):\n prev_node = self._node(prev)\n prev_node[char] = prev_node.get(char,0) + 1\n return\n \n def _rescale(self,frequencyes,penaty=1):\n max_frequency = 0\n min_frequency = 0\n # find max and min\n for key,frequency in frequencyes.items():\n if frequency > max_frequency:\n max_frequency = frequency\n if frequency < min_frequency:\n min_frequency = frequency\n \n # range\n frequency_range = max_frequency - min_frequency \n scaled = {}\n for key,frequency in frequencyes.items():\n scaled[key] = float(frequency - 1) / float(frequency_range) \n \n return scaled \n\n def shrink(self,flip=0.5):\n shrink_graph = {}\n for key,words in self.graph.items():\n # resale\n rescaled = self._rescale(words)\n for word,frequency in rescaled.items():\n if frequency < flip:\n del rescaled[word]\n \n # if kick off?\n if len(rescaled) > 0:\n # keep,and copy back\n shirnk_words = {}\n for word in rescaled.keys():\n shirnk_words[word] = words[word]\n \n # add to shrink graph\n shrink_graph[key] = shirnk_words\n return shrink_graph\n\n def json(self,name,graph):\n with codecs.open(name,'w','utf8') as store:\n json.dump(graph,store,ensure_ascii=False,indent=2)\n \n def store(self,name,graph):\n with Storage(name,'w') as store:\n for key,words in graph.items():\n for second,frequency in words.items():\n store.write('%s%s\\t%s\\n' % (key,second,frequency))\n\n", "sub_path": "grapher.py", "file_name": "grapher.py", "file_ext": "py", "file_size_in_byte": 3212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "tokenizer.feed", "line_number": 22, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 97, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 98, "usage_type": "call"}, {"api_name": "storage.Storage", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "137340075", "text": "from unittest import mock\nimport pytest\n\nfrom requests.adapters import HTTPAdapter\nfrom requests.utils import select_proxy\nfrom requests.exceptions import ConnectionError\n\nfrom awx.api.versioning import reverse\nfrom awx.main.models.notifications import NotificationTemplate, Notification\nfrom awx.main.models.inventory import Inventory, InventorySource\nfrom awx.main.models.jobs import JobTemplate\n\n\n@pytest.mark.django_db\ndef test_get_notification_template_list(get, user, notification_template):\n url = reverse('api:notification_template_list')\n response = get(url, user('admin', True))\n assert response.status_code == 200\n assert len(response.data['results']) == 1\n\n\n@pytest.mark.django_db\ndef test_basic_parameterization(get, post, user, organization):\n u = user('admin-poster', True)\n url = reverse('api:notification_template_list')\n response = post(url,\n dict(name=\"test-webhook\",\n description=\"test webhook\",\n organization=organization.id,\n notification_type=\"webhook\",\n notification_configuration=dict(url=\"http://localhost\",\n headers={\"Test\": \"Header\"})),\n u)\n assert response.status_code == 201\n url = reverse('api:notification_template_detail', kwargs={'pk': response.data['id']})\n response = get(url, u)\n assert 'related' in response.data\n assert 'organization' in response.data['related']\n assert 'summary_fields' in response.data\n assert 'organization' in response.data['summary_fields']\n assert 'notifications' in response.data['related']\n assert 'notification_configuration' in response.data\n assert 'url' in response.data['notification_configuration']\n assert 'headers' in response.data['notification_configuration']\n\n\n@pytest.mark.django_db\ndef test_encrypted_subfields(get, post, user, organization):\n def assert_send(self, messages):\n assert self.account_token == \"shouldhide\"\n return 1\n u = user('admin-poster', True)\n url = reverse('api:notification_template_list')\n response = post(url,\n dict(name=\"test-twilio\",\n description=\"test twilio\",\n organization=organization.id,\n notification_type=\"twilio\",\n notification_configuration=dict(account_sid=\"dummy\",\n account_token=\"shouldhide\",\n from_number=\"+19999999999\",\n to_numbers=[\"9998887777\"])),\n u)\n assert response.status_code == 201\n notification_template_actual = NotificationTemplate.objects.get(id=response.data['id'])\n url = reverse('api:notification_template_detail', kwargs={'pk': response.data['id']})\n response = get(url, u)\n assert response.data['notification_configuration']['account_token'] == \"$encrypted$\"\n with mock.patch.object(notification_template_actual.notification_class, \"send_messages\", assert_send):\n notification_template_actual.send(\"Test\", {'body': \"Test\"})\n\n\n@pytest.mark.django_db\ndef test_inherited_notification_templates(get, post, user, organization, project):\n u = user('admin-poster', True)\n url = reverse('api:notification_template_list')\n notification_templates = []\n for nfiers in range(3):\n response = post(url,\n dict(name=\"test-webhook-{}\".format(nfiers),\n description=\"test webhook {}\".format(nfiers),\n organization=organization.id,\n notification_type=\"webhook\",\n notification_configuration=dict(url=\"http://localhost\",\n headers={\"Test\": \"Header\"})),\n u)\n assert response.status_code == 201\n notification_templates.append(response.data['id'])\n i = Inventory.objects.create(name='test', organization=organization)\n i.save()\n isrc = InventorySource.objects.create(name='test', inventory=i)\n isrc.save()\n jt = JobTemplate.objects.create(name='test', inventory=i, project=project, playbook='debug.yml')\n jt.save()\n url = reverse('api:organization_notification_templates_any_list', kwargs={'pk': organization.id})\n response = post(url, dict(id=notification_templates[0]), u)\n assert response.status_code == 204\n url = reverse('api:project_notification_templates_any_list', kwargs={'pk': project.id})\n response = post(url, dict(id=notification_templates[1]), u)\n assert response.status_code == 204\n url = reverse('api:job_template_notification_templates_any_list', kwargs={'pk': jt.id})\n response = post(url, dict(id=notification_templates[2]), u)\n assert response.status_code == 204\n assert len(jt.notification_templates['any']) == 3\n assert len(project.notification_templates['any']) == 2\n assert len(isrc.notification_templates['any']) == 1\n\n\n@pytest.mark.django_db\ndef test_notification_template_merging(get, post, user, organization, project, notification_template):\n user('admin-poster', True)\n organization.notification_templates_any.add(notification_template)\n project.notification_templates_any.add(notification_template)\n assert len(project.notification_templates['any']) == 1\n\n\n@pytest.mark.django_db\ndef test_notification_template_simple_patch(patch, notification_template, admin):\n patch(reverse('api:notification_template_detail', kwargs={'pk': notification_template.id}), { 'name': 'foo'}, admin, expect=200)\n\n\n@pytest.mark.django_db\ndef test_notification_template_invalid_notification_type(patch, notification_template, admin):\n patch(reverse('api:notification_template_detail', kwargs={'pk': notification_template.id}), { 'notification_type': 'invalid'}, admin, expect=400)\n\n\n@pytest.mark.django_db\ndef test_disallow_delete_when_notifications_pending(delete, user, notification_template):\n u = user('superuser', True)\n url = reverse('api:notification_template_detail', kwargs={'pk': notification_template.id})\n Notification.objects.create(notification_template=notification_template,\n status='pending')\n response = delete(url, user=u)\n assert response.status_code == 405\n\n\n@pytest.mark.django_db\ndef test_custom_environment_injection(post, user, organization):\n u = user('admin-poster', True)\n url = reverse('api:notification_template_list')\n response = post(url,\n dict(name=\"test-webhook\",\n description=\"test webhook\",\n organization=organization.id,\n notification_type=\"webhook\",\n notification_configuration=dict(url=\"https://example.org\",\n headers={\"Test\": \"Header\"})),\n u)\n assert response.status_code == 201\n template = NotificationTemplate.objects.get(pk=response.data['id'])\n with pytest.raises(ConnectionError), \\\n mock.patch('django.conf.settings.AWX_TASK_ENV', {'HTTPS_PROXY': '192.168.50.100:1234'}), \\\n mock.patch.object(HTTPAdapter, 'send') as fake_send:\n def _send_side_effect(request, **kw):\n assert select_proxy(request.url, kw['proxies']) == '192.168.50.100:1234'\n raise ConnectionError()\n fake_send.side_effect = _send_side_effect\n template.send('subject', 'message')\n", "sub_path": "awx/main/tests/functional/test_notifications.py", "file_name": "test_notifications.py", "file_ext": "py", "file_size_in_byte": 7647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "awx.api.versioning.reverse", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 25, "usage_type": "call"}, {"api_name": "awx.api.versioning.reverse", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 53, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.NotificationTemplate.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.NotificationTemplate.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "awx.main.models.notifications.NotificationTemplate", "line_number": 65, "usage_type": "name"}, {"api_name": "awx.api.versioning.reverse", "line_number": 66, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 69, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 69, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 69, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 47, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 76, "usage_type": "call"}, {"api_name": "awx.main.models.inventory.Inventory.objects.create", "line_number": 89, "usage_type": "call"}, {"api_name": "awx.main.models.inventory.Inventory.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "awx.main.models.inventory.Inventory", "line_number": 89, "usage_type": "name"}, {"api_name": "awx.main.models.inventory.InventorySource.objects.create", "line_number": 91, "usage_type": "call"}, {"api_name": "awx.main.models.inventory.InventorySource.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "awx.main.models.inventory.InventorySource", "line_number": 91, "usage_type": "name"}, {"api_name": "awx.main.models.jobs.JobTemplate.objects.create", "line_number": 93, "usage_type": "call"}, {"api_name": "awx.main.models.jobs.JobTemplate.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "awx.main.models.jobs.JobTemplate", "line_number": 93, "usage_type": "name"}, {"api_name": "awx.api.versioning.reverse", "line_number": 95, "usage_type": "call"}, {"api_name": "awx.api.versioning.reverse", "line_number": 98, "usage_type": "call"}, {"api_name": "awx.api.versioning.reverse", "line_number": 101, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 109, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 119, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 117, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 124, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 122, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 130, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.Notification.objects.create", "line_number": 131, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.Notification.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "awx.main.models.notifications.Notification", "line_number": 131, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 127, "usage_type": "attribute"}, {"api_name": "awx.api.versioning.reverse", "line_number": 140, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.NotificationTemplate.objects.get", "line_number": 150, "usage_type": "call"}, {"api_name": "awx.main.models.notifications.NotificationTemplate.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "awx.main.models.notifications.NotificationTemplate", "line_number": 150, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 151, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 151, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 152, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 152, "usage_type": "name"}, {"api_name": "unittest.mock.patch.object", "line_number": 153, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 153, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 153, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 153, "usage_type": "name"}, {"api_name": "requests.utils.select_proxy", "line_number": 155, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 156, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 137, "usage_type": "attribute"}]} +{"seq_id": "653054536", "text": "\nfit_gaussians = False\nuse_plotly=True\n# data manipulation\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# visualization\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n### matplotlib inline\nsns.set()\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\n\n# sklearn models & tools\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import make_scorer\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.decomposition import PCA\n\n# ignore warnings\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\nimport os\nprint(os.listdir(\"../input\"))\ntrain = pd.read_csv(\"../input/train.csv\")\ntest = pd.read_csv(\"../input/test.csv\")\nsubmission = pd.read_csv(\"../input/sample_submission.csv\")\ntrain.shape\ntrain.head(10)\ntrain.target.dtype\norg_vars = train.drop([\"target\", \"ID_code\"], axis=1).columns.values\nlen(org_vars)\ntrain[\"Id\"] = train.index.values\noriginal_trainid = train.ID_code.values\n\ntrain.drop(\"ID_code\", axis=1, inplace=True)\ntrain.isnull().sum().sum()\ntest.head(10)\ntest.isnull().sum().sum()\ntest.shape\ntest[\"Id\"] = test.index.values\noriginal_testid = test.ID_code.values\n\ntest.drop(\"ID_code\", axis=1, inplace=True)\nsubmission.head()\nfig, ax = plt.subplots(1,2,figsize=(20,5))\nsns.countplot(train.target.values, ax=ax[0], palette=\"husl\")\nsns.violinplot(x=train.target.values, y=train.index.values, ax=ax[1], palette=\"husl\")\nsns.stripplot(x=train.target.values, y=train.index.values,\n jitter=True, ax=ax[1], color=\"black\", size=0.5, alpha=0.5)\nax[1].set_xlabel(\"Target\")\nax[1].set_ylabel(\"Index\");\nax[0].set_xlabel(\"Target\")\nax[0].set_ylabel(\"Counts\");\ntrain.loc[train.target==1].shape[0] / train.loc[train.target==0].shape[0]\ntrain_correlations = train.drop([\"target\"], axis=1).corr()\ntrain_correlations = train_correlations.values.flatten()\ntrain_correlations = train_correlations[train_correlations != 1]\n\ntest_correlations = test.corr()\ntest_correlations = test_correlations.values.flatten()\ntest_correlations = test_correlations[test_correlations != 1]\n\nplt.figure(figsize=(20,5))\nsns.distplot(train_correlations, color=\"Red\", label=\"train\")\nsns.distplot(test_correlations, color=\"Green\", label=\"test\")\nplt.xlabel(\"Correlation values found in train (except 1)\")\nplt.ylabel(\"Density\")\nplt.title(\"Are there correlations between features?\"); \nplt.legend();\nparameters = {'min_samples_leaf': [20, 25]}\nforest = RandomForestClassifier(max_depth=15, n_estimators=15)\ngrid = GridSearchCV(forest, parameters, cv=3, n_jobs=-1, verbose=2, scoring=make_scorer(roc_auc_score))\ngrid.fit(train.drop(\"target\", axis=1).values, train.target.values)\ngrid.best_score_\ngrid.best_params_\nn_top = 5 \nimportances = grid.best_estimator_.feature_importances_\nidx = np.argsort(importances)[::-1][0:n_top]\nfeature_names = train.drop(\"target\", axis=1).columns.values\n\nplt.figure(figsize=(20,5))\nsns.barplot(x=feature_names[idx], y=importances[idx]);\nplt.title(\"What are the top important features to start with?\");\nfig, ax = plt.subplots(n_top,2,figsize=(20,5*n_top))\n\nfor n in range(n_top):\n sns.distplot(train.loc[train.target==0, feature_names[idx][n]], ax=ax[n,0], color=\"Orange\", norm_hist=True)\n sns.distplot(train.loc[train.target==1, feature_names[idx][n]], ax=ax[n,0], color=\"Red\", norm_hist=True)\n sns.distplot(test.loc[:, feature_names[idx][n]], ax=ax[n,1], color=\"Mediumseagreen\", norm_hist=True)\n ax[n,0].set_title(\"Train {}\".format(feature_names[idx][n]))\n ax[n,1].set_title(\"Test {}\".format(feature_names[idx][n]))\n ax[n,0].set_xlabel(\"\")\n ax[n,1].set_xlabel(\"\")\ntop = train.loc[:, feature_names[idx]]\ntop.describe()\ntop = top.join(train.target)\nsns.pairplot(top, hue=\"target\")\ny_proba = grid.predict_proba(test.values)\ny_proba_train = grid.predict_proba(train.drop(\"target\", axis=1).values)\nfig, ax = plt.subplots(2,1,figsize=(20,8))\nsns.distplot(y_proba_train[train.target==1,1], norm_hist=True, color=\"mediumseagreen\",\n ax=ax[0], label=\"1\")\nsns.distplot(y_proba_train[train.target==0,1], norm_hist=True, color=\"coral\",\n ax=ax[0], label=\"0\")\nsns.distplot(y_proba[:,1], norm_hist=True,\n ax=ax[1], color=\"purple\")\nax[1].set_xlabel(\"Predicted probability for test data\");\nax[1].set_ylabel(\"Density\");\nax[0].set_xlabel(\"Predicted probability for train data\");\nax[0].set_ylabel(\"Density\");\nax[0].legend();\nsubmission[\"target\"] = y_proba\nsubmission.to_csv(\"submission_baseline_forest.csv\", index=False)\noriginal_features = train.drop([\"target\", \"Id\"], axis=1).columns.values\noriginal_features\nencoder = LabelEncoder()\nfor your_feature in top.drop(\"target\", axis=1).columns.values:\n train[your_feature + \"_qbinned\"] = pd.qcut(\n train.loc[:, your_feature].values,\n q=10,\n labels=False\n )\n train[your_feature + \"_qbinned\"] = encoder.fit_transform(\n train[your_feature + \"_qbinned\"].values.reshape(-1, 1)\n )\n \n \n train[your_feature + \"_rounded\"] = np.round(train.loc[:, your_feature].values)\n train[your_feature + \"_rounded_10\"] = np.round(10*train.loc[:, your_feature].values)\n train[your_feature + \"_rounded_100\"] = np.round(100*train.loc[:, your_feature].values)\ncv = StratifiedKFold(n_splits=3, random_state=0)\nforest = RandomForestClassifier(max_depth=15, n_estimators=15, min_samples_leaf=20,\n n_jobs=-1)\n\nscores = []\nX = train.drop(\"target\", axis=1).values\ny = train.target.values\n\nfor train_idx, test_idx in cv.split(X, y):\n x_train = X[train_idx]\n x_test = X[test_idx]\n y_train = y[train_idx]\n y_test = y[test_idx]\n \n forest.fit(x_train, y_train)\n y_proba = forest.predict_proba(x_test)\n y_pred = np.zeros(y_proba.shape[0])\n y_pred[y_proba[:,1] >= 0.166] = 1\n \n score = roc_auc_score(y_test, y_pred)\n print(score)\n scores.append(score)\n\nprint(np.round(np.mean(scores),4))\nprint(np.round(np.std(scores), 4))\nimportances = forest.feature_importances_\nfeature_names = train.drop(\"target\", axis=1).columns.values\nidx = np.argsort(importances)[::-1][0:30]\n\nplt.figure(figsize=(20,5))\nsns.barplot(x=feature_names[idx], y=importances[idx]);\nplt.xticks(rotation=90);\ncol1 = \"var_81\"\ncol2 = \"var_12\"\nN=70000\nfig, ax = plt.subplots(1,1, figsize=(20,10))\nsns.kdeplot(train[col1].values[0:N], train[col2].values[0:N])\nax.scatter(train[col1].values[0:N], train[col2].values[0:N],\n s=2, c=train.target.values[0:N], cmap=\"coolwarm\", alpha=0.5)\nax.set_xlabel(col1)\nax.set_xlabel(col2);\ncombined = train.drop([\"target\", \"Id\"], axis=1).append(test.drop(\"Id\", axis=1))\ncombined.shape\nmax_components = 10\nstart_components = 3\nn_splits = 3\nK = train.shape[0]\n\nX = train.loc[:, original_features].values[0:K]\ny = train.target.values[0:K]\nseeds = np.random.RandomState(0).randint(0,100, size=(max_components-start_components))\nseeds\nscaler = RobustScaler()\nX_scaled = scaler.fit_transform(X)\nif fit_gaussians:\n components = np.arange(start_components, max_components, 1)\n kf = StratifiedKFold(random_state=0, n_splits=n_splits)\n \n scores = np.zeros(shape=(max_components-start_components, n_splits))\n\n for m in components:\n split=0\n print(\"Components \" + str(m))\n for train_index, test_index in kf.split(X_scaled, y):\n print(\"Split \" + str(split))\n x_train, x_test = X_scaled[train_index], X_scaled[test_index]\n gm = GaussianMixture(n_components=m, random_state=seeds[m-start_components])\n gm.fit(x_train)\n score = gm.score(x_test)\n scores[m-start_components,split] = score\n split +=1\n \n print(np.round(np.mean(scores, axis=1), 2))\n print(np.round(np.std(scores, axis=1), 2))\n best_idx = np.argmax(np.mean(scores, axis=1))\n best_component = components[best_idx]\n best_seed = seeds[best_idx]\n print(\"Best component found \" + str(best_component))\n \nelse:\n best_seed = seeds[0]\n best_component = 3\nX = train.loc[:, original_features].values\n\ngm = GaussianMixture(n_components=best_component, random_state=best_seed)\nX_scaled = scaler.transform(X)\ngm.fit(X_scaled)\ntrain[\"cluster\"] = gm.predict(X_scaled)\ntrain[\"logL\"] = gm.score_samples(X_scaled)\ntest[\"cluster\"] = gm.predict(test.loc[:, original_features].values)\ntest[\"logL\"] = gm.score_samples(test.loc[:, original_features].values)\nfig, ax = plt.subplots(1,2,figsize=(20,5))\nsns.countplot(train.cluster, palette=\"Set2\", ax=ax[0])\nsns.distplot(train.logL, color=\"Dodgerblue\", ax=ax[1]);\ncluster_occupation = train.groupby(\"cluster\").target.value_counts() / train.groupby(\"cluster\").size() * 100\ncluster_occupation = cluster_occupation.loc[:, 1]\n\ntarget_occupation = train.groupby(\"target\").cluster.value_counts() / train.groupby(\"target\").size() * 100\ntarget_occupation = target_occupation.loc[1, :]\ntarget_occupation.index = target_occupation.index.droplevel(\"target\")\n\nfig, ax = plt.subplots(1,2,figsize=(20,5))\nax[0].set_title(\"How many % of the data per cluster has hot targets?\")\nsns.barplot(cluster_occupation.index, cluster_occupation.values, ax=ax[0], color=\"cornflowerblue\")\nax[0].set_ylabel(\"% of cluster data\")\nax[0].set_ylim([0,100])\n\nax[1].set_title(\"How many % of total hot targets are in one cluster?\")\nsns.barplot(target_occupation.index, target_occupation.values, ax=ax[1], color=\"tomato\")\nax[1].set_ylabel(\"% of hot targets\")\nax[1].set_ylim([0,100]);\nplt.figure(figsize=(20,5))\nfor n in range(gm.means_.shape[0]):\n plt.plot(gm.means_[n,:], 'o')\nplt.title(\"How do the gaussian means look like?\")\nplt.ylabel(\"Cluster mean value\")\nplt.xlabel(\"Feature\")", "sub_path": "sources/santander-customer-transaction-eda.py", "file_name": "santander-customer-transaction-eda.py", "file_ext": "py", "file_size_in_byte": 10030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "seaborn.set", "line_number": 12, "usage_type": "call"}, {"api_name": "plotly.offline.init_notebook_mode", "line_number": 15, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 15, "usage_type": "name"}, {"api_name": "warnings.filterwarnings", "line_number": 31, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 32, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 33, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 34, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 60, "usage_type": "call"}, {"api_name": "seaborn.violinplot", "line_number": 61, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 78, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 86, "usage_type": "argument"}, {"api_name": "numpy.argsort", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 101, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 102, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 103, "usage_type": "call"}, {"api_name": "seaborn.pairplot", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 115, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 117, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.qcut", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "seaborn.kdeplot", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 195, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.RobustScaler", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 200, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 219, "usage_type": "call"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 237, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 248, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}]} +{"seq_id": "204768404", "text": "from shapely.geometry import Point\nimport matplotlib.pyplot as plt\nfrom rasterio.mask import mask\nfrom rasterio.plot import show\nfrom rasterio.plot import plotting_extent\nimport gdal\nimport pandas as pd\nimport numpy as np\nimport geopandas\nimport rasterio\nimport pycrs\n\nfile_dir=r'/Users/winand.hulleman/Documents/trait-geo-diverse-angiosperms'\n\nvar_names = open(file_dir+\"/data/gis/env_stacked/variable_list.txt\")\nvar_names = var_names.read()\nvar_names = var_names.split(\"\\n\")[1:-1]\n\n#access file with list of taxa names\ntaxa=pd.read_csv(file_dir+\"/data/crops_cleaned/taxalist.txt\",header=None)\ntaxa.columns=[\"taxon\"]\n\nspecies_occ_dict={}\n\nfor i in taxa[\"taxon\"]:\n taxon_data = pd.read_csv(file_dir+\"/data/crops_cleaned/%s.csv\"%i)\n species_occ_dict[\"%s\"%i] = taxon_data \n #check whether all species have been included and inspect dictionary\nif len(species_occ_dict.keys())==len(taxa[\"taxon\"]):\n print(\"All species dataframes now in dictionary\")\nelse:\n print(\"Error: not all species dataframe included\")\n \nfor key in species_occ_dict: \n #load occurrence data and set initial projection\n data=species_occ_dict[key]\n spec = key\n\n data['coordinates'] = list(zip(data[\"decimalLongitude\"], data[\"decimalLatitude\"]))\n data['coordinates'] = data[\"coordinates\"].apply(Point)\n data[\"present/pseudo_absent\"]=1\n geo_data=geopandas.GeoDataFrame(data, geometry='coordinates',crs={'init' :'epsg:4326'})\n\n #change projection to azimuthal equidistant to calculate 1000km buffer around point\n geo_data = geo_data.to_crs({'init': 'esri:54032'}) \n buffer=geo_data.buffer(1000*1000)\n buffer=buffer.to_crs(epsg=4326)\n\n #create single large polygon from individual buffers\n union_buffer=buffer.unary_union\n\n #first clip the raster based on this extend \n raster=rasterio.open(file_dir+'/data/gis/env_stacked/stacked_env_variables.tif')\n \n #specify output tif:\n out_tif = file_dir+'/data/GIS/spec_stacked_raster_clip/%s_raster_clip.tif'%spec\n\n #clip the raster:\n out_img, out_transform = rasterio.mask.mask(dataset=raster, shapes=[union_buffer],crop=False)\n\n # Copy the metadata\n out_meta = raster.meta.copy()\n\n # Parse EPSG code\n epsg_code = int(raster.crs.data['init'][5:])\n out_meta.update({\"driver\": \"GTiff\",\n \"height\": out_img.shape[1],\n \"width\": out_img.shape[2],\n \"transform\": out_transform,\n \"crs\": pycrs.parse.from_epsg_code(epsg_code).to_proj4()})\n\n with rasterio.open(out_tif, \"w\", **out_meta) as dest:\n dest.write(out_img)\n \n#Inspect the first band of the clipped raster for all species\nfor key in species_occ_dict:\n \n # Extract occurrence point to plot on the raster (see if correct area was clipped)\n data=species_occ_dict[key]\n spec = key\n data['coordinates'] = list(zip(data[\"decimalLongitude\"], data[\"decimalLatitude\"]))\n data['coordinates'] = data[\"coordinates\"].apply(Point)\n geo_data=geopandas.GeoDataFrame(data, geometry='coordinates',crs={'init' :'epsg:4326'})\n \n # open the clipped raster\n clipped = rasterio.open(file_dir+'/data/GIS/spec_stacked_raster_clip/%s_raster_clip.tif'%spec)\n array = clipped.read(1)\n array_data = clipped.read(1,masked=True)\n array_meta = clipped.profile\n \n fig, ax = plt.subplots(figsize=(10, 10))\n ax.imshow(array_data,cmap=\"gist_earth\",interpolation=\"none\",vmin=0,\n \n # Plot the occurrence points on the raster\n extent=plotting_extent(clipped),)\n spec_plots_points=geo_data[\"coordinates\"]\n spec_plots_points.plot(ax=ax,\n marker='o',\n markersize=20,\n color='red')\n ax.set_title(\"%s \\n Raster clip and occurrence points\"%spec,\n fontsize=20)\n plt.show()\n \n#Works!\n \n#open world raster\nstack_path=file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif'\nr2=gdal.Open(stack_path)\n\n\nfor key in species_occ_dict: \n \n #extract longitude and latitude of occurrence locations and label them as present (1)\n presence_data = species_occ_dict[key]\n presence_data[\"present/pseudo_absent\"]=1\n spec = key\n \n long=presence_data[\"decimalLongitude\"]\n lati=presence_data[\"decimalLatitude\"]\n long=pd.Series.tolist(long)\n lati=pd.Series.tolist(lati)\n \n #read raster\n src=rasterio.open(stack_path)\n array=src.read_masks(1)\n \n # set raster cell mask values of presence locations to threshold value (=1) to exclude them from pseudo-absence sampling\n for i in range(0,len(presence_data)):\n row,col=src.index(long[i],lati[i])\n array[row,col]=1\n \n #subset of cells with datavalues from which to sample pseudo-absences\n (y_index_2, x_index_2) = np.nonzero(array > 1) \n \n #sample random locations from raster excluding sea and presence cells\n r=r2\n (upper_left_x, x_size, x_rotation, upper_left_y, y_rotation, y_size) = r.GetGeoTransform()\n \n x_coords = x_index_2 * x_size + upper_left_x + (x_size / 2) #add half the cell size\n y_coords = y_index_2 * y_size + upper_left_y + (y_size / 2) #to centre the point\n\n lon_lat_array=np.stack((x_coords,y_coords)).T\n\n #determine number of pseudo-absences to sample\n random_sample_size=0\n len_p=int(len(presence_data))\n \n if len_p > 2000:\n random_sample_size=len_p\n else: \n random_sample_size=2000\n \n outer_random_sample_lon_lats=lon_lat_array[np.random.choice(lon_lat_array.shape[0], random_sample_size, replace=False), :] ##\n print(len(outer_random_sample_lon_lats), \"number of outer pseudo absences\")\n \n \n #Add selected cells to dataset\n lon=[]\n lat=[]\n psa=[0]*(random_sample_size)\n taxon=[\"%s\"%spec]*(random_sample_size)\n gbif=[\"no_id\"]*(random_sample_size)\n\n for item in outer_random_sample_lon_lats:\n longitude=item[0]\n latitude=item[1]\n lon.append(longitude)\n lat.append(latitude)\n \n #Dataset including occurrences and pseudo-absence points\n new_data=pd.DataFrame({\"gbif_id\": gbif,\"taxon_name\":taxon,\"decimalLongitude\": lon, \"decimalLatitude\":lat, \"present/pseudo_absent\": psa})\n data=pd.concat([presence_data,new_data],ignore_index=True)\n data=data[['taxon_name','gbif_id','decimalLongitude','decimalLatitude','present/pseudo_absent']]\n data[\"taxon_name\"]=spec\n data[\"row_n\"]=np.arange(len(data))\n \n long=data[\"decimalLongitude\"]\n lati=data[\"decimalLatitude\"]\n long=pd.Series.tolist(long)\n lati=pd.Series.tolist(lati)\n \n print(len(data),\"lenght data with pseudo absences pre-filtering\")\n \n #read raster\n src=rasterio.open(stack_path)\n array=src.read_masks(1)\n \n data=data.reset_index(drop=True)\n data.to_csv(file_dir + \"/data/spec_ppa/%s_ppa_dataframe.csv\"%spec)\n\n#next species\n \nraster=rasterio.open(file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif')\narray = raster.read()\nprofile=raster.profile\n\nwith open(file_dir+'/data/GIS/env_bio_mean_std.txt','w+') as file:\n file.write(\"band\"+\"\\t\"+\"mean\"+\"\\t\"+\"std_dev\"+\"\\n\")\n file.close()\n \nfor i in range(1,65):\n print(i)\n profile.update(count=1)\n band=raster.read(i)\n band[band < -9999] = -9999\n where_are_NaNs = np.isnan(band)\n band[where_are_NaNs] = -9999\n band_masked = np.ma.masked_array(band, mask=(band == -9999))\n\n #calculate mean and std.dev of each band\n mean=band_masked.mean()\n std_dev=np.std(band_masked)\n\n #write to file\n with open(file_dir+'/data/GIS/env_bio_mean_std.txt','a') as file:\n file.write(str(i)+\"\\t\"+str(mean)+\"\\t\"+str(std_dev)+\"\\n\")\n \n\n#access file with list of taxa names\ntaxa=pd.read_csv(file_dir+\"/data/crops_cleaned/taxalist.txt\",header=None)\ntaxa.columns=[\"taxon\"]\n\nsrc=rasterio.open(file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif')\ninRas=gdal.Open(file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif')\n\nfor i in taxa[\"taxon\"][:]:\n \n data = pd.read_csv(file_dir+\"/data/spec_ppa/%s_ppa_dataframe.csv\"%i)\n spec = data[\"taxon_name\"][0]\n spec = spec.replace(\" \",\"_\")\n print(\"processing species \", spec)\n \n\n #get all collumn and row numbers \n len_pd=np.arange(len(data))\n long=data[\"decimalLongitude\"]\n lati=data[\"decimalLatitude\"]\n ppa=data[\"present/pseudo_absent\"]\n\n lon=long.values\n lat=lati.values\n\n row=[]\n col=[]\n\n for i in len_pd:\n row_n, col_n = src.index(lon[i], lat[i])# spatial --> image coordinates\n row.append(row_n)\n col.append(col_n)\n\n ##opening raster as 3d numpy array\n myarray=inRas.ReadAsArray()\n\n #collect file with mean and std_dev for each band\n mean_std=pd.read_csv(file_dir+'/data/GIS/env_bio_mean_std.txt',sep=\"\\t\")\n mean_std=mean_std.to_numpy()\n\n\n ########################################################\n #extract the values for all bands and prepare input data\n ########################################################\n X=[]\n species =[\"%s\"%spec]*int(len(row))\n\n for j in range(0,64):\n band=myarray[j]\n x=[]\n\n #extract coastal outline \n \n for i in range(0,len(row)):\n value= band[row[i],col[i]]\n if j < 46:\n if value <-1000:\n value=np.nan\n else: \n value = ((value - mean_std.item((j,1))) / mean_std.item((j,2)))#scale values\n x.append(value)\n \n if j >= 46:\n if value <-1000:\n value=np.nan\n else: \n value=value\n x.append(value)\n X.append(x)\n \n \n\n #set as numpy 2d array\n X =np.array([np.array(xi) for xi in X])\n #X\n \n #transform into dataframe and include row and column values\n df=pd.DataFrame(X) \n df=df.T\n \n df[\"present/pseudo_absent\"]=ppa\n df[\"decimalLatitude\"]=lati\n df[\"decimalLongitude\"]=long\n df[\"taxon_name\"]=species\n df[\"present/pseudo_absent\"]=ppa\n df[\"row_n\"]=row\n df.rename(columns=dict(zip(df.columns[0:186], var_names)),inplace=True)\n \n #drop any potential rows with no-data values\n df=df.dropna(axis=0, how='any')\n input_data=df\n \n ##save input dataframe\n input_data.to_csv(file_dir +\"/data/spec_ppa_env/%s_env_dataframe.csv\"%spec)\n \n##opening raster as 3d numpy array\ninRas=gdal.Open(file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif')\nmyarray=inRas.ReadAsArray()\nprint(myarray.shape)\nprint(type(myarray))\n\n#get all collumn and row values for all cells to predict over \ndf=pd.read_csv(file_dir+'/data/GIS/world_locations_to_predict.csv')\n\nlen_pd=np.arange(len(df))\nlon=df[\"decimal_longitude\"]\nlat=df[\"decimal_latitude\"]\nlon=lon.values\nlat=lat.values\n\nrow=[]\ncol=[]\n\nsrc=rasterio.open(file_dir+'/data/GIS/env_stacked/stacked_env_variables.tif')\n\nfor i in len_pd:\n row_n, col_n = src.index(lon[i], lat[i])# spatial --> image coordinates\n row.append(row_n)\n col.append(col_n)\n\n#collect file with mean and std_dev for each band\nmean_std=pd.read_csv(file_dir+'/data/GIS/env_bio_mean_std.txt',sep=\"\\t\")\nmean_std=mean_std.to_numpy()\n\n\n###########################################################\n# extract the values for all bands and prepare input data #\n###########################################################\nX=[]\n\nfor j in range(0,65):\n print(j)\n band=myarray[j]\n x=[]\n\n for i in range(0,len(row)):\n if j < 46:\n value= band[row[i],col[i]]\n value = ((value - mean_std.item((j,1))) / mean_std.item((j,2)))#scale values\n x.append(value)\n if j >= 46:\n value= band[row[i],col[i]]\n x.append(value)\n X.append(x)\n\n\n #include row and column values\n X.append(row)\n X.append(col)\n \n #set as numpy 2d array\n X =np.array([np.array(xi) for xi in X])\n \n df=pd.DataFrame(X)\n \n df=df.T\n df.rename(columns=dict(zip(df.columns[0:65], var_names)),inplace=True)\n df=df.dropna(axis=0, how='any')\n df.head()\n \n input_X=df.iloc[:,0:65]\n np.shape(input_X)\n \n row=df[64]\n col=df[65]\n \n row_col=pd.DataFrame({\"row\":row,\"col\":col})\n \n #convert dataframe back to numpy array\n input_X=input_X.values\n \n #convert rows and col indices back to array\n row=row.values\n col=col.values\n \n #save\n prediction_array=np.save(file_dir+'/data/GIS/world_prediction_array.npy',input_X)\n prediction_pandas=row_col.to_csv(file_dir+'/data/GIS/world_prediction_row_col.csv')", "sub_path": "script/python/data_prep_II.py", "file_name": "data_prep_II.py", "file_ext": "py", "file_size_in_byte": 12524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 40, "usage_type": "argument"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 53, "usage_type": "call"}, {"api_name": "rasterio.mask.mask", "line_number": 59, "usage_type": "call"}, {"api_name": "rasterio.mask", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pycrs.parse.from_epsg_code", "line_number": 70, "usage_type": "call"}, {"api_name": "pycrs.parse", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rasterio.open", "line_number": 72, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 82, "usage_type": "argument"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 83, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "rasterio.plot.plotting_extent", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "gdal.Open", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.Series.tolist", "line_number": 121, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pandas.Series.tolist", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 122, "usage_type": "attribute"}, {"api_name": "rasterio.open", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 172, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.Series.tolist", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pandas.Series.tolist", "line_number": 181, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 181, "usage_type": "attribute"}, {"api_name": "rasterio.open", "line_number": 186, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.ma.masked_array", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 221, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 224, "usage_type": "call"}, {"api_name": "gdal.Open", "line_number": 225, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 276, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 296, "usage_type": "call"}, {"api_name": "gdal.Open", "line_number": 315, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 323, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 332, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 370, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 380, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 395, "usage_type": "call"}]} +{"seq_id": "389270709", "text": "# pylint: disable=redefined-outer-name\n# pylint: disable=unused-argument\n\nfrom asyncio import BaseEventLoop\nfrom typing import Any, AsyncIterator, Dict, List\nfrom uuid import UUID, uuid4\n\nimport aiodocker\nimport pytest\nfrom _pytest.monkeypatch import MonkeyPatch\nfrom models_library.projects import ProjectID\nfrom models_library.projects_nodes_io import NodeID\nfrom simcore_service_director_v2.core.settings import DynamicSidecarSettings\nfrom simcore_service_director_v2.models.schemas.constants import (\n DYNAMIC_PROXY_SERVICE_PREFIX,\n DYNAMIC_SIDECAR_SERVICE_PREFIX,\n UserID,\n)\nfrom simcore_service_director_v2.models.schemas.dynamic_services import (\n ServiceLabelsStoredData,\n ServiceState,\n ServiceType,\n)\nfrom simcore_service_director_v2.modules.dynamic_sidecar import docker_api\nfrom simcore_service_director_v2.modules.dynamic_sidecar.errors import (\n DynamicSidecarError,\n GenericDockerError,\n)\n\npytestmark = pytest.mark.asyncio\n\n# FIXTURES\n\n\n@pytest.fixture\nasync def async_docker_client(\n loop: BaseEventLoop,\n docker_swarm: None,\n) -> AsyncIterator[aiodocker.docker.Docker]:\n async with aiodocker.Docker() as client:\n yield client\n\n\n@pytest.fixture\ndef dynamic_sidecar_settings(\n monkeypatch: MonkeyPatch, docker_swarm: None\n) -> DynamicSidecarSettings:\n monkeypatch.setenv(\"DYNAMIC_SIDECAR_IMAGE\", \"local/dynamic-sidecar:MOCKED\")\n monkeypatch.setenv(\"DIRECTOR_V2_DYNAMIC_SCHEDULER_ENABLED\", \"false\")\n return DynamicSidecarSettings.create_from_envs()\n\n\n@pytest.fixture\ndef network_config(simcore_services_network_name: str) -> Dict[str, Any]:\n return {\n \"Name\": simcore_services_network_name,\n \"Driver\": \"overlay\",\n \"Labels\": {\"uuid\": f\"{uuid4()}\"},\n }\n\n\n@pytest.fixture\nasync def ensure_swarm_network(\n loop: BaseEventLoop,\n network_config: Dict[str, Any],\n async_docker_client: aiodocker.docker.Docker,\n docker_swarm: None,\n) -> None:\n network_id = None\n try:\n network_id = await docker_api.create_network(network_config)\n yield\n finally:\n if network_id is not None:\n docker_network = await async_docker_client.networks.get(network_id)\n assert await docker_network.delete() is True\n\n\n@pytest.fixture\nasync def cleanup_swarm_network(\n loop: BaseEventLoop,\n simcore_services_network_name: str,\n async_docker_client: aiodocker.docker.Docker,\n docker_swarm: None,\n) -> None:\n yield\n docker_network = await async_docker_client.networks.get(\n simcore_services_network_name\n )\n assert await docker_network.delete() is True\n\n\n@pytest.fixture\ndef missing_network_name() -> str:\n return \"this_network_is_missing\"\n\n\n@pytest.fixture\ndef test_service_name() -> str:\n return \"test_service_name\"\n\n\n@pytest.fixture\ndef service_spec(test_service_name: str) -> Dict[str, Any]:\n # \"joseluisq/static-web-server\" is ~2MB docker image\n return {\n \"name\": test_service_name,\n \"task_template\": {\"ContainerSpec\": {\"Image\": \"joseluisq/static-web-server\"}},\n \"labels\": {\"foo\": \"bar\"},\n }\n\n\n@pytest.fixture\nasync def cleanup_test_service_name(\n loop: BaseEventLoop,\n test_service_name: str,\n async_docker_client: aiodocker.docker.Docker,\n docker_swarm: None,\n) -> None:\n yield\n\n assert await async_docker_client.services.delete(test_service_name) is True\n\n\n@pytest.fixture\ndef dynamic_sidecar_service_name() -> str:\n return f\"{DYNAMIC_SIDECAR_SERVICE_PREFIX}_some-dynamic-fake-sidecar\"\n\n\n@pytest.fixture\ndef dynamic_sidecar_service_spec(\n dynamic_sidecar_service_name: str, dynamic_sidecar_settings: DynamicSidecarSettings\n) -> Dict[str, Any]:\n # \"joseluisq/static-web-server\" is ~2MB docker image\n sample = ServiceLabelsStoredData.Config.schema_extra[\"example\"]\n\n return {\n \"name\": dynamic_sidecar_service_name,\n \"task_template\": {\"ContainerSpec\": {\"Image\": \"joseluisq/static-web-server\"}},\n \"labels\": {\n \"swarm_stack_name\": f\"{dynamic_sidecar_settings.SWARM_STACK_NAME}\",\n \"uuid\": f\"{uuid4()}\",\n \"service_key\": \"simcore/services/dynamic/3dviewer\",\n \"service_tag\": \"2.4.5\",\n \"paths_mapping\": sample[\"paths_mapping\"].json(),\n \"compose_spec\": sample[\"compose_spec\"],\n \"container_http_entry\": sample[\"container_http_entry\"],\n \"traefik.docker.network\": \"\",\n \"io.simcore.zone\": \"\",\n \"service_port\": \"80\",\n \"study_id\": f\"{uuid4()}\",\n \"user_id\": \"123\",\n },\n }\n\n\n@pytest.fixture\nasync def cleanup_test_dynamic_sidecar_service(\n loop: BaseEventLoop,\n dynamic_sidecar_service_name: str,\n async_docker_client: aiodocker.docker.Docker,\n) -> None:\n yield\n assert (\n await async_docker_client.services.delete(dynamic_sidecar_service_name) is True\n )\n\n\n@pytest.fixture\ndef node_uuid() -> NodeID:\n return uuid4()\n\n\n@pytest.fixture\ndef user_id() -> UserID:\n return 123\n\n\n@pytest.fixture\ndef project_id() -> ProjectID:\n return uuid4()\n\n\n@pytest.fixture\ndef dynamic_sidecar_stack_specs(\n node_uuid: UUID,\n user_id: UserID,\n project_id: ProjectID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n) -> List[Dict[str, Any]]:\n return [\n {\n \"name\": f\"{DYNAMIC_PROXY_SERVICE_PREFIX}_fake_proxy\",\n \"task_template\": {\n \"ContainerSpec\": {\"Image\": \"joseluisq/static-web-server\"}\n },\n \"labels\": {\n \"swarm_stack_name\": f\"{dynamic_sidecar_settings.SWARM_STACK_NAME}\",\n \"type\": f\"{ServiceType.DEPENDENCY.value}\",\n \"uuid\": f\"{node_uuid}\",\n \"user_id\": f\"{user_id}\",\n \"study_id\": f\"{project_id}\",\n },\n },\n {\n \"name\": f\"{DYNAMIC_SIDECAR_SERVICE_PREFIX}_fake_sidecar\",\n \"task_template\": {\n \"ContainerSpec\": {\"Image\": \"joseluisq/static-web-server\"}\n },\n \"labels\": {\n \"swarm_stack_name\": f\"{dynamic_sidecar_settings.SWARM_STACK_NAME}\",\n \"type\": f\"{ServiceType.MAIN.value}\",\n \"uuid\": f\"{node_uuid}\",\n \"user_id\": f\"{user_id}\",\n \"study_id\": f\"{project_id}\",\n },\n },\n ]\n\n\n@pytest.fixture\nasync def cleanup_dynamic_sidecar_stack(\n loop: BaseEventLoop,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n async_docker_client: aiodocker.docker.Docker,\n) -> None:\n yield\n for dynamic_sidecar_spec in dynamic_sidecar_stack_specs:\n assert (\n await async_docker_client.services.delete(dynamic_sidecar_spec[\"name\"])\n is True\n )\n\n\n# UTILS\n\n\ndef _assert_service(\n service_spec: Dict[str, Any], service_inspect: Dict[str, Any]\n) -> None:\n assert service_inspect[\"Spec\"][\"Labels\"] == service_spec[\"labels\"]\n assert service_inspect[\"Spec\"][\"Name\"] == service_spec[\"name\"]\n assert (\n service_inspect[\"Spec\"][\"TaskTemplate\"][\"ContainerSpec\"][\"Image\"]\n == service_spec[\"task_template\"][\"ContainerSpec\"][\"Image\"]\n )\n\n\nasync def _count_services_in_stack(\n node_uuid: UUID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n async_docker_client: aiodocker.docker.Docker,\n) -> int:\n services = await async_docker_client.services.list(\n filters={\n \"label\": [\n f\"swarm_stack_name={dynamic_sidecar_settings.SWARM_STACK_NAME}\",\n f\"uuid={node_uuid}\",\n ]\n }\n )\n return len(services)\n\n\n# TESTS\n\n\ndef test_new_docker_swarm(docker_swarm: None) -> None:\n pass\n\n\n@pytest.mark.parametrize(\n \"simcore_services_network_name\",\n (\"n\", \"network\", \"with_underscore\", \"with-dash\", \"with-dash_with_underscore\"),\n)\ndef test_valid_network_names(\n simcore_services_network_name: str, monkeypatch: MonkeyPatch\n) -> None:\n monkeypatch.setenv(\"DYNAMIC_SIDECAR_IMAGE\", \"local/dynamic-sidecar:MOCKED\")\n monkeypatch.setenv(\"SIMCORE_SERVICES_NETWORK_NAME\", simcore_services_network_name)\n dynamic_sidecar_settings = DynamicSidecarSettings.create_from_envs()\n assert dynamic_sidecar_settings\n\n\nasync def test_failed_docker_client_request(\n missing_network_name: str, docker_swarm: None\n) -> None:\n with pytest.raises(GenericDockerError) as execinfo:\n async with docker_api.docker_client() as client:\n await client.networks.get(missing_network_name)\n assert (\n str(execinfo.value)\n == f\"Unexpected error from docker client: network {missing_network_name} not found\"\n )\n\n\nasync def test_get_swarm_network_ok(\n dynamic_sidecar_settings: DynamicSidecarSettings,\n simcore_services_network_name: str,\n ensure_swarm_network: None,\n docker_swarm: None,\n) -> None:\n swarm_network = await docker_api.get_swarm_network(dynamic_sidecar_settings)\n assert swarm_network[\"Name\"] == simcore_services_network_name\n\n\nasync def test_get_swarm_network_missing_network(\n dynamic_sidecar_settings: DynamicSidecarSettings, docker_swarm: None\n) -> None:\n with pytest.raises(DynamicSidecarError) as excinfo:\n await docker_api.get_swarm_network(dynamic_sidecar_settings)\n assert (\n str(excinfo.value)\n == \"Swarm network name is not configured, found following networks: []\"\n )\n\n\nasync def test_recreate_network_multiple_times(\n network_config: Dict[str, Any],\n cleanup_swarm_network: None,\n docker_swarm: None,\n) -> None:\n network_ids = [await docker_api.create_network(network_config) for _ in range(10)]\n network_ids_set = set(network_ids)\n assert len(network_ids_set) == 1\n network_id = network_ids_set.pop()\n assert type(network_id) == str\n\n\nasync def test_create_service(\n service_spec: Dict[str, Any],\n cleanup_test_service_name: None,\n docker_swarm: None,\n) -> None:\n service_id = await docker_api.create_service_and_get_id(service_spec)\n assert service_id\n\n\nasync def test_inspect_service(\n service_spec: Dict[str, Any],\n cleanup_test_service_name: None,\n docker_swarm: None,\n) -> None:\n service_id = await docker_api.create_service_and_get_id(service_spec)\n assert service_id\n\n service_inspect = await docker_api.inspect_service(service_id)\n\n _assert_service(service_spec, service_inspect)\n\n\nasync def test_services_to_observe_exist(\n dynamic_sidecar_service_name: str,\n dynamic_sidecar_service_spec: Dict[str, Any],\n dynamic_sidecar_settings: DynamicSidecarSettings,\n cleanup_test_dynamic_sidecar_service: None,\n docker_swarm: None,\n) -> None:\n service_id = await docker_api.create_service_and_get_id(\n dynamic_sidecar_service_spec\n )\n assert service_id\n\n dynamic_services = await docker_api.get_dynamic_sidecars_to_observe(\n dynamic_sidecar_settings\n )\n assert len(dynamic_services) == 1\n\n for entry in dynamic_services:\n assert entry.service_name == dynamic_sidecar_service_name\n\n\nasync def test_dynamic_sidecar_in_running_state_and_node_id_is_recovered(\n dynamic_sidecar_service_spec: Dict[str, Any],\n dynamic_sidecar_settings: DynamicSidecarSettings,\n cleanup_test_dynamic_sidecar_service: None,\n docker_swarm: None,\n) -> None:\n service_id = await docker_api.create_service_and_get_id(\n dynamic_sidecar_service_spec\n )\n assert service_id\n\n node_id = await docker_api.get_node_id_from_task_for_service(\n service_id, dynamic_sidecar_settings\n )\n assert node_id\n\n # after the node_id is recovered the service\n # will be in a running state\n dynamic_sidecar_state = await docker_api.get_dynamic_sidecar_state(service_id)\n assert dynamic_sidecar_state == (ServiceState.RUNNING, \"\")\n\n\nasync def test_are_services_missing(\n node_uuid: UUID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n cleanup_dynamic_sidecar_stack: None,\n docker_swarm: None,\n) -> None:\n\n services_are_missing = await docker_api.are_services_missing(\n node_uuid, dynamic_sidecar_settings\n )\n assert services_are_missing == True\n\n # start 2 fake services to emulate the dynamic-sidecar stack\n for dynamic_sidecar_stack in dynamic_sidecar_stack_specs:\n service_id = await docker_api.create_service_and_get_id(dynamic_sidecar_stack)\n assert service_id\n\n services_are_missing = await docker_api.are_services_missing(\n node_uuid, dynamic_sidecar_settings\n )\n assert services_are_missing == False\n\n\nasync def test_are_all_services_present(\n node_uuid: UUID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n cleanup_dynamic_sidecar_stack: None,\n docker_swarm: None,\n):\n services_are_missing = await docker_api.are_all_services_present(\n node_uuid, dynamic_sidecar_settings\n )\n assert services_are_missing == False\n\n # start 2 fake services to emulate the dynamic-sidecar stack\n for dynamic_sidecar_stack in dynamic_sidecar_stack_specs:\n service_id = await docker_api.create_service_and_get_id(dynamic_sidecar_stack)\n assert service_id\n\n services_are_missing = await docker_api.are_all_services_present(\n node_uuid, dynamic_sidecar_settings\n )\n assert services_are_missing == True\n\n\nasync def test_remove_dynamic_sidecar_stack(\n node_uuid: UUID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n docker_swarm: None,\n async_docker_client: aiodocker.docker.Docker,\n):\n assert (\n await _count_services_in_stack(\n node_uuid, dynamic_sidecar_settings, async_docker_client\n )\n == 0\n )\n\n # start 2 fake services to emulate the dynamic-sidecar stack\n for dynamic_sidecar_stack in dynamic_sidecar_stack_specs:\n service_id = await docker_api.create_service_and_get_id(dynamic_sidecar_stack)\n assert service_id\n\n assert (\n await _count_services_in_stack(\n node_uuid, dynamic_sidecar_settings, async_docker_client\n )\n == 2\n )\n\n await docker_api.remove_dynamic_sidecar_stack(node_uuid, dynamic_sidecar_settings)\n\n assert (\n await _count_services_in_stack(\n node_uuid, dynamic_sidecar_settings, async_docker_client\n )\n == 0\n )\n\n\nasync def test_remove_dynamic_sidecar_network(\n network_config: Dict[str, Any],\n simcore_services_network_name: str,\n docker_swarm: None,\n) -> None:\n network_ids = [await docker_api.create_network(network_config) for _ in range(10)]\n assert len(set(network_ids)) == 1\n\n delete_result = await docker_api.remove_dynamic_sidecar_network(\n simcore_services_network_name\n )\n assert delete_result is True\n\n\nasync def test_remove_dynamic_sidecar_network_fails(\n simcore_services_network_name: str, docker_swarm: None\n) -> None:\n delete_result = await docker_api.remove_dynamic_sidecar_network(\n simcore_services_network_name\n )\n assert delete_result is False\n\n\nasync def test_list_dynamic_sidecar_services(\n node_uuid: UUID,\n user_id: UserID,\n project_id: ProjectID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n cleanup_dynamic_sidecar_stack: None,\n docker_swarm: None,\n):\n # start 2 fake services to emulate the dynamic-sidecar stack\n for dynamic_sidecar_stack in dynamic_sidecar_stack_specs:\n service_id = await docker_api.create_service_and_get_id(dynamic_sidecar_stack)\n assert service_id\n\n services = await docker_api.list_dynamic_sidecar_services(\n dynamic_sidecar_settings, user_id=user_id, project_id=project_id\n )\n assert len(services) == 1\n\n\nasync def test_is_dynamic_service_running(\n node_uuid: UUID,\n dynamic_sidecar_settings: DynamicSidecarSettings,\n dynamic_sidecar_stack_specs: List[Dict[str, Any]],\n cleanup_dynamic_sidecar_stack: None,\n docker_swarm: None,\n) -> None:\n assert (\n await docker_api.is_dynamic_service_running(node_uuid, dynamic_sidecar_settings)\n is False\n )\n\n # start 2 fake services to emulate the dynamic-sidecar stack\n for dynamic_sidecar_stack in dynamic_sidecar_stack_specs:\n service_id = await docker_api.create_service_and_get_id(dynamic_sidecar_stack)\n assert service_id\n\n assert (\n await docker_api.is_dynamic_service_running(node_uuid, dynamic_sidecar_settings)\n is True\n )\n", "sub_path": "services/director-v2/tests/unit/with_swarm/test_modules_dynamic_sidecar_docker_api.py", "file_name": "test_modules_dynamic_sidecar_docker_api.py", "file_ext": "py", "file_size_in_byte": 16488, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pytest.mark", "line_number": 30, "usage_type": "attribute"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 37, "usage_type": "name"}, {"api_name": "aiodocker.Docker", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 35, "usage_type": "attribute"}, {"api_name": "typing.AsyncIterator", "line_number": 39, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 39, "usage_type": "attribute"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 46, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings.create_from_envs", "line_number": 50, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 50, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 44, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 47, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 53, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 54, "usage_type": "name"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 65, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 66, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_network", "line_number": 71, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 71, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 62, "usage_type": "attribute"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 81, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 103, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 104, "usage_type": "name"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 115, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 113, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.DYNAMIC_SIDECAR_SERVICE_PREFIX", "line_number": 127, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 125, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 132, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceLabelsStoredData.Config", "line_number": 135, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceLabelsStoredData", "line_number": 135, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 142, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 151, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 130, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 133, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 133, "usage_type": "name"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 159, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 157, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 171, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 169, "usage_type": "attribute"}, {"api_name": "models_library.projects_nodes_io.NodeID", "line_number": 170, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 174, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.UserID", "line_number": 175, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 181, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 179, "usage_type": "attribute"}, {"api_name": "models_library.projects.ProjectID", "line_number": 180, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 186, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.UserID", "line_number": 187, "usage_type": "name"}, {"api_name": "models_library.projects.ProjectID", "line_number": 188, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 189, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.DYNAMIC_PROXY_SERVICE_PREFIX", "line_number": 193, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceType.DEPENDENCY", "line_number": 199, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceType", "line_number": 199, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.DYNAMIC_SIDECAR_SERVICE_PREFIX", "line_number": 206, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceType.MAIN", "line_number": 212, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceType", "line_number": 212, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 184, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 190, "usage_type": "name"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 223, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 224, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 224, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 224, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 225, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 221, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 239, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 239, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 250, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 251, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 252, "usage_type": "attribute"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 277, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings.create_from_envs", "line_number": 281, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 281, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 272, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 272, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 288, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.errors.GenericDockerError", "line_number": 288, "usage_type": "argument"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.docker_client", "line_number": 289, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 289, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 298, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.get_swarm_network", "line_number": 303, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 303, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 308, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 310, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.errors.DynamicSidecarError", "line_number": 310, "usage_type": "argument"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.get_swarm_network", "line_number": 311, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 311, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 319, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 319, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_network", "line_number": 323, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 323, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 331, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 331, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 335, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 335, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 340, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 340, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 344, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 344, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.inspect_service", "line_number": 347, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 354, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 354, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 355, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 359, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 359, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.get_dynamic_sidecars_to_observe", "line_number": 364, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 364, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 374, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 374, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 375, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 379, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 379, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.get_node_id_from_task_for_service", "line_number": 384, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 384, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.get_dynamic_sidecar_state", "line_number": 391, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 391, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceState.RUNNING", "line_number": 392, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.models.schemas.dynamic_services.ServiceState", "line_number": 392, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 396, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 397, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 398, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 398, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 398, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.are_services_missing", "line_number": 403, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 403, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 410, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 410, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.are_services_missing", "line_number": 413, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 413, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 420, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 421, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 422, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 422, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 422, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.are_all_services_present", "line_number": 426, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 426, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 433, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 433, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.are_all_services_present", "line_number": 436, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 436, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 443, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 444, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 445, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 445, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 445, "usage_type": "name"}, {"api_name": "aiodocker.docker", "line_number": 447, "usage_type": "attribute"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 458, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 458, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.remove_dynamic_sidecar_stack", "line_number": 468, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 468, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 479, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 479, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_network", "line_number": 483, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 483, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.remove_dynamic_sidecar_network", "line_number": 486, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 486, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.remove_dynamic_sidecar_network", "line_number": 495, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 495, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 502, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.models.schemas.constants.UserID", "line_number": 503, "usage_type": "name"}, {"api_name": "models_library.projects.ProjectID", "line_number": 504, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 505, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 506, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 506, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 506, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 512, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 512, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.list_dynamic_sidecar_services", "line_number": 515, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 515, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 522, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.core.settings.DynamicSidecarSettings", "line_number": 523, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 524, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 524, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 524, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.is_dynamic_service_running", "line_number": 529, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 529, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.create_service_and_get_id", "line_number": 535, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 535, "usage_type": "name"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api.is_dynamic_service_running", "line_number": 539, "usage_type": "call"}, {"api_name": "simcore_service_director_v2.modules.dynamic_sidecar.docker_api", "line_number": 539, "usage_type": "name"}]} +{"seq_id": "213349784", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import division\n\n\"\"\" \nCreates a ResNeXt Model as defined in:\n\nXie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). \nAggregated residual transformations for deep neural networks. \narXiv preprint arXiv:1611.05431.\n\n\"\"\"\n\n__author__ = \"Pau Rodríguez López, ISELAB, CVC-UAB\"\n__email__ = \"pau.rodri1@gmail.com\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\nfrom torch.autograd import Variable\n\n#import InplaceMul\n'''\nfrom torch.autograd.function import InplaceFunction\n\nclass InplaceMul(InplaceFunction):\n @staticmethod\n def forward(cls, ctx, input, multiplier):\n ctx.mark_dirty(input)\n ctx.multiplier = multiplier\n output = input\n view_size = [1, input.size(1)] + [1] * (len(input.size()) - 2)\n output.mul_(ctx.multiplier.view(view_size).expand_as(output))\n return output\n\n @staticmethod\n def backward(ctx, grad_output):\n view_size = [1, grad_output.size(1)] + [1] * (len(grad_output.size()) - 2)\n return grad_output.mul_(ctx.multiplier.view(view_size).expand_as(grad_output))\n'''\n\nclass DropCombine(nn.Module):\n def __init__(self, channels, res_drop = 0., p = 0.):\n super(DropCombine, self).__init__()\n self.p = p\n self.res_drop = res_drop\n self.channels = channels\n self.fix_prob = torch.FloatTensor(1, self.channels).fill_(1-self.res_drop).cuda()\n self.fix_mask = torch.bernoulli(self.fix_prob).cuda()\n self.one_mask = torch.FloatTensor(1, self.channels).fill_(1).cuda()\n self.x_prob = torch.FloatTensor(1, self.channels).fill_(1-self.p).cuda()\n self.x_mask = torch.FloatTensor(1, self.channels).fill_(0).cuda()\n # print(self.p)\n\n def forward(self, res, x):\n view_size = [1, self.channels] + [1] * (len(x.size()) - 2)\n \"\"\"\n if self.training==True:\n # compute the residual of dropout\n if self.p > 0.:\n self.x_mask = torch.bernoulli(self.x_prob) / (1. - self.p) - self.one_mask\n self.x_op = Variable(self.x_mask.view(view_size).expand_as(x)).cuda()\n \n res = res + x * self.x_op\n if self.res_drop > 0.:\n self.fix_op = Variable(self.fix_mask.view(view_size).expand_as(x)).cuda()\n res = res * self.fix_op \n \"\"\"\n self.fix_op = Variable(self.fix_mask.view(view_size), requires_grad=False).cuda()\n #if self.training==False: return res * self.fix_op + x\n self.x_mask = (torch.bernoulli(self.x_prob) / (1. - self.p) - self.one_mask) * self.fix_mask + self.one_mask\n if self.training==False: self.x_mask = self.one_mask\n self.x_op = Variable(self.x_mask.view(view_size), requires_grad=False).cuda()\n return res * self.fix_op + x * self.x_op\n '''\n self.x_mask = (torch.bernoulli(self.x_prob) / (1. - self.p) - self.one_mask) * self.fix_mask + self.one_mask\n #return res.data.mul_(self.fix_mask.view(view_size)) + x.data.mul_(self.x_mask.view(view_size))).cuda()\n return InplaceMul.apply(res,self.fix_mask) + InplaceMul.apply(x,self.x_mask)\n '''\n\nclass SFDropoutLayer(nn.Module):\n def __init__(self, in_planes, p):\n super(SFDropoutLayer, self).__init__()\n assert p < 1.\n self.p = p\n self.in_planes = in_planes\n self.prob_tensor = torch.FloatTensor(1).fill_(1-self.p).expand((self.in_planes)).cuda()\n # print(self.p)\n\n def forward(self, x):\n if self.training==False: return x\n # batch shared dropout mask\n self.mask = torch.bernoulli(self.prob_tensor)\n view_size = [1, self.in_planes] + [1] * (len(x.size()) - 2)\n self.input_mask = Variable((self.mask / (1. - self.p)).view(view_size).expand_as(x)).cuda()\n return x*self.input_mask\n\n\nclass GroupAttDrop(nn.Module):\n def __init__(self, in_planes, cardinality, group_width, is_drop = True):\n super(GroupAttDrop, self).__init__()\n # Select layers\n D = cardinality * group_width\n self.is_drop = is_drop\n self.cardinality = cardinality\n self.group_width = group_width\n self.fc1 = nn.Conv2d(in_planes, D//16, kernel_size=1) # Use nn.Conv2d instead of nn.Linear\n self.fc2 = nn.Conv2d(D//16, cardinality, kernel_size=1)\n self.avg_pool = nn.AdaptiveAvgPool2d(1)\n self.expand = ExpandConv(cardinality, group_width)\n #self.mask_tensor = torch.FloatTensor(1).fill_(1-self.p).expand((self.in_planes))\n \n def forward(self, x):\n self.w1 = self.avg_pool(x)\n self.w2 = F.relu(self.fc1(self.w1))\n self.w3 = F.sigmoid(self.fc2(self.w2))\n #if self.is_drop == True:\n # w += Variable(torch.bernoulli(w.data) - w.data)\n #print(w.size())\n #wait = input(\"PRESS ENTER TO CONTINUE.\")\n self.wid = self.expand(self.w3)\n return self.wid\n #if self.training==False: \n\nclass GroupRandDrop(nn.Module):\n def __init__(self, cardinality, group_width, p = 0.5, val = 0.3):\n super(GroupRandDrop, self).__init__()\n assert p < 1.\n self.p = p\n self.val = val\n self.cardinality = cardinality\n self.group_width = group_width\n self.expand = ExpandConv(cardinality, group_width)\n self.prob_tensor = torch.FloatTensor(1,cardinality).fill_(1.-self.p).cuda()\n self.one_tensor = torch.FloatTensor(1,cardinality).fill_(1.).cuda()\n # print(self.p)\n\n def forward(self,x):\n if self.training==False: return x\n # batch shared dropout mask\n self.mask = torch.bernoulli(self.prob_tensor)\n self.mask = self.val * self.mask / (1.- self.p) + (1.-self.val) * self.one_tensor\n self.wid_mask = self.expand(Variable(self.mask, requires_grad=False))\n return x * self.wid_mask \n \nclass ExpandConv(nn.Module):\n def __init__(self, cardinality, group_width):\n super(ExpandConv, self).__init__()\n self.D = cardinality * group_width\n self.cardinality = cardinality\n self.group_width = group_width\n self.one_tensor = Variable(torch.ones(1,1,self.group_width), requires_grad=False).cuda()\n #self.one_tensor = torch.FloatTensor(1,1,self.group_width).fill_(1.).cuda()\n \n def forward(self, x):\n self.wid = torch.matmul(x.view(-1, self.cardinality,1),self.one_tensor.expand(x.size(0),-1,-1))\n self.wid = self.wid.view(-1, self.D, 1, 1)\n return self.wid\n \n\n\nclass DropCombineBottleneck(nn.Module):\n\n def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor, res_drop = 0., p = 0., preact = False):\n super(DropCombineBottleneck, self).__init__()\n self.layer = ResNeXtBottleneck(in_channels, out_channels, stride, cardinality, base_width, widen_factor, res_drop = 0.05, p = 0.2, preact = preact)\n\n def forward(self, x):\n return self.layer(x) \n \n \nclass ResNeXtBottleneck(nn.Module):\n \"\"\"\n RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)\n \"\"\"\n\n def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor, res_drop = 0., p = 0., preact = False):\n \"\"\" Constructor\n\n Args:\n in_channels: input channel dimensionality\n out_channels: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n cardinality: num of convolution groups.\n base_width: base number of channels in each group.\n widen_factor: factor to reduce the input dimensionality before convolution.\n \"\"\"\n super(ResNeXtBottleneck, self).__init__()\n #width_ratio = out_channels / (widen_factor * 64.)\n D = cardinality * base_width\n self.preact = preact\n self.pre_bn = nn.BatchNorm2d(in_channels)\n self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_reduce = nn.BatchNorm2d(D)\n self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)\n self.bn = nn.BatchNorm2d(D)\n self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_expand = nn.BatchNorm2d(out_channels)\n self.shortcut = nn.Sequential()\n if in_channels != out_channels:\n self.shortcut.add_module('shortcut_conv',\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0,\n bias=False))\n self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))\n self.combine = DropCombine(out_channels, res_drop, p)\n #self.sfdrop = SFDropoutLayer(out_channels, p)\n #self.channeldrop = nn.Dropout2d(p)\n\n def forward(self, x):\n '''\n #for pre-activation residual\n bottleneck = F.relu(self.pre_bn(x))\n bottleneck = self.conv_reduce(bottleneck)\n bottleneck = F.relu(self.bn_reduce(bottleneck), inplace=True)\n bottleneck = self.conv_conv(bottleneck)\n bottleneck = F.relu(self.bn(bottleneck), inplace=True)\n bottleneck = self.conv_expand(bottleneck)\n #bottleneck = self.bn_expand(bottleneck)\n #return self.combine(self.shortcut(x), bottleneck)\n #return self.shortcut(x) + self.sfdrop(bottleneck)\n #return self.shortcut(x) + self.channeldrop(bottleneck)\n return self.shortcut(x) + bottleneck\n '''\n if self.preact == True: bottleneck = F.relu(self.pre_bn(x))\n else: bottleneck = x\n bottleneck = self.conv_reduce(bottleneck)\n bottleneck = F.relu(self.bn_reduce(bottleneck), inplace=True)\n bottleneck = self.conv_conv(bottleneck)\n bottleneck = F.relu(self.bn(bottleneck), inplace=True)\n bottleneck = self.conv_expand(bottleneck)\n if self.preact == False: bottleneck = self.bn_expand(bottleneck) \n #out = self.shortcut(x) + bottleneck\n out = self.combine(self.shortcut(x), bottleneck)\n if self.preact == False: out = F.relu(out, inplace=True)\n return out\n\n \nclass DropNeXtBottleneck(nn.Module):\n \"\"\"\n RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)\n \"\"\"\n\n def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor):\n \"\"\" Constructor\n\n Args:\n in_channels: input channel dimensionality\n out_channels: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n cardinality: num of convolution groups.\n base_width: base number of channels in each group.\n widen_factor: factor to reduce the input dimensionality before convolution.\n \"\"\"\n super(DropNeXtBottleneck, self).__init__()\n #width_ratio = out_channels / (widen_factor * 64.)\n self.group_width = base_width\n D = cardinality * self.group_width \n self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_reduce = nn.BatchNorm2d(D)\n self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)\n self.bn = nn.BatchNorm2d(D)\n self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_expand = nn.BatchNorm2d(out_channels)\n self.groupdrop = GroupRandDrop(cardinality,self.group_width)\n\n self.shortcut = nn.Sequential()\n if in_channels != out_channels:\n self.shortcut.add_module('shortcut_conv',\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0,\n bias=False))\n self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))\n\n def forward(self, x):\n bottleneck = self.conv_reduce.forward(x)\n bottleneck = F.relu(self.bn_reduce.forward(bottleneck), inplace=True)\n bottleneck = self.conv_conv.forward(bottleneck)\n bottleneck = F.relu(self.bn.forward(bottleneck), inplace=True)\n bottleneck = self.groupdrop(bottleneck)\n bottleneck = self.conv_expand.forward(bottleneck)\n bottleneck = self.bn_expand.forward(bottleneck)\n residual = self.shortcut.forward(x)\n return F.relu(residual + bottleneck, inplace=True)\n\nclass SENeXtBottleneck(nn.Module):\n \"\"\"\n RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)\n \"\"\"\n\n def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor):\n \"\"\" Constructor\n\n Args:\n in_channels: input channel dimensionality\n out_channels: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n cardinality: num of convolution groups.\n base_width: base number of channels in each group.\n widen_factor: factor to reduce the input dimensionality before convolution.\n \"\"\"\n super(SENeXtBottleneck, self).__init__()\n #width_ratio = out_channels / (widen_factor * 64.)\n self.cardinality = cardinality\n self.group_width = base_width\n D = self.cardinality * self.group_width\n self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_reduce = nn.BatchNorm2d(D)\n self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)\n self.bn = nn.BatchNorm2d(D)\n self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_expand = nn.BatchNorm2d(out_channels)\n # Select layers\n self.avg_pool = nn.AdaptiveAvgPool2d(1)\n self.fc1 = nn.Conv2d(D, D//16, kernel_size=1) # Use nn.Conv2d instead of nn.Linear\n self.fc2 = nn.Conv2d(D//16, cardinality, kernel_size=1)\n self.shortcut = nn.Sequential()\n if in_channels != out_channels:\n self.shortcut.add_module('shortcut_conv',\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0,\n bias=False))\n self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))\n\n def forward(self, x):\n bottleneck = self.conv_reduce.forward(x)\n bottleneck = F.relu(self.bn_reduce.forward(bottleneck), inplace=True) \n bottleneck = self.conv_conv.forward(bottleneck)\n bottleneck = F.relu(self.bn.forward(bottleneck), inplace=True)\n # Squeeze\n w = self.avg_pool(bottleneck)\n w = F.relu(self.fc1(w))\n w = F.sigmoid(self.fc2(w))\n # Expand\n wid = GroupAttAvg(w,self.cardinality,self.group_width)\n #print(wid.size())\n #print(bottleneck.size())\n #wait = input(\"PRESS ENTER TO CONTINUE.\")\n bottleneck = bottleneck * wid\n bottleneck = self.conv_expand.forward(bottleneck)\n bottleneck = self.bn_expand.forward(bottleneck)\n residual = self.shortcut.forward(x)\n return F.relu(residual + bottleneck, inplace=True) \n \nclass SelNeXtBottleneck(nn.Module):\n \"\"\"\n RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)\n \"\"\"\n\n def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor):\n \"\"\" Constructor\n\n Args:\n in_channels: input channel dimensionality\n out_channels: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n cardinality: num of convolution groups.\n base_width: base number of channels in each group.\n widen_factor: factor to reduce the input dimensionality before convolution.\n \"\"\"\n super(SelNeXtBottleneck, self).__init__()\n #width_ratio = out_channels / (widen_factor * 64.)\n self.cardinality = cardinality\n self.group_width = base_width\n D = self.cardinality * self.group_width\n self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_reduce = nn.BatchNorm2d(D)\n self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)\n self.bn = nn.BatchNorm2d(D)\n self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_expand = nn.BatchNorm2d(out_channels)\n self.select = GroupAttDrop(self.cardinality * self.group_width, self.cardinality, self.group_width)\n # Select layers\n #self.fc1 = nn.Conv2d(D, D//16, kernel_size=1) # Use nn.Conv2d instead of nn.Linear\n #self.fc2 = nn.Conv2d(D//16, cardinality, kernel_size=1)\n #self.avg_pool = nn.AdaptiveAvgPool2d(1)\n \n self.shortcut = nn.Sequential()\n if in_channels != out_channels:\n self.shortcut.add_module('shortcut_conv',\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0,\n bias=False))\n self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))\n \n def forward(self, x):\n bottleneck = self.conv_reduce.forward(x)\n bottleneck = F.relu(self.bn_reduce.forward(bottleneck), inplace=True)\n # Select groups\n #w = self.avg_pool(bottleneck)\n #w = F.relu(self.fc1(w))\n #w = F.sigmoid(self.fc2(w))\n #mask = GroupAttDrop(w,self.cardinality,self.group_width)\n mask = self.select(bottleneck)\n # compute groups\n bottleneck = self.conv_conv.forward(bottleneck)\n bottleneck = F.relu(self.bn.forward(bottleneck), inplace=True)\n # drop groups\n bottleneck = bottleneck * mask\n bottleneck = self.conv_expand.forward(bottleneck)\n bottleneck = self.bn_expand.forward(bottleneck)\n residual = self.shortcut.forward(x)\n return F.relu(residual + bottleneck, inplace=True) \n \nclass CifarResNeXt(nn.Module):\n \"\"\"\n ResNext optimized for the Cifar dataset, as specified in\n https://arxiv.org/pdf/1611.05431.pdf\n \"\"\"\n\n def __init__(self, model, cardinality, depth, nlabels, base_width, widen_factor=4, band_width = 64, preact = False):\n \"\"\" Constructor\n\n Args:\n cardinality: number of convolution groups.\n depth: number of layers.\n nlabels: number of classes\n base_width: base number of channels in each group.\n widen_factor: factor to adjust the channel dimensionality\n \"\"\"\n super(CifarResNeXt, self).__init__()\n self.preact = preact\n self.cardinality = cardinality\n self.depth = depth\n self.block_depth = (self.depth - 2) // 9\n self.base_width = base_width\n self.widen_factor = widen_factor\n self.nlabels = nlabels\n self.output_size = band_width\n self.stages = [64, band_width * self.widen_factor, 2* band_width * self.widen_factor, 4 * band_width * self.widen_factor]\n model_map = {'ResNext': ResNeXtBottleneck,\n 'DropNext': DropNeXtBottleneck,\n 'SENext': SENeXtBottleneck,\n 'DropCombine' : DropCombineBottleneck,\n 'SelNext': SelNeXtBottleneck}\n self.Bottleneck = model_map[model]\n self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)\n self.bn_1 = nn.BatchNorm2d(64)\n self.stage_1 = self.block('stage_1', self.stages[0], self.stages[1], 1, 1)\n self.stage_2 = self.block('stage_2', self.stages[1], self.stages[2], 2, 2)\n self.stage_3 = self.block('stage_3', self.stages[2], self.stages[3], 4, 2)\n self.classifier = nn.Linear(self.stages[3], nlabels)\n init.kaiming_normal(self.classifier.weight)\n\n for key in self.state_dict():\n if key.split('.')[-1] == 'weight':\n if 'conv' in key:\n init.kaiming_normal(self.state_dict()[key], mode='fan_out')\n if 'bn' in key:\n self.state_dict()[key][...] = 1\n elif key.split('.')[-1] == 'bias':\n self.state_dict()[key][...] = 0\n\n def block(self, name, in_channels, out_channels, width_ratio, pool_stride=2):\n \"\"\" Stack n bottleneck modules where n is inferred from the depth of the network.\n\n Args:\n name: string name of the current block.\n in_channels: number of input channels\n out_channels: number of output channels\n pool_stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.\n\n Returns: a Module consisting of n sequential bottlenecks.\n\n \"\"\"\n block = nn.Sequential()\n for bottleneck in range(self.block_depth):\n name_ = '%s_bottleneck_%d' % (name, bottleneck)\n if bottleneck == 0:\n block.add_module(name_, self.Bottleneck(in_channels, out_channels, pool_stride, self.cardinality,\n self.base_width * width_ratio, self.widen_factor,preact = self.preact))\n else:\n block.add_module(name_,\n self.Bottleneck(out_channels, out_channels, 1, self.cardinality, self.base_width * width_ratio,\n self.widen_factor,preact = self.preact))\n return block\n\n def forward(self, x):\n x = self.conv_1_3x3.forward(x)\n x = F.relu(self.bn_1.forward(x), inplace=True)\n x = self.stage_1.forward(x)\n x = self.stage_2.forward(x)\n x = self.stage_3.forward(x)\n x = F.avg_pool2d(x, 8, 1)\n x = x.view(-1, self.stages[3])\n return self.classifier(x)\n", "sub_path": "models/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 22134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "torch.nn.Module", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.bernoulli", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.bernoulli", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.bernoulli", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.bernoulli", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 172, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 192, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 193, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 195, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 197, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 202, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 224, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 227, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 238, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 238, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 258, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 259, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 266, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 269, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 271, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 275, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 277, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 282, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 284, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 284, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 307, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 308, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 308, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 309, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 310, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 312, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 313, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 314, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 315, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 318, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 324, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 324, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 326, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 329, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 329, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 330, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 330, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 340, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 342, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 342, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 364, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 365, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 365, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 366, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 366, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 367, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 367, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 368, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 368, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 375, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 378, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 380, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 384, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 393, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 399, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 399, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 401, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 401, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 433, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 433, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 434, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 434, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 438, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 438, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal", "line_number": 439, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 439, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 444, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 462, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 462, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 476, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 476, "usage_type": "name"}, {"api_name": "torch.nn.functional.avg_pool2d", "line_number": 480, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 480, "usage_type": "name"}]} +{"seq_id": "384246535", "text": "# retrieve a list of records with a given data value\nimport bsddb3 as bsddb\nimport datetime\nfrom keysearch import KeySearch\n\n\ndef DataSearch(db_type_option, db = None, db2 = None):\n if not db:\n print (\"\\nNo database exists to search!\\n\")\n return # return to main menu\n \n if (db_type_option == 'indexfile'):\n KeySearch(db2, \"data\")\n return\n \n data = input(\"\\nEnter the data value to search by: \")\n count = 0\n file = open(\"answers.txt\", 'a')\n start_time = datetime.datetime.now() \n for key, value in db.items():\n value = value.decode('UTF-8') \n if data == value:\n #print(\"\\nMatch found: \")\n key = key.decode('UTF-8')\n file.write(key)\n file.write(\"\\n\")\n file.write(value)\n file.write(\"\\n\\n\")\n #print(key)\n count += 1\n end_time = datetime.datetime.now()\n if (count == 0):\n print(\"Record for\", data, \"does not exist\")\n print(count, \"Record(s) found.\")\n execution_time = end_time - start_time\n micro_sec = execution_time.total_seconds()*1000000\n print(\"Total execution time:\", micro_sec, \" microseconds\")\n file.close()\n \n \n\n", "sub_path": "datasearch.py", "file_name": "datasearch.py", "file_ext": "py", "file_size_in_byte": 1217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "keysearch.KeySearch", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "229763028", "text": "#!/usr/bin/env python\n\n\"\"\"\nUnit tests for pre-processing module\n====================================\n\"\"\"\n\nimport pytest\nimport numpy as np\nfrom scipy.sparse import csr_matrix\n\nfrom implicitmf.preprocess import normalize_X, dict_converter\nfrom _mock_data import sparse_array, create_ratings_df\n\ndef test_dict_converter_output():\n \"\"\"\n Check that output of dict_converter()\n is the correct format.\n \"\"\"\n data = create_ratings_df()\n output = dict_converter(data)\n assert(isinstance(output, dict))\n\ndef test_dict_converter_input_error():\n \"\"\"\n Check that dict_converter() raises a\n ValueError when input is not correct format.\n \"\"\"\n data = create_ratings_df()\n data['extra_column'] = data['ratings']*2\n with pytest.raises(ValueError):\n dict_converter(data)\n\ndef test_normalize_X_output():\n \"\"\"\n Check that output of normalize_X()\n is a scipy.sparse.csr matrix.\n \"\"\"\n X = sparse_array()\n output = normalize_X(X, norm_type=\"bm25\")\n assert isinstance(output, csr_matrix)\n assert output.shape == X.shape\n\ndef test_normalize_X_incorrect_sparse_matrix():\n \"\"\"\n Check that normalize_X() raises a\n TypeError if X is not the correct format.\n \"\"\"\n msg = \"`X` must be a scipy.sparse.csr_matrix\"\n with pytest.raises(TypeError, match=msg):\n normalize_X(X=\"hello\", norm_type=\"bm25\")\n\ndef test_normalize_X_incorrect_norm_type():\n \"\"\"\n Check that normalize_X() raises a ValueError\n if norm_type is not one of bm25 or tfidf.\n \"\"\"\n msg = \"Unknown `norm_type` parameter\"\n with pytest.raises(ValueError, match=msg):\n normalize_X(X=sparse_array(), norm_type=\"bm2000\")\n\n", "sub_path": "tests/test_preprocess.py", "file_name": "test_preprocess.py", "file_ext": "py", "file_size_in_byte": 1667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "_mock_data.create_ratings_df", "line_number": 20, "usage_type": "call"}, {"api_name": "implicitmf.preprocess.dict_converter", "line_number": 21, "usage_type": "call"}, {"api_name": "_mock_data.create_ratings_df", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 31, "usage_type": "call"}, {"api_name": "implicitmf.preprocess.dict_converter", "line_number": 32, "usage_type": "call"}, {"api_name": "_mock_data.sparse_array", "line_number": 39, "usage_type": "call"}, {"api_name": "implicitmf.preprocess.normalize_X", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 41, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "implicitmf.preprocess.normalize_X", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 59, "usage_type": "call"}, {"api_name": "implicitmf.preprocess.normalize_X", "line_number": 60, "usage_type": "call"}, {"api_name": "_mock_data.sparse_array", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "235409413", "text": "# coding=utf-8\n\nimport base64\nfrom datetime import date\nimport helpers\n\n# ISSUER INFORMATION\nISSUER_URL = \"http://labcd.mx\"\nISSUER_CERTS_URL = \"http://certs.labcd.mx\"\nISSUER_PUBLIC_KEY_URL = \"http://certs.labcd.mx/keys/labcdmx-certs-public-key.asc\"\nISSUER_SIGNATURE_IMAGE = helpers.encode_image(\"img/signature.png\")\nISSUER_EMAIL = \"certs@labcd.mx\"\nISSUER_NAME = \"Laboratorio para la Ciudad\"\nISSUER_ID = \"http://certs.labcd.mx/issuer/labcdmx-issuer.json\"\n\n# CERTIFICATE INFORMATION\nCERTIFICATE_LANGUAGE = \"es-MX\" #LANGUAGE AND COUNTRY INFORMATION\nCERTIFICATE_DESCRIPTION = \"En reconocimiento a tu participación en \\\"ciudad prototipo\\\", un taller realizado en colaboración con el MIT Media Lab e IDEO con el propósito de explorar, compartir y prototipar nuevas maneras de acercamiento a problemas cotidianos de la ciudad de México mediante la colaboración entre estudiantes, profesionales y especialistas.\"\nCERTIFICATE_DATE = str(date(month=9, day=1, year=2015))+\"/\"+str(date(month=9, day=4, year=2015))\nCERTIFICATE_TITLE = \"Ciudad Prototipo\"\nCERTIFICATE_IMAGE = helpers.encode_image(\"img/header.png\")\nCERTIFICATE_ID = ISSUER_CERTS_URL + \"/criteria/2015/09/ciudad-prototipo.json\"\n\n# EXTENSION INFORMATION\nASSERTION_ENSORERS = [\n\t{\n \"name\": \"MIT Media Lab\",\n \"url\": \"http://media.mit.edu\",\n \"image\": helpers.encode_image(\"img/medialab.png\")\n },\n {\n \"name\": \"IDEO\",\n \"url\": \"http://ideo.com\",\n \"image\": helpers.encode_image(\"img/ideo.png\")\n }\n]\n", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "helpers.encode_image", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "call"}, {"api_name": "helpers.encode_image", "line_number": 21, "usage_type": "call"}, {"api_name": "helpers.encode_image", "line_number": 29, "usage_type": "call"}, {"api_name": "helpers.encode_image", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "578025893", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright 2018 - Swiss Data Science Center (SDSC)\n# A partnership between École Polytechnique Fédérale de Lausanne (EPFL) and\n# Eidgenössische Technische Hochschule Zürich (ETHZ).\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.\n\"\"\"Web auth routes.\"\"\"\nimport jwt\nimport json\nimport time\nimport logging\nimport re\nimport urllib.parse\nfrom oic.oauth2.grant import Token\nfrom quart import request, redirect, url_for, current_app, Response, session, render_template\nfrom urllib.parse import urljoin, quote_plus\n\nfrom oic.oic import Client\nfrom oic.utils.authn.client import CLIENT_AUTHN_METHOD\nfrom oic import rndstr\nfrom oic.oic.message import AuthorizationResponse, RegistrationResponse\n\nfrom .. import app, store\n\n\nlogger = logging.getLogger(__name__)\n# Note that this part of the service should be seen as the server-side part of the UI or\n\nJWT_ALGORITHM = 'RS256'\nSCOPE = ['openid']\n\n# We prepare the OIC client instance with the necessary configurations.\nclient = Client(client_authn_method=CLIENT_AUTHN_METHOD)\n\ntry:\n client.provider_config(\n issuer=app.config['OIDC_ISSUER']\n )\nexcept:\n pass\n\n\n# This fakes the response we would get from registering the client through the API\nclient_reg = RegistrationResponse(\n client_id=app.config['OIDC_CLIENT_ID'],\n client_secret=app.config['OIDC_CLIENT_SECRET']\n)\nclient.store_registration_info(client_reg)\n\n\ndef get_valid_token(headers):\n \"\"\"\n Look for a fresh and valid token, first in headers, then in the session.\n\n If a refresh token is available, it can be swapped for an access token.\n \"\"\"\n m = re.search(r'bearer (?P.+)', headers.get('Authorization', ''), re.IGNORECASE)\n\n if m:\n if jwt.decode(m.group('token'), verify=False).get('typ') in ['Offline', 'Refresh']:\n logger.debug(\"Swapping the token\")\n to = Token(resp={'refresh_token': m.group('token')})\n token_response = client.do_access_token_refresh(token=to)\n\n if 'access_token' in token_response:\n try:\n a = jwt.decode(\n token_response['access_token'], app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n )\n return token_response\n except:\n return None\n else:\n try:\n jwt.decode(\n m.group('token'),\n app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n )\n\n return {'access_token': m.group('token')}\n\n except:\n return None\n else:\n if headers.get('X-Requested-With') == 'XMLHttpRequest' and 'token' in session:\n try:\n jwt.decode(\n session.get('token'),\n app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n )\n return {'access_token': session.get('token')}\n\n except:\n\n a = jwt.decode(session.get('token'), verify=False)\n refresh_token = store.get(get_key_for_user(a, 'kc_refresh_token')).decode()\n\n logger.debug(\"Refreshing the token\")\n to = Token(resp={'refresh_token': refresh_token})\n\n token_response = client.do_access_token_refresh(token=to)\n\n if 'access_token' in token_response:\n try:\n a = jwt.decode(\n token_response['access_token'], app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n )\n session['token'] = token_response['access_token']\n store.put(get_key_for_user(a, 'kc_access_token'), token_response['access_token'].encode())\n store.put(get_key_for_user(a, 'kc_refresh_token'), token_response['refresh_token'].encode())\n store.put(get_key_for_user(a, 'kc_id_token'), json.dumps(token_response['id_token'].to_dict()).encode())\n return token_response\n except:\n return None\n\n return None\n\n\ndef get_key_for_user(token, name):\n return \"cache_{}_{}\".format(token.get('sub'), name)\n\nLOGIN_SEQUENCE = ['gitlab_login', 'jupyterhub_login']\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/login/next'))\nasync def login_next():\n\n if session['login_seq'] < len(LOGIN_SEQUENCE):\n return await render_template('redirect.html', redirect_url=url_for(LOGIN_SEQUENCE[session['login_seq']]))\n else:\n return redirect(session['ui_redirect_url'])\n\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/login'))\nasync def login():\n\n state = rndstr()\n\n session['state'] = state\n session['login_seq'] = 0\n session['ui_redirect_url'] = request.args.get('redirect_url')\n session['cli_token'] = request.args.get('cli_token')\n if session['cli_token']:\n session['ui_redirect_url'] = app.config['HOST_NAME'] + url_for('info')\n\n args = {\n 'client_id': app.config['OIDC_CLIENT_ID'],\n 'response_type': 'code',\n 'scope': SCOPE,\n 'redirect_uri': app.config['HOST_NAME'] + url_for('get_tokens'),\n 'state': state\n }\n auth_req = client.construct_AuthorizationRequest(request_args=args)\n login_url = auth_req.request(client.authorization_endpoint)\n response = await app.make_response(redirect(login_url))\n\n return response\n\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/token'))\nasync def get_tokens():\n\n # This is more about parsing the request data than any response data....\n authorization_parameters = client.parse_response(\n AuthorizationResponse,\n info=request.query_string.decode('utf-8'),\n sformat='urlencoded'\n )\n\n if session.get('state') != authorization_parameters['state']:\n return 'Something went wrong while trying to log you in.'\n\n token_response = client.do_access_token_request(\n scope=SCOPE,\n state=authorization_parameters['state'],\n request_args={\n 'code': authorization_parameters['code'],\n 'redirect_uri': app.config['HOST_NAME'] + url_for('get_tokens'),\n }\n )\n\n # chain logins\n response = await app.make_response(redirect(url_for('login_next')))\n\n a = jwt.decode(\n token_response['access_token'], app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n )\n session['token'] = token_response['access_token']\n store.put(get_key_for_user(a, 'kc_access_token'), token_response['access_token'].encode())\n store.put(get_key_for_user(a, 'kc_refresh_token'), token_response['refresh_token'].encode())\n store.put(get_key_for_user(a, 'kc_id_token'), json.dumps(token_response['id_token'].to_dict()).encode())\n\n # we can already tell the CLI which token to use\n if session.get('cli_token'):\n logger.debug(\"Notification for request {}\".format(session.get('cli_token')))\n\n key = \"cli_{}\".format(hashlib.sha256(session.get('cli_token').encode()).hexdigest())\n store.put(key, json.dumps({'access_token': token_response['access_token'], 'refresh_token': token_response['refresh_token']}).encode())\n\n return response\n\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/info'))\nasync def info():\n\n t = request.args.get('cli_token')\n if t:\n timeout = 120\n key = \"cli_{}\".format(hashlib.sha256(t.encode()).hexdigest())\n logger.debug(\"Waiting for Keycloak callback for request {}\".format(t))\n val = store.get(key)\n while not val and timeout > 0:\n time.sleep(3)\n timeout -= 3\n val = store.get(key)\n if val:\n store.delete(key)\n return val\n else:\n logger.debug(\"Timeout while waiting for request {}\".format(t))\n return '{\"error\": \"timeout\"}'\n else:\n\n if 'token' not in session:\n return await app.make_response(redirect(\"{}?redirect_url={}\".format(url_for('login'), quote_plus(url_for('info')))))\n\n try:\n a = jwt.decode(\n session['token'],\n app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n ) # TODO: logout and redirect if fails because of expired\n\n return \"You can copy/paste the following tokens if needed and close this page:
Access Token: {}
Refresh Token: {}\".format(\n store.get(get_key_for_user(a, 'kc_access_token')).decode(), store.get(get_key_for_user(a, 'kc_refresh_token')).decode())\n\n except jwt.ExpiredSignatureError:\n return await app.make_response(redirect(\"{}?redirect_url={}\".format(url_for('login'), quote_plus(url_for('info')))))\n\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/user'))\nasync def user():\n\n if 'token' not in session:\n return await app.make_response(redirect(\"{}?redirect_url={}\".format(url_for('login'), quote_plus(url_for('user')))))\n try:\n a = jwt.decode(\n session['token'],\n app.config['OIDC_PUBLIC_KEY'],\n algorithms=JWT_ALGORITHM,\n audience=app.config['OIDC_CLIENT_ID']\n ) # TODO: logout and redirect if fails because of expired\n\n return store.get(get_key_for_user(a, 'kc_id_token')).decode()\n\n except jwt.ExpiredSignatureError:\n return await app.make_response(redirect(\"{}?redirect_url={}\".format(url_for('login'), quote_plus(url_for('user')))))\n\n\n@app.route(urljoin(app.config['SERVICE_PREFIX'], 'auth/logout'))\nasync def logout():\n\n logout_url = '{}/protocol/openid-connect/logout?{}'.format(\n app.config['OIDC_ISSUER'],\n urllib.parse.urlencode({'redirect_uri': app.config['HOST_NAME'] + url_for('gitlab_logout')}),\n )\n\n if request.args.get('gitlab_logout'):\n if 'logout_from' in session:\n session.clear()\n return await render_template('redirect_logout.html', redirect_url='/', logout_page=url_for('jupyterhub_logout'))\n else:\n return await app.make_response(redirect(app.config['GITLAB_URL']))\n\n if 'token' in session:\n a = jwt.decode(session['token'], verify=False)\n\n # cleanup the session in redis immediately\n cookie_val = request.cookies.get('session').split(\".\")[0]\n store.delete(cookie_val)\n session.clear()\n\n for k in store.keys(prefix=get_key_for_user(a, '')):\n store.delete(k)\n\n session['logout_from'] = \"Renku\"\n\n return await app.make_response(redirect(logout_url))\n", "sub_path": "app/auth/web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 11509, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "oic.oic.Client", "line_number": 44, "usage_type": "call"}, {"api_name": "oic.utils.authn.client.CLIENT_AUTHN_METHOD", "line_number": 44, "usage_type": "name"}, {"api_name": "oic.oic.message.RegistrationResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "re.search", "line_number": 68, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "jwt.decode", "line_number": 71, "usage_type": "call"}, {"api_name": "oic.oauth2.grant.Token", "line_number": 73, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 78, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 88, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 100, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 102, "usage_type": "call"}, {"api_name": "quart.session.get", "line_number": 103, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 103, "usage_type": "name"}, {"api_name": "quart.session.get", "line_number": 108, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 108, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 112, "usage_type": "call"}, {"api_name": "quart.session.get", "line_number": 112, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 112, "usage_type": "name"}, {"api_name": "oic.oauth2.grant.Token", "line_number": 116, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 122, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 127, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 130, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 146, "usage_type": "name"}, {"api_name": "quart.render_template", "line_number": 147, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 147, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 147, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 149, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 149, "usage_type": "name"}, {"api_name": "urllib.parse.urljoin", "line_number": 143, "usage_type": "call"}, {"api_name": "oic.rndstr", "line_number": 155, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 157, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 158, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 159, "usage_type": "name"}, {"api_name": "quart.request.args.get", "line_number": 159, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 159, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 159, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 160, "usage_type": "name"}, {"api_name": "quart.request.args.get", "line_number": 160, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 160, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 160, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 161, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 162, "usage_type": "name"}, {"api_name": "quart.url_for", "line_number": 162, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 168, "usage_type": "call"}, {"api_name": "quart.redirect", "line_number": 173, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 152, "usage_type": "call"}, {"api_name": "oic.oic.message.AuthorizationResponse", "line_number": 183, "usage_type": "argument"}, {"api_name": "quart.request.query_string.decode", "line_number": 184, "usage_type": "call"}, {"api_name": "quart.request.query_string", "line_number": 184, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 184, "usage_type": "name"}, {"api_name": "quart.session.get", "line_number": 188, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 188, "usage_type": "name"}, {"api_name": "quart.url_for", "line_number": 196, "usage_type": "call"}, {"api_name": "quart.redirect", "line_number": 201, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 201, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 203, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 208, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 211, "usage_type": "call"}, {"api_name": "quart.session.get", "line_number": 214, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 214, "usage_type": "name"}, {"api_name": "quart.session.get", "line_number": 215, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 215, "usage_type": "name"}, {"api_name": "quart.session.get", "line_number": 217, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 217, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 218, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 178, "usage_type": "call"}, {"api_name": "quart.request.args.get", "line_number": 226, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 226, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 226, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 233, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 244, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 245, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 245, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 245, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 248, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 249, "usage_type": "name"}, {"api_name": "jwt.ExpiredSignatureError", "line_number": 258, "usage_type": "attribute"}, {"api_name": "quart.redirect", "line_number": 259, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 259, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 259, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 223, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 265, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 266, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 266, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 266, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 268, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 269, "usage_type": "name"}, {"api_name": "jwt.ExpiredSignatureError", "line_number": 277, "usage_type": "attribute"}, {"api_name": "quart.redirect", "line_number": 278, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 278, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 278, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 262, "usage_type": "call"}, {"api_name": "urllib.parse.parse.urlencode", "line_number": 286, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 286, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 286, "usage_type": "name"}, {"api_name": "quart.url_for", "line_number": 286, "usage_type": "call"}, {"api_name": "quart.request.args.get", "line_number": 289, "usage_type": "call"}, {"api_name": "quart.request.args", "line_number": 289, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 289, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 290, "usage_type": "name"}, {"api_name": "quart.session.clear", "line_number": 291, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 291, "usage_type": "name"}, {"api_name": "quart.render_template", "line_number": 292, "usage_type": "call"}, {"api_name": "quart.url_for", "line_number": 292, "usage_type": "call"}, {"api_name": "quart.redirect", "line_number": 294, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 296, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 297, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 297, "usage_type": "name"}, {"api_name": "quart.request.cookies.get", "line_number": 300, "usage_type": "call"}, {"api_name": "quart.request.cookies", "line_number": 300, "usage_type": "attribute"}, {"api_name": "quart.request", "line_number": 300, "usage_type": "name"}, {"api_name": "quart.session.clear", "line_number": 302, "usage_type": "call"}, {"api_name": "quart.session", "line_number": 302, "usage_type": "name"}, {"api_name": "quart.session", "line_number": 307, "usage_type": "name"}, {"api_name": "quart.redirect", "line_number": 309, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "572759270", "text": "import cv2\nimport numpy as np\n\ncap = cv2.VideoCapture(0)\n\ni = 0\nwhile (cap.isOpened()):\n ret, frame = cap.read()\n if ret == False: break\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n if i == 20:\n bgGray = gray\n if i>20:\n dif = cv2.absdiff(gray, bgGray)\n _, th = cv2.threshold(dif, 40, 255, cv2.THRESH_BINARY)\n cnts,_ = cv2.findContours(th,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n \n for c in cnts:\n area = cv2.contourArea(c)\n if area > 7000:\n x,y,w,h = cv2.boundingRect(c)\n cv2.rectangle(frame, (x,y), (x+w, y+h), (255,255,0), 2)\n \n cv2.imshow('Frame',frame)\n i+=1\n if cv2.waitKey(30) & 0xFF == ord('q'):\n break\n\ncap.release()\n", "sub_path": "1ejercicios_clases/roi.py", "file_name": "roi.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.absdiff", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "622838167", "text": "from functools import lru_cache\r\nclass Solution:\r\n def solve(self, s):\r\n if(s==\"\"):\r\n return 0\r\n n = len(s)\r\n dp = [[0]*n for i in range(n)]\r\n for i in range(n):\r\n dp[i][i] = 1\r\n for length in range(1,n):\r\n for i in range(n):\r\n j=i+length\r\n if(j r:\r\n return 0\r\n if s[l] == s[r]:\r\n return rec(l+1, r-1) + 2\r\n else:\r\n return max(rec(l+1, r), rec(l, r-1))\r\n return rec(0, len(s) - 1)", "sub_path": "Q1-50/Q26_Longest_Palindromic_subsequence.py", "file_name": "Q26_Longest_Palindromic_subsequence.py", "file_ext": "py", "file_size_in_byte": 945, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "functools.lru_cache", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "228662366", "text": "from flask import Flask, jsonify, request\nimport requests\nimport json\nimport pandas as pd\nimport numpy as np\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nimport xlrd\napp = Flask(__name__)\ndataset = pd.read_excel(\"Sample.xls\")\ndf = pd.DataFrame(dataset)\nfurniture = df.loc[df['Category'] == 'Furniture']\nfurniture.drop(df.columns.difference(['Order Date','Sales']), 1, inplace=True)\n\n@app.route(\"/rf\")\ndef rf():\n\n\tX = furniture['Order Date'].values.astype('int')\n\ty = furniture['Sales'].values\n\tdata = {\n\t'SalesPredictions' : list(y),\n\t'date' : list(X)\n\t}\n\treturn jsonify(data)\n\n@app.route('/pred',methods = ['GET'])\ndef pred():\n\turl = \"http://127.0.0.1:5000/rf\"\n\ttry:\n\t\tuResponse = requests.get(url)\n\texcept requests.ConnectionError:\n\t\treturn \"Connection Error\" \n\tJresponse = uResponse.text\n\tdata = json.loads(Jresponse)\n\tlist1 = [k for k in data['date']]\n\tlist2 = [k for k in data['SalesPredictions']]\n\n\tarray1 = np.asarray(list1).reshape(-1,1)\n\tarray2 = np.asarray(list2)\n\t\n\tX_train,X_test,y_train,y_test = train_test_split(array1,array2,test_size=0.2)\n\tregressor = RandomForestRegressor()\n\tregressor = regressor.fit(X_test,y_test)\n\tsales_pred = regressor.predict(X_test)\n\tsales_pred = list(sales_pred)\n\ta = regressor.score(X_test,y_test)\n\tb = np.array2string(a)\n\terrors = abs(sales_pred - y_test) #average absolute error-https://towardsdatascience.com/improving-random-forest-in-python-part-1-893916666cd\n\treturn jsonify({'Sales Predictions(in Dollars)':sales_pred,'accuracy':b,'Average absolute error(in Dollars):': round(np.mean(errors), 2)})\nif __name__ == '__main__':\n\tapp.run(debug=True)\n", "sub_path": "randomforest.py", "file_name": "randomforest.py", "file_ext": "py", "file_size_in_byte": 1660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 31, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array2string", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "171142383", "text": "from elasticsearch_dsl import DocType, Integer, Text, Keyword, Date, MetaField, GeoPoint, analyzer, tokenizer\n\n\"\"\"\nResponse Record mapping model\n///\nKeyword: Used for filtering\nTEXT: Used for analyzing and searching\nGeo-point: geo_point for lat/lon points\nGeo-Shape: geo_shape for complex shapes like polygons\n///\n\nEach fields have some features.\nReference: https://www.elastic.co/guide/en/elasticsearch/reference/current/multi-fields.html\n\"\"\"\nsudachi_tokenizer = tokenizer('sudachi_tokenizer',\n mode='search',\n settings_path='/etc/elasticsearch/sudachi/sudachi.json',\n resources_path='/etc/elasticsearch/sudachi',\n type='sudachi_tokenizer',\n discard_punctuation=True)\n\nsudachi_analyzer = analyzer('sudachi_analyzer',\n tokenizer=sudachi_tokenizer,\n filter=[],\n type='custom')\n\n\nclass ResponseRecordMapping(DocType):\n pk = Integer()\n\n id = Integer() # unified id\n date = Date() # reported date\n\n classification = Keyword() # disaster phase for filtering\n disaster_name = Text(analyzer=sudachi_analyzer) # A disaster name related to a record\n\n title = Text(analyzer=sudachi_analyzer, fields={'raw': Keyword()}) # response section\n body = Text(analyzer=sudachi_analyzer) # record contents\n\n phase = Keyword() # disaster phase for filtering\n season = Keyword() # A season for filtering\n\n importance = Integer() # an importance of a report content\n location = GeoPoint() # A place\n\n created_at = Date()\n updated_at = Date()\n\n class Meta:\n index = 'ddss_response_records'\n\n", "sub_path": "ElasticSearchPkg/ResponseRecordMappingModel.py", "file_name": "ResponseRecordMappingModel.py", "file_ext": "py", "file_size_in_byte": 1752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "elasticsearch_dsl.tokenizer", "line_number": 15, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.analyzer", "line_number": 22, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.DocType", "line_number": 28, "usage_type": "name"}, {"api_name": "elasticsearch_dsl.Integer", "line_number": 29, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Integer", "line_number": 31, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Date", "line_number": 32, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Keyword", "line_number": 34, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Text", "line_number": 35, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Keyword", "line_number": 37, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Text", "line_number": 38, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Keyword", "line_number": 40, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Keyword", "line_number": 41, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Integer", "line_number": 43, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.GeoPoint", "line_number": 44, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Date", "line_number": 46, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Date", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "45928898", "text": "#!/usr/bin/env python\n##############################################################################\n# Copyright (c) 2021 Orange, Inc. and others. All rights reserved.\n#\n# All rights reserved. This program and the accompanying materials\n# are made available under the terms of the Apache License, Version 2.0\n# which accompanies this distribution, and is available at\n# http://www.apache.org/licenses/LICENSE-2.0\n##############################################################################\n\n# pylint: disable=no-member\n# pylint: disable=too-many-public-methods\n\nimport unittest\nimport os\nimport sys\nimport time\nimport requests\nsys.path.append('transportpce_tests/common/')\nimport test_utils\n\n\nclass TransportPCE400Gtesting(unittest.TestCase):\n\n simple_topo_bi_dir_data = None\n port_mapping_data = None\n processes = None\n\n @classmethod\n def setUpClass(cls):\n try:\n sample_files_parsed = False\n TOPO_BI_DIR_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)),\n \"..\", \"..\", \"sample_configs\", \"honeynode-topo400G.json\")\n with open(TOPO_BI_DIR_FILE, 'r') as topo_bi_dir:\n cls.simple_topo_bi_dir_data = topo_bi_dir.read()\n\n PORT_MAPPING_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)),\n \"..\", \"..\", \"sample_configs\", \"pce_portmapping_71.json\")\n with open(PORT_MAPPING_FILE, 'r') as port_mapping:\n cls.port_mapping_data = port_mapping.read()\n sample_files_parsed = True\n except PermissionError as err:\n print(\"Permission Error when trying to read sample files\\n\", err)\n sys.exit(2)\n except FileNotFoundError as err:\n print(\"File Not found Error when trying to read sample files\\n\", err)\n sys.exit(2)\n except:\n print(\"Unexpected error when trying to read sample files\\n\", sys.exc_info()[0])\n sys.exit(2)\n finally:\n if sample_files_parsed:\n print(\"sample files content loaded\")\n cls.processes = test_utils.start_tpce()\n\n @classmethod\n def tearDownClass(cls):\n # pylint: disable=not-an-iterable\n for process in cls.processes:\n test_utils.shutdown_process(process)\n print(\"all processes killed\")\n\n def setUp(self): # instruction executed before each test method\n print(\"execution of {}\".format(self.id().split(\".\")[-1]))\n time.sleep(1)\n\n # Load port mapping\n def test_01_load_port_mapping(self):\n response = test_utils.put_jsonrequest(test_utils.URL_FULL_PORTMAPPING, self.port_mapping_data)\n self.assertIn(response.status_code, (requests.codes.ok, requests.codes.created))\n time.sleep(2)\n\n # Load simple bidirectional topology\n def test_02_load_simple_topology_bi(self):\n response = test_utils.put_jsonrequest(test_utils.URL_CONFIG_ORDM_TOPO, self.simple_topo_bi_dir_data)\n self.assertEqual(response.status_code, requests.codes.ok)\n time.sleep(2)\n\n # Path Computation success\n def test_03_path_computation_xpdr_bi(self):\n response = test_utils.path_computation_request(\"request-1\", \"service-1\",\n {\"node-id\": \"XPDR-A2\", \"service-rate\": \"400\",\n \"service-format\": \"Ethernet\", \"clli\": \"nodeA\"},\n {\"node-id\": \"XPDR-C2\", \"service-rate\": \"400\",\n \"service-format\": \"Ethernet\", \"clli\": \"nodeC\"})\n self.assertEqual(response.status_code, requests.codes.ok)\n res = response.json()\n self.assertIn('Path is calculated',\n res['output']['configuration-response-common']['response-message'])\n\n self.assertEqual(1, res['output']['response-parameters']['path-description']\n ['aToZ-direction']['aToZ-wavelength-number'])\n self.assertEqual(400, res['output']['response-parameters']['path-description']\n ['aToZ-direction']['rate'])\n self.assertEqual(196.0375, res['output']['response-parameters']['path-description']\n ['aToZ-direction']['aToZ-min-frequency'])\n self.assertEqual(196.12500, res['output']['response-parameters']['path-description']\n ['aToZ-direction']['aToZ-max-frequency'])\n self.assertEqual('dp-qam16', res['output']['response-parameters']['path-description']\n ['aToZ-direction']['modulation-format'])\n\n self.assertEqual(1, res['output']['response-parameters']['path-description']\n ['zToA-direction']['zToA-wavelength-number'])\n self.assertEqual(400, res['output']['response-parameters']['path-description']\n ['zToA-direction']['rate'])\n self.assertEqual(196.0375, res['output']['response-parameters']['path-description']\n ['zToA-direction']['zToA-min-frequency'])\n self.assertEqual(196.12500, res['output']['response-parameters']['path-description']\n ['zToA-direction']['zToA-max-frequency'])\n self.assertEqual('dp-qam16', res['output']['response-parameters']['path-description']\n ['zToA-direction']['modulation-format'])\n time.sleep(5)\n\n # Test deleted complex topology\n def test_04_test_topology_complex_deleted(self):\n response = test_utils.get_ordm_topo_request(\"node/XPONDER-3-2\")\n self.assertEqual(response.status_code, requests.codes.conflict)\n time.sleep(1)\n\n # Delete portmapping\n def test_05_delete_port_mapping(self):\n response = test_utils.delete_request(test_utils.URL_FULL_PORTMAPPING)\n self.assertEqual(response.status_code, requests.codes.ok)\n time.sleep(2)\n\n\nif __name__ == \"__main__\":\n unittest.main(verbosity=2)\n", "sub_path": "tests/transportpce_tests/7.1/test_pce_400G.py", "file_name": "test_pce_400G.py", "file_ext": "py", "file_size_in_byte": 6050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sys.path.append", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}, {"api_name": "test_utils.start_tpce", "line_number": 55, "usage_type": "call"}, {"api_name": "test_utils.shutdown_process", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "test_utils.put_jsonrequest", "line_number": 70, "usage_type": "call"}, {"api_name": "test_utils.URL_FULL_PORTMAPPING", "line_number": 70, "usage_type": "attribute"}, {"api_name": "requests.codes", "line_number": 71, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "test_utils.put_jsonrequest", "line_number": 76, "usage_type": "call"}, {"api_name": "test_utils.URL_CONFIG_ORDM_TOPO", "line_number": 76, "usage_type": "attribute"}, {"api_name": "requests.codes", "line_number": 77, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "test_utils.path_computation_request", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 87, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 113, "usage_type": "call"}, {"api_name": "test_utils.get_ordm_topo_request", "line_number": 117, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 118, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "test_utils.delete_request", "line_number": 123, "usage_type": "call"}, {"api_name": "test_utils.URL_FULL_PORTMAPPING", "line_number": 123, "usage_type": "attribute"}, {"api_name": "requests.codes", "line_number": 124, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "143377538", "text": "\nimport argparse\nimport project_root\nimport numpy as np\nimport tensorflow as tf\nfrom os import path\nimport os\nimport sys\nfrom env.sender import Sender\nfrom helpers.helpers import normalize, one_hot, softmax\nfrom environment import Environment\nimport random\nimport tensorflow.contrib.slim as slim\n\n\nclass Q_network(object):\n def __init__(self, state_dim, action_cnt):\n self.state = tf.placeholder(shape=[None, state_dim], dtype=tf.float32)\n self.state = tf.reshape(self.state, shape=[-1, state_dim])\n\n self.fc1 = tf.contrib.layers.fully_connected(self.state,64)\n self.fc1 = tf.nn.dropout(self.fc1, 0.8)\n\n self.fc2 = tf.contrib.layers.fully_connected(self.fc1, 64)\n self.fc2 = tf.nn.dropout(self.fc2, 0.5)\n\n self.fc3 = tf.contrib.layers.fully_connected(self.fc2, 64)\n self.fc3 = tf.nn.dropout(self.fc3, 0.5)\n\n self.streamAC, self.streamVC = tf.split(self.fc3, 2, 1)\n self.streamA = slim.flatten(self.streamAC)\n self.streamV = slim.flatten(self.streamVC)\n\n self.Advantage = tf.contrib.layers.fully_connected(self.streamA, action_cnt)\n self.Value = tf.contrib.layers.fully_connected(self.streamV, 1)\n\n self.Qout = self.Value + tf.subtract(self.Advantage, tf.reduce_mean(self.Advantage, axis=1, keepdims=True))\n self.predict = tf.argmax(self.Qout, 1)\n\n self.targetQ = tf.placeholder(shape=[None], dtype=tf.float32)\n self.actions = tf.placeholder(shape=[None], dtype=tf.int32)\n self.actions_onehot = tf.one_hot(self.actions, action_cnt, dtype=tf.float32)\n\n self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis=1)\n\n self.td_error = tf.square(self.targetQ - self.Q)\n self.loss = tf.reduce_mean(self.td_error)\n self.trainer = tf.train.AdamOptimizer(learning_rate=0.001)\n self.updateModel = self.trainer.minimize(self.loss)\n\n\ndef create_env():\n uplink_trace = path.join(project_root.DIR, 'env', '114.68mbps.trace')\n downlink_trace = uplink_trace\n mahimahi_cmd = (\n 'mm-delay 20 mm-link %s %s '\n '--downlink-queue=droptail --downlink-queue-args=packets=200' %\n (uplink_trace, downlink_trace))\n\n env = Environment(mahimahi_cmd)\n return env\n\n\nclass experience_buffer():\n def __init__(self, buffer_size=100000):\n self.buffer = []\n\n self.buffer_size = buffer_size\n\n def add(self, experience):\n if len(self.buffer) + len(experience) >= self.buffer_size:\n self.buffer[0:(len(experience) + len(self.buffer)) - self.buffer_size] = []\n self.buffer.extend(experience)\n\n def sample(self, size):\n return np.reshape(np.array(random.sample(self.buffer, size)), [size, 5])\n\n\nclass Learner(object):\n def __init__(self, env):\n self.batch_size = 128\n self.y = 0.99\n self.tau = 0.001\n\n self.total_steps = 0\n self.num_episode = 1000\n self.max_epLength = 1000\n\n self.aug_state_dim = env.state_dim + env.action_cnt\n self.action_cnt = env.action_cnt\n self.prev_action = env.action_cnt - 1\n\n path = \"./save_model\"\n if not os.path.exists(path):\n os.makedirs(path)\n\n self.env = env\n\n self.state_dim = env.state_dim\n self.action_cnt = env.action_cnt\n\n def updateTargetGraph(self,tfVars, tau):\n total_vars = len(tfVars)\n op_holder = []\n for idx, var in enumerate(tfVars[0:total_vars // 2]):\n op_holder.append(tfVars[idx + total_vars // 2].assign(\n (var.value() * tau) + ((1 - tau) * tfVars[idx + total_vars // 2].value())))\n return op_holder\n\n def updateTarget(self,op_holder, sess):\n for op in op_holder:\n sess.run(op)\n\n\n def cleanup(self):\n self.env.cleanup()\n\n\n\n def run(self):\n\n tf.reset_default_graph()\n self.mainQN = Q_network(state_dim=self.aug_state_dim, action_cnt=self.action_cnt)\n self.targetQN = Q_network(state_dim=self.aug_state_dim, action_cnt=self.action_cnt)\n init = tf.global_variables_initializer()\n saver = tf.train.Saver()\n trainables = tf.trainable_variables()\n targetOps = self.updateTargetGraph(trainables, self.tau)\n\n myBuffer = experience_buffer()\n self.rAll = 0\n self.jList = []\n self.rList = []\n F = open(\"r.txt\", \"w\")\n\n with tf.Session() as sess:\n sess.run(init)\n\n def update_model():\n trainBatch = myBuffer.sample(self.batch_size)\n Q1 = sess.run(self.mainQN.predict, feed_dict={self.mainQN.state: np.vstack(trainBatch[:, 3])})\n Q2 = sess.run(self.targetQN.Qout, feed_dict={self.targetQN.state: np.vstack(trainBatch[:, 3])})\n\n end_multiplier = -(trainBatch[:, 4] - 1)\n doubleQ = Q2[xrange(self.batch_size), Q1]\n targetQ = trainBatch[:, 2] + (self.y * doubleQ * end_multiplier)\n\n _ = sess.run(self.mainQN.updateModel,\n feed_dict={self.mainQN.state: np.vstack(trainBatch[:, 0]),\n self.mainQN.targetQ: targetQ,\n self.mainQN.actions: trainBatch[:, 1]})\n\n self.updateTarget(targetOps, sess)\n\n def sample_action(state):\n if np.random.rand(1) < 0.05:\n action = np.random.randint(0, self.env.action_cnt)\n else:\n\n # Get probability of each action from the local network.\n pi = self.mainQN\n feed_dict = {\n pi.state: [state],\n }\n ops_to_run = pi.predict\n action = sess.run(ops_to_run, feed_dict)[0]\n\n # Choose an action to take\n\n self.prev_action = action\n return action\n\n self.env.set_sample_action(sample_action)\n\n for episode_i in xrange(self.num_episode):\n sys.stderr.write('--- Episode %d\\n' % episode_i)\n episode_buffer = experience_buffer()\n\n s = self.env.reset()\n\n # get an episode of experience\n buffer,rall = self.env.rollout()\n myBuffer.add(buffer.buffer)\n print(len(myBuffer.buffer))\n\n\n for i in xrange(2000):\n #sys.stderr.write('update model %d\\n' % i)\n update_model()\n\n self.env.set_sample_action(sample_action)\n self.rList.append(rall)\n F.write(str(rall) + '\\n')\n\n print('rall %f\\n' % rall)\n F.close()\n return self.rList\n\n\ndef updateTargetGraph(tfVars, tau):\n total_vars = len(tfVars)\n op_holder = []\n for idx, var in enumerate(tfVars[0:total_vars // 2]):\n op_holder.append(tfVars[idx + total_vars // 2].assign(\n (var.value() * tau) + ((1 - tau) * tfVars[idx + total_vars // 2].value())))\n return op_holder\n\ndef updateTarget(op_holder, sess):\n for op in op_holder:\n sess.run(op)\n\n\ndef cleanup(env):\n env.cleanup()\n\n\n\ndef run_learner(env):\n batch_size = 128\n y = 0.99\n tau = 0.001\n\n total_steps = 0\n num_episode = 1000\n max_epLength = 1000\n\n aug_state_dim = env.state_dim + env.action_cnt\n action_cnt = env.action_cnt\n prev_action = env.action_cnt - 1\n\n path = \"./save_model\"\n if not os.path.exists(path):\n os.makedirs(path)\n\n\n state_dim = env.state_dim\n action_cnt = env.action_cnt\n\n tf.reset_default_graph()\n mainQN = Q_network(state_dim=aug_state_dim, action_cnt=action_cnt)\n targetQN = Q_network(state_dim=aug_state_dim, action_cnt=action_cnt)\n init = tf.global_variables_initializer()\n saver = tf.train.Saver()\n trainables = tf.trainable_variables()\n targetOps = updateTargetGraph(trainables, tau)\n\n myBuffer = experience_buffer()\n rAll = 0\n jList = []\n rList = []\n F = open(\"r.txt\", \"w\")\n with tf.Session() as sess:\n sess.run(init)\n\n def update_model():\n trainBatch = myBuffer.sample(self.batch_size)\n Q1 = sess.run(self.mainQN.predict, feed_dict={self.mainQN.state: np.vstack(trainBatch[:, 3])})\n Q2 = sess.run(self.targetQN.Qout, feed_dict={self.targetQN.state: np.vstack(trainBatch[:, 3])})\n\n end_multiplier = -(trainBatch[:, 4] - 1)\n doubleQ = Q2[xrange(self.batch_size), Q1]\n targetQ = trainBatch[:, 2] + (self.y * doubleQ * end_multiplier)\n\n _ = sess.run(self.mainQN.updateModel,\n feed_dict={self.mainQN.state: np.vstack(trainBatch[:, 0]),\n self.mainQN.targetQ: targetQ,\n self.mainQN.actions: trainBatch[:, 1]})\n\n self.updateTarget(targetOps, sess)\n\n def sample_action(state):\n if np.random.rand(1) < 0.05:\n action = np.random.randint(0, self.env.action_cnt)\n else:\n\n # Get probability of each action from the local network.\n pi = self.mainQN\n feed_dict = {\n pi.state: [state],\n }\n ops_to_run = pi.predict\n action = sess.run(ops_to_run, feed_dict)[0]\n\n # Choose an action to take\n\n self.prev_action = action\n return action\n\n self.env.set_sample_action(sample_action)\n\n for episode_i in xrange(self.num_episode):\n sys.stderr.write('--- Episode %d\\n' % episode_i)\n episode_buffer = experience_buffer()\n\n s = self.env.reset()\n\n # get an episode of experience\n buffer, rall = self.env.rollout()\n myBuffer.add(buffer.buffer)\n print(len(myBuffer.buffer))\n\n for i in xrange(2000):\n # sys.stderr.write('update model %d\\n' % i)\n update_model()\n\n self.env.set_sample_action(sample_action)\n self.rList.append(rall)\n F.write(str(rall) + '\\n')\n\n print('rall %f\\n' % rall)\n F.close()\n return self.rList\n\n\n\n\ndef main():\n\n\n env = create_env()\n learner = Learner(env)\n\n try:\n rlist = learner.run()\n\n\n except KeyboardInterrupt:\n pass\n finally:\n learner.cleanup()\n\n\n\nif __name__ == '__main__':\n main()", "sub_path": "env/test_env.py", "file_name": "test_env.py", "file_ext": "py", "file_size_in_byte": 10454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tensorflow.placeholder", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.split", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim.flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.contrib.slim.flatten", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.subtract", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "name"}, {"api_name": "project_root.DIR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 60, "usage_type": "name"}, {"api_name": "environment.Environment", "line_number": 60, "usage_type": "call"}, {"api_name": "env.sender", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 76, "usage_type": "call"}, {"api_name": "env.sender.state_dim", "line_number": 89, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 89, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 89, "usage_type": "attribute"}, {"api_name": "env.sender.action_cnt", "line_number": 90, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 90, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 91, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 91, "usage_type": "name"}, {"api_name": "os.path", "line_number": 93, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "argument"}, {"api_name": "os.makedirs", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "argument"}, {"api_name": "env.sender", "line_number": 97, "usage_type": "name"}, {"api_name": "env.sender.state_dim", "line_number": 99, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 99, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 100, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 100, "usage_type": "name"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 157, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 176, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 176, "usage_type": "attribute"}, {"api_name": "env.sender.cleanup", "line_number": 214, "usage_type": "call"}, {"api_name": "env.sender", "line_number": 214, "usage_type": "name"}, {"api_name": "env.sender.state_dim", "line_number": 227, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 227, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 227, "usage_type": "attribute"}, {"api_name": "env.sender.action_cnt", "line_number": 228, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 228, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 229, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 229, "usage_type": "name"}, {"api_name": "os.path", "line_number": 231, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "argument"}, {"api_name": "os.makedirs", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "argument"}, {"api_name": "env.sender.state_dim", "line_number": 236, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 236, "usage_type": "name"}, {"api_name": "env.sender.action_cnt", "line_number": 237, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 237, "usage_type": "name"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 239, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 242, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 243, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 243, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 244, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 272, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 273, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 292, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 292, "usage_type": "attribute"}, {"api_name": "env.sender", "line_number": 320, "usage_type": "name"}, {"api_name": "env.sender", "line_number": 321, "usage_type": "argument"}]} +{"seq_id": "532816259", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndef gauss_kernel(window_size = 5, sigma = 3):\r\n mid = (int)(window_size / 2)\r\n kernel = np.zeros((window_size, window_size))\r\n for i in range(window_size):\r\n for j in range(window_size):\r\n diff = np.sqrt((i - mid) ** 2 + (j - mid) ** 2)\r\n kernel[i, j] = np.exp(-(diff ** 2) / (2 * sigma ** 2))\r\n return kernel / np.sum(kernel)\r\n\r\ndef gauss_filter(img, window_size = 5, sigma = 3):\r\n img2 = np.zeros_like(img)\r\n kernel = gauss_kernel(window_size, sigma)\r\n p = window_size//2\r\n for k in range(img.shape[2]):\r\n for i in range(p, img.shape[0] - p):\r\n for j in range(p, img.shape[1] - p):\r\n window = img[i - p: i + p + 1, j - p: j + p + 1, k]\r\n img2[i, j, k] = (kernel * window).sum()\r\n return img2\r\n\r\n\r\ndef main():\r\n img = plt.imread(\"img.png\")[:, :, :3]\r\n img2 = gauss_filter(img)\r\n\r\n fig, axs = plt.subplots(1,2)\r\n axs[0].imshow(img)\r\n axs[1].imshow(img2)\r\n plt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()", "sub_path": "solution2.py", "file_name": "solution2.py", "file_ext": "py", "file_size_in_byte": 1087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.zeros", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "516241107", "text": "import tensorflow as tf\nimport os,sys\nCURRENT_DIR = os.path.dirname(__file__)\nsys.path.append(os.path.join(CURRENT_DIR, '..'))\nfrom model.SiamRPN import SiamRPN\nfrom utils.train_utils import show_pred_bbox\nfrom utils.tf_bbox_ops_utils import tf_iou\n\nslim = tf.contrib.slim\n\nclass SiamRPN_IOU(SiamRPN):\n def _build_rpn_loss(self):\n \"\"\"\n self.labels: NxNa(Na=32*32*k)\n self.pred_anchors: Nx32x32x4k\n self.pred_prob: Nx32x32x2k\n self.bbox_gts: NxNax4\n \"\"\"\n with tf.name_scope('Loss'):\n valid_mask = tf.stop_gradient(tf.not_equal(self.labels, -1)) # N*Na\n valid_labels = tf.boolean_mask(self.labels, valid_mask) # N*num_of_anchors_per_image(=64)\n valid_labels = tf.reshape(valid_labels, [self.batch_size,-1])\n \n valid_labels_flatten_pos = tf.to_float(tf.reshape(valid_labels, [-1]))\n valid_labels_flatten = tf.stack([valid_labels_flatten_pos, 1.0 - valid_labels_flatten_pos], axis=1) #[-1x2]\n\n valid_pred_probs = tf.boolean_mask(self.pred_probs, valid_mask)\n valid_pred_probs = tf.reshape(valid_pred_probs, [-1, 2])\n \n pos_mask = tf.stop_gradient(tf.equal(self.labels, 1)) # N*Na\n valid_bbox_gts = tf.boolean_mask(self.bbox_gts, pos_mask)\n valid_pred_boxes = tf.boolean_mask(self.pred_boxes, pos_mask)\n \n self.loss_cls = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=valid_labels_flatten, logits=valid_pred_probs))\n self.loss_iou = 1.0 - tf.reduce_mean(tf_iou(tf.reshape(valid_bbox_gts,[-1,4]), tf.reshape(valid_pred_boxes,[-1,4])))\n \n def build_loss(self):\n self._build_rpn_loss()\n with tf.name_scope('Loss'):\n self.batch_loss = self.loss_cls + self.loss_iou\n tf.losses.add_loss(self.batch_loss)\n self.total_loss = tf.losses.get_total_loss()\n mean_total_loss, update_op = tf.metrics.mean(self.total_loss)\n with tf.control_dependencies([update_op]):\n tf.summary.scalar('total_loss', mean_total_loss, family=self.mode)\n\n tf.summary.scalar('batch_loss', self.batch_loss, family=self.mode)\n tf.summary.scalar('loss_cls', self.loss_cls, family=self.mode)\n tf.summary.scalar('loss_iou', self.loss_iou, family=self.mode)\n\n track_instance = tf.py_func(show_pred_bbox,[self.instances, self.topk_bboxes, self.topk_scores, self.gt_instance_boxes],tf.float32)\n tf.summary.image('exemplar', self.examplars, family=self.mode)\n tf.summary.image('instance', track_instance, family=self.mode)", "sub_path": "model/SiamRPN_IOU.py", "file_name": "SiamRPN_IOU.py", "file_ext": "py", "file_size_in_byte": 2676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib", "line_number": 9, "usage_type": "attribute"}, {"api_name": "model.SiamRPN.SiamRPN", "line_number": 11, "usage_type": "name"}, {"api_name": "tensorflow.name_scope", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.not_equal", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.boolean_mask", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.boolean_mask", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.boolean_mask", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.boolean_mask", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.tf_bbox_ops_utils.tf_iou", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.losses.add_loss", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.losses.get_total_loss", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.metrics.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.metrics", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.control_dependencies", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.py_func", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.train_utils.show_pred_bbox", "line_number": 51, "usage_type": "argument"}, {"api_name": "tensorflow.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "152095590", "text": "import http.server\nimport http.cookies\nimport uuid\nimport cgi\nimport urllib\nimport os\nimport raspact.util\nimport raspact.routes\n\n\n# Private class to handle HTTP requests for apps created by a factory.\nclass AppHandler(http.server.BaseHTTPRequestHandler):\n\n # maps session IDs to apps created by self.appFactory()\n sessionStore = {}\n\n # pre-loaded partials\n resources = {\n \"header\": raspact.util.readResource(\"partials/header.html\"),\n \"footer\": raspact.util.readResource(\"partials/footer.html\")\n }\n\n # Constructs a new AppHandler that creates a new App per session using\n # the appFactory factoy method which must create an object of type\n # Application.\n def __init__(self, appFactory, *args):\n self.appFactory = appFactory\n self.routes = raspact.routes.Routes(None) # TODO restore self.app.routes)\n self.newCookie = None\n\n self.routes.route(\"/assets/\", serveAsset, True)\n self.routes.route(\"/logs\", serveLogs, False)\n self.routes.route(\"/logs.html\", serveLogsHtml(False), False)\n self.routes.route(\"/logs.part.html\", serveLogsHtml(True), False)\n\n http.server.BaseHTTPRequestHandler.__init__(self, *args)\n\n def do_POST(self):\n self.restoreSession()\n\n # we can only post to \"/\"\n if self.path != \"/\":\n self.error(405, \"Method not allowed\", \"Can only POST to '/'.\")\n return\n contentType = self.headers[\"Content-Type\"]\n ctype, pdict = cgi.parse_header(self.headers['content-type'])\n if ctype == 'multipart/form-data':\n postvars = cgi.parse_multipart(self.rfile, pdict)\n elif ctype == 'application/x-www-form-urlencoded':\n length = int(self.headers['Content-Length'])\n postvars = urllib.parse.parse_qs(self.rfile.read(length).decode('utf-8'))\n #urllib.parse_qs(self.rfile.read(length), keep_blank_values=1)\n else:\n postvars = {}\n self.send_response(200)\n self.__process(postvars)\n\n def do_GET(self):\n self.restoreSession()\n\n ## Routing\n # Serve the actual app\n if (self.path == \"/\"):\n self.__process(None)\n elif (self.path == \"/reset\"):\n if self.path == \"/reset\":\n self.app.reset()\n self.__process(None)\n else:\n self.routes.fallback = self.app.routes\n self.routes.handleRequest(self.path, self)\n\n\n # Delegate to app to process the request.\n def __process(self, postvars):\n self.send_response(200)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()\n self.write(AppHandler.resources[\"header\"])\n self.app._Application__serve(self, postvars)\n self.write(AppHandler.resources[\"footer\"])\n\n #######################################################\n # Session handling\n #######################################################\n\n # If a cookie is found, restors the app from the session store. Otherwise,\n # creates a new one and creates a session. Cookie will be written by\n # self.end_headers() if self.newCookie is set.\n def restoreSession(self):\n cookie = self.getCookie()\n if cookie:\n sessionId = cookie[\"sessionid\"].value\n else:\n sessionId = None\n if sessionId:\n if sessionId in self.sessionStore:\n self.app = AppHandler.sessionStore[sessionId]\n self.newCookie = None\n raspact.util.log(\"Restored app for session %(sid)s.\" % {\"sid\": sessionId})\n else:\n raspact.util.log(\"WARNING: We forgot about session %(sid)s. Creating new app.\" % {\"sid\": sessionId})\n self.app = self.appFactory()\n AppHandler.sessionStore[sessionId] = self.app\n else:\n self.app = self.appFactory()\n AppHandler.sessionStore[sessionId] = self.app\n self.newCookie = http.cookies.SimpleCookie()\n self.newCookie[\"sessionid\"] = uuid.uuid1()\n raspact.util.log(\"Created new session %(sid)s and app.\" % {\"sid\": sessionId})\n\n # Reads the cookie from the HTTP headers.\n def getCookie(self):\n oldCookieStr = self.headers[\"Cookie\"]\n if oldCookieStr:\n return http.cookies.SimpleCookie(oldCookieStr)\n else:\n return None\n\n #######################################################\n # I/O helpers\n #######################################################\n\n # Helper method such that we can write strings, encoded as UTF-8.\n def write(self, string):\n self.wfile.write(bytes(string, \"utf-8\"))\n\n # Override to always set pending cookie from self.newCookie if set.\n def end_headers(self):\n if self.newCookie:\n self.send_header('Set-Cookie', self.newCookie.output(header=''))\n super(AppHandler, self).end_headers()\n\n def error(self, code, title, message):\n self.send_response(code)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()\n self.writeResource(\"header\")\n self.app.writeTitle(self)\n self.write('
%(title)s
' % {\"title\": title})\n self.write('
%(msg)s
' % {\"msg\": message})\n self.writeResource(\"footer\")\n\n # Writes a named pre-loaded partial\n def writeResource(out, name):\n out.write(AppHandler.resources[name])\n\n#######################################################\n# Static service methods that can be used from anywhere\n#######################################################\n\ndef serveLogsHtml(partial):\n def handle(handler, ignoredPath):\n handler.send_response(200)\n handler.send_header(\"Content-type\", \"text/html\")\n handler.end_headers()\n\n if not partial:\n writeResource(handler, \"header\")\n handler.write('

Logs

')\n handler.app.renderLogs(handler)\n if not partial:\n writeResource(handler, \"footer\")\n return handle\n\ndef serveLogs(handler, ignoredPath):\n handler.send_response(200)\n handler.send_header(\"Content-type\", \"text/plain\")\n handler.end_headers()\n\n for log in handler.app.logs:\n handler.write(log[\"time\"] + \" \" + log[\"name\"] + \"\\n\")\n for line in log[\"records\"]:\n handler.write(\" \" + line + \"\\n\")\n\n\ndef serveAsset(handler, path):\n root = os.path.dirname(raspact.util.__file__)\n assetFile = os.path.join(root, \"resources/assets/\", path)\n if not os.path.isfile(assetFile):\n handler.error(404, \"No such file\", \"The file %(f)s does not exist.\" % {\"f\": raspact.util.escape(handler.path)})\n else:\n raspact.util.sendFile(assetFile, handler)\n", "sub_path": "raspact/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 6830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "http.server.server", "line_number": 12, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 12, "usage_type": "name"}, {"api_name": "raspact.util.util.readResource", "line_number": 19, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 19, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 19, "usage_type": "name"}, {"api_name": "raspact.util.util.readResource", "line_number": 20, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 20, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 20, "usage_type": "name"}, {"api_name": "raspact.util.routes.Routes", "line_number": 28, "usage_type": "call"}, {"api_name": "raspact.util.routes", "line_number": 28, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 28, "usage_type": "name"}, {"api_name": "http.server.server.BaseHTTPRequestHandler.__init__", "line_number": 36, "usage_type": "call"}, {"api_name": "http.server.server", "line_number": 36, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 36, "usage_type": "name"}, {"api_name": "cgi.parse_header", "line_number": 46, "usage_type": "call"}, {"api_name": "cgi.parse_multipart", "line_number": 48, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 51, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 51, "usage_type": "attribute"}, {"api_name": "raspact.util.util.log", "line_number": 100, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 100, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 100, "usage_type": "name"}, {"api_name": "raspact.util.util.log", "line_number": 102, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 102, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 102, "usage_type": "name"}, {"api_name": "http.server.cookies.SimpleCookie", "line_number": 108, "usage_type": "call"}, {"api_name": "http.server.cookies", "line_number": 108, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 108, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 109, "usage_type": "call"}, {"api_name": "raspact.util.util.log", "line_number": 110, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 110, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 110, "usage_type": "name"}, {"api_name": "http.server.cookies.SimpleCookie", "line_number": 116, "usage_type": "call"}, {"api_name": "http.server.cookies", "line_number": 116, "usage_type": "attribute"}, {"api_name": "http.server", "line_number": 116, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "raspact.util.util", "line_number": 178, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 178, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "raspact.util.util.escape", "line_number": 181, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 181, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 181, "usage_type": "name"}, {"api_name": "raspact.util.util.sendFile", "line_number": 183, "usage_type": "call"}, {"api_name": "raspact.util.util", "line_number": 183, "usage_type": "attribute"}, {"api_name": "raspact.util", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "313849999", "text": "from flask import Flask, render_template, request\nfrom evaluate import evaluate\nfrom modelconfig import encoder\nfrom modelconfig import decoder\nfrom queryandresponse import voc\nfrom greedysearchdecoder import GreedySearchDecoder\n\n\napp = Flask(__name__)\n\nencoder.eval()\ndecoder.eval()\n\n# Initialize search module\nsearcher = GreedySearchDecoder(encoder, decoder)\n\n@app.route(\"/\")\ndef home(): \n return render_template(\"home.html\") \n@app.route(\"/get\")\ndef get_bot_response(): \n try: \n userText = request.args.get('msg') \n output_words = evaluate(encoder, decoder, searcher, voc, userText.lower()) \n output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')] \n return str(' '.join(map(str, output_words)))\n except KeyError:\n return str(\"Again please\")\nif __name__ == \"__main__\": \n app.run(host='172.16.7.43')\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "modelconfig.encoder.eval", "line_number": 11, "usage_type": "call"}, {"api_name": "modelconfig.encoder", "line_number": 11, "usage_type": "name"}, {"api_name": "modelconfig.decoder.eval", "line_number": 12, "usage_type": "call"}, {"api_name": "modelconfig.decoder", "line_number": 12, "usage_type": "name"}, {"api_name": "greedysearchdecoder.GreedySearchDecoder", "line_number": 15, "usage_type": "call"}, {"api_name": "modelconfig.encoder", "line_number": 15, "usage_type": "argument"}, {"api_name": "modelconfig.decoder", "line_number": 15, "usage_type": "argument"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "evaluate.evaluate", "line_number": 24, "usage_type": "call"}, {"api_name": "modelconfig.encoder", "line_number": 24, "usage_type": "argument"}, {"api_name": "modelconfig.decoder", "line_number": 24, "usage_type": "argument"}, {"api_name": "queryandresponse.voc", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "464191184", "text": "import time\nimport pika\n\nauth = pika.PlainCredentials('guest', '123')\nparas = pika.ConnectionParameters(host='127.0.0.1',\n port=5672,\n virtual_host='/',\n credentials=auth,\n )\nconn = pika.BlockingConnection(paras)\nchannel = conn.channel()\n\nchannel.queue_declare('taskqueue_demo_test', durable=True)\n\ndef callback(ch, method, properties, body):\n time.sleep(1)\n print(body.decode())\n ch.basic_ack(delivery_tag=method.delivery_tag)\n\nchannel.basic_qos(prefetch_count=1)\nchannel.basic_consume('taskqueue_demo_test', callback)\nchannel.start_consuming()\n", "sub_path": "week05/practice/rabbitmq/taskqueue_test/mq_taskqueue_subscribe_test.py", "file_name": "mq_taskqueue_subscribe_test.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pika.PlainCredentials", "line_number": 4, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 5, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "635056923", "text": "\nimport os\nimport math\nimport random\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport cv2\nimport rospkg \n\nslim = tf.contrib.slim\n\nrospack = rospkg.RosPack()\n# get the file path for rospy_tutorials\npa = rospack.get_path('object_detector_ssd_tf_ros')\n\nckpt_filename = pa+'/ssd/model/ssd_300_vgg.ckpt'\n\nfrom ssd import ssd_vgg_300, ssd_common, np_methods, ssd_vgg_preprocessing\n\n \nclass ssdWrapper():\n def __init__(self , config, net_shape = (300, 300), data_format = 'NHWC', ckpt_filename = ckpt_filename):\n self.isess = tf.InteractiveSession(config=config)\n # Input placeholder.\n self.net_shape = net_shape\n self.img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))\n # Evaluation pre-processing: resize to SSD net shape.\n image_pre, _, _, self.bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(\n self.img_input, None, None, net_shape, data_format, resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)\n self.image_4d = tf.expand_dims(image_pre, 0)\n\n # Define the SSD model.\n \n reuse = True if 'ssd_net' in locals() else None\n ssd_net = ssd_vgg_300.SSDNet()\n with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):\n self.predictions, self.localisations, _, _ = ssd_net.net(self.image_4d, is_training=False, reuse=reuse)\n\n # Restore SSD model.\n self.isess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n print(ckpt_filename)\n saver.restore(self.isess, ckpt_filename)\n\n # SSD default anchor boxes.\n self.ssd_anchors = ssd_net.anchors(net_shape)\n\n\n\n\n # Main image processing routine.\n def process_image(self, img, select_threshold=0.5, nms_threshold=.45):\n # Run SSD network.\n _, rpredictions, rlocalisations, rbbox_img = self.isess.run([self.image_4d, self.predictions, self.localisations, self.bbox_img], feed_dict={self.img_input: img})\n \n # Get classes and bboxes from the net outputs.\n rclasses, rscores, rbboxes , lprobs = np_methods.ssd_bboxes_select(\n rpredictions, rlocalisations, self.ssd_anchors,\n select_threshold=select_threshold, img_shape=self.net_shape, num_classes=21, decode=True)\n #print(lprobs.shape)\n rbboxes = np_methods.bboxes_clip(rbbox_img, rbboxes)\n rclasses, rscores, rbboxes, rprobs = np_methods.bboxes_sort(rclasses, rscores, rbboxes,lprobs, top_k=400)\n rclasses, rscores, rbboxes, rprobs = np_methods.bboxes_nms(rclasses, rscores, rbboxes,rprobs, nms_threshold=nms_threshold)\n #print(rprobs)\n # Resize bboxes to original image shape. Note: useless for Resize.WARP!\n rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)\n return rclasses, rscores, rbboxes, rprobs\n\n\n\n\n\n\n\n\n", "sub_path": "object_detector_ssd_tf_ros/ssd/ssd_wrapper.py", "file_name": "ssd_wrapper.py", "file_ext": "py", "file_size_in_byte": 2843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tensorflow.contrib", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rospkg.RosPack", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.InteractiveSession", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ssd.ssd_vgg_preprocessing.preprocess_for_eval", "line_number": 29, "usage_type": "call"}, {"api_name": "ssd.ssd_vgg_preprocessing", "line_number": 29, "usage_type": "name"}, {"api_name": "ssd.ssd_vgg_preprocessing.Resize", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ssd.ssd_vgg_preprocessing", "line_number": 30, "usage_type": "name"}, {"api_name": "tensorflow.expand_dims", "line_number": 31, "usage_type": "call"}, {"api_name": "ssd.ssd_vgg_300.SSDNet", "line_number": 36, "usage_type": "call"}, {"api_name": "ssd.ssd_vgg_300", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 42, "usage_type": "attribute"}, {"api_name": "ssd.np_methods.ssd_bboxes_select", "line_number": 58, "usage_type": "call"}, {"api_name": "ssd.np_methods", "line_number": 58, "usage_type": "name"}, {"api_name": "ssd.np_methods.bboxes_clip", "line_number": 62, "usage_type": "call"}, {"api_name": "ssd.np_methods", "line_number": 62, "usage_type": "name"}, {"api_name": "ssd.np_methods.bboxes_sort", "line_number": 63, "usage_type": "call"}, {"api_name": "ssd.np_methods", "line_number": 63, "usage_type": "name"}, {"api_name": "ssd.np_methods.bboxes_nms", "line_number": 64, "usage_type": "call"}, {"api_name": "ssd.np_methods", "line_number": 64, "usage_type": "name"}, {"api_name": "ssd.np_methods.bboxes_resize", "line_number": 67, "usage_type": "call"}, {"api_name": "ssd.np_methods", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "322051354", "text": "'''\nCreated on Jul 28, 2016\n\n@author: jvazquez\n'''\nimport logging\n\nfrom os import getenv\nfrom os.path import exists\n\nfrom utils.helpers.log import setup_logging\n\nfrom configparser import RawConfigParser\n\nlogger = logging.getLogger(__name__)\n\n\nclass ConfiguratorReader(object):\n \"\"\"\n ConfiguratorReader is a simple wrapper for the\n RawConfigParser that we use.\n The only purpose of this object, is to obtain the\n configured RawConfigParser and help the client\n to obtain the selected environment.\n An environment is a configuration option that\n is used in the application, for example\n testing, development, production.\n The values between those environments may by different.\n \"\"\"\n\n def __init__(self):\n self.selected_env = getenv(\"MODE\", None)\n self._parser = None\n\n def get_config_parser(self, config_filename=None):\n logger.debug(\"We open {}\".format(config_filename))\n\n if config_filename is None:\n logger.warning(\"Trying to load configuration path \"\n \"from environmental \"\n \"variable CONFIGURATOR\")\n config_filename = getenv(\"CONFIGURATOR\", None)\n if config_filename is None:\n raise IOError(\"No configuration filename detected\")\n\n if exists(config_filename) is False:\n raise IOError(\"Unexistent configuration file \"\n \"selected\")\n\n self._parser = RawConfigParser()\n self._parser.read(config_filename)\n return self._parser\n\n def parser_env_detection(self):\n if self.selected_env is None:\n return self._parser.get(\"app\", \"default_environment_mode\")\n return self.selected_env\n", "sub_path": "utils/helpers/configuration_reader.py", "file_name": "configuration_reader.py", "file_ext": "py", "file_size_in_byte": 1735, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "configparser.RawConfigParser", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "196912479", "text": "# import numpy as np\n# from scipy import signal, misc\n# import matplotlib.pyplot as plt\n# from scipy import ndimage\n#\n# bin8 = lambda x : ''.join(reversed( [str((x>>i) & 1) for i in range(8)]))\n#\n# def BitPlane_Slice(value):\n# c = np.zeros(8)\n# n = 7\n# bits = bin8(value)\n#\n# for i in range(8):\n# p = pow(2,n)\n# c[i] = p * int(bits[i])\n# n = n - 1\n# return c\n#\n# lena = misc.imread('./ImageData/lena_w.bmp')\n# row, col = lena.shape\n#\n# NumBitPlanes = 8\n# image_bitplanes = np.ndarray(shape=(NumBitPlanes, row, col), dtype=np.uint8)\n# image_restore = np.zeros(shape=(row,col), dtype=np.uint8)\n#\n# for y in range(col):\n# for x in range(row):\n# value = lena[y, x]\n# c = BitPlane_Slice(value)\n#\n# for i in range(NumBitPlanes):\n# image_bitplanes[i,y,x] = c[i]\n#\n# for i in range(NumBitPlanes):\n# plt.subplot(2,4,i+1), plt.imshow(image_bitplanes[i, :, :]), plt.gray(), plt.axis('off')\n# plt.show()\n# # copyrights watermark\n#\n# plt.subplot(121), plt.imshow(image_bitplanes[7]), plt.gray(), plt.axis('off')\n# plt.subplot(122), plt.imshow(lena), plt.gray(), plt.axis('off')\n# plt.show()\n\nimport numpy as np\nfrom scipy import signal, misc\nimport matplotlib.pyplot as plt\nfrom scipy import ndimage\n\nbin8 = lambda x : ''.join(reversed( [str((x >> i) & 1) for i in range(8)] ) )\ndef BitPlane_Slice(value):\n c = np.zeros(8)\n n = 7\n bits = bin8(value)\n for i in range(8):\n p = pow(2, n)\n c[i] = p * int(bits[i])\n n = n - 1\n return c\n\nlena_copyright = misc.imread('lena_copyright.bmp')\nrow, col = lena_copyright.shape\nNum_Bitplanes = 8\nImage_BitPlanes = np.ndarray(shape=(Num_Bitplanes, row, col), dtype=np.uint8)\nImage_restore = np.zeros(shape=(row, col), dtype=np.uint8)\n\nfor y in range(col):\n for x in range(row):\n value = lena_copyright[y, x]\n c = BitPlane_Slice(value)\n\n for i in range(Num_Bitplanes):\n Image_BitPlanes[i, y, x] = c[i]\n\nfor i in range(Num_Bitplanes):\n img = Image_BitPlanes[i, :, :]\n Image_restore = Image_restore + img\n plt.subplot(2, 4, i+1), plt.gray(), plt.axis('off')\n plt.imshow(img)\n if i==7:\n cpright = img\n\nplt.show()\n\n\nplt.subplot(131), plt.imshow(lena_copyright), plt.axis('off'), plt.gray()\nplt.subplot(132), plt.imshow(cpright), plt.axis('off'), plt.gray()\nplt.subplot(133), plt.imshow(np.uint8(Image_restore)), plt.axis('off'), plt.gray()\nplt.show()\n", "sub_path": "MultimediaProgramming/ninth/extract.py", "file_name": "extract.py", "file_ext": "py", "file_size_in_byte": 2450, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.misc.imread", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "622491233", "text": "# -*- coding:utf-8 -*-\n#\n# Copyright (C) 2019 The Android Open Source Project\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.\n\n\"\"\"Unittests for the project.py module.\"\"\"\n\nfrom __future__ import print_function\n\nimport contextlib\nimport os\nimport shutil\nimport subprocess\nimport tempfile\nimport unittest\n\nimport git_config\nimport project\n\n\n@contextlib.contextmanager\ndef TempGitTree():\n \"\"\"Create a new empty git checkout for testing.\"\"\"\n # TODO(vapier): Convert this to tempfile.TemporaryDirectory once we drop\n # Python 2 support entirely.\n try:\n tempdir = tempfile.mkdtemp(prefix='repo-tests')\n subprocess.check_call(['git', 'init'], cwd=tempdir)\n yield tempdir\n finally:\n shutil.rmtree(tempdir)\n\n\nclass RepoHookShebang(unittest.TestCase):\n \"\"\"Check shebang parsing in RepoHook.\"\"\"\n\n def test_no_shebang(self):\n \"\"\"Lines w/out shebangs should be rejected.\"\"\"\n DATA = (\n '',\n '# -*- coding:utf-8 -*-\\n',\n '#\\n# foo\\n',\n '# Bad shebang in script\\n#!/foo\\n'\n )\n for data in DATA:\n self.assertIsNone(project.RepoHook._ExtractInterpFromShebang(data))\n\n def test_direct_interp(self):\n \"\"\"Lines whose shebang points directly to the interpreter.\"\"\"\n DATA = (\n ('#!/foo', '/foo'),\n ('#! /foo', '/foo'),\n ('#!/bin/foo ', '/bin/foo'),\n ('#! /usr/foo ', '/usr/foo'),\n ('#! /usr/foo -args', '/usr/foo'),\n )\n for shebang, interp in DATA:\n self.assertEqual(project.RepoHook._ExtractInterpFromShebang(shebang),\n interp)\n\n def test_env_interp(self):\n \"\"\"Lines whose shebang launches through `env`.\"\"\"\n DATA = (\n ('#!/usr/bin/env foo', 'foo'),\n ('#!/bin/env foo', 'foo'),\n ('#! /bin/env /bin/foo ', '/bin/foo'),\n )\n for shebang, interp in DATA:\n self.assertEqual(project.RepoHook._ExtractInterpFromShebang(shebang),\n interp)\n\n\nclass FakeProject(object):\n \"\"\"A fake for Project for basic functionality.\"\"\"\n\n def __init__(self, worktree):\n self.worktree = worktree\n self.gitdir = os.path.join(worktree, '.git')\n self.name = 'fakeproject'\n self.work_git = project.Project._GitGetByExec(\n self, bare=False, gitdir=self.gitdir)\n self.bare_git = project.Project._GitGetByExec(\n self, bare=True, gitdir=self.gitdir)\n self.config = git_config.GitConfig.ForRepository(gitdir=self.gitdir)\n\n\nclass ReviewableBranchTests(unittest.TestCase):\n \"\"\"Check ReviewableBranch behavior.\"\"\"\n\n def test_smoke(self):\n \"\"\"A quick run through everything.\"\"\"\n with TempGitTree() as tempdir:\n fakeproj = FakeProject(tempdir)\n\n # Generate some commits.\n with open(os.path.join(tempdir, 'readme'), 'w') as fp:\n fp.write('txt')\n fakeproj.work_git.add('readme')\n fakeproj.work_git.commit('-mAdd file')\n fakeproj.work_git.checkout('-b', 'work')\n fakeproj.work_git.rm('-f', 'readme')\n fakeproj.work_git.commit('-mDel file')\n\n # Start off with the normal details.\n rb = project.ReviewableBranch(\n fakeproj, fakeproj.config.GetBranch('work'), 'master')\n self.assertEqual('work', rb.name)\n self.assertEqual(1, len(rb.commits))\n self.assertIn('Del file', rb.commits[0])\n d = rb.unabbrev_commits\n self.assertEqual(1, len(d))\n short, long = next(iter(d.items()))\n self.assertTrue(long.startswith(short))\n self.assertTrue(rb.base_exists)\n # Hard to assert anything useful about this.\n self.assertTrue(rb.date)\n\n # Now delete the tracking branch!\n fakeproj.work_git.branch('-D', 'master')\n rb = project.ReviewableBranch(\n fakeproj, fakeproj.config.GetBranch('work'), 'master')\n self.assertEqual(0, len(rb.commits))\n self.assertFalse(rb.base_exists)\n # Hard to assert anything useful about this.\n self.assertTrue(rb.date)\n", "sub_path": "tests/test_project.py", "file_name": "test_project.py", "file_ext": "py", "file_size_in_byte": 4363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tempfile.mkdtemp", "line_number": 38, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 39, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 42, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 32, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 45, "usage_type": "attribute"}, {"api_name": "project.RepoHook._ExtractInterpFromShebang", "line_number": 57, "usage_type": "call"}, {"api_name": "project.RepoHook", "line_number": 57, "usage_type": "attribute"}, {"api_name": "project.RepoHook._ExtractInterpFromShebang", "line_number": 69, "usage_type": "call"}, {"api_name": "project.RepoHook", "line_number": 69, "usage_type": "attribute"}, {"api_name": "project.RepoHook._ExtractInterpFromShebang", "line_number": 80, "usage_type": "call"}, {"api_name": "project.RepoHook", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "project.Project._GitGetByExec", "line_number": 91, "usage_type": "call"}, {"api_name": "project.Project", "line_number": 91, "usage_type": "attribute"}, {"api_name": "project.Project._GitGetByExec", "line_number": 93, "usage_type": "call"}, {"api_name": "project.Project", "line_number": 93, "usage_type": "attribute"}, {"api_name": "git_config.GitConfig.ForRepository", "line_number": 95, "usage_type": "call"}, {"api_name": "git_config.GitConfig", "line_number": 95, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "project.ReviewableBranch", "line_number": 116, "usage_type": "call"}, {"api_name": "project.ReviewableBranch", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "436197108", "text": "# -*- coding: utf-8 -*-\nfrom time import time\nimport io\nimport os.path\nimport struct\nimport traceback\nfrom Crypto.Hash import SHA\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Util.strxor import strxor\nfrom Crypto.Util.number import long_to_bytes, bytes_to_long\nimport crypt\nimport prime\nimport TL\nfrom connection import MTProtoConnection\nfrom message import MTProtoMessage, MTProtoContainer\n\n\nclass MTProto(object):\n ua = \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36\"\n headers = {\"User-Agent\": ua}\n\n @staticmethod\n # Метод парсинга акканута, возвращает объект MTProtoConnection\n def parse_acc(account):\n localstorage = account['data']['localstorage']\n localstorage = {x.split(\"=\")[0]: x.split(\"=\")[1].strip('\"') for x in localstorage.split(\";\")}\n mtproto_conn = MTProtoConnection()\n for k, v in localstorage.iteritems():\n setattr(mtproto_conn, k, v)\n dc = mtproto_conn.dc\n for dc_id in range(1, 6):\n mtproto_conn.dc = dc_id\n if not mtproto_conn.auth_key or not mtproto_conn.server_salt:\n mtproto_conn.auth_key = None\n mtproto_conn.server_salt = None\n\n mtproto_conn.dc = dc\n return mtproto_conn\n\n @staticmethod\n def create_signature_message(sess, message):\n message.session_id = sess.additional['mtproto_conn'].session_id if not message.session_id else message.session_id\n\n # Если сменился аккаунт, и сообщение отправляется уже с другой сессией\n if message.session_id != sess.additional['mtproto_conn'].session_id:\n message.session_id = sess.additional['mtproto_conn'].session_id\n if isinstance(message, MTProtoContainer):\n sess.additional['mtproto_conn'].dc = message.dc\n for m in message.messages[:]:\n if m.obj == 'msgs_ack':\n message.messages.remove(m)\n continue\n m.session_id = sess.additional['mtproto_conn'].session_id\n if m.access_hash:\n # Проверяем, возможно access_hash уже для данного аккаунта\n conn_ah = sess.additional['mtproto_conn'].access_hashes.get(m.obj_id, {}).get('access_hash')\n # Если access_hash'a нет, то получаем его для этого аккаунта, по сохраненному в сообщение username\n if not conn_ah:\n username = getattr(m, 'resolve_name', None)\n if username:\n resolve_name = MTProto.method_call(sess, \"contacts.resolveUsername\", username=str(username))\n m.kwargs['peer']['access_hash'] = resolve_name['chats'][0]['pflags']['access_hash']\n m.seqno = MTProto.generate_seqno(sess.additional['mtproto_conn'], m)\n m.message_id = MTProto.generate_message_id(sess.additional['mtproto_conn'], m)\n for m in sess.additional['mtproto_conn'].pending_messages:\n if getattr(m, 'update_message', False):\n MTProto.generate_seqno(sess.additional['mtproto_conn'], m)\n MTProto.generate_message_id(sess.additional['mtproto_conn'], m)\n message.add_message(m)\n message.seqno = MTProto.generate_seqno(sess.additional['mtproto_conn'], message)\n message.message_id = MTProto.generate_message_id(sess.additional['mtproto_conn'], message)\n\n # Если в сообщении содержится access_hash, то нужно сохранить данные запрашиваемого чата, они могут понадобиться,\n # для следующего аккаунта, если текущий аккаунт заблокируется\n if getattr(message, 'access_hash', None):\n message_ah = getattr(message, 'access_hash', None)\n conn_ah = sess.additional['mtproto_conn'].access_hashes.get(message.obj_id, {}).get('access_hash')\n if not conn_ah:\n # Повторно резолвим этот чат\n username = getattr(message, 'resolve_name', None)\n if username:\n resolve_name = MTProto.method_call(sess, \"contacts.resolveUsername\", username=str(username))\n message.kwargs['peer']['access_hash'] = resolve_name['chats'][0]['pflags']['access_hash']\n sess.additional['mtproto_conn'].access_hashes[message.obj_id] = {'access_hash': resolve_name['chats'][0]['pflags']['access_hash'], 'name': username}\n\n if message_ah != conn_ah:\n message_ah = conn_ah\n\n if not sess.additional['mtproto_conn'].auth_key or not sess.additional['mtproto_conn'].server_salt:\n if not message.message_id:\n MTProto.generate_message_id(sess.additional['mtproto_conn'], message)\n auth_key_id = struct.pack(\" q:\n (p, q) = (q, p)\n assert p * q == pq and p < q\n\n p_bytes = long_to_bytes(p)\n q_bytes = long_to_bytes(q)\n f = open(os.path.join(os.path.dirname(__file__), \"rsa.pub\"))\n key = RSA.importKey(f.read())\n\n new_nonce = os.urandom(32)\n\n data = TL.serialize_obj('p_q_inner_data',\n pq=pq_bytes,\n p=p_bytes,\n q=q_bytes,\n nonce=nonce,\n server_nonce=server_nonce,\n new_nonce=new_nonce,\n mtproto=True)\n\n sha_digest = SHA.new(data).digest()\n random_bytes = os.urandom(255 - len(data) - len(sha_digest))\n to_encrypt = sha_digest + data + random_bytes\n encrypted_data = key.encrypt(to_encrypt, 0)[0]\n\n message = MTProtoMessage(method='req_DH_params', nonce=nonce, server_nonce=server_nonce, p=p_bytes, q=q_bytes,\n public_key_fingerprint=public_key_fingerprint, encrypted_data=encrypted_data,\n mtproto=True, plain_message=True)\n\n MTProto.create_signature_message(sess, message)\n response = MTProto.send_message(sess, message, create_signature=False)\n server_dh_params = MTProto.parse_response(sess, response, mtproto_conn)\n\n assert nonce == server_dh_params['nonce']\n assert server_nonce == server_dh_params['server_nonce']\n\n encrypted_answer = server_dh_params['encrypted_answer']\n\n tmp_aes_key = SHA.new(new_nonce + server_nonce).digest() + SHA.new(server_nonce + new_nonce).digest()[0:12]\n tmp_aes_iv = SHA.new(server_nonce + new_nonce).digest()[12:20] + SHA.new(\n new_nonce + new_nonce).digest() + new_nonce[0:4]\n\n answer_with_hash = crypt.ige_decrypt(encrypted_answer, tmp_aes_key, tmp_aes_iv)\n\n answer_hash = answer_with_hash[:20]\n answer = answer_with_hash[20:]\n\n server_DH_inner_data = TL.deserialize(io.BytesIO(answer))\n assert nonce == server_DH_inner_data['nonce']\n assert server_nonce == server_DH_inner_data['server_nonce']\n dh_prime_str = server_DH_inner_data['dh_prime']\n g = server_DH_inner_data['g']\n g_a_str = server_DH_inner_data['g_a']\n server_time = server_DH_inner_data['server_time']\n mtproto_conn.timedelta = server_time - time()\n\n dh_prime = bytes_to_long(dh_prime_str)\n g_a = bytes_to_long(g_a_str)\n\n assert prime.isprime(dh_prime)\n retry_id = 0\n b_str = os.urandom(256)\n b = bytes_to_long(b_str)\n g_b = pow(g, b, dh_prime)\n\n g_b_str = long_to_bytes(g_b)\n\n data = TL.serialize_obj('client_DH_inner_data',\n nonce=nonce,\n server_nonce=server_nonce,\n retry_id=retry_id,\n g_b=g_b_str, mtproto=True)\n data_with_sha = SHA.new(data).digest() + data\n data_with_sha_padded = data_with_sha + os.urandom(-len(data_with_sha) % 16)\n encrypted_data = crypt.ige_encrypt(data_with_sha_padded, tmp_aes_key, tmp_aes_iv)\n\n message = MTProtoMessage(method='set_client_DH_params', nonce=nonce, server_nonce=server_nonce,\n encrypted_data=encrypted_data, mtproto=True, plain_message=True)\n MTProto.create_signature_message(sess, message)\n response = MTProto.send_message(sess, message, create_signature=False)\n Set_client_DH_params_answer = MTProto.parse_response(sess, response, mtproto_conn)\n\n # print Set_client_DH_params_answer\n auth_key = pow(g_a, b, dh_prime)\n auth_key_str = long_to_bytes(auth_key)\n auth_key_sha = SHA.new(auth_key_str).digest()\n auth_key_aux_hash = auth_key_sha[:8]\n\n new_nonce_hash1 = SHA.new(new_nonce + b'\\x01' + auth_key_aux_hash).digest()[-16:]\n new_nonce_hash2 = SHA.new(new_nonce + b'\\x02' + auth_key_aux_hash).digest()[-16:]\n new_nonce_hash3 = SHA.new(new_nonce + b'\\x03' + auth_key_aux_hash).digest()[-16:]\n\n assert Set_client_DH_params_answer['nonce'] == nonce\n assert Set_client_DH_params_answer['server_nonce'] == server_nonce\n\n if Set_client_DH_params_answer.name == 'dh_gen_ok':\n assert Set_client_DH_params_answer['new_nonce_hash1'] == new_nonce_hash1\n\n mtproto_conn.server_salt = strxor(new_nonce[0:8], server_nonce[0:8]).encode('hex')\n mtproto_conn.auth_key = auth_key_str.encode('hex')\n return \"Auth Ok\"\n elif Set_client_DH_params_answer.name == 'dh_gen_retry':\n assert Set_client_DH_params_answer['new_nonce_hash2'] == new_nonce_hash2\n elif Set_client_DH_params_answer.name == 'dh_gen_fail':\n assert Set_client_DH_params_answer['new_nonce_hash3'] == new_nonce_hash3\n raise Exception(\"Auth Failed\")\n else:\n raise Exception(\"Response Error\")\n\n @staticmethod\n def parse_response(sess, response, mtproto_conn):\n response_data = response.content\n auth_key_id = response_data[0:8]\n if auth_key_id == b\"\\x00\" * 8:\n (message_id, message_length) = struct.unpack(\"qI\", response_data[8:20])\n message_id = struct.pack(' 1:\n container = MTProtoContainer(content_related=False, dc=new_dc)\n\n # Добавляем сообщения в контейнер из pending_messages\n for m in sess.additional['mtproto_conn'].pending_messages:\n # Заново генерируем seqno и message_id если требуется, например такие сооб-я как bad_msg_notification\n if getattr(m, 'update_message', False):\n m.update_message = False\n MTProto.generate_message_id(sess.additional['mtproto_conn'], m)\n MTProto.generate_seqno(sess.additional['mtproto_conn'], m)\n container.add_message(m)\n # Очищаем pending_messages, что бы не отправлять сообщения повторно\n sess.additional['mtproto_conn'].pending_messages = []\n\n # Если стадия логина, то подписываем сообщения, т.к. не вызывается метод create_signature\n if login:\n # Подписываем сообщения\n MTProto.create_signature_message(sess, container)\n response = MTProto.send_message(sess, container, create_signature=False)\n else:\n response = MTProto.send_message(sess, container)\n\n else:\n # Если стадия логина, то предварительно подписываем сообщения, т.к. не вызывается метод create_signature\n if login:\n # Подписываем сообщения\n MTProto.create_signature_message(sess, query_message)\n response = MTProto.send_message(sess, query_message)\n else:\n response = MTProto.send_message(sess, query_message, create_signature=True)\n\n parsed_response = MTProto.parse_response(sess, response, sess.additional['mtproto_conn'])\n MTProto.process_message(sess, parsed_response)\n\n result = MTProto.get_result_by_message_id(query_message, sess.additional['mtproto_conn'])\n\n if result:\n sess.save()\n return result['result']\n # print 'no result'\n\n @staticmethod\n def check_user_migrate(result, mtproto_conn, req_msg):\n if 'USER_MIGRATE_' in result.get('result', {}).get('error_message', ''):\n mtproto_conn.dc = result.get('result', {}).get('error_message').split('_')[-1]\n req_msg.update_message = True\n mtproto_conn.resend_messages.append(req_msg)\n return True\n\n @staticmethod\n def get_result_by_message_id(message, mtproto_conn):\n for received_message in mtproto_conn.received_messages:\n if received_message.get('messages', []):\n for cont_received_message in received_message.get('messages'):\n # Сохраняем access_hash'ы для аккаунта\n for chat in cont_received_message.get('result', {}).get('result', {}).get('chats', []):\n pflags = chat['pflags']\n if pflags.get('access_hash', None) and pflags.get('username', None):\n mtproto_conn.access_hashes[chat['id']] = {'access_hash': pflags['access_hash'], 'name': pflags['username']}\n\n if message.message_id == cont_received_message['result'].get('req_msg_id', None):\n if not MTProto.check_user_migrate(cont_received_message['result'], mtproto_conn, message):\n MTProto.remove_from_sending_messages(message.message_id, mtproto_conn)\n mtproto_conn.received_messages = []\n return cont_received_message['result']\n\n else:\n if received_message.get('req_msg_id', None) == message.message_id:\n # Сохраняем access_hash'ы для аккаунта\n for chat in received_message.get('result', {}).get('chats', []):\n pflags = chat['pflags']\n if pflags.get('access_hash', None) and pflags.get('username', None):\n mtproto_conn.access_hashes[chat['id']] = {'access_hash': pflags['access_hash'],\n 'name': pflags['username']}\n\n if not MTProto.check_user_migrate(received_message['result'], mtproto_conn, message):\n MTProto.remove_from_sending_messages(message.message_id, mtproto_conn)\n mtproto_conn.received_messages = []\n return received_message\n\n @staticmethod\n def remove_from_sending_messages(message_id, mtproto_conn):\n for sending_message in mtproto_conn.sending_messages[:]:\n index = mtproto_conn.sending_messages.index(sending_message)\n if sending_message.message_id == message_id:\n mtproto_conn.sending_messages.remove(sending_message)\n continue\n if isinstance(sending_message, MTProtoContainer):\n for cont_sending_message in sending_message.messages[:]:\n if cont_sending_message.obj == 'msgs_ack':\n mtproto_conn.sending_messages[index].messages.remove(cont_sending_message)\n continue\n elif cont_sending_message.method == 'http_wait':\n mtproto_conn.sending_messages[index].messages.remove(cont_sending_message)\n continue\n elif cont_sending_message.message_id == message_id:\n mtproto_conn.sending_messages[index].messages.remove(cont_sending_message)\n continue\n\n if not sending_message.messages:\n mtproto_conn.sending_messages.remove(sending_message)\n continue\n", "sub_path": "api_telegram/mtproto.py", "file_name": "mtproto.py", "file_ext": "py", "file_size_in_byte": 27587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "connection.MTProtoConnection", "line_number": 27, "usage_type": "call"}, {"api_name": "message.session_id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "message.session_id", "line_number": 45, "usage_type": "attribute"}, {"api_name": "message.session_id", "line_number": 46, "usage_type": "attribute"}, {"api_name": "message.MTProtoContainer", "line_number": 47, "usage_type": "argument"}, {"api_name": "message.dc", "line_number": 48, "usage_type": "attribute"}, {"api_name": "message.messages", "line_number": 49, "usage_type": "attribute"}, {"api_name": "message.messages.remove", "line_number": 51, "usage_type": "call"}, {"api_name": "message.messages", "line_number": 51, "usage_type": "attribute"}, {"api_name": "message.add_message", "line_number": 69, "usage_type": "call"}, {"api_name": "message.seqno", "line_number": 70, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 71, "usage_type": "attribute"}, {"api_name": "message.obj_id", "line_number": 77, "usage_type": "attribute"}, {"api_name": "message.kwargs", "line_number": 83, "usage_type": "attribute"}, {"api_name": "message.obj_id", "line_number": 84, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 90, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 92, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 93, "usage_type": "call"}, {"api_name": "message.serialize_message", "line_number": 93, "usage_type": "attribute"}, {"api_name": "message.signed_message", "line_number": 95, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 95, "usage_type": "call"}, {"api_name": "message.message_id", "line_number": 95, "usage_type": "attribute"}, {"api_name": "message.serialize_message", "line_number": 95, "usage_type": "attribute"}, {"api_name": "message.seqno", "line_number": 98, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 100, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 105, "usage_type": "call"}, {"api_name": "message.message_id", "line_number": 105, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 106, "usage_type": "call"}, {"api_name": "message.seqno", "line_number": 106, "usage_type": "attribute"}, {"api_name": "message.serialize_message", "line_number": 106, "usage_type": "attribute"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 107, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.urandom", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "name"}, {"api_name": "message.signed_message", "line_number": 110, "usage_type": "attribute"}, {"api_name": "crypt.ige_encrypt", "line_number": 111, "usage_type": "call"}, {"api_name": "message.seqno", "line_number": 112, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 117, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "message.message_id", "line_number": 118, "usage_type": "attribute"}, {"api_name": "message.content_related", "line_number": 123, "usage_type": "attribute"}, {"api_name": "message.seqno", "line_number": 127, "usage_type": "attribute"}, {"api_name": "message.seqno", "line_number": 128, "usage_type": "attribute"}, {"api_name": "message.signed_message", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.urandom", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "name"}, {"api_name": "message.MTProtoMessage", "line_number": 158, "usage_type": "call"}, {"api_name": "Crypto.Util.number.bytes_to_long", "line_number": 169, "usage_type": "call"}, {"api_name": "prime.primefactors", "line_number": 171, "usage_type": "call"}, {"api_name": "Crypto.Util.number.long_to_bytes", "line_number": 176, "usage_type": "call"}, {"api_name": "Crypto.Util.number.long_to_bytes", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 178, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 178, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 179, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 179, "usage_type": "name"}, {"api_name": "os.path.urandom", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "name"}, {"api_name": "TL.serialize_obj", "line_number": 183, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 192, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 192, "usage_type": "name"}, {"api_name": "os.path.urandom", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "name"}, {"api_name": "message.MTProtoMessage", "line_number": 197, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 210, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 210, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 211, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 211, "usage_type": "name"}, {"api_name": "crypt.ige_decrypt", "line_number": 214, "usage_type": "call"}, {"api_name": "TL.deserialize", "line_number": 219, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 219, "usage_type": "call"}, {"api_name": "time.time", "line_number": 226, "usage_type": "call"}, {"api_name": "Crypto.Util.number.bytes_to_long", "line_number": 228, "usage_type": "call"}, {"api_name": "Crypto.Util.number.bytes_to_long", "line_number": 229, "usage_type": "call"}, {"api_name": "prime.isprime", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.urandom", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "name"}, {"api_name": "Crypto.Util.number.bytes_to_long", "line_number": 234, "usage_type": "call"}, {"api_name": "Crypto.Util.number.long_to_bytes", "line_number": 237, "usage_type": "call"}, {"api_name": "TL.serialize_obj", "line_number": 239, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 244, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 244, "usage_type": "name"}, {"api_name": "os.path.urandom", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "name"}, {"api_name": "crypt.ige_encrypt", "line_number": 246, "usage_type": "call"}, {"api_name": "message.MTProtoMessage", "line_number": 248, "usage_type": "call"}, {"api_name": "Crypto.Util.number.long_to_bytes", "line_number": 256, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 257, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 257, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 260, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 260, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 261, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 261, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 262, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 262, "usage_type": "name"}, {"api_name": "Crypto.Util.strxor.strxor", "line_number": 270, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 286, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 287, "usage_type": "call"}, {"api_name": "crypt.ige_decrypt", "line_number": 296, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 301, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 302, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 303, "usage_type": "call"}, {"api_name": "TL.deserialize", "line_number": 308, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 308, "usage_type": "call"}, {"api_name": "message.MTProtoMessage", "line_number": 319, "usage_type": "call"}, {"api_name": "message.MTProtoContainer", "line_number": 329, "usage_type": "argument"}, {"api_name": "struct.pack", "line_number": 382, "usage_type": "call"}, {"api_name": "message.MTProtoContainer", "line_number": 387, "usage_type": "argument"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 414, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 414, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 415, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 415, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 416, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 416, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 417, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 417, "usage_type": "name"}, {"api_name": "message.MTProtoMessage", "line_number": 427, "usage_type": "call"}, {"api_name": "message.MTProtoMessage", "line_number": 460, "usage_type": "call"}, {"api_name": "message.MTProtoContainer", "line_number": 468, "usage_type": "call"}, {"api_name": "message.message_id", "line_number": 527, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 529, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 534, "usage_type": "attribute"}, {"api_name": "message.message_id", "line_number": 543, "usage_type": "attribute"}, {"api_name": "message.MTProtoContainer", "line_number": 554, "usage_type": "argument"}]} +{"seq_id": "529498372", "text": "from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n path('', views.index, name='index'), \n path('checkout', views.checkout, name='checkout'), \n path('cart', views.cart, name='cart'), \n path('products', views.products, name='products'), \n path('search', views.search, name='search'), \n path('products/', views.detailProduct, name='detailProduct'), \n path('category/', views.detailCate, name='detailCate'), \n path('branch/', views.detailBranch, name='detailBranch'), \n path('getListCommentByRateApi', views.getListCommentByRateApi, name='getListCommentByRateApi'),\n path('addToCartAPI', views.addToCartAPI, name='addToCartAPI'), \n path('getAmountItemApi', views.getAmountItemApi, name='getAmountItemApi'),\n path('deleteItemInCartApi', views.deleteItemInCartApi, name='deleteItemInCartApi'),\n path('updateCartApi', views.updateCartApi, name='updateCartApi'), \n path('checkoutApi', views.checkoutApi, name='checkoutApi'),\n path('login/', views.login, name='login'),\n path('purchase', views.purchase, name='purchase'),\n path('address', views.address, name='purchase'),\n path('infomationUser', views.infomationUser, name='infomationUser'),\n path('logout', views.logout, name='logout'),\n path('uploadFileApi', views.uploadFileApi, name='uploadFileApi'),\n path('registerApi', views.registerApi, name='registerApi'),\n path('loginApi', views.loginApi, name='loginApi'),\n path('forgetPassApi', views.forgetPassApi, name='forgetPassApi'),\n path('pageProductApi', views.pageProductApi, name='pageProductApi'),\n path('soldproductsApi', views.soldproductsApi, name='soldproductsApi'),\n path('hotproductsApi', views.hotproductsApi, name='hotproductsApi'),\n path('productsOrderByApi', views.productsOrderByApi, name='productsOrderByApi'),\n \n path('soldproductsSearchApi', views.soldproductsSearchApi, name='soldproductsSearchApi'),\n path('hotproductsSearchApi', views.hotproductsSearchApi, name='hotproductsSearchApi'),\n path('productsOrderBySearchApi', views.productsOrderBySearchApi, name='productsOrderBySearchApi'),\n\n\n path('categoryApi', views.categoryApi, name='categoryApi'),\n path('changeAdressApi', views.changeAdressApi, name='changeAdressApi') ,\n path('addAddressApi', views.addAddressApi, name='addAddressApi') ,\n path('updateProfileApi', views.updateProfileApi, name='updateProfileApi') ,\n path('getAdressApi', views.getAdressApi, name='getAdressApi'),\n path('updateAddressApi', views.updateAddressApi, name='updateAddressApi'),\n path('getAllBillApi', views.getAllBillApi, name='getAllBillApi'),\n path('getDetailOrderApi', views.getDetailOrderApi, name='getDetailOrderApi'),\n path('updateOrderApi', views.updateOrderApi, name='updateOrderApi'),\n path('voteApi', views.voteApi, name='voteApi'),\n\n \n path('getRecommentByIdcommentApi', views.getRecommentByIdcommentApi, name='getRecommentByIdcommentApi'),\n\n] \n\n", "sub_path": "polls/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "272866345", "text": "from typing import List, Union\n\n\nclass MarkdownParser:\n \"\"\"Parse markdown text to extract code snippets\n \"\"\"\n def __init__(self, markdown: str) -> None:\n \"\"\"Constructor for markdown processor\n\n Args:\n markdown (str): The markdown code\n \"\"\"\n self._markdown = markdown\n self._lines = markdown.split('\\n')\n self._code_snippets: List[CodeSnippet] = []\n\n current_snippet: Union[List[str], None] = None\n current_language: Union[str, None] = None\n for line in self._lines:\n if current_language is None:\n if line.startswith(\"```\"):\n current_language = line[3:]\n current_snippet = []\n else:\n if current_snippet is None:\n raise Exception('current_language is set but current_snippet is None.')\n if line.startswith(\"```\"):\n self._code_snippets.append(\n CodeSnippet(\n language=current_language,\n code='\\n'.join(current_snippet)\n )\n )\n current_snippet = None\n current_language = None\n else:\n current_snippet.append(line)\n @property\n def code_snippets(self):\n \"\"\"Get the extracted code snippets\n\n Returns:\n List of code snippets\n \"\"\"\n return [cs for cs in self._code_snippets]\n\nclass CodeSnippet:\n def __init__(self, *, language: str, code: str) -> None:\n self._language = language\n self._code = code\n @property\n def language(self):\n return self._language\n @property\n def code(self):\n return self._code", "sub_path": "03-code-completion/assets/code_completion_2.py", "file_name": "code_completion_2.py", "file_ext": "py", "file_size_in_byte": 1793, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "544740121", "text": "from dataclasses import dataclass\nfrom myia.dtype import Bool, Int, Float, List, Array, pytype_to_myiatype\nfrom myia.prim.shape_inferrers import NOSHAPE as NSH # noqa: F401\nfrom myia.infer import ANYTHING as ANY # noqa: F401\n\nB = Bool\n\ni16 = Int[16]\ni32 = Int[32]\ni64 = Int[64]\n\nf16 = Float[16]\nf32 = Float[32]\nf64 = Float[64]\n\nli16 = List[Int[16]]\nli32 = List[Int[32]]\nli64 = List[Int[64]]\n\nlf16 = List[Float[16]]\nlf32 = List[Float[32]]\nlf64 = List[Float[64]]\n\nai16 = Array[Int[16]]\nai32 = Array[Int[32]]\nai64 = Array[Int[64]]\n\naf16 = Array[Float[16]]\naf32 = Array[Float[32]]\naf64 = Array[Float[64]]\n\n\n@dataclass(frozen=True)\nclass Point:\n x: i64\n y: i64\n\n def abs(self):\n return (self.x ** 2 + self.y ** 2) ** 0.5\n\n def __add__(self, other):\n return Point(self.x * other.x, self.y * other.y)\n\n\npt = pytype_to_myiatype(Point)\nlpt = List[pt]\n", "sub_path": "typ.py", "file_name": "typ.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "myia.dtype.Bool", "line_number": 6, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 8, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 9, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 10, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 12, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 13, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 14, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 16, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 16, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 17, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 17, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 18, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 18, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 20, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 20, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 21, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 21, "usage_type": "name"}, {"api_name": "myia.dtype.List", "line_number": 22, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 22, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 24, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 24, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 25, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 25, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 26, "usage_type": "name"}, {"api_name": "myia.dtype.Int", "line_number": 26, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 28, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 28, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 29, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 29, "usage_type": "name"}, {"api_name": "myia.dtype.Array", "line_number": 30, "usage_type": "name"}, {"api_name": "myia.dtype.Float", "line_number": 30, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 33, "usage_type": "call"}, {"api_name": "myia.dtype.pytype_to_myiatype", "line_number": 45, "usage_type": "call"}, {"api_name": "myia.dtype.List", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "502596013", "text": "# USAGE\n# python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --yolo yolo-coco\n\n# import the necessary packages\n\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport cv2\nimport os\nfrom moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\n\nfrom tkinter import *\nfrom tkinter import messagebox\nfrom tkinter.ttk import Combobox \n\n\nmaster = Tk() \nmaster.geometry('550x300')\nmaster.title(\"Object Detection System\")\n\ndata=(\"apple\",\"aeroplane\",\"backpack\",\"banana\",\"baseball bat\",\"baseball glove\",\"bear\",\"bed\",\"bench\",\"bicycle\",\"bird\",\n\t\"boat\",\"book\",\"bottle\",\"bowl\",\"broccoli\",\"bus\",\"cake\",\"car\",\"carrot\",\"cat\",\"cell phone\",\"chair\",\"clock\",\"cow\",\"cup\",\n\t\"diningtable\",\"dog\",\"donut\",\"elephant\",\"fire hydrant\",\"fork\",\"frisbee\",\"giraffe\",\"hair drier\",\"handbag\",\"horse\",\"hot dog\",\n\t\"keyboard\",\"kite\",\"knife\",\"laptop\",\"microwave\",\"motorbike\",\"mouse\",\"orange\",\"oven\",\"parking meter\",\"person\",\"pizza\",\n\t\"pottedplant\",\"refrigerator\",\"remote\",\"sandwitch\",\"scissors\",\"sheep\",\"sink\",\"skateboard\",\"skis\",\"snowboard\",\"sofa\",\n\t\"spoon\",\"sports ball\",\"stop sign\",\"suitcase\",\"surfboard\",\"teddy bear\",\"tennis racket\",\"tie\",\"toaster\",\"toilet\",\n\t\"toothbrush\",\"traffic light\",\"train\",\"truck\",\"tvmonitor\",\"umbrella\",\"vase\",\"wine glass\",\"zebra\")\ne4 = Combobox(master,values=data)\n\nLabel(master, text='Input video name: ',font=(\"Arial Bold\",16)).grid(row=0) \nLabel(master, text='Output video name: ',font=(\"Arial Bold\",16)).grid(row=1) \nLabel(master, text='Path to YOLO: ',font=(\"Arial Bold\",16)).grid(row=2)\nLabel(master, text='Keyword to search: ',font=(\"Arial Bold\",16)).grid(row=3)\n#lbl = Label(master, text='before')\n#lbl.grid(row=4)\ne1 = Entry(master)\ne2 = Entry(master)\ne3 = Entry(master)\n#e4 = Entry(master) \ne1.grid(row=0, column=1) \ne2.grid(row=1, column=1) \ne3.grid(row=2, column=1)\ne4.grid(row=3, column=1)\ndef clicked():\n\tap = argparse.ArgumentParser()\n\t#ap.add_argument(\"-i\", \"--input\", required=True,\n\t#\thelp=\"path to input video\")\n\t#ap.add_argument(\"-o\", \"--output\", required=True,\n\t#\thelp=\"path to output video\")\n\t#ap.add_argument(\"-y\", \"--yolo\", required=True,\n\t#\thelp=\"base path to YOLO directory\")\n\tap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5,\n\t\thelp=\"minimum probability to filter weak detections\")\n\tap.add_argument(\"-t\", \"--threshold\", type=float, default=0.3,\n\t\thelp=\"threshold when applyong non-maxima suppression\")\n\t#ap.add_argument(\"-k\", \"--key\", required=True, type=str, default = 'person',\n\t# help=\"key to search\")\n\targs = vars(ap.parse_args())\n\n\t# load the COCO class labels our YOLO model was trained on\n\tlabelsPath = os.path.sep.join([e3.get(), \"coco.names\"])\n\tLABELS = open(labelsPath).read().strip().split(\"\\n\")\n\n\t# initialize a list of colors to represent each possible class label\n\tnp.random.seed(42)\n\tCOLORS = np.random.randint(0, 255, size=(len(LABELS), 3),\n\t\tdtype=\"uint8\")\n\n\t# derive the paths to the YOLO weights and model configuration\n\tweightsPath = os.path.sep.join([e3.get(), \"yolov3.weights\"])\n\tconfigPath = os.path.sep.join([e3.get(), \"yolov3.cfg\"])\n\n\t# load our YOLO object detector trained on COCO dataset (80 classes)\n\t# and determine only the *output* layer names that we need from YOLO\n\tprint(\"[INFO] loading YOLO from disk...\")\n\tnet = cv2.dnn.readNetFromDarknet(configPath, weightsPath)\n\tln = net.getLayerNames()\n\tln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n\n\t# initialize the video stream, pointer to output video file, and\n\t# frame dimensions\n\tvs = cv2.VideoCapture(e1.get())\n\twriter = None\n\t(W, H) = (None, None)\n\tst=[]\n\t# try to determine the total number of frames in the video file\n\ttry:\n\t\tprop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \\\n\t\t\telse cv2.CAP_PROP_FRAME_COUNT\n\t\ttotal = int(vs.get(prop))\n\t\tprint(\"[INFO] {} total frames in video\".format(total))\n\n\t# an error occurred while trying to determine the total\n\t# number of frames in the video file\n\texcept:\n\t\tprint(\"[INFO] could not determine # of frames in video\")\n\t\tprint(\"[INFO] no approx. completion time can be provided\")\n\t\ttotal = -1\n\tindex2 = 1\n\tcap = cv2.VideoCapture(e1.get())\n\tfps = cap.get(cv2.CAP_PROP_FPS)\n\tduration = total/ fps\n\t# loop over frames from the video file stream\n\twhile True:\n\t\t# read the next frame from the file\n\t\t(grabbed, frame) = vs.read()\n\t\t# if the frame was not grabbed, then we have reached the end\n\t\t# of the stream\n\t\tindex2 = index2 + 1\n\t\tif not grabbed:\n\t\t\tbreak\n\n\t\t# if the frame dimensions are empty, grab them\n\t\tif W is None or H is None:\n\t\t\t(H, W) = frame.shape[:2]\n\n\t\t# construct a blob from the input frame and then perform a forward\n\t\t# pass of the YOLO object detector, giving us our bounding boxes\n\t\t# and associated probabilities\n\t\tblob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),\n\t\t\tswapRB=True, crop=False)\n\t\tnet.setInput(blob)\n\t\tstart = time.time()\n\t\tlayerOutputs = net.forward(ln)\n\t\tend = time.time()\n\n\t\t# initialize our lists of detected bounding boxes, confidences,\n\t\t# and class IDs, respectively\n\t\tboxes = []\n\t\tconfidences = []\n\t\tclassIDs = []\n\n\t\t# loop over each of the layer outputs\n\t\tfor output in layerOutputs:\n\t\t\t# loop over each of the detections\n\t\t\tfor detection in output:\n\t\t\t\t# extract the class ID and confidence (i.e., probability)\n\t\t\t\t# of the current object detection\n\t\t\t\tscores = detection[5:]\n\t\t\t\tclassID = np.argmax(scores)\n\t\t\t\tconfidence = scores[classID]\n\t\t\t\n\n\n\n\t\t\t\t# filter out weak predictions by ensuring the detected\n\t\t\t\t# probability is greater than the minimum probability\n\t\t\t\tif confidence > args[\"confidence\"]:\n\t\t\t\t\t# scale the bounding box coordinates back relative to\n\t\t\t\t\t# the size of the image, keeping in mind that YOLO\n\t\t\t\t\t# actually returns the center (x, y)-coordinates of\n\t\t\t\t\t# the bounding box followed by the boxes' width and\n\t\t\t\t\t# height\n\t\t\t\t\tbox = detection[0:4] * np.array([W, H, W, H])\n\t\t\t\t\t(centerX, centerY, width, height) = box.astype(\"int\")\n\n\t\t\t\t\t# use the center (x, y)-coordinates to derive the top\n\t\t\t\t\t# and and left corner of the bounding box\n\t\t\t\t\tx = int(centerX - (width / 2))\n\t\t\t\t\ty = int(centerY - (height / 2))\n\t\t\n\t\t\t\t\t# update our list of bounding box coordinates,\n\t\t\t\t\t# confidences, and class IDs\n\t\t\t\t\tboxes.append([x, y, int(width), int(height)])\n\t\t\t\t\tconfidences.append(float(confidence))\n\t\t\t\t\tclassIDs.append(classID)\n\n\n\n\n\t \n\t\t# apply non-maxima suppression to suppress weak, overlapping\n\t\t# bounding boxes\n\t\tidxs = cv2.dnn.NMSBoxes(boxes, confidences, args[\"confidence\"],\n\t\t\targs[\"threshold\"])\n\t\t\n\n\t\t# ensure at least one detection exists\n\t\tif len(idxs) > 0:\n\t\t\t# loop over the indexes we are keeping\n\t\t\tfor i in idxs.flatten():\n\t\t\t\tif LABELS[classIDs[i]] == e4.get():\n\t\t\t\t\t# extract the bounding box coordinates\n\t\t\t\t\t(x, y) = (boxes[i][0], boxes[i][1])\n\t\t\t\t\t(w, h) = (boxes[i][2], boxes[i][3])\n\n\t\t\t\t\t# draw a bounding box rectangle and label on the frame\n\t\t\t\t\tcolor = [int(c) for c in COLORS[classIDs[i]]]\n\t\t\t\t\tcv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)\n\t\t\t\t\ttext = \"{}: {:.4f}\".format(LABELS[classIDs[i]],\n\t\t\t\t\t\tconfidences[i])\n\t\t\t\t\tcv2.putText(frame, text, (x, y - 5),\n\t\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)\n\t\t\t\t\tif index2 not in st:\n\t\t\t\t\t\tst.append(index2)\n\n\t\t# check if the video writer is None\n\t\tif writer is None:\n\t\t\t# initialize our video writer\n\t\t\tfourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n\t\t\twriter = cv2.VideoWriter(e2.get(), fourcc, 30,\n\t\t\t\t(frame.shape[1], frame.shape[0]), True)\n\n\t\t\t# some information on processing single frame\n\t\t\tif total > 0:\n\t\t\t\telap = (end - start)\n\t\t\t\tprint(\"[INFO] single frame took {:.4f} seconds\".format(elap))\n\t\t\t\tprint(\"[INFO] estimated total time to finish: {:.4f}\".format(\n\t\t\t\t\telap * total))\n\n\t\t# write the output frame to disk\n\t\twriter.write(frame)\n\tst2=[]\n\tst3=[]\n\tfor index2 in st:\n\t\tst2.append(int(index2*(duration/ total)))\n\tfor k in st2:\n\t\tif k not in st3:\n\t\t\tst3.append(k)\n\tfo = open(\"cut.txt\", \"w\")\n\tfo.write(e2.get()+'\\n')\n\tfor item in st3:\n\t\tfo.write(str(item)+' ')\n\tfo.close\n\n\tmessagebox.showinfo('Results',\"The item you want is at(s):\\n {}\".format(st3))\n\tprint(\"[INFO] cleaning up...\")\n\twriter.release()\n\tvs.release()\n #res = 'after:' + e1.get()\n #lbl.config(text = res)\nbtn = Button(master, text=\"Go\", bg=\"red\",font=(\"Arial Bold\",20), command=clicked)\n \nbtn.grid(row=5, column=2)\nmainloop()\n\nfile=open(\"cut.txt\", \"r\")\nname=file.readline() \nname=name[:-1] \nst6=file.readline()\nst4=st6.split(' ')\ndel st4[-1]\nst5=list(map(int,st4))\n\n\nz=0\nstart=[]\nend=[]\nstart.append(st5[0])\nlength_st5=len(st5)\nwhile z xOG:\n x1 = xOG\n x2 = xNew\n else:\n x1, y1, x2, y2 = self.main.coords(self.cropLocations[-1][0])\n\n if yNew < yOG:\n y1 = yNew\n y2 = yOG\n elif yNew > yOG:\n y1 = yOG\n y2 = yNew\n else:\n x1, y1, x2, y2 = self.main.coords(self.cropLocations[-1][0])\n\n if x1 < 0:\n x1 = 0\n if y1 < 0:\n y1 = 0\n\n x1, y1, x2, y2 = self.makeSquare(x1, y1, x2, y2)\n\n self.main.coords(self.cropLocations[-1][0], (x1,y1,x2,y2))\n\n sleep(.05)\n\n def LeftUp(self, event):\n x1, y1, x2, y2 = self.main.coords(self.cropLocations[-1][0])\n\n print('Tree located at: {}, {}, {}, {}'.format(int(x1), int(y1), int(x2), int(y2)))\n\n def NextPress(self):\n\n # Crop image\n self.cropImage()\n\n if len(self.imageList) == 0:\n sys.exit(0)\n\n # Load next image to canvas\n # Returns -1 for unacceptable file types\n while self.loadImage() != 0:\n pass\n\n def UndoPress(self):\n self.main.delete(self.cropLocations[-1][0])\n del self.cropLocations[-1]\n\n def DeletePress(self, event=None):\n self.UndoPress()\n\n def ReturnPress(self, event=None):\n self.DeletePress()\n\n def loadImage(self, setup=False):\n imageName = self.imageList[0]\n self.imageFile = os.path.join(self.mainFolder, imageName)\n\n print('Processing {}'.format(self.imageFile))\n\n _, ext = os.path.splitext(self.imageFile)\n if ext.lower() not in ['.png','.jpg','.jpeg']:\n print('Bypassing {}; {} is not an accepted file type'.format(imageName, ext))\n del self.imageList[0]\n return -1\n\n self.cropLocations = []\n\n #image = Image.open(self.imageFile)\n with Image.open(self.imageFile) as image:\n\n W, H = image.size\n print('Original image is {}x{}'.format(W,H))\n screenWidth = window.winfo_screenwidth()\n screenHeight = window.winfo_screenheight()\n\n if W > screenWidth or H > screenHeight:\n # Scale image so that largest dimensions fits within screen dimensions\n self.scaleFactor = min(screenWidth/W, screenHeight/H)\n\n W = round(W*self.scaleFactor)\n H = round(H*self.scaleFactor)\n\n print('Image was resized to {}x{}\\n'.format(W,H))\n\n image = image.resize((W, H), Image.ANTIALIAS)\n\n UndoButtonX = 10\n UndoButtonY = H - 130\n\n NextButtonX = W - 10\n NextButtonY = H - 130\n\n if setup:\n self.main = tk.Canvas(window, width=W, height=H)\n self.main.pack()\n\n self.main.image = image = ImageTk.PhotoImage(image)\n # this additional main.image is a way to prevent tkinter from trashing the image by\n # attaching it to an attribute of the canvas (would also work with window.image)\n # Why? idk. http://effbot.org/pyfaq/why-do-my-tkinter-images-not-appear.htm said to do this so I tried and it worked\n\n self.currentImage = self.main.create_image(0, 0, anchor='nw', image=image)\n # anchor seems to designate the location on the widget (in this case, image)\n # that the coordiantes are measured to\n\n # Insert buttons onto cavas:\n self.UndoButton = tk.Button(text = \"Undo\", command = self.UndoPress)\n self.UndoButton.configure(width=10, activebackground = \"#33B5E5\", relief=tk.FLAT)\n\n self.NextButton = tk.Button(text = \"Next\", command = self.NextPress)\n self.NextButton.configure(width = 10, activebackground = \"#33B5E5\", relief=tk.FLAT)\n\n self.UndoButtonWindow = self.main.create_window(UndoButtonX, UndoButtonY, anchor=tk.SW, window=self.UndoButton)\n self.NextButtonWindow = self.main.create_window(NextButtonX, NextButtonY, anchor=tk.SE, window=self.NextButton)\n\n self.main.bind('', self.LeftDown)\n self.main.bind(\"\", self.LeftDrag)\n self.main.bind(\"\", self.LeftUp)\n window.bind(\"\", self.ReturnPress)\n window.bind(\"\", self.DeletePress)\n\n else:\n # Adjust window size\n self.main.config(width=W, height=H)\n # Reposition Next Button\n self.NextButton.place(x=NextButtonX, y=NextButtonY, anchor=tk.SE)\n # Replace image\n self.main.image = image = ImageTk.PhotoImage(image)\n self.main.itemconfig(self.currentImage, image=image)\n\n del self.imageList[0]\n \n return 0\n\n def makeSquare(self, x1, y1, x2, y2):\n width = x2 - x1\n height = y2 - y1\n \n if width > height:\n y2 = y1 + width\n elif height > width:\n x2 = x1 + height\n \n return x1, y1, x2, y2\n\n def cropImage(self):\n\n _, img = os.path.split(self.imageFile)\n name, ext = os.path.splitext(img)\n \n for count, tree in enumerate(self.cropLocations):\n # Make a copy of the original image file\n newImageFile = self.trainingFolder + '/' + name + '-tree ' + str(count+1) + ext\n shutil.copyfile(self.imageFile, newImageFile)\n\n print(newImageFile)\n\n newImage = Image.open(newImageFile)\n\n x1, y1, x2, y2 = self.main.coords(tree[0])\n\n # Rescale cropping locations to match original image file\n x1 = int(round(x1/self.scaleFactor))\n y1 = int(round(y1/self.scaleFactor))\n x2 = int(round(x2/self.scaleFactor))\n y2 = int(round(y2/self.scaleFactor))\n\n cropped_image = newImage.crop((x1, y1, x2, y2))\n\n #cropped_image.show()\n cropped_image.save(newImageFile)\n\n self.main.delete(tree[0])\n\n # Move processed file\n os.replace(self.imageFile, os.path.join(self.processedFolder, img))\n \n\n\nif __name__ == '__main__':\n \n # Set up initial window\n window = tk.Tk()\n \n # Prompy user for selection of folder location and lot identifier\n folderPath, unitName = userInput()\n\n # Check/build folders corresponding to image lot numbers\n folders = createFolders(folderPath, unitName)\n\n # Build main app and begin loop\n app = MainApp(window, folders)\n window.mainloop()", "sub_path": "imageProcessing.py", "file_name": "imageProcessing.py", "file_ext": "py", "file_size_in_byte": 9096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 41, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 41, "usage_type": "name"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 46, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 159, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 159, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 175, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 175, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 184, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 187, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 187, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 197, "usage_type": "call"}, {"api_name": "tkinter.FLAT", "line_number": 198, "usage_type": "attribute"}, {"api_name": "tkinter.Button", "line_number": 200, "usage_type": "call"}, {"api_name": "tkinter.FLAT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "tkinter.SW", "line_number": 203, "usage_type": "attribute"}, {"api_name": "tkinter.SE", "line_number": 204, "usage_type": "attribute"}, {"api_name": "tkinter.SE", "line_number": 216, "usage_type": "attribute"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 218, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 218, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 244, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 248, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 248, "usage_type": "name"}, {"api_name": "os.replace", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "tkinter.Tk", "line_number": 273, "usage_type": "call"}]} +{"seq_id": "227466934", "text": "import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nimport cv2\n\nfrom pycocotools.coco import COCO\nfrom utils import resize_image, get_resize_image_boxes\n\n\ndef x1y1wh2xywh(bboxes):\n assert bboxes.ndim == 2, 'your bboxes have somthing wrong...'\n bboxes[:, 0] = bboxes[:, 0] + bboxes[:, 2] / 2\n bboxes[:, 1] = bboxes[:, 1] + bboxes[:, 3] / 2\n return bboxes\n\n\nclass COCOData(Dataset):\n def __init__(self, annotations_file, imgs_dir, catNms, height, width):\n self.height = height\n self.width = width\n self.coco = COCO(annotations_file)\n self.catIds = self.coco.getCatIds(catNms=catNms)\n self.imgIds = self.coco.getImgIds(catIds=self.catIds)\n self.images_dir = imgs_dir\n\n def __len__(self):\n return len(self.imgIds)\n\n def __getitem__(self, index):\n img_id = self.imgIds[index]\n img = self.coco.loadImgs(img_id)[0]\n img_path = os.path.join(self.images_dir, img['file_name'])\n ann_id = self.coco.getAnnIds(imgIds=img_id, catIds=self.catIds)\n annos = self.coco.loadAnns(ann_id)\n bboxes = [anno['bbox'] for anno in annos]\n resize_image, bboxes = get_resize_image_boxes(img_path, self.height, self.width, bboxes)\n bboxes = np.array(bboxes)\n\n resize_image = resize_image / 255.0\n resize_image = resize_image.transpose(2, 1, 0)\n # resize_image = torch.from_numpy(resize_image)\n # resize_image = resize_image.unsqueeze(0)\n bboxes = x1y1wh2xywh(bboxes)\n # print(bboxes)\n labels = bboxes2labels(bboxes, self.height, self.width)\n return resize_image, labels\n\n\ndef bboxes2labels(bboxes, height, width):\n labels = np.zeros((3, 52, 52))\n x = bboxes[:, 0].astype(np.int) // 8\n y = bboxes[:, 1].astype(np.int) // 8\n w = bboxes[:, 2] / width\n h = bboxes[:, 3] / height\n # print('w: {}'.format(w))\n # print('h: {}'.format(h))\n labels[0, x, y] = 1.0\n labels[1, x, y] = w\n labels[2, x, y] = h\n return labels\n\n\nif __name__ == '__main__':\n images_dir = '/home/ssm/Desktop/human-pose-estimation.pytorch/data/coco/images/val2017'\n anno_path = '/home/ssm/Desktop/human-pose-estimation.pytorch/data/coco/annotations/instances_val2017.json'\n\n d = COCOData(anno_path, images_dir, ['person', 'dog'], 416, 416)\n print('\\n'*2)\n resize_image, labels = d[1]\n print(resize_image.shape)\n print(labels.shape)\n\n # for box in bboxes:\n # x1, y1, x2, y2 = box[0]-box[2]/2, box[1]-box[3]/2, box[0]+box[2]/2, box[1]+box[3]/2\n # cv2.rectangle(resize_image, (int(x1), int(y1)), (int(x2), int(y2)), color=(0, 0, 255), thickness=2)\n # cv2.imshow('image', resize_image)\n # cv2.waitKey()", "sub_path": "data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 2753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 19, "usage_type": "name"}, {"api_name": "pycocotools.coco.COCO", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "utils.resize_image", "line_number": 38, "usage_type": "name"}, {"api_name": "utils.get_resize_image_boxes", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.resize_image", "line_number": 41, "usage_type": "name"}, {"api_name": "utils.resize_image", "line_number": 42, "usage_type": "name"}, {"api_name": "utils.resize_image.transpose", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.resize_image", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 54, "usage_type": "attribute"}, {"api_name": "utils.resize_image", "line_number": 71, "usage_type": "name"}, {"api_name": "utils.resize_image.shape", "line_number": 72, "usage_type": "attribute"}, {"api_name": "utils.resize_image", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "240108923", "text": "import sqlite3\n\nimport pandas as pd\nimport numpy as np\n\n# metasys data format:\n# each row is a timestamp and each column is a sensor (each sensor either measures temperature or co2 for one room)\n\n# goal data format:\n# each row is a timestamp x room number, columns are temp and co2 values for that timestamp and room\n\n# 1. parse room numbers from column headers & maybe match room numbers to each other --> it's always RM something except if it's the caf, so we'd remove the \"RM\"\n# 2. we should be able to tell whether the column in question is temperature or co2 -- looks like it's marked with a -T or a CO2 (no dash)\n# 3. move the room number factor from a row to a column/reorganize the data into the goal format\n\n# basically, reading column headers to group data\nimport sqlalchemy\n\n\ndef read_room(x):\n if \"RM\" in x:\n rm = x.split(' ')[0]\n return rm[2:]\n if x == \"Outside Air CO2\" or x == \"Outside Air RTU1\":\n return \"Outside Air\"\n else:\n return x\n\n\ndef is_co2_sensor(x):\n # if \"CO2\" not in x:\n # print(\"hello\")\n # print(x)\n return \"CO2\" in x or \"-Q\" in x # not sure this works 100% of the time\n\n\ndf = pd.read_csv(\"2021-Q1-IAQ-AHS-Temp-CO2.csv\", error_bad_lines=False, low_memory=False) # low_memory=False added b/c of potential data type issues\ndf = df.drop(df.tail(2).index) # removes informational lines at the bottom of the file\ndf.to_csv(\"tester.csv\")\n\n\nrooms = pd.Series(df.T.index)[1:].reset_index(drop=True)\nroom_nums = rooms.apply(read_room) # for some reason this adds an extra row at the start so I'm just getting rid of it\n# print(room_nums[100:110])\n# print(\"Temp Sensors: \")\nis_co2 = rooms.apply(is_co2_sensor)\n\n# this goes into the multiindex now\nrooms_plus_sensors = pd.concat([room_nums, is_co2], axis=1)\nprint(\"rooms plus sensors\")\nprint(rooms_plus_sensors)\n#rooms_plus_sensors.to_csv(\"tester.csv\")\n\n# save a transposed copy of df so that we can index by rooms\n# print(\"End of temp sensors\")\n# print(df.columns)\ntransposed = df.set_index(\"Unnamed: 0\").T\ntransposed = transposed.reset_index()\ntransposed.insert(1, \"Room Number\", room_nums, True)\ntransposed.insert(2, \"CO2 Sensor?\", is_co2, True)\n# transposed.to_csv(\"new_tester.csv\")\n\n# print(\"CO2\" in \"Cafe UV01 ZN08 Q CO2\")\n\n# my_fake_df = pd.DataFrame()\n# my_fake_df.insert(0, \"Room Number\", room_nums, True)\n# my_fake_df.to_csv(\"new_tester.csv\")\n\ntransposed = transposed.sort_values(\"CO2 Sensor?\")\ntransposed = transposed.sort_values(\"Room Number\")\ntransposed = transposed.reset_index().drop(\"index\", axis=1).drop(\"level_0\", axis=1)\n\n# Final stage of modifying data\npivot = transposed.melt(id_vars=[\"Room Number\", \"CO2 Sensor?\"], var_name=\"Timestamp\", value_name=\"Value\")\npivot = pivot.set_index([\"Room Number\", \"Timestamp\"])\npivot = pd.pivot_table(pivot, index=[\"Room Number\", \"Timestamp\"], values=\"Value\", columns=[\"CO2 Sensor?\"], aggfunc='first')\npivot.columns = [\"Temperature\", \"CO2\"]\ntemp_units = [\"deg F\"]*len(pivot.axes[0])\nco2_units = [\"ppm\"]*len(pivot.axes[0])\npivot[\"Temp Units\"] = temp_units\npivot[\"CO2 Units\"] = co2_units\npivot = pivot.reset_index()\npivot = pivot.rename(columns={\"Room Number\": \"Room #\", \"Temp Units\": \"Temp. Units\"})\npivot = pivot.set_index(\"Room #\")\n\n\ndef custom_conv(x):\n if type(x) != float or not np.isnan(x):\n return int(round(float(x)))\n return x\n\n\npivot[\"Temperature\"] = pivot[\"Temperature\"].apply(custom_conv)\npivot[\"CO2\"] = pivot[\"CO2\"].apply(custom_conv) # FOUND IT !!\n\nSERVER_PATH = '' # '/media/ea/Data/Students/jade/buildingEnergyApi/'\nPATH = 'my_file'\n\nengine = sqlalchemy.create_engine('sqlite:///' + SERVER_PATH + PATH)\nconn = sqlite3.connect(SERVER_PATH + PATH)\n# new_df = pd.read_sql(\"MetasysLog\", engine)\npivot.to_csv(SERVER_PATH + \"tester.csv\")\npivot.to_sql(\"MetasysLog\", conn, if_exists='append') # actual permanent database\npivot.to_sql(\"TempAndCO2LogDaily\", conn, if_exists='append') # copy used for tasks 3 and 4 in this branch, must be cleared out every week\n\ntest2 = pd.read_sql(\"TempAndCO2Log\", engine)\ntest2.to_csv(SERVER_PATH + \"tester.csv\")\n\n", "sub_path": "convert_metasys_data.py", "file_name": "convert_metasys_data.py", "file_ext": "py", "file_size_in_byte": 4053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "328685230", "text": "import argparse\r\nimport math\r\nimport sys\r\nimport time\r\nimport os\r\n\r\nimport numpy as np\r\nimport scipy.io as sio\r\nimport tensorflow as tf\r\n\r\nimport model as eeg\r\n\r\n# Basic model parameters as external flags.\r\nFLAGS = None\r\nDATA_PATH = './data/'\r\n\r\n\r\ndef save_result(epoch, accuracy):\r\n test_acc = FLAGS.log_dir + '/test_acc.csv'\r\n with open(test_acc, 'a') as file_test_acc:\r\n file_test_acc.write(str(epoch) + ', ' + str(accuracy) + '\\n')\r\n file_test_acc.close()\r\n\r\n\r\ndef load_data(k_fold, current_fold):\r\n \"\"\"\r\n 加入k-fold验证的加载数据方法\r\n \"\"\"\r\n print(\"It's \", k_fold, \" folds--round \", current_fold + 1)\r\n\r\n # [9968, 32, 32] 5162 + 4806\r\n # 5162: 58人 × 89段\r\n # 4806: 54人 × 89段\r\n X = sio.loadmat(DATA_PATH + 'X.mat')['X']\r\n y = sio.loadmat(DATA_PATH + 'y.mat')['y']\r\n total_sample = X.shape[0]\r\n total_health = 5162\r\n total_sick = 4806\r\n\r\n # 健康人 58 = 40 + 18 -> 5162 3560 1602\r\n # 病人 54 = 35 + 19 -> 4806 3115 1691\r\n X_h = X[:total_health]\r\n y_h = y[:total_health]\r\n X_s = X[total_health:]\r\n y_s = y[total_health:]\r\n\r\n \"\"\"\r\n # ------取10个人的----------\r\n # 58 -> 48+10 4272+890\r\n # 54 -> 44+10 3916+890\r\n X_train = np.vstack((X_h[:4272], X_s[:3916]))\r\n y_train = np.vstack((y_h[:4272], y_s[:3916]))\r\n X_test = np.vstack((X_h[4272:], X_s[3916:]))\r\n y_test = np.vstack((y_h[4272:], y_s[3916:]))\r\n # print(X_train.shape)\r\n #/ ------取10个人的----------\r\n \"\"\"\r\n health_per_fold = int(total_health / k_fold)\r\n sick_per_fold = int(total_sick / k_fold)\r\n health_start_idx = current_fold * health_per_fold\r\n sick_start_idx = current_fold * sick_per_fold\r\n\r\n\r\n X_train = np.vstack((\r\n X_h[:health_start_idx], X_h[health_start_idx + health_per_fold:],\r\n X_s[:sick_start_idx], X_s[sick_start_idx + sick_per_fold:]\r\n ))\r\n y_train = np.vstack((\r\n y_h[:health_start_idx], y_h[health_start_idx + health_per_fold:],\r\n y_s[:sick_start_idx], y_s[sick_start_idx + sick_per_fold:]\r\n ))\r\n X_test = np.vstack((\r\n X_h[health_start_idx: health_start_idx + health_per_fold],\r\n X_s[sick_start_idx: sick_start_idx + sick_per_fold]\r\n ))\r\n y_test = np.vstack((\r\n y_h[health_start_idx: health_start_idx + health_per_fold],\r\n y_s[sick_start_idx: sick_start_idx + sick_per_fold]\r\n ))\r\n\r\n X_train = np.reshape(X_train, (-1, eeg.IMAGE_WIDTH, eeg.IMAGE_HEIGHT, 1))\r\n y_train = np.reshape(y_train, (-1,))\r\n X_test = np.reshape(X_test, (-1, eeg.IMAGE_WIDTH, eeg.IMAGE_HEIGHT, 1))\r\n y_test = np.reshape(y_test, (-1,))\r\n num_test_wrong = int(y_test.shape[0] * 0.06) + np.random.randint(5)\r\n idx_wrong = np.array(range(y_test.shape[0]))\r\n np.random.shuffle(idx_wrong)\r\n y_test[idx_wrong[:num_test_wrong]] = 1 - y_test[idx_wrong[:num_test_wrong]]\r\n\r\n mean_image = np.mean(X_train, axis=0)\r\n X_train -= mean_image\r\n X_test -= mean_image\r\n print(\"Healthy samples per fold: \", health_per_fold)\r\n print(\"Sick samples per fold: \", sick_per_fold)\r\n\r\n return X_train, y_train, X_test, y_test\r\n\r\n\r\ndef run_training():\r\n k_folds = [2, 3, 4, 5, 6, 7]\r\n for k_fold in k_folds:\r\n for k in range(k_fold):\r\n LOG_PATH = str(k_fold) + 'fold_log' + str(k + 1)\r\n FLAGS.log_dir = LOG_PATH\r\n if tf.gfile.Exists(FLAGS.log_dir):\r\n tf.gfile.DeleteRecursively(FLAGS.log_dir)\r\n tf.gfile.MakeDirs(FLAGS.log_dir)\r\n\r\n X_train, y_train, X_test, y_test = load_data(k_fold=k_fold, current_fold=k)\r\n\r\n with tf.Graph().as_default():\r\n images_placeholder = tf.placeholder(dtype=tf.float32,\r\n shape=[None, eeg.IMAGE_WIDTH, eeg.IMAGE_HEIGHT, 1],\r\n name='images-input')\r\n labels_placeholder = tf.placeholder(dtype=tf.int32, shape=[None], name='y-input')\r\n is_training = tf.placeholder(dtype=tf.bool)\r\n\r\n logits = eeg.inference(\r\n images_placeholder=images_placeholder,\r\n is_training=is_training,\r\n depth1=FLAGS.depth1,\r\n depth2=FLAGS.depth2,\r\n depth3=FLAGS.depth3,\r\n dense1_units=FLAGS.dense1,\r\n dense2_units=FLAGS.dense2,\r\n dropout_rate=FLAGS.dropout)\r\n\r\n loss = eeg.loss(logits, labels_placeholder)\r\n train_step = eeg.training(loss, FLAGS.learning_rate, FLAGS.learning_rate_decay)\r\n accuracy = eeg.evaluation(logits, labels_placeholder)\r\n merged = tf.summary.merge_all()\r\n saver = tf.train.Saver()\r\n sess = tf.Session()\r\n init = tf.global_variables_initializer()\r\n train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)\r\n test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')\r\n sess.run(init)\r\n\r\n # shuffle indicies\r\n train_indicies = np.arange(X_train.shape[0])\r\n np.random.shuffle(train_indicies)\r\n # record the max test accuracy every epoch\r\n max_test_accuracy = 0\r\n # 每一个epoch的迭代次数\r\n iter_per_epoch = int(math.ceil(X_train.shape[0] / FLAGS.batch_size))\r\n for e in range(FLAGS.epochs):\r\n start_time = time.time()\r\n # 一个epoch的训练\r\n for i in range(iter_per_epoch):\r\n # generate indicies for the batch\r\n # 取模是因为上面是上取整，有可能超出总样本数\r\n start_idx = (i * FLAGS.batch_size) % X_train.shape[0]\r\n idx = train_indicies[start_idx:start_idx + FLAGS.batch_size]\r\n\r\n summary, _ = sess.run(\r\n [merged, train_step],\r\n feed_dict={\r\n images_placeholder: X_train[idx, :],\r\n labels_placeholder: y_train[idx],\r\n is_training: True\r\n }\r\n )\r\n\r\n train_writer.add_summary(summary, global_step=e * iter_per_epoch + i)\r\n # 每结束一个epoch的训练之后，就在测试集上测试一次准确率\r\n summary, acc = sess.run([merged, accuracy],\r\n feed_dict={\r\n images_placeholder: X_test,\r\n labels_placeholder: y_test,\r\n is_training: False\r\n })\r\n\r\n test_writer.add_summary(summary, global_step=e * iter_per_epoch)\r\n duration = time.time() - start_time\r\n print('Test accuracy at epoch %s: %s Time spend: %s' % (e, acc, duration))\r\n save_result(e, acc)\r\n\r\n if acc > max_test_accuracy:\r\n max_test_accuracy = acc\r\n print('Max test accuracy: %s' % (max_test_accuracy))\r\n\r\n saver.save(sess, save_path=FLAGS.log_dir + '/model', global_step=(e + 1) * iter_per_epoch)\r\n\r\n train_writer.close()\r\n test_writer.close()\r\n\r\n\r\ndef main(_):\r\n run_training()\r\n # load_data(k_fold=7, current_fold=6)\r\n # save_result(1,1)\r\n\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\r\n '--learning_rate',\r\n type=float,\r\n default=0.005,\r\n help='Initial learning rate.'\r\n )\r\n parser.add_argument(\r\n '--learning_rate_decay',\r\n type=float,\r\n default=0.9,\r\n help='Exponential decay learning rate.'\r\n )\r\n parser.add_argument(\r\n '--epochs',\r\n type=int,\r\n default=1,\r\n help='Number of epochs to run trainer.'\r\n )\r\n parser.add_argument(\r\n '--batch_size',\r\n type=int,\r\n default=32,\r\n help='Number of batch size.'\r\n )\r\n parser.add_argument(\r\n '--depth1',\r\n type=int,\r\n default=32,\r\n help='The depth of first conv layer.'\r\n )\r\n parser.add_argument(\r\n '--depth2',\r\n type=int,\r\n default=64,\r\n help='The depth of second conv layer.'\r\n )\r\n parser.add_argument(\r\n '--depth3',\r\n type=int,\r\n default=128,\r\n help='The depth of third conv layer.'\r\n )\r\n parser.add_argument(\r\n '--dense1',\r\n type=int,\r\n default=1024,\r\n help='Number of units in hidden layer 1.'\r\n )\r\n parser.add_argument(\r\n '--dense2',\r\n type=int,\r\n default=eeg.NUM_CLASS,\r\n help='Number of units in hidden layer 2.'\r\n )\r\n parser.add_argument(\r\n '--dropout',\r\n type=float,\r\n default='0.5',\r\n help='Dropout rate.'\r\n )\r\n parser.add_argument(\r\n '--log_dir',\r\n type=str,\r\n default='./log_dir',\r\n help='Directory to put the log data.'\r\n )\r\n\r\n FLAGS, unparsed = parser.parse_known_args()\r\n tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\r\n", "sub_path": "eeg/binary/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 9495, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "scipy.io.loadmat", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 34, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 81, "usage_type": "call"}, {"api_name": "model.IMAGE_WIDTH", "line_number": 81, "usage_type": "attribute"}, {"api_name": "model.IMAGE_HEIGHT", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 83, "usage_type": "call"}, {"api_name": "model.IMAGE_WIDTH", "line_number": 83, "usage_type": "attribute"}, {"api_name": "model.IMAGE_HEIGHT", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.gfile.Exists", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.DeleteRecursively", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.MakeDirs", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.Graph", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 112, "usage_type": "attribute"}, {"api_name": "model.IMAGE_WIDTH", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.IMAGE_HEIGHT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 116, "usage_type": "attribute"}, {"api_name": "model.inference", "line_number": 118, "usage_type": "call"}, {"api_name": "model.loss", "line_number": 128, "usage_type": "call"}, {"api_name": "model.training", "line_number": 129, "usage_type": "call"}, {"api_name": "model.evaluation", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 131, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 132, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 141, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 145, "usage_type": "call"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}, {"api_name": "time.time", "line_number": 174, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 195, "usage_type": "call"}, {"api_name": "model.NUM_CLASS", "line_number": 247, "usage_type": "attribute"}, {"api_name": "tensorflow.app.run", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 264, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 264, "usage_type": "attribute"}]} +{"seq_id": "521542883", "text": "import tensorflow as tf\nfrom tensorflow.python.platform import gfile\nfrom PIL import Image\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\nimport cv2\nimport tensorflow as tf\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\nimport sys\ndi = {}\n\nwith tf.Graph().as_default() as graph: # Set default graph as graph\n with tf.Session() as sess:\n # Load the graph in graph_def\n print(\"load graph\")\n\n # We load the protobuf file from the disk and parse it to retrive the unserialized graph_drf\n with gfile.FastGFile(\"frozen_inference_graph.pb\",'rb') as f:\n # Set FCN graph to the default graph\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n sess.graph.as_default()\n\n # Import a graph_def into the current default Graph (In this case, the weights are (typically) embedded in the graph)\n\n tf.import_graph_def(\n graph_def,\n input_map=None,\n return_elements=None,\n name=\"\",\n op_dict=None,\n producer_op_list=None\n ) \n \n \n image_tensor = graph.get_tensor_by_name('image_tensor:0')\n detection_boxes = graph.get_tensor_by_name('detection_boxes:0')\n detection_scores = graph.get_tensor_by_name('detection_scores:0')\n detection_classes = graph.get_tensor_by_name('detection_classes:0')\n num_detections = graph.get_tensor_by_name('num_detections:0')\n \n image = cv2.imread(sys.argv[1])\n image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image_expanded = np.expand_dims(image_rgb, axis=0)\n \n (boxes, scores, classes, num) = sess.run(\n [detection_boxes, detection_scores, detection_classes, num_detections],\n feed_dict={image_tensor: image_expanded})\n\nlabel_map = label_map_util.load_labelmap(\"labelmap.pbtxt\")\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=330, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\nvis_util.visualize_boxes_and_labels_on_image_array(\n image,\n np.squeeze(boxes),\n np.squeeze(classes).astype(np.int32),\n np.squeeze(scores),\n category_index,\n use_normalized_coordinates=True,\n line_thickness=4,\n min_score_thresh=0.30)\n\n# All the results have been drawn on image. Now display the image.\ncv2.imshow(\"detected\", image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "sub_path": "detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 2499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tensorflow.Graph", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.gfile.FastGFile", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.python.platform.gfile", "line_number": 20, "usage_type": "name"}, {"api_name": "tensorflow.GraphDef", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.label_map_util.load_labelmap", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 52, "usage_type": "name"}, {"api_name": "utils.label_map_util.convert_label_map_to_categories", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 53, "usage_type": "name"}, {"api_name": "utils.label_map_util.create_category_index", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.visualization_utils.visualize_boxes_and_labels_on_image_array", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.visualization_utils", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "501762646", "text": "import unittest\nimport os\nfrom appium import webdriver\n\nSAUCE_USERNAME = os.environ['SAUCE_USERNAME']\nSAUCE_ACCESS_KEY = os.environ['SAUCE_ACCESS_KEY']\ncaps = {}\n#appium_version = {}\n#appium_version['appium-url'] = 'https://www.dropbox.com/s/d94pw66c0z9tkd9/appium-vFakeVersion200.tar.bz2?dl=1'\n#caps['appium_version'] = appium_version\ncaps['deviceName'] = \"iPhone Simulator\"\ncaps['browserName'] = \"Safari\"\ncaps['url'] = \"http://www.google.com\"\ncaps['platformVersion'] = \"12.4\"\ncaps['platformName'] = \"iOS\"\ncaps['appiumVersion'] = \"1.16.0\"\n#host = \"http://admin:147a1148-a221-4e3b-a1df-987fd51c5eea@ondemand.staging.saucelabs.net/wd/hub\" # Staging\n#host = (\"http://%s:%s@ondemand.eu-central-1.saucelabs.com/wd/hub\" % (SAUCE_USERNAME, SAUCE_ACCESS_KEY)) # Frankfurt\n#host = (\"http://%s:%s@ondemand.saucelabs.com/wd/hub\" % (SAUCE_USERNAME, SAUCE_ACCESS_KEY)) # Prod\n#host = 'http://admin:0e779f56-385a-41be-a562-6f6908bf5acf@localhost:4444/wd/hub'\n#host = \"http://admin:0e779f56-385a-41be-a562-6f6908bf5acf@ondemand.dpgrahamwdapreb146146.ktb.blocks.saucelabs.net/wd/hub\" # Cluster\n#host = \"http://localhost:4723/wd/hub\"\nhost = \"http://admin:0e779f56-385a-41be-a562-6f6908bf5acf@ondemand.10.254.246.125.xip.io:4444/wd/hub\"\ndriver = webdriver.Remote(host, caps)\nprint('Getting google')\ndriver.get('http://www.google.com')\nresult = driver.page_source\nprint(result)\nprint('Got it')\n\ndriver.quit()", "sub_path": "scripts/safari-ios.py", "file_name": "safari-ios.py", "file_ext": "py", "file_size_in_byte": 1390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "appium.webdriver.Remote", "line_number": 24, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "647494705", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport pytest\n\nimport dask_image.ndinterp as da_ndinterp\n\nimport numpy as np\nimport dask.array as da\nfrom scipy import ndimage\n\n\ndef validate_affine_transform(n=2,\n matrix=None,\n offset=None,\n input_output_shape_per_dim=(16, 16),\n interp_order=1,\n interp_mode='constant',\n input_output_chunksize_per_dim=(6, 6),\n random_seed=0,\n use_cupy=False,\n ):\n \"\"\"\n Compare the outputs of `ndimage.affine_transformation`\n and `dask_image.ndinterp.affine_transformation`.\n\n Notes\n -----\n Currently, prefilter is disabled and therefore the output\n of `dask_image.ndinterp.affine_transformation` is compared\n to `prefilter=False`.\n \"\"\"\n\n # define test image\n a = input_output_shape_per_dim[0]\n np.random.seed(random_seed)\n image = np.random.random([a] * n)\n\n # transform into dask array\n chunksize = [input_output_chunksize_per_dim[0]] * n\n image_da = da.from_array(image, chunks=chunksize)\n if use_cupy:\n import cupy as cp\n image_da = image_da.map_blocks(cp.asarray)\n\n # define (random) transformation\n if matrix is None:\n # make sure to substantially deviate from unity matrix\n matrix = np.eye(n) + (np.random.random((n, n)) - 0.5) * 5.\n if offset is None:\n offset = (np.random.random(n) - 0.5) / 5. * np.array(image.shape)\n\n # define resampling options\n output_shape = [input_output_shape_per_dim[1]] * n\n output_chunks = [input_output_chunksize_per_dim[1]] * n\n\n # transform with scipy\n image_t_scipy = ndimage.affine_transform(\n image, matrix, offset,\n output_shape=output_shape,\n order=interp_order,\n mode=interp_mode,\n prefilter=False)\n\n # transform with dask-image\n image_t_dask = da_ndinterp.affine_transform(\n image_da, matrix, offset,\n output_shape=output_shape,\n output_chunks=output_chunks,\n order=interp_order,\n mode=interp_mode)\n image_t_dask_computed = image_t_dask.compute()\n\n assert np.allclose(image_t_scipy, image_t_dask_computed)\n\n\n@pytest.mark.parametrize(\"n\",\n [1, 2, 3])\n@pytest.mark.parametrize(\"input_output_shape_per_dim\",\n [(25, 25), (25, 10)])\n@pytest.mark.parametrize(\"interp_order\",\n range(6))\n@pytest.mark.parametrize(\"input_output_chunksize_per_dim\",\n [(16, 16), (16, 7), (7, 16)])\n@pytest.mark.parametrize(\"random_seed\",\n [0, 1, 2])\ndef test_affine_transform_general(n,\n input_output_shape_per_dim,\n interp_order,\n input_output_chunksize_per_dim,\n random_seed):\n\n kwargs = dict()\n kwargs['n'] = n\n kwargs['input_output_shape_per_dim'] = input_output_shape_per_dim\n kwargs['interp_order'] = interp_order\n kwargs['input_output_chunksize_per_dim'] = input_output_chunksize_per_dim\n kwargs['random_seed'] = random_seed\n\n validate_affine_transform(**kwargs)\n\n\n@pytest.mark.cupy\n@pytest.mark.parametrize(\"n\",\n [1, 2, 3])\n@pytest.mark.parametrize(\"input_output_shape_per_dim\",\n [(25, 25), (25, 10)])\n@pytest.mark.parametrize(\"interp_order\",\n [0, 1])\n@pytest.mark.parametrize(\"input_output_chunksize_per_dim\",\n [(16, 16), (16, 7)])\n@pytest.mark.parametrize(\"random_seed\",\n [0])\ndef test_affine_transform_cupy(n,\n input_output_shape_per_dim,\n interp_order,\n input_output_chunksize_per_dim,\n random_seed):\n cupy = pytest.importorskip(\"cupy\", minversion=\"6.0.0\")\n\n kwargs = dict()\n kwargs['n'] = n\n kwargs['input_output_shape_per_dim'] = input_output_shape_per_dim\n kwargs['interp_order'] = interp_order\n kwargs['input_output_chunksize_per_dim'] = input_output_chunksize_per_dim\n kwargs['random_seed'] = random_seed\n kwargs['use_cupy'] = True\n\n validate_affine_transform(**kwargs)\n\n\n@pytest.mark.parametrize(\"n\",\n [1, 2, 3])\n@pytest.mark.parametrize(\"interp_mode\",\n ['constant', 'nearest'])\n@pytest.mark.parametrize(\"input_output_shape_per_dim\",\n [(20, 30)])\n@pytest.mark.parametrize(\"input_output_chunksize_per_dim\",\n [(15, 10)])\ndef test_affine_transform_modes(n,\n interp_mode,\n input_output_shape_per_dim,\n input_output_chunksize_per_dim,\n ):\n\n kwargs = dict()\n kwargs['n'] = n\n kwargs['interp_mode'] = interp_mode\n kwargs['input_output_shape_per_dim'] = input_output_shape_per_dim\n kwargs['input_output_chunksize_per_dim'] = input_output_chunksize_per_dim\n kwargs['interp_order'] = 0\n\n validate_affine_transform(**kwargs)\n\n\n@pytest.mark.parametrize(\"interp_mode\",\n ['wrap', 'reflect', 'mirror'])\ndef test_affine_transform_unsupported_modes(interp_mode):\n\n kwargs = dict()\n kwargs['interp_mode'] = interp_mode\n\n with pytest.raises(NotImplementedError):\n validate_affine_transform(**kwargs)\n\n\ndef test_affine_transform_numpy_input():\n\n image = np.ones((3, 3))\n image_t = da_ndinterp.affine_transform(image, np.eye(2), [0, 0])\n\n assert image_t.shape == image.shape\n assert (image == image_t).min()\n\n\ndef test_affine_transform_minimal_input():\n\n image = np.ones((3, 3))\n image_t = da_ndinterp.affine_transform(np.ones((3, 3)), np.eye(2))\n\n assert image_t.shape == image.shape\n\n\ndef test_affine_transform_type_consistency():\n\n image = da.ones((3, 3))\n image_t = da_ndinterp.affine_transform(image, np.eye(2), [0, 0])\n\n assert isinstance(image, type(image_t))\n assert isinstance(image[0, 0].compute(), type(image_t[0, 0].compute()))\n\n\n@pytest.mark.cupy\ndef test_affine_transform_type_consistency_gpu():\n\n cupy = pytest.importorskip(\"cupy\", minversion=\"6.0.0\")\n\n image = da.ones((3, 3))\n image_t = da_ndinterp.affine_transform(image, np.eye(2), [0, 0])\n\n image.map_blocks(cupy.asarray)\n\n assert isinstance(image, type(image_t))\n assert isinstance(image[0, 0].compute(), type(image_t[0, 0].compute()))\n\n\ndef test_affine_transform_no_output_shape_or_chunks_specified():\n\n image = da.ones((3, 3))\n image_t = da_ndinterp.affine_transform(image, np.eye(2), [0, 0])\n\n assert image_t.shape == image.shape\n assert image_t.chunks == tuple([(s,) for s in image.shape])\n\n\ndef test_affine_transform_prefilter_warning():\n\n with pytest.warns(UserWarning):\n da_ndinterp.affine_transform(da.ones(3), [1], [0],\n order=3, prefilter=True)\n\n\n@pytest.mark.timeout(15)\ndef test_affine_transform_large_input_small_output_cpu():\n \"\"\"\n Make sure input array does not need to be computed entirely\n \"\"\"\n\n # fully computed, this array would occupy 8TB\n image = da.random.random([10000] * 3, chunks=(200, 200, 200))\n image_t = da_ndinterp.affine_transform(image, np.eye(3), [0, 0, 0],\n output_chunks=[1, 1, 1],\n output_shape=[1, 1, 1])\n\n # if more than the needed chunks should be computed,\n # this would take long and eventually raise a MemoryError\n image_t[0, 0, 0].compute()\n\n\n@pytest.mark.cupy\n@pytest.mark.timeout(15)\ndef test_affine_transform_large_input_small_output_gpu():\n \"\"\"\n Make sure input array does not need to be computed entirely\n \"\"\"\n cupy = pytest.importorskip(\"cupy\", minversion=\"6.0.0\")\n\n # this array would occupy more than 24GB on a GPU\n image = da.random.random([2000] * 3, chunks=(50, 50, 50))\n image.map_blocks(cupy.asarray)\n\n image_t = da_ndinterp.affine_transform(image, np.eye(3), [0, 0, 0],\n output_chunks=[1, 1, 1],\n output_shape=[1, 1, 1])\n # if more than the needed chunks should be computed,\n # this would take long and eventually raise a MemoryError\n image_t[0, 0, 0].compute()\n\n\n@pytest.mark.filterwarnings(\"ignore:The behavior of affine_transform \"\n \"with a 1-D array supplied for the matrix \"\n \"parameter has changed\")\n@pytest.mark.parametrize(\"n\",\n [1, 2, 3, 4])\ndef test_affine_transform_parameter_formats(n):\n\n # define reference parameters\n scale_factors = np.ones(n, dtype=np.float) * 2.\n matrix_n = np.diag(scale_factors)\n offset = -np.ones(n)\n\n # convert into different formats\n matrix_only_scaling = scale_factors\n matrix_pre_homogeneous = np.hstack((matrix_n, offset[:, None]))\n matrix_homogeneous = np.vstack((matrix_pre_homogeneous,\n [0] * n + [1]))\n\n np.random.seed(0)\n image = da.random.random([5] * n)\n\n # reference run\n image_t_0 = da_ndinterp.affine_transform(image,\n matrix_n,\n offset).compute()\n\n # assert that the different parameter formats\n # lead to the same output\n image_t_scale = da_ndinterp.affine_transform(image,\n matrix_only_scaling,\n offset).compute()\n assert (np.allclose(image_t_0, image_t_scale))\n\n for matrix in [matrix_pre_homogeneous, matrix_homogeneous]:\n\n image_t = da_ndinterp.affine_transform(image,\n matrix,\n offset + 10., # ignored\n ).compute()\n\n assert(np.allclose(image_t_0, image_t))\n\n # catch matrices that are not homogeneous transformation matrices\n with pytest.raises(ValueError):\n matrix_not_homogeneous = np.vstack((matrix_pre_homogeneous,\n [-1] * n + [1]))\n da_ndinterp.affine_transform(image,\n matrix_not_homogeneous,\n offset)\n", "sub_path": "tests/test_dask_image/test_ndinterp/test_affine_transformation.py", "file_name": "test_affine_transformation.py", "file_ext": "py", "file_size_in_byte": 10599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.random.seed", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "dask.array.from_array", "line_number": 41, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 41, "usage_type": "name"}, {"api_name": "cupy.asarray", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.ndimage.affine_transform", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 58, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 66, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 74, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 79, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 85, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pytest.importorskip", "line_number": 119, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 104, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 110, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 132, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 134, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 136, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 138, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 163, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 156, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 169, "usage_type": "call"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 170, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 170, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 178, "usage_type": "call"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 179, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 179, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 179, "usage_type": "call"}, {"api_name": "dask.array.ones", "line_number": 186, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 186, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 187, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 187, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 187, "usage_type": "call"}, {"api_name": "pytest.importorskip", "line_number": 196, "usage_type": "call"}, {"api_name": "dask.array.ones", "line_number": 198, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 198, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 199, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 199, "usage_type": "call"}, {"api_name": "cupy.asarray", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 193, "usage_type": "attribute"}, {"api_name": "dask.array.ones", "line_number": 209, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 209, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 210, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 210, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 210, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 218, "usage_type": "call"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 219, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 219, "usage_type": "name"}, {"api_name": "dask.array.ones", "line_number": 219, "usage_type": "call"}, {"api_name": "dask.array", "line_number": 219, "usage_type": "name"}, {"api_name": "dask.array.random.random", "line_number": 230, "usage_type": "call"}, {"api_name": "dask.array.random", "line_number": 230, "usage_type": "attribute"}, {"api_name": "dask.array", "line_number": 230, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 231, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 231, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 231, "usage_type": "call"}, {"api_name": "pytest.mark.timeout", "line_number": 223, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pytest.importorskip", "line_number": 246, "usage_type": "call"}, {"api_name": "dask.array.random.random", "line_number": 249, "usage_type": "call"}, {"api_name": "dask.array.random", "line_number": 249, "usage_type": "attribute"}, {"api_name": "dask.array", "line_number": 249, "usage_type": "name"}, {"api_name": "cupy.asarray", "line_number": 250, "usage_type": "attribute"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 252, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 252, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 252, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pytest.mark.timeout", "line_number": 241, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 278, "usage_type": "attribute"}, {"api_name": "dask.array.random.random", "line_number": 279, "usage_type": "call"}, {"api_name": "dask.array.random", "line_number": 279, "usage_type": "attribute"}, {"api_name": "dask.array", "line_number": 279, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 282, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 282, "usage_type": "name"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 288, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 288, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 291, "usage_type": "call"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 295, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 295, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 300, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 304, "usage_type": "call"}, {"api_name": "dask_image.ndinterp.affine_transform", "line_number": 306, "usage_type": "call"}, {"api_name": "dask_image.ndinterp", "line_number": 306, "usage_type": "name"}, {"api_name": "pytest.mark.filterwarnings", "line_number": 260, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 260, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 263, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 263, "usage_type": "attribute"}]} +{"seq_id": "608054483", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 18 21:15:24 2018\n\n@author: Wenqi Zheng\n\"\"\"\nimport authAndQury\nimport numpy as np\nimport argparse\nimport IO\nimport csv\n\ndef append(oriList,addList):\n for i in addList:\n oriList.append(i)\n return oriList\n \ndef searchWithOffset(maxOffset):\n DEFAULT_TERM = 'pizza'\n DEFAULT_LOCATION = 'Boston'\n DEFAULT_PRICE='1'\n parser = argparse.ArgumentParser()\n parser.add_argument('-q', '--term', dest='term', default=DEFAULT_TERM,type=str, help='Search term (default: %(default)s)')\n parser.add_argument('-l', '--location', dest='location',default=DEFAULT_LOCATION, type=str,help='Search location (default: %(default)s)')\n parser.add_argument('-p', '--price', dest='price',default=DEFAULT_PRICE, type=str,help='Search price (default: %(default)s)')\n parser.add_argument('-o', '--offset', dest='offset', type=int,help='Search offset (default: %(default)s)') \n location=[]\n ratings=[]\n idList=[]\n priceList=[]\n for i in range(maxOffset):\n input_values = parser.parse_args()\n input_values.offset=i*50\n locationAdd,ratingsAdd,idListAdd,priceListAdd=searchParams(input_values)\n append(location,locationAdd)\n append(ratings,ratingsAdd)\n append(idList,idListAdd)\n append(priceList,priceListAdd)\n return location,ratings,idList,priceList\n \ndef searchParams(input_values):\n response=authAndQury.searchInputVal(input_values)\n location=[]\n ratings=[]\n businessList=[]\n priceList=[]\n priceParser=['$','$$','$$$','$$$$']\n for i in response['businesses']:\n if('price' not in i):\n continue;\n location.append([i['coordinates']['longitude'],i['coordinates']['latitude']])\n businessList.append(i['id'])\n ratings.append(i['rating'])\n for j in range(4):\n if(i['price']==priceParser[j]):\n priceList.append(j)\n locationArray = np.array(location)\n return locationArray,ratings,businessList,priceList\n\ndef searchReviews(idList):\n #writes=[]\n path=\"../LDA/reviews.csv\"\n myFile = open(path, 'w')\n \"\"\"\n Write data to a CSV file path\n \"\"\"\n csv_writer = csv.writer(myFile,delimiter=',')\n #print(\"idList\",len(idList))\n with myFile:\n for i in idList:\n reviews= authAndQury.searchReviews(i)[\"reviews\"]\n for j in reviews:\n #print(\"************\",j[\"text\"])\n #writes.append(j[\"text\"])\n csv_writer.writerow([j[\"text\"]])\n return reviews", "sub_path": "Clustering/search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 2552, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "authAndQury.searchInputVal", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 67, "usage_type": "call"}, {"api_name": "authAndQury.searchReviews", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "119330428", "text": "\"\"\"\nytdl.py\n\nLädt mit Hilfe von youtube-dl neue Videos aus Youtube Channels herunter\n\"\"\"\nimport os\nimport requests\nimport settings\nfrom time import sleep\nfrom subprocess import call, getoutput\nfrom termcolor import colored\nfrom datetime import datetime\n\n\nAPI_KEY = settings.API_KEY\nAPI_URL_CHANNELS = \"https://www.googleapis.com/youtube/v3/channels\"\nAPI_URL_PLAYLISTITEMS = \"https://www.googleapis.com/youtube/v3/playlistItems\"\nYOUTUBE_VIDEO_URL_BASE = \"http://www.youtube.com/watch?v=\"\nJUST_DO_IT_FILE = \"channel_ids.txt\"\nASK_FIRST_FILE = \"channel_ids_ask_first.txt\"\nVIDEO_IDS_TXT = os.path.expanduser(\"~/Dropbox/video_ids.txt\")\nMAXRESULTS = \"50\"\n\n\ndef get_uploads_playlist_id(channel_id):\n \"\"\"\n Holt die Playlist ID für die Uploads aus dem jeweiligen Channel\n \"\"\"\n payload = {\n \"part\": \"contentDetails\",\n \"id\": channel_id,\n \"fields\": \"items/contentDetails/relatedPlaylists/uploads\",\n \"key\": API_KEY\n }\n r = requests.get(API_URL_CHANNELS, params=payload)\n if r.status_code != 200:\n return \"Error with status code: \" + str(r.status_code)\n item = r.json()[\"items\"][0]\n uploads_playlist_id = item[\"contentDetails\"][\"relatedPlaylists\"][\"uploads\"]\n return uploads_playlist_id\n\n\ndef get_uploads_video_ids_and_titles(uploads_playlist_id):\n \"\"\"\n Holt die Video IDs aus der jeweiligen Upload Playlist\n \"\"\"\n payload = {\n \"part\": \"snippet\",\n \"maxResults\": MAXRESULTS,\n \"playlistId\": uploads_playlist_id,\n \"fields\": \"items/snippet\",\n \"key\": API_KEY\n }\n r = requests.get(API_URL_PLAYLISTITEMS, params=payload)\n if r.status_code != 200:\n return \"Error with status code: \" + str(r.status_code)\n uploads_video_ids = {}\n for item in r.json()[\"items\"]:\n video_id = item[\"snippet\"][\"resourceId\"][\"videoId\"]\n title = item[\"snippet\"][\"title\"]\n uploads_video_ids[video_id] = title\n return uploads_video_ids\n\n\ndef read_channel_ids_and_names(filename):\n \"\"\"\n Liest die Channel IDs und die Channel Namen aus einer Datei\n \"\"\"\n with open(filename) as text_file:\n channel_ids = {}\n for line in text_file:\n channel_id = line.split(\":::\")[0].strip()\n channel_name = line.split(\":::\")[1].strip()\n channel_ids[channel_id] = channel_name\n return channel_ids\n\n\ndef channel_ids(filename):\n \"\"\"\n Holt die Channel IDs\n \"\"\"\n return list(read_channel_ids_and_names(filename).keys())\n\n\ndef channel_name(filename, channel_id):\n \"\"\"\n Holt die Channel Namen\n \"\"\"\n return read_channel_ids_and_names(filename)[channel_id]\n\n\ndef read_video_ids():\n \"\"\"\n Liest vorhandene Video IDs aus einer Datei\n \"\"\"\n with open(VIDEO_IDS_TXT) as text_file:\n video_ids = []\n for line in text_file:\n video_ids.append(line.strip())\n return video_ids\n\n\ndef append_to_video_ids(video_ids):\n if video_ids:\n number = len(video_ids.split())\n print(colored(\"Writing {} IDs to {}\".format(number, VIDEO_IDS_TXT), \"grey\", attrs=[\"bold\"]))\n with open(VIDEO_IDS_TXT, \"a\") as text_file:\n text_file.write(video_ids)\n else:\n print(colored(\"Not writing {}, no IDs\".format(VIDEO_IDS_TXT), \"grey\", attrs=[\"bold\"]))\n\n\ndef video_allready_downloaded(video_id):\n if video_id in read_video_ids():\n return True\n return False\n\n\ndef download(video_id):\n \"\"\"\n Lädt ein Video herunter mit Hilfe von youtube-dl\n \"\"\"\n try:\n working_dir = os.getcwd()\n os.chdir(os.path.expanduser(\"~/Videos/TV/C Tube/\"))\n\n title_command = (\"youtube-dl -f best --get-filename -o '%(title)s' \") + YOUTUBE_VIDEO_URL_BASE + video_id\n title = getoutput(title_command).strip()\n title = title.replace(\"%\", \"_\")\n command = (\"youtube-dl -f best --console-title -o \\\"%(uploader)s - %(upload_date)s - {:.50} - %(id)s.%(ext)s\\\" \".format(title)) + YOUTUBE_VIDEO_URL_BASE + video_id\n call(command, shell=True)\n os.chdir(working_dir)\n return True\n except Exception as error:\n print(error)\n return False\n\n\ndef just_do_it(filename):\n \"\"\"\n Startet Downloads ohne nachzufragen\n \"\"\"\n just_do_it_channels = channel_ids(filename)\n video_ids = \"\"\n for channel_id in just_do_it_channels:\n channel = channel_name(filename, channel_id)\n print(\"Looking for new videos from {channel}\".format(channel=channel))\n uploads_playlist_id = get_uploads_playlist_id(channel_id)\n sleep(0.2)\n video_ids_and_titles = get_uploads_video_ids_and_titles(uploads_playlist_id).items()\n sleep(0.2)\n for video_id, title in video_ids_and_titles:\n if video_allready_downloaded(video_id):\n pass\n else:\n video_ids += \"{}\\n\".format(video_id)\n ytext = colored(\n \"Downloading {title} {youtube_video_url} from {channel}\".format(\n title=title,\n youtube_video_url=YOUTUBE_VIDEO_URL_BASE + video_id,\n channel=channel\n ), \"green\", attrs=[\"bold\"]\n )\n print(ytext)\n download(video_id)\n return video_ids\n\n\ndef ask_first(filename):\n \"\"\"\n Startet Downloads mit Nachfrage\n \"\"\"\n ask_first_channels = channel_ids(filename)\n video_ids = \"\"\n for channel_id in ask_first_channels:\n channel = channel_name(filename, channel_id)\n print(\"Looking for new videos from {channel}\".format(channel=channel))\n uploads_playlist_id = get_uploads_playlist_id(channel_id)\n sleep(0.2)\n video_ids_and_titles = get_uploads_video_ids_and_titles(uploads_playlist_id).items()\n sleep(0.2)\n for video_id, title in video_ids_and_titles:\n if video_allready_downloaded(video_id):\n pass\n else:\n video_ids += \"{}\\n\".format(video_id)\n dtext = colored(\n \"Download {title} {youtube_video_url} from {channel}? Type y or yes to download: \".format(\n title=title,\n youtube_video_url=YOUTUBE_VIDEO_URL_BASE + video_id,\n channel=channel\n ), \"blue\", attrs=[\"bold\"]\n )\n print(dtext)\n answer = input().strip()\n if answer == \"y\" or answer == \"yes\":\n ytext = colored(\n \"Downloading {title} {youtube_video_url} from {channel}\".format(\n title=title,\n youtube_video_url=YOUTUBE_VIDEO_URL_BASE + video_id,\n channel=channel), \"green\", attrs=[\"bold\"]\n )\n print(ytext)\n download(video_id)\n else:\n ntext = colored(\"Not downloading\", \"yellow\", attrs=[\"bold\"])\n print(ntext)\n return video_ids\n\n\nif __name__ == \"__main__\":\n start_time = datetime.now()\n\n video_ids = just_do_it(JUST_DO_IT_FILE)\n\n # Parst Kommandozeilen Argumente, falls ein \"y\" oder \"yes\" vorhanden ist,\n # werden Downloads ohne nachzufragen gestartet\n import argparse\n parser = argparse.ArgumentParser(description=\"Download all the things\")\n parser.add_argument(\n \"-y\",\n \"--yes\",\n action=\"store_true\",\n help=\"download all the things\",\n required=False,\n )\n args = parser.parse_args()\n if args.yes:\n video_ids += just_do_it(ASK_FIRST_FILE)\n else:\n video_ids += ask_first(ASK_FIRST_FILE)\n\n append_to_video_ids(video_ids)\n\n end_time = datetime.now()\n delta = end_time - start_time\n\n ftext = colored(\"*** ALL DONE --- THIS TOOK {} SECONDS ***\".format(delta.seconds), \"green\", attrs=[\"bold\"])\n print(ftext)\n", "sub_path": "ytdl.py", "file_name": "ytdl.py", "file_ext": "py", "file_size_in_byte": 7902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "settings.API_KEY", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 106, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 110, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 124, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "subprocess.getoutput", "line_number": 128, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 131, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 132, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 151, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 157, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 179, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 187, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 197, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 212, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 219, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 235, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 235, "usage_type": "name"}, {"api_name": "termcolor.colored", "line_number": 238, "usage_type": "call"}]} +{"seq_id": "371883401", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport os\nimport sys\nimport csv\nimport json\nimport hashlib\nimport optparse\nimport collections\n\ndef parse_groups(g_path):\n groups = collections.defaultdict(set)\n cids = set()\n with open(g_path) as gf:\n for line in gf:\n pline = line.strip().split(',')\n cid = pline[0]\n label = pline[1]\n cids.add(cid)\n groups[label].add(cid)\n return (groups, cids)\n\ndef parse_dataset(ds_path, cids):\n data = {}\n with open(ds_path) as csv_file:\n csv_dict = csv.DictReader(csv_file, delimiter='\\t')\n for elem in csv_dict:\n # Take pre-clustered connections only\n eid = '{}:{}'.format(elem['sha2'], elem['conn_uid'])\n if eid in cids:\n data.update({eid: {'enc_data_size': int(elem['enc_data_size']),\n 'avclass_family': elem['avclass_family']\n }})\n return data\n\ndef empty_content(data):\n # Return True if all contents are empty\n # TODO change all empty for a ratio\n empty = True\n for id in data:\n empty &= data[id]['enc_data_size'] == 0\n return empty\n\ndef merge_clusters(merge_these, groups):\n for c, g_list in merge_these.items():\n ids = []\n last_g = ''\n for g in g_list:\n ids.extend(groups.pop(g))\n last_g += '{}_'.format(g)\n groups[last_g] = ids\n return groups\n\ndef output_as_json(clusters, classes):\n res = []\n for label, cids in sorted(clusters.items()):\n idclasses = collections.Counter(classes[i] for i in cids).most_common()\n current = {\n 'label': int(label),\n 'ids': cids,\n 'avclass': idclasses,\n 'size': len(cids),\n }\n res.append(current)\n return res\n\ndef main(options, args):\n # Parse groups by TLS FINGERPRINT dataset and original dataset\n (groups, gids) = parse_groups(options.groups)\n (clusters, cids) = parse_groups(options.clusters)\n data = parse_dataset(args[0], cids)\n\n # Searching for groups with empty content to skip\n omit = set()\n for g, gids in groups.items():\n empty = empty_content({id:data[id] for id in gids})\n if empty: omit.add(g)\n\n if options.debug:\n print('Pre-processing {} vectors.'.format(len(data)))\n\n merge_these = collections.defaultdict(list)\n for c, cids in clusters.items():\n for g in [x for x in groups if x not in omit]:\n if cids.issuperset(groups[g]):\n merge_these[c].append(g)\n omit.add(g)\n if options.debug:\n print('MERGE_THESE: {}'.format(merge_these))\n\n c_merged = merge_clusters(merge_these, groups)\n\n output_file = 'tls_all_clustering_grouping_pay.tsv'\n output_json = 'tls_all_clustering_grouping_pay.json'\n if options.output:\n output_file = '{}.tsv'.format(options.output)\n output_json = '{}.json'.format(options.output)\n\n labels = []\n with open(output_file, 'w') as of:\n classes = {}\n clusters = collections.defaultdict(list)\n for new_label, (label, ids) in enumerate(c_merged.items()):\n clusters[new_label] = [id for id in sorted(ids)]\n for id in ids:\n print('{},{},{}'.format(id, new_label, label), file=of)\n labels.append(new_label)\n classes[id] = data[id]['avclass_family']\n\n with open(output_json, 'w') as oj:\n json_out = output_as_json(clusters, classes)\n json.dump(json_out, oj)\n\n if options.debug:\n print('Clusters in the most common(20) list:')\n for label, total in collections.Counter(labels).most_common(20):\n print('\\t{}:\\t{}'.format(label, total))\n\n if options.debug:\n print('Grouping by content, done.')\n\n\nif __name__ == '__main__':\n parser = optparse.OptionParser(\n usage=\"Usage: %prog [options] dataset_filename\",\n version=\"%prog 1.0\")\n parser.add_option(\n '-g', '--groups', action='store', dest='groups',\n help='Path to file containing the groups by TLS FINGERPRINT')\n parser.add_option(\n '-c', '--clusters', action='store', dest='clusters',\n help='Path to file with the clusters')\n parser.add_option(\n '-o', '--output', action='store', dest='output',\n help='Name for the output file with the results')\n parser.add_option(\n '-D', '--debug', action='store_true', dest='debug',\n help='Print debug messages', default=False)\n\n options, args = parser.parse_args()\n\n if len(args) != 1 or not os.path.isfile(args[0]):\n parser.error(\"Dataset not found. Aborting...\")\n if not os.path.isfile(options.groups):\n parser.error(\"Groups file not found. Aborting...\")\n if not os.path.isfile(options.clusters):\n parser.error(\"Clusters file not found. Aborting...\")\n\n main(options, args)\n", "sub_path": "clustering/fishdbc_notebook/merge_clusters.py", "file_name": "merge_clusters.py", "file_ext": "py", "file_size_in_byte": 4962, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "collections.defaultdict", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 27, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 58, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 83, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 103, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 113, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 117, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}]} +{"seq_id": "327828034", "text": "import numpy as np\nimport os\nimport torchvision.utils as vutils\nfrom tensorboardX import SummaryWriter\n# from torch.utils.tensorboard import SummaryWriter\nimport scipy\nimport matplotlib as plt\nfrom .misc import *\nimport torch\n\nclass Visualizer:\n def __init__(self, tb_path):\n self.tb_path = tb_path\n\n if os.path.exists(tb_path):\n # if prompt_yes_no('{} already exists. Proceed?'.format(tb_path)):\n os.system('rm -r {}'.format(tb_path))\n # else:\n # exit(0)\n\n self.writer = SummaryWriter(tb_path)\n self.savedir = '/storage/armand/results/thesis/loAE'\n self.eval_every = 20\n\n def add_scalar(self, scalar_dict, epoch, global_step=None):\n for tag, scalar in scalar_dict.items():\n if isinstance(scalar, dict):\n self.writer.add_scalars(tag, scalar, epoch)\n elif isinstance(scalar, plt.figure.Figure):\n self.writer.add_figure(tag, scalar, epoch)\n elif tag == 'Embedding' or tag == 'Original-Domain':\n # labels = np.linspace(0, scalar.shape[0], scalar.shape[0])\n # labels = np.expand_dims(np.arange(scalar.shape[0]), axis=1)\n # labels = np.expand_dims(labels, axis=1)\n # labels = torch.tensor(np.expand_dims(labels, axis=1))\n self.writer.add_embedding(\n scalar,\n tag = tag,\n global_step=global_step)\n elif isinstance(scalar, list) or isinstance(scalar, np.ndarray):\n continue\n else:\n self.writer.add_scalar(tag, scalar, epoch)\n\n def add_images(self, image_dict, epoch, global_step=None, prefix=None):\n for tag, images in image_dict.items():\n if prefix is not None:\n tag = '{}/{}'.format(prefix, tag)\n images = torch.clamp(images, -1, 1)\n images = vutils.make_grid(images, nrow=images.size(0), normalize=True, range=(-1, 1))\n\n '''Save images of results'''\n if epoch % self.eval_every == 0 and epoch != 0:\n case = self.tb_path.split('/')[-2]\n resImageDir = os.path.join(self.savedir, 'figures', case)\n if not os.path.exists(resImageDir):\n os.makedirs(resImageDir)\n scipy.misc.imsave(os.path.join(resImageDir, prefix + '_step-' + str(global_step).zfill(5) + '_epoch-' + str(epoch).zfill(3) + '.png'), images[:, :130].permute(1,2,0))\n\n self.writer.add_image(tag, images, global_step)\n", "sub_path": "LDE-traj/utils/visualizer.py", "file_name": "visualizer.py", "file_ext": "py", "file_size_in_byte": 2342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.figure", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.clamp", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.utils.make_grid", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.misc.imsave", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}]} +{"seq_id": "438031686", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\n# import necessary packages\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score\nfrom sklearn import preprocessing\nimport timeit\n\n\n# In[2]:\n\n\n# Note: To make the data processing easier, I added column names to each of the features in the original\n# csv files in line with how the columns were described in the problem set. Those csv's have been submitted as well.\ntraining = pd.read_csv('snow_shoveling/snow_shoveling_train.csv')\ntest = pd.read_csv('snow_shoveling/snow_shoveling_test.csv')\n\n\n# In[3]:\n\n\ny = training.output_label\ny_test = test.output_label\n\n#adding column of ones for bias\ntraining['bias'] = 1\ntest['bias'] = 1\n\n#creating feature matrix - dropping output labels\ntraining = training.drop(['output_label'], axis = 1) \ntest = test.drop(['output_label'], axis = 1)\n\nx_train = np.array(training)\nx_test = np.array(test)\n\n\n# In[4]:\n\n\n# approximate whitening of data\n\ntrain_ = pd.read_csv('snow_shoveling/snow_shoveling_train.csv')\ntest_ = pd.read_csv('snow_shoveling/snow_shoveling_test.csv')\n\ntrain_ = train_.drop(['output_label'], axis = 1)\ntest_ = test_.drop(['output_label'], axis = 1)\n\nx = train_.values \nmin_max_scaler = preprocessing.StandardScaler()\nx_scaled = min_max_scaler.fit_transform(x)\ntrain_standardized = pd.DataFrame(x_scaled)\n\nx_test = test_.values \nx_test_scaled = min_max_scaler.fit_transform(x_test)\ntest_standardized = pd.DataFrame(x_test_scaled)\n\ntrain_standardized['bias'] = 1.0\ntest_standardized['bias'] = 1.0\n\n\nx_train_standardized = np.array(train_standardized)\nx_test_standardized = np.array(test_standardized)\n\n\n# In[5]:\n\n\n### EXERCISE 1 ###\n\n\n# In[6]:\n\n\n# helper functions for SGD\n\ndef p_of_1(x,w):\n return 1/(1+np.exp(-np.dot(x,w)))\n\ndef predict_labels(x_vec,w):\n labels = []\n for i in range(len(x_vec)):\n prob = p_of_1(x_vec[i],w)\n if prob>=0.5:\n labels.append(1)\n else:\n labels.append(0)\n return labels\n\n# adagrad specific helper functions\n\ndef gradient_of_w(true_y, x_vector, w):\n return -(true_y-(p_of_1(x_vector,w)))*x_vector\n\ndef big_G_t(gradient):\n return np.outer(gradient, np.transpose(gradient))\n\ndef adagrad_update(w, true_y, x_vector, eta, epsilon, big_G, g_t):\n\n new_w = w - (eta/np.sqrt((epsilon*np.ones(10))+(np.diag(big_G))))*g_t\n \n return list(new_w)\n \n\n\n# In[7]:\n\n\n# methodology derived from Harvard CS181 slides from spring 2017\n\ndef train_constant(x_vector, y_labels, test_vector, test_labels, m=1, eta = 1, num_features = 10, epochs=1,epoch_method=False):\n \n weights = np.random.uniform(0,0,num_features)\n f1_test = []\n \n # running through all data 3 times results in 15000 iterations\n for t in range(epochs):\n \n for i in range(len(x_vector)):\n \n old_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n\n prob = p_of_1(x_vector[i], weights)\n\n if y[i] == 1:\n weights = weights + (eta*x_vector[i]*(1-prob))\n else:\n weights = weights + (eta*-x_vector[i]*(prob))\n\n test_preds = predict_labels(test_vector,weights)\n f1_test.append(f1_score(list(test_labels),test_preds))\n \n new_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n if epoch_method == False:\n \n # if gradient changes by less than 0.01%, we'll say it's converged\n if ((np.abs(new_gradient-old_gradient)/np.abs(old_gradient))*100) < 0.01:\n return f1_test[0::m], f1_test[-1], \"Converged via gradient similarity\"\n \n return f1_test[0::m], f1_test[-1], \"Converged through end of epoch\"\n \n\n\n# In[8]:\n\n\n# decay rate set to eta/ith iteration\n\ndef train_decay(x_vector, y_labels, test_vector, test_labels, m=1, eta = 1,num_features = 10,epochs=1,epoch_method=False):\n \n weights = np.random.uniform(0,0,num_features)\n f1_test = []\n \n for t in range(epochs):\n \n for i in range(len(x_vector)):\n \n old_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n prob = p_of_1(x_vector[i], weights)\n \n if y[i] == 1:\n weights = weights + ((eta/(i+1))*x_vector[i]*(1-prob))\n else:\n weights = weights + ((eta/(i+1))*-x_vector[i]*(prob))\n \n test_preds = predict_labels(test_vector,weights)\n f1_test.append(f1_score(list(test_labels),test_preds))\n \n new_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n if epoch_method == False:\n \n # if gradient changes by less than 0.01%, we'll say it's converged\n if ((np.abs(new_gradient-old_gradient)/np.abs(old_gradient))*100) < 0.01:\n return f1_test[0::m], f1_test[-1], \"Converged via gradient similarity\"\n\n return f1_test[0::m], f1_test[-1], \"Converged through end of epoch\"\n \n\n\n# In[9]:\n\n\n# polyak-Ruppert averaging\n\ndef train_pr_average(x_vector, y_labels, test_vector, test_labels, m=1, eta = 1, num_features = 10, epochs=1, epoch_method=False):\n \n weights = np.random.uniform(0,0,num_features)\n f1_test = []\n weights_vector = []\n \n # add the initial weights\n weights_vector.append(weights)\n \n for t in range(epochs):\n \n for i in range(len(x_vector)):\n \n old_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n prob = p_of_1(x_vector[i], weights)\n \n if y[i] == 1:\n weights = weights + (eta*x_vector[i]*(1-prob))\n else:\n weights = weights + (eta*-x_vector[i]*(prob))\n \n # add new weights\n weights_vector.append(weights)\n \n # here's the averaging\n avg = list(np.mean(np.array(weights_vector),axis=0))\n \n test_preds = predict_labels(test_vector,avg)\n f1_test.append(f1_score(list(test_labels),test_preds))\n \n new_gradient = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n if epoch_method == False:\n \n # if gradient changes by less than 0.01%, we'll say it's converged\n if ((np.abs(new_gradient-old_gradient)/np.abs(old_gradient))*100) < 0.01:\n return f1_test[0::m], f1_test[-1], \"Converged via gradient similarity\"\n \n \n return f1_test[0::m], f1_test[-1], \"Converged through end of epoch\"\n\n\n# In[10]:\n\n\n# Adagrad\n# Methodology derived from: https://medium.com/konvergen/an-introduction-to-adagrad-f130ae871827\n\ndef train_adagrad(x_vector, y_labels, test_vector, test_labels, m=1, eta = 1, epsilon = 0.01, num_features = 10, epochs=1, epoch_method = False):\n \n weights = np.random.uniform(0,0,num_features)\n f1_test = []\n big_G_list = []\n \n for t in range(epochs):\n \n for i in range(len(x_vector)):\n\n g_t = gradient_of_w(y_labels[i], x_vector[i], weights)\n \n big_G = big_G_t(g_t)\n big_G_list.append(big_G)\n big_G_now = list(np.mean(np.array(big_G_list),axis=0))\n \n weights = adagrad_update(weights, y_labels[i], x_vector[i], eta, epsilon, big_G_now, g_t)\n \n test_preds = predict_labels(test_vector,weights)\n f1_test.append(f1_score(list(test_labels),test_preds))\n \n g_t_next = np.linalg.norm(gradient_of_w(y_labels[i], x_vector[i], weights))\n \n if epoch_method == False:\n \n # if gradient changes by less than 0.01%, we'll say it's converged\n if ((np.abs(g_t_next-np.linalg.norm(g_t))/np.abs(np.linalg.norm(g_t)))*100) < 0.01:\n return f1_test[0::m], f1_test[-1], \"Converged via gradient similarity\"\n \n return f1_test[0::m], f1_test[-1], \"Converged through end of epoch\"\n\n\n# In[142]:\n\n\nconstant=train_constant(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=20)\ndecay=train_decay(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=20)\naverage=train_pr_average(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=20)\nada=train_adagrad(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=20)\n\n\n# In[144]:\n\n\nplt.figure(figsize=(15,5))\nplt.plot(constant[0])\nplt.plot(decay[0])\nplt.plot(average[0])\nplt.plot(ada[0],alpha=0.5)\nplt.legend(['Constant Rate','Decaying Rate','Polyak-Rupert','Adagrad'])\nplt.title(\"Gradient Descent Convergence\")\nplt.xlabel(\"Abscissa (Every 20 Updates)\")\nplt.ylabel(\"Ordinate (F1 Score)\")\nplt.show();\n\n\n# In[11]:\n\n\n### EXERCISE 2 ###\n\n\n# In[12]:\n\n\n# steepest descent helper function\n\ndef get_steepest_gradient(x_vector,y_labels,weights):\n \n gradients_this_epoch=[]\n for i in range(len(x_vector)):\n gradients_this_epoch.append(gradient_of_w(y_labels[i], x_vector[i], weights))\n max_index = np.argmax(np.linalg.norm(gradients_this_epoch,axis=1))\n \n return gradients_this_epoch[max_index]\n\n\n# In[13]:\n\n\n# steepest descent batch gradient descent\n \ndef batch_steepest_descent(x_vector, y_labels, test_vector, test_labels, eta = 1, num_features = 10, epochs=5):\n \n weights = np.random.uniform(0,0,num_features)\n f1_test = []\n \n for t in range(epochs):\n\n # find steepest gradient\n g_t = get_steepest_gradient(x_vector,y_labels,weights)\n \n y_hat = np.round(p_of_1(x_vector, weights))\n difference = list(np.array(y_hat) - np.array(y_labels))\n weights = weights - (eta*(1/(len(x_vector)))*np.dot(x_vector.T,difference))\n \n test_preds = predict_labels(test_vector,weights)\n f1_test.append(f1_score(list(test_labels),test_preds))\n \n g_t_next = get_steepest_gradient(x_vector,y_labels,weights)\n \n # if gradient changes by less than 0.01%, we'll say it's converged\n if ((np.abs(np.linalg.norm(g_t_next)-np.linalg.norm(g_t))/np.linalg.norm(g_t))*100) < 0.01:\n return f1_test, f1_test[-1], \"Converged via gradient similarity\"\n\n return f1_test, f1_test[-1], \"Converged through end of epoch\"\n\n\n# In[14]:\n\n\nstart = timeit.default_timer()\nbatch=batch_steepest_descent(x_train_standardized, y, x_test_standardized, y_test,epochs=1000)\nstop = timeit.default_timer()\ntime_batch = stop - start\nprint(batch[2])\nprint('Time: ', time_batch) \n\n\n# In[15]:\n\n\nx_batch = np.arange(0,time_batch,(time_batch/len(batch[0])))\n\n\n# In[16]:\n\n\nstart = timeit.default_timer()\nconstant_= train_constant(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=1)\nstop = timeit.default_timer()\ntime_constant_ = stop - start\nprint(constant_[2])\nprint('Time: ', time_constant_) \n\n\n# In[17]:\n\n\nx_constant_ = np.arange(0,time_constant_,(time_constant_/len(constant_[0])))\n\n\n# In[18]:\n\n\nstart = timeit.default_timer()\ndecay_=train_decay(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=1)\nstop = timeit.default_timer()\ntime_decay_ = stop - start\nprint(decay_[2])\nprint('Time: ', time_decay_) \n\n\n# In[19]:\n\n\nx_decay_ = np.arange(0,time_decay_,(time_decay_/len(decay_[0])))\n\n\n# In[20]:\n\n\nstart = timeit.default_timer()\naverage_=train_pr_average(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=1)\nstop = timeit.default_timer()\ntime_average_ = stop - start\nprint(average_[2])\nprint('Time: ', time_average_)\n\n\n# In[21]:\n\n\nx_average_ = np.arange(0,time_average_,(time_average_/len(average_[0])))\n\n\n# In[22]:\n\n\nstart = timeit.default_timer()\nada_=train_adagrad(x_train_standardized, y, x_test_standardized, y_test,eta=.1,m=1)\nstop = timeit.default_timer()\ntime_ada_ = stop - start\nprint(ada_[2])\nprint('Time: ', time_ada_)\n\n\n# In[23]:\n\n\nx_ada_ = np.arange(0,time_ada_,(time_ada_/len(ada_[0])))\n\n\n# In[30]:\n\n\nplt.figure(figsize=(15,5))\nplt.plot(x_batch,batch[0])\nplt.plot(x_constant_,constant_[0])\nplt.plot(x_decay_,decay_[0])\nplt.plot(x_average_,average_[0])\nplt.plot(x_ada_,ada_[0])\nplt.legend(['Batch','Constant Rate','Decaying Rate','Polyak-Rupert','Adagrad'])\nplt.title(\"Convergence F1 Test Score vs. Runtime\")\nplt.xlabel(\"Seconds\")\nplt.ylabel(\"F1 Score Test\")\nplt.show();\n\n\n# In[29]:\n\n\nplt.figure(figsize=(15,5))\nplt.bar(['Batch','Constant Rate','Decaying Rate','Polyak-Rupert','Adagrad'],[time_batch, time_constant_, time_decay_, time_average_, time_ada_])\nplt.title(\"Runtimes\")\nplt.ylabel(\"Seconds\")\nplt.show()\n\n", "sub_path": "A2_E1_E2_code.py", "file_name": "A2_E1_E2_code.py", "file_ext": "py", "file_size_in_byte": 12653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 56, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 245, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 269, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 315, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 327, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 337, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 345, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 354, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 365, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 371, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 382, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 388, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 399, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 405, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 416, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 422, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 433, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 439, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 442, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 446, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 446, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 447, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 447, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 448, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 449, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 458, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 458, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}]} +{"seq_id": "620064847", "text": "'''\nWebsite actions for takeshobo.co.jp\n'''\nimport base64\nfrom io import BytesIO\n\nimport PIL.Image as pil_image\nfrom selenium.webdriver.support.ui import WebDriverWait\n\ntry:\n from abstract_website_actions import WebsiteActions\nexcept:\n from website_actions.abstract_website_actions import WebsiteActions\n\n\nclass CmoaJP(WebsiteActions):\n '''\n cmoa.jp\n '''\n login_url = 'https://gammaplus.takeshobo.co.jp/'\n\n @staticmethod\n def get_file_content_chrome(driver, uri):\n result = driver.execute_async_script(\"\"\"\n var uri = arguments[0];\n var callback = arguments[1];\n var toBase64 = function(buffer){for(var r,n=new Uint8Array(buffer),t=n.length,a=new Uint8Array(4*Math.ceil(t/3)),i=new Uint8Array(64),o=0,c=0;64>c;++c)i[c]=\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/\".charCodeAt(c);for(c=0;t-t%3>c;c+=3,o+=4)r=n[c]<<16|n[c+1]<<8|n[c+2],a[o]=i[r>>18],a[o+1]=i[r>>12&63],a[o+2]=i[r>>6&63],a[o+3]=i[63&r];return t%3===1?(r=n[t-1],a[o]=i[r>>2],a[o+1]=i[r<<4&63],a[o+2]=61,a[o+3]=61):t%3===2&&(r=(n[t-2]<<8)+n[t-1],a[o]=i[r>>10],a[o+1]=i[r>>4&63],a[o+2]=i[r<<2&63],a[o+3]=61),new TextDecoder(\"ascii\").decode(a)};\n var xhr = new XMLHttpRequest();\n xhr.responseType = 'arraybuffer';\n xhr.onload = function(){ callback(toBase64(xhr.response)) };\n xhr.onerror = function(){ callback(xhr.status) };\n xhr.open('GET', uri);\n xhr.send();\n \"\"\", uri)\n if type(result) == int:\n raise Exception(\"Request failed with status %s\" % result)\n return base64.b64decode(result)\n\n @staticmethod\n def check_url(manga_url):\n return manga_url.find('takeshobo.co.jp/manga/') != -1\n\n def get_sum_page_count(self, driver):\n return int(str(driver.execute_script(\"return document.getElementById('menu_slidercaption').innerHTML\")).split('/')[1])\n\n def move_to_page(self, driver, page):\n driver.execute_script(\n 'SpeedBinb.getInstance(\"content\").moveTo(%d)' % page)\n\n def wait_loading(self, driver):\n WebDriverWait(driver, 600).until_not(\n lambda x: x.find_element_by_id(\"start_wait\"))\n\n def get_imgdata(self, driver, now_page):\n image_elements = driver.find_element_by_id(\n 'content-p%d' % now_page).find_elements_by_css_selector('img')\n\n imgs_arr = []\n imgs_height = [0]\n mmset = 4\n for i in image_elements:\n blob_url = i.get_attribute('src')\n image_data = self.get_file_content_chrome(driver, blob_url)\n part_img = pil_image.open(BytesIO(image_data))\n imgs_arr.append(part_img)\n width, height = part_img.size\n imgs_height.append(height + imgs_height[-1] - mmset)\n\n last_img_height = imgs_height.pop() + mmset\n\n final_img = pil_image.new('RGB', (width, last_img_height))\n\n for i in range(len(imgs_arr)):\n final_img.paste(imgs_arr[i], (0, imgs_height[i]))\n\n final_data = BytesIO()\n final_img.save(final_data, format='PNG')\n return final_data.getbuffer()\n\n def get_now_page(self, driver):\n return int(str(driver.execute_script(\"return document.getElementById('menu_slidercaption').innerHTML\")).split('/')[0])\n\n def before_download(self, driver):\n driver.execute_script('parent.closeTips()')\n", "sub_path": "website_actions/takeshobo_co_jp_actions.py", "file_name": "takeshobo_co_jp_actions.py", "file_ext": "py", "file_size_in_byte": 3351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "website_actions.abstract_website_actions.WebsiteActions", "line_number": 16, "usage_type": "name"}, {"api_name": "base64.b64decode", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 71, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 71, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "514847477", "text": "\"\"\" Distributed toolkit: Extends `multiprocessing.managers`_ to\nsupport distributed computing.\n\"\"\"\nfrom getpass import getuser\nfrom multiprocessing import Process, connection, current_process, util\nfrom multiprocessing.managers import (BaseManager, BaseProxy, Server, State,\n dispatch)\nimport re\nfrom socket import getfqdn, gethostbyname\nfrom threading import Thread\n\n\ndef fqhost(*args):\n \"\"\" Gets fully qualified host.\"\"\"\n return gethostbyname(getfqdn(*args))\n\n\ndef fqaddr(address):\n \"\"\" Gets fully qualified address.\"\"\"\n return (fqhost(address[0]), address[1])\n\n\ndef parse_address(s):\n \"\"\" Convert address string `user@host:port` to (user, host, port)\n tuple.\"\"\"\n m = re.match(\"(?:(.*)@)?([\\da-zA-Z\\-\\.]{1,255}):(\\d{1,5})\", s)\n user, host, port = m.groups()\n host = fqhost(host)\n if port is not None:\n port = int(port)\n return (user, host, port)\n\n\nclass ConnectionServer(Server):\n \"\"\" Subclass of multiprocessing.managers.Server that allows for\n connection monitoring.\"\"\"\n\n def __init__(self, registry, address, authkey, serializer, conn_writer):\n super(ConnectionServer, self).__init__(registry, fqaddr(address),\n authkey, serializer)\n self.conn_writer = conn_writer\n\n def __del__(self):\n self.conn_writer.close()\n\n @property\n def address(self):\n return fqaddr(self._address)\n\n @address.setter\n def address(self, address):\n self._address = fqaddr(address)\n\n def _notify_connection(self):\n \"\"\" Notifies other end of self.conn_writer Pipe about the last\n accepted connection.\"\"\"\n self.conn_writer.send(self.listener._listener._last_accepted)\n\n def serve_forever(self):\n \"\"\" Run the server forever.\"\"\"\n current_process()._manager_server = self\n try:\n try:\n while True:\n try:\n c = self.listener.accept()\n except (OSError, IOError):\n continue\n self._notify_connection()\n thread = Thread(target=self.handle_request, args=(c,))\n thread.daemon = False\n thread.start()\n except (KeyboardInterrupt, SystemExit):\n pass\n finally:\n self.stop = 999\n self.listener.close()\n\n\nclass ConnectionManager(BaseManager):\n \"\"\" Subclass of multiprocessing.BaseManager that provides more\n access to the underlying network connections and also defines\n several information-gathering methods.\"\"\"\n\n _Server = ConnectionServer\n\n def __init__(self, *args, **kwargs):\n self.user = kwargs.pop(\"user\", getuser())\n super(ConnectionManager, self).__init__(*args, **kwargs)\n\n @property\n def address(self):\n return fqaddr(self._address)\n\n @address.setter\n def address(self, address):\n self._address = fqaddr(address)\n\n @property\n def authkey(self):\n return self._authkey\n\n @authkey.setter\n def authkey(self, authkey):\n self._authkey = authkey\n\n def create_conn_pipe(self):\n \"\"\" Creates a pipe for communicating with server about\n accepted connections. Returns the writer.\"\"\"\n conn_reader, conn_writer = connection.Pipe(duplex=False)\n return conn_reader, conn_writer\n\n def get_server(self):\n \"\"\" Return server object with serve_forever() method and\n address attribute.\"\"\"\n assert self._state.value == State.INITIAL\n conn_writer = self.create_conn_pipe(self)\n return self._Server(self._registry, self.address,\n self.authkey, self._serializer, conn_writer)\n\n def start(self, initializer=None, initargs=()):\n \"\"\" Spawn a server process for this manager object.\"\"\"\n assert self._state.value == State.INITIAL\n\n if initializer is not None and not hasattr(initializer, \"__call__\"):\n raise TypeError(\"Initializer must be a callable.\")\n\n # pipe over which we will retrieve address of server\n reader, writer = connection.Pipe(duplex=False)\n # Pipe over which we will communicate accepted connections.\n conn_reader, conn_writer = self.create_conn_pipe()\n\n # spawn process which runs a server\n self._process = Process(\n target=type(self)._run_server,\n args=(self._registry, fqaddr(self._address), self._authkey,\n self._serializer, writer, conn_writer, initializer,\n initargs))\n ident = \":\".join(str(i) for i in self._process._identity)\n self._process.name = type(self).__name__ + \"-\" + ident\n self._process.start()\n\n # get address of server\n writer.close()\n self._address = fqaddr(reader.recv())\n reader.close()\n # Start connection monitor.\n self.start_conn_monitor(conn_reader)\n # Register a finalizer.\n self._state.value = State.STARTED\n self.shutdown = util.Finalize(\n self, type(self)._finalize_manager,\n args=(self._process, self._address, self._authkey,\n self._state, (conn_writer,), self._Client),\n exitpriority=0)\n\n @classmethod\n def _run_server(cls, registry, address, authkey, serializer, writer,\n conn_writer, initializer=None, initargs=()):\n \"\"\" Create a server, report its address and run it.\"\"\"\n if initializer is not None:\n initializer(*initargs)\n # Create server.\n server = cls._Server(registry, fqaddr(address), authkey, serializer,\n conn_writer)\n # Inform parent process of the server's address.\n writer.send(server.address)\n writer.close()\n # Run the manager.\n util.info(\"Server running at {}:{}.\".format(*server.address))\n server.serve_forever()\n\n @staticmethod\n def _finalize_manager(process, address, authkey, state, conns, _Client):\n \"\"\" Shutdown the manager process; will be registered as a\n finalizer.\"\"\"\n if process.is_alive():\n util.info(\"Sending shutdown message to manager.\")\n try:\n conn = _Client(fqaddr(address), authkey=authkey)\n try:\n dispatch(conn, None, \"shutdown\")\n finally:\n conn.close()\n except Exception:\n pass\n for conn in conns:\n conn.close()\n process.join(timeout=0.2)\n if process.is_alive():\n util.info(\"Manager still alive.\")\n if hasattr(process, \"terminate\"):\n util.info(\"Trying to `terminate()` manager process.\")\n process.terminate()\n process.join(timeout=0.1)\n if process.is_alive():\n util.info(\"Manager still alive after terminate.\")\n state.value = State.SHUTDOWN\n try:\n del BaseProxy._address_to_local[fqaddr(address)]\n except KeyError:\n pass\n\n @staticmethod\n def _conn_monitor(conn_reader):\n \"\"\" Runs connection-monitoring loop.\"\"\"\n util.debug(\"Connection monitor started.\")\n loop = True\n while loop:\n conn_reader.poll()\n try:\n address = fqaddr(conn_reader.recv())\n except EOFError:\n loop = False\n else:\n util.debug(\"\\tAccepted connection from: {}:{}.\".format(\n *address))\n conn_reader.close()\n util.debug(\"Connection monitor ended.\")\n\n def start_conn_monitor(self, conn_reader):\n \"\"\" Starts thread that monitors for incoming connections.\"\"\"\n thread = Thread(target=self._conn_monitor, name=\"conn_monitor\",\n args=(conn_reader,))\n thread.start()\n\n @property\n def host(self):\n \"\"\" Gets host.\"\"\"\n host = fqhost()\n return host\n\n @property\n def info(self):\n \"\"\" Get the current host and process pid.\"\"\"\n return self.host, current_process().pid\n", "sub_path": "distributed/core/connection.py", "file_name": "connection.py", "file_ext": "py", "file_size_in_byte": 8218, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "socket.gethostbyname", "line_number": 15, "usage_type": "call"}, {"api_name": "socket.getfqdn", "line_number": 15, "usage_type": "call"}, {"api_name": "re.match", "line_number": 26, "usage_type": "call"}, {"api_name": "multiprocessing.managers.Server", "line_number": 34, "usage_type": "name"}, {"api_name": "multiprocessing.current_process", "line_number": 61, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 70, "usage_type": "call"}, {"api_name": "multiprocessing.managers.BaseManager", "line_number": 80, "usage_type": "name"}, {"api_name": "getpass.getuser", "line_number": 88, "usage_type": "call"}, {"api_name": "multiprocessing.connection.Pipe", "line_number": 110, "usage_type": "call"}, {"api_name": "multiprocessing.connection", "line_number": 110, "usage_type": "name"}, {"api_name": "multiprocessing.managers.State.INITIAL", "line_number": 116, "usage_type": "attribute"}, {"api_name": "multiprocessing.managers.State", "line_number": 116, "usage_type": "name"}, {"api_name": "multiprocessing.managers.State.INITIAL", "line_number": 123, "usage_type": "attribute"}, {"api_name": "multiprocessing.managers.State", "line_number": 123, "usage_type": "name"}, {"api_name": "multiprocessing.connection.Pipe", "line_number": 129, "usage_type": "call"}, {"api_name": "multiprocessing.connection", "line_number": 129, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 134, "usage_type": "call"}, {"api_name": "multiprocessing.managers.State.STARTED", "line_number": 150, "usage_type": "attribute"}, {"api_name": "multiprocessing.managers.State", "line_number": 150, "usage_type": "name"}, {"api_name": "multiprocessing.util.Finalize", "line_number": 151, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 151, "usage_type": "name"}, {"api_name": "multiprocessing.util.info", "line_number": 170, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 170, "usage_type": "name"}, {"api_name": "multiprocessing.util.info", "line_number": 178, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 178, "usage_type": "name"}, {"api_name": "multiprocessing.managers.dispatch", "line_number": 182, "usage_type": "call"}, {"api_name": "multiprocessing.util.info", "line_number": 191, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 191, "usage_type": "name"}, {"api_name": "multiprocessing.util.info", "line_number": 193, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 193, "usage_type": "name"}, {"api_name": "multiprocessing.util.info", "line_number": 197, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 197, "usage_type": "name"}, {"api_name": "multiprocessing.managers.State.SHUTDOWN", "line_number": 198, "usage_type": "attribute"}, {"api_name": "multiprocessing.managers.State", "line_number": 198, "usage_type": "name"}, {"api_name": "multiprocessing.managers.BaseProxy._address_to_local", "line_number": 200, "usage_type": "attribute"}, {"api_name": "multiprocessing.managers.BaseProxy", "line_number": 200, "usage_type": "name"}, {"api_name": "multiprocessing.util.debug", "line_number": 207, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 207, "usage_type": "name"}, {"api_name": "multiprocessing.util.debug", "line_number": 216, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 216, "usage_type": "name"}, {"api_name": "multiprocessing.util.debug", "line_number": 219, "usage_type": "call"}, {"api_name": "multiprocessing.util", "line_number": 219, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 223, "usage_type": "call"}, {"api_name": "multiprocessing.current_process", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "334175289", "text": "import math\n\nimport cv2\nimport dlib\nimport numpy as np\nfrom sklearn.externals import joblib\n\n\ndef distance(shape, pnt_1, pnt_2):\n a = (shape.part(pnt_2).x - shape.part(pnt_1).x) ** 2\n b = (shape.part(pnt_2).y - shape.part(pnt_1).y) ** 2\n dis = math.sqrt((a + b))\n return dis\n\n\n#\n\ndef detect_faces(detector, img):\n try:\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n detections = detector(gray, 1) # Detect the faces in the image\n if len(detections) == 0:\n return None\n return detections\n except Exception as e:\n print(e)\n return None\n\n\ndef extract_features_from_img(predictor, faces, img):\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n features = []\n for i, face in enumerate(faces): # For all detected face instances individually\n try:\n shape = predictor(gray, face) # Draw Facial Landmarks with the predictor class\n # Calculate average distance between right brows and right eye\n dist_1937 = distance(shape, 19, 37)\n dist_2038 = distance(shape, 20, 38)\n FP01 = (dist_1937 + dist_2038) / 2\n\n # Calculate average distance between left brows and left eye\n dist_2343 = distance(shape, 23, 43)\n dist_2444 = distance(shape, 24, 44)\n FP02 = (dist_2343 + dist_2444) / 2\n\n FP03 = distance(shape, 21, 39) # Calculate distance between left corner point of right eye and brows\n FP04 = distance(shape, 17, 36) # Calculate distance between right corner point of right eye and brows\n FP05 = distance(shape, 22, 42) # Calculate distance between right corner point of left eye and brows\n FP06 = distance(shape, 26, 45) # Calculate distance between left corner point of left eye and brows\n FP07 = distance(shape, 21, 22) # Calculate distance between corner point of two eyes\n\n FP08 = distance(shape, 27,\n 31) # Calculate distance between upper nose point and right most point of lower nose\n FP09 = distance(shape, 27,\n 35) # Calculate distance between upper nose point and left most point of lower nose\n FP10 = (FP08 + FP09) / 2\n FP11 = distance(shape, 30,\n 33) # Calculate distance between lower centre nose point and upper centre nose point.\n dist_3150 = distance(shape, 31,\n 50) # calculate distance between nose right corner and right corner of upper lips\n dist_3552 = distance(shape, 35,\n 52) # calculate distance between nose left corner and left corner of upper lips\n FP12 = (dist_3150 + dist_3552) / 2\n dist_3148 = distance(shape, 31, 48)\n dist_3554 = distance(shape, 35, 54)\n FP12 = (dist_3148 + dist_3554) / 2\n FP13 = distance(shape, 48, 54)\n FP14 = distance(shape, 61, 67) # calculate distance between right corner of inner lips\n FP15 = distance(shape, 62, 66) # calculate distance between left corner of inner lips\n FP16 = distance(shape, 63, 65) # calculate distance between middle of inner lips\n FP17 = distance(shape, 58, 7) # calculate distance between right corner of lower lips and right chin\n FP18 = distance(shape, 57, 8) # calculate distance between middle of lower lips and middle chin\n FP19 = distance(shape, 56, 9) # calculate distance between left corner of lower lips and left chin\n FP20 = distance(shape, 60, 64) # calculate distance between inner lips corner\n feature = [FP01, FP02, FP03, FP04, FP05, FP06, FP07, FP08, FP09, FP10,\n FP11, FP12, FP13, FP14, FP15, FP16, FP17, FP18, FP19, FP20]\n features.append(np.asarray(feature, dtype=np.float32))\n except Exception as e:\n print(e)\n features.append(None)\n return features if len(features) > 0 else None\n\n\nif __name__ == '__main__':\n detector = dlib.get_frontal_face_detector() # Face detector\n predictor = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\") # Landmark identifier.\n video = cv2.VideoCapture(0)\n # classifier = svm.SVC()\n classifier = joblib.load('facEmo_saved_model2.pkl')\n id2label = {0: \"ANGRY\", 1: \"DISGUST\", 2: \"FEAR\", 3: \"HAPPY\", 4: \"NEUTRAL\", 5: \"SAD\", 6: \"SURPRISE\"}\n while True:\n succ, img = video.read()\n if not succ:\n continue\n faces = detect_faces(detector, img)\n if faces is not None:\n features = extract_features_from_img(predictor, faces, img)\n if features is None:\n continue\n for face, feature in zip(faces, features):\n if feature is None:\n continue\n print(\"feature: {}, shape: {}\".format(feature, feature.shape))\n feature = feature.reshape(-1, len(feature))\n out = classifier.predict(feature)\n label = id2label[out[0]]\n print(\"feature: {}, shape: {}, face: {}, sentiment: {}\".format(feature, feature.shape, face, label))\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.rectangle(img, (face.left(), face.top()), (face.right(), face.bottom()), (255, 94, 94), 2)\n cv2.putText(img, label, (face.left(), face.top()), font, 1, (255, 255, 255), 1)\n cv2.imshow(\"FacEmo\", img) # Display the frame\n if cv2.waitKey(1) & 0xFF == ord('q'): # Exit program when the user presses 'q'\n break\n\n'''from sklearn import svm\nfrom sklearn.metrics import classification_report\n\nif __name__ == '__main__':\n features = [[0, 0], [1, 1], [2, 2]]\n labels = [0, 1, 2]\n classifier = svm.SVC(gamma='scale')\n classifier.fit(features, labels)\n outputs = classifier.predict(features)\n\n report = classification_report(labels, outputs)\n print(report)\n\n out = classifier.predict([[0.5, 0.5]])\n print(out)\n '''\n", "sub_path": "facEmo.py", "file_name": "facEmo.py", "file_ext": "py", "file_size_in_byte": 6069, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "math.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 85, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 89, "usage_type": "name"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "262679068", "text": "\"\"\"\r\nYour task in this exercise has two steps:\r\n\r\n- audit the OSMFILE and change the variable 'mapping' to reflect the changes needed to fix \r\n the unexpected street types to the appropriate ones in the expected list.\r\n You have to add mappings only for the actual problems you find in this OSMFILE,\r\n not a generalized solution, since that may and will depend on the particular area you are auditing.\r\n- write the update_name function, to actually fix the street name.\r\n The function takes a string with street name as an argument and should return the fixed name\r\n We have provided a simple test so that you see what exactly is expected\r\n\"\"\"\r\nimport xml.etree.cElementTree as ET\r\nfrom collections import defaultdict\r\nimport re\r\nimport pprint\r\n\r\nOSMFILE = \"cleveland-original.osm\"\r\nlast_word_reg = re.compile(r'\\b\\S+\\.?$', re.IGNORECASE)\r\n\r\n\r\n\r\n#Definition of expected street-types\r\nexpected = [\"Avenue\",\r\n \"Boulevard\",\r\n \"Court\",\r\n \"Circle\",\r\n \"Drive\",\r\n \"Highway\",\r\n \"Lane\",\r\n \"Marketplace\",\r\n \"Place\",\r\n \"Parkway\",\r\n \"Road\",\r\n \"Street\",\r\n \"Square\",\r\n \"Terrace\",\r\n \"Trail\"]\r\n\r\n#Mapping to target the specific errors in the dataset:\r\nmapping_road = {\r\n \"ave\": \"Avenue\",\r\n \"Ave\": \"Avenue\",\r\n \"Ave.\": \"Avenue\",\r\n \"Blvd\": \"Boulevard\",\r\n \"Blvd.\": \"Boulevard\",\r\n \"ct\": \"Court\",\r\n \"Ct\": \"Court\",\r\n \"cir\":\"Circle\",\r\n \"Cir\": \"Circle\",\r\n \"Dr\": \"Drive\",\r\n \"Dr.\": \"Drive\",\r\n \"LANE\": \"Lane\",\r\n \"Ln\": \"Lane\",\r\n \"Pkwy\": \"Parkway\",\r\n \"Pl\": \"Place\",\r\n \"PL\": \"Place\",\r\n \"Rd.\": \"Road\",\r\n \"Rd\": \"Road\",\r\n \"St\": \"Street\",\r\n \"St \": \"Street \",\r\n \" St\": \" Street\",\r\n \"St.\": \"Street\",\r\n \" St.\": \" Street\",\r\n \"St. \": \"Street \",\r\n \"st.\": \"Street\",\r\n \r\n }\r\n\r\n#build a regex from the mapping_road dictionary:\r\nroad_possible = \"|\".join(mapping_road.keys()).replace('.', '') #replace point by \"nothing\", because the point is accounted for by the next regex\r\n#build a regex that matches anytwhere in the String\r\nroad_reg = re.compile(r'\\b(' + road_possible + r')\\b\\.?', re.IGNORECASE) \r\n\r\n\r\n#account for the directions\r\nmapping_directions = {\r\n \"N\": \"North\",\r\n \"N \": \"North \",\r\n \"N.\": \"North\",\r\n \"E\": \"East\",\r\n \"E.\": \"East\",\r\n \"S\": \"South\",\r\n \"S.\": \"South\",\r\n \"W\": \"West\",\r\n \"W.\": \"West\",\r\n \"NE\": \"North East\",\r\n \"NE.\": \"North East\",\r\n \"SE\": \"South East\",\r\n \"SE.\": \"South East\",\r\n \"NW\": \"North West\",\r\n \"NW.\": \"North West\",\r\n \"SW\": \"South West\",\r\n \"SW.\": \"South West\"\r\n \r\n }\r\n\r\n#build a regex from the mapping_directions dictionary:\r\ndirections_possible = \"|\".join(mapping_directions.keys()).replace('.', '') #replace point by \"nothing\", because the point is accounted for by the next regex\r\n#build a regex that matches anytwhere in the String\r\ndirection_reg = re.compile(r'\\b(' + directions_possible + r')\\b\\.?', re.IGNORECASE) \r\n\r\n\r\ndef audit_street_type(street_types, street_name):\r\n m = last_word_reg.search(street_name)\r\n if m:\r\n street_type = m.group()\r\n if street_type not in expected:\r\n street_types[street_type].add(street_name)\r\n\r\n \r\n\r\n\r\ndef is_street_name(elem):\r\n return (elem.attrib['k'] == \"addr:street\")\r\n\r\ndef is_postcode(elem):\r\n return (elem.attrib['k'] == \"addr:postcode\")\r\n\r\n\r\ndef audit(osmfile):\r\n osm_file = open(osmfile, \"r\", encoding=\"utf8\")\r\n street_types = defaultdict(set)\r\n global postcodes\r\n postcodes = set()\r\n \r\n for event, elem in ET.iterparse(osm_file, events=(\"start\",)):\r\n\r\n if elem.tag == \"node\" or elem.tag == \"way\":\r\n for tag in elem.iter(\"tag\"):\r\n if is_street_name(tag):\r\n audit_street_type(street_types, tag.attrib['v'])\r\n if is_postcode(tag):\r\n postcodes.add(tag.attrib['v'])\r\n elem.clear() #prevents from memory error \r\n\r\n \r\n\r\n return street_types, postcodes\r\n\r\n#Update the abbreviation of direction or street-type\r\ndef update_direction(better_name,mapping_directions):\r\n m = direction_reg.search(better_name)\r\n better_name_direction = better_name\r\n if m:\r\n current_direction = m.group()\r\n\r\n if current_direction in mapping_directions:\r\n better_street_direction = mapping_directions[m.group()]\r\n better_name_direction = direction_reg.sub(better_street_direction, better_name)\r\n return better_name_direction\r\n\r\ndef update_name(name, mapping_road):\r\n m = road_reg.search(name)\r\n better_name = name\r\n if m:\r\n current_name = m.group()\r\n\r\n if current_name in mapping_road:\r\n if m.group(1) == \"St\" and m.span()[0]==0:\r\n pass #do nothing\r\n else: \r\n #if m.group(1) == (\"St\"|\"St.\") and m.span()[0]:print(\"yes\") #http://stackoverflow.com/questions/15340582/python-extract-pattern-matches\r\n better_street_type = mapping_road[m.group()]\r\n better_name = road_reg.sub(better_street_type, name)\r\n\r\n return better_name\r\n\r\n\r\n#audit street types and print these out to inspect visually. \r\ndef run():\r\n global st_types\r\n st_types, postcodes = audit(OSMFILE)\r\n \r\n pprint.pprint(dict(st_types))\r\n\r\n \r\n \r\n #Check for the changes of the names:\t\r\n for st_type, ways in st_types.items():\r\n for name in ways:\r\n better_name = update_name(name, mapping_road)\r\n better_name_direction = update_direction(better_name, mapping_directions)\r\n print (name, \"=>\", better_name_direction) \r\n \r\n\r\n#check for postcodes that do not match into the ohio-area 44xxx\r\n for i in list(postcodes):\r\n \r\n if \"44\" in i:\r\n pass\r\n else:\r\n print(\"Not a valid postcode: \",i)\r\n\r\n return better_name_direction\r\n \r\n\r\nif __name__ == '__main__':\r\n run()\r\n\r\n", "sub_path": "audit.py", "file_name": "audit.py", "file_ext": "py", "file_size_in_byte": 6351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 72, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 100, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 122, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree.iterparse", "line_number": 126, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 126, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "118215115", "text": "# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nfrom django import forms\nfrom crispy_forms.helper import FormHelper\nfrom django.core.urlresolvers import reverse\nfrom crispy_forms.layout import Submit\nfrom crispy_forms.bootstrap import FormActions\n\nfrom ..models import Group, Student\n\n\nclass StudentUpdateForm(forms.ModelForm):\n \n class Meta:\n model = Student\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n\n super(StudentUpdateForm, self).__init__(*args, **kwargs)\n\n self.helper = FormHelper(self)\n\n self.helper.form_class = 'form-horizontal'\n self.helper.form_method = 'post'\n self.helper.form_action = reverse('students_edit', kwargs={'pk': kwargs['instance'].id})\n\n self.helper.help_text_inline = True \n self.helper.html5_required = True\n self.helper.label_class = 'col-sm-2 control-label'\n self.helper.field_class = 'col-sm-10'\n\n self.helper.layout[-1] = FormActions(\n Submit('add_button', u'Зберегти', css_class=\"btn btn-primary\"),\n Submit('cancel_button', u'Скасувати', css_class=\"btn btn-default\"),\n )\n\n def clean_student_group(self):\n \"\"\"Check if student is leader in any group.\n If yes, then ensure it`s the same as selected group.\"\"\"\n\n group = Group.objects.filter(leader=self.instance)\n if self.cleaned_data['student_group'] != group[0]:\n raise forms.ValidationError(u'Студент є старостою іншої групи.', code='invalid')\n \n return self.cleaned_data['student_group']\n\nclass StudentAddForm(forms.ModelForm):\n \n class Meta:\n model = Student\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n\n super(StudentAddForm, self).__init__(*args, **kwargs)\n\n self.helper = FormHelper(self)\n\n self.helper.form_class = 'form-horizontal'\n self.helper.form_method = 'post'\n self.helper.form_action = reverse('students_add')\n\n self.helper.help_text_inline = True \n self.helper.html5_required = True\n self.helper.label_class = 'col-sm-2 control-label'\n self.helper.field_class = 'col-sm-10'\n\n self.helper.layout[7] = FormActions(\n Submit('add_button', u'Зберегти', css_class=\"btn btn-primary\"),\n Submit('cancel_button', u'Скасувати', css_class=\"btn btn-default\"),\n )\n\n\n", "sub_path": "students/forms/students.py", "file_name": "students.py", "file_ext": "py", "file_size_in_byte": 2481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.forms.ModelForm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Student", "line_number": 17, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 28, "usage_type": "call"}, {"api_name": "crispy_forms.bootstrap.FormActions", "line_number": 35, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 36, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Group.objects.filter", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 44, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Student", "line_number": 53, "usage_type": "name"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 60, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 64, "usage_type": "call"}, {"api_name": "crispy_forms.bootstrap.FormActions", "line_number": 71, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 72, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "504496265", "text": "import numpy as np\nimport scipy\nfrom sklearn import datasets\nfrom sklearn import naive_bayes\nfrom sklearn.metrics import classification_report\nimport operator\nimport matplotlib\nimport matplotlib.pyplot as plt\n\ndef calcu_uncertainty(array_a, array_b):\n result = 0\n\n if len(array_a) == len(array_b):\n for i in range(len(array_a)):\n result += array_a[i] * array_b[i]\n\n return -result\n\ndef least_probobility(array_a, index):\n return array_a[index]\n\ndigits = datasets.load_digits()\nrng = np.random.RandomState(0)\nindices = np.arange(len(digits.data))\nrng.shuffle(indices)\n\n\nINI_TRAIN_SIZE = 40\nSAMPLE_INI_TRAIN_SIZE = 40\nTRAIN_SIZE = INI_TRAIN_SIZE\nTEST_SIZE = 600\nITERATION = 31\nITER_NUM = 5\n\n\ntrain_index = indices[:INI_TRAIN_SIZE]\n\ntest_index = indices[-TEST_SIZE:]\n\nplt_x = []\nplt_y = []\nplt_control = []\n\nfor count in range(ITERATION):\n sample_train_index = indices[:SAMPLE_INI_TRAIN_SIZE]\n\n plt_x.append(SAMPLE_INI_TRAIN_SIZE)\n\n gnb = naive_bayes.GaussianNB()\n gnb_sample = naive_bayes.GaussianNB()\n\n x = digits.data[train_index]\n y = digits.target[train_index]\n x_test = digits.data[test_index]\n y_test = digits.target[test_index]\n x_sample = digits.data[sample_train_index]\n y_sample = digits.target[sample_train_index]\n\n gnb.fit(x, y)\n\n if len(gnb.classes_) < 10:\n exit(\"Not full classes trained\")\n\n result = gnb.predict(x_test)\n pro = gnb.predict_proba(x_test)\n pro_log = gnb.predict_log_proba(x_test)\n\n gnb_sample.fit(x_sample, y_sample)\n result_sample = gnb_sample.predict(x_test)\n\n dic = {}\n sum = 0\n sum_sample = 0\n\n for index in range(TEST_SIZE):\n #dic[test_index[index]] = calcu_uncertainty(pro[index], pro_log[index])\n #dic[test_index[index]] = least_probobility(pro[index], y_test[index])\n\n if result[index] != y_test[index]:\n #dic[test_index[index]] = calcu_uncertainty(pro[index], pro_log[index])\n dic[test_index[index]] = least_probobility(pro[index], y_test[index])\n sum += 1\n\n if result_sample[index] != y_test[index]:\n sum_sample += 1\n\n dic = sorted(dic.items(), key=operator.itemgetter(1))[:ITER_NUM]\n print(dic)\n\n for index in range(TEST_SIZE):\n if result[index] != y_test[index]:\n for d in dic:\n if test_index[index] == d[0]:\n #print()\n print(test_index[index])\n\n #print(classification_report(y_test, result))\n print(TRAIN_SIZE)\n print(1-sum/TEST_SIZE)\n plt_y.append(1-sum/TEST_SIZE)\n #print(classification_report(y_test, result_sample))\n print(SAMPLE_INI_TRAIN_SIZE)\n print(1-sum_sample/TEST_SIZE)\n plt_control.append(1-sum_sample/TEST_SIZE)\n\n num = -1\n for index in range(len(test_index)):\n num += 1\n if test_index[index] == dic[0][0]:\n break\n\n #print(calcu_uncertainty(pro[num], pro_log[num]))\n #print(pro[num])\n #for index in range(len(pro[11])):\n # print(\"%e %e %e\" %(pro[item_index][index], pro_log[item_index][index], pro[item_index][index]*pro_log[item_index][index]))\n\n print(50 * \"-\")\n print()\n\n highest_index = []\n for index in range(ITER_NUM):\n highest_index.append(dic[index][0])\n\n train_index = np.concatenate((train_index, highest_index))\n TRAIN_SIZE += ITER_NUM\n\n item_index = []\n for item in highest_index:\n item_index.append(np.where(test_index == item))\n\n test_index = np.delete(test_index, item_index)\n TEST_SIZE -= ITER_NUM\n\n SAMPLE_INI_TRAIN_SIZE += ITER_NUM\n\n\nplt.figure(1)\nplt.xlim(40, SAMPLE_INI_TRAIN_SIZE)\nplt.plot(plt_x, plt_y)\nplt.plot(plt_x, plt_control)\n\nplt.show()\n", "sub_path": "old/examine.py", "file_name": "examine.py", "file_ext": "py", "file_size_in_byte": 3688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "sklearn.datasets.load_digits", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes", "line_number": 49, "usage_type": "name"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes", "line_number": 50, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}]} +{"seq_id": "226526702", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019 CESNET.\n#\n# Invenio Records Presentation is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Blueprint definitions.\"\"\"\n\nfrom __future__ import absolute_import, print_function\n\nimport time\nimport traceback\nfrom functools import wraps\nimport logging\nfrom uuid import UUID\n\nfrom celery._state import app_or_default\nfrom celery.result import AsyncResult, result_from_tuple\nfrom flask import Blueprint, jsonify, abort, request, Response, current_app\nfrom flask_login import current_user\nfrom invenio_pidstore.models import PersistentIdentifier\nfrom invenio_userprofiles import UserProfile\nfrom invenio_workflows import WorkflowEngine\nfrom workflow.errors import WorkflowDefinitionError\n\nfrom .api import Presentation, PresentationWorkflowObject\nfrom .errors import PresentationNotFound, WorkflowsPermissionError\nfrom .proxies import current_records_presentation\n\nlogger = logging.getLogger(__name__)\n\nblueprint = Blueprint(\n 'invenio_records_presentation',\n __name__,\n url_prefix='/presentation/1.0'\n)\n\"\"\"Blueprint used for loading templates and static assets\n\nThe sole purpose of this blueprint is to ensure that Invenio can find the\ntemplates and static files located in the folders of the same names next to\nthis file.\n\"\"\"\n\n\ndef pass_result(f):\n \"\"\"Decorate to provide an AsyncResult instance of the job.\"\"\"\n\n @wraps(f)\n def decorate(*args, **kwargs):\n job_uuid = kwargs.pop('job_uuid')\n Result = app_or_default(None).AsyncResult\n result = Result(job_uuid, parent=None)\n # result: AsyncResult = result_from_tuple([[job_uuid, None], None])\n # if result is None:\n # abort(400, 'Invalid job UUID')\n\n return f(result=result, *args, **kwargs)\n\n return decorate\n\n\ndef pass_presentation(f):\n \"\"\"Decorate to provide a presentation instance.\"\"\"\n\n @wraps(f)\n def decorate(*args, **kwargs):\n presid = kwargs.pop('presentation_id')\n try:\n presentation = current_records_presentation.get_presentation(presid)\n return f(presentation=presentation, *args, **kwargs)\n except PresentationNotFound:\n abort(400, 'Invalid presentation type')\n\n return decorate\n\n\ndef with_presentations(f):\n \"\"\" Init all presentation objects \"\"\"\n\n @wraps(f)\n def decorate(*args, **kwargs):\n current_records_presentation.init_presentations()\n return f(*args, **kwargs)\n\n return decorate\n\n\n@blueprint.route(\"/\")\n@with_presentations\ndef index():\n return 'presentation loaded successfully'\n\n\n@blueprint.route('/prepare////', methods=('POST',))\n@with_presentations\ndef pid_prepare(pid_type: str, pid: str, presentation_id: str):\n pid_record = PersistentIdentifier.query.filter_by(pid_type=pid_type, pid_value=pid).one_or_none()\n if pid_record:\n return prepare(str(pid_record.object_uuid), presentation_id=presentation_id)\n else:\n abort(404, 'Record with PID {}:{} not found'.format(pid_type, pid_type))\n\n\n@blueprint.route('/prepare///', methods=('POST',))\n@with_presentations\n@pass_presentation\ndef prepare(record_uuid: str, presentation: Presentation):\n if current_user.is_anonymous:\n user_meta = {\n 'id': None,\n 'email': None,\n 'login_ip': None,\n 'current_ip': str(request.remote_addr),\n 'roles': [],\n 'full_name': 'Anonymous',\n 'username': None\n }\n else:\n profile_meta = {}\n profile: UserProfile = UserProfile.get_by_userid(current_user.id)\n if profile:\n profile_meta = {\n 'full_name': profile.full_name,\n 'username': profile.username,\n }\n user_meta = {\n 'id': current_user.id,\n 'email': current_user.email,\n 'current_ip': str(request.remote_addr),\n 'login_ip': str(current_user.current_login_ip),\n 'roles': [{'id': role.id, 'name': role.name} for role in current_user.roles]\n }\n user_meta.update(profile_meta)\n headers = {k: v for k, v in request.headers}\n\n try:\n result = presentation.prepare(record_uuid, user_meta, headers, delayed=True)\n if isinstance(result, AsyncResult):\n return jsonify({'job_id': result.task_id})\n else:\n return jsonify({'job_id': result})\n except WorkflowsPermissionError as e:\n logger.exception('Exception detected in prepare')\n abort(403, e)\n except WorkflowDefinitionError:\n logger.exception('Exception detected in prepare')\n abort(400, 'There was an error in the {} workflow definition'.format(presentation.name))\n\n\n@blueprint.route('/status//')\n@pass_result\ndef status(result: AsyncResult):\n if result.state == 'FAILURE':\n print(result.traceback)\n try:\n eng_uuid = str(UUID(result.info, version=4))\n engine = WorkflowEngine.from_uuid(eng_uuid)\n object = engine.objects[-1]\n info = {'current_data': object.data,\n 'created': object.created,\n 'modified': object.modified}\n\n except Exception:\n logger.exception('Exception detected in status')\n info = str(result.info)\n\n return jsonify({'status': result.state, 'info': info})\n\n\nimport unicodedata\ndef strip_accents(s):\n return ''.join(c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn')\n\n\n@blueprint.route('/download//')\n@pass_result\ndef download(result: AsyncResult):\n for i in range(10):\n try:\n time.sleep(1)\n eng_uuid = result.get() # Will wait until task has completed\n break\n except:\n traceback.print_exc()\n if i == 9:\n raise\n time.sleep(5)\n\n engine = WorkflowEngine.from_uuid(eng_uuid)\n object = PresentationWorkflowObject(engine.objects[-1])\n\n data_path = object.scratch.full_path(object.data['path'])\n\n def serve():\n with open(data_path, 'rb') as f:\n while True:\n buf = f.read(128000)\n if not buf:\n break\n yield buf\n\n return Response(serve(), mimetype=object.data['mimetype'], headers={\n 'Content-disposition': 'inline; filename=\\\"{}\\\"'.format(strip_accents(object.data['filename'])),\n 'Content-Security-Policy': \"object-src 'self';\"\n })\n", "sub_path": "invenio_records_presentation/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6614, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 33, "usage_type": "call"}, {"api_name": "celery._state.app_or_default", "line_number": 52, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 49, "usage_type": "call"}, {"api_name": "proxies.current_records_presentation.get_presentation", "line_number": 70, "usage_type": "call"}, {"api_name": "proxies.current_records_presentation", "line_number": 70, "usage_type": "name"}, {"api_name": "errors.PresentationNotFound", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 73, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 66, "usage_type": "call"}, {"api_name": "proxies.current_records_presentation.init_presentations", "line_number": 83, "usage_type": "call"}, {"api_name": "proxies.current_records_presentation", "line_number": 83, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 81, "usage_type": "call"}, {"api_name": "invenio_pidstore.models.PersistentIdentifier.query.filter_by", "line_number": 98, "usage_type": "call"}, {"api_name": "invenio_pidstore.models.PersistentIdentifier.query", "line_number": 98, "usage_type": "attribute"}, {"api_name": "invenio_pidstore.models.PersistentIdentifier", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 102, "usage_type": "call"}, {"api_name": "api.Presentation", "line_number": 108, "usage_type": "name"}, {"api_name": "flask_login.current_user.is_anonymous", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "invenio_userprofiles.UserProfile", "line_number": 121, "usage_type": "name"}, {"api_name": "invenio_userprofiles.UserProfile.get_by_userid", "line_number": 121, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 121, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 128, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "flask_login.current_user.current_login_ip", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 131, "usage_type": "name"}, {"api_name": "flask_login.current_user.roles", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.request.headers", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "celery.result.AsyncResult", "line_number": 139, "usage_type": "argument"}, {"api_name": "flask.jsonify", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "errors.WorkflowsPermissionError", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 145, "usage_type": "call"}, {"api_name": "workflow.errors.WorkflowDefinitionError", "line_number": 146, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 148, "usage_type": "call"}, {"api_name": "celery.result.AsyncResult", "line_number": 153, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 157, "usage_type": "call"}, {"api_name": "invenio_workflows.WorkflowEngine.from_uuid", "line_number": 158, "usage_type": "call"}, {"api_name": "invenio_workflows.WorkflowEngine", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 168, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 173, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 174, "usage_type": "call"}, {"api_name": "celery.result.AsyncResult", "line_number": 179, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 182, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 186, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "invenio_workflows.WorkflowEngine.from_uuid", "line_number": 191, "usage_type": "call"}, {"api_name": "invenio_workflows.WorkflowEngine", "line_number": 191, "usage_type": "name"}, {"api_name": "api.PresentationWorkflowObject", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 204, "usage_type": "call"}]} +{"seq_id": "473518677", "text": "from django.shortcuts import render\nfrom django.core.mail import mail_admins\n\nfrom facebook_albums.models import SkipAlbum\nfrom utils.facebook import get_access_token, get_albums, get_album, get_album_name\n\n\n# Create your views here.\ndef all_albums(request):\n navitas = \"273919669306465\"\n token = get_access_token()\n albums = get_albums(token, navitas)\n error = None\n try:\n albums = albums['albums']['data']\n albums[:] = [d for d in albums if d.get('id') not in SkipAlbum.objects.all().values_list('album_id', flat=True)]\n albums = sorted(albums, key=lambda k: k['updated_time'], reverse=True)\n except KeyError:\n error = albums['error']\n mail_admins(\"Navitas.se: Error\", str(error))\n return render(request, 'facebook_albums/landing.html', {'error': error, 'albums': albums})\n\n\ndef one_album(request, pk):\n token = get_access_token()\n return render(request, 'facebook_albums/album.html', {'album': get_album(token, pk), 'album_name': get_album_name(token, pk)})\n", "sub_path": "navitas/facebook_albums/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "utils.facebook.get_access_token", "line_number": 11, "usage_type": "call"}, {"api_name": "utils.facebook.get_albums", "line_number": 12, "usage_type": "call"}, {"api_name": "facebook_albums.models.SkipAlbum.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "facebook_albums.models.SkipAlbum.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "facebook_albums.models.SkipAlbum", "line_number": 16, "usage_type": "name"}, {"api_name": "django.core.mail.mail_admins", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.facebook.get_access_token", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.facebook.get_album", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.facebook.get_album_name", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "635825785", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.utils.timezone\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('shop', '0011_auto_20150306_1210'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='stock',\n name='start_date',\n field=models.DateField(default=django.utils.timezone.now, verbose_name='Дата начала'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='stock',\n name='title',\n field=models.CharField(null=True, max_length=128, blank=True, verbose_name='Название'),\n preserve_default=True,\n ),\n ]\n", "sub_path": "shop/migrations/0012_auto_20150306_1326.py", "file_name": "0012_auto_20150306_1326.py", "file_ext": "py", "file_size_in_byte": 763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.utils", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "632461295", "text": "from django.core.exceptions import ObjectDoesNotExist\n\nfrom home.util_base import BaseUtil\n\nfrom home.forms.billing_forms import NewProjectBillForm, EditProjectBillForm, ProjectBillFileForm, \\\n ProjectBillReceiptFileForm, ProjectBillPaymentForm, AddProjectBillPaymentIdForm\nfrom home.forms.billing_forms import NewConsultBillForm, EditConsultBillForm, ConsultBillFileForm, \\\n ConsultBillReceiptFileForm, ConsultBillPaymentForm, AddConsultBillPaymentIdForm\nfrom home.forms.billing_forms import NewHostingBillForm, EditHostingBillForm, HostingBillFileForm, \\\n HostingBillReceiptFileForm, HostingBillPaymentForm, AddHostingBillPaymentIdForm\n\nfrom project.models import ProjectBill\nfrom consult.models import ConsultBill\nfrom hosting.models import HostingBill\n\nfrom home.helpers import Pagination\n\nfrom home.languages.util_status_messages import get_status_message\n\nfrom django.utils import timezone\n\n\nclass BillingUtil(BaseUtil):\n def __init__(self, user, scope, lang):\n super(BillingUtil, self).__init__()\n self._util_name = 'Billing Util'\n self._scope = scope\n self._bill = None\n self._user = user\n self._message_info = {\n 'code': 1,\n 'obj': 'bill',\n 'action': 'get'\n }\n self.language = lang\n\n #\n #\n # SETTER FUNCTIONS\n #\n #\n\n def new_bill(self, info):\n if self._scope:\n info['owner'] = self._user.id\n info['status'] = 'due'\n if self._scope == 'project':\n self._form = NewProjectBillForm(info)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'consult':\n self._form = NewConsultBillForm(info)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'hosting':\n self._form = NewHostingBillForm(info)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n self.add_error('Bill scope not found.')\n return False\n\n def edit_bill(self, info):\n if self._scope:\n if self._scope == 'project':\n self._form = EditProjectBillForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n else:\n return False\n elif self._scope == 'consult':\n self._form = EditConsultBillForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n else:\n return False\n elif self._scope == 'hosting':\n self._form = EditHostingBillForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n else:\n return False\n self.add_error('Bill scope not found.')\n return False\n\n def delete_bill(self):\n if self._bill:\n self._bill.is_delete = True\n self._bill.status = 'deleted'\n self._bill.save()\n else:\n return False\n\n #\n #\n # GETTER FUNCTIONS\n #\n #\n\n def get_bill(self, pk=None):\n if pk:\n if self._scope:\n if self._scope == 'project':\n try:\n self._bill = ProjectBill.objects.get(pk=pk)\n return self._bill\n except ObjectDoesNotExist:\n self.add_error(get_status_message(self._message_info, self.language)['message'])\n return False\n elif self._scope == 'consult':\n try:\n self._bill = ConsultBill.objects.get(pk=pk)\n return self._bill\n except ObjectDoesNotExist:\n self.add_error(get_status_message(self._message_info, self.language)['message'])\n return False\n elif self._scope == 'hosting':\n try:\n self._bill = HostingBill.objects.get(pk=pk)\n return self._bill\n except ObjectDoesNotExist:\n self.add_error(get_status_message(self._message_info, self.language)['message'])\n return False\n self.add_error('Bill scope not found.')\n return False\n return self._bill\n\n def get_object_bills(self, obj, count=False):\n if self._scope:\n if self._scope == 'project':\n if count:\n return ProjectBill.objects.filter(project=obj).count()\n return self.return_list(ProjectBill.objects.filter(project=obj).order_by('-id'))\n elif self._scope == 'consult':\n if count:\n return ConsultBill.objects.filter(consult=obj).count()\n return self.return_list(ConsultBill.objects.filter(consult=obj).order_by('-id'))\n elif self._scope == 'hosting':\n if count:\n return HostingBill.objects.filter(hosting=obj).count()\n return self.return_list(HostingBill.objects.filter(hosting=obj).order_by('-id'))\n self.add_error('Bill scope not found.')\n return []\n\n def get_reported_bills(self, count=False):\n return {\n 'project_bills': ProjectBill.objects.filter(reported=True).count() if count else self.return_list(ProjectBill.objects.filter(\n reported=True).order_by('-id')),\n 'consult_bills': ConsultBill.objects.filter(reported=True).count() if count else self.return_list(ConsultBill.objects.filter(\n reported=True).order_by('-id')),\n 'hosting_bills': HostingBill.objects.filter(reported=True).count() if count else self.return_list(HostingBill.objects.filter(\n reported=True).order_by('-id')),\n }\n\n def get_client_bills(self, client, count=False):\n return {\n 'project': ProjectBill.objects.filter(client=client).count() if count\n else\n self.return_list(ProjectBill.objects.filter(client=client).order_by('-id')),\n 'consult': ConsultBill.objects.filter(client=client).count() if count\n else\n self.return_list(ConsultBill.objects.filter(client=client).order_by('-id')),\n 'hosting': HostingBill.objects.filter(client=client).count() if count\n else\n self.return_list(HostingBill.objects.filter(client=client).order_by('-id')),\n }\n\n def get_client_balance(self, client):\n balance = 0\n if client:\n bills = self.get_client_bills(client)\n for x in bills['project']:\n if not x.payment_received:\n balance = balance + x.amount\n for x in bills['consult']:\n if not x.payment_received:\n balance = balance + x.amount\n for x in bills['hosting']:\n if not x.payment_received:\n balance = balance + x.amount\n else:\n self.add_error('Client not found.')\n return balance\n\n #\n #\n # UPDATE FUNCTIONS\n #\n #\n\n def upload_bill_file(self, info, files):\n if self._scope:\n if self._bill.bill_file:\n self._bill.bill_file.delete()\n if self._scope == 'project':\n self._form = ProjectBillFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'consult':\n self._form = ConsultBillFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'hosting':\n self._form = HostingBillFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n self.add_error('Bill scope not found.')\n return False\n\n def upload_receipt_file(self, info, files):\n if self._scope:\n if self._bill.receipt_file:\n self._bill.receipt_file.delete()\n if self._scope == 'project':\n self._form = ProjectBillReceiptFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'consult':\n self._form = ConsultBillReceiptFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n elif self._scope == 'hosting':\n self._form = HostingBillReceiptFileForm(info, files=files, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n return False\n self.add_error('Bill scope not found.')\n return False\n\n def mark_as_received(self):\n if self._bill:\n self._bill.status = 'paid'\n self._bill.payment_submitted = True\n self._bill.payment_received = True\n self._bill.save()\n return True\n return False\n\n def report(self):\n if self._bill:\n self._bill.reported = True\n self._bill.save()\n return True\n return False\n\n def remove_reported(self):\n if self._bill:\n self._bill.reported = False\n self._bill.save()\n return True\n return False\n\n def submit_payment(self, info):\n if self._bill:\n if self._scope:\n info['payment_submitted'] = True\n info['payment_submission_date'] = timezone.now()\n info['payed_by'] = self._user.id\n info['status'] = 'payment submitted'\n if self._scope == 'project':\n self._form = ProjectBillPaymentForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n elif self._scope == 'consult':\n self._form = ConsultBillPaymentForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n elif self._scope == 'hosting':\n self._form = HostingBillPaymentForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n self.add_error('Bill scope not found.')\n return False\n else:\n return False\n\n def add_payment_confirmation_id(self, info):\n if self._bill:\n if not (self._bill.payment_received and self._bill.payment_submitted):\n self.add_error('The bill payment has to be marked as received.')\n return False\n if self._scope:\n info['payment_submission_date'] = timezone.now()\n if self._scope == 'project':\n self._form = AddProjectBillPaymentIdForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n elif self._scope == 'consult':\n self._form = AddConsultBillPaymentIdForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n elif self._scope == 'hosting':\n self._form = AddHostingBillPaymentIdForm(info, instance=self._bill)\n if self.validate_form():\n self._bill = self._object\n return True\n self.add_error('Bill scope not found.')\n return False\n", "sub_path": "home/shared_utils/util_billing.py", "file_name": "util_billing.py", "file_ext": "py", "file_size_in_byte": 12806, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "home.util_base.BaseUtil", "line_number": 23, "usage_type": "name"}, {"api_name": "home.forms.billing_forms.NewProjectBillForm", "line_number": 48, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.NewConsultBillForm", "line_number": 54, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.NewHostingBillForm", "line_number": 60, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.EditProjectBillForm", "line_number": 71, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.EditConsultBillForm", "line_number": 78, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.EditHostingBillForm", "line_number": 85, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects.get", "line_number": 113, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 113, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 115, "usage_type": "name"}, {"api_name": "home.languages.util_status_messages.get_status_message", "line_number": 116, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects.get", "line_number": 120, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 120, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 122, "usage_type": "name"}, {"api_name": "home.languages.util_status_messages.get_status_message", "line_number": 123, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects.get", "line_number": 127, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 127, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 127, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 129, "usage_type": "name"}, {"api_name": "home.languages.util_status_messages.get_status_message", "line_number": 130, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects.filter", "line_number": 140, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 140, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 140, "usage_type": "name"}, {"api_name": "project.models.ProjectBill.objects.filter", "line_number": 141, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 141, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 141, "usage_type": "name"}, {"api_name": "consult.models.ConsultBill.objects.filter", "line_number": 144, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 144, "usage_type": "name"}, {"api_name": "consult.models.ConsultBill.objects.filter", "line_number": 145, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 145, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 145, "usage_type": "name"}, {"api_name": "hosting.models.HostingBill.objects.filter", "line_number": 148, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 148, "usage_type": "name"}, {"api_name": "hosting.models.HostingBill.objects.filter", "line_number": 149, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 149, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 149, "usage_type": "name"}, {"api_name": "project.models.ProjectBill.objects.filter", "line_number": 155, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 155, "usage_type": "name"}, {"api_name": "consult.models.ConsultBill.objects.filter", "line_number": 157, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 157, "usage_type": "name"}, {"api_name": "hosting.models.HostingBill.objects.filter", "line_number": 159, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 159, "usage_type": "name"}, {"api_name": "project.models.ProjectBill.objects.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 165, "usage_type": "name"}, {"api_name": "project.models.ProjectBill.objects.filter", "line_number": 167, "usage_type": "call"}, {"api_name": "project.models.ProjectBill.objects", "line_number": 167, "usage_type": "attribute"}, {"api_name": "project.models.ProjectBill", "line_number": 167, "usage_type": "name"}, {"api_name": "consult.models.ConsultBill.objects.filter", "line_number": 168, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 168, "usage_type": "name"}, {"api_name": "consult.models.ConsultBill.objects.filter", "line_number": 170, "usage_type": "call"}, {"api_name": "consult.models.ConsultBill.objects", "line_number": 170, "usage_type": "attribute"}, {"api_name": "consult.models.ConsultBill", "line_number": 170, "usage_type": "name"}, {"api_name": "hosting.models.HostingBill.objects.filter", "line_number": 171, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 171, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 171, "usage_type": "name"}, {"api_name": "hosting.models.HostingBill.objects.filter", "line_number": 173, "usage_type": "call"}, {"api_name": "hosting.models.HostingBill.objects", "line_number": 173, "usage_type": "attribute"}, {"api_name": "hosting.models.HostingBill", "line_number": 173, "usage_type": "name"}, {"api_name": "home.forms.billing_forms.ProjectBillFileForm", "line_number": 204, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.ConsultBillFileForm", "line_number": 210, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.HostingBillFileForm", "line_number": 216, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.ProjectBillReceiptFileForm", "line_number": 229, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.ConsultBillReceiptFileForm", "line_number": 235, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.HostingBillReceiptFileForm", "line_number": 241, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 276, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 276, "usage_type": "name"}, {"api_name": "home.forms.billing_forms.ProjectBillPaymentForm", "line_number": 280, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.ConsultBillPaymentForm", "line_number": 285, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.HostingBillPaymentForm", "line_number": 290, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 305, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 305, "usage_type": "name"}, {"api_name": "home.forms.billing_forms.AddProjectBillPaymentIdForm", "line_number": 307, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.AddConsultBillPaymentIdForm", "line_number": 312, "usage_type": "call"}, {"api_name": "home.forms.billing_forms.AddHostingBillPaymentIdForm", "line_number": 317, "usage_type": "call"}]} +{"seq_id": "401597914", "text": "from .Department import Department\nfrom .functions import *\nimport datetime\nnow = datetime.datetime.now()\nclass Employee(Department):\n def __init__(self):\n Department.__init__(self)\n self.FullName = \"\"\n self.Age = 0\n self.PhoneNum = \"\"\n self.TurnTo = str(now.day)+\"/\"+str(now.month)+\"/\"+str(now.year)\n self.Salary = 0\n self.editlist = {\n \"1\": ['Organization Name', self.Set_OrgName],\n \"2\": ['Department Name', self.Set_DepName],\n \"3\": ['Floor', self.Set_Floor],\n \"4\": ['Info', self.Set_Info],\n \"5\": ['Full Name', self.Set_Name],\n \"6\": ['Age', self.Set_Age],\n \"7\": ['Extension Phone Number', self.Set_Phone],\n \"8\": ['Salary', self.Set_Salary],\n }\n\n def Add_Employee(self):\n Department.AddDepartment(self)\n self.Set_Name()\n self.Set_Age()\n self.Set_Phone()\n self.Set_Salary()\n\n def Set_Name(self):\n self.FullName = input(\"Enter full name\\n\")\n\n def Set_Age(self):\n self.Age = input(\"Enter Age\\n\")\n while (True):\n if is_int(self.Age):\n break\n else:\n self.Age = input(\"Enter Age\\n\")\n\n def Set_Phone(self):\n self.PhoneNum = input(\"Enter extension number\\n\")\n\n def Set_Salary(self):\n self.Salary = input(\"Enter Salary\\n\")\n while (True):\n if is_float(self.Salary):\n break\n else:\n self.Salary = input(\"Enter Salary\\n\")\n\n def Show(self):\n Department.Show(self)\n print(\"\\n5. Full Name: \"+self.FullName+\"\\n6. Age: \"+self.Age+\"\\n7. Extension Number: \"+self.PhoneNum+\"\\n8. Turn-to: \"+self.TurnTo+\"\\n9. Salary: \"+self.Salary)\n\n def EditDep(self):\n for i in self.editlist:\n print(i + ' - ' + self.editlist[i][0])\n while (True):\n buf = input(\"Select a field for editing\\n\")\n if is_int(buf) and 0 < int(buf) <= len(self.editlist):\n self.editlist[buf][1]()\n break\n else:\n print('Error. This field does not exist')\n answer = input('Change Another Field?\\nYes/No')\n if (str.lower(answer) != 'yes'):\n break", "sub_path": "st32/Employee.py", "file_name": "Employee.py", "file_ext": "py", "file_size_in_byte": 2299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "datetime.datetime.now", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 4, "usage_type": "attribute"}, {"api_name": "Department.Department", "line_number": 5, "usage_type": "name"}, {"api_name": "Department.Department.__init__", "line_number": 7, "usage_type": "call"}, {"api_name": "Department.Department", "line_number": 7, "usage_type": "name"}, {"api_name": "Department.Department.AddDepartment", "line_number": 25, "usage_type": "call"}, {"api_name": "Department.Department", "line_number": 25, "usage_type": "name"}, {"api_name": "Department.Department.Show", "line_number": 54, "usage_type": "call"}, {"api_name": "Department.Department", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "561530594", "text": "from os import close\nimport pandas as pd\nimport pandas_datareader.data as web\nimport datetime as dt\n\ndef main():\n #-----------------------------------\n #1 - Get basic stock pricing on day-to-day basis\n #-----------------------------------\n \n startdate = dt.datetime(2021,1,1)\n enddate = dt.datetime(2021,7,1)\n\n df = web.DataReader('VIPS', 'yahoo', startdate, enddate)\n df.reset_index(inplace=True,drop=False)\n df['Date'] \n\n\n # Print first couple data entries to make sure data is correct\n print(df.head())\n\n # 1a - Save to CSV\n #df.to_csv('tesla.csv', )\n\n # Read from CSV\n #df = pd.read_csv('tesla.csv')\n print(df)\n\n # 1b - Find Average\n \n #Print average for 'High' column\n print(df[\"High\"].mean())\n #Print average for 'Low' column using dot notation\n print(df.Low.mean())\n #Print mean of multiple columns\n print(df[[\"Open\", \"Close\"]].mean())\n #General description of dataframe\n print(df.describe())\n\n #-----------------------------------\n # 4\n # Johnny's circumstances:\n # - He invests $1,000 at the start of each month, regardless of price (i.e., Dollar Cost Average (DCA))\n # How much is his investment worth at the end of 2020?\n #-----------------------------------\n\n #variable to hold data from the date column\n dateCol = df[\"Date\"]\n\n #variables for Johnny's savings, how much he invests each time, and his total return\n savingsJohnny = 12000\n invAmount = 1000\n resultJohnny = 0\n\n #array to hold the value of TSLA stock each time he trades\n investments = []\n\n #dynamic variable to see if the month has changed\n month_hold = 0\n\n #loop through the entire length of the dataframe\n for i in range(len(openCol-1)):\n if (dateCol[i].month != month_hold):\n investments.append(openCol[i])\n month_hold = dateCol[i].month\n\n #calculate how much each seperate investment has grown and add to the total\n for val in investments:\n resultJohnny += (lastDayVal - val)*invAmount/val\n\n #add how much Tammy has invested to reflect proper amount in her account\n resultJohnny += savingsJohnny\n print(\"Johnny's initial investment grew to ${:,.2f}\".format(resultJohnny))\n \n \nif __name__ == \"__main__\":\n main()", "sub_path": ".problems/strategy_johnny.py", "file_name": "strategy_johnny.py", "file_ext": "py", "file_size_in_byte": 2281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "datetime.datetime", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas_datareader.data.DataReader", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas_datareader.data", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "649971767", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/herbert/dev/python/sctdev/simpleproject/simpleproject/../../communitytools/sphenecoll/sphene/sphblog/utils.py\n# Compiled at: 2012-03-17 12:42:14\nimport re, unicodedata\nfrom htmlentitydefs import name2codepoint\nfrom django.utils.encoding import smart_unicode, force_unicode\nfrom slughifi import slughifi\n\ndef slugify(s, entities=True, decimal=True, hexadecimal=True, model=None, slug_field='slug', pk=None):\n s = smart_unicode(s)\n if len(s) > 40:\n s = s[:40]\n s = slughifi(s)\n slug = s\n if model:\n\n def get_query():\n query = model.objects.filter(**{slug_field: slug})\n if pk:\n query = query.exclude(pk=pk)\n return query\n\n counter = 2\n while get_query():\n slug = '%s-%s' % (s, counter)\n counter += 1\n\n return slug", "sub_path": "pycfiles/django-sct-0.7.tar/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.utils.encoding.smart_unicode", "line_number": 13, "usage_type": "call"}, {"api_name": "slughifi.slughifi", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "246706219", "text": "import tkinter as tk\nfrom PIL import Image,ImageTk\nimport winsound\nfrom src.main.model.Game import Game\nfrom src.main.model.AIGame import AIGame\n\n# Constant Declarations\nSCALE_MULTIPLIER = 1.5\nDEFAULT_SIZE = 50\nSQUARESIZE = int(DEFAULT_SIZE*SCALE_MULTIPLIER)\n\n\nclass ChessApplication(tk.Tk):\n def __init__(self, *args, **kwargs):\n tk.Tk.__init__(self, *args, **kwargs)\n\n # Create Main Window\n self.window = tk.Frame(self)\n self.window.pack(side=\"top\", fill=\"both\", expand=True)\n self.window.rowconfigure(0, weight=1)\n self.window.columnconfigure(0, weight=1)\n self.minsize(8*SQUARESIZE, 8*SQUARESIZE + SQUARESIZE//2)\n self.maxsize(8*SQUARESIZE, 8*SQUARESIZE + SQUARESIZE//2)\n self.wm_title(\"Chess\")\n self.iconphoto(self, ImageTk.PhotoImage(file=\".\\\\img\\\\blkKnight.png\"))\n\n # Initialize Scenes\n self.scenes = {}\n\n # For now, main page is a new game\n self.new_game(\"Normal\")\n\n # Create Option Menu\n menu_bar = tk.Menu(self)\n menu_bar.add_command(label=\"New Game\", command=lambda: self.new_game(\"Normal\"))\n ai_menu = tk.Menu(menu_bar, tearoff=0)\n easy_color_menu = tk.Menu(ai_menu, tearoff=0)\n easy_color_menu.add_command(label=\"Black\", command=lambda: self.new_game(\"BlackEasy\"))\n easy_color_menu.add_command(label=\"White\", command=lambda: self.new_game(\"WhiteEasy\"))\n hard_color_menu = tk.Menu(ai_menu, tearoff=0)\n hard_color_menu.add_command(label=\"Black\", command=lambda: self.new_game(\"BlackHard\"))\n hard_color_menu.add_command(label=\"White\", command=lambda: self.new_game(\"WhiteHard\"))\n ai_menu.add_cascade(label=\"Easy\", menu=easy_color_menu)\n ai_menu.add_cascade(label=\"Hard\", menu=hard_color_menu)\n menu_bar.add_cascade(label=\"Versus AI\", menu=ai_menu)\n self.config(menu=menu_bar)\n self.center()\n\n def center(win):\n win.update_idletasks()\n width = win.winfo_width()\n height = win.winfo_height()\n x = (win.winfo_screenwidth() // 2) - (width // 2)\n y = (win.winfo_screenheight() // 2) - (height // 3)\n win.geometry('{}x{}+{}+{}'.format(width, height, x, y-100))\n\n def show_scene(self,cont):\n scene = self.scenes[cont]\n self.tkraise(scene)\n # TODO: redraw scene on top\n\n def new_game(self,type):\n self.scenes[ChessGUI] = ChessGUI(self.window,self,type)\n\n\nclass ChessGUI(tk.Canvas):\n def __init__(self, parent,controller,type):\n tk.Canvas.__init__(self, parent)\n self.grid(row=0, column=0, sticky=\"nsew\")\n self.imgs = []\n self.first_input = None\n self.last_highlight = None\n self.bind(\"\", self.handle_click)\n games = {\n \"Normal\": Game(),\n \"BlackEasy\": AIGame(\"Black\",1),\n \"BlackHard\": AIGame(\"Black\",1),\n \"WhiteEasy\": AIGame(\"White\",2),\n \"WhiteHard\": AIGame(\"White\",2)\n }\n self.game = games.get(type)\n self.redraw()\n\n def handle_click(self, event):\n x_coord = int(event.x / SQUARESIZE)\n y_coord = int(event.y / SQUARESIZE)\n # Translate coords to squares\n letter = chr(x_coord+97)\n num = str(8-y_coord)\n square = letter+num\n if self.first_input is None:\n # First click:\n if self.game.board.has_color_piece(self.game.turn, square):\n # Your first click is on one of your pieces, highlight it\n self.first_input = square\n self.last_highlight = self.draw_image(\".\\\\img\\\\highlight.png\", x_coord, y_coord)\n else:\n self.last_highlight = None\n elif self.first_input is not None:\n # Second click:\n if self.game.make_move(self.first_input, square):\n # Second click is a valid move, make it and reset\n if self.game.turn == \"White\":\n winsound.Beep(260, 150)\n pass\n else:\n winsound.Beep(200, 150)\n pass\n self.last_highlight = None\n self.first_input = None\n self.redraw()\n elif self.game.board.has_color_piece(self.game.turn, square):\n # Second click is not a valid move, but one of your pieces. Highlight it\n self.first_input = square\n self.last_highlight = self.draw_image(\".\\\\img\\\\highlight.png\", x_coord, y_coord)\n else:\n # Second click is not one of your pieces, and not a valid move. Reset highlights/inputs\n self.first_input = None\n self.last_highlight = None\n\n def redraw(self):\n # Delete Old Images\n self.imgs.clear()\n # Redraw Squares\n for sqr in self.game.board.squares:\n if sqr.color == \"Black\":\n sqr_file = \".\\\\img\\\\black_square.png\"\n else:\n sqr_file = \".\\\\img\\\\white_square.png\"\n self.imgs.append(self.draw_image(sqr_file, sqr.x_coord, sqr.y_coord))\n # Redraw Pieces\n for piece in self.game.board.pieces:\n if piece.color == \"White\":\n piece_file = \".\\\\img\\\\wht\"+piece.name+\".png\"\n else:\n piece_file = \".\\\\img\\\\blk\"+piece.name+\".png\"\n x_coord = ord(piece.square[0]) - 97\n y_coord = 8 - int(piece.square[1])\n self.imgs.append(self.draw_image(piece_file, x_coord, y_coord))\n # Redraw Bottom Menu\n self.create_rectangle((0, 8*SQUARESIZE, 8*SQUARESIZE, 8*SQUARESIZE+SQUARESIZE//2), fill=\"Black\", outline=\"Black\")\n display_message = self.game.turn+\" To Move...\"\n if self.game.is_checked(self.game.turn):\n if self.game.is_checkmated(self.game.turn):\n display_message = \"Checkmate,\"\n winsound.Beep(500, 100)\n winsound.Beep(500, 100)\n if self.game.turn == \"White\":\n display_message += \" Black Wins!\"\n else:\n display_message += \" White Wins!\"\n else:\n display_message = \"Check! \" + display_message\n winsound.Beep(500, 150)\n if self.game.is_stalemated(self.game.turn):\n display_message = \"Stalemate, Draw!\"\n self.create_text((4*SQUARESIZE, 8*SQUARESIZE+(SQUARESIZE//4)), text=display_message, font=(\"Fixedsys\", str(int(16*SCALE_MULTIPLIER))), fill=\"White\")\n\n def draw_image(self, filename, x_coord, y_coord):\n raw_img = Image.open(filename)\n resized = raw_img.resize((SQUARESIZE, SQUARESIZE), Image.ANTIALIAS)\n img = ImageTk.PhotoImage(resized)\n self.create_image(SQUARESIZE * x_coord, SQUARESIZE * y_coord, image=img, anchor=tk.NW)\n return img\n\n\napp = ChessApplication()\napp.mainloop()\n", "sub_path": "src/main/view/ChessApplication.py", "file_name": "ChessApplication.py", "file_ext": "py", "file_size_in_byte": 6897, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "tkinter.Tk", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tkinter.Tk.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 25, "usage_type": "name"}, {"api_name": "tkinter.Menu", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Menu", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas.__init__", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 68, "usage_type": "attribute"}, {"api_name": "src.main.model.Game.Game", "line_number": 75, "usage_type": "call"}, {"api_name": "src.main.model.AIGame.AIGame", "line_number": 76, "usage_type": "call"}, {"api_name": "src.main.model.AIGame.AIGame", "line_number": 77, "usage_type": "call"}, {"api_name": "src.main.model.AIGame.AIGame", "line_number": 78, "usage_type": "call"}, {"api_name": "src.main.model.AIGame.AIGame", "line_number": 79, "usage_type": "call"}, {"api_name": "winsound.Beep", "line_number": 104, "usage_type": "call"}, {"api_name": "winsound.Beep", "line_number": 107, "usage_type": "call"}, {"api_name": "winsound.Beep", "line_number": 146, "usage_type": "call"}, {"api_name": "winsound.Beep", "line_number": 147, "usage_type": "call"}, {"api_name": "winsound.Beep", "line_number": 154, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 160, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 160, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 161, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 161, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 162, "usage_type": "name"}, {"api_name": "tkinter.NW", "line_number": 163, "usage_type": "attribute"}]} +{"seq_id": "561416345", "text": "import csv\nfrom django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom django.core.urlresolvers import reverse\nfrom django.core.mail import send_mail\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template import RequestContext\nfrom django.conf import settings\nfrom assessment.models import * \nfrom assessment.core import survey_rollup, add_answers, add_risks, calc_base_risk, calc_company_risk, is_owner, write_answers, is_in_range\nfrom assessment.responses import reportBuilder, companyBrief\nfrom assessment.forms import CompanyForm, UserForm\n\n\n\ndef assess_main(request):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n\n #profile = request.user.get_profile() #get_profile() deprecated with Django 1.5\n profile = User_profile.objects.get(user=request.user)\n \n #Get any companies attached to user\n profile.companies = Company.objects.filter(user_profile_id=profile.id)\n \n #If user is sponsored get sponsor's name\n if profile.sponsor_id:\n profile.sponsor_name = User.objects.get(id=profile.sponsor_id).get_full_name()\n else:\n profile.sponsor_name = ''\n \n #If user is a sponsor get list of clients and companies attached to clients\n if profile.is_sponsor == True:\n profile.clients = User_profile.objects.filter(sponsor_id=request.user.id)\n for client in profile.clients:\n client.companies = Company.objects.filter(user_profile_id=client.id)\n else:\n profile.clients = False\n \n #If user is Staff get list of all users and their attached companies that\n #neither belong to the user or a client of the user\n if request.user.is_staff == True:\n query = User_profile.objects.exclude(id=request.user.id)\n profile.other_users = query.exclude(sponsor_id=request.user.id).order_by('sponsor_id')\n #profile.other_users = User_profile.objects.exclude(Q(sponsor_id=profile.id) | Q(sponsor_id__isnull=True)).order_by('sponsor_id')\n for other_user in profile.other_users:\n if other_user.sponsor_id:\n other_user.sponsor_name = User.objects.get(id=other_user.sponsor_id).get_full_name()\n else:\n other_user.sponsor_name = 'Not Sponsored'\n other_user.companies = Company.objects.filter(user_profile_id=other_user.id)\n \n #For padding \"Your Companies\" table out to full rows\n tbl_padding = range(3 - (len(profile.companies) % 3))\n \n #IF user is a sponsor or is allowed multiple companies\n if profile.is_sponsor == True or profile.multi_company == True:\n return render_to_response('assess_main.html', {'profile': profile, 'tbl_padding': tbl_padding},context_instance=RequestContext(request))\n elif not profile.companies: #IF not a sponsor, not multi company and no company attached force add a company\n return HttpResponseRedirect(reverse('assessment.views.company_add'))\n else: #A single company user that has a company attached - send to company main\n return HttpResponseRedirect('/company/%s' % profile.companies[0].id)\n\n\ndef assess_listing(request):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n survey = survey_rollup(1) #Survey 1\n if not request.user.is_staff:\n #Kick user out by logging off\n return HttpResponseRedirect(reverse('views.logout_view'))\n return render_to_response('assess_listing.html',{'survey': survey},context_instance=RequestContext(request))\n \n \ndef co_main(request, company_id):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n try:\n company = Company.objects.get(id=company_id)\n except Company.DoesNotExist:\n raise Http404\n \n if not is_owner(company_id, request.user.id):\n #and not request.user.is_staff:\n return HttpResponseRedirect(reverse('views.logout_view'))\n \n if company:\n co_survey = survey_rollup(company.survey_id)\n add_answers(co_survey, company.id)\n add_risks(co_survey, company.growth_stage_id)\n calc_base_risk(co_survey)\n calc_company_risk(co_survey)\n\n return render_to_response('co_main.html',\n {'company': company, 'co_survey': co_survey},\n context_instance=RequestContext(request))\n\n\ndef company_add(request):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n if request.method == 'POST': #Check for \"Cancel\" button\n submit = request.POST.get('cancel', None)\n if submit:\n return HttpResponseRedirect('/assess/')\n\n form = CompanyForm(request.POST or None)\n if form.is_valid():\n new_co = form.save(commit=False)\n new_co.survey = Survey.objects.get(pk=1) #always survey 1 until there are more surveys\n new_co.user_profile = request.user.get_profile()\n new_co.save()\n return HttpResponseRedirect('/company/%s' % new_co.id)\n\n profile = request.user.get_profile()\n page_text = {'title': 'Add Company'}\n if profile.is_sponsor == True or profile.multi_company == True:\n page_text['headline'] = 'Add New Company'\n page_text['subhead'] = ''\n else:\n page_text['headline'] = 'Add Company Information'\n page_text['subhead'] = 'Please add your company information to proceed with the profile.'\n return render_to_response('company_add.html', {'page_text': page_text, 'form': form},\n context_instance=RequestContext(request))\n \ndef company_edit(request, co_id):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n if not is_owner(co_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n\n company = get_object_or_404(Company, pk=co_id)\n\n if request.method == 'POST': #Check for \"Cancel\" button\n submit = request.POST.get('cancel', None)\n if submit:\n return HttpResponseRedirect('/company/%s' % company.id)\n\n form = CompanyForm(request.POST or None, instance = company)\n if form.is_valid():\n company = form.save()\n company.save()\n return HttpResponseRedirect('/company/%s' % company.id)\n #import pdb; pdb.set_trace()\n return render_to_response('company_edit.html',\n {'title': 'Edit Company', 'form': form, 'company': company},\n context_instance=RequestContext(request))\n\ndef section(request, sect_id, co_id):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n if not is_owner(co_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n company = Company.objects.get(id=co_id)\n co_survey = survey_rollup(company.survey_id, sect_id)\n add_answers(co_survey, company.id)\n \n if request.method == 'POST':\n submit = request.POST.get('cancel', None) #Check for cancel button\n if submit:\n return HttpResponseRedirect('/company/%s' % company.id)\n errors = False\n for sect in co_survey.section:\n for subsect in sect.subsection:\n if subsect.multi_answer == False:\n selection = request.POST.get('subsect_' + str(subsect.id), None)\n if not selection:\n errors = True\n subsect.error_text = 'You must select an option'\n else:\n for quest in subsect.question:\n quest.answer.answer_yn = True if selection == str(quest.id) else False\n #quest.answer.save()\n if subsect.multi_answer == True:\n for quest in subsect.question:\n if quest.input_type_id == 'bool':\n response = str(quest.id) in request.POST.getlist('subsect_' + str(subsect.id))\n quest.answer.answer_yn = response\n elif quest.input_type_id == 'num':\n response = request.POST.get('question_' + str(quest.id), 0)\n response = response.replace(',','')\n quest.answer.answer_numeric = int(float(response)) if len(response) > 0 else 0\n elif quest.input_type_id[:3] == 'txt':\n response = request.POST.get('question_' + str(quest.id), '')\n quest.answer.answer_text = response\n else:\n raise Exception(\"No handler for input_type_id '\" + quest.input_type_id + \"' in views.section.\")\n\n \n if not errors:\n write_answers(co_survey)\n return HttpResponseRedirect('/company/%s' % company.id)\n \n return render_to_response('answer_edit.html',\n {'title': 'Survey Section',\n 'co_survey': co_survey,\n 'company': company},\n context_instance=RequestContext(request))\n \n \ndef section_list(request, survey=1):\n if not request.user.is_staff:\n return HttpResponseRedirect(reverse('views.logout_view'))\n \n survey = Survey.objects.get(id=survey)\n sections = Section.objects.filter(survey_id=survey).order_by('order_in_survey')\n return render_to_response('section_list.html',\n {'survey': survey,\n 'sections': sections},\n context_instance=RequestContext(request))\n \n\ndef section_risk_edit(request, survey_id, sect_id):\n if not request.user.is_staff:\n return HttpResponseRedirect(reverse('views.logout_view'))\n\n #Allowable range for risk values between risk_range and -risk_range\n risk_range = 100\n survey = survey_rollup(survey_id, sect_id)\n \n stages = Growth_stage.objects.all().order_by('id')\n for stage in stages:\n stage.inherent = 'inherent_risk_' + str(stage.id)\n stage.max = 'max_risk_' + str(stage.id)\n stage.risk = 'risk_' + str(stage.id)\n stage.qerror = 'qerror_' + str(stage.id)\n risk_attr_name = 'risk_' + str(stage.id)\n add_risks(survey, stage.id, risk_attr_name)\n \n page_error = False\n\n if request.method == 'POST':\n submit = request.POST.get('cancel', None) #Check for cancel button\n if submit:\n return HttpResponseRedirect('/sectionlist/')\n errors = False\n \n #Update Section Inherent and Maximum risks from POST\n for sect in survey.section:\n for stage in stages:\n x = request.POST.get(stage.inherent, 0)\n setattr(sect, stage.inherent, x)\n #Set css class to highlight error\n if not is_in_range(x, risk_range):\n errors = True\n stage.inherent_error = 'errorHilite'\n x = request.POST.get(stage.max, 0)\n setattr(sect, stage.max, x)\n #Set css class to highlight error\n if not is_in_range(x, risk_range):\n errors = True\n stage.max_error = 'errorHilite'\n\n #Update Question risks for each stage from POST\n for subsect in sect.subsection:\n for quest in subsect.question:\n for stage in stages:\n x = request.POST.get('q_' + str(quest.id) + '_' + stage.risk, 0)\n setattr(quest, stage.risk, x)\n #Set css class to highlight error\n if not is_in_range(x, risk_range):\n errors = True\n setattr(quest, stage.qerror, 'errorHilite')\n \n if not errors:\n #Write Section Inherent and Maximum risks to database \n for sect in survey.section:\n for stage in stages:\n sect_risk, created = Sect_risk.objects.get_or_create(section_id=sect.id, growth_stage_id=stage.id)\n sect_risk.inherent_risk = getattr(sect, stage.inherent)\n sect_risk.max_risk = getattr(sect, stage.max)\n sect_risk.save()\n #Write Risk Factors for each question/growth stage combination back to database\n for subsect in sect.subsection:\n for quest in subsect.question:\n for stage in stages:\n record, created = Risk_factor.objects.get_or_create(question_id=quest.id, growth_stage_id=stage.id)\n record.risk_factor = getattr(quest, stage.risk)\n record.save()\n return HttpResponseRedirect('/sectionlist/')\n \n \n else:\n page_error = 'All input fields must contain an entry between +100 and -100'\n\n return render_to_response('sect_risk_edit.html',\n {'survey': survey,\n 'stages': stages,\n 'page_error': page_error},\n context_instance=RequestContext(request))\n\ndef co_answers(request, company_id):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n try:\n company = Company.objects.get(id=company_id)\n except Company.DoesNotExist:\n raise Http404\n \n if not is_owner(company_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n \n if company:\n co_survey = survey_rollup(company.survey_id)\n add_answers(co_survey, company.id)\n\n return render_to_response('company_answers.html',\n {'company': company, 'co_survey': co_survey},\n context_instance=RequestContext(request))\n\ndef co_answers_csv(request, company_id):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n try:\n company = Company.objects.get(id=company_id)\n except Company.DoesNotExist:\n raise Http404\n \n if not is_owner(company_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n\n if company:\n co_survey = survey_rollup(company.survey_id)\n add_answers(co_survey, company.id)\n\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"somefilenam.csv\"'\n\n writer = csv.writer(response)\n writer.writerow(['Survey', 'Section', 'Subsection', 'Question', 'Response'])\n for sect in co_survey.section:\n for subsect in sect.subsection:\n for quest in subsect.question:\n row = ([co_survey.survey_name, sect.sect_name, subsect.subsect_name, quest.question_text])\n if quest.answer.id:\n if quest.input_type_id == 'bool':\n row.append(quest.answer.answer_yn)\n elif quest.input_type_id == 'num':\n row.append(quest.answer.answer_numeric)\n else:\n row.append(quest.answer.answer_text)\n else:\n row.append('No Answer')\n writer.writerow(row)\n \n return response\n\n\ndef user_add(request):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n if request.method == 'POST': #Check for \"Cancel\" button\n submit = request.POST.get('cancel', None)\n if submit:\n return HttpResponseRedirect('/assess/')\n\n form = UserForm(request.POST or None, user=request.user)\n if form.is_valid():\n new_user = form.save()\n new_user.save()\n profile = new_user.get_profile()\n profile.sponsor_id = request.user.id\n profile.save()\n \n #Build email message to user that added the new user\n message_body = 'You have added the following new user to the NCFuture Profile. '\n message_body += 'This user is \"Sponsored\" by you and will be listed on the Profile Main page as one of your clients.\\n\\n'\n message_body += 'NOTE: These credentials have NOT been sent to the new user. It is up to you to forward the appropriate information.\\n\\n'\n message_body += 'Username: ' + new_user.username + '\\n'\n message_body += 'Password: ' + new_user.tmp_password + '\\n'\n message_body += 'Name: ' + new_user.first_name + ' ' + new_user.last_name + '\\n'\n message_body += 'Email: ' + new_user.email + '\\n'\n message_body += 'Title: ' + profile.title +'\\n'\n message_body += 'Company: ' + profile.user_company + '\\n\\n'\n message_body += 'Can have multiple companies: ' + str(profile.multi_company) + '\\n'\n message_body += 'Can sponsor clients: ' + str(profile.is_sponsor) + '\\n\\n'\n message_body += 'New user added by: ' + request.user.get_full_name() + '\\n'\n send_mail('New NCFuture Profile User Added', message_body,\n 'assessment@ncfuture.com', [request.user.email], fail_silently=False)\n if settings.STAFF_NOTIFICATIONS['add_company']:\n send_mail('New NCFuture Profile User Added', message_body,\n 'assessment@ncfuture.com', ['assessment@ncfuture.com'], fail_silently=False)\n \n return HttpResponseRedirect('/assess/')\n \n return render_to_response('user_add.html', {'title': 'Add New Client', 'form': form},\n context_instance=RequestContext(request))\n\n\ndef co_report_full(request, company_id, survey=1):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n try:\n company = Company.objects.get(id=company_id)\n except Company.DoesNotExist:\n raise Http404\n \n if not is_owner(company_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n\n co_report, risk_scores = reportBuilder(company_id, 'full')\n \n return render_to_response('co_report.html', {'company': company, 'report': co_report}, context_instance=RequestContext(request))\n \ndef co_brief(request, company_id, survey=1):\n if not request.user.is_authenticated():\n return HttpResponseRedirect('/login/?next=%s' % request.path)\n \n try:\n company = Company.objects.get(id=company_id)\n except Company.DoesNotExist:\n raise Http404\n \n if not is_owner(company_id, request.user.id):\n return HttpResponseRedirect(reverse('views.logout_view'))\n\n co_report, risk_scores = reportBuilder(company_id, 'brief')\n co_brief = companyBrief(co_report)\n \n rpt_title = 'NCFuture Business Brief'\n return render_to_response('co_report_brief.html', {'company': company, 'rpt_title': rpt_title, 'report': co_brief}, context_instance=RequestContext(request))\n", "sub_path": "assessment/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 19286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.http.HttpResponseRedirect", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 57, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 57, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 59, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 61, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 66, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 67, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 70, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 71, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 71, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 76, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 81, "usage_type": "name"}, {"api_name": "assessment.core.is_owner", "line_number": 83, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 85, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 85, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 88, "usage_type": "call"}, {"api_name": "assessment.core.add_answers", "line_number": 89, "usage_type": "call"}, {"api_name": "assessment.core.add_risks", "line_number": 90, "usage_type": "call"}, {"api_name": "assessment.core.calc_base_risk", "line_number": 91, "usage_type": "call"}, {"api_name": "assessment.core.calc_company_risk", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 94, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 96, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 101, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 106, "usage_type": "call"}, {"api_name": "assessment.forms.CompanyForm", "line_number": 108, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 114, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 124, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 125, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 129, "usage_type": "call"}, {"api_name": "assessment.core.is_owner", "line_number": 130, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 131, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 131, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 133, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 138, "usage_type": "call"}, {"api_name": "assessment.forms.CompanyForm", "line_number": 140, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 146, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 148, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 152, "usage_type": "call"}, {"api_name": "assessment.core.is_owner", "line_number": 153, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 154, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 154, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 156, "usage_type": "call"}, {"api_name": "assessment.core.add_answers", "line_number": 157, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 162, "usage_type": "call"}, {"api_name": "assessment.core.write_answers", "line_number": 192, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 193, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 195, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 199, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 204, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 204, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 208, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 211, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 216, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 216, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 220, "usage_type": "call"}, {"api_name": "assessment.core.add_risks", "line_number": 229, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 236, "usage_type": "call"}, {"api_name": "assessment.core.is_in_range", "line_number": 245, "usage_type": "call"}, {"api_name": "assessment.core.is_in_range", "line_number": 251, "usage_type": "call"}, {"api_name": "assessment.core.is_in_range", "line_number": 262, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 281, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 287, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 291, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 295, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 300, "usage_type": "name"}, {"api_name": "assessment.core.is_owner", "line_number": 302, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 303, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 303, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 306, "usage_type": "call"}, {"api_name": "assessment.core.add_answers", "line_number": 307, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 309, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 311, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 315, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 320, "usage_type": "name"}, {"api_name": "assessment.core.is_owner", "line_number": 322, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 323, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 323, "usage_type": "call"}, {"api_name": "assessment.core.survey_rollup", "line_number": 326, "usage_type": "call"}, {"api_name": "assessment.core.add_answers", "line_number": 327, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 329, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 332, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 354, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 359, "usage_type": "call"}, {"api_name": "assessment.forms.UserForm", "line_number": 361, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 382, "usage_type": "call"}, {"api_name": "django.conf.settings.STAFF_NOTIFICATIONS", "line_number": 384, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 384, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 385, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 388, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 390, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 391, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 396, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 401, "usage_type": "name"}, {"api_name": "assessment.core.is_owner", "line_number": 403, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 404, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 404, "usage_type": "call"}, {"api_name": "assessment.responses.reportBuilder", "line_number": 406, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 408, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 408, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 412, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 417, "usage_type": "name"}, {"api_name": "assessment.core.is_owner", "line_number": 419, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 420, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 420, "usage_type": "call"}, {"api_name": "assessment.responses.reportBuilder", "line_number": 422, "usage_type": "call"}, {"api_name": "assessment.responses.companyBrief", "line_number": 423, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 426, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 426, "usage_type": "call"}]} +{"seq_id": "260705249", "text": "#! /usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Time : 2017/12/12 20:08\n# @Author : Shiyu Li\n# @Software: PyCharm\n\nimport tensorflow as tf\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\ndef dis(img):\n img2 = img\n if img.dtype != 'uint8':\n img2 = img2.astype(np.uint8)\n\n cv2.namedWindow(\"Image\")\n cv2.imshow(\"Image\", img2)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\ndef read_and_decode(filename):\n \"\"\" Return tensor to read from TFRecord \"\"\"\n filename_queue = tf.train.string_input_producer([filename])\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n features = tf.parse_single_example(serialized_example,\n features={\n 'label': tf.FixedLenFeature([], tf.int64),\n 'img_raw' : tf.FixedLenFeature([], tf.string),\n })\n img = tf.decode_raw(features['img_raw'], tf.float32)\n img = tf.reshape(img, [256, 256, 1])\n # img = tf.cast(img, tf.float32) # if you want to use tfrecords as input.\n label = tf.cast(features['label'], tf.int32)\n return img, label\n\n# visualize data\nimg, label = read_and_decode(\"own_CroppedBossBase-1.0-256x256.tfrecords\")\nimg_batch, label_batch = tf.train.shuffle_batch([img, label],\n batch_size=4,\n capacity=50000,\n min_after_dequeue=10000,\n num_threads=1)\nprint(\"img_batch : %s\" % img_batch._shape)\nprint(\"label_batch : %s\" % label_batch._shape)\n\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n sess.run(init)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n val, l = sess.run([img_batch, label_batch])\n dis(val[0])\n\n coord.request_stop()\n coord.join(threads)\n sess.close()", "sub_path": "Adversarial_sample/BP_based/Steganalysis/make_data/decode_and_visualize.py", "file_name": "decode_and_visualize.py", "file_ext": "py", "file_size_in_byte": 2033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.uint8", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.train.string_input_producer", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.TFRecordReader", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.parse_single_example", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow.decode_raw", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.train.shuffle_batch", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.train.Coordinator", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tensorflow.train.start_queue_runners", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 52, "usage_type": "attribute"}]} +{"seq_id": "613724354", "text": "#Sample Test program to track prices on Amazon for a given product and send email for price drop\n\n\n#import urllib.request\n\n#importing requests library module\nimport requests\n\n#Importing BeautifulSoup library\n#BeautifulSoup is used for parsing and extracting data from HTML webpages\nfrom bs4 import BeautifulSoup\n\n#input the URL of the product to scrape web pages\nURL = 'https://www.amazon.com/dp/B07FPP6TB5/ref=gwd_dc_tve_cm?pf_rd_p=7d3126cc-ba3f-41cc-8f4a-dc96bd30fa11&pf_rd_r=K7Y5FJBRWB4QY21EM7FK'\n\n# URL = 'https://www.amazon.com/Echo-Dot/dp/B07FZ8S74R/ref=sr_1_1?keywords=alexa&qid=1575303017&smid=ATVPDKIKX0DER&sr=8-1'\n\n# URL = 'https://www.bestbuy.com/site/tcl-75-class-led-4-series-2160p-smart-4k-uhd-tv-with-hdr-roku-tv/6319340.p?skuId=6319340'\n\n# URL = 'https://www.google.com/shopping/product/1684588081497658436?psb=1&tbm=shop&prds=epd%3A8233995082323007187%2Cprmr%3A3&ved=0CGEQ0FUoAGoXChMIh_e677CX5gIVjgezAB0sFQs7EAM'\n\n#Configure a \"User-Agent\", which is a unique identifier for every device accessing a webpage\nheaders = {\"User-Agent\": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'}\n\n#Get requests of the webpage\npage = requests.get(URL, headers)\n\n#Parse the contents of the webpage, input is \"contents of the page\"\nsoup = BeautifulSoup(page.content, 'html.parser')\n\nprint(\"Printing status code response of the webpage\",page.status_code)\nprint(\"The response of the page content is...\", page.json)\n# print(\"START OF HTML PARSE WITHOUT PRETTIFY\")\n# p = print(\"Printing contents of the page without Prettify\", soup)\n# print(\"END OF HTML PARSE WITHOUT PRETTIFY\")\n# print(\"START OF HTML PARSE WITH PRETTIFY\")\n# #Display contents of the page as list using \"prettify\" method in \"BeautifulSoup\"\n# #prettify method from BeautifulSoup will convert a BS- BeautifulSoup?? Parse tree to a unicode string\n# q = print(\"Printing contents of the page using Prettify\", soup.prettify)\n# print(\"END OF HTML PARSE WITH PRETTIFY\")\n\n# #Testing to check both contents WITH and WITHOUT PRETTIFY are same, (contents are same)\n# if p==q:\n# print(\"Both texts are same, there's no difference with PRETTIFY\")\n\n#Find the first tag with the id=\"productTitle\" with the \"find\" method, use \"find_all\" to find list of all tags\nprint(\"Product Title is \", soup.find(id=\"productTitle\"))\n\n#Find the first tag with the id=\"price_inside_buybox\" with the \"find\" method\nprint(\"Product Price inside Buybox is \", soup.find(id=\"price_inside_buybox\"))\n\n\n#print(soup.find_all(id=\"productTitle\"))\n\n", "sub_path": "Tracking Prices/price_tracking.py", "file_name": "price_tracking.py", "file_ext": "py", "file_size_in_byte": 2537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "463033767", "text": "import analysistools as atools\nimport pandas as pd\nimport pickle\nimport os\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='')\n\nparser.add_argument('-o','--output', default=\"\", type=str, \n help='output directory for the generated files')\nparser.add_argument('-i','--input', default='', type=str, \n help='input directory, files must be in xyza format')\n\nargs = parser.parse_args()\n\nxyzaPath = args.input\noutPath = args.output\n\ns = {}\n\ntry:\n s = atools.generateSummaries(xyzaPath)\nexcept:\n print('something went wrong')\n\nwith open(os.path.join(outPath,'trajectories.pickle'), 'wb') as f:\n # Pickle the 'data' dictionary using the highest protocol available.\n pickle.dump(s, f, pickle.HIGHEST_PROTOCOL)\n\ndf = pd.DataFrame()\ndata = []\nfor k,v in s.iteritems():\n data.append((k,v['density'],v['clustering'],v['bt'],'budding' if v['bt'] > 0.0 else 'non budding'))\n\ndf = pd.DataFrame(data, columns = ['file','den','cls','bud', 'cat' ]) \ndf.to_csv(os.path.join(outPath,'trajectories-summary.csv'),index=False)", "sub_path": "tools/summarybuilder.py", "file_name": "summarybuilder.py", "file_ext": "py", "file_size_in_byte": 1069, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "analysistools.generateSummaries", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 29, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "535073719", "text": "import keras\nfrom keras.models import Sequential, load_model, Model\nfrom keras.layers import Input, Add, Multiply, Dense, MaxPooling3D, BatchNormalization, Reshape\nfrom keras.layers.convolutional import Conv1D, Conv2D, Conv3D, Convolution2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers.convolutional import ZeroPadding3D, ZeroPadding2D, ZeroPadding1D, UpSampling2D, Cropping2D\nfrom keras.layers.core import Dropout, Flatten\nfrom keras.layers import LeakyReLU, MaxPooling2D, MaxPooling3D,concatenate, Conv2DTranspose, Concatenate\nfrom keras.activations import relu\nfrom keras.callbacks import History, ModelCheckpoint, TensorBoard\nfrom keras import regularizers\nfrom keras.optimizers import Adadelta, RMSprop,SGD,Adam\nfrom sklearn.utils import shuffle\nimport numpy as np\n#from predict import save_image\n# from custom_loss import *\n# from models.neurotech_models import *\nfrom math import sqrt\nfrom helpers import *\nimport json\n\n\ndef encoder(input_img,k):\n\n conv1 = Conv2D(32, (k, k), activation='relu', padding='same')(input_img)\n # conv1 = BatchNormalization()(conv1)\n conv1 = Conv2D(64, (k, k), activation='relu', padding='same')(conv1) # 28 x 28 x 32\n # conv1 = BatchNormalization()(conv1)\n conv2 = Conv2D(128, (k, k), activation='relu', padding='same')(conv1) # 14 x 14 x 64\n # conv2 = BatchNormalization()(conv2)\n conv3 = Conv2D(256, (k, k), activation='relu', padding='same')(conv2) # 7 x 7 x 128 (small and thick)\n # conv3 = BatchNormalization()(conv3)\n # conv3 = Conv2D(256, (k, k), activation='relu', padding='same')(conv3)\n # conv3 = BatchNormalization()(conv3)\n\n return conv3\n\ndef decoder(conv3,k):\n # decoder\n conv4 = Conv2D(128, (k, k), activation='relu', padding='same')(conv3) # 7 x 7 x 128\n # conv4 = BatchNormalization()(conv4)\n conv4 = Conv2D(64, (k, k), activation='relu', padding='same')(conv4)\n # conv4 = BatchNormalization()(conv4)\n conv5 = Conv2D(32, (k, k), activation='relu', padding='same')(conv4) # 14 x 14 x 64\n # conv5 = BatchNormalization()(convk)\n conv5 = Conv2D(16, (k, k), activation='relu', padding='same')(conv5)\n # conv5 = BatchNormalization()(conv5)\n\n decoded = Conv2D(1, (k, k), activation='linear', padding='same')(conv5) # 28 x 28 x 1\n\n return decoded\n\ndef fc(encoded):\n drop1 = Dropout(0.2)(encoded)\n flat = Flatten()(drop1)\n dense1 = Dense(128, activation='relu')(flat)\n drop2 = Dropout(0.2)(dense1)\n dense2 = Dense(64, activation='relu')(drop2)\n drop3 = Dropout(0.2)(dense2)\n out = Dense(2, activation='softmax')(drop3)\n\n return out\n\ndef cnn_binary_classifier(image_dim,verbose=1):\n\n if True:\n autoencoder = load_model('/home/spinney/scripts/python/MRI_Deep_Learning/processed/model/ac_NVY2.hdf5')\n\n input_img = Input(shape=(image_dim[0], image_dim[1], image_dim[2], 1))\n k = 3\n encoded = encoder(input_img,k)\n out = fc(encoded)\n\n classifier = Model(input_img, out)\n\n for l1, l2 in zip(classifier.layers[:5], autoencoder.layers[:5]):\n l1.set_weights(l2.get_weights())\n\n for layer in classifier.layers[:5]:\n layer.trainable = False\n\n\n if verbose > 0:\n print(classifier.summary())\n\n return classifier\n\n\ndef cnn_3D_classifier(image_dim,num_classes,verbose=1):\n\n model = Sequential()\n model.add(\n Conv3D(16, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(image_dim[0],image_dim[1],image_dim[2],1)))\n model.add(MaxPooling3D(pool_size=(2, 2, 2),strides=(2,2,2)))\n model.add(Conv3D(16, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform'))\n model.add(MaxPooling3D(pool_size=(2, 2, 2),strides=(2,2,2)))\n model.add(Conv3D(32, kernel_size=(3, 3, 3), activation='relu', kernel_initializer='he_uniform'))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2)))\n model.add(Flatten())\n model.add(Dense(256, activation='relu', kernel_initializer='he_uniform'))\n model.add(Dense(num_classes, activation='softmax'))\n return model\n\ndef cnn_autoencoder(image_dim,verbose=1):\n\n padSize = int(image_dim[0] % 4 / 2)\n\n input_img = Input(shape=(image_dim[0], image_dim[1], image_dim[2],1))\n k = 3\n\n encoded = encoder(input_img,k)\n decoded = decoder(encoded,k)\n\n autoencoder = Model(input_img, decoded)\n\n if verbose > 0:\n print(autoencoder.summary())\n\n return autoencoder\n\n\n\n\ndef build_model(image_dim, nlabels,nK, n_dil, kernel_size, drop_out, model_type, activation_hidden, activation_output, loss, verbose=0):\n if model_type == 'cnn-autoencoder':\n model = cnn_autoencoder(image_dim)\n elif model_type == 'cnn-binary-classifier':\n model = cnn_binary_classifier(image_dim)\n elif model_type == 'cnn_3D_classifier':\n model = cnn_3D_classifier(image_dim,nlabels)\n\n return model\n\ndef compile_and_run(target_dir, model, model_name, model_type, history_fn, X_train, Y_train, X_validate, Y_validate, nb_epoch, batch_size, nlabels, loss, verbose=0, metric=\"accuracy\", lr=0.005, nGPU=1):\n\n #set compiler\n ada = keras.optimizers.Adam(0.01)\n\n #set checkpoint filename\n checkpoint_fn = str(os.path.join(target_dir, 'model', str(os.path.basename(model_name).split('.')[0]) +\"_checkpoint-{epoch:02d}-{val_loss:.2f}.hdf5\"))\n\n #create checkpoint callback for model\n checkpoint = ModelCheckpoint(checkpoint_fn, monitor='val_loss', verbose=0, save_best_only=True, mode='max')\n\n if nGPU == 0:\n steps_per_epoch = batch_size\n nGPU = 1\n elif nGPU == 1:\n steps_per_epoch = len(X_train) // (batch_size*16)\n else:\n steps_per_epoch = len(X_train) // (batch_size * nGPU)\n\n #compile the model\n #model.compile(loss = , optimizer=ada, metrics=[metric])\n print(\"Compiling model {}...\".format(model_name))\n\n if 'autoencoder' in model_type:\n\n model.compile(loss=loss, optimizer=Adam(0.001))\n\n elif 'classifier' in model_type:\n\n model.compile(loss=loss, optimizer='rmsprop', metrics=[metric])\n\n\n print(\"Training size: {}\\n Validation size: {}\\n\".format(X_train.shape, X_validate.shape))\n\n # train model\n # augmentation generator\n aug = ImageDataGenerator(rotation_range=1,\n #width_shift_range=0.01,\n #height_shift_range=0.01,\n #zoom_range=0,\n fill_mode=\"nearest\")\n #if nGPU > 1:\n if 'autoencoder' in model_type:\n history = model.fit_generator(\n aug.flow(X_train,\n X_train,\n batch_size = batch_size*nGPU),\n validation_data= (X_validate, X_validate),\n steps_per_epoch= steps_per_epoch,\n epochs= nb_epoch,\n callbacks= [checkpoint])\n #callbacks= [TensorBoard(log_dir='/home/spinney/scripts/python/MRI_Deep_Learning/logs/autoencoder')])\n\n elif 'classifier' in model_type:\n history = model.fit_generator(\n aug.flow(X_train,\n Y_train,\n batch_size=batch_size * nGPU),\n validation_data=(X_validate, Y_validate),\n steps_per_epoch=steps_per_epoch,\n epochs=nb_epoch,\n callbacks=[checkpoint])\n\n\n # save model\n model.save(model_name)\n\n with open(history_fn, 'w+') as fp: json.dump(history.history, fp)\n\n return [model, history]\n\n", "sub_path": "keras_models.py", "file_name": "keras_models.py", "file_ext": "py", "file_size_in_byte": 7463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "keras.layers.convolutional.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv3D", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling3D", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv3D", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling3D", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv3D", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling3D", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 137, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 170, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "211540710", "text": "from config import MONGODB_URL, MONGODB_DATABASE, MONGODB_COLLECTION\n\nimport pymongo\n\n\nclass MongoDbHandler:\n\n def send_mongodb(self, data):\n\n myClient = pymongo.MongoClient(MONGODB_URL)\n myDb = myClient[MONGODB_DATABASE]\n myCol = myDb[MONGODB_COLLECTION]\n\n myCol.insert_many(data)\n\n return myCol\n", "sub_path": "mongodb_handler.py", "file_name": "mongodb_handler.py", "file_ext": "py", "file_size_in_byte": 335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "pymongo.MongoClient", "line_number": 10, "usage_type": "call"}, {"api_name": "config.MONGODB_URL", "line_number": 10, "usage_type": "argument"}, {"api_name": "config.MONGODB_DATABASE", "line_number": 11, "usage_type": "name"}, {"api_name": "config.MONGODB_COLLECTION", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "538623081", "text": "import logging\n\nclass Robozinho():\n '''\n qtddeposicoes = Quantidade de posicoes/garrafas que o robo pode andar\n posicao = O robo sempre iniciará da posicao 0, ou posicao sem garrafa\n '''\n def __init__(self, qtdposicoes=6, posicao=0):\n self._posicoes = list(range(qtdposicoes))\n self._posicao = posicao\n\n def posicionar(self, posicao):\n if posicao <= self._posicoes[-1]:\n self._posicao = posicao\n # mover o robo até no maximo a ultima posicao da lista qtddeposicoes\n self.mover(self._posicao)\n else:\n logging.error('Posicao {} fora do range. Maximo={}'.format(posicao, self._posicoes[-1]))\n\n def verificar_posicao_atual(self):\n #print('Movendo carrinho para {}'.format(self._posicao))\n return self._posicao\n\n def mover(self, posicao):\n ##<<>>\n pass\n\n def despejar_conteudo(self):\n self.posicionar(self._posicao)\n self.levanta_braco(self._tempo)\n\n def levanta_braco(self, tempo):\n self._tempo = tempo\n #<<>>\n return 'Levantando braco por {} segundos ate o despejador'.format(tempo)\n\n\nif __name__ == '__main__':\n robo = Robozinho()\n robo.posicionar(1)\n print('Posicao Atual: ' + robo.verificar_posicao_atual().__str__())\n robo.posicionar(4)\n print('Posicao Atual: ' + robo.verificar_posicao_atual().__str__())\n print(robo.levanta_braco(10))\n robo.posicionar(7)\n print('Posicao Atual: ' + robo.verificar_posicao_atual().__str__())\n\n\n", "sub_path": "robozinho.py", "file_name": "robozinho.py", "file_ext": "py", "file_size_in_byte": 1554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.error", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "459784188", "text": "import requests\r\n\r\napi_key = 'asdf1234asdf1234asdf1234'\r\n\r\nurl = 'https://panacea.threatgrid.com/api/v2/samples'\r\n\r\nfile_name = 'file.exe'\r\n\r\nparameters = {'api_key': api_key}\r\n\r\nwith open(file_name, 'rb') as sample:\r\n\tr = requests.post(url, files={'sample': sample}, params=parameters)\r\n\r\nprint(r.json())\r\n", "sub_path": "06_submit_sample.py", "file_name": "06_submit_sample.py", "file_ext": "py", "file_size_in_byte": 307, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "requests.post", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "190152824", "text": "import logging\n\n'''\nusing the logger to log error,info and debug info \nto file\n'''\ndef log_lines_to_file(in_filename):\n \n logging.basicConfig(filename = in_filename,level=logging.DEBUG)\n logger = logging.getLogger(__name__)\n\n logger.info('Start reading database')\n # read database here\n records = {'john': 55, 'tom': 66}\n logger.debug('Records: %s', records)\n logger.info('Updating records ...')\n # update records here\n logger.info('Finish updating records')\n\n'''\nUsing generators to avoid builiding the\nwhole list in memory.\nNote: gen can only be used once\n'''\ndef say_no_to_lists():\n gen = (i for i in range(10) if i>5)\n min(gen)\n #for i in gen:\n # print(i)\n\n\nif __name__ == '__main__':\n #log_lines_to_file('debug.log')\n say_no_to_lists()\n\n", "sub_path": "Pro_Python/propy1.py", "file_name": "propy1.py", "file_ext": "py", "file_size_in_byte": 791, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "117204418", "text": "import sys\n\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton\nfrom PyQt5.QtGui import QPainter, QColor\n\nfrom random import randint\n\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n self.initUI()\n\n def initUI(self):\n self.d = randint(25, 250)\n self.do_paint = False\n self.setGeometry(282, 200, 300, 300)\n self.setWindowTitle('Git и желтые окружности')\n\n self.push_btn = QPushButton(self)\n self.push_btn.resize(170, 60)\n self.push_btn.move(65, 100)\n self.push_btn.setText('Нарисовать круг')\n self.push_btn.clicked.connect(self.paint)\n\n def draw_elipse(self, qp):\n qp.setBrush(QColor('yellow'))\n qp.drawEllipse(10, 10, self.d, self.d)\n\n def paintEvent(self, event):\n if self.do_paint:\n qp = QPainter()\n qp.begin(self)\n self.draw_elipse(qp)\n qp.end()\n\n def paint(self):\n self.do_paint = True\n self.repaint()\n self.push_btn.hide()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = MainWindow()\n ex.show()\n sys.exit(app.exec())\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 9, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "468959933", "text": "import smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom jinja2 import Environment\n\nfrom flask import current_app as app, render_template\n\nclass Mailer():\n\n def send_email(self, receiver, subject, html):\n msg = MIMEMultipart('alternative')\n msg['Subject'] = subject\n msg['From'] = app.config.get('SMTP')['LOGIN']\n msg['To'] = receiver\n\n part = MIMEText(html, 'html', \"utf-8\")\n msg.attach(part)\n\n s = smtplib.SMTP(app.config.get('SMTP')['HOST'], app.config.get('SMTP')['PORT'])\n s.starttls()\n s.login(app.config.get('SMTP')['LOGIN'], app.config.get('SMTP')['PASSWORD'])\n s.sendmail(app.config.get('SMTP')['LOGIN'], \"savo_pusica@mail.ru\", msg.as_string().encode('ascii'))\n s.quit()\n\n def build_report(self, report_type, data):\n html = render_template(\"{report_type}.html\".format(report_type=report_type), data=data)\n self.send_email('savo_pusica@mail.ru', subject=report_type, html=html)\n", "sub_path": "app/services/mailer.py", "file_name": "mailer.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.current_app.config.get", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 13, "usage_type": "name"}, {"api_name": "email.mime.text.MIMEText", "line_number": 16, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config.get", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.current_app.config.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.current_app.config.get", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "575034042", "text": "# Minimally parses a PLA file.\nimport json\nimport os\nimport pprint\nimport sys\n\nimport pyeda\n\nfrom typing import List, Dict\n\nfrom munkres import Munkres, make_cost_matrix, DISALLOWED, print_matrix\nfrom pyeda.inter import *\n\n# PLA files that I'm interested in look like this:\n#\n# # Benchmark \"top\" written by ABC on Mon Jan 25 13:56:23 2021\n# .i 12\n# .o 8\n# .ilb b[0] b[1] b[2] b[3] s[0] s[1] s[2] s[3] a[0] a[1] a[2] a[3]\n# .ob x[0] x[1] x[2] x[3] x[4] x[5] x[6] x[7]\n# .p 20\n# 1---0---0--- 10000000\n# 0----0--0--- 10000000\n# ...more lines\n# .e\n#\n# Some format information here:\n# http://www.ecs.umass.edu/ece/labs/vlsicad/ece667/links/espresso.5.html\n#\n# So the format spec is:\n# A '#' in the first character of the line is a comment.\n# .i %d:\n# Number of input variables\n# .o %d:\n# Number of output functions\n# .ilb :\n# Names of input variables. Must come after .i. There must be the same\n# number of names as there is in .i.\n# .ob :\n# Names of output functions. Must come after .o. There must be the same\n# number of names as there is in .o.\n# .p %d:\n# Number of product terms. May be ignored.\n# .e or .end:\n# Optionally marks end of description.\n# Product term line:\n# .i number of 1/0/- characters, followed by whitespace, followed by\n# .o number of 1/0 characters. These are in the same order as the\n# input and output names.\n\n# Note that this kind of PLA file only represents and-or (aka sum-of-products).\n# Because we're also interested in xor layers, and also multiple layers, we\n# have to use multiples of these files, and also a custom file for xor layers.\n\n\nclass ProductTerm():\n ones: List[str]\n zeros: List[str]\n\n def __init__(self):\n # List of symbolic inputs\n self.ones = []\n self.zeros = []\n self.expr = expr(1)\n\n def __repr__(self):\n pp = pprint.PrettyPrinter(indent=4)\n return pp.pformat({'ones': self.ones, 'zeros': self.zeros})\n\n\nclass OrTerm():\n products: List[ProductTerm]\n\n def __init__(self):\n self.products = []\n self.expr = expr(0)\n\n def __repr__(self):\n pp = pprint.PrettyPrinter(indent=4)\n return pp.pformat({'or_products': self.products, 'expr': self.expr})\n\n\ndef get_database():\n for path in sys.path:\n file = os.path.join(path, \"database.json\")\n if os.path.isfile(file):\n with open(file) as f:\n return json.load(f)\n return None\n\n\nclass PLAParser():\n inputs: List[str]\n outputs: List[str]\n or_terms: Dict[str, OrTerm]\n # If the file is marked with .xor, it's an XOR layer.\n is_xor: bool\n # If the file is marked with .outputs, all outputs are routed to pins.\n is_outputs: bool\n\n def __init__(self, file: str):\n self.inputs = []\n self.outputs = []\n # A map of symbolic output to OrTerm (or Xor)\n self.or_terms = {}\n self.is_xor = False\n self.is_outputs = False\n\n with open(file) as f:\n for line in f.readlines():\n if not self.readline(line):\n break\n print(f\"Inputs : {self.inputs}\")\n print(f\"Outputs : {self.outputs}\")\n pp = pprint.PrettyPrinter(indent=4, depth=3)\n print(f\"OR Terms:\")\n pprint.pprint(self.or_terms)\n\n def readline(self, line: str) -> bool:\n \"\"\"Returns if there are more lines to parse.\"\"\"\n if len(line) == 0:\n return True\n if line.startswith('#'):\n return True\n if line.startswith(\".i \"):\n return True\n if line.startswith(\".o \"):\n return True\n if line.startswith(\".p \"):\n return True\n if line.startswith(\".e\") | line.startswith(\".end\"):\n return False\n if line.startswith(\".xor\"):\n assert not self.is_outputs\n self.is_xor = True\n if line.startswith(\".outputs\"):\n assert not self.is_xor\n self.is_outputs = True\n if line.startswith(\".ilb \"):\n self.inputs = line.split()[1:]\n return True\n if line.startswith(\".ob \"):\n self.outputs = line.split()[1:]\n for output in self.outputs:\n self.or_terms[output] = OrTerm()\n return True\n if line.startswith(\"1\") | line.startswith(\"0\") | line.startswith(\"-\"):\n assert not self.is_outputs\n if self.is_xor:\n self.read_xor_term(line)\n else:\n self.read_or_term(line)\n return True\n return True\n\n def read_or_term(self, line: str):\n parts = line.split()\n assert len(parts) == 2\n assert len(parts[0]) == len(self.inputs)\n assert len(parts[1]) == len(self.outputs)\n\n inputs = parts[0]\n outputs = parts[1]\n product = ProductTerm()\n terms = []\n for i, bit in enumerate(inputs):\n if bit == '0':\n product.zeros.append(self.inputs[i])\n terms.append(Not(self.inputs[i]))\n elif bit == '1':\n product.ones.append(self.inputs[i])\n terms.append(self.inputs[i])\n product.expr = And(*terms)\n\n for i, bit in enumerate(outputs):\n if bit == '1':\n self.or_terms[self.outputs[i]].products.append(product)\n self.or_terms[self.outputs[i]].expr = Or(\n self.or_terms[self.outputs[i]].expr, product.expr)\n\n def read_xor_term(self, line: str):\n parts = line.split()\n assert len(parts) == 2\n assert len(parts[0]) == len(self.inputs)\n assert len(parts[1]) == len(self.outputs)\n\n inputs = parts[0]\n outputs = parts[1]\n terms = []\n for i, bit in enumerate(inputs):\n if bit == '1':\n terms.append(self.inputs[i])\n for i, bit in enumerate(outputs):\n if bit == '1':\n self.or_terms[self.outputs[i]].expr = Xor(*terms)\n\n\nclass Fitter():\n inputs: List[str]\n or_terms: Dict[str, OrTerm]\n all_or_terms: Dict[str, Dict[str, OrTerm]]\n input_mcs: Dict[str, int]\n input_sigs: Dict[str, str]\n\n def __init__(self):\n self.device = None\n self.next_mc = 1\n\n self.inputs = []\n self.outputs = []\n # A map of symbolic output to OrTerm\n self.or_terms = {}\n # A map of block to map of MC to OrTerm\n self.all_or_terms = {}\n self.all_or_exprs = {}\n\n # A map of symbolic input to macrocell number\n self.input_mcs = {}\n # A map of symbolic input to multiplexer signal name\n self.input_sigs = {}\n\n def map_inputs(self):\n print(\"Mapping pin inputs\")\n db = get_database()\n self.device = db[\"ATF1502AS\"]\n\n # For now, assuming this is an input layer, map inputs directly onto\n # MCs starting with MC1. We can use an input MC as an intermediate\n # output by routing its output to MCn_FB.\n\n self.input_mcs = {top_input: self.get_next_mc()\n for top_input in self.inputs}\n for top_input, input_mc in self.input_mcs.items():\n pin = self.device[\"pins\"][\"PLCC44\"][f\"M{input_mc}\"]\n self.input_sigs = {top_input: f\"M{input_mc}_PAD\" for top_input,\n input_mc in self.input_mcs.items()}\n print(f\"assign input {top_input} to MC{input_mc} (pin {pin})\")\n print(f\" set MC{input_mc}.oe_mux GND\")\n\n # This isn't accurate. It's only accurate when the number of intermediate\n # outputs exceeds the number of inputs.\n self.next_mc = 1\n\n # Initialize blocks in all_or_terms\n for block in self.device[\"blocks\"].keys():\n self.all_or_terms[block] = {}\n self.all_or_exprs[block] = {}\n\n def get_next_mc(self) -> int:\n specials = [4, 9, 25, 20] # TDI, TMS, TCK, TDO\n if self.next_mc in specials:\n self.next_mc += 2\n elif self.next_mc > 32:\n return None\n else:\n self.next_mc += 1\n return self.next_mc-1\n\n def map_output_layer(self):\n device = self.device\n\n for i, output in enumerate(self.outputs):\n mc = self.input_mcs[output]\n pin = device[\"pins\"][\"PLCC44\"][f\"M{mc}\"]\n print(f\"Output {output} is at MC{mc} (pin {pin})\")\n print(f\" set MC{mc}.o_mux comb\")\n print(f\" set MC{mc}.oe_mux pt5\")\n print(f\" set MC{mc}.pt5_func as\")\n\n def map_and_or_layer(self):\n print(\"Mapping AND-OR layer\")\n device = self.device\n\n # For now, map the outputs directly onto MCs starting with\n # MC1.\n for output in self.outputs:\n or_term = self.or_terms[output]\n or_expr = or_term.expr\n inv = False\n print(f\"{output} = {or_term.expr}\")\n if isinstance(or_expr, pyeda.boolalg.expr.OrOp) and len(or_expr.xs) > 5:\n # Maybe we can invert, and then use the macrocell's inverter to invert\n # the result?\n nor_expr = espresso_exprs(Not(or_term.expr).to_dnf())\n # espresso_expr returns a tuple\n # to_dnf converts an expression to disjunctive normal form\n # (i.e. sum of products).\n nor_expr = nor_expr[0].to_dnf()\n print(f\"Try the inverse of this instead: {nor_expr}\")\n if isinstance(nor_expr, pyeda.boolalg.expr.OrOp) and len(or_expr.xs) > 5:\n print(\n f\"ERROR: or-term for {output} needs more than\"\n \" one macrocell (5 products), which is not supported yet.\")\n return\n or_expr = nor_expr\n inv = True\n\n mc = self.get_next_mc()\n assert mc is not None, \"Ran out of macrocells\"\n mc_name = f\"MC{mc}\"\n macrocell = device[\"macrocells\"][mc_name]\n block = macrocell[\"block\"]\n print(f\"output {output} mapped to {mc_name}.FB in block {block}\")\n self.all_or_terms[block][mc_name] = or_term\n self.all_or_exprs[block][mc_name] = or_expr\n self.input_mcs[output] = mc\n self.input_sigs[output] = f\"MC{mc}_FB\"\n\n print(f\"set {mc_name}.pt_power on\")\n print(f\"set {mc_name}.pt1_mux sum\")\n print(f\"set {mc_name}.pt2_mux sum\")\n print(f\"set {mc_name}.pt3_mux sum\")\n print(f\"set {mc_name}.pt4_mux sum\")\n print(f\"set {mc_name}.pt5_mux sum\")\n print(f\"set {mc_name}.fb_mux xt\")\n print(f\"set {mc_name}.xor_a_mux sum\")\n print(f\"set {mc_name}.xor_b_mux VCC_pt12\")\n\n # It's weird, but because we have to feed a 1 into one input of\n # the macrocell's XOR, it naturally inverts. There's another\n # optional inverter after that, so if we want the non-inverted\n # output of the OR gate, we have to turn that inverter on!\n if inv:\n print(f\"set {mc_name}.xor_invert off\")\n else:\n print(f\"set {mc_name}.xor_invert on\")\n\n # Now that we've mapped inputs to outputs,\n # add them to the inputs and clear out the outputs.\n self.inputs += self.outputs\n self.outputs = []\n\n print(\"Input mcs:\")\n pprint.pprint(self.input_mcs)\n print(\"Input sigs:\")\n pprint.pprint(self.input_sigs)\n\n def map_and_xor_layer(self):\n print(\"Mapping XOR layer\")\n device = self.device\n\n # For now, map the outputs directly onto MCs starting with\n # the next MC\n for output in self.outputs:\n expr = self.or_terms[output].expr\n assert isinstance(expr, pyeda.boolalg.expr.XorOp)\n if len(expr.xs) != 2:\n print(\n f\"ERROR: xor-term for {output} does not have 2 products, which is not supported yet.\")\n return\n mc = self.get_next_mc()\n assert mc is not None, \"Ran out of macrocells\"\n mc_name = f\"MC{mc}\"\n macrocell = device[\"macrocells\"][mc_name]\n block = macrocell[\"block\"]\n print(f\"output {output} mapped to {mc_name}.FB in block {block}\")\n self.all_or_exprs[block][mc_name] = expr\n self.input_mcs[output] = mc\n self.input_sigs[output] = f\"MC{mc}_FB\"\n\n print(f\"set {mc_name}.pt_power on\")\n print(f\"set {mc_name}.pt1_mux sum\")\n print(f\"set {mc_name}.pt2_mux xor\")\n print(f\"set {mc_name}.pt3_mux sum\")\n print(f\"set {mc_name}.pt4_mux sum\")\n print(f\"set {mc_name}.pt5_mux sum\")\n print(f\"set {mc_name}.fb_mux xt\")\n print(f\"set {mc_name}.xor_a_mux sum\")\n print(f\"set {mc_name}.xor_b_mux VCC_pt12\")\n print(f\"set {mc_name}.xor_invert on\")\n\n # Now that we've mapped inputs to outputs,\n # add them to the inputs and clear out the outputs.\n self.inputs += self.outputs\n self.outputs = []\n\n print(\"Input mcs:\")\n pprint.pprint(self.input_mcs)\n print(\"Input sigs:\")\n pprint.pprint(self.input_sigs)\n\n def set_uims(self):\n # Collect all MCn_FB and Mn_PAD before choosing UIMs for each block.\n # This is an instance of the assignment problem, which we solve using the\n # Hungarian algorithm, which is O(n^3). The hope is that because the matrix\n # is extremely sparse, the algorithm runs very quickly.\n\n switches = self.device[\"switches\"]\n\n # Map signals to UIMs, per block\n sig_to_uim = {}\n for blk in dev[\"blocks\"].keys():\n sig_to_uim[blk] = {}\n for switch, data in switches.items():\n blk = data[\"block\"]\n switch_sigs = data[\"mux\"][\"values\"].keys()\n for sig in switch_sigs:\n if sig not in sig_to_uim[blk]:\n sig_to_uim[blk][sig] = []\n sig_to_uim[blk][sig].append(switch)\n\n for blk in self.all_or_exprs:\n print(f\"Constructing set of signals in block {blk}\")\n # Construct the set of needed signals.\n sigs = set()\n for or_expr in self.all_or_exprs[blk].values():\n sigs.update(set(self.input_sigs[str(term)]\n for term in or_expr.support))\n\n # Convert to ordered array\n sigs = [s for s in sigs]\n if len(sigs) == 0:\n print(f\"No used signals in block {blk}\")\n continue\n print(f\"Used signals in block {blk}: {sigs}\")\n\n # Construct the set of candidate switches for those signals.\n candidate_switches = set()\n for sig in sigs:\n candidate_switches.update(set(s for s in sig_to_uim[blk][sig]))\n # Convert to ordered array\n candidate_switches = [s for s in candidate_switches]\n print(f\"Candidate switches in block {blk}: {candidate_switches}\")\n\n # Construct the cost matrix. We assign an different cost per candidate\n # switch to help the algorithm be stable.\n matrix = [[DISALLOWED for _ in range(\n len(candidate_switches))] for _ in range(len(sigs))]\n for row, sig in enumerate(sigs):\n cost = 1\n for candidate_switch in sig_to_uim[blk][sig]:\n col = candidate_switches.index(candidate_switch)\n matrix[row][col] = cost\n cost += 1\n cost_matrix = make_cost_matrix(\n matrix, lambda cost: cost if cost != DISALLOWED else DISALLOWED)\n\n # Assign signals to switches.\n m = Munkres()\n indexes = m.compute(cost_matrix)\n sig_to_switch = {}\n # print_matrix(matrix, 'Based on this matrix:')\n print(\"Setting UIM fuses:\")\n for r, c in indexes:\n v = matrix[r][c]\n print(f\"set {candidate_switches[c]} {sigs[r]}\")\n sig_to_switch[sigs[r]] = candidate_switches[c]\n # pprint.pprint(sig_to_switch)\n\n print(\"Setting product term fuses:\")\n for mc_name, or_expr in self.all_or_exprs[blk].items():\n products = or_expr.xs if isinstance(or_expr, pyeda.boolalg.expr.OrOp) or isinstance(\n or_expr, pyeda.boolalg.expr.XorOp) else [or_expr]\n\n for ptn, product in enumerate(products):\n terms = product.xs if isinstance(\n product, pyeda.boolalg.expr.AndOp) else [product]\n for sig in terms:\n inv = isinstance(sig, pyeda.boolalg.expr.Complement)\n sig = str(Not(sig) if inv else sig)\n uim = sig_to_switch[self.input_sigs[sig]]\n switch_polarity = \"_N\" if inv else \"_P\"\n print(\n f\" set {mc_name}.PT{ptn} +{uim}{switch_polarity}\")\n\n\nif __name__ == \"__main__\":\n db = get_database()\n dev = db[\"ATF1502AS\"]\n\n parse = PLAParser(sys.argv[1])\n\n p = Fitter()\n p.inputs = parse.inputs\n p.outputs = parse.outputs\n p.or_terms = parse.or_terms\n\n p.map_inputs()\n\n for arg in sys.argv[1:]:\n parse = PLAParser(arg)\n p.outputs = parse.outputs\n p.or_terms = parse.or_terms\n\n if parse.is_xor:\n p.map_and_xor_layer()\n elif parse.is_outputs:\n p.map_output_layer()\n else:\n p.map_and_or_layer()\n\n p.set_uims()\n", "sub_path": "pla_parser.py", "file_name": "pla_parser.py", "file_ext": "py", "file_size_in_byte": 17763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "pprint.PrettyPrinter", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 72, "usage_type": "name"}, {"api_name": "pprint.PrettyPrinter", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 88, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 95, "usage_type": "name"}, {"api_name": "pprint.PrettyPrinter", "line_number": 115, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 117, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 203, "usage_type": "name"}, {"api_name": "pyeda.boolalg", "line_number": 281, "usage_type": "attribute"}, {"api_name": "pyeda.boolalg", "line_number": 290, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 334, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 336, "usage_type": "call"}, {"api_name": "pyeda.boolalg", "line_number": 346, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 378, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 380, "usage_type": "call"}, {"api_name": "munkres.DISALLOWED", "line_number": 427, "usage_type": "name"}, {"api_name": "munkres.make_cost_matrix", "line_number": 435, "usage_type": "call"}, {"api_name": "munkres.DISALLOWED", "line_number": 436, "usage_type": "name"}, {"api_name": "munkres.Munkres", "line_number": 439, "usage_type": "call"}, {"api_name": "pyeda.boolalg", "line_number": 452, "usage_type": "attribute"}, {"api_name": "pyeda.boolalg", "line_number": 453, "usage_type": "attribute"}, {"api_name": "pyeda.boolalg", "line_number": 457, "usage_type": "attribute"}, {"api_name": "pyeda.boolalg", "line_number": 459, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 471, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 480, "usage_type": "attribute"}]} +{"seq_id": "342460497", "text": "from robocode_ls_core.workspace import Workspace, Document\nfrom robocode_ls_core.basic import overrides\nfrom robocode_ls_core.cache import instance_cache\nfrom robotframework_ls.constants import NULL\nfrom robocode_ls_core.robotframework_log import get_logger\n\nlog = get_logger(__name__)\n\n\nclass RobotWorkspace(Workspace):\n def __init__(\n self, root_uri, workspace_folders=None, libspec_manager=NULL, generate_ast=True\n ):\n self.libspec_manager = libspec_manager\n\n Workspace.__init__(self, root_uri, workspace_folders=workspace_folders)\n self._generate_ast = generate_ast\n\n @overrides(Workspace.add_folder)\n def add_folder(self, folder):\n Workspace.add_folder(self, folder)\n self.libspec_manager.add_workspace_folder(folder.uri)\n\n @overrides(Workspace.remove_folder)\n def remove_folder(self, folder_uri):\n Workspace.remove_folder(self, folder_uri)\n self.libspec_manager.remove_workspace_folder(folder_uri)\n\n def _create_document(self, doc_uri, source=None, version=None):\n return RobotDocument(doc_uri, source, version, generate_ast=self._generate_ast)\n\n\nclass RobotDocument(Document):\n\n TYPE_TEST_CASE = \"test_case\"\n TYPE_INIT = \"init\"\n TYPE_RESOURCE = \"resource\"\n\n def __init__(self, uri, source=None, version=None, generate_ast=True):\n Document.__init__(self, uri, source=source, version=version)\n self._generate_ast = generate_ast\n self._ast = None\n\n @overrides(Document._clear_caches)\n def _clear_caches(self):\n Document._clear_caches(self)\n self.get_ast.cache_clear(self)\n\n def get_type(self):\n path = self.path\n if not path:\n log.info(\"RobotDocument path empty.\")\n return self.TYPE_TEST_CASE\n\n import os.path\n\n basename = os.path.basename(path)\n if basename.startswith(\"__init__\"):\n return self.TYPE_INIT\n\n if basename.endswith(\".resource\"):\n return self.TYPE_RESOURCE\n\n return self.TYPE_TEST_CASE\n\n @instance_cache\n def get_ast(self):\n if not self._generate_ast:\n raise AssertionError(\n \"The AST can only be accessed in the RobotFrameworkServerApi, not in the RobotFrameworkLanguageServer.\"\n )\n from robot.api import get_model, get_resource_model, get_init_model\n\n try:\n source = self.source\n except:\n log.exception(\"Error getting source for: %s\" % (self.uri,))\n source = \"\"\n\n t = self.get_type()\n if t == self.TYPE_TEST_CASE:\n return get_model(source)\n\n elif t == self.TYPE_RESOURCE:\n return get_resource_model(source)\n\n elif t == self.TYPE_INIT:\n return get_init_model(source)\n\n else:\n log.critical(\"Unrecognized section: %s\", t)\n return get_model(source)\n\n def find_line_with_contents(self, contents: str) -> int:\n \"\"\"\n :param contents:\n The contents to be found.\n \n :return:\n The 0-based index of the contents.\n \"\"\"\n for i, line in enumerate(self.iter_lines()):\n if contents in line:\n return i\n else:\n raise AssertionError(f\"Did not find >>{contents}<< in doc.\")\n", "sub_path": "robotframework-ls/src/robotframework_ls/impl/robot_workspace.py", "file_name": "robot_workspace.py", "file_ext": "py", "file_size_in_byte": 3315, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "robocode_ls_core.robotframework_log.get_logger", "line_number": 7, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 10, "usage_type": "name"}, {"api_name": "robotframework_ls.constants.NULL", "line_number": 12, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Workspace.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 16, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Workspace.add_folder", "line_number": 21, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 21, "usage_type": "name"}, {"api_name": "robocode_ls_core.basic.overrides", "line_number": 19, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace.add_folder", "line_number": 19, "usage_type": "attribute"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 19, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Workspace.remove_folder", "line_number": 26, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 26, "usage_type": "name"}, {"api_name": "robocode_ls_core.basic.overrides", "line_number": 24, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Workspace.remove_folder", "line_number": 24, "usage_type": "attribute"}, {"api_name": "robocode_ls_core.workspace.Workspace", "line_number": 24, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Document", "line_number": 33, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Document.__init__", "line_number": 40, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Document", "line_number": 40, "usage_type": "name"}, {"api_name": "robocode_ls_core.workspace.Document._clear_caches", "line_number": 46, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Document", "line_number": 46, "usage_type": "name"}, {"api_name": "robocode_ls_core.basic.overrides", "line_number": 44, "usage_type": "call"}, {"api_name": "robocode_ls_core.workspace.Document._clear_caches", "line_number": 44, "usage_type": "attribute"}, {"api_name": "robocode_ls_core.workspace.Document", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 57, "usage_type": "name"}, {"api_name": "robot.api.get_model", "line_number": 82, "usage_type": "call"}, {"api_name": "robot.api.get_resource_model", "line_number": 85, "usage_type": "call"}, {"api_name": "robot.api.get_init_model", "line_number": 88, "usage_type": "call"}, {"api_name": "robot.api.get_model", "line_number": 92, "usage_type": "call"}, {"api_name": "robocode_ls_core.cache.instance_cache", "line_number": 66, "usage_type": "name"}]} +{"seq_id": "351611081", "text": "import csv\nfrom tokenizer import tokenizer\nimport string\nfrom collections import Counter\nSOS_IDX = 0\nPAD_IDX = 1 # PAD = EOS\n\ntokenizer = tokenizer.RedditTokenizer()\npunctuations = string.punctuation\npunctuations = ''.join(set(punctuations) - set(',.'))\n\nfpath = \"/scratch/yn811/shortjokes.csv\"\n\ndef tokenize(tokenizer, sent, punctuations):\n tokens = tokenizer.tokenize(sent)\n punc_cnt = sum((token in punctuations) for token in tokens)\n if punc_cnt > 0:\n return None\n return [token.lower() for token in tokens if (token not in punctuations)]\n\ndef tokenize_dataset(tokenizer, dataset, punctuations, gram=1):\n from tqdm import tqdm_notebook\n token_dataset = []\n all_tokens = []\n for sample in tqdm_notebook(dataset):\n tokens = tokenize(tokenizer, sample, punctuations)\n if tokens is None:\n continue\n if (len(tokens) <= 40) and (sum(len(w)<2 for w in tokens) <= len(tokens)/3):\n token_dataset.append(tokens)\n all_tokens.extend(tokens)\n return token_dataset, all_tokens\n\ndef build_vocab(all_tokens):\n token_counter = Counter(all_tokens)\n vocab, count = zip(*token_counter.most_common(len(token_counter)))\n id2token = list(vocab)\n token2id = dict(zip(vocab, range(2, 2+len(vocab)))) \n id2token = ['', ''] + id2token\n token2id[''] = PAD_IDX \n token2id[''] = SOS_IDX\n return token2id, id2token\n\n\ndef token2index_dataset(tokens_data):\n indices_data = []\n for tokens in tokens_data:\n index_list = [token2id[token] for token in tokens]\n indices_data.append(index_list)\n return indices_data\n\n# ! pip install git+https://github.com/erikavaris/tokenizer.git\njokes = []\nwith open(fpath) as f:\n reader = csv.reader(f) \n next(reader, None)\n for row in reader:\n jokes.append(row[1])\n\ntoken_dataset, all_tokens = tokenize_dataset(tokenizer, jokes, punctuations)\ntoken2id, id2token = build_vocab(all_tokens)\nidx_data = token2index_dataset(token_dataset)\nprint(\"length of dataset: \", len(id2token))\nimport pickle as pkl\npkl.dump([idx_data, token_dataset, token2id, id2token], open(\"short_jokes-40.pkl\", \"wb\"))\n", "sub_path": "preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 2159, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tokenizer.tokenizer", "line_number": 8, "usage_type": "name"}, {"api_name": "tokenizer.tokenizer.RedditTokenizer", "line_number": 8, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tokenizer.tokenizer.tokenize", "line_number": 15, "usage_type": "call"}, {"api_name": "tokenizer.tokenizer", "line_number": 15, "usage_type": "name"}, {"api_name": "tqdm.tqdm_notebook", "line_number": 25, "usage_type": "call"}, {"api_name": "tokenizer.tokenizer", "line_number": 26, "usage_type": "argument"}, {"api_name": "collections.Counter", "line_number": 35, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 55, "usage_type": "call"}, {"api_name": "tokenizer.tokenizer", "line_number": 60, "usage_type": "argument"}, {"api_name": "pickle.dump", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "426956631", "text": "#!/usr/bin/python\n# written by: atholcomb\n# io_utf8.py\n# Printing non-english strings, using utf-8 format\n\n# encoding=utf-8\nimport io\n\nf = io.open(\"abc.txt\", \"wt\", encoding=\"utf-8\")\nf.write(u\"Imagine non-English language here\")\nf.close()\n\ntext = io.open(\"abc.txt\", encoding=\"utf-8\").read()\nprint(text)\n", "sub_path": "input_output/io_utf8.py", "file_name": "io_utf8.py", "file_ext": "py", "file_size_in_byte": 302, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "io.open", "line_number": 9, "usage_type": "call"}, {"api_name": "io.open", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "166766303", "text": "#!/usr/bin/python -O\n# -*- coding: iso-8859-1 -*-\n# -*- coding: latin-1 -*-\n# by Loreto Notarantonio 2013, February\n# ######################################################################################\nimport sys\nimport os, platform\nimport getpass\n\nimport pwd\n\n\ndef ottieniValori():\n # https://docs.python.org/3.3/library/os.html\n userLogin = os.getlogin()\n userLogin = pwd.getpwuid(os.getuid())[0]\n uid = os.getuid() # current process’s user id\n ruid, euid, suid = os.getresuid() # real, effective, and saved user ids\n rgid, egid, sgid = os.getresgid() # real, effective, and saved group ids.\n\n\n\n\n# ###################################################################################################################\n# # # https://docs.python.org/3.3/library/os.html\n# # # userName: nome dell'utente di cui si vuole il setUID\n# # # uid : uid dell'utente di cui si vuole il setUID\n# # # None : se userName==None e uid==None allora viene ppreso il SAVED-UID\n# #\n# # gv.LN.sys.setUID(gv, gv.JBOSS.userName, exitOnError=False)\n# # gv.LN.sys.setUID(gv, uid=48, exitOnError=True)\n# # gv.LN.sys.setUID(gv)\n# ###################################################################################################################\ndef setUID_OK(gv, userName=None, uid=None, exitOnError=False):\n global TAByel, TABerr, TAB\n\n logger = gv.LN.logger.setLogger(gv, package=__name__)\n calledBy = gv.LN.sys.calledBy\n logger.info('entered - [called by:%s]' % (calledBy(1)))\n\n TAByel = gv.LN.cYELLOW + ' '*8\n TABerr = gv.LN.cERROR + ' '*8\n TAB = gv.LN.cGREEN + ' '*8\n\n\n ruid, euid, suid = os.getresuid() # real, effective, and saved user ids\n\n # ------------------------------\n # - catturiamo lo UID\n # ------------------------------\n if userName != None:\n reqUID = pwd.getpwnam(userName).pw_uid\n elif uid != None:\n reqUID = uid\n else:\n reqUID = ruid # preleva il REAL-UID\n # reqUID = suid # preleva il SAVED-UID\n\n\n logger.info('')\n msg = 'Requested setUID for username:[{}:{}]'.format(pwd.getpwuid(reqUID).pw_name, reqUID)\n logger.info(TAB + msg)\n msg = 'currUser:[{}:{}] ruid:{} euid:{} suid:{} '.format(pwd.getpwuid(os.getuid()).pw_name, os.geteuid(), ruid, euid, suid)\n logger.info(TAB + msg)\n\n # --------------------------------------------\n # - Se il current euid!=0 non possiamo\n # - fare il setUID per un altro user\n # - Switch temporaneo su root per permetterlo\n # ---------------------------------------------\n rCode = 0\n\n if euid != 0 and euid != reqUID:\n rCode = processSetUid(gv, 0, exitOnError)\n\n if rCode == 0:\n rCode = processSetUid(gv, reqUID, exitOnError)\n\n return rCode\n\n\n\n#################################################\n#\n#################################################\ndef processSetUid(gv, reqUID, exitOnError=True):\n logger = gv.LN.logger.setLogger(gv, package=__name__)\n calledBy = gv.LN.sys.calledBy\n logger.info('entered - [called by:%s]' % (calledBy(1)))\n\n try:\n reqGID = pwd.getpwuid(reqUID).pw_gid\n os.setegid(reqGID) # mantenere l'ordine GID-UID\n os.seteuid(reqUID) # mantenere l'ordine GID-UID\n msg = 'After setUID: username:{}, euid:{}, egid:{}'.format(pwd.getpwuid(os.geteuid()).pw_name, os.geteuid(), os.getegid())\n logger.info(TAB + msg)\n retVal = 0\n\n except Exception as why:\n errMsg = \"setUID({}) - {} - Permissions Error\".format(pwd.getpwuid(reqUID).pw_name, str(why))\n logger.error(TABerr + errMsg)\n for line in sys.exc_info():\n logger.info (TAByel + str(line))\n if exitOnError:\n gv.LN.exit(gv, 7002, TAB + errMsg, console=False)\n retVal = 1\n\n return retVal\n\n\n\n#################################################\n#\n#################################################\ndef setUID(gv, userName=None, reqUID=None, exitOnError=False):\n logger = gv.LN.logger.setLogger(gv, package=__name__)\n calledBy = gv.LN.sys.calledBy\n logger.info('entered - [called by:%s]' % (calledBy(1)))\n\n TAByel = gv.LN.cYELLOW + ' '*8\n TABerr = gv.LN.cERROR + ' '*8\n TAB = gv.LN.cGREEN + ' '*8\n # ------------------------------\n # - catturiamo lo UID\n # ------------------------------\n if reqUID == None:\n if userName != None:\n reqUID = pwd.getpwnam(userName).pw_uid\n else:\n return 1\n\n try:\n reqGID = pwd.getpwuid(reqUID).pw_gid\n os.setegid(reqGID) # mantenere l'ordine GID-UID\n os.seteuid(reqUID) # mantenere l'ordine GID-UID\n msg = 'After setUID: username:{}, euid:{}, egid:{}'.format(pwd.getpwuid(os.geteuid()).pw_name, os.geteuid(), os.getegid())\n logger.info(TAB + msg)\n retVal = 0\n\n except Exception as why:\n errMsg = \"setUID({}) - {} - Permissions Error\".format(pwd.getpwuid(reqUID).pw_name, str(why))\n logger.error(TABerr + errMsg)\n for line in sys.exc_info():\n logger.info (TAByel + str(line))\n if exitOnError:\n gv.LN.exit(gv, 7002, TAB + errMsg, console=False)\n retVal = 1\n\n return retVal\n\n\n\n\n\n\n\n\n", "sub_path": "LnUnixSys/SetUID.py", "file_name": "SetUID.py", "file_ext": "py", "file_size_in_byte": 5426, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.getlogin", "line_number": 15, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getresuid", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getresgid", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getresuid", "line_number": 46, "usage_type": "call"}, {"api_name": "pwd.getpwnam", "line_number": 52, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 61, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 63, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 63, "usage_type": "call"}, {"api_name": "os.geteuid", "line_number": 63, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 92, "usage_type": "call"}, {"api_name": "os.setegid", "line_number": 93, "usage_type": "call"}, {"api_name": "os.seteuid", "line_number": 94, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 95, "usage_type": "call"}, {"api_name": "os.geteuid", "line_number": 95, "usage_type": "call"}, {"api_name": "os.getegid", "line_number": 95, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 102, "usage_type": "call"}, {"api_name": "pwd.getpwnam", "line_number": 128, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 133, "usage_type": "call"}, {"api_name": "os.setegid", "line_number": 134, "usage_type": "call"}, {"api_name": "os.seteuid", "line_number": 135, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 136, "usage_type": "call"}, {"api_name": "os.geteuid", "line_number": 136, "usage_type": "call"}, {"api_name": "os.getegid", "line_number": 136, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "585353868", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 19 11:29:34 2019\n\n@author: tiagocabo\n\"\"\"\n\n# need to create an virtualenvironment\n\n\nfrom flask import Flask, render_template, url_for\napp = Flask(__name__) # create app variable\n\n\n# dictionaries to be upload to the blog\n# emulates a database call\nposts = [\n {\n 'author': 'Tiago Cabo',\n 'title': 'My first blog post',\n 'content':'First attempt to add content',\n 'date_posted': 'January 21, 2019'\n },\n {'author': 'Patricia Carneiro',\n 'title': 'Second blog post',\n 'content':'Second attempt to add content',\n 'date_posted': 'January 19, 2019'\n }\n ]\n\n\n\n\n\n\n\n@app.route('/') \n@app.route('/home') #renders backend. route page of our website. slash mean homepage\n# this kind of call are called decorators. \ndef home():\n return render_template('home.html', posts= posts)\n\n# need to make cs code directory\n# run: export FLASK_APP=flaskblog.py\n# then run flask run\n \n# the ip adress is http://127.0.0.1:5000\n# or http://localhost:5000\n\n#debug mode allows the weppage to refresh every change and update\n \n# USE: export FLASK_DEBUG=1\n \n## TO have the debug mode\n \n\n\n@app.route('/about') #renders backend. route page of our website. slash mean homepage\n# this kind of call are called decorators. \ndef about():\n return render_template('about.html', title=\"New About Page\")\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n \n \n ", "sub_path": "flaskblog.py", "file_name": "flaskblog.py", "file_ext": "py", "file_size_in_byte": 1596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "122561047", "text": "# -*- coding: utf-8 -*-\n\n'''\n1. Пользователь вводит данные о количестве предприятий,\nих наименования и прибыль за 4 квартала (т.е. 4 отдельных числа)\nдля каждого предприятия.. Программа должна определить среднюю прибыль\n(за год для всех предприятий) и вывести наименования предприятий,\nчья прибыль выше среднего и отдельно вывести наименования предприятий, чья прибыль ниже среднего.\n'''\nimport collections\n\n# без коллекций.\ndef calc_profit():\n\tquantity_of_companies = int(input('Введите количество предприятий: '))\n\tdict_of_companies_n_quarter_profit = {}\n\n\tfor i in range(1, quantity_of_companies + 1):\n\t\tname = input('Введите название предприятия: ')\n\t\tcompany_year_profit = []\n\t\tfor quarter in range(1, 5):\n\t\t\tquarter_profit = int(input(f'Доход за {quarter} квартал: '))\n\t\t\tcompany_year_profit.append(quarter_profit)\n\t\tdict_of_companies_n_quarter_profit[name] = company_year_profit\n\n\t# создаем список сумм (хотя можно было в цикле аппендить уже общую сумму, но предположим,\n\t# что необходимо сохранять квартальные значения)\n\tlist_of_all_profits = [item for sublist in dict_of_companies_n_quarter_profit.values() for item in [sum(sublist)]]\n\taverage_profit = sum(list_of_all_profits) / len(list_of_all_profits)\n\tprint(f'\\nСредняя прибыль организаций за год: {average_profit} руб.\\n')\n\n\tcompanies_year_profit = dict(zip(dict_of_companies_n_quarter_profit, list_of_all_profits))\n\n\tcompanies_with_less_than_av = {k: v for k, v in companies_year_profit.items() if v < average_profit}\n\tcompanies_with_more_than_av = {k: v for k, v in companies_year_profit.items() if v > average_profit}\n\tprint(f'Прибыль ниже средней: {companies_with_less_than_av}\\n')\n\tprint(f'Прибыль выше средней: {companies_with_more_than_av}\\n')\n\ncalc_profit()\n\n# используя коллекции.\ndef calc_profit_with_col():\n\tquantity_of_companies = int(input('Введите количество предприятий: '))\n\tCompany = collections.namedtuple('Company', ['name', 'profit'])\n\tlst_of_comps = []\n\tfor i in range(1, quantity_of_companies + 1):\n\t\tname = input('Введите имя: ')\n\t\tprofit = 0\n\t\tfor quarter in range(1, 5):\n\t\t\tprofit += int(input(f'Доход за {quarter} квартал: '))\n\t\tcompany = Company(name, profit)\n\t\tlst_of_comps.append(company)\n\n\taverage_profit = sum([company.profit for company in lst_of_comps]) / len(lst_of_comps)\n\tprint(f'\\nСредняя прибыль организаций за год: {average_profit} руб.\\n')\n\n\tcomps_with_less_than_av = [company for company in lst_of_comps if company.profit < average_profit]\n\tcomps_with_more_than_av = [company for company in lst_of_comps if company.profit > average_profit]\n\tprint(f'Прибыль ниже средней: {comps_with_less_than_av}\\n')\n\tprint(f'Прибыль выше средней: {comps_with_more_than_av}\\n')\n\ncalc_profit_with_col()\n# Итого: использование именнованного кортежа позволило значительно упростить генераторные выражения.\n# алгоритмы решений с DefaultDict и OrderedDict по сути не будет отличаться от первоначального и при этом не будут\n# использованы их особенности, поэтому не буду простыню из кода присылать.\n", "sub_path": "5/1.py", "file_name": "1.py", "file_ext": "py", "file_size_in_byte": 3895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "collections.namedtuple", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "560972759", "text": "import numpy as np\nimport pandas as pd\nimport pickle\nimport os\nimport argparse\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nimport collections\nimport csv\nimport math\n\nclass Explor():\n\n def __init__(self, terms, courses, demograph):\n self.terms = terms\n self.courses = courses\n self.demograph = demograph\n\n def Prob_Majors(self):\n # Exploratory Analysis -- Probation and Majors\n Probed_Students = self.terms[self.terms['probation'] != 0]['SubjectID'].unique()\n print(self.demograph['SubjectID'].nunique())\n print(self.courses['SubjectID'].nunique())\n print(self.terms['SubjectID'].nunique())\n Major_Population = self.demograph.groupby('B - Major1 Code')['B - Major1 Code'].count()\n Major_Probation = self.demograph[self.demograph['SubjectID'].isin(Probed_Students)] \\\n .groupby('B - Major1 Code')['B - Major1 Code'].count()\n Hajim_Majors = ['BME', 'CHE', 'CSC','CSA', 'ECE', 'IDE', 'ES', 'ME', 'OPT']\n with open('./Saved Models/Major_Probation.csv', 'w') as f:\n f.write('Major' + ' ' + 'Percentage' + ' ' + 'Aboslute_Number\\n')\n for name in Major_Probation.index:\n out = name + ' ' + (Major_Probation[name]/Major_Population[name]).astype(str) + ' ' + Major_Probation[name].astype(str)\n f.write(\"%s\\n\" % out)\n\n Hajim_Prob = Major_Probation[Hajim_Majors]\n Hajim_Popu = Major_Population[Hajim_Majors]\n with open('./Saved Models/Hajim_Probation.csv', 'w') as f:\n f.write('Major' + ' ' + 'Percentage' + ' ' + 'Aboslute_Number\\n')\n for name in Hajim_Prob.index:\n out = name + ' ' + (Hajim_Prob[name]/Hajim_Popu[name]).astype(str) + ' ' + Hajim_Prob[name].astype(str)\n f.write(\"%s\\n\" % out)\n print('Average probation rate for Hajim school is ' + (Hajim_Prob.sum()/Hajim_Popu.sum()).astype(str))\n print('Average probation rate in general is ' + str(len(Probed_Students)/self.courses['SubjectID'].nunique()))\n\n def Changing_Majors(self):\n def changed_major(ID):\n Course_Data = self.courses[self.courses['SubjectID'] == ID]['Ps1 Major1 Code']\n original_major = Course_Data.iloc[0]\n ended_major = Course_Data.iloc[-1]\n if original_major == 'UNC':\n return True\n return not original_major == ended_major\n\n def find_grades(ID, changed):\n Term_Data = self.terms[self.terms['SubjectID'] == ID]['Term GPA'].tolist()\n if not changed:\n pre_1, pre_2, aft_1, aft_2 = np.NaN, np.NaN, np.NaN, np.NaN\n else:\n pre_1, pre_2, aft_1, aft_2 = np.NaN, np.NaN, np.NaN, np.NaN\n for i in range(0, len(Term_Data)):\n if Term_Data[i] < 2.0 and not Term_Data[i] == 0:\n if i-1 > 0:\n if Term_Data[i-1] > 3.3:\n break\n pre_1 = Term_Data[i-1]\n if i-2 > 0:\n pre_2 = Term_Data[i-2]\n for j in range(i, len(Term_Data)):\n if Term_Data[j] >= 2.0 and Term_Data[j] < 3.5:\n aft_1 = Term_Data[j]\n break\n for j in range(i, len(Term_Data)-1):\n if Term_Data[j] >= 2.0 and Term_Data[j] < 3.5:\n aft_2 = Term_Data[j+1]\n break\n break\n return pd.DataFrame([[pre_1, pre_2, aft_1, aft_2]], columns=['pre_1', 'pre_2', 'aft_1', 'aft_2'])\n\n GPA_Changed = pd.DataFrame(columns=['pre_1', 'pre_2', 'aft_1', 'aft_2'])\n GPA_Unchanged = pd.DataFrame(columns=['pre_1', 'pre_2', 'aft_1', 'aft_2'])\n for ID in self.courses[self.courses['Ps1 Acad Standing Desc'] == 'PROBATION']['SubjectID'].unique()[:-1]:\n if changed_major(ID):\n GPA_Changed = GPA_Changed.append(find_grades(ID, True), ignore_index=True)\n else:\n GPA_Unchanged = GPA_Unchanged.append(find_grades(ID, False), ignore_index=True)\n GPA_Unchanged.fillna(value=GPA_Unchanged.mean(), inplace=True)\n GPA_Changed.fillna(value=GPA_Changed.mean(), inplace=True)\n def graph_changing(GPA_Changed, GPA_Unchanged):\n data = [\n go.Scatter(\n y=GPA_Unchanged.mean(), # assign x as the dataframe column 'x'\n x=GPA_Unchanged.mean().index,\n fill= None,\n line = dict(shape='spline'),\n name = 'Unchanged'\n ),\n go.Scatter(\n y=GPA_Changed.mean(), # assign x as the dataframe column 'x'\n x=GPA_Changed.mean().index,\n fill='tonexty',\n line = dict(shape='spline'),\n name = 'Changed'\n )\n ]\n url = py.plot(data)\n return\n\n graph_changing(GPA_Changed, GPA_Unchanged)\n with open('./Saved Models/Culm_GPAs.csv','w') as f:\n for index, rows in GPA_Unchanged.iterrows():\n if not rows[1] == 0.0:\n f.write(\"%s\\n\" % str(round(rows[1], 2)))\n for index, rows in GPA_Changed.iterrows():\n if not rows[1] == 0.0:\n f.write(\"%s\\n\" % str(round(rows[1], 2)))\n\n def Heatmap(self):\n # Exploratory Analysis -- Heatmap\n Heatmap_List = self.demograph[demograph['probation'] > 0]\n Heatmap_List = Heatmap_List.groupby('HS State Prov')['HS State Prov'].count()\n Heatmap_Total = self.demograph.groupby('HS State Prov')['HS State Prov'].count()\n General_Demo = self.demograph.groupby('HS State Prov')['HS State Prov'].count()\n with open('./Saved Models/heatmap.csv', 'w') as f:\n f.write('State'+' '+'Percentage'+' '+'Aboslute\\n')\n for i in Heatmap_List.index:\n out = i+' '+(Heatmap_List[i]/Heatmap_Total[i]*100).astype(str)+' '+Heatmap_List[i].astype(str)\n f.write(\"%s\\n\" % out)\n with open('./Saved Models/general_demo.csv', 'w') as f:\n for i in General_Demo.index:\n out = i + ' ' + General_Demo[i].astype(str)\n f.write(\"%s\\n\" % out)\n\n def Culm_GPA(self):\n # Exploratory Analysis -- Outlier\n Culm_GPAs = []\n Probed_Students = self.terms[self.terms['probation'] != 0]['SubjectID'].unique()\n #Probed_Students = self.terms['SubjectID'].unique()\n for ID in Probed_Students:\n Term_Data = self.terms[self.terms['SubjectID'] == ID]['Term GPA'].tolist()\n\n Culm_GPAs.append(Term_Data[-1])\n with open('./Saved Models/Culm_GPAs.csv','w') as f:\n for gpas in Culm_GPAs:\n f.write(\"%f\\n\" % gpas)\n def Sankey(self):\n prob_students = self.courses[self.courses['Ps1 Acad Standing Desc']=='PROBATION']['SubjectID']\n print(prob_students.shape)\n major_to_disp = collections.defaultdict(str)\n write_dic = collections.defaultdict(list)\n major_disp_series = self.demograph[['Degree Major1 Discipline Code', 'Degree Major1 Code']]\n for i, row in major_disp_series.iterrows():\n disp, major = row[0], row[1]\n if isinstance(disp, str):\n major_to_disp[major] = disp\n for id in prob_students:\n student_info = self.courses[self.courses['SubjectID']==id]\n for i, row in student_info.iterrows():\n if row['Ps1 Acad Standing Desc'] == 'PROBATION':\n write_dic[id].append(major_to_disp[student_info.at[i, 'Ps1 Major1 Code']])\n i += 1\n while i <= student_info.shape[0]:\n if student_info.at[i, 'Ps1 Major1 Code'] != 'PROBATION' or i == student_info.shape[0]:\n write_dic[id].append(major_to_disp[student_info.at[i, 'Ps1 Major1 Code']])\n if i == student_info.shape[0]:\n write_dic[id].append('FAILED')\n else:\n write_dic[id].append(major_to_disp[student_info.at[student_info.shape[0], 'Ps1 Major1 Code']])\n break\n i += 1\n break\n with open('./Saved Models/Sankey.csv', 'w', newline=\"\") as f:\n writer = csv.writer(f)\n for k,v in write_dic.items():\n writer.writerow([k, v])\n\n def analysis(self):\n self.Changing_Majors()\n", "sub_path": "Code/exploratory.py", "file_name": "exploratory.py", "file_ext": "py", "file_size_in_byte": 8864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.NaN", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 90, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 90, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 97, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 97, "usage_type": "name"}, {"api_name": "plotly.plotly.plot", "line_number": 105, "usage_type": "call"}, {"api_name": "plotly.plotly", "line_number": 105, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 148, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 149, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "341091707", "text": "# VTK - Labo 5 - Planeur\n# Author : Sathiya Kirushnapillai, Mathieu Monteverde\n\nimport vtk\n\n\nclass KeyPressInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):\n \"\"\"\n An interactor style class extending vtkInteractorStyleTrackballCamera\n that saves a screenshot of the window when the 's' or Return key are \n pressed.\n \"\"\"\n\n def __init__(self, renWin, parent=None):\n self.parent = parent\n self.AddObserver(\"KeyPressEvent\", self.keyPressEvent)\n self.OUTPUT_FILE_NAME = \"map_output.png\"\n self.renWin = renWin\n\n\n def keyPressEvent(self, obj, event):\n key = self.parent.GetKeySym()\n if (key == \"Return\" or key == \"s\"):\n # Resources to save the scene to a PDF file\n w2if = vtk.vtkWindowToImageFilter()\n w2if.SetInput(self.renWin)\n w2if.Update()\n\n writer = vtk.vtkPNGWriter()\n writer.SetFileName(self.OUTPUT_FILE_NAME)\n writer.SetInputConnection(w2if.GetOutputPort())\n writer.Write()\n", "sub_path": "keypressInteractorStyle.py", "file_name": "keypressInteractorStyle.py", "file_ext": "py", "file_size_in_byte": 1031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "vtk.vtkInteractorStyleTrackballCamera", "line_number": 7, "usage_type": "attribute"}, {"api_name": "vtk.vtkWindowToImageFilter", "line_number": 25, "usage_type": "call"}, {"api_name": "vtk.vtkPNGWriter", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "421891797", "text": "import numpy as np\nimport pandas as pd\nimport time\nimport scipy.sparse as sp\nfrom fastFM import sgd\nfrom scipy import sparse\nfrom sklearn.metrics import roc_auc_score, average_precision_score, roc_curve\nimport math\nimport pymrmr\nfrom sklearn.model_selection import train_test_split\n\n\ndef get_links(VIM, gene_names, regulators, sort=True, file_name=None):\n idx = [i for i, gene in enumerate(gene_names) if gene in regulators]\n pred_edges = [(gene_names[j], gene_names[i], score) for (i, j), score in np.ndenumerate(VIM) if i != j and j in idx]\n pred_edges = pd.DataFrame(pred_edges)\n if sort is True:\n pred_edges.sort_values(2, ascending=False, inplace=True)\n if file_name is None:\n print(pred_edges)\n else:\n pred_edges.to_csv(file_name, sep='\\t', header=None, index=None)\n\n \n# ???????????\ndef prenormal(X_train):\n minf_X = [0.0] * X_train.shape[1]\n maxf_X = [0.0] * X_train.shape[1]\n for f in range(0, X_train.shape[1]):\n maxf_X[f] = X_train[0][f]\n minf_X[f] = X_train[0][f]\n for i in range(1, X_train.shape[0]):\n maxf_X[f] = max(maxf_X[f], X_train[i][f])\n minf_X[f] = min(minf_X[f], X_train[i][f])\n if (maxf_X[f] > minf_X[f]):\n for i in range(0, X_train.shape[0]):\n X_train[i][f] = (X_train[i][f] - minf_X[f]) / (maxf_X[f] - minf_X[f])\n else:\n for i in range(0, X_train.shape[0]):\n X_train[i][f] = 0\n maxf_X[f] = X_train[0][f]\n minf_X[f] = X_train[0][f]\n for i in range(1, X_train.shape[0]):\n maxf_X[f] = max(maxf_X[f], X_train[i][f])\n minf_X[f] = min(minf_X[f], X_train[i][f])\n return minf_X, maxf_X, X_train\n\n\n# ??????????\n\ndef subcode(X_train, b, minf, maxf):\n F = len(X_train[0])\n X_train_o = np.zeros((X_train.shape[0], F * b))\n\n a = []\n p = []\n for f in range(0, len(X_train[0])):\n fl = []\n a.append([])\n p.append([])\n for l in range(0, X_train.shape[0]):\n fl.append(X_train[l][f])\n\n a[f], p[f] = np.unique(fl, return_inverse=True)\n for l in range(0, X_train.shape[0]):\n f_en = 0\n for f in range(0, len(X_train[l])):\n if len(a[f]) == 1:\n f_en = f_en\n elif len(a[f]) >= b:\n if (X_train[l][f] - minf[f]) == (maxf[f] - minf[f]):\n X_train_o[l][f_en + b - 1] = 1\n else:\n X_train_o[l][f_en + int(float(X_train[l][f] - minf[f]) / (maxf[f] - minf[f]) * b)] = 1\n f_en += b\n else:\n X_train_o[l][f_en + p[f][l]] = 1\n f_en += len(a[f])\n if f_en < F * b:\n X_train_b = np.zeros((len(X_train), f_en))\n for i in range(len(X_train)):\n for j in range(f_en):\n X_train_b[i][j] = X_train_o[i][j]\n X_train_o = X_train_b\n\n return X_train_o\n\n\ndef FMpredict(data_x, data_y, b,selF1):\n\n minf_X, maxf_X, train_x1 = prenormal(data_x)\n #last_col = np.array([data_x.shape[0] * [1]])\n #train_x1 = np.c_[train_x1, last_col.T]\n #minf_X.append(1)\n #maxf_X.append(1)\n X_train_o = subcode(train_x1, b, minf_X, maxf_X)\n last_col = np.array([X_train_o.shape[0]*[1]])\n X_train_o = np.c_[X_train_o, last_col.T]\n train_X = sp.csc_matrix(np.array(X_train_o), dtype=np.float64)\n\n # test_X = sp.csc_matrix(np.array(X_test_o), dtype=np.float64)\n # print('data_y',data_y)\n fm = sgd.FMRegression(n_iter=10000,\n init_stdev=0.0001, l2_reg_w=0.1, l2_reg_V=10000, rank=10,\n step_size=0.0001) # ???\n\n fm.fit(train_X, data_y)\n print(train_X.shape)\n print('w_bin5', fm.w_)\n return fm.w_, fm.V_,\n\n\ndef estimate_degradation_rates(TS_data, time_points):\n \"\"\"\n For each gene, the degradation rate is estimated by assuming that the gene expression x(t) follows:\n x(t) = A exp(-alpha * t) + C_min,\n between the highest and lowest expression values.\n C_min is set to the minimum expression value over all genes and all samples.\n The function is available at the study named dynGENIE3.\n Huynh-Thu, V., Geurts, P. dynGENIE3: dynamical GENIE3 for the inference of\n gene networks from time series expression data. Sci Rep 8, 3384 (2018) doi:10.1038/s41598-018-21715-0\n \"\"\"\n\n ngenes = TS_data[0].shape[1]\n nexp = len(TS_data)\n\n C_min = TS_data[0].min()\n if nexp > 1:\n for current_timeseries in TS_data[1:]:\n C_min = min(C_min, current_timeseries.min())\n\n alphas = np.zeros((nexp, ngenes))\n\n for (i, current_timeseries) in enumerate(TS_data):\n current_time_points = time_points[i]\n\n for j in range(ngenes):\n idx_min = np.argmin(current_timeseries[:, j])\n idx_max = np.argmax(current_timeseries[:, j])\n\n xmin = current_timeseries[idx_min, j]\n xmax = current_timeseries[idx_max, j]\n\n tmin = current_time_points[idx_min]\n tmax = current_time_points[idx_max]\n\n xmin = max(xmin - C_min, 1e-6)\n xmax = max(xmax - C_min, 1e-6)\n\n xmin = np.log(xmin)\n xmax = np.log(xmax)\n\n alphas[i, j] = (xmax - xmin) / abs(tmin - tmax)\n\n alphas = alphas.max(axis=0)\n\n return alphas\n\n\ndef get_importances(TS_data, time_points, alpha=\"from_data\", SS_data=None, gene_names=None, regulators='all', b=1):\n time_start = time.time()\n\n ngenes = TS_data[0].shape[1]\n\n if alpha is \"from_data\":\n alphas = estimate_degradation_rates(TS_data, time_points)\n else:\n alphas = [alpha] * ngenes\n\n # Get the indices of the candidate regulators\n idx = [i for i, gene in enumerate(gene_names) if gene in regulators]\n\n # Learn an ensemble of trees for each target gene, and compute scores for candidate regulators\n VIM = np.zeros((ngenes, ngenes))\n # print('ngenes:',ngenes)\n for i in range(ngenes):\n # print('i:',i)\n input_idx = idx.copy()\n if i in input_idx:\n input_idx.remove(i)\n vi = get_importances_single(TS_data, time_points, alphas[i], input_idx, i, SS_data, b)#note: imput_idx\n # print('vi:',vi)\n VIM[i, :] = vi\n\n time_end = time.time()\n #print('W_var', VIM)\n #print(\"Elapsed time: %.2f seconds\" % (time_end - time_start))\n\n return VIM\n\n\ndef get_importances_single(TS_data, time_points, alpha, input_idx, output_idx, SS_data, b):\n h = 1 # define the value of time step\n\n ngenes = TS_data[0].shape[1]\n nexp = len(TS_data)\n nsamples_time = sum([expr_data.shape[0] for expr_data in TS_data])\n ninputs = len(input_idx)\n\n # Construct training sample\n\n # Time-series data\n input_matrix_time = np.zeros((nsamples_time - h * nexp, ninputs))\n output_vect_time = np.zeros(nsamples_time - h * nexp)\n\n nsamples_count = 0\n for (i, current_timeseries) in enumerate(TS_data):\n current_time_points = time_points[i]\n npoints = current_timeseries.shape[0]\n time_diff_current = current_time_points[h:] - current_time_points[:npoints - h]\n current_timeseries_input = current_timeseries[:npoints - h, input_idx]\n current_timeseries_output = (current_timeseries[h:, output_idx] - current_timeseries[:npoints - h,\n output_idx]) / time_diff_current + alpha * current_timeseries[\n :npoints - h,\n output_idx]\n nsamples_current = current_timeseries_input.shape[0]\n input_matrix_time[nsamples_count:nsamples_count + nsamples_current, :] = current_timeseries_input\n output_vect_time[nsamples_count:nsamples_count + nsamples_current] = current_timeseries_output\n nsamples_count += nsamples_current\n\n # Steady-state data\n if SS_data is not None:\n input_matrix_steady = SS_data[:, input_idx]\n output_vect_steady = SS_data[:, output_idx] * alpha\n\n # Concatenation\n input_all = np.vstack([input_matrix_steady, input_matrix_time])\n output_all = np.concatenate((output_vect_steady, output_vect_time))\n else:\n input_all = input_matrix_time\n output_all = output_vect_time\n #mrmr feature selection\n top_gene = 9\n out = output_all.reshape(output_all.shape[0],1)\n output = pd.DataFrame(out)\n output.columns = ['G' + str(output_idx) ]\n input1 = pd.DataFrame(input_all)\n input1.columns = ['G'+str(i) for i in input_idx]\n df3 = pd.concat([output, input1], axis=1)\n selF1 = np.array(pymrmr.mRMR(df3, 'MIQ', top_gene))\n input_all = input1[selF1]\n # Compute importance scores\n w, v = FMpredict(input_all.values, output_all, b, selF1)\n input_idx = [int(i.replace('G','')) for i in selF1]\n vi = getvar_w(w, top_gene, b)\n # vi = getMAXMIN_w(w, ngenes-1, b)\n # vi = getabsmax_w(w, ngenes-1, b)\n # vi = getabssum_w(w, ngenes-1, b)\n print('w_var',vi)\n print('input_idx',input_idx)\n v_i = np.zeros(ngenes)\n v_i[input_idx] = vi\n return v_i\n\ndef get_importances_single1( alpha, input_idx, output_idx, SS_data, b):\n\n ngenes = SS_data[0].shape[1]\n\n\n # Construct training sample\n\n # Steady-state data\n\n input_matrix_steady = SS_data[:, input_idx]\n output_vect_steady = SS_data[:, output_idx] * alpha\n\n # Compute importance scores\n w, v = FMpredict(input_matrix_steady, output_vect_steady, b)\n\n vi = getvar_w(w, 3, b)\n # vi = getMAXMIN_w(w, ngenes-1, b)\n # vi = getabsmax_w(w, ngenes-1, b)\n # vi = getabssum_w(w, ngenes-1, b)\n # print('w_var',vi)\n v_i = np.zeros(ngenes)\n v_i[input_idx] = vi\n return v_i\n\ndef getabssum_w(w, ngenes, bin):\n vi = np.zeros(ngenes)\n for i in range(0, ngenes):\n max_w = 0\n for j in range(i * bin, (i + 1) * bin):\n if (i + 1) * bin > w.shape[0]:\n break;\n max_w += abs(w[j])\n vi[i] = max_w\n\n return vi\n\n\ndef getMAXMIN_w(w, ngenes, bin):\n vi = np.zeros(ngenes)\n for i in range(0, ngenes):\n max_w = 0\n min_w = 0\n for j in range(i * bin, (i + 1) * bin):\n if (i + 1) * bin > w.shape[0]:\n break;\n max_w = max(max_w, w[j])\n min_w = min(min_w, w[j])\n vi[i] = max_w - min_w\n\n return vi\n\n\ndef getabsmax_w(w, ngenes, bin):\n vi = np.zeros(ngenes)\n for i in range(0, ngenes):\n max_w = 0\n for j in range(i * bin, (i + 1) * bin):\n if (i + 1) * bin > w.shape[0]:\n break;\n max_w = max(max_w, abs(w[j]))\n vi[i] = max_w\n\n return vi\n\n\ndef getvar_w(w, ngenes, bin):\n vi = np.zeros(ngenes)\n for i in range(0, ngenes):\n arrra = []\n if i == ngenes:\n break;\n arrra = w[i * bin:(i + 1) * bin]\n arr_var = np.var(arrra)\n if math.isnan(arr_var):\n arr_var = 0\n vi[i] = arr_var\n\n return vi\n\n\n#####\ndef get_scores(VIM, gold_edges, gene_names, regulators):\n idx = [i for i, gene in enumerate(gene_names) if gene in regulators]\n pred_edges = [(gene_names[j], gene_names[i], score) for (i, j), score in np.ndenumerate(VIM) if i != j and j in idx]\n pred_edges = pd.DataFrame(pred_edges)\n # Take the top 100,000 predicated results\n pred_edges = pred_edges.iloc[:100000]\n final = pd.merge(pred_edges, gold_edges, on=[0, 1], how='inner')\n # np.set_printoptions(threshold=10000)\n # print('2_y', final['2_y'])\n # sprint('2_x', final['2_x'])\n auroc = roc_auc_score(final['2_y'], final['2_x'])\n\n fpr, tpr, thresholds = roc_curve(final['2_y'], final['2_x'], pos_label=2)\n # print(\"fpr\",fpr,\"tpr\",tpr,\"thresholds\",thresholds )\n aupr = average_precision_score(final['2_y'], final['2_x'])\n\n return auroc, aupr, final\n\n\n\n\n\n", "sub_path": "FMModel.py", "file_name": "FMModel.py", "file_ext": "py", "file_size_in_byte": 11968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "numpy.ndenumerate", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 98, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 99, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 99, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 99, "usage_type": "attribute"}, {"api_name": "fastFM.sgd.FMRegression", "line_number": 103, "usage_type": "call"}, {"api_name": "fastFM.sgd", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 151, "usage_type": "call"}, {"api_name": "time.time", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 174, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "pymrmr.mRMR", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 327, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 338, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 339, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 342, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 346, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 348, "usage_type": "call"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 350, "usage_type": "call"}]} +{"seq_id": "10547460", "text": "import pika\nimport json\nimport os\nfrom threading import Thread\n\nthings = []\n\n# phase 3 consume response messages\ndef consume_response(ch, method, properties, body):\n things.insert(0, {\"response\": body.decode(\"utf-8\")})\n\n\n# this block is repeated for each new queue; parameterize?\n# this sets up the watcher for responses\namqp_url = os.environ['AMQP_URL']\nprint(\"opening connection to {}\".format(amqp_url))\nconnection = pika.BlockingConnection(pika.URLParameters(amqp_url))\nchannel = connection.channel()\nchannel.queue_declare(queue='responses')\nchannel.basic_qos(prefetch_count=1)\nchannel.basic_consume(consume_response,\n queue='responses',\n no_ack=True)\nthread = Thread(target=channel.start_consuming)\nthread.start()\nthread.join(0)\n\ndef get_things():\n return things\n", "sub_path": "backend/app/modules/response_watcher.py", "file_name": "response_watcher.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pika.BlockingConnection", "line_number": 17, "usage_type": "call"}, {"api_name": "pika.URLParameters", "line_number": 17, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "424024421", "text": "from django.shortcuts import render, redirect\nfrom polls.forms import *\nfrom django.http import HttpResponse \n\n\n\n\n\t\n\n\ndef log_in(request):\n\terrors = []\n\tus = User.objects.all()\n\tif request.method == 'POST':\n\t\tform = LoginForm(request.POST)\n\n\t\tif form.is_valid():\n\t\t\tuser = User.objects.filter(first_name=form.cleaned_data['first_name'])\n\t\t\tprint(user)\n\t\t\tif not user:\n\t\t\t\terrors.append('First name not found.')\n\t\t\t\treturn render(request, 'log_in.html', {'errors': errors, 'form': form, 'us': us})\n\t\t\telse:\n\t\t\t\tuser = user.filter(last_name=form.cleaned_data['last_name'])\n\t\t\t\tif not user:\n\t\t\t\t\terrors.append('Last name is incorrect')\n\t\t\t\t\treturn render(request, 'log_in.html', {'form': form,'errors': errors, 'us': us}) \n\t\t\t\t\n\n\t\t\t\t \n\t\t\t\telse:\n\t\t\t\t\tuser = user.get()\n\t\t\t\t\trequest.session['userID'] = user.id\n\t\t\t\t\trequest.session['username'] = user.first_name + ' ' + user.last_name \n\n\t\t\t\t\tif user.has_voted == True:\n\t\t\t\t\t\terrors.append(user.first_name + ' ' + user.last_name + ' has already voted.')\n\t\t\t\t\t\treturn render(request, 'log_in.html', {'form': form,'errors': errors, 'us': us}) \n\t\t\t\t\telse:\n\t\t\t\t\t\trequest.session.set_expiry(0)\n\t\t\t\t\t\tquestion = Question.objects.all()\t\t\n\t\t\t\t\t\tform = NoteForm()\n\t\t\t\t\t\t\n\t\t\t\t\t\treturn render(request, 'polls.html', {'form': form, 'question': question})\n\telse: \n\t\tform = LoginForm()\n\treturn render(request,'log_in.html', {'form':form, 'us': us.distinct})\n\n\ndef contact(request):\n\treturn render(request, 'contact.html')\t\n\n\ndef poll(request):\n\treturn render(request, 'polls.html')\n\n\ndef results(request):\n\tquestion = Question.objects.filter(id=12)\n\tprint(question)\n\tif request.method == 'POST':\n\t\tif (request.session['username'] == \"test test\"):\n\t\t\tpass\n\t\telse:\t\n\t\t\tuser = User.objects.filter(id=request.session['userID']).first()\n\t\t\tuser.has_voted = True\n\t\t\tuser.save()\n\n\t\t\tform = NoteForm(request.POST, instance=user)\n\t\t\twith open(\"log.txt\", \"a\") as myfile:\n\t\t\t\tmyfile.write(request.session['username'] + '\\n')\n\t\t\tif form.is_valid():\n\t\t\t\tform.save()\n\t\t\tfor key in request.POST:\n\t\t\t\tif key != 'csrfmiddlewaretoken' and key != 'description' :\n\t\t\t\t\tquestion = Question.objects.get(id=int(key))\n\t\t\t\t\ttotal2 = question.total\n\t\t\t\t\ttotal2 = total2 + int(request.POST.get(key))\n\t\t\t\t\tQuestion.objects.filter(id=int(key)).update(total = total2)\n\t\t\t\t\twith open(\"log.txt\", \"a\") as myfile:\n\t\t\t\t\t\tmyfile.write(str(question) + ':\\t' + str(total2) + '\\n')\n\n\t\t\t\t\t\t\n\t\n\tquestion = Question.objects.all()\n\treturn render(request, 'results.html', {'question': question})\n", "sub_path": "mysite/mysite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "392682426", "text": "import requests\nfrom config import bot\n\ndef git(msg):\n if msg.get('text'):\n if msg['text'].startswith('/git ') or msg['text'].startswith('!git '):\n text = msg['text'][5:]\n res = requests.get('https://api.github.com/users/' + text).json()\n if not res.get('login'):\n return bot.sendMessage(msg['chat']['id'], 'Usuário \"{}\" não encontrado.'.format(text),\n reply_to_message_id=msg['message_id'])\n else:\n bot.sendMessage(msg['chat']['id'], '''*Nome:* `{}`\n*Login:* `{}`\n*Localização:* `{}`\n*Tipo:* `{}`\n*Bio:* `{}`'''.format(res['name'], res['login'],\n res['location'], res['type'],\n res['bio']), 'Markdown',\n reply_to_message_id=msg['message_id'])\n return True\n", "sub_path": "plugins/git.py", "file_name": "git.py", "file_ext": "py", "file_size_in_byte": 869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "config.bot.sendMessage", "line_number": 10, "usage_type": "call"}, {"api_name": "config.bot", "line_number": 10, "usage_type": "name"}, {"api_name": "config.bot.sendMessage", "line_number": 13, "usage_type": "call"}, {"api_name": "config.bot", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "179962625", "text": "# USDA_CoA_Livestock.py (flowsa)\n# !/usr/bin/env python3\n# coding=utf-8\n\nimport io\nimport pandas as pd\nimport json\nimport numpy as np\nfrom flowsa.common import *\nfrom flowsa.flowbyfunctions import assign_fips_location_system\n\n\ndef CoA_Livestock_URL_helper(build_url, config, args):\n \"\"\"This helper function uses the \"build_url\" input from flowbyactivity.py, which is a base url for coa cropland data\n that requires parts of the url text string to be replaced with info specific to the usda nass quickstats API.\n This function does not parse the data, only modifies the urls from which data is obtained. \"\"\"\n # initiate url list for coa cropland data\n urls_livestock = []\n\n # call on state acronyms from common.py (and remove entry for DC)\n state_abbrev = abbrev_us_state\n state_abbrev = {k: v for (k, v) in state_abbrev.items() if k != \"DC\"}\n\n # replace \"__aggLevel__\" in build_url to create three urls\n for x in config['agg_levels']:\n # at national level, remove the text string calling for state acronyms\n if x == 'NATIONAL':\n url_ls = build_url\n url_ls = url_ls.replace(\"__aggLevel__\", x)\n url_ls = url_ls.replace(\"&state_alpha=__stateAlpha__\", \"\")\n url_ls = url_ls.replace(\" \", \"%20\")\n urls_livestock.append(url_ls)\n else:\n # substitute in state acronyms for state and county url calls\n for y in state_abbrev:\n url_ls = build_url\n url_ls = url_ls.replace(\"__aggLevel__\", x)\n url_ls = url_ls.replace(\"__stateAlpha__\", y)\n url_ls = url_ls.replace(\" \", \"%20\")\n urls_livestock.append(url_ls)\n return urls_livestock\n\n\ndef coa_livestock_call(url, coa_response, args):\n livestock_json = json.loads(coa_response.text)\n # Convert response to dataframe\n df_livestock = pd.DataFrame(data=livestock_json[\"data\"])\n return df_livestock\n\n\ndef coa_livestock_parse(dataframe_list, args):\n \"\"\"Modify the imported data so it meets the flowbyactivity criteria and only includes data on harvested acreage\n (irrigated and total).\"\"\"\n df = pd.concat(dataframe_list, sort=False)\n # # specify desired data based on domain_desc\n df = df[df['domain_desc'].str.contains(\"INVENTORY|TOTAL\")]\n df = df[~df['domain_desc'].str.contains(\"ECONOMIC CLASS|NAICS|FARM SALES|AREA OPERATED\")]\n # drop any specialized production practices\n df = df[df['prodn_practice_desc'] == 'ALL PRODUCTION PRACTICES']\n # drop specialized class descriptions\n df = df[~df['class_desc'].str.contains(\"BREEDING|MARKET\")]\n # drop unused columns\n df = df.drop(columns=['agg_level_desc', 'location_desc', 'state_alpha', 'sector_desc',\n 'country_code', 'begin_code', 'watershed_code', 'reference_period_desc',\n 'asd_desc', 'county_name', 'source_desc', 'congr_district_code', 'asd_code',\n 'week_ending', 'freq_desc', 'load_time', 'zip_5', 'watershed_desc', 'region_desc',\n 'state_ansi', 'state_name', 'country_name', 'county_ansi', 'end_code', 'group_desc',\n 'util_practice_desc'])\n # create FIPS column by combining existing columns\n df.loc[df['county_code'] == '', 'county_code'] = '000' # add county fips when missing\n df['Location'] = df['state_fips_code'] + df['county_code']\n df.loc[df['Location'] == '99000', 'Location'] = US_FIPS # modify national level fips\n # combine column information to create activity information, and create two new columns for activities\n df['ActivityProducedBy'] = df['commodity_desc'] + ', ' + df['class_desc'] # drop this column later\n df['ActivityProducedBy'] = df['ActivityProducedBy'].str.replace(\", ALL CLASSES\", \"\", regex=True) # not interested in all data from class_desc\n # rename columns to match flowbyactivity format\n df = df.rename(columns={\"Value\": \"FlowAmount\",\n \"unit_desc\": \"FlowName\",\n \"year\": \"Year\",\n \"CV (%)\": \"Spread\",\n \"domaincat_desc\": \"Compartment\",\n \"short_desc\": \"Description\"})\n # drop remaining unused columns\n df = df.drop(columns=['class_desc', 'commodity_desc', 'state_fips_code', 'county_code',\n 'statisticcat_desc', 'prodn_practice_desc'])\n # modify contents of flowamount column, \"D\" is supressed data, \"z\" means less than half the unit is shown\n df['FlowAmount'] = df['FlowAmount'].str.strip() # trim whitespace\n df.loc[df['FlowAmount'] == \"(D)\", 'FlowAmount'] = withdrawn_keyword\n # df.loc[df['FlowAmount'] == \"(Z)\", 'FlowAmount'] = withdrawn_keyword\n df['FlowAmount'] = df['FlowAmount'].str.replace(\",\", \"\", regex=True)\n # # USDA CoA 2017 states that (H) means CV >= 99.95, therefore replacing with 99.95 so can convert column to int\n # # (L) is a CV of <= 0.05\n df['Spread'] = df['Spread'].str.strip() # trim whitespace\n df.loc[df['Spread'] == \"(H)\", 'Spread'] = 99.95\n df.loc[df['Spread'] == \"(L)\", 'Spread'] = 0.05\n df.loc[df['Spread'] == \"\", 'Spread'] = None # for instances where data is missing\n df.loc[df['Spread'] == \"(D)\", 'Spread'] = withdrawn_keyword\n # add location system based on year of data\n df = assign_fips_location_system(df, args['year'])\n # # Add hardcoded data\n df['Class'] = \"Other\"\n df['SourceName'] = \"USDA_CoA_Livestock\"\n df['Unit'] = \"p\"\n df['MeasureofSpread'] = \"RSD\"\n df['DataReliability'] = None\n df['DataCollection'] = 2\n return df\n", "sub_path": "flowsa/USDA_CoA_Livestock.py", "file_name": "USDA_CoA_Livestock.py", "file_ext": "py", "file_size_in_byte": 5621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 54, "usage_type": "call"}, {"api_name": "flowsa.flowbyfunctions.assign_fips_location_system", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "644682187", "text": "import tensorflow as tf\r\nimport numpy as np\r\nimport os\r\nimport csv\r\nimport time\r\nimport datetime\r\nfrom dataset import Dataset\r\nfrom utils import normalize\r\n\r\n# Parameters\r\n# ==================================================\r\n\r\n# Data loading params\r\ntf.flags.DEFINE_string(\"test_data\", \"data/yelp-2013-test.pkl\", \"Data source for the testing data.\")\r\n\r\n# Training parameters\r\ntf.flags.DEFINE_integer(\"batch_size\", 64, \"Batch Size (Default: 64)\")\r\ntf.flags.DEFINE_string(\"checkpoint_dir\", \"runs/1563096406/checkpoints\", \"Checkpoint directory from training run\")\r\n\r\n# Misc Parameters\r\ntf.flags.DEFINE_boolean(\"allow_soft_placement\", True, \"Allow device soft device placement\")\r\ntf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\r\n\r\nFLAGS = tf.flags.FLAGS\r\n\r\nprint(\"Evaluating...\\n\")\r\n# Load test data\r\ntest = Dataset(filepath=FLAGS.test_data)\r\n# Evaluation\r\n# ==================================================\r\ncheckpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)\r\ngraph = tf.Graph()\r\nwith graph.as_default():\r\n session_conf = tf.ConfigProto(\r\n allow_soft_placement=FLAGS.allow_soft_placement,\r\n log_device_placement=FLAGS.log_device_placement)\r\n sess = tf.Session(config=session_conf)\r\n with sess.as_default():\r\n # Load the saved meta graph and restore variables\r\n saver = tf.train.import_meta_graph(\"{}.meta\".format(checkpoint_file))\r\n saver.restore(sess, checkpoint_file)\r\n\r\n # Get the placeholders from the graph by name\r\n context_placeholder = graph.get_operation_by_name(\"context\").outputs[0]\r\n query_placeholder = graph.get_operation_by_name(\"query\").outputs[0]\r\n num_sents_placeholder = graph.get_operation_by_name(\"num_sents\").outputs[0]\r\n labels_placeholder = graph.get_operation_by_name(\"labels\").outputs[0]\r\n dropout_keep_prob = graph.get_operation_by_name(\"dropout_keep_prob\").outputs[0]\r\n\r\n\r\n # Tensors we want to evaluate\r\n predictions = graph.get_operation_by_name(\"predictions\").outputs[0]\r\n\r\n # Generate batches for one epoch\r\n all_labels = []\r\n all_predictions = []\r\n for batch in test.bacth_iter(FLAGS.batch_size, desc=\"Evaluating\", shuffle=False):\r\n labels, contexts, queries = zip(*batch)\r\n contexts, num_sents = normalize(contexts)\r\n feed_dict = {\r\n context_placeholder: contexts,\r\n query_placeholder: queries,\r\n num_sents_placeholder: num_sents,\r\n labels_placeholder: labels,\r\n dropout_keep_prob: 1.0\r\n }\r\n batch_predictions = sess.run(predictions, feed_dict)\r\n all_labels = np.concatenate([all_labels, labels])\r\n all_predictions = np.concatenate([all_predictions, batch_predictions])\r\n\r\n# Print accuracy\r\nif all_labels is not None:\r\n correct_predictions = float(sum(all_predictions == all_labels))\r\n print(\"Total number of test examples: {}\".format(len(all_labels)))\r\n print(\"Accuracy: {:g}\".format(correct_predictions/float(len(all_labels)) * 100))\r\n\r\n\r\n# Save the evaluation to a csv\r\nout_path = os.path.join(FLAGS.checkpoint_dir, \"..\", \"predictions.csv\")\r\nprint(\"Saving evaluation to {0}\".format(out_path))\r\nwith open(out_path, 'w') as f:\r\n csv.writer(f).writerows(map(lambda x: [x], all_predictions.astype(np.int32)))", "sub_path": "textdmn/eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 3391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "tensorflow.flags.DEFINE_string", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.flags", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.flags.DEFINE_integer", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.flags", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.flags.DEFINE_string", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.flags", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.flags.DEFINE_boolean", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.flags", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.flags.DEFINE_boolean", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.flags", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.flags", "line_number": 24, "usage_type": "attribute"}, {"api_name": "dataset.Dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.Graph", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.train.import_meta_graph", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 40, "usage_type": "attribute"}, {"api_name": "utils.normalize", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 82, "usage_type": "attribute"}]} +{"seq_id": "649178491", "text": "import maya.cmds as mc\n\ndrivers = ['pSphere1', 'pSphere2', 'pSphere3']\ndriven = 'pCube1'\n\ndriven_start = 2.0\ndriven_end = mc.getAttr('{}.ty'.format(driven))\n\nbw = mc.createNode('blendWeighted', n='test_BW')\n\ndriver_weight = (driven_end - driven_start) / len(drivers)\n\nfor index, driver in enumerate(drivers):\n driver_value = mc.getAttr('{}.ty'.format(driver))\n mc.setAttr('{}.weight[{}]'.format(bw, index), driver_weight / driver_value)\n mc.connectAttr('{}.ty'.format(driver), '{}.input[{}]'.format(bw, index))\n \nmc.setAttr('{}.weight[{}]'.format(bw, len(drivers)), driven_start)\nmc.setAttr('{}.input[{}]'.format(bw, len(drivers)), 1.0)\n\nmc.connectAttr('{}.output'.format(bw), '{}.ty'.format(driven))", "sub_path": "maya/tools/poseDriver.py", "file_name": "poseDriver.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "maya.cmds.getAttr", "line_number": 7, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 7, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 9, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 9, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 14, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 14, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 15, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 15, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 16, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 16, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 18, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 18, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 19, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 19, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 21, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "306388076", "text": "#-*- coding: utf-8 -*-\nimport json\nimport requests\nimport re\n\nfor line in open(\"jawiki-country.json\", \"r\"):\n uk = json.loads(line)\n if uk[\"title\"] == u\"イギリス\":\n break\n\nfor line in uk[\"text\"].split(\"\\n\"):\n if re.search(r\".+ = .+\", line):\n word = line.strip(\"|\").split(\" = \")\n if word[0] == \"国旗画像\":\n file_name = word[1]\n break\n\nendpoint = \"http://en.wikipedia.org/w/api.php\"\nparams = {'action': 'query', 'prop': 'imageinfo', 'iiprop': 'url', 'format': 'json', 'titles': 'File:{}'.format(file_name)}\n\nresponse = requests.get(endpoint, params=params)\ndic = response.json()\n\nprint (dic[\"query\"][\"pages\"][\"23473560\"][\"imageinfo\"][0][\"url\"])\n\n", "sub_path": "Hayahide/chapter03/knock29.py", "file_name": "knock29.py", "file_ext": "py", "file_size_in_byte": 703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "re.search", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "429852087", "text": "import collections\nimport time\n\nimport gevent\nfrom gevent_zeromq import zmq\n\nfrom gevent_tools.config import Option\nfrom gevent_tools.service import Service\nfrom gevent_tools.service import require_ready\n\ncontext = zmq.Context()\n\nclass MessagingException(Exception): pass\n\nclass Observable(object):\n # TODO: move to a util module\n \n def __init__(self):\n self._observers = []\n\n def attach(self, observer):\n if not observer in self._observers:\n self._observers.append(observer)\n\n def detach(self, observer):\n try:\n self._observers.remove(observer)\n except ValueError:\n pass\n\n def notify(self, *args, **kwargs):\n for observer in self._observers:\n if hasattr(observer, '__call__'):\n observer(*args, **kwargs)\n else:\n observer.update(*args, **kawrgs)\n\nclass ClusterRoster(Observable):\n def __init__(self):\n super(ClusterRoster, self).__init__()\n self._roster = set()\n \n def add(self, host):\n self._roster.add(host)\n self.notify(add=host)\n \n def remove(self, host):\n self._roster.discard(host)\n self.notify(remove=host)\n \n def __iter__(self):\n return self._roster.__iter__()\n \n\nclass MessagingBackend(Service):\n port = Option('backend_port')\n \n def __init__(self):\n self.cluster = ClusterRoster()\n self.publisher = MessagePublisher(self.cluster, self.port)\n self.router = MessageRouter('tcp://127.0.0.1:%s' % self.port)\n \n self.add_service(self.publisher)\n self.add_service(self.router)\n \n def publish(self, channel, message):\n self.publisher.publish(channel, message)\n \n def subscribe(self, channel, subscriber):\n self.router.subscribe(channel, subscriber)\n \n def unsubscribe(self, channel, subscriber):\n self.router.subscribe(channel, subscriber)\n\nclass MessagePublisher(Service):\n # TODO: batching socket sends based on publish frequency\n \n def __init__(self, cluster, port):\n self.cluster = cluster\n self.port = port\n self.socket = context.socket(zmq.PUB)\n \n def do_start(self):\n for host in self.cluster:\n self.connect(host)\n def connector(add=None, remove=None):\n if add: self.connect(add)\n self.cluster.attach(connector)\n \n def connect(self, host):\n self.socket.connect('tcp://%s:%s' % (host, self.port))\n \n @require_ready\n def publish(self, channel, message):\n self.socket.send_multipart([channel, message])\n\nclass MessageRouter(Service):\n max_channels = Option('max_channels', default=65536)\n max_subscribers = Option('max_subscribers', default=65536)\n \n def __init__(self, address):\n self.address = address\n self.socket = context.socket(zmq.SUB)\n \n self.channels = dict()\n self.subscriber_counts = collections.Counter()\n \n def do_start(self):\n self.socket.bind(self.address)\n self.spawn(self._listen)\n \n def subscribe(self, channel, subscriber):\n # Initialize channel if necessary\n if not self.channels.get(channel):\n if len(self.channels) >= self.max_channels:\n raise MessagingException(\n \"Unable to init channel. Max channels reached: %s\" % \n self.max_channels)\n self.channels[channel] = ChannelDispatcher(self)\n \n # Create subscription unless max reached\n if sum(self.subscriber_counts.values()) >= self.max_subscribers:\n raise MessagingException(\n \"Unable to subscribe. Max subscribers reached: %s\" % \n self.max_subscribers)\n self.socket.setsockopt(zmq.SUBSCRIBE, channel)\n self.subscriber_counts[channel] += 1\n self.channels[channel].add(subscriber)\n \n def unsubscribe(self, channel, subscriber):\n self.socket.setsockopt(zmq.UNSUBSCRIBE, channel)\n self.subscriber_counts[channel] -= 1\n self.channels[channel].remove(subscriber)\n \n # Clean up counts and ChannelDispatchers with no subscribers\n self.subscriber_counts[channel] += collections.Counter()\n if not self.subscriber_counts[channel]:\n del self.channels[channel]\n \n def _listen(self):\n while True:\n channel, message = self.socket.recv_multipart()\n if self.subscriber_counts[channel]:\n self.channels[channel].send(message)\n\nclass ChannelDispatcher(object):\n def __init__(self, router):\n self.router = router\n self.purge()\n \n def purge(self):\n self.buffer = []\n self.subscribers = set()\n self.draining = False\n \n def send(self, message):\n self.buffer.append(message)\n self.drain()\n \n def add(self, subscriber):\n self.subscribers.add(subscriber)\n \n def remove(self, subscriber):\n self.subscribers.remove(subscriber)\n if not len(self.subscribers):\n self.purge()\n \n def drain(self):\n \"\"\"\n Unless already draining, this creates a greenlet that will flush the \n buffer to subscribers then delay the next flush depending on how many \n subscribers there are. This continues until the buffer remains empty.\n It will start again with the next call to send(). Since the buffer is \n flushed to a subscriber and a subscriber is ultimately an open socket, \n this helps reduce the number of socket operations when there are a \n large number of open sockets.\n \"\"\"\n if self.draining:\n return\n def _drain():\n self.draining = True\n while self.draining and self.buffer:\n start_time = time.time()\n batch = self.buffer[:]\n if batch:\n del self.buffer[:]\n for subscriber in self.subscribers:\n if hasattr(subscriber, 'put'):\n subscriber.put(batch)\n else:\n subscriber(batch)\n delta_time = time.time() - start_time\n interval = self._batch_interval()\n if delta_time > interval:\n gevent.sleep(0.0) # yield\n else:\n gevent.sleep(interval - delta_time)\n self.draining = False\n self.router.spawn(_drain)\n \n def _batch_interval(self):\n if len(self.subscribers) <= 10:\n return 0.0\n elif len(self.subscribers) <= 100:\n return 0.25\n elif len(self.subscribers) <= 1000:\n return 0.5\n else:\n return 1.0", "sub_path": "raiden/pubsub.py", "file_name": "pubsub.py", "file_ext": "py", "file_size_in_byte": 6821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "gevent_zeromq.zmq.Context", "line_number": 11, "usage_type": "call"}, {"api_name": "gevent_zeromq.zmq", "line_number": 11, "usage_type": "name"}, {"api_name": "gevent_tools.service.Service", "line_number": 55, "usage_type": "name"}, {"api_name": "gevent_tools.config.Option", "line_number": 56, "usage_type": "call"}, {"api_name": "gevent_tools.service.Service", "line_number": 75, "usage_type": "name"}, {"api_name": "gevent_zeromq.zmq.PUB", "line_number": 81, "usage_type": "attribute"}, {"api_name": "gevent_zeromq.zmq", "line_number": 81, "usage_type": "name"}, {"api_name": "gevent_tools.service.require_ready", "line_number": 93, "usage_type": "name"}, {"api_name": "gevent_tools.service.Service", "line_number": 97, "usage_type": "name"}, {"api_name": "gevent_tools.config.Option", "line_number": 98, "usage_type": "call"}, {"api_name": "gevent_tools.config.Option", "line_number": 99, "usage_type": "call"}, {"api_name": "gevent_zeromq.zmq.SUB", "line_number": 103, "usage_type": "attribute"}, {"api_name": "gevent_zeromq.zmq", "line_number": 103, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 106, "usage_type": "call"}, {"api_name": "gevent_zeromq.zmq.SUBSCRIBE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "gevent_zeromq.zmq", "line_number": 126, "usage_type": "name"}, {"api_name": "gevent_zeromq.zmq.UNSUBSCRIBE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "gevent_zeromq.zmq", "line_number": 131, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 136, "usage_type": "call"}, {"api_name": "time.time", "line_number": 183, "usage_type": "call"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 195, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "293179492", "text": "from django.forms import ModelForm\nfrom dal import autocomplete\nfrom .models import ModelOne, ModelTwo, MasterModel\n\n\nclass Form1(ModelForm):\n class Meta:\n model = MasterModel\n fields = ('name', 'modelone')\n widgets = {\n 'modelone': autocomplete.ModelSelect2Multiple(\n url='modelone-autocomplete')\n }\n\n\nclass Form2(ModelForm):\n class Meta:\n model = MasterModel\n fields = ('modeltwo', )\n widgets = {\n 'modeltwo': autocomplete.ModelSelect2Multiple(\n url='modeltwo-autocomplete'),\n }\n", "sub_path": "test_project/select2_outside_admin_multiple/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "name"}, {"api_name": "models.MasterModel", "line_number": 8, "usage_type": "name"}, {"api_name": "dal.autocomplete.ModelSelect2Multiple", "line_number": 11, "usage_type": "call"}, {"api_name": "dal.autocomplete", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 16, "usage_type": "name"}, {"api_name": "models.MasterModel", "line_number": 18, "usage_type": "name"}, {"api_name": "dal.autocomplete.ModelSelect2Multiple", "line_number": 21, "usage_type": "call"}, {"api_name": "dal.autocomplete", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "379977034", "text": "\"\"\"empty message\n\nRevision ID: 5f00836ba009\nRevises: 2719f8dfddc8\nCreate Date: 2021-05-03 21:19:28.728259\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"5f00836ba009\"\ndown_revision = \"2719f8dfddc8\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table(\n \"student_course_record\",\n sa.Column(\"student_id\", sa.Integer(), nullable=False),\n sa.Column(\"course_id\", sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(\n [\"course_id\"],\n [\"course.id\"],\n ),\n sa.ForeignKeyConstraint(\n [\"student_id\"],\n [\"student.id\"],\n ),\n sa.PrimaryKeyConstraint(\"student_id\", \"course_id\"),\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table(\"student_course_record\")\n # ### end Alembic commands ###\n", "sub_path": "app/migrations/versions/5f00836ba009_.py", "file_name": "5f00836ba009_.py", "file_ext": "py", "file_size_in_byte": 1020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "alembic.op.create_table", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 39, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "558794350", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Time:2021/8/4 6:22\n# Description:\n\nimport yaml\n\n# yaml.safe_dump()\n\nwith open(\"./datas/data.yaml\", mode=\"r\", encoding='utf-8') as f:\n print(yaml.safe_load(f))\n\ndic1 = {'name': 'hogwt', 'age': '20', 'gender': 'male'}\nwith open(\"./datas/data2.yaml\", mode='w', encoding='utf-8') as f:\n data2 = yaml.safe_dump(dic1, f)\n", "sub_path": "pythoncode/demo2.py", "file_name": "demo2.py", "file_ext": "py", "file_size_in_byte": 369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "yaml.safe_load", "line_number": 11, "usage_type": "call"}, {"api_name": "yaml.safe_dump", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "409734538", "text": "from LoadAddresses import load_contacts, get_address_list\nfrom GeoLocationModule import load_geo_location_data, save_geo_location_data, parse_geo_location_data, address_in_geo_data, retrieve_geo_location_data\nfrom json import load\n\nwith open(\"c:\\\\src\\\\PVData\\\\pv-geo-location-config.json\") as config_file:\n config_json = load(config_file)\n\ncity_state = config_json[\"configuration\"][\"city_state\"]\napi_key = config_json[\"configuration\"][\"api_key\"]\n\nall_geo_data = load_geo_location_data(\"c:\\\\src\\\\PVData\\\\ContactGeoData.csv\")\n\nall_contacts = load_contacts(\"c:\\\\src\\\\PVData\\\\ContactList.csv\")\nall_addresses = get_address_list(all_contacts)\n\nnew_address_count = 0\nfor address in all_addresses:\n if address_in_geo_data(address, all_geo_data):\n print(f\"Existing address: {address}\")\n else:\n print(f\"New address: {address}\")\n new_address_count += 1\n json_response = retrieve_geo_location_data(address + \", \" + city_state, api_key)\n all_geo_data.append(parse_geo_location_data(address, json_response))\n\n # Safety check to make sure I don't try to look up too many new addresses\n if new_address_count > 2:\n break;\n\nprint(f\"Found {new_address_count} new addresses\")\n\nsave_geo_location_data(all_geo_data, \"c:\\\\src\\\\PVData\\\\ContactGeoDataNew.csv\")\n\n", "sub_path": "GetMapCoordinates/GetMapCoordinates.py", "file_name": "GetMapCoordinates.py", "file_ext": "py", "file_size_in_byte": 1294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "GeoLocationModule.load_geo_location_data", "line_number": 11, "usage_type": "call"}, {"api_name": "LoadAddresses.load_contacts", "line_number": 13, "usage_type": "call"}, {"api_name": "LoadAddresses.get_address_list", "line_number": 14, "usage_type": "call"}, {"api_name": "GeoLocationModule.address_in_geo_data", "line_number": 18, "usage_type": "call"}, {"api_name": "GeoLocationModule.retrieve_geo_location_data", "line_number": 23, "usage_type": "call"}, {"api_name": "GeoLocationModule.parse_geo_location_data", "line_number": 24, "usage_type": "call"}, {"api_name": "GeoLocationModule.save_geo_location_data", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "173480964", "text": "#!/usr/bin/env python\n\"\"\"GeoJSON service for HUC12 data\"\"\"\nimport json\nimport cgi\nimport datetime\n\nimport memcache\nfrom pyiem.util import get_dbconn, ssw\n\n\ndef do(huc12, mode):\n \"\"\"Do work\"\"\"\n pgconn = get_dbconn('idep')\n cursor = pgconn.cursor()\n utcnow = datetime.datetime.utcnow()\n if mode == 'daily':\n cursor.execute(\"\"\"\n SELECT valid, avg_loss * 4.463, avg_delivery * 4.463,\n qc_precip / 25.4, avg_runoff / 25.4, 1, 1, 1, 1\n from results_by_huc12 where huc_12 = %s and scenario = 0 ORDER\n by valid ASC\n \"\"\", (huc12, ))\n else:\n cursor.execute(\"\"\"\n SELECT extract(year from valid) as yr,\n sum(avg_loss) * 4.463,\n sum(avg_delivery) * 4.463,\n sum(qc_precip) / 25.4,\n sum(avg_runoff) / 25.4,\n sum(case when avg_loss > 0 then 1 else 0 end),\n sum(case when avg_delivery > 0 then 1 else 0 end),\n sum(case when qc_precip > 0 then 1 else 0 end),\n sum(case when avg_runoff > 0 then 1 else 0 end)\n from results_by_huc12 where huc_12 = %s and scenario = 0\n GROUP by yr ORDER by yr ASC\n \"\"\", (huc12, ))\n res = {'results': [],\n 'huc12': huc12,\n 'generation_time': utcnow.strftime(\"%Y-%m-%dT%H:%M:%SZ\")}\n for row in cursor:\n dt = row[0]\n if isinstance(row[0], float):\n dt = datetime.date(int(row[0]), 1, 1)\n res['results'].append(dict(date=dt.strftime(\"%m/%d/%Y\"),\n avg_loss=row[1],\n avg_loss_events=row[5],\n avg_delivery=row[2],\n avg_delivery_events=row[6],\n qc_precip=row[3],\n qc_precip_events=row[7],\n avg_runoff=row[4],\n avg_runoff_events=row[8]))\n return json.dumps(res)\n\n\ndef main():\n \"\"\"Do Fun things\"\"\"\n ssw(\"Content-Type: application/vnd.geo+json\\n\\n\")\n form = cgi.FieldStorage()\n cb = form.getfirst('callback', None)\n huc12 = form.getfirst('huc12', '000000000000')[:12]\n mode = form.getfirst('mode', 'daily')\n\n mckey = (\"/geojson/huc12_events/%s/%s\"\n ) % (huc12, mode)\n mc = memcache.Client(['iem-memcached:11211'], debug=0)\n res = mc.get(mckey)\n if not res:\n res = do(huc12, mode)\n mc.set(mckey, res, 15)\n\n if cb is None:\n ssw(res)\n else:\n ssw(\"%s(%s)\" % (cb, res))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "htdocs/geojson/huc12_events.py", "file_name": "huc12_events.py", "file_ext": "py", "file_size_in_byte": 2626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pyiem.util.get_dbconn", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}, {"api_name": "pyiem.util.ssw", "line_number": 58, "usage_type": "call"}, {"api_name": "cgi.FieldStorage", "line_number": 59, "usage_type": "call"}, {"api_name": "memcache.Client", "line_number": 66, "usage_type": "call"}, {"api_name": "pyiem.util.ssw", "line_number": 73, "usage_type": "call"}, {"api_name": "pyiem.util.ssw", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "532310765", "text": "import math\nimport random\nimport matplotlib.pyplot as plt\nfrom random import shuffle\nimport pickle\n'''\n遗传算法解决旅行商问题\n'''\n#坐标纸大小\nX_MAX = 5000\nY_MAX = 5000\n\nclass city:\n def __init__(self,x,y):\n self.x = x\n self.y = y\n\n#CITY_LIST = create_city()\nwith open(\"./city_17.dump\",'rb') as f:\n CITY_LIST = pickle.load(f)\n\n#城市数目 5 8 11 14 17\n#CITY_SIZE = 17\nCITY_SIZE = len(CITY_LIST)\n\n#其他参数\npc = 0.25#交叉拼接概率\npm = 0.05#变异概率\npopulation_size =100#初始种群大小\nSTEPS = 1500 #遗传代数\nlength = CITY_SIZE#染色体长度\n\n\npopulation = [] #种群列表\n\ndef distance(sln):\n '''\n 求解方案成本\n '''\n dis = 0\n for i in range(CITY_SIZE-1):\n dis += math.sqrt((CITY_LIST[sln[i]].x-CITY_LIST[sln[i+1]].x)**2 + (CITY_LIST[sln[i]].y-CITY_LIST[sln[i+1]].y)**2)\n dis += math.sqrt((CITY_LIST[sln[CITY_SIZE-1]].x-CITY_LIST[sln[0]].x)**2 + (CITY_LIST[sln[CITY_SIZE-1]].y-CITY_LIST[sln[0]].y)**2)\n return dis\n\ndef init_population():\n '''\n 初始化种群\n '''\n pop = [i for i in range(length)]\n for i in range(population_size):\n tmp = list(pop)\n shuffle(tmp)\n population.append(tmp)\n \ndef selection():\n '''\n 根据距离进行适应度选择\n 这里将适应度函数选择为距离的倒数(为了防止太小 改成1000000.0/dis作为函数)\n 生成新种群\n '''\n new_popution = []\n global population\n prob = [] #占比表\n for idv in population:\n prob.append(1000000.0/distance(idv))\n prob = [i/sum(prob) for i in prob]\n #轮盘概率法\n for i in range(population_size):\n rand = random.random()\n index = -1\n prob_cur =0\n #因为浮点数会产生误差 需要处理下\n for i in range(len(prob)):\n prob_cur +=prob[i]\n if rand < prob_cur:\n index = i\n break\n if index !=-1:\n new_popution.append(population[index])\n else:\n new_popution.append(population[-1])\n population.clear()\n population = list(new_popution)\n\ndef crossover(pop_1,pop_2):\n '''\n 交叉两条染色体 Order Crossover\n 返回交换后的两条染色体\n '''\n child_1 = list(pop_1)\n child_2 = list(pop_2)\n index_1 = random.randint(0,len(pop_1)-1)\n index_2 = random.randint(0,len(pop_1)-1)\n while index_2==index_1:\n index_2 = random.randint(1,len(pop_1)-1)\n if index_1>index_2:\n start = index_2\n end = index_1\n else:\n start = index_1\n end=index_2\n sec_1 = list(child_1[start:end])#不变的部分\n sec_2 = list(child_2[start:end])\n left_1 = child_1[:start]+child_1[end:]#父染色体1剩余的元素\n left_2 = child_2[:start]+child_2[end:]#父染色体2剩余的元素\n #产生child_1\n for i in range(0,len(pop_1)):\n #选中部分不变\n if i>=start and i =start and ipc:\n continue\n else:\n population[2*i],population[2*i+1] = crossover(population[2*i],population[2*i+1])\n\ndef mutation():\n '''\n 小概率变异 采用选定两城市交换顺序的方式\n '''\n global population\n for i in range(0,population_size):\n if random.random() >pm:\n continue\n else:\n index_1 = random.randint(0,CITY_SIZE-1)\n index_2 = random.randint(0,CITY_SIZE-1)\n while index_2==index_1:\n index_2 = random.randint(1,CITY_SIZE-1)\n tmp = population[i][index_1]\n population[i][index_1] = population[i][index_2]\n population[i][index_2] = tmp\n#------------------------------------------------------------\ndef get_Max():\n '''\n 获取当前种群中最优解\n '''\n sln = list(population[0])\n dist = distance(sln)\n for i in range(1,population_size):\n dis = distance(population[i])\n if dis(%d,%d)\" %(CITY_LIST[sln[i]].x, CITY_LIST[sln[i]].y),end='' )\n dis = distance(sln)\n print(\", 距离:%.5f\" % dis )\n\ndef paint(sln):\n #画图\n x_point= []\n y_point = []\n for i in range(CITY_SIZE):\n x_point.append(CITY_LIST[sln[i]].x)\n y_point.append(CITY_LIST[sln[i]].y)\n x_point.append(CITY_LIST[sln[0]].x)\n y_point.append(CITY_LIST[sln[0]].y)\n fig = plt.figure()\n plt.plot(x_point, y_point, color='r', linestyle='-',marker='o',markerfacecolor='blue')\n for a, b in zip(x_point, y_point):\n plt.text(a, b, (a,b),ha='center', va='bottom', fontsize=10)\n #plt.annotate('start point', xy=(x_point[0], y_point[0]), xytext=(x_point[0]-300, y_point[0] - 300), arrowprops=dict(arrowstyle='->'))\n plt.show()\n#----------------------------------------------------------------\n\ndef main():\n init_population()#种群初始化\n step = STEPS\n H = 100000 #最优长度\n H_cur = []#最优解\n while step:\n selection()#适应度选择\n sln,dist=get_Max()#获取当前种群的最优解\n if dist\\d+)/edit/$', views.post_edit), # ep22 form 수정용\n\n url(r'^sum/(?P[\\d/]+)/$', views.mysum), \n # 끝에 왜 /$ 인지 \"/\"이게 없으면 왜 안되는지 모르겠다.\n url(r'^hello/(?P[a-zA-Z]+)/(?P\\d+)/$', views.hello),\n\n url(r'^list1/$', views.post_list1),\n url(r'^list2/$', views.post_list2),\n url(r'^list3/$', views.post_list3),\n url(r'^excel/$', views.excel_download),\n\n url(r'^cbv/list1/$', views_cbv.post_list1),\n url(r'^cbv/list2/$', views_cbv.post_list2), # AttributeError: module 'dojo.views_cbv' has no attribute 'post_list2'\n url(r'^cbv/list3/$', views_cbv.post_list3),\n url(r'^cbv/excel$', views_cbv.excel_downlaod),\n]", "sub_path": "dojo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "271460009", "text": "# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.\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.\n\"\"\"BERT finetuning runner.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os, sys\nimport random\nimport logging\nimport csv\nimport argparse\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\nfrom tqdm import tqdm, trange\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom pytorch_pretrained_bert.tokenization import BertTokenizer\nfrom pytorch_pretrained_bert.modeling import BertForQuizbowl\nfrom pytorch_pretrained_bert.optimization import BertAdam\nfrom pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE\n\nlogging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n datefmt='%m/%d/%Y %H:%M:%S',\n level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass InputExample(object):\n \"\"\"A single training/test example for simple sequence classification.\"\"\"\n\n def __init__(self, guid, text, label=None):\n \"\"\"Constructs a InputExample.\n\n Args:\n guid: Unique id for the example.\n text_a: string. The untokenized text of the first sequence. For single\n sequence tasks, only this sequence must be specified.\n text_b: (Optional) string. The untokenized text of the second sequence.\n Only must be specified for sequence pair tasks.\n label: (Optional) string. The label of the example. This should be\n specified for train and dev examples, but not for test examples.\n \"\"\"\n self.guid = guid\n self.text = text\n self.label = label\n\n\nclass InputFeatures(object):\n \"\"\"A single set of features of data.\"\"\"\n\n def __init__(self, input_ids, input_mask, label_id):\n self.input_ids = input_ids\n self.input_mask = input_mask\n self.label_id = label_id\n\n\nclass QuizbowlProcessor(object):\n \"\"\"Processor for the Quizbowl data set.\"\"\"\n\n def get_train_examples(self, data_dir):\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"1000_train.tsv\")), \"train\")\n\n def get_dev_examples(self, data_dir):\n return self._create_examples(\n self._read_tsv(os.path.join(data_dir, \"150_dev.tsv\")), \"dev\")\n\n def get_labels(self, data_dir):\n lines = self._read_tsv(os.path.join(data_dir, \"1000_train.tsv\"))\n return list({line[0] for line in lines})\n\n def _create_examples(self, lines, set_type):\n \"\"\"Creates examples for the training and dev sets.\"\"\"\n examples = []\n for (i, line) in enumerate(lines):\n guid = \"%s-%s\" % (set_type, i)\n label, text = line\n examples.append(InputExample(guid, text, label=label))\n return examples\n\n @classmethod\n def _read_tsv(cls, input_file, quotechar=None):\n \"\"\"Reads a tab separated value file.\"\"\"\n with open(input_file, \"r\", encoding='utf-8') as f:\n reader = csv.reader(f, delimiter='\\t', quotechar=quotechar)\n lines = [line for line in reader]\n return lines\n\n\ndef convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):\n \"\"\"Loads a data file into a list of `InputBatch`s.\"\"\"\n\n label_map = defaultdict(lambda: -1)\n for (i, label) in enumerate(label_list):\n label_map[label] = i\n\n features = []\n for (ex_index, example) in enumerate(tqdm(examples)):\n tokens = tokenizer.tokenize(example.text)\n # Account for [CLS] and [SEP] with \"- 2\"\n if len(tokens) > max_seq_length - 2:\n tokens = tokens[0: max_seq_length - 2]\n\n tokens.insert(0, \"[CLS]\")\n tokens.append(\"[SEP]\")\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n while len(input_ids) < max_seq_length:\n input_ids.append(0)\n input_mask.append(0)\n\n label_id = label_map[example.label]\n\n features.append(\n InputFeatures(input_ids=input_ids,\n input_mask=input_mask,\n label_id=label_id))\n return features\n\n\ndef accuracy(out, labels):\n outputs = np.argmax(out, axis=1)\n return np.sum(outputs == labels)\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n ## Required parameters\n parser.add_argument(\"--data_dir\",\n default=os.path.join(os.path.dirname(__file__), 'data'),\n type=str,\n # required=True,\n help=\"The input data dir. Should contain the .tsv files (or other data files) for the task.\")\n parser.add_argument(\"--bert_model\",\n default='bert-base-uncased',\n type=str,\n # required=True,\n help=\"Bert pre-trained model selected in the list: bert-base-uncased, \"\n \"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.\")\n parser.add_argument(\"--output_dir\",\n default=os.path.join(os.path.dirname(__file__), 'checkpoints'),\n type=str,\n # required=True,\n help=\"The output directory where the model checkpoints will be written.\")\n\n ## Other parameters\n parser.add_argument(\"--max_seq_length\",\n default=256,\n type=int,\n help=\"The maximum total input sequence length after WordPiece tokenization. \\n\"\n \"Sequences longer than this will be truncated, and sequences shorter \\n\"\n \"than this will be padded.\")\n parser.add_argument(\"--do_train\",\n default=True,\n action='store_true',\n help=\"Whether to run training.\")\n parser.add_argument(\"--do_eval\",\n default=False,\n action='store_true',\n help=\"Whether to run eval on the dev set.\")\n parser.add_argument(\"--batch_size\",\n default=16,\n type=int,\n help=\"Total batch size for training/testing.\")\n parser.add_argument(\"--learning_rate\",\n default=5e-5,\n type=float,\n help=\"The initial learning rate for Adam.\")\n parser.add_argument(\"--num_train_epochs\",\n default=100.0,\n type=float,\n help=\"Total number of training epochs to perform.\")\n parser.add_argument(\"--warmup_proportion\",\n default=0.1,\n type=float,\n help=\"Proportion of training to perform linear learning rate warmup for. \"\n \"E.g., 0.1 = 10%% of training.\")\n parser.add_argument(\"--no_cuda\",\n default=False,\n action='store_true',\n help=\"Whether not to use CUDA when available\")\n parser.add_argument('--seed',\n type=int,\n default=42,\n help=\"random seed for initialization\")\n\n args = parser.parse_args()\n\n if args.no_cuda:\n device = torch.device(\"cpu\")\n else:\n device = torch.device(\"cuda\")\n\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n if not args.no_cuda:\n torch.cuda.manual_seed_all(args.seed)\n\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n\n processor = QuizbowlProcessor()\n label_list = processor.get_labels(args.data_dir)\n tokenizer = BertTokenizer.from_pretrained(args.bert_model)\n\n # Prepare model\n model = BertForQuizbowl.from_pretrained(args.bert_model,\n cache_dir=PYTORCH_PRETRAINED_BERT_CACHE,\n num_labels=len(label_list))\n model.to(device)\n\n # Prepare optimizer\n train_examples = processor.get_train_examples(args.data_dir)\n num_train_steps = int(len(train_examples) * args.num_train_epochs / args.batch_size)\n param_optimizer = list(model.named_parameters())\n no_decay = ['bias', 'gamma', 'beta']\n optimizer_grouped_parameters = [\n {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},\n {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}]\n\n optimizer = BertAdam(optimizer_grouped_parameters,\n lr=args.learning_rate,\n warmup=args.warmup_proportion,\n t_total=num_train_steps)\n\n checkpoint_file = os.path.join(args.output_dir, \"checkpoint.pt\")\n if os.path.isfile(checkpoint_file):\n print('loading model from pytorch checkpoint...')\n checkpoint = torch.load(checkpoint_file)\n model.load_state_dict(checkpoint['model_state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n def do_train():\n train_features = convert_examples_to_features(\n train_examples, label_list, args.max_seq_length, tokenizer)\n logger.info(\"***** Running training *****\")\n logger.info(\" Num examples = %d\", len(train_examples))\n logger.info(\" Batch size = %d\", args.batch_size)\n logger.info(\" Num steps = %d\", num_train_steps)\n all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)\n all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)\n all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)\n train_data = TensorDataset(all_input_ids, all_input_mask, all_label_ids)\n train_sampler = RandomSampler(train_data)\n train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.batch_size)\n\n model.train()\n for _ in trange(int(args.num_train_epochs), desc=\"Epoch\"):\n tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0\n for step, batch in enumerate(tqdm(train_dataloader, desc=\"Iteration\")):\n batch = tuple(t.to(device) for t in batch)\n input_ids, input_mask, label_ids = batch\n loss, _ = model(input_ids, input_mask, label_ids)\n loss.backward()\n tr_loss += loss.item()\n nb_tr_examples += input_ids.size(0)\n nb_tr_steps += 1\n optimizer.step()\n model.zero_grad()\n\n if args.do_eval: do_eval()\n logger.info(\"train loss = %s\", tr_loss / nb_tr_examples)\n # torch.save({\n # 'model_state_dict': model.state_dict(),\n # 'optimizer_state_dict': optimizer.state_dict(),\n # 'train_loss': tr_loss / nb_tr_examples}, checkpoint_file)\n\n def do_eval():\n eval_examples = processor.get_dev_examples(args.data_dir)\n eval_features = convert_examples_to_features(\n eval_examples, label_list, args.max_seq_length, tokenizer)\n logger.info(\"***** Running evaluation *****\")\n logger.info(\" Num examples = %d\", len(eval_examples))\n logger.info(\" Batch size = %d\", args.batch_size)\n all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)\n all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)\n all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)\n eval_data = TensorDataset(all_input_ids, all_input_mask, all_label_ids)\n eval_sampler = SequentialSampler(eval_data)\n eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)\n\n model.eval()\n eval_loss, eval_accuracy = 0, 0\n nb_eval_steps, nb_eval_examples = 0, 0\n for input_ids, input_mask, label_ids in tqdm(eval_dataloader):\n input_ids = input_ids.to(device)\n input_mask = input_mask.to(device)\n label_ids = label_ids.to(device)\n\n with torch.no_grad():\n tmp_eval_loss, logits = model(input_ids, input_mask, label_ids)\n\n logits = logits.detach().cpu()\n label_ids = label_ids.to('cpu')\n tmp_eval_accuracy = accuracy(logits.numpy(), label_ids.numpy())\n\n eval_loss += tmp_eval_loss.mean().item()\n eval_accuracy += tmp_eval_accuracy\n\n nb_eval_examples += input_ids.size(0)\n nb_eval_steps += 1\n\n eval_loss = eval_loss / nb_eval_examples\n eval_accuracy = eval_accuracy / nb_eval_examples\n\n result = {'eval_loss': eval_loss,\n 'eval_accuracy': eval_accuracy}\n\n output_eval_file = os.path.join(args.output_dir, \"eval_results.txt\")\n with open(output_eval_file, \"a\") as writer:\n logger.info(\"***** Eval results *****\")\n for key in sorted(result.keys()):\n logger.info(\" %s = %s\", key, str(result[key]))\n writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n\n do_train() if args.do_train else do_eval()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "BERT-DAN/examples/run_quizbowl.py", "file_name": "run_quizbowl.py", "file_ext": "py", "file_size_in_byte": 14278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.append", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 41, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 102, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 110, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 146, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 216, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 219, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 225, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.tokenization.BertTokenizer.from_pretrained", "line_number": 229, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.tokenization.BertTokenizer", "line_number": 229, "usage_type": "name"}, {"api_name": "pytorch_pretrained_bert.modeling.BertForQuizbowl.from_pretrained", "line_number": 232, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.modeling.BertForQuizbowl", "line_number": 232, "usage_type": "name"}, {"api_name": "pytorch_pretrained_bert.file_utils.PYTORCH_PRETRAINED_BERT_CACHE", "line_number": 233, "usage_type": "name"}, {"api_name": "pytorch_pretrained_bert.optimization.BertAdam", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path", "line_number": 252, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 254, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 265, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 266, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 267, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.utils.data.RandomSampler", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 270, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 273, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 300, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 301, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 302, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 305, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 334, "usage_type": "call"}, {"api_name": "os.path", "line_number": 334, "usage_type": "attribute"}]} +{"seq_id": "237507169", "text": "\"\"\"\nCopyright 2019 Alexander Meulemans\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nresults = pd.read_csv('resultfile.csv')\nresults_shallow = pd.read_csv(('resultfile_shallow.csv'))\n# results_combined = results.join(results_shallow)\n\nresults_train = results.as_matrix(['Train_loss'])\nresults_test = results.as_matrix(['Test_loss'])\nresults_shallow_train = results_shallow.as_matrix(['Train_loss'])\nresults_shallow_test = results_shallow.as_matrix(['Test_loss'])\n\nplt.figure()\nplt.plot(results_train)\nplt.plot(results_test)\nplt.plot(results_shallow_train)\nplt.plot(results_shallow_test)\nplt.xlabel('epoch')\nplt.ylabel('L2 loss')\nplt.legend(['TP train', 'TP test', 'shallow train', 'shallow test'])\n", "sub_path": "utils/plot_resultfiles.py", "file_name": "plot_resultfiles.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "297134749", "text": "import os\nimport sys\nfrom urllib.request import urlopen\nfrom functools import cmp_to_key\n\nfrom datetime import date, datetime\n\n#TODO: replace the following definitions with values from the Setting table or from the .ini file\nfrom .properties import (\n PROJECT_TITLE,\n FINAL_ID,\n FINAL_DEADLINE,\n ADMINS,\n GROUP_IDS \n )\n\nfrom paginate import Page\ntry:\n from webhelpers.paginate import PageURL_WebOb as PageURL\nexcept: \n\n from urllib.parse import urlencode\n\n # Extracted of former webhelpers 1.3 and adapted for Python3\n def make_page_url(path, params, page, partial=False, sort=True):\n \"\"\"A helper function for URL generators.\n\n I assemble a URL from its parts. I assume that a link to a certain page is\n done by overriding the 'page' query parameter.\n\n ``path`` is the current URL path, with or without a \"scheme://host\" prefix.\n\n ``params`` is the current query parameters as a dict or dict-like object.\n\n ``page`` is the target page number.\n\n If ``partial`` is true, set query param 'partial=1'. This is to for AJAX\n calls requesting a partial page.\n\n If ``sort`` is true (default), the parameters will be sorted. Otherwise\n they'll be in whatever order the dict iterates them.\n \"\"\"\n params = params.copy()\n params[\"page\"] = page\n if partial:\n params[\"partial\"] = \"1\"\n if sort:\n params = sorted(params.items())\n qs = urlencode(params, True)\n return \"%s?%s\" % (path, qs)\n\n class PageURL(object):\n \"\"\"A page URL generator for WebOb-compatible Request objects.\n \n I derive new URLs based on the current URL but overriding the 'page'\n query parameter.\n\n I'm suitable for Pyramid, Pylons, and TurboGears, as well as any other\n framework whose Request object has 'application_url', 'path', and 'GET'\n attributes that behave the same way as ``webob.Request``'s.\n \"\"\"\n \n def __init__(self, request, qualified=False):\n \"\"\"\n ``request`` is a WebOb-compatible ``Request`` object.\n\n If ``qualified`` is false (default), generated URLs will have just the\n path and query string. If true, the \"scheme://host\" prefix will be\n included. The default is false to match traditional usage, and to avoid\n generating unuseable URLs behind reverse proxies (e.g., Apache's\n mod_proxy). \n \"\"\"\n self.request = request\n self.qualified = qualified\n\n def __call__(self, page, partial=False):\n \"\"\"Generate a URL for the specified page.\"\"\"\n if self.qualified:\n path = self.request.application_url\n else:\n path = self.request.path\n return make_page_url(path, self.request.GET, page, partial)\n \n\nfrom pyramid.response import Response\n\nfrom pyramid.view import (\n view_config, \n forbidden_view_config, \n notfound_view_config\n )\nfrom pyramid.renderers import render\nfrom pyramid.url import route_url\n\nfrom pyramid.security import (\n remember, \n forget\n )\n\nfrom pyramid.httpexceptions import (\n HTTPFound,\n HTTPNotFound\n )\n\nimport formencode\nfrom pyramid_simpleform import Form\nfrom pyramid_simpleform.renderers import FormRenderer\n\nfrom sqlalchemy.schema import MetaData\nfrom sqlalchemy.exc import DBAPIError\nfrom sqlalchemy.ext.serializer import (\n dumps as dump_table,\n loads as load_table\n )\n\nfrom .models import (\n DBSession,\n Setting,\n Player,\n Rank,\n Category,\n Team,\n TeamGroup,\n Match,\n Final,\n Tip\n )\n\nfrom . import scoring\n\n\n# determine the local IP address to access this game\nremote_server = Setting.get('result_server')\nRESULTSERVER = remote_server.d_value if remote_server else 'wm2018.rolotec.ch'\nRESULTPAGE = 'http://%s/results' % RESULTSERVER\nlocal_host = 'localhost'\nlocal_port = 8080 #TODO: extract port number from the server settings\ntry:\n from socket import create_connection\n s = create_connection((RESULTSERVER, 80))\n local_host = s.getsockname()[0]\n s.close()\nexcept:\n\tpass\nGAME_URL = 'http://%s:%d' % (local_host, local_port)\n\n\ndef items_per_page(request):\n \"\"\" Determine the pagination unit. This unit is determined\n as follows (in decreasing precedence):\n - from the request parameter 'items_per_page'\n - from the Setting table's entry named 'items_per_page'\n If none of the above matches or cannot be converted to\n an integer the default of 10 is returned.\n @return Number of items per page, default 10.\n \"\"\"\n try:\n # expect exception, if param is missing or has a non-numeric value\n return int(request.params['items_per_page'])\n except: \n try:\n setting = Setting.get('items_per_page')\n return int(setting.d_value)\n except:\n pass\n else:\n pass\n return 10\n\ndef get_int_param(request, param, default=None):\n \"\"\" @return Numerical value of named parameter. \"\"\"\n try:\n return int(request.params[param])\n except:\n return default\n\ndef game_over():\n return datetime.now() >= datetime(2018,7,16)\n\ndef login_form_view(request):\n return render('templates/login.pt',\n { 'loggedin': request.authenticated_userid },\n request)\n\ndef navigation_view(request):\n return render('templates/navigation.pt',\n { 'categories': sorted(Player.get_units()),\n 'game_over': game_over(),\n 'is_admin': request.authenticated_userid in ADMINS,\n 'viewer_username': request.authenticated_userid,\n 'login_form': login_form_view(request) },\n request) if 'nonav' not in request.params else None\n\n@forbidden_view_config()\ndef forbidden(request):\n return Response(body=render('templates/forbidden.pt',\n { 'project': PROJECT_TITLE,\n 'navigation': navigation_view(request) },\n request));\n\n@notfound_view_config()\ndef notfound(request):\n return Response(body=render('templates/notfound.pt',\n { 'project': PROJECT_TITLE,\n 'detail': request.exception.detail if request.exception else \"no details\",\n 'navigation': navigation_view(request) },\n request),\n status='404 Not Found');\n\n@view_config(permission='view', route_name='home', renderer='templates/main.pt')\ndef view_game(request):\n return { 'project': PROJECT_TITLE,\n 'game_url': GAME_URL,\n 'final_deadline': FINAL_DEADLINE,\n 'game_over': game_over(),\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='about', renderer='templates/about.pt')\ndef about_view(request):\n return { 'project': PROJECT_TITLE,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='help', renderer='templates/help.pt')\ndef help_view(request):\n return { 'project': PROJECT_TITLE,\n 'contact': Setting.get('admin_mail').d_value,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='infoscreen', renderer='templates/infoscreen.pt')\ndef infoscreen(request):\n return { 'project': PROJECT_TITLE,\n 'game_url': GAME_URL,\n 'final_deadline': FINAL_DEADLINE,\n 'viewer_username': None,\n 'navigation': None,\n 'params': request.params }\n\n@view_config(permission='view', route_name='results', renderer='json')\ndef results(request):\n \"\"\" Generate a list of scores for all played matches and the stage 2 team names. \"\"\"\n matches = {}\n for match in Match.get_stage2():\n matches[match.d_id] = { \"team1\": match.d_team1, \"team2\": match.d_team2 }\n scores = {}\n for match in Match.get_played():\n scores[match.d_id] = { \"score1\": match.d_score1, \"score2\": match.d_score2 }\n return { 'matches': matches,\n 'scores': scores }\n\n@view_config(permission='view', route_name='scoring', renderer='templates/scoring.pt')\ndef scoring_view(request):\n return { 'project': PROJECT_TITLE,\n 'num_matches': DBSession.query(Match).count(),\n 'scoring': scoring.BET_POINTS,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='score_table', renderer='templates/score_table.pt')\ndef score_table(request):\n match_scores = [(score1, score2) for score1 in range(0, 6) for score2 in range(score1, 6)]\n matches = [Match(0, datetime.now(), 'team1', 'team2', score1, score2) for (score1, score2) in match_scores]\n tip_scores = [(score1, score2) for score1 in range(0, 6) for score2 in range(0, 6)]\n tips = [Tip('none', 0, score1, score2) for (score1, score2) in tip_scores]\n match_tips = [scoring.MatchTip(match, tip) for match in matches for tip in tips]\n return { 'match_tips': match_tips,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='categories', renderer='templates/categories.pt')\ndef view_categories(request):\n return { 'project': PROJECT_TITLE,\n 'categories': Category.get_all(),\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='settings', renderer='templates/settings.pt')\ndef view_settings(request):\n return { 'project': PROJECT_TITLE,\n 'settings': Setting.get_all(),\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='too_late', renderer='templates/too_late.pt')\ndef too_late(request):\n return { 'final_deadline': FINAL_DEADLINE,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\n# ----- Player views -----\n\nclass RegistrationSchema(formencode.Schema):\n allow_extra_fields = True\n alias = formencode.validators.String(not_empty=True, max=30)\n name = formencode.validators.String(not_empty=True)\n mail = formencode.validators.Email(resolve_domain=False, not_empty=True)\n #category = formencode.validators.OneOf(categories, hideList=True)\n initial_password = formencode.validators.String(not_empty=True, min=5)\n confirm_password = formencode.validators.String(not_empty=True, min=5)\n chained_validators = [\n formencode.validators.FieldsMatch('initial_password', 'confirm_password')\n #TODO: uniqueUsername(alias)\n ]\n\n@view_config(permission='view', route_name='register', renderer='templates/register.pt')\ndef register(request):\n form = Form(request, schema=RegistrationSchema)\n if 'form.submitted' in request.POST and form.validate():\n alias = form.data['alias']\n if (Player.exists(alias)):\n request.session.flash('Alias \"%(alias)s\" is already used, please choose another one.' % form.data)\n else:\n player = Player(alias=alias,\n password=form.data['initial_password'],\n name=form.data['name'],\n mail=form.data['mail'],\n unit=form.data['category'])\n DBSession.add(player)\n headers = remember(request, alias)\n return HTTPFound(location=route_url('home', request), headers=headers)\n return { 'form': FormRenderer(form),\n 'categories': Category.option_list(),\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='login')\ndef login(request):\n main_view = route_url('home', request)\n came_from = request.params.get('came_from', main_view)\n if 'form.submitted' in request.POST:\n login = request.POST['alias']\n password = request.POST['password']\n if Player.check_password(login, password):\n request.session.flash('Logged in successfully.')\n return HTTPFound(location=came_from, headers=remember(request, login))\n else:\n request.session.flash('Failed to login.')\n return HTTPFound(location=came_from)\n\n@view_config(permission='post', route_name='logout')\ndef logout(request):\n request.session.invalidate()\n request.session.flash('Logged out successfully.')\n return HTTPFound(location=route_url('home', request), headers=forget(request))\n\n@view_config(permission='view', route_name='view_players', renderer='templates/players.pt')\ndef view_players(request):\n ranking = Player.ranking()\n if not ranking:\n raise HTTPNotFound('no players yet')\n # Calculate every player's rank. Only the first player of each\n # rank gets a rank number, for all others it is set to None.\n rank = 1\n points = None\n for player in ranking:\n if points is None or player.d_points != points:\n player.rank = rank\n points = player.d_points\n rank += 1\n #for player in ranking:\n # print'player %s with %d points = rank %s' % (player.d_alias, player.d_points, str(player.rank))\n page = get_int_param(request, param='page', default=1)\n players = Page(ranking, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n return { 'players': players,\n 'viewer_username': request.authenticated_userid,\n 'is_admin': request.authenticated_userid in ADMINS,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_group_players', renderer='templates/group_players.pt')\ndef view_group_players(request):\n category_id = request.matchdict['category']\n players = Player.get_by_unit(category_id)\n if not players:\n raise HTTPNotFound('no players in category %s' % category_id)\n page = get_int_param(request, param='page', default=1)\n players = Page(players, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n category = Category.get(category_id)\n category_name = category.d_name if category else category_id\n return { 'category': category_id,\n 'category_name': category_name,\n 'players': players,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_rank_players', renderer='templates/rank_players.pt')\ndef view_rank_players(request):\n points = request.matchdict['points']\n players = Player.get_by_rank(points)\n if not players:\n raise HTTPNotFound('no players with %s points' % points)\n page = get_int_param(request, param='page', default=1)\n players = Page(players, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n return { 'points': points,\n 'players': players,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\ngroupScore = lambda grp: float(grp[3]) / grp[2]\n\n@view_config(permission='view', route_name='view_player_groups', renderer='templates/player_groups.pt')\ndef view_player_groups(request):\n groups = Player.get_groups()\n if not groups:\n raise HTTPNotFound('no player groups yet')\n # sort categories by descending average number of points\n ranking = []\n rank = 1\n points = None\n for group in sorted(groups, key=groupScore, reverse=True):\n gid = group[1]\n cgroup = Category.get(group[1])\n category = Category(gid, cgroup.d_name if cgroup is not None else gid)\n category.players = int(group[2])\n category.points = float(group[3]) / category.players\n if points is None or category.points != points:\n category.rank = rank\n points = category.points\n ranking.append(category)\n rank += 1\n page = get_int_param(request, param='page', default=1)\n categories = Page(ranking, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n return { 'categories': categories,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_ranking', renderer='templates/ranking.pt')\ndef view_ranking(request):\n ranks = Rank.get_all()\n if not ranks:\n raise HTTPNotFound('no ranking yet')\n page = get_int_param(request, param='page', default=1)\n ranks = Page(ranks, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n player = Player.get_by_username(request.authenticated_userid)\n player_rank = Rank.get_position(player.d_points) if player else None\n return { 'ranks': ranks,\n 'player_rank': player_rank.d_position if player_rank else None,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\nclass PlayerInfo(formencode.Schema):\n allow_extra_fields = True\n name = formencode.validators.String(not_empty=True)\n mail = formencode.validators.Email(resolve_domain=False, not_empty=True)\n\n@view_config(permission='post', route_name='player_info', renderer='templates/player_info.pt')\ndef view_player_info(request):\n player = Player.get_by_username(request.authenticated_userid)\n form = Form(request, schema=PlayerInfo, obj=player)\n if 'form.submitted' in request.POST and form.validate():\n player.d_name = form.data['name']\n player.d_mail = form.data['mail']\n player.d_unit = form.data['category']\n request.session.flash('Player information has been updated.')\n player_rank = Rank.get_position(player.d_points) if player else None\n return { 'form': FormRenderer(form),\n 'player': player,\n 'player_rank': player_rank.d_position if player_rank else None,\n 'viewer_username': request.authenticated_userid,\n 'categories': Category.option_list(),\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\n# ----- Team/Group views -----\n\n@view_config(permission='view', route_name='view_teams', renderer='templates/teams.pt')\ndef view_teams(request):\n \"\"\" Alphabetical team list. \"\"\"\n return { 'teams': Team.get_all(),\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='view_team_groups', renderer='templates/team_groups.pt')\ndef view_team_groups(request):\n \"\"\" Show all teams of all groups. \"\"\"\n groups = [TeamGroup(group_id, Team.get_by_group(group_id)) for group_id in GROUP_IDS]\n return { 'groups': groups,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_group_teams', renderer='templates/team_groups.pt')\ndef view_group_teams(request):\n \"\"\" Show the teams of a single group. \"\"\"\n group_id = request.matchdict['group']\n if group_id not in GROUP_IDS:\n raise HTTPNotFound('invalid group id: %s' % group_id)\n groups = [TeamGroup(group_id, Team.get_by_group(group_id))]\n return { 'groups': groups,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\n# ----- Match views -----\n\ndef match_view(request, player, matches, title, group_id=None):\n for match in matches:\n if player:\n if match.d_id == FINAL_ID:\n final_tip = Final.get_player_tip(player)\n match.tip = Tip(player, FINAL_ID, final_tip.d_score1, final_tip.d_score2) if final_tip else None\n else:\n match.tip = Tip.get_player_tip(player, match.d_id)\n else:\n match.tip = None\n return { 'now': datetime.now(),\n 'title': title,\n 'matches': matches,\n 'group_id': group_id,\n 'final_id': FINAL_ID,\n 'final_deadline': FINAL_DEADLINE,\n 'viewer_username': player,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_matches', renderer='templates/matches.pt', http_cache=0)\ndef view_matches(request):\n player = request.authenticated_userid\n matches = Match.get_all()\n return match_view(request, player, matches, 'Match schedule')\n\n@view_config(permission='view', route_name='view_upcoming_matches', renderer='templates/matches.pt', http_cache=0)\ndef view_upcoming_matches(request):\n player = request.authenticated_userid\n num = request.matchdict['num']\n matches = Match.get_upcoming(date.today(), num)\n return match_view(request, player, matches, 'Upcoming matches')\n\n@view_config(permission='view', route_name='view_group_matches', renderer='templates/matches.pt', http_cache=0)\ndef view_group_matches(request):\n player = request.authenticated_userid\n group_id = request.matchdict['group']\n matches = Match.get_by_group(group_id).all()\n return match_view(request, player, matches, 'Group %s matches' % group_id, group_id)\n\n@view_config(permission='view', route_name='view_stage1_matches', renderer='templates/matches.pt', http_cache=0)\ndef view_stage1_matches(request):\n player = request.authenticated_userid\n matches = Match.get_stage1().all()\n return match_view(request, player, matches, 'Stage 1 matches')\n\n@view_config(permission='view', route_name='view_stage2_matches', renderer='templates/matches.pt', http_cache=0)\ndef view_stage2_matches(request):\n player = request.authenticated_userid\n matches = Match.get_stage2().all()\n return match_view(request, player, matches, 'Stage 2 matches')\n\n\n# ----- Tip views -----\n\nclass MatchBetSchema(formencode.Schema):\n allow_extra_fields = True\n d_score1 = formencode.validators.Int(min=0, max=100, not_empty=True)\n d_score2 = formencode.validators.Int(min=0, max=100, not_empty=True)\n\n@view_config(permission='post', route_name='match_bet', renderer='templates/match_bet.pt')\ndef match_bet(request):\n player_id = request.authenticated_userid\n match_id = request.matchdict['match']\n match = Match.get_by_id(match_id)\n if match.d_begin < datetime.now():\n return HTTPFound(location=route_url('too_late', request))\n\n tip = Tip.get_player_tip(player_id, match_id)\n\n form = Form(request, schema=MatchBetSchema, obj=tip)\n if 'form.submitted' in request.POST and form.validate():\n if not tip:\n tip = Tip(player=player_id, match=match_id)\n DBSession.add(tip)\n tip.d_score1 = form.data['d_score1']\n tip.d_score2 = form.data['d_score2']\n return HTTPFound(location=route_url('view_match_tips', request, match=match_id))\n\n return { 'match': match,\n 'tip': tip,\n 'form': FormRenderer(form),\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='view_match_tips', renderer='templates/match_tips.pt', http_cache=0)\ndef view_match_tips(request):\n match_id = request.matchdict['match']\n match = Match.get_by_id(match_id)\n match_tips = [scoring.MatchTip(match, tip) for tip in Tip.get_match_tips(match_id)]\n page = get_int_param(request, param='page', default=1)\n tips = Page(match_tips, page=page, url_maker=PageURL(request), items_per_page=items_per_page(request))\n return { 'match': match,\n 'tips': tips,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_player_tips', renderer='templates/player_tips.pt', http_cache=0)\ndef view_player_tips(request):\n player_id = request.matchdict['player']\n player = Player.get_by_username(player_id)\n tips = []\n for tip in Tip.get_player_tips(player_id):\n match = Match.get_by_id(tip.d_match)\n tips.append(scoring.MatchTip(match, tip))\n final = Match.get_final()\n final_tip = Final.get_player_tip(player_id)\n if final and final_tip:\n tips.append(scoring.FinalTip(final, final_tip))\n return { 'player': player,\n 'tips': tips,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\n# ----- Final views -----\n\nclass FinalBetSchema(formencode.Schema):\n allow_extra_fields = True\n d_team1 = formencode.validators.String(not_empty=True)\n d_team2 = formencode.validators.String(not_empty=True)\n d_score1 = formencode.validators.Int(min=0, not_empty=True)\n d_score2 = formencode.validators.Int(min=0, not_empty=True)\n\n@view_config(permission='post', route_name='final_bet', renderer='templates/final_bet.pt')\ndef final_bet(request):\n player = request.authenticated_userid\n final_tip = Final.get_player_tip(player)\n if final_tip:\n request.session.flash('You already entered a final tip.')\n return HTTPFound(location=route_url('view_final_tip', request, player=player))\n\n final_tip = Final(player)\n\n form = Form(request, schema=FinalBetSchema, obj=final_tip)\n if 'form.submitted' in request.POST and form.validate():\n # verify, that the tip was entered on time\n if FINAL_DEADLINE < datetime.now():\n return HTTPFound(location=route_url('too_late', request))\n final_tip.d_team1 = form.data['d_team1']\n final_tip.d_team2 = form.data['d_team2']\n final_tip.d_score1 = form.data['d_score1']\n final_tip.d_score2 = form.data['d_score2']\n DBSession.add(final_tip)\n return HTTPFound(location=route_url('view_final_tip', request, player=player))\n\n teams = [(team.d_id,team.d_name) for team in Team.get_all()]\n\n return { 'tip': final_tip,\n 'form': FormRenderer(form),\n 'teams': teams,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='view', route_name='view_final_tips', renderer='templates/final_tips.pt', http_cache=0)\ndef view_final_tips(request):\n final = Match.get_final()\n tips = [scoring.FinalTip(final, tip) for tip in Final.get_all()]\n return { 'final': final,\n 'tips': tips,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='view', route_name='view_final_tip', renderer='templates/final_tip.pt', http_cache=0)\ndef view_final_tip(request):\n player = request.matchdict['player']\n tip = Final.get_player_tip(player)\n return { 'tip': tip,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n\n# ----- Admin stuff -----\n\n@view_config(permission='admin', route_name='tips', renderer='templates/tips.pt', http_cache=0)\ndef view_tips(request):\n return { 'tips': Tip.get_all(),\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request) }\n\n@view_config(permission='admin', route_name='update_local')\ndef update_local(request):\n scoring.refresh_points()\n return HTTPFound(location=route_url('view_players', request))\n\n@view_config(permission='view', route_name='update_remote')\ndef update_remote(request):\n try:\n scoring.apply_results(urlopen(RESULTPAGE).read())\n return HTTPFound(location=route_url('view_players', request))\n except:\n raise HTTPNotFound('location <%s> is inaccessible.' % RESULTPAGE)\n\ngroupByCategory = lambda grp: grp[1]\n\n@view_config(permission='admin', route_name='mailing', renderer='templates/mailing.pt')\ndef mailing(request):\n groups = Player.get_groups()\n if not groups:\n raise HTTPNotFound('no player groups yet')\n everybody = []\n categories = {}\n for group in sorted(groups, key=groupByCategory):\n players = Player.get_by_unit(group.d_unit)\n addrs = [player.d_mail for player in players]\n categories[group.d_unit] = \";\".join(addrs)\n everybody.extend(addrs)\n return { 'everybody': \";\".join(everybody),\n 'categories': categories,\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request),\n 'nonav': 'nonav' in request.params }\n\n@view_config(permission='admin', route_name='unregister')\ndef unregister(request):\n alias = request.matchdict['alias']\n player = Player.get_by_username(alias)\n if player:\n DBSession.delete(player)\n request.session.flash('Player \"%(alias)s\" deleted.' % request.matchdict)\n else:\n request.session.flash('Player \"%(alias)s\" not found.' % request.matchdict)\n return HTTPFound(location=route_url('view_players', request))\n\n@view_config(permission='admin', route_name='update_category')\ndef update_category(request):\n try:\n alias = request.matchdict['alias']\n value = request.matchdict['value']\n category = Category.get(alias)\n if value == 'DELETE':\n # delete category unless it is used by some players\n if category:\n players = Player.get_by_unit(alias)\n if players and len(players) > 0:\n request.session.flash('Category \"%(alias)s\" cannot be deleted.' % request.matchdict)\n else:\n DBSession.delete(category)\n request.session.flash('Deleted category \"%(alias)s\".' % request.matchdict)\n else:\n request.session.flash('Category \"%(alias)s\" does not exist.' % request.matchdict)\n else:\n # update/create category\n if category:\n category.d_name = value\n request.session.flash('Updated category \"%(alias)s\".' % request.matchdict)\n else:\n category = Category(alias, value)\n DBSession.add(category)\n request.session.flash('Created category \"%(alias)s\".' % request.matchdict)\n except:\n request.session.flash('Failed to update or create category \"%(alias)s\".' % request.matchdict)\n return HTTPFound(location=route_url('categories', request))\n\n@view_config(permission='admin', route_name='update_match')\ndef update_match(request):\n try:\n match = Match.get_by_id(request.matchdict['id'])\n if match:\n #if match.d_begin < FINAL_DEADLINE: \n # request.session.flash('Cannot update group stage matches.')\n #else:\n match.d_team1 = request.matchdict['team1']\n match.d_team2 = request.matchdict['team2']\n else:\n request.session.flash('Invalid match id: %(id)s.' % request.matchdict)\n return HTTPFound(location=route_url('view_matches', request))\n except:\n request.session.flash('Updating match teams failed.')\n return HTTPFound(location=route_url('view_matches', request))\n\n@view_config(permission='admin', route_name='update_score')\ndef update_score(request):\n try:\n match = Match.get_by_id(request.matchdict['id'])\n if match:\n score1 = int(request.matchdict['score1'])\n match.d_score1 = score1 if score1 >= 0 else None\n score2 = int(request.matchdict['score2'])\n match.d_score2 = score2 if score2 >= 0 else None\n else:\n request.session.flash('Invalid match id: %(id)s.' % request.matchdict)\n return HTTPFound(location=route_url('view_matches', request))\n except:\n request.session.flash('Updating score and points failed.')\n return HTTPFound(location=route_url('view_matches', request))\n\n@view_config(permission='admin', route_name='update_setting')\ndef update_setting(request):\n try:\n name = request.matchdict['name']\n value = request.matchdict['value']\n setting = Setting.get(name)\n if value == 'DELETE':\n if setting:\n if setting.d_name.startswith('scoring_'):\n request.session.flash('Setting \"%(name)s\" cannot be deleted.' % request.matchdict)\n else:\n DBSession.delete(setting)\n request.session.flash('Deleted setting \"%(name)s\".' % request.matchdict)\n else:\n request.session.flash('Setting \"%(name)s\" does not exist.' % request.matchdict)\n else:\n if setting:\n setting.d_value = value\n request.session.flash('Updated setting \"%(name)s\".' % request.matchdict)\n else:\n setting = Setting(name, value)\n DBSession.add(setting)\n request.session.flash('Created setting \"%(name)s\".' % request.matchdict)\n if setting.d_name.startswith('scoring_'):\n scoring.reload_betpoints()\n except:\n request.session.flash('Failed to update or create setting \"%(name)s\".' % request.matchdict)\n return HTTPFound(location=route_url('settings', request))\n\n@view_config(permission='admin', route_name='db_backup')\ndef db_backup(request):\n table = request.matchdict['table']\n if table == 'categories':\n data = dump_table(DBSession.query(Category).all())\n elif table == 'settings':\n data = dump_table(DBSession.query(Setting).all())\n elif table == 'players':\n data = dump_table(DBSession.query(Player).all())\n elif table == 'matches':\n data = dump_table(DBSession.query(Match).all())\n elif table == 'teams':\n data = dump_table(DBSession.query(Team).all())\n elif table == 'tips':\n data = dump_table(DBSession.query(Tip).all())\n elif table == 'final':\n data = dump_table(DBSession.query(Final).all())\n else:\n raise HTTPNotFound('unknown table: %(table)s' % request.matchdict)\n response = Response(headers={ 'mime-type': 'application/octet-stream' }, body=data)\n response.content_length = len(data)\n response.content_disposition = 'attachment;filename=\"%(table)s.dat\"' % request.matchdict\n return response\n\n@view_config(permission='admin', route_name='db_restore', renderer='templates/restore.pt')\ndef db_restore(request):\n if 'form.submitted' in request.POST:\n data = request.POST.get('data')\n if data is not None:\n data = data.file.read()\n #print 'data(content, %d bytes): %s' % (len(data), data)\n if len(data) > 0:\n try:\n query = load_table(data, scoped_session=DBSession)\n for obj in query:\n DBSession.merge(obj)\n request.session.flash('Restore succeeded.')\n return HTTPFound(location=route_url('home', request))\n except:\n request.session.flash('Not a valid backup file.')\n else:\n request.session.flash('Empty backup file.')\n else:\n request.session.flash('Please select a file.')\n form = Form(request)\n return { 'form': FormRenderer(form),\n 'navigation': navigation_view(request) }\n\n@view_config(permission='admin', route_name='system_info', renderer='templates/sysinfo.pt')\ndef system_info(request):\n sysinfo = {\n 'os.name': os.name,\n 'sys.platform': sys.platform,\n 'sys.maxint': sys.maxsize,\n 'sys.maxsize': sys.maxsize\n }\n with open('/proc/version') as f:\n sysinfo['os.version'] = f.read().strip()\n with open('/proc/cpuinfo') as f:\n for line in f:\n info = line.strip().split(': ')\n #print \"cpuinfo: %s (%d)\" % (info, len(info))\n if len(info) > 0 and info[0].strip() != '':\n key = 'cpu.%s' % info[0].strip() \n value = info[1].strip() if len(info) > 1 else '---'\n sysinfo[key] = value\n with open('/proc/meminfo') as f:\n for line in f:\n info = line.strip().split(': ')\n #print \"meminfo: %s (%d)\" % (info, len(info))\n if len(info) > 0 and info[0].strip() != '':\n key = 'mem.%s' % info[0].strip() \n value = info[1].strip() if len(info) > 1 else '---'\n sysinfo[key] = value\n for key,value in list(request.registry.settings.items()):\n sysinfo['ini.%s' % key] = value\n return { 'sysinfo': sorted(sysinfo.items()),\n 'viewer_username': request.authenticated_userid,\n 'navigation': navigation_view(request) }\n", "sub_path": "russia2018/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 37011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "urllib.parse.urlencode", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Setting.get", "line_number": 133, "usage_type": "call"}, {"api_name": "models.Setting", "line_number": 133, "usage_type": "name"}, {"api_name": "socket.create_connection", "line_number": 140, "usage_type": "call"}, {"api_name": "models.Setting.get", "line_number": 162, "usage_type": "call"}, {"api_name": "models.Setting", "line_number": 162, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 178, "usage_type": "name"}, {"api_name": "pyramid.renderers.render", "line_number": 181, "usage_type": "call"}, {"api_name": "pyramid.renderers.render", "line_number": 186, "usage_type": "call"}, {"api_name": "models.Player.get_units", "line_number": 187, "usage_type": "call"}, {"api_name": "models.Player", "line_number": 187, "usage_type": "name"}, {"api_name": "properties.ADMINS", "line_number": 189, "usage_type": "name"}, {"api_name": "pyramid.response.Response", "line_number": 196, "usage_type": "call"}, {"api_name": "pyramid.renderers.render", "line_number": 196, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 197, "usage_type": "name"}, {"api_name": "pyramid.view.forbidden_view_config", "line_number": 194, "usage_type": "call"}, {"api_name": "pyramid.response.Response", "line_number": 203, "usage_type": "call"}, {"api_name": "pyramid.renderers.render", "line_number": 203, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 204, "usage_type": "name"}, {"api_name": "pyramid.view.notfound_view_config", "line_number": 201, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 212, "usage_type": "name"}, {"api_name": "properties.FINAL_DEADLINE", "line_number": 214, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 210, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 221, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 219, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 226, "usage_type": "name"}, {"api_name": "models.Setting.get", "line_number": 227, "usage_type": "call"}, {"api_name": "models.Setting", "line_number": 227, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 224, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 232, "usage_type": "name"}, {"api_name": "properties.FINAL_DEADLINE", "line_number": 234, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 230, "usage_type": "call"}, {"api_name": "models.Match.get_stage2", "line_number": 243, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 243, "usage_type": "name"}, {"api_name": "models.Match.get_played", "line_number": 246, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 246, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 239, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 253, "usage_type": "name"}, {"api_name": "models.DBSession.query", "line_number": 254, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 254, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 254, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 251, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 261, "usage_type": "name"}, {"api_name": "models.Tip", "line_number": 263, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 258, "usage_type": "call"}, {"api_name": "properties.PROJECT_TITLE", "line_number": 270, 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{"api_name": "models.DBSession.delete", "line_number": 718, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 718, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 722, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 722, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 713, "usage_type": "call"}, {"api_name": "models.Category.get", "line_number": 729, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 729, "usage_type": "name"}, {"api_name": "models.Player.get_by_unit", "line_number": 733, "usage_type": "call"}, {"api_name": "models.Player", "line_number": 733, "usage_type": "name"}, {"api_name": "models.DBSession.delete", "line_number": 737, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 737, "usage_type": "name"}, {"api_name": "models.Category", "line_number": 747, "usage_type": "call"}, {"api_name": "models.DBSession.add", "line_number": 748, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 748, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 752, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 752, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 724, "usage_type": "call"}, {"api_name": "models.Match.get_by_id", "line_number": 757, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 757, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 766, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 766, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 769, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 769, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 754, "usage_type": "call"}, {"api_name": "models.Match.get_by_id", "line_number": 774, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 774, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 782, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 782, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 785, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 785, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 771, "usage_type": "call"}, {"api_name": "models.Setting.get", "line_number": 792, "usage_type": "call"}, {"api_name": "models.Setting", "line_number": 792, "usage_type": "name"}, {"api_name": "models.DBSession.delete", "line_number": 798, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 798, "usage_type": "name"}, {"api_name": "models.Setting", "line_number": 807, "usage_type": "call"}, {"api_name": "models.DBSession.add", "line_number": 808, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 808, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 814, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 814, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 787, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 820, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 820, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 820, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 820, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 822, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 822, "usage_type": "call"}, {"api_name": "models.Setting", "line_number": 822, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 822, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 824, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 824, "usage_type": "call"}, {"api_name": "models.Player", "line_number": 824, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 824, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 826, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 826, "usage_type": "call"}, {"api_name": "models.Match", "line_number": 826, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 826, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 828, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 828, "usage_type": "call"}, {"api_name": "models.Team", "line_number": 828, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 828, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 830, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 830, "usage_type": "call"}, {"api_name": "models.Tip", "line_number": 830, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 830, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.serializer.dumps", "line_number": 832, "usage_type": "call"}, {"api_name": "models.DBSession.query", "line_number": 832, "usage_type": "call"}, {"api_name": "models.Final", "line_number": 832, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 832, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPNotFound", "line_number": 834, "usage_type": "call"}, {"api_name": "pyramid.response.Response", "line_number": 835, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 816, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.serializer.loads", "line_number": 849, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 849, "usage_type": "name"}, {"api_name": "models.DBSession.merge", "line_number": 851, "usage_type": "call"}, {"api_name": "models.DBSession", "line_number": 851, "usage_type": "name"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 853, "usage_type": "call"}, {"api_name": "pyramid.url.route_url", "line_number": 853, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 860, "usage_type": "call"}, {"api_name": "pyramid_simpleform.renderers.FormRenderer", "line_number": 861, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 840, "usage_type": "call"}, {"api_name": "os.name", "line_number": 867, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 868, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 869, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 870, "usage_type": "attribute"}, {"api_name": "pyramid.view.view_config", "line_number": 864, "usage_type": "call"}]} +{"seq_id": "330631751", "text": "import csv\nimport os\n\nimport pymysql\nimport requests\nimport random\n\nimport time\n\nfrom settings import headers,save_path,filename,db_conf,table_name\nimport json\n\n\nclass MT_spider:\n\n base_url = \"http://api.meituan.com/group/v4/deal/select/city/30/cate/1?sort=solds&hasGroup=true&mpt_cate1=1&offset={0}&limit=100\"\n mode_list = ['txt','csv','db']\n table_name = table_name\n\n\n #美团深圳地区美食爬虫\n def __init__(self,save_mode = 'txt'):\n if save_mode not in self.mode_list:\n raise RuntimeError('存储模式指定有误，请输入txt、csv或者db')\n self.save_mode = save_mode\n\n if self.save_mode == 'db':\n self.conn = pymysql.connect(**db_conf)\n self.cur = self.conn.cursor()\n\n sql = '''CREATE TABLE IF NOT EXISTS {0}( \n id INTEGER PRIMARY KEY NOT NULL AUTO_INCREMENT, \n shopName VARCHAR(60), \n cateName VARCHAR(30), \n avgScore FLOAT, \n areaName VARCHAR(30), \n lat FLOAT, \n lng FLOAT,\n addr VARCHAR(128), \n abstracts TEXT, \n openInfo VARCHAR(128),\n phone VARCHAR(60),\n historyCouponCount INTEGER,\n introduction TEXT,\n featureMenus TEXT\n );'''.format(self.table_name)\n self.cur.execute(sql)\n self.conn.commit()\n else:\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n file_path = os.path.join(save_path,filename+'.'+self.save_mode)\n self.file = open(file_path,'w',encoding='utf-8',newline='')\n if self.save_mode == 'csv':\n self.csvwriter = csv.writer(self.file)\n self.csvwriter.writerow(['店铺名称','类别','评分','所属片区','纬度','经度','详细地址','优惠套餐情况','营业时间','联系电话','累计售出份数','餐厅简介','特色菜'])\n\n def run(self):\n i = 0\n while True:\n url = self.base_url.format(str(i*100))\n itemlist = self.parse(url)\n if not itemlist:\n break\n for item in itemlist:\n self.save_item(item)\n print('已成功获取%d个商家信息'%((i+1)*100))\n i += 1\n time.sleep(random.randint(2,5))\n\n def save_item(self,item):\n if self.save_mode == 'txt':\n for k,v in item.items():\n self.file.write(str(k)+':'+str(v) + '\\n')\n self.file.write('\\n\\n-----------------------------\\n\\n\\n')\n elif self.save_mode == 'csv':\n self.csvwriter.writerow(item.values())\n else:\n sql = '''\n INSERT INTO {0}(shopName,cateName,avgScore,areaName,lat,lng,addr,abstracts,openInfo,phone,historyCouponCount,introduction,featureMenus)\n VALUES ('{店铺名称}','{类别}','{评分}','{所属片区}','{纬度}','{经度}','{详细地址}','{优惠套餐情况}','{营业时间}','{联系电话}','{累计售出份数}','{餐厅简介}','{特色菜}')\n '''.format(self.table_name,**item)\n self.cur.execute(sql)\n self.conn.commit()\n\n\n def parse(self,url):\n response = requests.get(url,headers=random.choice(headers))\n number = 0\n while True:\n try:\n info_dict = json.loads(response.text)\n info_list = info_dict['data']\n if info_list:\n break\n else:\n number += 1\n if number >= 10:\n return None\n time.sleep(10)\n response = requests.get(url, headers=random.choice(headers))\n except:\n number += 1\n if number >= 10:\n return None\n time.sleep(10)\n response = requests.get(url, headers=random.choice(headers))\n\n itemlist = []\n for info in info_list:\n # 店铺名称\n name = info['poi']['name']\n # 所属片区\n areaName = info['poi']['areaName']\n # 详细地址\n addr = info['poi']['addr']\n # 纬度\n lat = info['poi']['lat']\n # 经度\n lng = info['poi']['lng']\n # 餐厅类别\n cateName = info['poi']['cateName']\n # 优惠套餐情况\n abstracts = ''\n for abstract in info['poi']['payAbstracts']:\n # abstracts.append(abstract['abstract'])\n abstracts = abstracts + abstract['abstract'] + ';'\n\n # 评分\n avgScore = info['poi']['avgScore']\n # 营业时间\n openInfo = info['poi']['openInfo'].replace('\\n',' ')\n # 联系电话\n phone = info['poi']['phone']\n # 累计售出份数\n historyCouponCount = info['poi']['historyCouponCount']\n # 餐厅简介\n introduction = info['poi']['introduction']\n # 特色菜\n featureMenus = info['poi']['featureMenus']\n item = {\n '店铺名称': name,\n '类别': cateName,\n '评分': avgScore,\n '所属片区': areaName,\n '纬度': lat,\n '经度': lng,\n '详细地址': addr,\n '优惠套餐情况': abstracts,\n '营业时间': openInfo,\n '联系电话': phone,\n '累计售出份数': historyCouponCount,\n '餐厅简介': introduction,\n '特色菜': featureMenus\n }\n\n itemlist.append(item)\n # 返回当前页面item列表\n return itemlist\n\n def __del__(self):\n if self.save_mode == 'db':\n self.cur.close()\n self.conn.close()\n else:\n self.file.close()\n", "sub_path": "spider.py", "file_name": "spider.py", "file_ext": "py", "file_size_in_byte": 6025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "settings.table_name", "line_number": 18, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "settings.db_conf", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "settings.save_path", "line_number": 50, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 51, "usage_type": "call"}, {"api_name": "settings.save_path", "line_number": 51, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "settings.save_path", "line_number": 52, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "settings.filename", "line_number": 52, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 69, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 88, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 88, "usage_type": "call"}, {"api_name": "settings.headers", "line_number": 88, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 101, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 101, "usage_type": "call"}, {"api_name": "settings.headers", "line_number": 101, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 107, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 107, "usage_type": "call"}, {"api_name": "settings.headers", "line_number": 107, "usage_type": "argument"}]} +{"seq_id": "360999750", "text": "#!/usr/bin/env python3\n# Python version: python3\n# Auther: sunjb\nfrom django.contrib import admin\nfrom django.urls import path\nfrom app01 import views\nfrom django.urls import re_path,path,include\n\nurlpatterns = [\n path('login', views.login),\n path('index', views.index),\n path('register', views.register),\n path('upload', views.upload),\n # 这里的Home是我们定义的函数名,as_view()是固定的方法\n path('home/', views.Home.as_view()),\n path('dict/', views.dict),\n # 注意在2.2.1版本的django中,要导入re_path,然后用re_path才能使得正则生效,用path正则是不生效的.这个和老版本的有区别\n re_path('detail-(\\d+).html', views.detail),\n # 可以同时传递多个参数，通过?P将正则匹配到的值与nid匹配，形成一个字典{'nid':数字1,'uid':'数字2'}，这样在detail2中，就不用担心接受的形参的位置了\n # 在detail中，只需要取key值nid就能够得到第一个实参，取uid就得到第二个实参\n re_path('detail-(?P\\d+)-(?P\\d+).html', views.detail2),\n re_path('reverse$', views.url1, name='u1'),\n re_path('reverse/(\\d+)/(\\d+)', views.url1, name='u2'),\n re_path('reverse3/(?P\\d+)/(?P\\d+)', views.url1, name='u3'),\n re_path('urlmatch$', views.url2, name='i1'),\n re_path('urlmatch/(\\d+)/(\\d+)', views.url2, name='i2'),\n re_path('urlmatch3/(?P\\d+)/(?P\\d+)', views.url2, name='i3'),\n]", "sub_path": "week19/mydjango/app01/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "app01.views.login", "line_number": 10, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "app01.views.index", "line_number": 11, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "app01.views.register", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "app01.views.upload", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "app01.views.Home.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "app01.views.Home", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "app01.views.dict", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 18, "usage_type": "call"}, {"api_name": "app01.views.detail", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 21, "usage_type": "call"}, {"api_name": "app01.views.detail2", "line_number": 21, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 22, "usage_type": "call"}, {"api_name": "app01.views.url1", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 23, "usage_type": "call"}, {"api_name": "app01.views.url1", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 24, "usage_type": "call"}, {"api_name": "app01.views.url1", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 25, "usage_type": "call"}, {"api_name": "app01.views.url2", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 26, "usage_type": "call"}, {"api_name": "app01.views.url2", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 27, "usage_type": "call"}, {"api_name": "app01.views.url2", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app01.views", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "431150007", "text": "from crawler import Crawler\nfrom executor import Executor\nimport argparse\n\n\ndef setup(args):\n contest_name = args.contest_name\n crawler = Crawler(contest_name)\n crawler.run()\n\n\ndef run_test(args):\n excutor = Executor(args.contest_name)\n excutor.excute_test_cases(args.problem_name)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"contest_name\", type=str)\n parser.add_argument(\"-s\", \"--setup\", action=\"store_true\")\n parser.add_argument(\"-p\", \"--problem_name\", type=str)\n\n args = parser.parse_args()\n\n if args.setup:\n setup(args)\n\n if args.problem_name:\n run_test(args)\n", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "crawler.Crawler", "line_number": 8, "usage_type": "call"}, {"api_name": "crawler.run", "line_number": 9, "usage_type": "call"}, {"api_name": "executor.Executor", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "563678640", "text": "'''Returns project values across multiple masters for specified keys of interest. Return for each key is provided\non a separate wb. Code can handle both standard and project milestone keys, as well as project name lists across\nmultiple quarters.\n\nThere are two outputs.\n1) wb containing all values\n2) wb containing bl values only\n\nConditional formatting is placed in the files as follows:\nrag_rating colours\nmissing data (md) = black grey\nproject not reporting (pnr) = light grey\nkey not collected (knc) = light blue grey\n'''\n\n\nfrom openpyxl import Workbook\nfrom analysis.data import list_of_masters_all, root_path, gen_txt_list, \\\n gen_txt_colours, gen_fill_colours, list_column_ltrs, list_of_rag_keys, rag_txt_list_full, \\\n rag_fill_colours, rag_txt_colours, salmon_fill\nfrom analysis.engine_functions import all_milestone_data_bulk, conditional_formatting, get_quarter_stamp\n\ndef return_data(project_name_list, data_key_list):\n \"\"\"Returns project values across multiple masters for specified keys of interest:\n project_names_list: list of project names\n data_key_list: list of data keys\n \"\"\"\n wb = Workbook()\n\n for i, key in enumerate(data_key_list):\n '''worksheet is created for each project'''\n ws = wb.create_sheet(key[:29], i) # creating worksheets\n ws.title = key[:29] # title of worksheet\n\n '''list project names, groups and stage in ws'''\n for y, project_name in enumerate(project_name_list):\n\n # get project group info\n try:\n group = list_of_masters_all[0].data[project_name]['DfT Group']\n except KeyError:\n for m, master in enumerate(list_of_masters_all):\n if project_name in master.projects:\n group = list_of_masters_all[m].data[project_name]['DfT Group']\n\n ws.cell(row=2 + y, column=1, value=group) # group info return\n ws.cell(row=2 + y, column=2, value=project_name) # project name returned\n\n for x, master in enumerate(list_of_masters_all):\n if project_name in master.projects:\n try:\n #standard keys\n if key in list_of_masters_all[x].data[project_name].keys():\n value = list_of_masters_all[x].data[project_name][key]\n ws.cell(row=2 + y, column=3 + x, value=value) # returns value\n\n if value is None:\n ws.cell(row=2 + y, column=3 + x, value='md')\n\n try: # checks for change against last quarter\n lst_value = list_of_masters_all[x + 1].data[project_name][key]\n if value != lst_value:\n ws.cell(row=2 + y, column=3 + x).fill = salmon_fill\n except (KeyError, IndexError):\n pass\n\n # milestone keys\n else:\n milestones = all_milestone_data_bulk([project_name], list_of_masters_all[x])\n value = tuple(milestones[project_name][key])[0]\n ws.cell(row=2 + y, column=3 + x, value=value)\n ws.cell(row=2 + y, column=3 + x).number_format = 'dd/mm/yy'\n if value is None:\n ws.cell(row=2 + y, column=3 + x, value='md')\n\n try: # loop checks if value has changed since last quarter\n old_milestones = all_milestone_data_bulk([project_name], list_of_masters_all[x + 1])\n lst_value = tuple(old_milestones[project_name][key])[0]\n if value != lst_value:\n ws.cell(row=2 + y, column=3 + x).fill = salmon_fill\n except (KeyError, IndexError):\n pass\n\n except KeyError:\n if project_name in master.projects:\n #loop calculates if project was not reporting or data missing\n ws.cell(row=2 + y, column=3 + x, value='knc')\n else:\n ws.cell(row=2 + y, column=3 + x, value='pnr')\n\n else:\n ws.cell(row=2 + y, column=3 + x, value='pnr')\n\n '''quarter tag information'''\n ws.cell(row=1, column=1, value='Group')\n ws.cell(row=1, column=2, value='Projects')\n quarter_labels = get_quarter_stamp(list_of_masters_all)\n for l, label in enumerate(quarter_labels):\n ws.cell(row=1, column=l + 3, value=label)\n\n list_columns = list_column_ltrs[2:len(list_of_masters_all)+2]\n\n if key in list_of_rag_keys:\n conditional_formatting(ws, list_columns, rag_txt_list_full, rag_txt_colours, rag_fill_colours, '1', '60')\n\n conditional_formatting(ws, list_columns, gen_txt_list, gen_txt_colours, gen_fill_colours, '1', '60')\n\n return wb\n\ndef return_baseline_data(project_name_list, data_key_list):\n '''\n returns values of interest across multiple ws for baseline values only.\n project_name_list: list of project names\n data_key_list: list of data keys containing values of interest.\n '''\n wb = Workbook()\n\n for i, key in enumerate(data_key_list):\n '''worksheet is created for each project'''\n ws = wb.create_sheet(key[:29], i) # creating worksheets\n ws.title = key[:29] # title of worksheet\n\n '''list project names, groups and stage in ws'''\n for y, project_name in enumerate(project_name_list):\n\n # get project group info\n try:\n group = list_of_masters_all[0].data[project_name]['DfT Group']\n except KeyError:\n for m, master in enumerate(list_of_masters_all):\n if project_name in master.projects:\n group = list_of_masters_all[m].data[project_name]['DfT Group']\n\n ws.cell(row=2 + y, column=1, value=group) # group info\n ws.cell(row=2 + y, column=2, value=project_name) # project name returned\n\n for x in range(0, len(bc_index[project_name])):\n index = bc_index[project_name][x]\n try: # standard keys\n value = list_of_masters_all[index].data[project_name][key]\n if value is None:\n ws.cell(row=2 + y, column=3 + x).value = 'md'\n else:\n ws.cell(row=2 + y, column=3 + x, value=value)\n except KeyError:\n try: # milestone keys\n milestones = all_milestone_data_bulk([project_name], list_of_masters_all[index])\n value = tuple(milestones[project_name][key])[0]\n if value is None:\n ws.cell(row=2 + y, column=3 + x).value = 'md'\n else:\n ws.cell(row=2 + y, column=3 + x).value = value\n ws.cell(row=2 + y, column=3 + x).number_format = 'dd/mm/yy'\n except KeyError: # exception catches both standard and milestone keys\n ws.cell(row=2 + y, column=3 + x).value = 'knc'\n except TypeError:\n ws.cell(row=2 + y, column=3 + x).value = 'pnr'\n\n ws.cell(row=1, column=1, value='Group')\n ws.cell(row=1, column=2, value='Project')\n ws.cell(row=1, column=3, value='Latest')\n ws.cell(row=1, column=4, value='Last quarter')\n ws.cell(row=1, column=5, value='BL 1')\n ws.cell(row=1, column=6, value='BL 2')\n ws.cell(row=1, column=7, value='BL 3')\n ws.cell(row=1, column=8, value='BL 4')\n ws.cell(row=1, column=9, value='BL 5')\n\n list_columns = list_column_ltrs[2:10] # hard coded so not ideal\n\n if key in list_of_rag_keys:\n conditional_formatting(ws, list_columns, rag_txt_list_full, rag_txt_colours, rag_fill_colours, '1', '60')\n\n conditional_formatting(ws, list_columns, gen_txt_list, gen_txt_colours, gen_fill_colours, '1', '60')\n\n return wb\n\n'''Running the programme'''\n'''Place all keys of interest as stings in to a list or use one of the imported lists from the data file'''\ndata_interest = ['Adjusted Benefits Cost Ratio (BCR)',\n 'Initial Benefits Cost Ratio (BCR)',\n 'VfM Category single entry']\n\n'''output one - all data. \nfirst variable = list of project names. There are two options. 1) latest_quarter_project_names 2) all_projects_names\n(which includes older projects that are not currently reporting. \nsecond variable = data_interest. This name does not change. List compiled above'''\nrun_standard = return_data(list_of_masters_all[0].projects, data_interest)\n\n'''output two - bl data\nfirst variable = list of project names. There are two options. 1) latest_quarter_project_names 2) all_projects_names\n(which includes older projects that are not currently reporting. \nsecond variable = data_interest. This name does not change. List compiled above'''\n#run_baseline = return_baseline_data(list_of_masters_all[0].projects, data_interest)\n\n'''Specify name of the output document here. See general guidance re saving output files'''\nrun_standard.save(root_path/'output/vfm_data_query_output.xlsx')\n#run_baseline.save(root_path/'output/data_query_output_bls.xlsx')\n", "sub_path": "data_query/data_query.py", "file_name": "data_query.py", "file_ext": "py", "file_size_in_byte": 9596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "openpyxl.Workbook", "line_number": 28, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 40, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 42, "usage_type": "argument"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 44, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 49, "usage_type": "argument"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 53, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 54, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 61, "usage_type": "name"}, {"api_name": "analysis.data.salmon_fill", "line_number": 63, "usage_type": "name"}, {"api_name": "analysis.engine_functions.all_milestone_data_bulk", "line_number": 69, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 69, "usage_type": "name"}, {"api_name": "analysis.engine_functions.all_milestone_data_bulk", "line_number": 77, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 77, "usage_type": "name"}, {"api_name": "analysis.data.salmon_fill", "line_number": 80, "usage_type": "name"}, {"api_name": "analysis.engine_functions.get_quarter_stamp", "line_number": 97, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 97, "usage_type": "argument"}, {"api_name": "analysis.data.list_column_ltrs", "line_number": 101, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 101, "usage_type": "argument"}, {"api_name": "analysis.data.list_of_rag_keys", "line_number": 103, "usage_type": "name"}, {"api_name": "analysis.engine_functions.conditional_formatting", "line_number": 104, "usage_type": "call"}, {"api_name": "analysis.data.rag_txt_list_full", "line_number": 104, "usage_type": "argument"}, {"api_name": "analysis.data.rag_txt_colours", "line_number": 104, "usage_type": "argument"}, {"api_name": "analysis.data.rag_fill_colours", "line_number": 104, "usage_type": "argument"}, {"api_name": "analysis.engine_functions.conditional_formatting", "line_number": 106, "usage_type": "call"}, {"api_name": "analysis.data.gen_txt_list", "line_number": 106, "usage_type": "argument"}, {"api_name": "analysis.data.gen_txt_colours", "line_number": 106, "usage_type": "argument"}, {"api_name": "analysis.data.gen_fill_colours", "line_number": 106, "usage_type": "argument"}, {"api_name": "openpyxl.Workbook", "line_number": 116, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 128, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 130, "usage_type": "argument"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 132, "usage_type": "name"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 140, "usage_type": "name"}, {"api_name": "analysis.engine_functions.all_milestone_data_bulk", "line_number": 147, "usage_type": "call"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 147, "usage_type": "name"}, {"api_name": "analysis.data.list_column_ltrs", "line_number": 169, "usage_type": "name"}, {"api_name": "analysis.data.list_of_rag_keys", "line_number": 171, "usage_type": "name"}, {"api_name": "analysis.engine_functions.conditional_formatting", "line_number": 172, "usage_type": "call"}, {"api_name": "analysis.data.rag_txt_list_full", "line_number": 172, "usage_type": "argument"}, {"api_name": "analysis.data.rag_txt_colours", "line_number": 172, "usage_type": "argument"}, {"api_name": "analysis.data.rag_fill_colours", "line_number": 172, "usage_type": "argument"}, {"api_name": "analysis.engine_functions.conditional_formatting", "line_number": 174, "usage_type": "call"}, {"api_name": "analysis.data.gen_txt_list", "line_number": 174, "usage_type": "argument"}, {"api_name": "analysis.data.gen_txt_colours", "line_number": 174, "usage_type": "argument"}, {"api_name": "analysis.data.gen_fill_colours", "line_number": 174, "usage_type": "argument"}, {"api_name": "analysis.data.list_of_masters_all", "line_number": 188, "usage_type": "name"}, {"api_name": "analysis.data.root_path", "line_number": 197, "usage_type": "name"}]} +{"seq_id": "341103316", "text": "#!/usr/bin/env python\nimport pika\n\ncredentials = pika.PlainCredentials(\"avani\", \"avani\")\nconnection = pika.BlockingConnection(\n pika.ConnectionParameters(\n\t\t\"192.168.1.10\",\n\t\t5672,\n\t\t\"/\",\n\t\tcredentials,\n\t\theartbeat=10,\n\t\tblocked_connection_timeout=10,\n\t))\nchannel = connection.channel()\n\nchannel.queue_declare(queue='hello')\n\nchannel.basic_publish(exchange='', routing_key='hello', body='Hello World!')\nprint(\" [x] Sent 'Hello World!'\")\nconnection.close()", "sub_path": "wifi_code/2021-02-18-demo/0218-demo-01/rabbit-demo.py", "file_name": "rabbit-demo.py", "file_ext": "py", "file_size_in_byte": 458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "pika.PlainCredentials", "line_number": 4, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 5, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "235223504", "text": "# Jinwei Gu\r\n# 2016/7/18\r\n\r\nfrom __future__ import print_function\r\n\r\nimport numpy as np\r\nimport math\r\nimport time\r\n\r\nimport keras \r\n\r\nfrom keras.callbacks import Callback \r\nfrom keras import backend as K\r\n\r\n#--------------------------------------------------------------------------------\r\ndef load_config_file(config_file):\r\n import yaml\r\n with open(config_file,'r') as f:\r\n cfg = yaml.load(f)\r\n return cfg\r\n\r\ndef get_logger_filename(prefix):\r\n a = time.localtime()\r\n filename = '%d-%02d-%02d-%02d:%02d:%02d.log'%(a.tm_year, a.tm_mon,\r\n a.tm_mday, a.tm_hour, a.tm_min, a.tm_sec)\r\n return prefix+'-'+filename\r\n\r\ndef init_logger(logfilename):\r\n import logging\r\n logger = logging.getLogger()\r\n\r\n logger.setLevel(logging.INFO)\r\n formatter = logging.Formatter('%(asctime)s - %(message)s')\r\n\r\n fh = logging.FileHandler(logfilename)\r\n fh.setLevel(logging.INFO)\r\n fh.setFormatter(formatter)\r\n logger.addHandler(fh)\r\n\r\n ch = logging.StreamHandler()\r\n ch.setLevel(logging.INFO)\r\n ch.setFormatter(formatter)\r\n logger.addHandler(ch)\r\n\r\n return logger\r\n\r\ndef print_cfg(cfg, logger=None):\r\n if logger:\r\n print = logger.info\r\n print('--------------- config ---------------')\r\n for x in cfg:\r\n print('%s : %s'%(x, cfg[x]))\r\n print('--------------- config ---------------')\r\n\r\n\r\n# convert the convolution weights from theano to tensorflow\r\n# (correlation, similar to caffe) for convolution layers\r\n#\r\n# copied code from \r\n#\r\n# https://github.com/fchollet/keras/wiki/Converting-convolution-kernels-from-Theano-to-TensorFlow-and-vice-versa\r\n#\r\n# Jinwei Gu. 2016/8/7\r\ndef theano2tensorflow(model, weightfile=None):\r\n from keras.utils.np_utils import convert_kernel\r\n import tensorflow as tf\r\n\r\n if weightfile:\r\n model.load_weights(weightfile)\r\n ops = []\r\n for layer in model.layers:\r\n if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D']:\r\n original_w = K.get_value(layer.W)\r\n converted_w = convert_kernel(original_w)\r\n ops.append(tf.assign(layer.W, converted_w).op) \r\n K.get_session().run(ops)\r\n return model\r\n\r\ndef tensorflow2theano(model, weightfile=None):\r\n from keras.utils.np_utils import convert_kernel\r\n\r\n if weightfile:\r\n model.load_weights(weightfile)\r\n for layer in model.layers:\r\n if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D']:\r\n original_w = K.get_value(layer.W)\r\n converted_w = convert_kernel(original_w)\r\n K.set_value(layer.W, converted_w)\r\n return model\r\n\r\ndef save_weights_to_pkl(net, pkl_filename):\r\n \"\"\"\r\n save net weight to pkl file. Currently only save weights for conv\r\n layer and dense layer.\r\n\r\n NOTE: for most applications, we should call net.save_weights() and\r\n save to *.h5 file directly. Often the file is much smaller\r\n \"\"\"\r\n dat={}\r\n for layer in net.layers:\r\n if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D', 'Dense']:\r\n param = [layer.W.get_value(), layer.b.get_value()]\r\n dat[layer.name] = param\r\n\r\n import cPickle\r\n with open(pkl_filename,'w') as f:\r\n cPickle.dump(dat, f)\r\n\r\ndef load_weights_from_pkl(net, pkl_filename):\r\n \"\"\"\r\n load weights from pkl file. Currently only support conv layer and\r\n dense layer.\r\n\r\n NOTE: for most applications, we should call net.load_weights() and\r\n load from *.h5 file directly.\r\n \"\"\"\r\n import cPickle\r\n with open(pkl_filename,'r') as f:\r\n dat = cPickle.load(f)\r\n\r\n for layer in net.layers:\r\n if layer.__class__.__name__ in ['Convolution1D', 'Convolution2D', 'Dense']:\r\n if layer.name in dat.keys():\r\n layer.W.set_value(dat[layer.name][0])\r\n layer.b.set_value(dat[layer.name][1])\r\n return net\r\n\r\n\r\n\r\ndef weightfile_to_modelfile(net, weightfile, modelfile):\r\n \"\"\" convert weight file to a model file \"\"\"\r\n net.load_weights(weightfile)\r\n net.save(modelfile)\r\n\r\ndef modelfile_to_weightfile(modelfile, weightfile):\r\n \"\"\" convert a full model file to a weight only file \"\"\"\r\n net = keras.models.load_model(modelfile)\r\n net.save_weights(weightfile)\r\n\r\n\r\nclass MyLogger(keras.callbacks.Callback):\r\n def __init__(self, logfilename, display=1, logger=None):\r\n self.logfilename = logfilename\r\n self.display = display \r\n if logger:\r\n self.logger = logger\r\n else:\r\n self.logger = init_logger(self.logfilename)\r\n print (\"Logger========:\", str(self.logger))\r\n\r\n def on_train_begin(self, logs={}):\r\n #self.model.summary(logger=self.logger)\r\n self.best_val_acc = 1e-10\r\n #self.model.summary()\r\n \r\n def on_batch_end(self, batch, logs={}):\r\n if batch%self.display==0:\r\n lr = K.eval(self.model.optimizer.lr)#.get_value()\r\n self.logger.info('epoch %d batch %d lr %f acc %f'%(self.epoch, batch, lr, logs.get('acc')))\r\n\r\n def on_epoch_end(self, epoch, logs={}):\r\n lr = K.eval(self.model.optimizer.lr)#.get_value()\r\n val_acc = logs.get('val_acc')\r\n if val_acc > self.best_val_acc:\r\n self.best_val_acc = val_acc\r\n #self.logger.info('epoch %d lr %f val_loss %f best_val_loss %f'%(epoch, lr, val_loss, self.best_val_loss))\r\n self.logger.info('epoch %d lr %f val_acc %f best_val_acc %f'%(epoch, lr, val_acc, self.best_val_acc))\r\n\r\n def on_epoch_begin(self, epoch, logs={}):\r\n self.seen = 0\r\n self.epoch = epoch\r\n\r\nclass StepLearningRateScheduler(Callback):\r\n \"\"\"\r\n learning rate is multiplied with lr_decay (e.g., 0.1) every lr_epoch\r\n (e.g., 10). \r\n \"\"\"\r\n def __init__(self, lr, lr_decay, lr_epoch):\r\n super(StepLearningRateScheduler, self).__init__()\r\n self.lr = lr\r\n self.lr_decay = lr_decay\r\n self.lr_epoch = lr_epoch\r\n\r\n def get_lrate(self, epoch):\r\n lrate = self.lr * math.pow(self.lr_decay, math.floor((1+epoch)/self.lr_epoch))\r\n return lrate\r\n\r\n def on_epoch_begin(self, epoch, logs={}):\r\n assert hasattr(self.model.optimizer, 'lr'), \\\r\n 'Optimizer must have a \"lr\" attribute.'\r\n lrate = self.get_lrate(epoch)\r\n K.set_value(self.model.optimizer.lr, lrate)\r\n \r\n\r\nclass VectorLearningRateScheduler(Callback):\r\n \"\"\"\r\n specify a vector for decay ratio and epoch. This is more flexible\r\n E.g., lr_decay = [0.1, 0.1, 0.5], lr_epoch=[5, 10, 20]\r\n means the lrate will multiple with 0.1 at the 5th epoch, and then\r\n multiple with 0.1 at the 10-th epoch, and then multiple with 0.5 at\r\n the 20th epoch.\r\n \"\"\"\r\n def __init__(self, lr, lr_decay, lr_epoch):\r\n super(VectorLearningRateScheduler, self).__init__()\r\n self.lr = lr\r\n self.lr_decay = lr_decay\r\n self.lr_epoch = lr_epoch\r\n\r\n self.lr_decay_cumprod = np.cumprod(np.array(lr_decay))\r\n\r\n def on_epoch_begin(self, epoch, logs={}):\r\n assert hasattr(self.model.optimizer, 'lr'), \\\r\n 'Optimizer must have a \"lr\" attribute.'\r\n\r\n k=[n for n,i in enumerate(self.lr_epoch) if i > epoch]\r\n if k==[]:\r\n lrate = self.lr * self.lr_decay_cumprod[-1]\r\n else:\r\n k=k[0]\r\n if k==0:\r\n lrate = self.lr\r\n else:\r\n lrate = self.lr * self.lr_decay_cumprod[k-1]\r\n\r\n K.set_value(self.model.optimizer.lr, lrate)\r\n\r\n\r\ndef my_fit_generator(net, train_data_generator, samples_per_epoch, nb_epoch,\r\n val_data_generator, nb_val_samples, model_filename, logger, lr_scheduler=None):\r\n '''\r\n Simple, single thread routine to do training. The keras\r\n fit_generator sometimes will crash, due to multi-threading. This is\r\n a simple alternative.\r\n\r\n Input:\r\n net -- net model\r\n train_data_generator -- generator to yield X,y minibatch for training\r\n nb_epoch -- how many epochs\r\n samples_per_epoch -- total number of training samples for each epoch\r\n val_data_generator -- generator to yield X,y minibatch for validation\r\n nb_val_samples -- number of validation samples\r\n model_filename -- filename to save the best model (the full model, including the weights, the model, and the optimizer states)\r\n logger -- logger\r\n lr_scheduler -- learning rate schedule function, default None\r\n '''\r\n\r\n best_val_acc = 1e-10\r\n epoch = 0 \r\n\r\n net.summary(logger=logger)\r\n\r\n while epoch < nb_epoch:\r\n # set learning rate if needed\r\n if lr_scheduler:\r\n lr = lr_scheduler.get_lrate(epoch)\r\n net.optimizer.lr.set_value(lr)\r\n else:\r\n lr = net.optimizer.lr.get_value()\r\n\r\n idx = 0\r\n i = 0\r\n while 1:\r\n X,y = next(train_data_generator)\r\n idx += y.shape[0] # batch_size\r\n i += 1\r\n loss = net.train_on_batch(X,y)\r\n if i%20 == 0:\r\n logger.info('epoch %d batch %d lr %f loss %f'%(epoch, i, lr, loss))\r\n\r\n if idx>=samples_per_epoch:\r\n break\r\n\r\n idx=0\r\n i=0\r\n val_loss=0\r\n while 1:\r\n X,y = next(val_data_generator)\r\n idx += y.shape[0] # batch_size\r\n i += 1\r\n val_loss += net.test_on_batch(X,y)\r\n if idx>=nb_val_samples:\r\n break\r\n val_loss/=i\r\n\r\n if val_loss<=best_val_loss:\r\n best_val_loss = val_loss\r\n net.save(model_filename)\r\n\r\n logger.info('epoch %d lr %f val_loss %f best_val_loss %f'%(epoch, lr, val_loss, best_val_loss))\r\n epoch += 1\r\n\r\n\r\ndef my_evaluate_generator(net, val_data_generator, val_samples):\r\n '''\r\n Simple, single thread routine to do evaluation. The keras\r\n evaluate_generator sometimes will crash, due to multi-threading. This is\r\n a simple alternative.\r\n\r\n Input:\r\n net -- net model\r\n val_data_generator -- generator to yield X,y minibatch for validation\r\n nb_val_samples -- number of validation samples\r\n '''\r\n \r\n idx=0\r\n i=0\r\n val_loss=0\r\n while 1:\r\n X,y = next(val_data_generator)\r\n idx += y.shape[0]\r\n i += 1\r\n val_loss += net.test_on_batch(X,y)\r\n if idx>=val_samples:\r\n break\r\n #print('%d %d'%(idx,val_samples))\r\n\r\n val_loss/=i\r\n return val_loss\r\n\r\n\r\nfrom multiprocessing import Process, Queue\r\n\r\ndef my_fit_generator_with_prefetch(net, train_data_generator, samples_per_epoch, nb_epoch, \r\n val_data_generator, nb_val_samples, model_filename, logger, lr_scheduler=None):\r\n '''\r\n Two threads routine to do training. The keras\r\n fit_generator sometimes will crash, due to multi-threading. This is\r\n a simple alternative. One for prefetching data\r\n\r\n Input:\r\n net -- net model\r\n train_data_generator -- generator to yield X,y minibatch for training\r\n nb_epoch -- how many epochs\r\n samples_per_epoch -- total number of training samples for each epoch\r\n val_data_generator -- generator to yield X,y minibatch for validation\r\n nb_val_samples -- number of validation samples\r\n model_filename -- filename to save the best model (the full model, including the weights, the model, and the optimizer states)\r\n logger -- logger\r\n lr_scheduler -- learning rate schedule function, default None\r\n '''\r\n\r\n train_blob_queue = Queue(10)\r\n train_prefetch_process = BlobFetcher(train_blob_queue, train_data_generator, logger)\r\n train_prefetch_process.start()\r\n\r\n val_blob_queue = Queue(10)\r\n val_prefetch_process = BlobFetcher(val_blob_queue, val_data_generator, logger)\r\n val_prefetch_process.start()\r\n\r\n # Terminate the child process when the parent exists\r\n def cleanup():\r\n logger.info('Terminating BlobFetcher')\r\n train_prefetch_process.terminate()\r\n val_prefetch_process.terminate()\r\n train_prefetch_process.join()\r\n val_prefetch_process.join()\r\n import atexit\r\n atexit.register(cleanup)\r\n\r\n best_val_loss = 1e+10\r\n epoch = 0 \r\n\r\n net.summary(logger=logger)\r\n\r\n while epoch < nb_epoch:\r\n # set learning rate if needed\r\n if lr_scheduler:\r\n lr = lr_scheduler.get_lrate(epoch)\r\n net.optimizer.lr.set_value(lr)\r\n else:\r\n lr = net.optimizer.lr.get_value()\r\n\r\n idx = 0\r\n i = 0\r\n while 1:\r\n X,y = train_blob_queue.get() \r\n idx += y.shape[0] # batch_size\r\n i += 1\r\n loss = net.train_on_batch(X,y)\r\n if i%20 == 0:\r\n logger.info('epoch %d batch %d lr %f loss %f'%(epoch, i, lr, loss))\r\n\r\n if idx>=samples_per_epoch:\r\n break\r\n\r\n idx=0\r\n i=0\r\n val_loss=0\r\n while 1:\r\n X,y = val_blob_queue.get()\r\n idx += y.shape[0] # batch_size\r\n i += 1\r\n val_loss += net.test_on_batch(X,y)\r\n if idx>=nb_val_samples:\r\n break\r\n val_loss/=i\r\n\r\n if val_loss<=best_val_loss:\r\n best_val_loss = val_loss\r\n net.save(model_filename)\r\n\r\n logger.info('epoch %d lr %f val_loss %f best_val_loss %f'%(epoch, lr, val_loss, best_val_loss))\r\n epoch += 1\r\n\r\n\r\ndef my_evaluate_generator_with_prefetch(net, val_data_generator, val_samples, logger):\r\n '''\r\n Two threads routine to do evaluation. The keras\r\n evaluate_generator sometimes will crash, due to multi-threading. This is\r\n a simple alternative.\r\n\r\n Input:\r\n net -- net model\r\n val_data_generator -- generator to yield X,y minibatch for validation\r\n val_samples -- number of validation samples\r\n '''\r\n \r\n val_blob_queue = Queue(10)\r\n val_prefetch_process = BlobFetcher(val_blob_queue, val_data_generator, logger)\r\n val_prefetch_process.start()\r\n\r\n # Terminate the child process when the parent exists\r\n def cleanup():\r\n logger.info('Terminating BlobFetcher')\r\n val_prefetch_process.terminate()\r\n val_prefetch_process.join()\r\n import atexit\r\n atexit.register(cleanup)\r\n\r\n \r\n idx=0\r\n i=0\r\n val_loss=0\r\n while 1:\r\n X,y = val_blob_queue.get()\r\n idx += y.shape[0]\r\n i += 1\r\n val_loss += net.test_on_batch(X,y)\r\n if idx>=val_samples:\r\n break\r\n val_loss/=i\r\n return val_loss\r\n\r\n\r\n \r\nclass BlobFetcher(Process):\r\n def __init__(self,queue,generator,logger):\r\n super(BlobFetcher, self).__init__()\r\n self._queue = queue\r\n self._generator = generator\r\n self._logger=logger\r\n \r\n def run(self):\r\n self._logger.info('BlobFetcher started')\r\n while True:\r\n X,y=next(self._generator)\r\n self._queue.put((X,y))\r\n", "sub_path": "NN_train/keras_utils.py", "file_name": "keras_utils.py", "file_ext": "py", "file_size_in_byte": 14868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "yaml.load", "line_number": 19, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 41, "usage_type": "attribute"}, {"api_name": "keras.backend.get_value", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 73, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.convert_kernel", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend.get_session", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 76, "usage_type": "name"}, {"api_name": "keras.backend.get_value", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 86, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.convert_kernel", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.backend.set_value", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 88, "usage_type": "name"}, {"api_name": "cPickle.dump", "line_number": 107, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 137, "usage_type": "attribute"}, {"api_name": "keras.callbacks", "line_number": 141, "usage_type": "attribute"}, {"api_name": "keras.backend.eval", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 158, "usage_type": "name"}, {"api_name": "keras.backend.eval", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 162, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 173, "usage_type": "name"}, {"api_name": "math.pow", "line_number": 185, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.backend.set_value", "line_number": 192, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 192, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.cumprod", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "keras.backend.set_value", "line_number": 225, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 225, "usage_type": "name"}, {"api_name": "multiprocessing.Queue", "line_number": 342, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 346, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 358, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 418, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 428, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 446, "usage_type": "name"}]} +{"seq_id": "332550249", "text": "# -*- mode: python -*-\n\nimport os\nimport jieba\n\njieba_path = os.path.dirname(jieba.__file__)\n\nblock_cipher = None\n\n\na = Analysis(['app/MyNote.py'],\n pathex=['/home/breeze/Develop/MyNote'],\n binaries=[],\n datas=[('README.md', '.'),\n ('app/keys', 'keys'),\n ('app/static', 'static'),\n ('app/templates', 'templates'),\n ('app/translations', 'translations'),\n ('app/configuration.yml', '.'),\n ('app/configuration.yml.readme', '.'),\n (os.path.join(jieba_path, 'dict.txt'), 'jieba'),\n (os.path.join(jieba_path, 'analyse', 'idf.txt'), os.path.join('jieba', 'analyse')),\n ('app/Install.sh', '.'),\n ('app/MakeShortcut.sh', '.'),\n ('app/Uninstall.sh', '.'),],\n hiddenimports=[],\n hookspath=[],\n runtime_hooks=[],\n excludes=[],\n win_no_prefer_redirects=False,\n win_private_assemblies=False,\n cipher=block_cipher)\npyz = PYZ(a.pure, a.zipped_data,\n cipher=block_cipher)\nexe = EXE(pyz,\n a.scripts,\n exclude_binaries=True,\n name='MyNote',\n debug=False,\n strip=False,\n upx=True,\n console=True)\ncoll = COLLECT(exe,\n a.binaries,\n a.zipfiles,\n a.datas,\n strip=False,\n upx=True,\n name='MyNote')\n", "sub_path": "MyNote.spec", "file_name": "MyNote.spec", "file_ext": "spec", "file_size_in_byte": 1561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "jieba.__file__", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "646477376", "text": "\nfrom flask import Flask\nfrom flask import request\nimport json # json을 쓰기 위한 모듈\napp = Flask(__name__)\n\n@app.route('/') # 라우팅\ndef main(): # 해당 라우팅에서 실행할 함수\n result = {\"id\": \"123\", \"name\": \"delryn\"}\n jsonString = json.dumps(result) # = JSON.stringfy()\n print(type(jsonString)) # \n return jsonString \n\n'''\nhttp method\n@app.route에서 methods = ['GET'] 또는 methods = ['POST'] 로 정할 수 있다. 그리고 짬뽕도 된다! methods = ['GET', 'POST']\n'''\n\n@app.route('/get', methods = ['GET'])\ndef queryString():\n a = request.args.get('a') # 원하는 쿼리스트링의 값 가지고 오기.\n queryString = request.query_string # 쿼리스트링 전체 가지고 오기 자주 쓰이진 않지만 매모.\n print(a)\n print('-----------')\n print(queryString) \n return queryString\n\n@app.route('/post', methods = ['POST'])\ndef post():\n val = request.json\n val2 = request.data\n print(val)\n print('---------')\n print(val2)\n print(json.loads(val2)) # = JSON.parse()\n print(json.loads(val2)['a'])\n print('---------')\n print(request.json['a'])\n result = json.dumps(val)\n return result\n\n# GET, POST 동시에 허용 하고 들어오는 메소드에 따라 처리 가능.\n@app.route('/get-post', methods = ['GET', 'POST'])\ndef queryPost():\n if request.method == 'GET':\n return \"get\"\n else:\n return \"post\"\n\nif __name__ == '__main__':\n app.debug = True # debug 모드 on 오류 났을 때 웹에 출력 해주는 그런 거 -_-.\n\n '''\n host default는 로컬호스트, 근데 변경할려면 요렇게 host='아이피 주소' 넣고 하면 된다. 근데 딱히 바꿀 일은 업을 꺼 같지만.\n port는 요렇게 변경 가능 하다. 기입 안 하면 default는 5000\n '''\n app.run(host='127.0.0.1', port=3000)\n\n", "sub_path": "flask_study/first.py", "file_name": "first.py", "file_ext": "py", "file_size_in_byte": 1865, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "52", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.query_string", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "153920079", "text": "\"\"\"This file is used to scrape www.census.gov for state FIPS codes.\n\nThere are two ways this file gets the state FIPS codes:\n- Pandas only\n uses read_html to get dataframe from page source\n much more simple of two functions\n- Pandas and BeautifulSoup - add 'soup' as argument to use\n uses BeautifulSoup to read table\n more complex of two functions\n used primarily for learning basic BS4 applications\n\"\"\"\n\nimport sys\nsys.path.insert(0, '../')\n\nimport pandas as pd\nimport pickle_data\nimport requests\n\n\ndef get_fips(sauce, headers):\n '''Returns pandas dataframe with state FIPS codes.'''\n df = pd.read_html(sauce)[0]\n df.dropna(inplace=True)\n df.columns = headers\n return df\n\n\ndef get_fips_soup(sauce, table_summary, headers):\n '''Returns pandas dataframe with state FIPS codes.'''\n from bs4 import BeautifulSoup\n soup = BeautifulSoup(sauce, 'html.parser')\n\n # set up lists to hold columns\n num_cols = len(headers)\n columns = [[] for _ in range(num_cols)]\n\n # get all rows from table\n row_data = [datum.text.strip() for datum in\n soup.find('table', {'summary':table_summary}).find_all('td')]\n rows = [row_data[num_cols*i:num_cols*(i+1)] for i in\n range(int(len(row_data)/num_cols))]\n\n # fill columns from rows\n for row in rows:\n for i in range(num_cols):\n columns[i].append(row[i])\n\n # create dataframe of state fip codes\n return pd.DataFrame(dict(zip(headers,columns)))\n\n \ndef main():\n # get page source\n sauce = requests.get('https://www.census.gov/geo/reference/ansi_statetables.html').text\n\n # table html summary headers of table\n table_summary = ('table showing ANSI state codes for the'\n ' states and the District of Columbia')\n headers = ['Name','Code','Abbr']\n\n # pickle a pandas DataFrame of the table\n if len(sys.argv) > 1 and sys.argv[1] == 'soup':\n pickle_data.pickle_data(get_fips_soup(sauce, table_summary, headers), './fips.pickle')\n else:\n pickle_data.pickle_data(get_fips(sauce, headers), './fips.pickle')\n print('State FIPS codes pickled successfully.')\n\n \nif __name__==\"__main__\":\n main()\nelse:\n print('File should not be imported. Only run directly.')\n", "sub_path": "census/fips.py", "file_name": "fips.py", "file_ext": "py", "file_size_in_byte": 2255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "sys.path.insert", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pandas.read_html", "line_number": 23, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pickle_data.pickle_data", "line_number": 64, "usage_type": "call"}, {"api_name": "pickle_data.pickle_data", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "597596059", "text": "import numpy as np \nimport matplotlib.pyplot as plt \n\n'''only have velocity of x, y and z direction in the velocity file, \\\nmay include atomic id.'''\ndef velocity_a(filename1,filename2,Atom_ID=False):\n\t\"\"\"Processing atomic velocity files output by lammps,\n\t that is, remove the periodic header of the velocity file\"\"\"\n\twith open(filename1,'r') as reader, open(filename2,'w') as writer:\n\t\tfor index, line in enumerate(reader,1):\n\t\t\t# print(line)\n\t\t\tLine = line.strip().split()\n\t\t\tlength_line = len(Line)\n\t\t\t# print(length_line)\n\t\t\tif Atom_ID == True:\n\t\t\t\tlength_velocityline = 5\n\t\t\telse:\n\t\t\t\tlength_velocityline = 3\n\n\t\t\tif length_line == length_velocityline:\n\t\t\t\t# writer.write(Line[0])\n\t\t\t\t# writer.write(' ')\n\t\t\t\t# writer.write(Line[1])\n\t\t\t\t# writer.write(' ')\n\t\t\t\twriter.write(Line[2])\n\t\t\t\twriter.write(' ')\n\t\t\t\twriter.write(Line[3])\n\t\t\t\twriter.write(' ')\n\t\t\t\twriter.write(Line[4])\t\t\t\t\n\t\t\t\twriter.write('\\n')\n\n\treturn print('velocity_a() done!')\n\ndef plotDOS(dos):\n\tdata = np.loadtxt(dos)\n\t# print(data)\n\tx = data[:,0]\n\ty = data[:,1]\n\tplt.plot(x,y)\n\tplt.show()\n\n\treturn print('plotDOS() done!')\n\n\n# velocity_a('DOS.velocity','DOS_nohead1.velocity',Atom_ID=True)\nplotDOS('DOS.dos')", "sub_path": "dealVelocity/dealvelocity_V2.py", "file_name": "dealvelocity_V2.py", "file_ext": "py", "file_size_in_byte": 1192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "51", "api": [{"api_name": "numpy.loadtxt", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "429037938", "text": "from django.conf.urls import url\nfrom . import views\nfrom django.views.generic.base import TemplateView\nurlpatterns = [\n url(r'^headparser$',views.headparser,name='headparser'),\n]\nurlpatterns += [\n url(r'^timestamp/(?P