diff --git "a/1767.jsonl" "b/1767.jsonl" new file mode 100644--- /dev/null +++ "b/1767.jsonl" @@ -0,0 +1,704 @@ +{"seq_id":"19518652","text":"import pandas as pd\n\ndf = pd.read_csv('/Users/chloe/Desktop/timedict_new.csv')\nprint(df.columns)\n\nfor col in df.columns:\n\tdf[col] = df[col]/df['MakeMove']\n\nprint(df.mean())\n\n","sub_path":"scripts/test_time.py","file_name":"test_time.py","file_ext":"py","file_size_in_byte":174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"587958501","text":"def oneaway(s,r):\n\n r=list(r)\n s=list(s)\n t=r[:]\n c=False\n max1=\"\"\n min1=\"\"\n if len(s)>len(r):\n max1 = s\n min1=r\n elif len(s)==len(r):\n c=True\n\n else:\n max1=r\n min1=s\n for i in max1:\n\n if i not in min1 and c==True:\n max1.remove(i)\n min1.remove(i)\n break\n elif i not in min1:\n max1.remove(i)\n break\n\n\n\n\n\n\n\n if ''.join(max1)==''.join(min1) :\n print(\"true\")\n else:\n print(\"false\")\n\n\noneaway(\"pale\",\"ela\")\n","sub_path":"learning/oneaway.py","file_name":"oneaway.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"547669556","text":"#!/usr/bin/env python\n# -*-coding:utf-8-*-\n# @Time : 2017/11/1 ~ 2019/9/1\n# @Author : Allen Woo\nfrom flask import request\nfrom apps.core.flask.login_manager import osr_login_required\nfrom apps.configs.sys_config import METHOD_WARNING\nfrom apps.core.blueprint import api\nfrom apps.core.flask.permission import permission_required\nfrom apps.core.flask.response import response_format\nfrom apps.modules.user.process.profile import profile_update, public_profile, user_basic_edit, all_profile\n\n\n@api.route('/account/profile/public', methods=['GET'])\n@permission_required(use_default=False)\ndef user_public():\n \"\"\"\n GET:\n 获取用户公开信息\n user_id:\n is_basic:, 0或1,默认1. 为１时只获取最基本的用户信息\n :return:\n \"\"\"\n data = public_profile()\n return response_format(data)\n\n\n@api.route('/account/basic', methods=['PUT'])\n@osr_login_required\n@permission_required(use_default=False)\ndef api_account_basic():\n \"\"\"\n 用户基础设置\n PUT:\n 编辑用户基础设置\n username:, 新的用户名\n custom_domain:, 个性域名\n editor:, 'rich_text' or 'markdown' 如果你有多个文本编辑器的话，可以加入这个选项\n :return:\n \"\"\"\n\n data = user_basic_edit()\n return response_format(data)\n\n\n@api.route('/account/profile', methods=['GET', 'PUT'])\n@osr_login_required\n@permission_required(use_default=False)\ndef api_account_profile():\n \"\"\"\n 用户资料\n GET:\n 获取当前登录用户的信息\n is_basic:, 0或1,默认1. 为１时只获取最基本的用户信息\n PUT\n 更新用户资料\n gender:, m or f or secret\n birthday:, The format must be \"YYYYMMDD\" ,such as: 20170101\n address:, The format must be: {countries:'string', provinces:'string',\n city:'string', district:'string', detailed:'string'}\n info:\n\n :return:\n \"\"\"\n\n if request.c_method == \"GET\":\n\n data = all_profile()\n elif request.c_method == \"PUT\":\n data = profile_update()\n else:\n data = {\"msg_type\": \"w\", \"msg\": METHOD_WARNING, \"custom_status\": 405}\n return response_format(data)\n","sub_path":"apps/modules/user/apis/profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"487291943","text":"import time\nstart = time.time()\n\nhr, mn, sec = map(int, input().split())\nt = int(input())\n\nwhile t > 0 :\n sec += 1\n if sec == 60 :\n sec = 0\n mn += 1\n if mn == 60 :\n mn = 0\n hr += 1\n if hr == 24 :\n hr = 0\n t -= 1\n\nprint(hr, mn ,sec)\nprint(time.time() - start)","sub_path":"Baekjoon/2530.py","file_name":"2530.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"63833508","text":"from collections import defaultdict\r\nimport math\r\n\r\ndef isHappy(n):\r\n l = splitNumber(n)\r\n sum = 0\r\n mem = set()\r\n while True:\r\n for i in l:\r\n sum += int(math.pow(i,2))\r\n if sum in mem:\r\n return False\r\n elif sum == 1:\r\n return True\r\n else:\r\n mem.add(sum)\r\n l = splitNumber(sum)\r\n sum = 0\r\n\r\ndef splitNumber(n):\r\n quot, rem, output = n, 0, []\r\n while quot != 0:\r\n rem = quot % 10\r\n quot = quot // 10\r\n output.append(rem)\r\n return output\r\n\r\nisHappy(1931)\r\nisHappy(19)\r\n","sub_path":"Leetcode/202-Happy Number.py","file_name":"202-Happy Number.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"12696308","text":"from django.core.management.base import BaseCommand\nfrom leaderboard.leaderboard import Leaderboard\n\nfrom mac.users.models import User, MonthlyScore\n\n\nclass Command(BaseCommand):\n help = \"Populate leaderboard\"\n\n def handle(self, *args, **options):\n # All time leaderboard\n highscores = Leaderboard('highscores')\n users = User.objects.filter()\n for user in users:\n highscores.rank_member(user.id, user.skill_point)\n\n # Monthly leaderboards\n monthly_scores = MonthlyScore.objects.filter()\n for monthly in monthly_scores:\n monthly_highscores = Leaderboard(str(monthly.month.month) + '-' + str(monthly.month.year) + '_highscores', host=\"redis\")\n monthly_highscores.rank_member(monthly.user.id, monthly.skill_point)\n","sub_path":"mac-backend-master/mac-backend/mac/core/management/commands/populate_leaders.py","file_name":"populate_leaders.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"190263677","text":"#Default Database\nfrom replit import clear\nimport Final_Application_Database\nrestart = True\n\n#Looper\nwhile restart == True:\n clear()\n condition = \"\"\n control = \"\"\n to_be_encoded = []\n check_control = False\n check_text = False\n check_factor = False\n\n#INPUT VERIFICATION\n while check_control != True:\n control = (input(\"\\nWhat do you want to do? 'Encode' or 'Decode'?: \")).lower()\n clear()\n if control == \"encode\" or control == \"decode\":\n check_control = True\n else:\n print(\"\\nINVALID INPUT!\")\n\n#ENCODE AND DECODE AI MODIFIER\n if control == \"encode\":\n condition = \"message\"\n else:\n condition = \"code\"\n\n#TEXT INPUT VERIFICATION\n while check_text != True:\n text = (input(f\"Enter the {condition} below to {control}\\n\"))\n response_text = input(\"Are you sure? Y/N: \")\n clear()\n if response_text == \"Y\":\n check_text = True\n\n#STRING FACTOR INPUT VERIFICATION\n while check_factor != True:\n factor = int(input(\"\\nHow many string factors you want to use?: \"))\n response_factor = input(\"Are you sure? Y/N: \")\n clear()\n if response_factor == \"Y\":\n check_factor = True\n\n#Word Encoder and Decoder\n def encode(t = text, fad = Final_Application_Database, f = factor):\n translate_encode = []\n translated_encode = []\n output = \"\"\n for n in range(len(t)):\n translate_encode += t[n]\n if translate_encode[n] in fad.letters:\n x = (fad.letters).index(translate_encode[n])\n translated_encode += fad.letters[x + f]\n output += translated_encode[n]\n elif translate_encode[n] in fad.numbers:\n x = (fad.numbers).index(translate_encode[n])\n translated_encode += fad.numbers[x + f]\n output += translated_encode[n]\n else:\n translated_encode += t[n]\n output += translated_encode[n]\n print(f\"\\nYour encrypted message is: {output}\")\n \n def decode(t = text, fad = Final_Application_Database, f = factor):\n translate_decode = []\n translated_decode = []\n output = \"\"\n for n in range(len(t)):\n translate_decode += t[n]\n if translate_decode[n] in fad.letters:\n x = (fad.letters).index(translate_decode[n])\n translated_decode += fad.letters[x - f]\n output += translated_decode[n]\n elif translate_decode in fad.numbers:\n x = (fad.numbers).index(translate_decode[n])\n translated_decode += fad.numbers[x - f]\n output += translated_decode[n]\n else:\n translated_decode += t[n]\n output += translated_decode[n]\n print(f\"\\nYour decrypted message is: {output}\")\n \n if control == \"encode\":\n encode()\n else:\n decode()\n \n again = input(\"\\nDo you want to restart again? Y/N: \")\n if again == \"N\":\n restart = False\n clear()\n print(\"\\nGoodbye!\")","sub_path":"008_Function_Parameters_&_Caesar_Cipher/Final_Application.py","file_name":"Final_Application.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"127406389","text":"import json\nimport os\nimport time\n\nimport requests\n\n\n# Download match data from opendota API\ndef get_match_by_id(match_id):\n m_id = str(match_id)\n # fetch match data\n response = requests.get(\"https://api.opendota.com/api/matches/\" + m_id)\n # if successful\n if response.ok:\n print(\"GET:\", m_id)\n # response.json data\n data = response.json()\n # write it to file\n file = open(\"download\" + os.path.sep + m_id + '_data.json', 'w')\n json.dump(data, file, indent=\"\")\n file.close()\n\n# starting match_id\n#match_id = 5915008308\n\n# fetch 1000 matches\nfor i in range(1, 1000):\n get_match_by_id(match_id + i)\n time.sleep(2)\n","sub_path":"fetch_dota_match.py","file_name":"fetch_dota_match.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"632917719","text":"# detailed_differences.py\n# 19 January 2020\n# Python solution to \"Detailed Differences\" problem on kattis.com (https://open.kattis.com/problems/detaileddifferences)\n# By Frederik Hoengaard - @frederikhoengaard - frph@itu.dk\n\n# Is tested and works as of 19 Jan 2020\n\n#====================================================================================================\n#====================================================================================================\n#====================================================================================================\n\nimport sys\n\nx = sys.stdin.readlines()\n\ndef show_differences(sample):\n\tlb = '\\n'\n\ttmp = []\n\tfor i in sample:\n\t\tx = i.replace(\"\\n\",\"\")\n\t\ttmp.append(x.replace(\"\\r\",\"\"))\n\ttmp = tmp[1:]\n\n\tdef comparer(string1,string2):\n\t\ttmp = ''\n\t\tfor i in range(0,len(string1)):\n\t\t\tif string1[i] == string2[i]:\n\t\t\t\ttmp = tmp + '.'\n\t\t\telse:\n\t\t\t\ttmp = tmp + '*'\n\t\treturn tmp\n\n\tdef output_builder(input_list):\n\t\tif not input_list:\n\t\t\treturn ''\n\t\telif len(input_list) == 2:\n\t\t\ttmp = comparer(input_list[0],input_list[1])\n\t\t\treturn input_list[0] + lb + input_list[1] + lb + tmp + lb\n\t\telse:\n\t\t\ttmp = comparer(input_list[0],input_list[1])\n\t\t\treturn input_list[0] + lb + input_list[1] + lb + tmp + 2*lb + output_builder(input_list[2:])\n\n\toutput = output_builder(tmp)\n\treturn output\n\n#====================================================================================================\n\ndef main():\n\tprint(show_differences(x))\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"detailed_differences/detailed_differences.py","file_name":"detailed_differences.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"457672592","text":"import tensorflow as tf\nfrom tensorflow.python.keras.datasets import mnist, cifar10\nfrom tensorflow.python.keras import Model, Input\nfrom tensorflow.python.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\nfrom tensorflow.python.keras.optimizers import Adadelta\nfrom tensorflow.python.keras.callbacks import TensorBoard\n\nimport numpy as np\nimport h5py\nfrom time import time\n\ntraining_file = '../data/hdf5/test.h5'\nvalidation_file = '../data/hdf5/validation.h5'\nmodel_file = '../models/firstcnn_v4.h5'\n\ntrain = h5py.File(training_file)\nimages = train['images'].value\nlabels = train['labels'].value\n\ny_train = tf.keras.utils.to_categorical(labels, 7)\nx_train = images/255\n\ntest = h5py.File(validation_file)\nimages = test['images'].value\nlabels = test['labels'].value\n\ny_test = tf.keras.utils.to_categorical(labels, 7)\nx_test = images/255\n\ninputs = Input(shape=(112, 112, 3))\nx = Conv2D(32, (3, 3), activation='relu')(inputs)\nx = Conv2D(64, (3, 3), activation='relu')(x)\nx = MaxPooling2D(pool_size=(2, 2))(x)\nx = Dropout(0.25)(x)\nx = Flatten()(x)\nx = Dense(128, activation='relu')(x)\nx = Dropout(0.5)(x)\ny = Dense(7, activation='softmax')(x)\n\nmodel = Model(inputs=inputs, outputs=y)\n\nmodel.compile(optimizer = Adadelta(), \n loss='categorical_crossentropy', \n metrics=['accuracy'])\n\ntensorboard = TensorBoard(log_dir='../logs/{0}'.format(time()))\n\nmodel.fit(x_train, y_train, batch_size=32, epochs=25, verbose=1, callbacks=[tensorboard])\n\n\nmodel.save(model_file)\n\n","sub_path":"scripts/archive/firstcnn.py","file_name":"firstcnn.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"111154483","text":"from keras.models import Sequential\r\nfrom keras.layers import Dense, Activation\r\nfrom keras.layers.core import Dropout, Flatten\r\nfrom keras.layers.pooling import MaxPooling2D\r\nfrom keras.layers.convolutional import Conv2D\r\nfrom keras.optimizers import Adam\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\nfrom tensorflow import reshape\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n# need the input data and batching it. \r\n# for a certain number of epochs, feed the batched data to the net\r\n# calc the the reduce_mean output on the cross entropy....use a softmax classifier\r\n# every X iterations of training use test values to get a test error. \r\n\r\ndef build_conv_net():\r\n\tnet = Sequential()\r\n\tnet.add(Conv2D(16, (5,5), padding='same', input_shape=(28,28,1)))\r\n\tnet.add(Conv2D(32, (5,5), padding='same'))\r\n\tnet.add(Activation('relu'))\r\n\tnet.add(MaxPooling2D(pool_size=(2,2)))\r\n\tnet.add(Dropout(0.25))\r\n\tnet.add(Conv2D(32, (5,5), padding='same'))\r\n\tnet.add(Activation('relu'))\r\n\tnet.add(MaxPooling2D(pool_size=(2,2))) \r\n\tnet.add(Dropout(0.25))\r\n\t# now flatten the net and give to a dense layer\r\n\tnet.add(Flatten())\r\n\tnet.add(Dense(units=512)) #, input_dim=25088))\r\n\tnet.add(Activation('relu'))\r\n\tnet.add(Dropout(0.25))\r\n\tnet.add(Dense(units=10))#, input_dim=512))\r\n\tnet.add(Activation('softmax'))\r\n\r\n\toptimizer = Adam(lr=0.0015)\r\n\tnet.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\r\n\treturn net\r\n\r\n\r\ndef main():\r\n\t#get the input data, nicely provided via TensorFlow\r\n\tmnist = input_data.read_data_sets('./MNIST_data', one_hot=True) \r\n\r\n\t\r\n\t## Test to show what the image is and to make sure the reformatting works!\r\n\t# imgs = mnist.train.next_batch(2)\r\n\t# img_label_1, img_label_2 = imgs[1][0], imgs[1][1]\r\n\t# imgs = np.reshape(imgs[0], (2,28,28,1))\r\n\t# print(img_label_1)\r\n\t# plt.imshow(imgs[0,:,:,0])\r\n\t# plt.show()\r\n\t# print(img_label_2)\r\n\t# plt.imshow(imgs[1,:,:,0])\r\n\t# plt.show()\r\n\r\n\t# build the net \r\n\r\n\tconv_net = build_conv_net()\r\n\r\n\ttotal_epochs = 1000\r\n\tbatch_size = 128\r\n\ttest_batch_size = 64\r\n\r\n\tfor i in range(total_epochs):\r\n\t\tbatch = mnist.train.next_batch(batch_size)\r\n\t\tdata, data_labels = np.reshape(batch[0], (batch_size,28,28,1)), batch[1]\r\n\t\tconv_net.train_on_batch(data, data_labels)\r\n\r\n\t\tif i % 100 == 0:\r\n\t\t\ttest_batch = mnist.test.next_batch(test_batch_size)\r\n\t\t\ttest_data, test_labels = np.reshape(test_batch[0], (test_batch_size,28,28,1)), test_batch[1]\r\n\t\t\ttraining_metrics = conv_net.evaluate(test_data, test_labels, batch_size=test_data.shape[0])\r\n\t\t\tprint('step %d, training metrics: ' %(i), training_metrics)\r\n\r\n\tformatted_test = np.reshape(mnist.test.images, (mnist.test.images.shape[0],28,28,1))\r\n\tscore = conv_net.evaluate(formatted_test,mnist.test.labels, batch_size=128)\r\n\tprint(score)\r\n\r\nif __name__ == '__main__':\r\n\tmain()","sub_path":"mnist_keras_conv.py","file_name":"mnist_keras_conv.py","file_ext":"py","file_size_in_byte":2823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"635542715","text":"import plotly.graph_objects as go\nimport json\nfrom collections import OrderedDict\nimport os\ndata_path = os.path.join(os.getcwd(),os.path.dirname(__file__)) + '/../data/'\n\n##with open('../data/MasterData.txt') as f:\nwith open(data_path + 'MasterData.txt', encoding='utf-8') as f:\n mdata = json.load(f, object_pairs_hook=OrderedDict)\n\n##with open('../data/riket.txt') as f:\nwith open(data_path + 'riket.txt', encoding='utf-8') as f:\n riket_data = json.load(f)\n\n##with open('../data/sekom.json') as f:\nwith open(data_path + 'sekom.json', encoding='utf-8') as f:\n sekom_data = json.load(f)\n\nkey_to_desc = {\n \"N15419\": \"Kunskapskrav uppnått, procent\",\n \"N15505\": \"Meritvärde\",\n \"N15436\": \"Behörighet, yrkesprogram, procent\",\n \"U15461\": \"Elever i år 9 som är behöriga till yrkespr. avvikelse från modellberäknat värde kommunala skolor, procentenheter\",\n \"N15485\": \"Elever i åk 6 med lägst betyget E i matematik, kommunala skolor, andel (%)\",\n \"N15488\": \"Elever i åk 6 med lägst betyget E i svenska, kommunala skolor, andel (%)\",\n \"N15574\": \"Elever i år 9 som fått ett högre betyg än provbetyg för ämnesprov i engelska, kommunala skolor, andel (%)\",\n \"N15573\": \"Elever i år 9 som fått ett lägre betyg än provbetyg för ämnesprov i engelska, kommunala skolor, andel (%)\",\n \"N15572\": \"Elever i år 9 som fått ett högre betyg än provbetyg för ämnesprov i matematik, kommunala skolor, andel (%)\",\n \"N15571\": \"Elever i år 9 som fått ett lägre betyg än provbetyg för ämnesprov i matematik, kommunala skolor, andel (%)\",\n \"N15570\": \"Elever i år 9 som fått ett högre betyg än provbetyg för ämnesprov i svenska, kommunala skolor, andel (%)\",\n \"N15569\": \"Elever i år 9 som fått ett lägre betyg än provbetyg för ämnesprov i svenska, kommunala skolor, andel (%)\",\n \"N15814\": \"Lärare (heltidstjänster) med lärarlegitimation och behörighet i minst ett ämne i grundskola åk 1-9, kommunala skolor, andel (%)\",\n \"N15034\": \"Elever/lärare (årsarbetare) i kommunal grundskola åk 1-9, lägeskommun, antal\",\n \"N15008\": \"Kostnad för kommunal grundskola åk 1-9, kr/elev\",\n \"N15902\": \"Nyinvandrade och elever med okänd bakgrund i kommunal grundskola åk. 1-9, andel (%)\",\n \"N15823\": \"Nyinvandrade och elever med okänd bakgrund i år 9, kommunala skolor, andel (%)\",\n \"N15820\": \"Elever vars föräldrar har eftergymnasial utbildning, åk 1-9 i kommunal grundskola, lägeskommun, andel (%)\"\n}\n\ndesc_to_key = {v:k for k,v in key_to_desc.items()}\n\n\nclass plot:\n\n def __init__(self):\n self._fig = go.Figure()\n\n def clear(self):\n \"\"\"\n Reset the current canvas.\n \"\"\"\n self._fig = go.Figure()\n\n def show(self, CONFIG = {}):\n \"\"\"\n Display the canvas.\n \"\"\"\n self._fig.show(config=CONFIG)\n\n def plot_line(self,x_0,y_0,x_1,y_1,col=\"black\",line_width=1,line_type=\"solid\"):\n \"\"\"\n Draw a line on the canvas.\n\n Arguments:\n (x_0,y_0) -- Starting point\n (x_1,y_1) -- End_point\n col -- Colour\n line_width -- Width\n line_type -- Type\n \"\"\"\n self._fig.add_shape(\n go.layout.Shape(\n type=\"line\",\n x0=x_0,\n y0=y_0,\n x1=x_1,\n y1=y_1,\n line=dict(\n color=col,\n width=line_width,\n dash=line_type\n )\n )\n )\n\n\n\n def add_def(self,Diagram,RikeAvg):\n \"\"\"\n Add a box with text in\n\n Arguments:\n RikeAvg --National avgrage.\n \"\"\"\n if Diagram==True:\n self._fig.update_layout(\n annotations=[\n go.layout.Annotation(\n text = 'Den sträckade linjen visar
rikets medel: '+str(RikeAvg)+' procent',\n align='left',\n showarrow=False,\n xref='paper',\n yref='paper',\n x=0.05,\n y=0.95,\n bordercolor='black',\n borderwidth=1,\n bgcolor = 'white'\n )\n ]\n )\n else: \n self._fig.update_layout(\n annotations=[\n go.layout.Annotation(\n text = \"Flickor har högre resultat än pojkar\",\n align='left',\n showarrow=False,\n xref='paper',\n yref='paper',\n x=0.04,\n y=1.00,\n font=dict(family=\"Open Sans, sans-serif\",size=12,color=\"black\")\n \n ), go.layout.Annotation(\n text = \"Pojkar har högre resultat än flickor\",\n align='left',\n showarrow=False,\n xref='paper',\n yref='paper',\n x=1.00,\n y=0.025,\n font=dict(family=\"Open Sans, sans-serif\",size=12,color=\"black\")\n \n )\n ]\n ) \n\n\n\n\n\n\n def add_scatter(self, data_x, data_y, data_text, colors, xlabel, ylabel):\n \"\"\"\n Add a scatter plot to the canvas.\n\n Arguments:\n data_x -- Data plotted against the x-axis.\n data_y -- Data plotted against the y-axis.\n data_text -- Information displayed on hover.\n colors -- Decides the colour of each data point.\n xlabel -- Information displayed on hover.\n ylabel -- Information displayed on hover.\n \"\"\"\n\n self._fig.add_trace(go.Scatter(\n x=data_x,\n y=data_y,\n customdata=list(zip(list(map(round,data_y)),list(map(round,data_x)))),\n hovertemplate = '%{text}

'+\n '{}'.format(ylabel) + ': %{customdata[0]}
'+\n '{}'.format(xlabel)+': %{customdata[1]}
',\n hoverlabel = dict(\n bgcolor = 'white'\n ),\n text=data_text,\n marker=dict(\n color=colors,\n ),\n showlegend=False))\n\n self._fig.update_traces(mode='markers', marker=dict(symbol='circle', size=8))\n\n\n def add_bar(self, data_x, data_y, colors, x_ticks = True, text=\"\", show_legend = False, legend_name=\"\"):\n \"\"\"\n Add a bar plot to the canvas.\n\n Arguments:\n data_x -- Data/labels plotted against the x-axis.\n data_y -- Data plotted against the y-axis.\n colors -- Decides the colour of each data point.\n xticks -- Show x-ticks or not, default is True\n text -- Text shown on hover-labels, default is \"\"\n show_legend -- Show legend or not, default is False\n legend_name -- Name of the bar trace shown in the legend, default is \"\"\n \"\"\"\n data_y_rounded = list(map(round,(list(map(lambda x: x + 0.001, list(map(abs,data_y)))))))\n\n self._fig.add_trace(go.Bar(\n x=data_x,\n y=data_y,\n hoverinfo= 'none',\n customdata=data_y_rounded, # Här skickar vi med en lista av absolutvärden, vilket blir de värden som visas vid hovring.\n hovertemplate = '%{x}

'+\n text + '%{customdata}
',\n hoverlabel = dict(\n bgcolor = 'white',\n namelength = 0),\n text = data_y_rounded,\n textfont = dict(color=\"white\"),\n textposition='auto',\n name = legend_name,\n marker_color=colors,\n cliponaxis = False,\n showlegend=show_legend,\n textangle=0))\n\n\n self._fig.update_layout(barmode=\"relative\",\n xaxis = dict(showticklabels=x_ticks),\n uniformtext_minsize=18, uniformtext_mode='hide') #Denna rad bestämmer hur små värdena får vara inuti stapeln innan de kapas bort.\n #Är de mindre än 20 punkter så visas de inte. Tar man bort \"uniformtext_minsize\" och \"uniformtext_mode\"\n #kommer siffrona alltid att visas, även om de blir oändligt små om stapeln är liten.\n\n def add_title(self, title, x_title = \"\", y_title = \"\"):\n \"\"\"\n Add/change the title of the plot.\n\n Arguments:\n title -- Title as string.\n x_title -- Add a title to the x-axis.\n y_title -- Add a title to the y-axis.\n \"\"\"\n self._fig.update_layout(title_text = title,\n xaxis_title = x_title,\n yaxis_title = y_title)\n\n\n def format_layout(self, show_x_grid=False, show_y_grid=False):\n \"\"\"\n Update the appearence of the canvas.\n\n Arguments:\n plot_title -- Add a title to the plot.\n show_x_grid -- If True, display a vertical line on each x-axis tick.\n show_y_grid -- If True, display a horizontal line on each y-axis tick.\n\n \"\"\"\n self._fig.update_layout(\n title=dict(\n y=0.9,\n x=0.5,\n xanchor='center',\n yanchor='top'),\n font=dict(\n family=\"Open Sans, sans-serif\",\n size=18,\n color=\"#7f7f7f\"),\n xaxis=dict(\n showgrid=show_x_grid,\n gridwidth=1,\n gridcolor='LightGrey',\n showline=True,\n linecolor='rgb(102, 102, 102)',\n tickfont_color='rgb(102, 102, 102)',\n showticklabels=True,\n ticks='outside',\n tickcolor='rgb(102, 102, 102)',\n ),\n yaxis=dict(\n showgrid=show_y_grid,\n gridwidth=1,\n gridcolor='LightGrey',\n showline=True,\n linecolor='rgb(102, 102, 102)',\n tickfont_color='rgb(102, 102, 102)',\n showticklabels=True,\n ticks='outside',\n tickcolor='rgb(102, 102, 102)',\n ),\n margin=dict(l=140, r=40, b=50, t=120),\n legend=dict(\n font_size=10,\n yanchor='middle',\n xanchor='right',\n ),\n paper_bgcolor='white',\n plot_bgcolor='white',\n hovermode='closest',\n )\n\n def show_zero_line(self):\n\n self._fig.update_layout(\n xaxis = dict(\n zerolinecolor='black',\n zerolinewidth=1),\n yaxis = dict(\n zerolinecolor='black',\n zerolinewidth=1)\n )\n\n def format_size(self, WIDTH, HEIGHT):\n \"\"\"\n Change the dimensions of the canvas.\n \"\"\"\n self._fig.update_layout(\n width=WIDTH,\n height=HEIGHT,\n )\n\n def format_x_axis(self, x_tick, x_limits):\n \"\"\"\n Alter the scope of the x-axis.\n\n Arguments:\n x_tick -- Change the frequency of the x-axis ticks.\n x_limits -- Tuple, containing the end_points of the displayed x-axis.\n \"\"\"\n self._fig.update_layout(\n xaxis=dict(\n range=x_limits,\n dtick=x_tick,\n ),\n )\n\n def format_y_axis(self, y_tick, y_limits):\n \"\"\"\n Alter the scope of the y-axis.\n\n Arguments:\n y_tick -- Change the frequency of the y-axis ticks.\n y_limits -- Tuple, containing the end_points of the displayed y-axis.\n \"\"\"\n self._fig.update_layout(\n yaxis=dict(\n range=y_limits,\n dtick=y_tick,\n ),\n )\n\n def edit_toolbar(self, filename, format, height=750,width=1050):\n \"\"\"\n Edit the options of the toolbar at the top of the diagram canvas.\n\n Argument description:\n filename \t-- a string specifying the default name of the file that downloads when pressing the “save plot” icon.\n format \t\t-- one of “png”, “svg”, “jpeg”, “webp”. Specifies the file format of the file that downloads when pressing the “save plot” icon.\n height\t\t-- the height of the download, in pixels. Default is 750px.\n width\t\t-- the width of the download, in pixels. Default is 1050px.\n \"\"\"\n return {\n 'displaylogo': False,\n 'toImageButtonOptions': {\n 'format': format, # png, svg, jpeg, webp\n 'filename': filename,\n 'height': height,\n 'width': width,\n 'scale': 1\n },\n #'modeBarButtonsToRemove': ['toggleSpikelines','hoverCompareCartesian','hoverClosestCartesian','autoScale2d','zoom2d', 'pan2d','lasso2d','select2d']\n }\n\n def dotted_line(self,legend_text,x_0,y_0,x_1,y_1,col=\"black\",line_width=1):\n self._fig.add_trace(go.Scatter(\n x=[x_0,x_1], \n y=[y_0,y_1], \n name=legend_text,\n line = dict(color=col, width=line_width, dash='dot'),\n hoverinfo='skip',\n xaxis=\"x2\",\n mode='lines'))\n self._fig.update_layout(legend = dict(font = dict(color=\"black\", size = 12, family=\"Open Sans, sans-serif\")),\n xaxis2=dict(showticklabels=False, overlaying= 'x',showgrid=False))","sub_path":"src/plot_funcs.py","file_name":"plot_funcs.py","file_ext":"py","file_size_in_byte":14127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"88842855","text":"\ndef ThresholdSelector():\n #answering the following examples\n \"\"\"\n (list of number, int) -> list of number\n\n Return a new list consisting of those numbers in nums\n\n that are below threshold,\n\n in the same order as in nums.\n\n >>> collect_underperformers([1, 2, 3, 4], 3)\n\n [1, 2]\n\n >>> collect_underperformers([1, 2, 108, 3, 4], 50)\n\n [1, 2, 3, 4]\n\n \"\"\"\n #defining the function\n def collect_underperformers(n,thres):\n #Initialize an empty list\n outlist = list()\n #loops the following code until it hits the length of n\n for i in range(0, len(n)):\n #checks whether the value is less than the threshold\n if n[i] max_total_length:\n batch_datas.append(current_batch)\n max_len_en = len_en\n max_len_zh = len_zh\n current_bs = 1\n current_batch = []\n else:\n max_len_en = temp_max_len_en\n max_len_zh = temp_max_len_zh\n current_batch.append(data_ids)\n if current_batch:\n batch_datas.append(current_batch)\n return batch_datas\n\n\ndef read_data():\n if os.path.exists(data_dump_path):\n LOG(\"Load data from %s.\" % data_dump_path)\n return joblib.load(data_dump_path)\n \n tokenizer_zh = tokenization.FullTokenizer(\"vocabulary/zh_vocab.txt\")\n tokenizer_en = tokenization.FullTokenizer(\"vocabulary/en_vocab.txt\")\n vocab_size_en = len(tokenizer_en.vocab)\n vocab_size_zh = len(tokenizer_zh.vocab)\n \n train_list = []\n valid_list = []\n load_file(\"translation2019zh_valid.json\", valid_list)\n LOG(len(valid_list))\n # load_file(\"translation2019zh_valid.json\", train_list)\n load_file(\"translation2019zh_train.json\", train_list)\n LOG(len(train_list))\n \n valid_id_list = [encode_sentence(en, zh, tokenizer_en, tokenizer_zh,\n vocab_size_en, vocab_size_zh) for en, zh in valid_list]\n LOG(len(valid_id_list))\n train_id_list = [encode_sentence(en, zh, tokenizer_en, tokenizer_zh,\n vocab_size_en, vocab_size_zh) for en, zh in tqdm(train_list, desc=\"processing traindata\")]\n LOG(len(train_id_list))\n \n input_vocab_size = vocab_size_en + 2\n target_vocab_size = vocab_size_zh + 2\n joblib.dump((train_id_list, valid_id_list, input_vocab_size,\n target_vocab_size), \"datasets_en2zh.dat\")\n return train_id_list, valid_id_list, input_vocab_size, target_vocab_size\n\n\ndef batch_to_tensor(batch_data):\n batch_size = len(batch_data)\n random.shuffle(batch_data)\n maxlen_0 = max(len(x[0]) for x in batch_data)\n maxlen_1 = max(len(x[1]) for x in batch_data)\n inp = np.zeros((batch_size, maxlen_0), dtype=np.int32)\n tar = np.zeros((batch_size, maxlen_1), dtype=np.int32)\n for i, (en, zh) in enumerate(batch_data):\n inp[i, :len(en)] = en\n tar[i, :len(zh)] = zh\n return tf.convert_to_tensor(inp, dtype=tf.int32), tf.convert_to_tensor(tar, dtype=tf.int32)\n\n\ndef get_tensor_batch(data_list):\n random.shuffle(data_list)\n for batch_data in data_list:\n yield batch_to_tensor(batch_data)\n\n\ndef load_embeddings(path):\n embed_data = joblib.load(path)\n vocab_size, d_model = embed_data.shape\n embedings = np.random.random((vocab_size+2, d_model))\n embedings[:vocab_size, :] = embed_data\n return embedings\n\n\ndef main():\n train_id_list, valid_id_list, input_vocab_size, target_vocab_size = read_data()\n LOG(\"Load data finished, %d training, %d validation\" %\n (len(train_id_list), len(valid_id_list)))\n train_dataset = batchify(train_id_list, MAX_TOTAL_LENGTH)\n val_dataset = batchify(valid_id_list, MAX_TOTAL_LENGTH)\n LOG(\" %d batches of training data, %d batches of validation data.\" %\n (len(train_dataset), len(val_dataset)))\n en_embed_data = load_embeddings(\"vocabulary/en_embedding.dat\")\n zh_embed_data = load_embeddings(\"vocabulary/zh_embedding.dat\")\n\n d_model = dff = zh_embed_data.shape[-1]\n learning_rate = CustomSchedule(d_model)\n optimizer = tf.keras.optimizers.Adam(learning_rate)\n train_loss = tf.keras.metrics.Mean(name='train_loss')\n train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n name='train_accuracy')\n transformer = Transformer(num_layers, num_heads, dff,\n # input_vocab_size, target_vocab_size,\n en_embed_data, zh_embed_data,\n pe_input=input_vocab_size,\n pe_target=target_vocab_size,\n rate=dropout_rate)\n ckpt = tf.train.Checkpoint(transformer=transformer,\n optimizer=optimizer)\n\n ckpt_manager = tf.train.CheckpointManager(\n ckpt, checkpoint_path, max_to_keep=5)\n\n # 如果检查点存在，则恢复最新的检查点。\n if ckpt_manager.latest_checkpoint:\n ckpt.restore(ckpt_manager.latest_checkpoint)\n print('Latest checkpoint restored!!')\n\n # train_step_signature = [\n # tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n # tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n # ]\n # @tf.function(input_signature=train_step_signature)\n def train_step(inp, tar):\n tar_inp = tar[:, :-1]\n tar_real = tar[:, 1:]\n\n enc_padding_mask, combined_mask, dec_padding_mask = create_masks(\n inp, tar_inp)\n\n with tf.GradientTape() as tape:\n predictions, _ = transformer(inp, tar_inp,\n True,\n enc_padding_mask,\n combined_mask,\n dec_padding_mask)\n loss = loss_function(tar_real, predictions)\n gradients = tape.gradient(loss, transformer.trainable_variables)\n optimizer.apply_gradients(\n zip(gradients, transformer.trainable_variables))\n\n train_loss(loss)\n train_accuracy(tar_real, predictions)\n\n def evaluate_step(inp, tar):\n tar_inp = tar[:, :-1]\n tar_real = tar[:, 1:]\n\n enc_padding_mask, combined_mask, dec_padding_mask = create_masks(\n inp, tar_inp)\n\n predictions, _ = transformer(inp, tar_inp,\n False,\n enc_padding_mask,\n combined_mask,\n dec_padding_mask)\n loss = loss_function(tar_real, predictions)\n\n train_loss(loss)\n train_accuracy(tar_real, predictions)\n LOG(\"Params MAX_TOTAL_LENGTH:%d, d_model:%d, dff:%d, num_layers:%d, num_heads:%d.\" %\n (MAX_TOTAL_LENGTH, d_model, dff, num_layers, num_heads))\n for epoch in range(EPOCHS):\n start = time.time()\n\n train_loss.reset_states()\n train_accuracy.reset_states()\n\n # inp -> portuguese, tar -> english\n for batch, (inp, tar) in enumerate(get_tensor_batch(train_dataset)):\n train_step(inp, tar)\n\n if batch % 1000 == 0:\n LOG('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n epoch + 1, batch, train_loss.result(), train_accuracy.result()))\n LOG('Train Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1,\n train_loss.result(),\n train_accuracy.result()))\n\n train_loss.reset_states()\n train_accuracy.reset_states()\n for batch_data in val_dataset:\n inp, tar = batch_to_tensor(batch_data)\n evaluate_step(inp, tar)\n\n LOG('Valid Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1,\n train_loss.result(),\n train_accuracy.result()))\n\n if (epoch + 1) % 5 == 0:\n ckpt_save_path = ckpt_manager.save()\n print('Saving checkpoint for epoch {} at {}'.format(epoch + 1,\n ckpt_save_path))\n\n print('Time taken for 1 epoch: {} secs\\n'.format(time.time() - start))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"train_transformer_en2zh.py","file_name":"train_transformer_en2zh.py","file_ext":"py","file_size_in_byte":9552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"96746049","text":"# --- coding: utf-8 ---\n\"\"\"\nbook-walker の操作を行うためのクラスモジュール\n\n@see https://github.com/xuzhengyi1995/Bookwalker_Downloader\n\"\"\"\n\nimport time\nimport re\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom manager import AbstractManager\n\n\nclass Manager(AbstractManager):\n \"\"\"\n book-walker の操作を行うためのクラス\n \"\"\"\n\n def __init__(self, driver, config=None, directory='./', prefix=''):\n \"\"\"\n book-walker の操作を行うためのコンストラクタ\n @param driver splinter のブラウザインスタンス\n \"\"\"\n super().__init__(driver, config, directory, prefix)\n\n self.next_key = Keys.ARROW_LEFT\n \"\"\"\n 次のページに進むためのキー\n \"\"\"\n\n def start(self, url=None):\n \"\"\"\n ページの自動スクリーンショットを開始する\n @return エラーが合った場合にエラーメッセージを、成功時に True を返す\n \"\"\"\n self._wait()\n\n self._wait_loading()\n\n total = self._get_total_page()\n if total is None:\n return '全ページ数の取得に失敗しました'\n\n self._sleep(2)\n\n # get original size\n canvas = self.driver.find_element_by_css_selector(\"canvas.dummy\")\n self.driver.set_window_size(int(canvas.get_attribute('width')),\n int(canvas.get_attribute('height')))\n print(f'size: {canvas.get_attribute(\"width\")}x{canvas.get_attribute(\"height\")}')\n\n self._sleep()\n\n self._set_total(total)\n for count in range(0, total):\n\n canvas = self.driver.find_element_by_css_selector(\".currentScreen canvas\")\n self._save_image_of_web_element(count, canvas)\n self.pbar.update(1)\n\n self._next()\n self._sleep()\n\n return True\n\n def _get_total_page(self):\n \"\"\"\n 全ページ数を取得する\n 最初にフッタの出し入れをする\n @return 取得成功時に全ページ数を、失敗時に None を返す\n \"\"\"\n for _ in range(Manager.MAX_LOADING_TIME):\n elements = self.driver.find_elements_by_id('pageSliderCounter')\n if len(elements) != 0:\n # print(elements[0].get_attribute('innerHTML'))\n if re.match('^\\\\d+/\\\\d+$', elements[0].get_attribute('innerHTML').strip()):\n return int(elements[0].get_attribute('innerHTML').split('/')[1])\n time.sleep(1)\n return None\n\n def _next(self):\n \"\"\"\n 次のページに進む\n \"\"\"\n self._press_key(self.next_key)\n self._wait_loading()\n\n def _wait_loading(self):\n WebDriverWait(self.driver, 30).until_not(lambda x: self._check_is_loading(\n x.find_elements_by_css_selector(\".loading\")))\n\n @staticmethod\n def _check_is_loading(list_ele):\n is_loading = False\n for i in list_ele:\n if i.is_displayed() is True:\n is_loading = True\n break\n return is_loading\n","sub_path":"bookwalker/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":3174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"264169193","text":"import os\nfrom zipfile import ZipFile\n\nif __name__ == \"__main__\":\n with ZipFile(\"Otter.zip\", \"w\") as zip_obj:\n # Zip specific files.\n zip_obj.write(\"README.md\")\n zip_obj.write(\"requirements.txt\")\n # Zip the src directory.\n for folder, subfolders, file_names in os.walk(\"src\"):\n for file_name in file_names:\n # Ignore .pyc files (cached files)\n file, ext = os.path.splitext(file_name)\n if ext == \".pyc\":\n continue\n file_path = os.path.join(folder, file_name)\n print(file_path)\n zip_obj.write(file_path)\n","sub_path":"submit.py","file_name":"submit.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"173753046","text":"# Import package\nimport paho.mqtt.client as mqtt\nimport ssl\nimport time\nimport json\nimport time\n\n# Define Variables\n\nclass mqtt_client:\n def __init__( self, accel, gyro, mag ):\n self.accel = accel\n self.mag = mag\n self.gyro = gyro\n self.user = \"Joe\"\n self.sessionID = 1\n \n self.MQTT_PORT = 8883\n self.MQTT_KEEPALIVE_INTERVAL = 45\n self.MQTT_TOPIC = \"myTopic\"\n \n self.MQTT_HOST = \"a35pq9rp66r1yo.iot.us-east-1.amazonaws.com\"\n self.CA_ROOT_CERT_FILE = \"/home/pi/Desktop/Stride/Raspberry Pi Code/Certificates/New Certificates/VeriSign-Class 3-Public-Primary-Certification-Authority-G5.pem.txt\"\n self.THING_CERT_FILE = \"/home/pi/Desktop/Stride/Raspberry Pi Code/Certificates/New Certificates/6943a8d54a-certificate.pem.crt\"\n self.THING_PRIVATE_KEY = \"/home/pi/Desktop/Stride/Raspberry Pi Code/Certificates/New Certificates/6943a8d54a-private.pem.key\"\n \n self.total_meas = len(accel)\n\n\n\n def store_data( self ):\n TIMESTAMP = time.time()\n print(TIMESTAMP)\n\n SESSIONID = 1\n MEA = 1\n\n # Initiate MQTT Client\n mqttc = mqtt.Client()\n\n # Configure TLS Set\n mqttc.tls_set(self.CA_ROOT_CERT_FILE, certfile=self.THING_CERT_FILE, keyfile=self.THING_PRIVATE_KEY,\n cert_reqs=ssl.CERT_REQUIRED, tls_version=ssl.PROTOCOL_TLSv1_2, ciphers=None)\n\n # Connect with MQTT Broker\n mqttc.connect(self.MQTT_HOST, self.MQTT_PORT, self.MQTT_KEEPALIVE_INTERVAL)\t\t\n mqttc.loop_start()\n\n counter = 0\n while counter < self.total_meas:\n SESSIONID1 = str(SESSIONID) + \"_\" + str(counter)\n MQTT_MSG = json.dumps({\"user\": \"Joe\",\"sessionID\": SESSIONID1,\n \"accelerometer x\": self.accel[counter][0], \"accelerometer y\": self.accel[counter][1], \"accelerometer z\": self.accel[counter][2],\n \"gyroscope x\": self.gyro[counter][0], \"gyroscope y\": self.gyro[counter][1], \"gyroscope z\": self.gyro[counter][2],\n \"magnetometer x\": self.mag[counter][0], \"magnetometer y\": self.mag[counter][1], \"magnetometerz\": self.mag[counter][2]})\n\n mqttc.publish(self.MQTT_TOPIC, MQTT_MSG ,qos=1)\n counter+=1\n time.sleep(.1)\n #print(counter)\n\n # Disconnect from MQTT_Broker\n mqttc.disconnect()\n\n\n\n","sub_path":"Raspberry Pi Code/Code_0/AWSIoTPythonSDK/mqtt_client.py","file_name":"mqtt_client.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"181258130","text":"from __future__ import absolute_import\nfrom happybase import Connection\nfrom frontera.contrib.backends.hbase import HBaseState, HBaseMetadata, HBaseQueue\nfrom frontera.core.models import Request, Response\nfrom frontera.core.components import States\nfrom binascii import unhexlify\n\n\nr1 = Request('https://www.example.com', meta={'fingerprint': '10',\n 'domain': {'name': 'www.example.com', 'fingerprint': '81'}})\nr2 = Request('http://example.com/some/page/', meta={'fingerprint': '11',\n 'domain': {'name': 'example.com', 'fingerprint': '82'}})\nr3 = Request('http://www.scrapy.org', meta={'fingerprint': '12',\n 'domain': {'name': 'www.scrapy.org', 'fingerprint': '83'}})\nr4 = r3.copy()\n\n\nclass TestHBaseBackend(object):\n\n def delete_rows(self, table, row_keys):\n batch = table.batch()\n for key in row_keys:\n batch.delete(unhexlify(key))\n batch.send()\n\n def test_metadata(self):\n connection = Connection(host='hbase-docker', port=9090)\n metadata = HBaseMetadata(connection, 'metadata', True, False, 300000, True)\n metadata.add_seeds([r1, r2, r3])\n resp = Response('https://www.example.com', request=r1)\n metadata.page_crawled(resp, [r2, r3])\n metadata.request_error(r4, 'error')\n metadata.frontier_stop()\n table = connection.table('metadata')\n assert set([data['m:url'] for _, data in table.scan()]) == \\\n set([r1.url, r2.url, r3.url])\n self.delete_rows(table, ['10', '11', '12'])\n\n def test_queue(self):\n connection = Connection(host='hbase-docker', port=9090)\n queue = HBaseQueue(connection, 1, 'queue', True)\n batch = [('10', 0.5, r1, True), ('11', 0.6, r2, True),\n ('12', 0.7, r3, True)]\n queue.schedule(batch)\n assert set([r.url for r in queue.get_next_requests(10, 0, min_requests=3, min_hosts=1,\n max_requests_per_host=10)]) == set([r1.url, r2.url, r3.url])\n\n def test_state(self):\n connection = Connection(host='hbase-docker', port=9090)\n state = HBaseState(connection, 'metadata', 300000)\n state.set_states([r1, r2, r3])\n assert [r.meta['state'] for r in [r1, r2, r3]] == [States.NOT_CRAWLED]*3\n state.update_cache([r1, r2, r3])\n assert state._state_cache == {'10': States.NOT_CRAWLED,\n '11': States.NOT_CRAWLED,\n '12': States.NOT_CRAWLED}\n r1.meta['state'] = States.CRAWLED\n r2.meta['state'] = States.CRAWLED\n r3.meta['state'] = States.CRAWLED\n state.update_cache([r1, r2, r3])\n state.flush(True)\n assert state._state_cache == {}\n state.fetch(['10', '11', '12'])\n assert state._state_cache == {'10': States.CRAWLED,\n '11': States.CRAWLED,\n '12': States.CRAWLED}\n r4.meta['state'] = States.ERROR\n state.set_states([r1, r2, r4])\n assert r4.meta['state'] == States.CRAWLED\n state.flush(True)\n assert state._state_cache == {}\n","sub_path":"tests/contrib/backends/hbase/test_hbase.py","file_name":"test_hbase.py","file_ext":"py","file_size_in_byte":3139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"208632939","text":"\n\ndef findrank(A):\n\n rank = 0\n \n def chars_less_than(i,A):\n \n less_than = 0\n\n for j in A[i:]:\n\n if(j= 'A' and j <= 'Z'):\n\n repeat_array[ord(j)-ord('A')] = repeat_array[ord(j)-ord('A')]+1\n \n else:\n\n repeat_array[ord(j)-ord('a')+26] = repeat_array[ord(j)-ord('a')+26]+1\n\n product = 1\n \n for e in repeat_array:\n \n product = product*fac(e)\n\n return product\n\n def fac(n):\n\n if(n==0):\n\n return 1\n\n else:\n\n return n*fac(n-1)\n \n n = len(A)\n \n for i in range(len(A)):\n\n less_than = chars_less_than(i,A)\n\n product_of_repeated_chars =repeat_chars_freq(i,A)\n\n product = less_than*fac(n-i-1)//(product_of_repeated_chars)\n \n rank = rank + product \n\n rank = rank + 1\n return rank % 1000003\n\nprint(findrank('acB'))\n","sub_path":"Arrays/sorted_permutation_rank_string.py","file_name":"sorted_permutation_rank_string.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"441009754","text":"import cyclone.web\nfrom cyclone.web import HTTPError\n# from django.conf import settings\nfrom back import settings\nimport os\nimport simplejson\n\nos.environ['DJANGO_SETTINGS_MODULE'] = 'back.settings'\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom django.utils.encoding import smart_unicode\n\nimport hashlib, urllib\n\nimport tempfile\nimport datetime\nimport decimal\nimport functools\nimport logging\nfrom UserDict import UserDict\n# from checkword import checkword\n\nfrom django.db.models.query import QuerySet\nfrom django.core.exceptions import ObjectDoesNotExist as DoesNotExist, MultipleObjectsReturned\nfrom django.core.exceptions import ValidationError\n\nfrom django.core.cache import cache as user_status_cache\nfrom django.core.files.uploadedfile import TemporaryUploadedFile, InMemoryUploadedFile\n\n\nclass objid(str):\n pass\n\n\nclass dbid(str):\n pass\n\n\nclass ExampleParam(dict):\n def __init__(self, parent, name):\n self['_parent'] = parent\n self['_name'] = name\n\n def __getattr__(self, name):\n if not self.has_key(name):\n self[name] = ExampleParam(self, name)\n return self[name]\n\n def __str__(self):\n if self['_parent']:\n return '%s.%s' % (self['_parent'], self['_name'])\n else:\n return self['_name']\n\n def print_tree(self, pre=''):\n ps = ['%s%s%s' % (pre, k, (lambda vp: vp and ':\\r\\n%s' % vp or '')(v.print_tree(pre + '\\t'))) for k, v in\n self.items() if k not in ('_parent', '_name')]\n if ps:\n return \"%s\" % '\\r\\n'.join(ps)\n else:\n return \"\"\n\n\nex = ExampleParam({}, 'ex')\n\n\nclass ApiDefined(UserDict):\n def __init__(self, name, method, uri, params=[], result=None, need_login=False, need_appkey=False, handler=None,\n module=None, filters=[], description=''):\n UserDict.__init__(self)\n self['name'] = name\n self['method'] = method\n self['module'] = module\n self['uri'] = uri\n self['handler'] = handler\n self['params'] = params\n self['result'] = result\n self['need_login'] = need_login\n self['need_appkey'] = need_appkey\n self['filters'] = filters\n self['description'] = description\n\n def get_handler_name(self):\n return self['handler'].__name__\n\n def doc(self):\n d = '%s\\n%s %s' % (self['name'], self['method'], self['uri'])\n d = d + '\\nname\\trequired\\ttype\\tdefault\\texample\\t\\tdesc'\n d = d + '\\n------------------------------------------------'\n for p in self['params']:\n d = d + '\\n%s\\t%s\\t%s\\t%s\\t%s\\t%s' % (\n p.name, p.required, p.param_type.__name__, p.default, p.display_example(), p.description)\n if self['result']:\n d = d + '\\nResult:\\n%s' % self['result']\n return d\n\n def __getattr__(self, name):\n try:\n return self[name]\n except Exception:\n return \"\"\n\n\nclass Param(UserDict):\n def __init__(self, name, required=False, param_type=str, default=None, example=None, description=\"\", hidden=False):\n UserDict.__init__(self)\n self['name'] = name\n self['required'] = required\n self['param_type'] = param_type\n self['default'] = default\n self['example'] = example\n self['description'] = description\n self['hidden'] = hidden\n\n def display_type(self, _t=None):\n _t = _t or self['param_type']\n if type(_t) in (list, tuple) and _t:\n return '[%s,..]' % self.display_type(_t[0])\n return _t.__name__\n\n def display_example(self):\n if self['hidden']: return ''\n if self['param_type'] is bool:\n return self['example'] and 'true' or 'false'\n else:\n return str(self['example'])\n\n def html_example(self):\n if self['hidden']: return ''\n\n if type(self['example']) is ExampleParam:\n return 'E' \\\n % (self['name'], str(self['example']))\n if self['param_type'] is file:\n return '' % self['name']\n if self['param_type'] is bool:\n return '' % \\\n (self['name'], self['example'] and ' selected' or '', (not self['example']) and ' selected' or '')\n elif self['param_type'] in (str, int, float):\n if type(self['example']) in (list, tuple):\n return '' % (\n self['name'], ''.join(['' % (v, v) for v in self['example']]))\n return self['name'], str(self['example'])\n\n def __getattr__(self, name):\n try:\n return self[name]\n except Exception:\n return \"\"\n\n\nclass ApiHolder(object):\n apis = []\n\n def __init__(self):\n pass\n\n def addapi(self, api):\n api['id'] = len(self.apis) + 1\n self.apis.append(api)\n\n def get_apis(self, name=None, module=None, handler=None):\n all_apis = self.apis\n if name:\n name = name.replace(' ', '_').lower()\n all_apis = filter(lambda api: api.name.lower().replace(' ', '_') == name, all_apis)\n if module:\n all_apis = filter(lambda api: api['module'] == module, all_apis)\n if handler:\n handler = handler.lower()\n all_apis = filter(lambda api: api['handler'].__name__.lower() == handler or api[\n 'handler'].__name__.lower() == '%shandler' % handler,\n all_apis)\n return all_apis\n\n def get_urls(self):\n urls = {}\n for api in self.apis:\n if not urls.has_key(api['uri']):\n urls[api['uri']] = api['handler']\n return [(r'%s$' % uri, handler) for uri, handler in urls.items()]\n\n\napi_manager = ApiHolder()\n\n\ndef api(name, uri, params=[], result=None, filters=[], description=''):\n def wrap(method):\n if not hasattr(method, 'apis'):\n setattr(method, 'apis', [])\n getattr(method, 'apis').append(\n ApiDefined(name, method.__name__.upper(), uri, params, result, module=method.__module__, filters=filters,\n description=description))\n return method\n\n return wrap\n\n\ndef handler(cls):\n for m in [getattr(cls, i) for i in dir(cls) if callable(getattr(cls, i)) and hasattr(getattr(cls, i), 'apis')]:\n method_filters = getattr(m, 'api_filters', None)\n for api in m.apis:\n api['handler'] = cls\n if method_filters:\n for f in method_filters:\n f(api)\n if api['filters']:\n for f in api['filters']:\n f(api)\n api_manager.addapi(api)\n return cls\n\n\ndef ps_filter(api):\n api.params.extend(\n [Param('start', False, int, 0, 0, 'Data Start'), Param('count', False, int, 25, 25, 'Data Count')])\n\n\nclass ApiJSONEncoder(DjangoJSONEncoder):\n def default(self, o):\n if isinstance(o, datetime.datetime):\n return str(o)\n # return dt2ut(o)\n elif isinstance(o, decimal.Decimal):\n return str(o)\n else:\n try:\n return super(ApiJSONEncoder, self).default(o)\n except Exception:\n return smart_unicode(o)\n","sub_path":"src/front/wiapi.py","file_name":"wiapi.py","file_ext":"py","file_size_in_byte":7573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"595360633","text":"import gmodels\r\nimport datetime\r\nimport json\r\nfrom gmodels import MBank\r\nfrom google.appengine.ext import db\r\n\r\n\r\n\r\ndef init_mbank():\r\n mbank = db.get(db.Key.from_path('MBank', 'main'))\r\n if mbank:\r\n return\r\n \r\n mbank = gmodels.MBank(key_name='main')\r\n mbank.balance = 0.0\r\n mbank.put()\r\n \r\n \r\ndef get_user(email):\r\n user = db.get(db.Key.from_path('MBank', 'main', 'MUser', email))\r\n return user\r\n\r\ndef get_recent_record(user):\r\n q = gmodels.BillingRecord.all().ancestor(user).order('-date')\r\n return q.run(limit=20)\r\n \r\n \r\n@db.transactional\r\ndef new_user(name, email):\r\n \r\n user = db.get(db.Key.from_path('MBank', 'main', 'MUser', email))\r\n if user:\r\n raise Exception('user %s already exists' % email)\r\n \r\n mbank = db.get(db.Key.from_path('MBank', 'main'))\r\n muser = gmodels.MUser(name=name, email=email, balance=0.0, parent=mbank, key_name=email)\r\n muser.put()\r\n \r\n\r\n@db.transactional\r\ndef new_bill(amount, raw_bills):\r\n \r\n date = db.datetime.datetime.now()\r\n \r\n # fetch account\r\n key = db.Key.from_path('MBank', 'main')\r\n mbank = db.get(key)\r\n \r\n # fetch users\r\n result = {}\r\n splits = 0\r\n common_amount = amount\r\n for rb in raw_bills:\r\n result[rb.email] = {'count': len(rb.extra_people), \"raw_bill\" : rb}\r\n splits += len(rb.extra_people) + 1\r\n common_amount = common_amount - rb.extra_amount\r\n \r\n key = db.Key.from_path('MBank', 'main', 'MUser', rb.email)\r\n person = db.get(key)\r\n if person is None:\r\n raise Exception(\"cannot find user %s\" % rb.email)\r\n else:\r\n rb.user = person\r\n \r\n \r\n # update mbank\r\n mbank.balance -= amount\r\n mbank.put()\r\n \r\n unit = common_amount / splits\r\n\r\n summary = [] \r\n # update each person\r\n for rb in raw_bills:\r\n uamount = unit * (len(rb.extra_people) + 1) + rb.extra_amount\r\n \r\n rb.user.balance -= uamount\r\n rb.user.put()\r\n \r\n extra = []\r\n if rb.extra_people:\r\n extra.append(\"extra people = \" + ', '.join(rb.extra_people))\r\n if rb.extra_amount:\r\n extra.append(\"extra amount = \" + str(rb.extra_amount))\r\n \r\n br = gmodels.BillingRecord(amount= -uamount,\r\n user=rb.user,\r\n date=date,\r\n balance=rb.user.balance,\r\n extra= ','.join(extra),\r\n parent=rb.user\r\n )\r\n br.put()\r\n result[rb.email]['record'] = br\r\n result[rb.email]['user'] = rb.user\r\n \r\n if extra:\r\n summary.append(\"%s(%s)\" % (rb.user.name, br.extra))\r\n else:\r\n summary.append(rb.user.name)\r\n \r\n \r\n summary.sort()\r\n summary = ', '.join(summary) if summary else \"\"\r\n \r\n sbill = gmodels.SumRecord(balance=mbank.balance, amount=-amount, date=date, details=summary, parent=mbank)\r\n sbill.put()\r\n \r\n return result\r\n\r\n\r\n@db.transactional\r\ndef topup(amount, email):\r\n amount = float(amount)\r\n date = datetime.datetime.now();\r\n \r\n user = db.get(db.Key.from_path('MBank', 'main', 'MUser', email))\r\n \r\n if not user:\r\n raise Exception('cannot find user %s' % email)\r\n \r\n # fetch mbank\r\n mbank = db.get(db.Key.from_path('MBank', 'main'))\r\n mbank.balance += amount\r\n mbank.put()\r\n \r\n old_balance = user.balance\r\n user.balance += amount\r\n \r\n user.put()\r\n \r\n br = gmodels.BillingRecord(amount=amount,\r\n user=user,\r\n date=date,\r\n balance=user.balance,\r\n extra='topup',\r\n parent=user\r\n )\r\n br.put()\r\n \r\n summary = '%s topup %.2f' % (user.name, amount)\r\n sbill = gmodels.SumRecord(balance=mbank.balance, amount=amount, date=date, details=summary, parent=mbank)\r\n sbill.put()\r\n \r\n return (br, old_balance)\r\n \r\n \r\n@db.transactional\r\ndef delete_user(email):\r\n user = db.get(db.Key.from_path('MBank', 'main', 'MUser', email))\r\n if not user:\r\n raise Exception('cannot find user %s' % email)\r\n \r\n date = datetime.datetime.now()\r\n\r\n mbank = db.get(db.Key.from_path('MBank', 'main'))\r\n mbank.balance -= user.balance\r\n mbank.put()\r\n \r\n summary = '%s is removed from system, return balance %.2f' % (user.name, user.balance)\r\n \r\n sbill = gmodels.SumRecord(balance=mbank.balance, amount=-user.balance, date=date, details=summary, parent=mbank)\r\n sbill.put()\r\n \r\n user.delete()\r\n \r\n \r\ndef current_time():\r\n pass\r\n \r\n","sub_path":"logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"312656437","text":"#!/usr/bin/env python3\n\nimport logging\nimport sys\nlogging.debug(str(sys.version_info))\nif sys.version_info[0] < 3 or sys.version_info[1] < 5:\n raise Exception(\"Requires python 3.5+, try module load python/3.6-anaconda-4.4\")\n\n# sample entry looks like:\n#@prefix : .\n#:\n# a logset:LogSet ;\n# dct:title \"Sample NERSC logs\" ;\n# rdfs:label \"nersc-logs-001\" ; # is this necessary/useful?\n# dct:description \"a partial sample of cori log data for testing logset tools\" ;\n# dct:publisher nersc:nersc ;\n# dcat:landingPage \n# dct:contactPoint [ a vcard:Individual ;\n# vcard:fn \"Steve Leak\" ;\n# vcard:email \"sleak@lbl.gov\" ;\n# ] ;\n# dcat:distribution\n# :console-20170906,\n# :console-20170907,\n# :consumer-20170906,\n# :consumer-20170907,\n# :messages-20170906,\n# :messages-20170907 ;\n# .\n\nfrom util import UI, Context, MultiDict, ran_str\nfrom Node import Node\nfrom graph import entities_ns, set_ns_base, the_graph\nimport rdflib\nfrom rdflib.term import URIRef, BNode\n\nclass Agent(Node):\n rdf_class = \"foaf:Organization\"\n rdf_superclass = \"foaf:Agent\"\n getters = {\n 'foaf:name': 'ask', \n 'foaf:page': 'ask' \n }\n required_properties = set(['foaf:name'])\n prompts = {\n 'foaf:name': \"Name of {0}? (generally an organization) \",\n 'foaf:page': \"URL with more information about {0}? \"\n }\n# finder_query = ''' SELECT ?uri (SAMPLE(?name) as ?name) (SAMPLE(?page) as ?page) WHERE {\n# ?uri a ?type .\n# ?type rdfs:subClassOf* foaf:Agent . \n# ?uri foaf:name ?name .\n# OPTIONAL {\n# ?uri foaf:page ?page .\n# }\n# } GROUP BY ?uri\n# '''\n# finder_fields = [ 'uri', 'foaf:name', 'foaf:page' ]\n label_property:str = 'foaf:name'\n label_alternate:str = 'the publisher'\n\n# @property\n# def label(self):\n# #def label(self, context:Context=None) -> str:\n# #return self._properties.one(self.label_property) or 'the publisher'\n# return self.get_one_value(self.label_property) or 'the publisher'\n\n @property\n def uri(self):\n # lazily find uri so there is a chance of deriving a readable one from foaf:name\n if self._uri is None:\n ns = self._namespace or entities_ns()\n self._uri = self.make_uri(self.label_property, ns)\n return self._uri\n\n\nclass Vcard(Node):\n rdf_class = \"vcard:Individual\"\n # FIXME I'm not reading in the vcard graph properly for some reason,\n # so the link between Kind and Individual isn't being found. Sidestep it\n # for now:\n #rdf_superclass = \"vcard:Kind\"\n getters = {\n 'vcard:fn': 'ask', \n 'vcard:email': 'ask'\n }\n required_properties = set(getters.keys())\n prompts = {\n 'vcard:fn': \"Full name of {0}? \",\n 'vcard:email': \"Email address for {0}? \"\n }\n finder_query = ''' SELECT ?uri (SAMPLE(?name) as ?name) (SAMPLE(?email) as ?email) WHERE {\n ?uri a ?type .\n ?type rdfs:subClassOf* vcard:Kind . \n ?uri vcard:fn ?name .\n OPTIONAL {\n ?uri vcard:email ?email .\n }\n } GROUP BY ?uri\n '''\n finder_fields = [ 'uri', 'vcard:fn', 'vcard:email' ]\n label_property:str = 'vcard:fn'\n label_alternate:str = 'contact person'\n\n# #@property\n# #def label(self):\n# def label(self, context:Context=None) -> str:\n# #return self._properties.one(self.label_property) or 'contact person'\n# return self.get_one_value(self.label_property) or 'contact person'\n\n @property\n def uri(self):\n # lazily find uri so there is a chance of deriving a readable one from the label\n if self._uri is None:\n ns = self._namespace or entities_ns()\n self._uri = self.make_uri(self.label_property, ns)\n return self._uri\n\n\n\nclass LogSet(Node):\n\n rdf_class = \"logset:LogSet\"\n\n getters = {\n 'dct:title': 'ask',\n 'dct:description': 'ask',\n 'dct:publisher': 'select',\n 'dct:contactPoint': 'select',\n 'logset:isClosed': 'truefalse',\n 'dcat:landingPage': 'ask',\n 'dcat:distribution': 'skip'\n }\n required_properties = set(['dct:title'])\n\n targets = {\n 'dct:publisher': Agent,\n 'dct:contactPoint': Vcard\n }\n\n # prompts for simple questions system can \"ask\" to get some properties:\n prompts = {\n 'dct:title': \"Give {0} a title (eg \\\"Cori smw logs p0-20170906t151820\\\"): \",\n 'dct:description': \"Please enter short description of {0}: \",\n 'dcat:landingPage': \"URL for information about or access to {0}? (may be blank) \",\n 'dct:publisher': \"Which organization is the publisher for {0}?\",\n 'dct:contactPoint': \"Who is the contact person for {0}?\",\n 'logset:isClosed': \"is this a closed (ie no new data will arrive) dataset? \" }\n label_property:str = 'dct:title'\n label_alternate:str = 'this log set'\n\n def __init__(self, properties:MultiDict = None, **kwargs) -> None: \n super().__init__(properties, **kwargs)\n uri_str = str(self._uri)\n if '#' not in uri_str:\n raise Exception(\"LogSet requires a URI in the form 'namespace#label' {0}\".format(uri_str))\n cut = uri_str.rfind('#')+1\n \n #self._uri = uri\n self.prefix = uri_str[cut:]\n self.namespace = rdflib.Namespace(uri_str[:cut])\n #self._uri = self.namespace.uri\n self.graph.bind(self.prefix, self.namespace)\n #the_graph.bind(self.prefix, self.namespace)\n\n # set a base for where new entities or local dict can be placed:\n cut2 = uri_str[:cut].rfind('/')\n set_ns_base(uri_str[:cut2])\n\n\n @property\n def label(self):\n #def label(self, context:Context=None) -> str:\n #title = self._properties.one('dct:title') or ''\n title = self.get_one_value('dct:title') or ''\n if title: \n # put the title in quotes, if there is one:\n return \"this log set \\\"{0}\\\"\".format(title)\n else:\n return \"this log set\"\n\n","sub_path":"src/LogSet.py","file_name":"LogSet.py","file_ext":"py","file_size_in_byte":6573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"148655689","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sept 23 2018\r\n@author: ALEX\r\n\"\"\"\r\n\r\nimport numpy as np # 引入numpy\r\nimport scipy as sp\r\nimport pylab as pl\r\nfrom scipy.optimize import leastsq # 引入最小二乘函数\r\n\r\n'''\r\n 借助scipy模块中的leastsq函数完成最小二乘拟合回归\r\n'''\r\nn = 9 # 多项式次数\r\n\r\n\r\n# 目标函数\r\ndef real_func(x):\r\n return np.sin(2 * np.pi * x)\r\n\r\n\r\n# 多项式函数\r\ndef fit_func(p, x):\r\n f = np.poly1d(p)\r\n return f(x)\r\n\r\n\r\n# 残差函数\r\ndef residuals_func(p, y, x):\r\n ret = fit_func(p, x) - y\r\n return ret\r\n\r\n\r\nx = np.linspace(0, 1, 9) # 随机选择9个点作为x\r\nx_points = np.linspace(0, 1, 1000) # 画图时需要的连续点\r\n\r\ny0 = real_func(x) # 目标函数\r\ny1 = [np.random.normal(0, 0.1) + y for y in y0] # 添加正太分布噪声后的函数\r\n\r\np_init = np.random.randn(n) # 随机初始化多项式参数\r\n\r\nplsq = leastsq(residuals_func, p_init, args=(y1, x))\r\n\r\nprint('Fitting Parameters: ', plsq[0]) # 输出拟合参数\r\n\r\npl.plot(x_points, real_func(x_points), label='real')\r\npl.plot(x_points, fit_func(plsq[0], x_points), label='fitted curve')\r\npl.plot(x, y1, 'bo', label='with noise')\r\npl.legend()\r\npl.show()\r\n","sub_path":"least square method/leastsq_fitting.py","file_name":"leastsq_fitting.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"437150161","text":"from sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom sqlalchemy import (\n create_engine,\n Table,\n Column,\n Integer,\n String,\n MetaData,\n ForeignKey,\n)\n\nengine = create_engine('sqlite:///resumes.db')\nBase = declarative_base()\n\nperson_interest = Table('person_interest', Base.metadata,\n Column('person_id', Integer, ForeignKey('person.id')),\n Column('interest_id', Integer, ForeignKey('interest.id')),\n)\n\nclass Person(Base):\n __tablename__ = 'person'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n awards = Column(String)\n publications = Column(String)\n certifications = Column(String)\n\n interests = relationship('Interest', secondary=person_interest,\n backref='people')\n\nclass Resume(Base):\n __tablename__ = 'resume'\n id = Column(Integer, primary_key=True)\n title = Column(String)\n summary = Column(String)\n person_id = Column(Integer, ForeignKey('person.id'))\n\nclass School(Base):\n __tablename__ = 'school'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Education(Base):\n __tablename__ = 'education'\n id = Column(Integer, primary_key=True)\n dates = Column(String)\n area_of_study = Column(String)\n person_id = Column(Integer, ForeignKey('person.id'))\n school_id = Column(Integer, ForeignKey('school.id'))\n activities = Column(String)\n\nclass Company(Base):\n __tablename__ = 'company'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Employment(Base):\n __tablename__ = 'employment'\n id = Column(Integer, primary_key=True)\n dates = Column(String)\n position = Column(String)\n summary = Column(String)\n person_id = Column(Integer, ForeignKey('person.id'))\n company_id = Column(Integer, ForeignKey('company.id'))\n\nclass Spoken(Base):\n __tablename__ = 'spoken'\n id = Column(Integer, primary_key=True)\n person_id = Column(Integer, ForeignKey('person.id'))\n language_proficiency_id = Column(Integer, ForeignKey('language_proficiency.id'))\n language_id = Column(Integer, ForeignKey('language.id'))\n\nclass Language(Base):\n __tablename__ = 'language'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass LanguageProficiency(Base):\n __tablename__ = 'language_proficiency'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass PersonSkill(Base):\n __tablename__ = 'person_skill'\n id = Column(Integer, primary_key=True)\n person_id = Column(Integer, ForeignKey('person.id'))\n skill_proficiency_id = Column(Integer, ForeignKey('skill_proficiency.id'))\n skill_id = Column(Integer, ForeignKey('skill.id'))\n\nclass Skill(Base):\n __tablename__ = 'skill'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass SkillProficiency(Base):\n __tablename__ = 'skill_proficiency'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Interest(Base):\n __tablename__ = 'interest'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Award(Base):\n __tablename__ = 'award'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Relationship(Base):\n __tablename__ = 'relationship'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n\nclass Reccomendation(Base):\n __tablename__ = 'reccomendation'\n id = Column(Integer, primary_key=True)\n text = Column(String)\n reccomender_location = Column(String)\n reccomender_name = Column(String)\n person_id = Column(Integer, ForeignKey('person.id'))\n relationship_id = Column(Integer, ForeignKey('relationship.id'))\n\nif __name__ == '__main__':\n Base.metadata.create_all(engine)\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"620093719","text":"from graph import Graph\nfrom util import Queue\n\n\ndef earliest_ancestor(ancestors, starting_node):\n\n # instantiate a graph and a queue\n gr = Graph()\n q = Queue()\n\n # instantiate the starting node as the first vertex and first item in graph + queue\n gr.add_vertex(starting_node)\n q.enqueue(starting_node)\n\n # loop through the queue\n while q.size() > 0:\n # grab the item off the front of the queue\n node = q.dequeue()\n # loop through the relationships\n for relationship in ancestors:\n # if the child in the relationship is the current node\n if relationship[1] == node:\n # grab the parent, enqueue the parent, add the parent as a vertex,\n # and add an edge between the node and parent (so it's in reverse order with the youngest first)\n parent = relationship[0]\n q.enqueue(parent)\n gr.add_vertex(parent)\n gr.add_edge(node, parent)\n\n # get the tree (returns a tuple of relatives + index in family tree as a signature)\n raw_tree = gr.dft(starting_node)\n # filter out the start node\n tree = list(filter(lambda x: x[0] != starting_node, raw_tree))\n # if the node has no ancestors, return -1\n if len(tree) == 0:\n return -1\n # otherwise mark the largest/oldest index, filter our for oldest ancestors,\n # and return the smallest id from the list of oldest ancestors\n else:\n oldest = 0\n for item in tree:\n if item[1] > oldest:\n oldest = item[1]\n ancestors = []\n for family_member in tree:\n if family_member[1] == oldest:\n ancestors.append(family_member[0])\n return sorted(ancestors)[0]\n","sub_path":"projects/ancestor/ancestor.py","file_name":"ancestor.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"76643844","text":"from __future__ import print_function\nfrom sandpile import Sandpile, grid_edges\nimport networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef score(G):\n k = 'current'\n loss = sum([G.node[n0]['current'] == G.node[n1]['current'] for n0,n1 in G.edges()\n if n1 != 'sink'])\n return loss\n\n\ndef main(n,n_iter,colors):\n\n G = Sandpile(grid_edges(n))\n s = {i:6 for i in range(n**2)}\n G.set_state(s)\n history = G.stabilize(hist=False)\n np.random.seed(234)\n initial = np.random.choice(colors,size=n**2,replace=True)\n for node in range(n**2):\n if node != 'sink':\n G.node[node]['options'] = colors\n G.node[node]['current'] = str(initial[node])\n\n loss = score(G)\n loss0 = loss\n # print(initial)\n # for i in range(n):\n # print(initial[i*n:i*n+n])\n # print(loss)\n for i_ in range(n_iter):\n if i_ % 10000 == 0:\n print(i_,loss)\n node = np.random.choice(range(n**2),size=1)\n fires = G.add_chip(node[0])\n if fires != {}:\n # print('#'*80)\n # print(fires)\n for v in fires.iterkeys():\n G.node[v]['previous'] = G.node[v]['current']\n G.node[v]['current'] = np.random.choice([x for x in G.node[v]['options'] if x != G.node[v]['previous']],size=1)[0]\n final = [G.node[node]['current'] for node in range(n**2)]\n # for i in range(n):\n # print(final[i*n:i*n+n])\n loss1 = score(G)\n # print(loss1)\n if loss1 == 0:\n print('got to 0 after {}!'.format(i_))\n loss = loss1\n break\n elif loss1 <= loss:\n loss = loss1\n pass\n else:\n # print('reverting')\n for v in fires.iterkeys():\n G.node[v]['current'] = G.node[v]['previous']\n final = [G.node[node]['current'] for node in range(n**2)]\n print('&'*80)\n print('started',loss0)\n for i in range(n):\n print(initial[i*n:i*n+n])\n print(loss)\n print('ended',loss)\n for i in range(n):\n print(final[i*n:i*n+n])\n\n # print('stable:',G.is_stable())\n # print(G.get_state())\n # print(history)\n\n # np.random.seed(987)\n # nodes = np.random.randint(0,n**2,size=n_iter,dtype=int)\n # fires = map(G.add_chip,nodes)\n # fire_lengths = map(lambda x: len(x),fires)\n # plt.figure()\n # plt.hist(fire_lengths,bins=25)\n #\n # plt.savefig('soc_plots/power_rule.png')\n # plt.close()\n # idxs = np.random.choice([i for i,x in enumerate(fire_lengths) if x > 0],\n # size=20,replace=False)\n # # idxs.sort()\n # for idx in idxs:\n # plt.figure()\n # toplot = np.zeros((n,n))\n # h = map(lambda x: (int(x/n),x%n),fires[idx].keys())\n # for r,c in h:\n # toplot[r,c]=1\n # plt.imshow(toplot)\n # plt.savefig('soc_plots/avalanche_{}.png'.format(idx))\n # plt.close()\n\n\n\n\nif __name__ == '__main__':\n colors = ['red','green',]#'blue','black','yellow']#,'magenta','cyan']\n main(n=20,n_iter=1000000,colors=colors)\n","sub_path":"graph_coloring.py","file_name":"graph_coloring.py","file_ext":"py","file_size_in_byte":3161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"577554447","text":"import threading\r\nfrom graia.application.event.mirai import *\r\nfrom graia.broadcast import Broadcast, BaseEvent, ExecutionStop\r\nfrom graia.application import GraiaMiraiApplication, Session, GroupMessage\r\nfrom graia.application.message.chain import MessageChain\r\nimport asyncio\r\nfrom graia.application.message.elements.internal import *\r\nfrom graia.application.friend import *\r\nfrom graia.application.group import Group\r\nfrom graia.broadcast.builtin.decoraters import Depend\r\nfrom graia.broadcast.entities.event import EventMeta\r\nfrom config import *\r\nfrom pulgin import *\r\nfrom MsgObj import Msg, asSendable_creat\r\n\r\n'''\r\n各文件说明：\r\n bot.py 运行的main文件，包括消息指令入口\r\n config.py bot运行所需要的配置，端口号，QQ号，黑名单，白名单，超级管理员账号等，配置参见Graia文档\r\n MsgObj.py 独立封装的message消息类，便于对消息数据进行保存和调用\r\n pulgin.py bot所需要到的一些函数的封装\r\n'''\r\n\r\n'''\r\n监听新人入群并欢迎\r\n'''\r\n\r\n\r\n@bcc.receiver(\"MemberJoinEvent\")\r\nasync def MemberJoin(event: MemberJoinEvent):\r\n group = event.member.group\r\n if group.id in WelcomeScence:\r\n talk = WelcomeScence[group.id]\r\n else:\r\n talk = \"欢迎小可爱来到本群\"\r\n await app.sendGroupMessage(group, MessageChain.create([\r\n Plain(talk),\r\n At(event.member.id)\r\n ]))\r\n\r\n\r\n'''\r\n修改群迎新词\r\n'''\r\n\r\n\r\n@bcc.receiver('GroupMessage', headless_decoraters=[\r\n Depend(judge_depend_target)\r\n])\r\nasync def changeWelcome(message: GroupMessage, group: Group):\r\n if not parser(message, \"修改迎新词 \"): return\r\n if not message.messageChain.has(Plain): return\r\n plain = message.messageChain.get(Plain)\r\n txt = plain[0].text.replace(\"修改迎新词 \", \"\")\r\n if len(txt) > 0:\r\n WelcomeScence[group.id] = txt\r\n status = \"修改成功\"\r\n else:\r\n status = \"修改失败，不合法！\"\r\n await app.sendGroupMessage(group, MessageChain.create([Plain(status)]))\r\n\r\n\r\n'''\r\n问答模块，集合了\r\n 添加问题\r\n 修改问题\r\n 会话管理\r\n 删除问题\r\n 问答功能\r\n（注：��部分代码十分恶心，请谨慎阅读，之后会重构）\r\n'''\r\n\r\n\r\nasync def FQA(message: GroupMessage, group: Group) -> bool:\r\n if not (message.messageChain.has(At) or message.messageChain.has(Plain)): return False\r\n msg = Msg(message)\r\n msgChain = message.messageChain\r\n # 首先对消息进行问答解析\r\n Question = msg.txt.strip()\r\n if Question == '列表' and message.messageChain.has(At):\r\n await FQA_list(message, group)\r\n del msg\r\n return False\r\n at = msgChain.get(At)[0].target if msgChain.has(At) else 0\r\n tempQ = search(Question, group)\r\n if tempQ is not None:\r\n send_msg = tempQ.get_msg_graia(msgChain)\r\n else:\r\n if at == BOTQQ:\r\n send_msg = asSendable_creat(list=[\r\n Plain(\"没有找到这个问题，请等待学长学姐来回答或回复“列表”查看已有问题\")\r\n ], MC=msgChain)\r\n else:\r\n send_msg = None\r\n if send_msg is not None:\r\n await app.sendGroupMessage(group, send_msg)\r\n del msg\r\n return True\r\n del msg\r\n return False\r\n\r\n\r\n@bcc.receiver(\"GroupMessage\")\r\nasync def group_message_handler(app: GraiaMiraiApplication, message: GroupMessage, group: Group):\r\n if await FQA(message,group):return\r\n msg = Msg(message)\r\n Question = message.messageChain.get(Plain)[0].text if message.messageChain.has(Plain) else None\r\n if Question is None: return\r\n hasSession = temp_talk.get(msg.user_id)\r\n if hasSession:\r\n if await session_manager(message, group): return\r\n if parser(message, \"添加问题 \"):\r\n # 创建添加问题的新会话\r\n Question = Question.replace(\"添加问题\", \"\").strip()\r\n if not hasSession:\r\n add_temp_talk(msg.user_id, 'Add', True, Question)\r\n await AddQA(message, group)\r\n del msg\r\n return\r\n if parser(message, \"修改问题 \"):\r\n # 创建修改问题的新会话\r\n Question = Question.replace(\"修改问题\", \"\").strip()\r\n if not hasSession:\r\n add_temp_talk(msg.user_id, 'Change', True, Question)\r\n await change(group=group,GM=message)\r\n del msg\r\n return\r\n if parser(message, \"删除问题 \"):\r\n # 删除问题\r\n Question = Question.replace(\"删除问题\", \"\").strip()\r\n isdeleteOK = \"删除成功\" if deleteQA(Question, group) else \"不存在这个问题\"\r\n await app.sendGroupMessage(group, message.messageChain.create([\r\n Plain(isdeleteOK)\r\n ]))\r\n await saveQA()\r\n del msg\r\n return\r\n\r\n\r\nif __name__ == '__main__':\r\n # 初始化GroupQA\r\n # loop.run_until_complete(Compatible_old_index())\r\n loop.run_until_complete(ReadQA())\r\n\r\n app.launch_blocking()\r\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"546190021","text":"\n\nfrom xai.brain.wordbase.nouns._riser import _RISER\n\n#calss header\nclass _RISERS(_RISER, ):\n\tdef __init__(self,): \n\t\t_RISER.__init__(self)\n\t\tself.name = \"RISERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"riser\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_risers.py","file_name":"_risers.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"510864899","text":"import asyncio\n\nimport pytest\n\nfrom aristaeus.dispatcher import Dispatcher\n\n\nclass Registry:\n _registry: list = []\n\n @classmethod\n def call(cls, name):\n cls._registry.append(name)\n\n @classmethod\n def retrieve(cls):\n output = cls._registry\n cls._registry = []\n return output\n\n\n@Dispatcher.subscribe(\"test.hello\")\ndef coucou():\n Registry.call(\"hello\")\n\n\n@Dispatcher.subscribe(\"test.world\")\nasync def world(cool):\n Registry.call(cool)\n\n\n@Dispatcher.subscribe(\"test.hello\")\nasync def olleh():\n Registry.call(\"olleh\")\n\n\nasync def test_dispatched_nominal():\n Dispatcher.init()\n Dispatcher.publish(\"test.hello\")\n Dispatcher.publish(\"test.world\", cool=True)\n\n await asyncio.sleep(0.1)\n assert Registry.retrieve() == [\"hello\", \"olleh\", True]\n\n\nasync def test_dispatcher__stop():\n Dispatcher.stop()\n Dispatcher.publish(\"test.hello\")\n Dispatcher.publish(\"test.world\", cool=True)\n\n await asyncio.sleep(0.1)\n assert Registry.retrieve() == []\n","sub_path":"aristaeus/tests/unit/test_dispatcher.py","file_name":"test_dispatcher.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"374445000","text":"import argparse\nimport os\nfrom tqdm import tqdm\nimport torch\n\nimport numpy as np\nfrom torch.utils.data import DataLoader\nfrom sklearn.svm import LinearSVC\nfrom sklearn.decomposition import PCA\nfrom sklearn.decomposition import TruncatedSVD\n\nimport cluster\nimport train_func as tf\nimport utils\n\ndef calc_acc(test_features, test_labels):\n _, test_pred = torch.max(test_features, 1)\n # test_pred = test_pred.values.detach()\n acc = utils.compute_accuracy(test_pred.numpy(), test_labels.numpy())\n print(\"Test Acc: {}\".format(acc))\n return acc\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Evaluation for Sequential Learning with CE')\n parser.add_argument('--model_dir', type=str, help='base directory for saving PyTorch model.')\n parser.add_argument('--epoch', type=int, default=None, help='which epoch for evaluation')\n parser.add_argument('--label_batch', type=int, default=None, help='which label batch for evaluation')\n parser.add_argument('--cpb', type=int, default=10, help='number of classes in each learning batch (default: 10)')\n \n parser.add_argument('--save', action='store_true', help='save labels')\n parser.add_argument('--data_dir', default='./data/', help='path to dataset')\n args = parser.parse_args()\n\n print(\"evaluate using label_batch: {}\".format(args.label_batch))\n\n params = utils.load_params(args.model_dir)\n # get train features and labels\n train_transforms = tf.load_transforms('test')\n trainset = tf.load_trainset(params['data'], train_transforms, train=True, path=args.data_dir)\n if 'lcr' in params.keys(): # supervised corruption case\n trainset = tf.corrupt_labels(trainset, params['lcr'], params['lcs'])\n new_labels = trainset.targets\n assert (trainset.num_classes % args.cpb == 0),\"Number of classes not divisible by cpb\"\n ## load model\n net, epoch = tf.load_checkpoint_ce(args.model_dir, trainset.num_classes, args.epoch, eval_=True, label_batch_id=args.label_batch)\n net = net.cuda().eval()\n \n classes = np.unique(trainset.targets)\n class_batch_num = trainset.num_classes//args.cpb\n class_batch_list = classes.reshape(class_batch_num,args.cpb)\n\n # get test features and labels\n test_transforms = tf.load_transforms('test')\n testset = tf.load_trainset(params['data'], test_transforms, train=False)\n subtestset = tf.get_subset(class_batch_list[0,:],testset)\n testloader = DataLoader(subtestset, batch_size=200)\n test_features, test_labels = tf.get_features(net, testloader)\n\n calc_acc(test_features, test_labels)\n\n\n","sub_path":"evaluate_seq_ce.py","file_name":"evaluate_seq_ce.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"260539745","text":"# Significant digit calculator\n\"\"\"\nRules\n+- Round to smallest decimal place\n*/ Round to smallest number of significant digits\nFinding siginificant digits:\nLook for the first non-zero digit from the left. Count all the digits right of that. Here are some examples:\n3.00040m has 6 sig-digs\n0.0060m has 2\n0.5000055m has 7\n2018.340m has 7\n3.000 * 4.00 = 12.0\n12 / 4.0 = 3.0\nRules differ for +-:\n4 + 15 = 19\n54 - 50 = 04 or 4\n4.00501 + 5.03 = 9.04\n\"\"\"\n\n#Definitions\n\n\ndef length(number):\n number = list(str(number))\n while True:\n if \".\" in number:\n number.remove(\".\")\n elif number[0] == \"0\":\n number.remove(number[0])\n else:\n return len(number)\n\n \ndef decimal(number):\n number = list(str(number))\n decCount = 0\n if \".\" in number:\n while number[0]!= \".\":\n number.remove(number[0])\n number.remove(number[0])\n while len(number) > 0:\n number.remove(number[0])\n decCount += 1\n return decCount\n else:\n return 0\n\n\ndef sci_note(number): #Places decimal right after first number\n number = list(str(number))\n exponent = -1 #Records the exponent for scientific notation\n num = number.copy() #Stores original list\n note_Log = {} \n i = 0\n if \".\" in number: #For floats\n while num[0] == \"0\": #Removes all frontal zeros\n num.remove(number[0])\n number.remove(number[0])\n if num[0] == \".\": #Takes out decimal\n num.remove(number[0])\n number.remove(number[0]) \n while num[0] == \"0\": #Removes frontal zeros after decimal for negative exponents\n if i == 1: # --------------------------------------------------------------I don't understand this part, i will never equal to 1 in the part\n exponent = 0 #Resets exponent\n i = 1\n num.remove(number[0])\n number.remove(number[0])\n exponent -= 1 #Negative exps\n \n while number[0] != \".\" and \".\" in number: #Find required positive exponent\n number.remove(number[0])\n exponent += 1\n if \".\" in num: #Converts to scientific number\n num.remove(\".\")\n num.insert(1, \".\")\n num = \"\".join(num)\n \n note_Log = {0: num, 1: exponent}#Final\n \n else: #For integral numbers\n while number[0] == \"0\": #Removes frontal zeros\n number.remove(number[0])\n num.remove(num[0])\n while i < len(num): #Finds total sig digs in list, for exponent value\n number.remove(number[0])\n i += 1 #i records iteration count\n exponent += 1\n num.insert(1, \".\")\n num = \"\".join(num)\n note_Log = {0: num, 1: exponent}#Final\n\n return str(note_Log[0]) + \" * 10^\" + str(note_Log[1]) #Returns in scientific notation\n\n\ndef rnd_sci_note(number, sig): #Creates scientific notation as well. Input only floats\n sig = int(sig) - 1\n \n note = sci_note(number) #Finds and rounds number\n copy = note\n exp = []\n note = note.split(\" * \")\n num = float(note[0])\n num = list(str(round(num, sig)))\n if \".\" in num: \n sig += 2\n\n copy = list(copy[::-1]) #Finds and stores exponent value\n while copy[0] != '^':\n exp.insert(0, copy[0])\n copy.remove(copy[0])\n exp = \"\".join(exp)\n exp_num = int(exp)\n\n while len(num) < sig: #Adds zeros for proper sig digs\n num.append(\"0\")\n \n num_str = \"\".join(num) #In case of rounding up, so 10 * 10^3 == 1 * 10^4\n if \"10.\" in num_str:\n num.remove(num[1])\n num.insert(2, \"0\")\n \n exp_num += 1\n copy = copy[::-1]\n copy.append(str(exp_num))\n copy = \"\".join(copy)\n copy = copy.split(\" * \")\n \n num = \"\".join(num)\n copy.remove(copy[0]) \n copy.insert(0, num)\n rnd = \" * \".join(copy)\n return rnd\n \n num = \"\".join(num)\n note.remove(note[0]) #Creates string with rounded number\n note.insert(0, num)\n rnd = \" * \".join(note)\n\n return rnd\n'''\n\ndef rnd_Dec(number, dec):\n dec = int(dec)\n number = list(str(number))\n org = number\n wholeList = []\n length = 0\n while True:\n if number[0] != \".\":\n wholeList = wholeList.extend(number[0])\n number.remove(number[0])\n elif number[0] == \".\":\n wholeList = wholeList.extend(number[0])\n number.remove(number[0])\n if int(number[dec]) >= 5:\n if int(number[dec - 1]) <= 8:\n number.remove(number[dec-1]) int(number[dec-1]) + 1 #Start off here\n\n\n\ndef rnd_sig(number, sig)\n number = list(str(number))\n\n\nsigDig_Log = {}\ndec_Log = {}\nop_Log = {}\nanswer_Log = \"Some sort of Error: No answer found\"\n\n\n#Processing\n\n\nuser_input = input('First number: ')\n\nsigDig_Log[0] = length(user_input)\ndec_Log[0] = decimal(user_input)\n\nsigDig = sigDig_Log[0]\ndec1 = dec_Log[0]\n\n\n\n#Final print\n\n\n\nif sigDig != 1 and dec1 != 1:\n print(\"There are \" + str(sigDig) + \" significant digits\" +\" and \" + str(dec1) + \" decimal places here\")\nelif sigDig == 1 and dec1 != 1:\n print(\"There is \" + str(sigDig) + \" significant digit\" +\" and \" + str(dec1) + \" decimal places here\")\nelif sigDig != 1 and dec1 == 1:\n print(\"There are \" + str(sigDig) + \" significant digits\" +\" and \" + str(dec1) + \" decimal place here\")\nelif sigDig == 1 and dec1 == 1:\n print(\"There is \" + str(sigDig) + \" significant digit\" +\" and \" + str(dec1) + \" decimal place here\")\nelse:\n print(\"Your values are invalid. Make sure you only enter numerical inputs!\")\n\n'''\n\n\n\n","sub_path":"Vova's Calculator.py","file_name":"Vova's Calculator.py","file_ext":"py","file_size_in_byte":5995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"1346920","text":"from keras.engine.topology import Layer\n\nimport numpy as np\n\nimport tensorflow as tf\n\nclass MaxPooling2D(Layer):\n def __init__(self, pool_size = 2, stride = None, padding = 'VALID', **kwargs):\n self.pool_size = pool_size\n assert(isinstance(self.pool_size, int))\n self.stride = stride\n if self.stride is None:\n self.stride = self.pool_size\n assert(isinstance(self.stride, int))\n self.padding = padding\n assert(padding in ['VALID', 'SAME'])\n super(MaxPooling2D, self).__init__(**kwargs)\n\n def build(self, input_shape):\n super(MaxPooling2D, self).build(input_shape)\n\n def call(self, inp):\n out, pos = tf.nn.max_pool_with_argmax(inp, \n ksize = [1, self.pool_size, self.pool_size, 1],\n strides = [1, self.stride, self.stride, 1],\n padding = self.padding)\n return [out, pos]\n\n def compute_output_shape(self, input_shape):\n output_shape = list(input_shape)\n if self.padding == 'VALID':\n output_shape[1] = output_shape[1] - self.pool_size + 1\n output_shape[2] = output_shape[2] - self.pool_size + 1\n output_shape[1] = (output_shape[1] + self.stride - 1) // self.stride\n output_shape[2] = (output_shape[2] + self.stride - 1) // self.stride\n output_shape = tuple(output_shape)\n return [output_shape, output_shape]\n\nclass UndoMaxPooling2D(Layer):\n def __init__(self, out_shape, **kwargs):\n self.out_shape = out_shape\n assert(isinstance(self.out_shape, tuple))\n assert(len(self.out_shape) == 4)\n super(UndoMaxPooling2D, self).__init__(**kwargs)\n\n def build(self, input_shape):\n super(UndoMaxPooling2D, self).build(input_shape)\n\n def call(self, inp):\n x, pos = inp\n pos = tf.cast(pos, dtype = tf.int32)\n x = tf.reshape(x, [-1])\n pos = tf.reshape(pos, [-1])\n out = tf.Variable(tf.zeros(np.prod(self.out_shape)))\n out = tf.scatter_update(out, pos, x)\n return tf.reshape(out, self.out_shape)\n\n def compute_output_shape(self, input_shape):\n return self.out_shape","sub_path":"code/pool_unpool.py","file_name":"pool_unpool.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"240354080","text":"# -*- coding: utf-8 -*-\n\n# Standard libraries\nimport os\n\n# Related 3rd party libraries\nimport cv2 as cv\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\n\n# morphological\nfrom scipy.ndimage.morphology import binary_fill_holes\nfrom skimage.morphology import binary_erosion, disk, opening, closing\n\n# Local libraries\n#import src.utils as ut\n#from src import weeks as w\nimport src.utils as ut\nimport utilsBG as bg\n\nROOT_DIR = os.path.dirname(\n os.path.dirname(\n os.path.abspath(__file__)))\nOUTPUT_DIR = os.path.join(ROOT_DIR, 'output')\nSRC_DIR = os.path.join(ROOT_DIR, 'src')\nDATA_DIR = os.path.join(ROOT_DIR, 'data')\n\n# Some constant for the script\n# Precentage of images for training\nN = 0.10\nGT = 'on'\n# Number of Channels\nDIM = 3\n\n# Color_space: in the cv format OR none\n# cv.COLOR_BGR2HSV\nCOLOR_SPACE = None\n\n# If we want to select only a few channels from the color space\n# Only valid if it isnt Greyscale\nCOLOR_CHANNELS = []\n\n# refine mask with morphological filters\n# Morphology:\nF_MORPH = True\n\n\n# Connected components:\nAREA_MIN = None\nAREA_MAX = None\nFF_MIN = None\nFF_MAX = None\nFR_MIN = None\nPLOT_BBOX = False\n\nPLOT_FLAG = False\n\nIMG_SHAPE = (1080, 1920)\n\nthreshold = 0.5 # IoU Threshold\n\nADAPTIVE = True\np=0.5\n\nEXP_NAME = '{}GT_N{}_DIM{}'.format(GT, N, DIM)\n\nif __name__ == '__main__':\n \"\"\"\n Script to compute the background and foreground of a sequence\n of frames using a Gaussian distribution method.\n\n This script uses the 100*N % first frames for training and the\n rest for testing.\n\n Meaningful variables are defined at the beginning of this script.\n \"\"\"\n # Estimating on 25% of the video frame\n # - Estimation of the background without consideration of the foreground in the gt.txt\n # - Estimation \" \" with respect to the BBox - ignoring them from the calculation\n\n # Set useful directories\n frames_dir = os.path.join(ROOT_DIR, 'frames')\n results_dir = os.path.join(OUTPUT_DIR, 'week2', 'task1', EXP_NAME)\n # Ground truth file path\n gt_file = os.path.join(ROOT_DIR,\n 'datasets', 'AICity_data', 'train', 'S03',\n 'c010', 'gt', 'gt.txt')\n\n # Create folders if they don't exist\n if not os.path.isdir(results_dir):\n os.mkdir(results_dir)\n\n # Get file paths for each of the frames and sort them according\n # to the frame number\n frame_paths = ut.get_files_from_dir2(frames_dir, ext='.jpg')\n frame_paths.sort(key=ut.natural_keys)\n\n # Total number of frames\n num_frames = len(frame_paths)\n\n # Get the the images for training\n num_frames_test = int(num_frames * N)\n\n # Separate frames for training and testing\n train_frames = frame_paths[:num_frames_test]\n test_frames = frame_paths[num_frames_test:num_frames_test+10]\n\n # Print useful information\n print(\"Total number of frames : {}\".format(num_frames))\n print(\"Number of frames for training: {}\".format(len(train_frames)))\n print(\"Number of frames for testing : {}\".format(len(test_frames)))\n\n\n \"\"\"\n I. Training\n \"\"\"\n # Model numpy files\n mu_file = os.path.join(results_dir, 'mu.npy')\n std_file = os.path.join(results_dir, 'std.npy')\n\n\n\n # If the files exist, load the values. If not, compute them\n if os.path.isfile(mu_file):\n mu_bg = np.load(mu_file)\n std_bg = np.load(std_file)\n else:\n print('Training Background .....')\n if GT=='no':\n mu_bg, std_bg = bg.getGauss_bg(train_frames,\n D=DIM,\n gt_file=None,\n color_space=COLOR_SPACE,\n color_channels= COLOR_CHANNELS)\n else:\n mu_bg, std_bg = bg.getGauss_bg(train_frames,\n D=DIM,\n gt_file=gt_file,\n color_space=COLOR_SPACE,\n color_channels= COLOR_CHANNELS)\n\n # Save the model of the specific exp\n #np.save(output_dir+output_subdir+exp_name+'_mu.npy',muBG)\n #np.save(output_dir+output_subdir+exp_name+'_std.npy',stdBG)\n\n # Plot the mean and the standard deviation computed and save it\n\n if PLOT_FLAG:\n fig = plt.figure(1, figsize=(6, 8))\n ax1 = plt.subplot(211)\n ax2 = plt.subplot(212)\n if DIM == 3:\n ax1.imshow(mu_bg, vmin=0, vmax=255)\n ax2.imshow(std_bg, vmin=0, vmax=255)\n elif DIM==2:\n s = np.shape(mu_bg)\n mu_bg2 = np.dstack((mu_bg,np.zeros((s[0],s[1]))))\n std_bg2 = np.dstack((std_bg,np.zeros((s[0],s[1]))))\n\n ax1.imshow(mu_bg2,vmin=0,vmax=255)\n ax2.imshow(std_bg2,vmin=0,vmax=255)\n else:\n ax1.imshow(mu_bg, cmap='gray')\n ax2.imshow(std_bg, cmap='gray')\n ax1.set_title(\n \"Mean background model over {} frames\".format(len(train_frames)))\n ax2.set_title(\"Standard noise backgrond model\")\n plt.savefig(os.path.join(results_dir, 'mean_std_testing.png'))\n\n\n\n \"\"\"\n II. Testing\n \"\"\"\n\n\n # Size of images\n s = np.shape(ut.getImg_D(test_frames[1],D=DIM,color_space = COLOR_SPACE))\n # Get bounding boxes from ground truth\n bboxes_gt = ut.get_bboxes_from_MOTChallenge(gt_file)\n # Threshold to create different masks\n alphas = np.linspace(2, 10, 6)\n\n fscore_tot = []\n iou_tot = []\n map_tot = []\n bboxTP_tot = 0\n bboxFN_tot = 0\n bboxFP_tot = 0\n precision = []\n recall = []\n fsc = []\n # Loop on all the alphas\n\n print('Testing Background .....')\n for alpha in alphas:\n # Get image size of the frames\n #frame_img = cv.imread(test_frames[1], color_flag)\n #loc = 1\n print('alpha ={} .....'.format(alpha))\n\n # Iterate over testing frames to choose one with bounding boxes\n for test_frame in test_frames:\n # Get frame ID from frame filename\n\n frm = ut.frameIdfrom_filename(test_frame)\n\n # Get mask and list of bounding boxes from the\n _, cbbox = ut.getbboxmask(bboxes_gt, frm, (s[0],s[1]))\n # get current frame in the experiment format\n I = ut.getImg_D(test_frame,D=DIM,color_space = COLOR_SPACE,color_channels= COLOR_CHANNELS)\n if DIM ==1:\n I=np.squeeze(I,axis=2)\n # get the mask\n map_bg,mu_bg, std_bg = bg.get_BG(mu_bg, std_bg,I,th=alpha,adaptive =ADAPTIVE, p=p)\n map_bg = bg.foreground_from_GBGmodel(mu_bg, std_bg, I, th=alpha)\n\n # refine mask with morphological filter\n if F_MORPH:\n kernel = np.ones((11, 11))\n\n morp_masks = binary_fill_holes(closing(map_bg, kernel))\n morp_masks = binary_fill_holes(opening(morp_masks, kernel))\n\n map_bg = morp_masks\n # Get BBox from mask\n bboxes_in_img = bg.connected_components(map_bg, area_min=AREA_MIN, area_max=AREA_MAX,\n ff_min=FF_MIN, ff_max=FF_MAX, fr_min=FR_MIN, plot=PLOT_BBOX)\n\n\n # Get mesurments for current frame\n fscore, iou, map, bboxTP, bboxFN, bboxFP = bg.compute_metrics_general(cbbox, bboxes_in_img,\n k = 5,\n iou_thresh = 0.5)\n\n\n # collect of the measures\n fscore_tot.append(fscore)\n iou_tot.append(iou)\n map_tot.append(map)\n\n bboxTP_tot += bboxTP\n bboxFN_tot += bboxFN\n bboxFP_tot += bboxFP\n\n\n # Precision -Recall of this Experiment\n pr = ut.precision(bboxTP_tot, bboxFP_tot)\n r = ut.recall(bboxTP_tot, bboxFN_tot)\n precision.append(pr )\n recall.append( r)\n fsc.append( ut.fscore(bboxTP_tot, bboxFP_tot, bboxFN_tot))\n print('for alpha : {}'.format(alpha))\n print('pr : {}, recall: {}'.format(pr,r))\n\n # Compute the mAP over the precision and Recall\n\n mAp = ut.map_precision_recall(precision,recall)\n print('mAP : {}'.format(mAp))\n # If there are bounding boxes in the ground truth\n #if any(cbbox):\n # check if it is a good example for ploting:\n #if PLOT_FLAG:\n\n\n # Plot different thresholds\n #fig, axs = plt.subplots(2, 3, figsize=(15, 6), facecolor='w', edgecolor='g')\n #fig.subplots_adjust(hspace=.5, wspace=.01)\n\n # axs = axs.ravel()\n # axs[0].imshow(frame_img, cmap='gray')\n # axs[0].set_title(os.path.basename(test_frame))\n #\n # # Iterate over each bounding box in the ground truth and add them\n # # to the image\n # for bbox in cbbox:\n # # Draw rectangle in the image\n # rect = patches.Rectangle((bbox[0], bbox[2]),\n # bbox[1] - bbox[0],\n # bbox[3] - bbox[2],\n # linewidth=1, edgecolor='r', facecolor='none')\n # # Add the patch to the Axes\n # axs[0].add_patch(rect)\n # axs[1].imshow(fore_mask, cmap='gray')\n # axs[1].set_title('GT map')\n\n # Plot the mask for the different thresholds\n # i = 2\n #\n #\n # axs[i].imshow(map_bg, cmap='gray')\n # axs[i].set_title(\"th = {}\".format(alpha))\n # i += 1\n #\n # plt.savefig(os.path.join(results_dir, 'thresholds.png'))\n # print(\"Done!\")\n","sub_path":"src/organize_code/main_task.py","file_name":"main_task.py","file_ext":"py","file_size_in_byte":9588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"257107372","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n#\n# 训练数据图像\n#\n\n\ndef add_layer(inputs, in_size, out_size, activation_function=None):\n Weights = tf.Variable(tf.random_normal([in_size, out_size]))\n biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n W_x_plus_b = tf.matmul(inputs, Weights) + biases\n if activation_function is None:\n outputs = W_x_plus_b\n else:\n outputs = activation_function(W_x_plus_b)\n return outputs\n\n\n# 数据集\n# linspace 指定的间隔内返回均匀间隔的数字\n# newaxis 给数组增加维度，与:组合使用\nx_data = np.linspace(-1, 1, 300)[:, np.newaxis]\nnoise = np.random.normal(0, 0.05, x_data.shape)\ny_data = np.square(x_data) - 0.5 + noise\n\nx_s = tf.placeholder(tf.float32, [None, 1])\ny_s = tf.placeholder(tf.float32, [None, 1])\n\nl1 = add_layer(x_s, 1, 10, activation_function=tf.nn.relu)\nprediction = add_layer(l1, 10, 1, activation_function=None)\n# reduction_indices 坍塌维度([0]:行坍塌；[1]:列坍塌；[0,1]:先按行，再按列)\nloss = tf.reduce_mean(tf.reduce_sum(tf.square(y_s - prediction), reduction_indices=[1]))\ntrain_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)\n\ninit = tf.global_variables_initializer()\n\n# plot 绘制图形\nfig = plt.figure()\n# 画布分割与MATLAB中的subplot一致\nax = fig.add_subplot(1, 1, 1)\n# scatter绘制散点\nax.scatter(x_data, y_data)\n# 动态显示图片\nplt.ion()\nplt.show()\nwith tf.Session() as sess:\n sess.run(init)\n for i in range(1000):\n sess.run(train_step, feed_dict={x_s: x_data, y_s: y_data})\n if i % 50 == 0:\n try:\n ax.lines.remove(lines[0])\n except Exception:\n pass\n prediction_value = sess.run(prediction, feed_dict={x_s: x_data})\n lines = ax.plot(x_data, prediction_value, 'r-', lw=5)\n # loss_value = sess.run(loss, feed_dict={x_s: x_data})\n # lines = ax.plot(x_data, loss_value, 'r-', lw=5)\n plt.pause(0.1)\n","sub_path":"demo/demo_neural_net_3.py","file_name":"demo_neural_net_3.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"312863558","text":"import json\n\nimport pytest\nimport mock\nfrom mock import Mock\n\nfrom mlflow.exceptions import IllegalArtifactPathError, MlflowException\nfrom mlflow.store.dbfs_artifact_repo import DbfsArtifactRepository\n\n\n@pytest.fixture()\ndef dbfs_artifact_repo():\n return DbfsArtifactRepository('dbfs:/test/', {})\n\n\n@pytest.fixture()\ndef test_file(tmpdir):\n p = tmpdir.join(\"test.txt\")\n p.write(\"content\")\n return p\n\n\n@pytest.fixture()\ndef test_dir(tmpdir):\n tmpdir.mkdir('subdir').join('test.txt').write('content')\n tmpdir.join('test.txt').write('content')\n return tmpdir\n\n\nLIST_ARTIFACTS_RESPONSE = {\n 'files': [{\n 'path': '/test/a.txt',\n 'is_dir': False,\n 'file_size': 100,\n }, {\n 'path': '/test/dir',\n 'is_dir': True,\n 'file_size': 0,\n }]\n}\n\n\ndef assert_endpoints(call_args_list, expected_endpoint):\n actual_endpoints = []\n for _, kwargs in call_args_list:\n actual_endpoints.append(kwargs['endpoint'])\n actual_endpoints = set(actual_endpoints)\n assert len(expected_endpoint.difference(actual_endpoints)) == 0\n\n\nclass TestDbfsArtifactRepository(object):\n def test_init_validation_and_cleaning(self):\n repo = DbfsArtifactRepository('dbfs:/test/', {})\n assert repo.artifact_uri == 'dbfs:/test'\n with pytest.raises(MlflowException):\n DbfsArtifactRepository('s3://test', {})\n\n @pytest.mark.parametrize(\"artifact_path,expected_endpoint\", [\n (None, '/dbfs/test/test.txt'),\n ('output', '/dbfs/test/output/test.txt'),\n ])\n def test_log_artifact(self, dbfs_artifact_repo, test_file, artifact_path, expected_endpoint):\n with mock.patch('mlflow.store.dbfs_artifact_repo.http_request') as http_request_mock:\n dbfs_artifact_repo.log_artifact(test_file.strpath, artifact_path)\n assert http_request_mock.called\n _, kwargs = http_request_mock.call_args\n assert kwargs['endpoint'] == expected_endpoint\n\n def test_log_artifact_empty(self, dbfs_artifact_repo, test_file):\n with pytest.raises(IllegalArtifactPathError):\n dbfs_artifact_repo.log_artifact(test_file.strpath, '')\n\n @pytest.mark.parametrize(\"artifact_path\", [\n None,\n '', # should behave like '/' and exclude base name of logged_dir\n # We should add '.',\n ])\n def test_log_artifacts(self, dbfs_artifact_repo, test_dir, artifact_path):\n with mock.patch('mlflow.store.dbfs_artifact_repo.http_request') as http_request_mock:\n dbfs_artifact_repo.log_artifacts(test_dir.strpath, artifact_path)\n basename = test_dir.basename\n assert_endpoints(http_request_mock.call_args_list, {\n '/dbfs/test/%s/subdir/test.txt' % basename,\n '/dbfs/test/%s/test.txt' % basename\n })\n\n @pytest.mark.parametrize(\"artifact_path,expected_endpoints\", [\n ('a', {'/dbfs/test/a/subdir/test.txt', '/dbfs/test/a/test.txt'}),\n ('a/', {'/dbfs/test/a/subdir/test.txt', '/dbfs/test/a/test.txt'}),\n ('/', {'/dbfs/test/subdir/test.txt', '/dbfs/test/test.txt'}),\n ])\n def test_log_artifacts_with_artifact_path(self, dbfs_artifact_repo, test_dir, artifact_path,\n expected_endpoints):\n with mock.patch('mlflow.store.dbfs_artifact_repo.http_request') as http_request_mock:\n dbfs_artifact_repo.log_artifacts(test_dir.strpath, artifact_path)\n assert_endpoints(http_request_mock.call_args_list, expected_endpoints)\n\n def test_list_artifacts(self, dbfs_artifact_repo):\n with mock.patch('mlflow.store.dbfs_artifact_repo.http_request') as http_request_mock:\n http_request_mock.return_value.text = json.dumps(LIST_ARTIFACTS_RESPONSE)\n artifacts = dbfs_artifact_repo.list_artifacts()\n assert len(artifacts) == 2\n assert artifacts[0].path == 'a.txt'\n assert artifacts[0].is_dir is False\n assert artifacts[0].file_size == 100\n assert artifacts[1].path == 'dir'\n assert artifacts[1].is_dir is True\n assert artifacts[1].file_size is None\n\n def test_download_artifacts(self, dbfs_artifact_repo):\n with mock.patch('mlflow.store.dbfs_artifact_repo._dbfs_is_dir') as is_dir_mock,\\\n mock.patch('mlflow.store.dbfs_artifact_repo._dbfs_list_api') as list_mock, \\\n mock.patch('mlflow.store.dbfs_artifact_repo._dbfs_download') as download_mock:\n is_dir_mock.side_effect = [\n True,\n False,\n True,\n ]\n list_mock.side_effect = [\n Mock(text=json.dumps(LIST_ARTIFACTS_RESPONSE)),\n Mock(text='{}') # this call is for listing `/dir`.\n ]\n dbfs_artifact_repo.download_artifacts('/')\n assert list_mock.call_count == 2\n assert download_mock.call_count == 1\n _, kwargs = download_mock.call_args\n assert kwargs['endpoint'] == '/dbfs/test/a.txt'\n","sub_path":"tests/store/test_dbfs_artifact_repo.py","file_name":"test_dbfs_artifact_repo.py","file_ext":"py","file_size_in_byte":5055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"7735996","text":"from stack_array import Stack\nimport operator\n\n\n# You do not need to change this class\nclass PostfixFormatException(Exception):\n pass\n\n\ndef postfix_eval(input_str):\n \"\"\"Evaluates a postfix expression\"\"\"\n\n \"\"\"Input argument: a string containing a postfix expression where tokens \n are space separated. Tokens are either operators + - * / ^ or numbers\n Returns the result of the expression evaluation. \n Raises an PostfixFormatException if the input is not well-formed\"\"\"\n\n stack = Stack(30)\n input_list = input_str.split(\" \")\n ops = {\"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.truediv, \"^\": operator.pow}\n opera = [\"+\", \"-\", \"/\", \"*\", \"^\"]\n\n for i in input_list:\n # pushes onto the stack if it is a number\n if is_num(i):\n i = float(i)\n stack.push(i)\n # performs the operation for the 2 numbers on the stack, then pushes final val to stack\n elif i in opera:\n # raises error if there are not enough operands to do the operation\n if stack.size() < 2:\n raise PostfixFormatException(\"Insufficient operands\")\n\n num1 = stack.pop()\n num2 = stack.pop()\n # raises an error if the program is about to divide by 0\n if (i is \"/\") and (num1 == 0):\n raise ValueError\n\n temp_val = ops[i](num2, num1)\n stack.push(temp_val)\n # if the token is not valid raise an error\n else:\n raise PostfixFormatException(\"Invalid token\")\n\n # raises error if more then 1 element is on the stack to end\n if stack.size() > 1:\n raise PostfixFormatException(\"Too many operands\")\n\n # returns the final number from the stack\n return stack.pop()\n\n\ndef is_num(input_num):\n \"\"\"\n checks to see if the string is a number or not\n :param input_num: is a string that is to be tested\n :return: returns if the string is a number\n \"\"\"\n try:\n float(input_num)\n return True\n except:\n return False\n\n\ndef infix_to_postfix(input_str):\n \"\"\"Converts an infix expression to an equivalent postfix expression\"\"\"\n\n \"\"\"Input argument: a string containing an infix expression where tokens are \n space separated. Tokens are either operators + - * / ^ parentheses ( ) or numbers\n Returns a String containing a postfix expression \"\"\"\n\n stack = Stack(30)\n r_val = \"\"\n input_list = input_str.split(\" \")\n comp_op = {\"+\": 1, \"-\": 1, \"*\": 2, \"/\": 2, \"^\": 3}\n\n for i in input_list:\n # adds to the final return if it is a number\n if is_num(i):\n r_val += i + \" \"\n else:\n # pushes to the stack if it is small enough or an open par\n if (stack.size() < 1) or (i is \"(\"):\n stack.push(i)\n # checks to see if it is an end par\n elif i is \")\":\n while stack.peek() is not \"(\":\n r_val += stack.pop() + \" \"\n stack.pop()\n # checks to see if it is right-associative\n elif comp_op[i] == 3:\n while (stack.size() > 0) and (stack.peek() is not \"(\") and (comp_op[i] < comp_op[stack.peek()]):\n r_val += stack.pop() + \" \"\n stack.push(i)\n # checks for all other left-associative cases\n else:\n while (stack.size() > 0) and (stack.peek() is not \"(\") and (comp_op[i] <= comp_op[stack.peek()]):\n r_val += stack.pop() + \" \"\n stack.push(i)\n\n # pops the remaining elements in the stack\n while stack.size() > 0:\n r_val += stack.pop() + \" \"\n\n # removes a space at the end for grading purposes\n if r_val[len(r_val)-1:]is \" \":\n r_val = r_val[0:len(r_val)-1]\n\n return r_val\n\n\ndef prefix_to_postfix(input_str):\n \"\"\"Converts a prefix expression to an equivalent postfix expression\"\"\"\n \"\"\"Input argument: a string containing a prefix expression where tokens are \n space separated. Tokens are either operators + - * / ^ parentheses ( ) or numbers\n Returns a String containing a postfix expression(tokens are space separated)\"\"\"\n\n stack = Stack(30)\n input_list = input_str.split(\" \")\n\n for i in range(len(input_list)):\n # pushes onto the stack if it is a number\n if is_num(input_list[len(input_list) - i - 1]):\n stack.push(input_list[len(input_list) - i - 1])\n # converts strings currently in stack into 1 str\n else:\n str1 = stack.pop()\n str2 = stack.pop()\n temp_str = str1 + \" \" + str2 + \" \" + input_list[len(input_list) - i - 1]\n stack.push(temp_str)\n\n # pops the final str\n return stack.pop()\n","sub_path":"p2-hegglinmichael/exp_eval.py","file_name":"exp_eval.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"183711263","text":"#!/usr/bin/env python\nfrom nornir import InitNornir\nfrom nornir.core.filter import F\nfrom nornir.plugins.tasks.networking import netmiko_send_command\nfrom nornir.plugins.functions.text import print_result\nfrom ciscoconfparse import CiscoConfParse\nimport re\n\n\ndef checkType1(the_string, contains, avoids):\n contains_count = 0\n avoids_count = 0\n #for more exact match use'^'+contain+'$'\n #any string match in the contains array, but no string match from avoids array\n #the_string is a single string\n for contain in contains:\n if re.search(contain, the_string):\n contains_count = contains_count + 1\n for avoid in avoids:\n if re.search(avoid, the_string):\n avoids_count = avoids_count + 1\n if (avoids_count == 0) and (contains_count >= 1 or len(contains) == 0):\n return True\n return False\n\n\ndef checkType2(the_string, contains, avoids):\n contains_count = 0\n avoids_count = 0\n #for more exact match use '^'+contain+'$'\n #all the string matches in the contains array, but no string match from avoids array\n #remember if doing interface config there could be multiple leading spaces\n #the_string is an array of strings\n contain_store = []\n for contain in contains:\n for line in the_string:\n if re.search(contain, line):\n if contain not in contain_store:\n #only increment once if there are multiple matches\n contains_count = contains_count + 1\n contain_store.append(contain)\n for avoid in avoids:\n for line in the_string:\n if re.search(avoid, line):\n avoids_count = avoids_count + 1\n if (avoids_count == 0) and (contains_count == len(contains)):\n return True\n return False\n\n\ndef filterByShowVersion(host, search_array=[]):\n #Filter by data found in show version\n\n the_show_version = host['show_version']\n the_hostname = the_show_version['hostname']\n the_hardware = the_show_version['hardware'][0]\n the_version = the_show_version['version']\n the_running_image = the_show_version['running_image']\n # etc\n\n #find index of search stage\n check_hostname = next((i for i, item in enumerate(search_array) if item[\"name\"] == \"hostname\"), None)\n check_version = next((i for i, item in enumerate(search_array) if item[\"name\"] == \"version\"), None)\n # etc\n\n #insert host show version data into search array\n if check_hostname is not None:\n search_array[check_hostname]['string'] = the_hostname\n if check_version is not None:\n search_array[check_version]['string'] = the_version\n #etc\n\n is_found = False\n for stage in search_array:\n if checkType1(the_string=stage['string'], contains=stage['contains'], avoids=stage['avoids']):\n print(f'host {host} found in stage {stage[\"name\"]}')\n is_found = True\n continue\n else:\n print(f'host {host} not found in stage {stage[\"name\"]}')\n is_found = False\n break\n\n return is_found\n\n\ndef filterByConfig(host, search_dict={}):\n \n the_string = host['config']\n contains = search_dict.get('contains',[])\n avoids = search_dict.get('avoids',[])\n\n is_found = False\n if checkType2(the_string=the_string, contains=contains, avoids=avoids):\n is_found = True\n print(f'host {host} has the config you are looking for')\n else:\n print(f'host {host} does not have the config you are looking for')\n\n return is_found\n\n\ndef filterByInterfaceConfig(host, search_dict={}):\n\n the_string = host['config']\n host['f_ports'] = []\n\n the_parent = search_dict.get('parent', None)\n contains = search_dict.get('contains',[])\n avoids = search_dict.get('avoids',[])\n\n is_found = False\n\n if not the_parent:\n return is_found\n \n #find parent and children\n parse = CiscoConfParse(the_string,factory=True)\n intf = parse.find_objects(the_parent)\n #import ipdb; ipdb.set_trace()\n for int_obj in intf:\n intf_config = []\n for obj_child in int_obj.children:\n intf_config.append(obj_child.text)\n if checkType2(the_string=intf_config,contains=contains,avoids=avoids):\n host['f_ports'].append(int_obj.text)\n print(f'host {host} and int {int_obj.text} found')\n is_found = True\n \n return is_found\n \n \ndef getVersion(task):\n host = task.host\n results = task.run(task=netmiko_send_command, command_string=\"show version\",\n use_textfsm=True)\n print(results[0].result[0])\n host['show_version'] = results[0].result[0] \n\n \ndef getConfig(task):\n host = task.host\n results = task.run(task=netmiko_send_command, command_string=\"show run\")\n host['config'] = results[0].result.split('\\n')\n\n\ndef main():\n\n nr = InitNornir(config_file='config.yaml')\n print(nr.inventory.hosts)\n\n result = nr.run(task=getVersion)\n #print_result(result)\n\n #dynamic filter on show version results\n search_array = [\n {'name': 'hostname',\n 'contains': ['R.*'],\n 'avoids': ['R3']\n },\n {'name': 'version',\n 'contains': ['15.2'],\n 'avoids': ['15.7']\n } \n ]\n nr = nr.filter(filter_func=filterByShowVersion, search_array=search_array)\n print(nr.inventory.hosts)\n\n #get config from filtered results\n result = nr.run(task=getConfig)\n #print_result(result)\n\n #dynamic filter on running config from the device\n search_dict = {\n 'contains': ['hostname R','no ip http'],\n 'avoids': ['hostname R4']\n }\n nr = nr.filter(filter_func=filterByConfig, search_dict=search_dict)\n print(nr.inventory.hosts)\n\n #dynamic filter on interface config from the device\n search_dict={\n 'parent': 'Ethernet',\n 'contains': ['ip address'],\n 'avoids': ['^ duplex half']\n }\n nr = nr.filter(filter_func=filterByInterfaceConfig, search_dict=search_dict)\n print(nr.inventory.hosts)\n\n #print host and interface names that were found\n print('final results')\n for host, host_obj in nr.inventory.hosts.items():\n print(host, host_obj['f_ports'])\n\n #you can now configure just the hosts and interfaces you are interested in\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"custom_filter.py","file_name":"custom_filter.py","file_ext":"py","file_size_in_byte":6406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"393636873","text":"# -*- coding:utf-8 -*-\nimport re\nimport os\nimport pickle\nimport json\nfrom elasticsearch import Elasticsearch\nfrom Song.commentProvider import CommentProvider\nfrom Song.CheckCopyRight import music_search\nfrom collections import Counter\n\n\n\nclass songSpider:\n def __init__(self,target = ['http://chitchat-index-int.eastasia.cloudapp.azure.com:19200/'],\n http_auth = ('esuser', 'Kibana123!'),\n port=9200, timeout=50000,\n base_save_dir = 'E:\\\\PycharmProjects\\\\FirstDayOnMS2\\\\Data\\\\Song',\n filter_by_comment = True,\n pklName = 5):\n super(songSpider,self).__init__()\n self.target = target\n self.http_auth = http_auth\n self.port = port\n self.timeout = timeout\n self.es = Elasticsearch(target,http_auth= http_auth,port=port,timeout=timeout)\n print(\"connect succeed!!!\")\n '''\n 连接数据库\n '''\n self.ch_pattern = re.compile('[\\u4e00-\\u9fa5]+')\n self.ch_word_ratio = 0.7 #中文单词超过这个比例才算是中文歌\n '''\n 判中文\n '''\n self.ResultList = []\n self.ResultNum = 0\n self.pklName = pklName\n self.base_save_dir = base_save_dir\n self.filter_by_comment = filter_by_comment\n self.sources_to_check = [music_search.MusicSource.MIGU]\n self.copyRightChecker = music_search.MusicSearch()\n\n def JudgeChinese(self,content,ratio = 0.7):\n content = content.split()\n '''\n 最容易弄混的是日文歌，因为日文歌中可以有中文的汉字\n '''\n num_Chinese_word = 0\n num_nonChinese_word = 0\n for word in content:\n flag = True\n for ch in word:\n if not ('\\u4e00' <= ch <= '\\u9fa5'):\n flag = False\n if flag is False:\n break\n if flag is False:\n num_nonChinese_word += 1\n else:\n num_Chinese_word += 1\n isJapan = False\n for ch in word:\n if '\\u3040' <= ch <= '\\u309F' or '\\u30A0' <= ch <= '\\u30FF':\n #日文平假名， 日文片假名\n isJapan = True\n break\n if isJapan:\n return False\n if num_nonChinese_word + num_Chinese_word == 0:\n return False\n if num_Chinese_word / (num_Chinese_word + num_nonChinese_word) > ratio:\n return True\n return False\n\n def JudgeCopyRight(self,song):\n '''\n 已经利用can_play字段是否为true，在查询的时候就修改了\n '''\n song_name = song['music_name']['format_name']\n singers = []\n for sig in song['singer_info']:\n singers.append(sig['format_name'])\n try:\n has_copyright_sources = self.copyRightChecker.check_copyright(name=song_name, singers=singers,\n sources_to_check=self.sources_to_check)\n except Exception(\"找版权的时候有问题的了\"):\n return False\n if len(has_copyright_sources) > 0: # 有版权\n return True\n return False\n\n\n def crawlSong(self,index = 'netease_music_merged_current/',doc_type=\"e_music\"):\n # Initialize the scroll\n\n # body = {\n # \"query\": {\n # \"match_all\": {}\n # }\n # }\n body = {\n \"query\" : {\n \"filtered\" : {\n \"filter\" : {\n \"bool\" : {\n \"must_not\":[{\"match\":{\"lyric.wb_lyric\": \"pure music\"}}],\n \"must\" : [\n {\"exists\" : [{ \"field\" : \"lyric\" }]},\n {\"nested\": {\n \"path\": \"tags\",\n \"query\": {\n \"exists\": {\n \"field\": \"tags\"\n }\n }\n\n }\n\n },\n { \"match\": { \"can_play\": \"true\" }}\n ]\n }\n }\n }\n },\n \"_source\": [\"album_info\", \"lyric\", \"music_id\", \"music_name\", \"singer_info\", \"similar_musics\", \"tags\",\n \"popularity\"]\n }\n '''\n body里面一共有4个查询条件：\n 1 lyric.web_lyric字段不等于pure music 确保有歌词\n 2 lyric字段存在，确保有歌词\n 3 tags字段存在，确保有tag\n 4 can_play字段为true，确保有版权\n 剩下需要在程序中判断的就是：\"歌曲是不是中文歌词的了\"\n '''\n body_migu = {\n \"query\" : {\n \"bool\" : {\n \"filter\" : {\n \"bool\" : {\n \"must_not\":[{\"match\":{\"lyric.wb_lyric\": \"pure music\"}},\n {\"match\":{\"lyric.wb_lyric\":\"暂 无 歌词\"}},\n {\"match\":{\"lyric.wb_lyric\":\"此 歌曲 为 没有 填词 的 纯 音乐 请 您 欣赏\"}}],\n \"must\" : [\n {\"exists\" : { \"field\" : \"lyric\" }\n },\n {\"nested\": {\n \"path\": \"tags\",\n \"query\": {\n \"exists\": {\n \"field\": \"tags\"\n }\n }\n\n }\n\n },\n { \"match\": { \"can_play\": \"true\" }}\n ]\n }\n }\n }\n },\n \"_source\": [\"album_info\", \"lyric\", \"music_id\", \"music_name\", \"singer_info\", \"similar_musics\", \"tags\",\n \"popularity\"]\n }\n\n page = self.es.search(\n index=index,\n doc_type=doc_type,\n scroll='2m',\n # search_type='scan',\n size=1000,\n body=body_migu\n )\n sid = page['_scroll_id']\n scroll_size = page['hits']['total']\n print(\"scroll_size = \",scroll_size)\n\n\n # Start scrolling\n while (scroll_size > 0):\n print(\"Scrolling...\")\n # Update the scroll ID\n sid = page['_scroll_id']\n # Get the number of results that we returned in the last scroll\n scroll_size = len(page['hits']['hits'])\n print(\"scroll size: \" + str(scroll_size))\n # Do something with the obtained page\n try:\n page = self.es.scroll(scroll_id=sid, scroll='2m')\n #print(\"length = \",page['hits']['hits'])\n for i, ele in enumerate(page['hits']['hits']): # type(page['hits']['hits']) == list , len(page['hits']['hits'])==1000\n # for key in ele['_source']: # _source 里面才有真正有用的东西 music_id , album_info, alias = [] , singer_info, music_name ,\n # print(\"key = \", key)\n # print(\"value = \", ele['_source'][key])\n # exit(89)\n # if i == 2:\n # exit(56789)\n if len(ele['_source']['lyric']['wb_lyric']) > 0:\n # print(ele['_source']['lyric']['ori_lyric'])#分段的，但是没有分词\n # print('\\n')\n # print(ele['_source']['lyric']['notime_lyric'])#同上\n # print('\\n')\n lyric = ele[\"_source\"]['lyric']['wb_lyric'] # 分过词的，一定要用分过词的判断，否则，不公平，英文中一个单词有很多字符\n # print('i= ', i, \" len(page) = \", len(page['hits']['hits']), \" lyric=\", lyric[:30])\n # if self.JudgeChinese(lyric) and self.JudgeCopyRight(ele[\"_source\"]):\n if self.JudgeChinese(lyric):\n\n # 加入到最终的结果中\n #print(\"Judge Chinese Succeed \",'i= ',i,\" len(page) = \",len(page['hits']['hits']) , \" lyric=\",lyric[:30])\n # tempzhengquan = input()\n ori_name, ori_singers, ori_album = ele[\"_source\"]['music_name']['ori_name'],\\\n ele[\"_source\"]['singer_info'][0]['ori_name'], \\\n None# ele[\"_source\"]['album_info'][0]['ori_name']\n temp = ele[\"_source\"]\n # comments , byReplied = CommentProvider.get_comments(ori_name, ori_singers, ori_album)#ori_name, ori_singers, ori_album\n # if len(comments) == 0:\n # print(\"comments = = 0\")\n # continue\n\n # comments = Counter(comments)\n # byReplied = Counter(byReplied)\n # comments = comments - byReplied\n # temp[\"comments\"] = comments\n self.ResultList.append(temp)\n self.ResultNum += 1\n print(\"in while in for self.ResultNum = \",self.ResultNum)\n # if len(self.ResultList) >= 30:\n # break\n # print(\"ResultNum = \",self.ResultNum)\n # pickle.dump(self.ResultList, open(\"song\" + str(self.pklName) + \".pkl\", \"wb\"))\n # self.ResultList = []\n # self.ResultNum = 0\n # self.pklName += 1\n # print(\"save pkl succeed!\")\n # print(\"ResultNum = \",self.ResultNum)\n # print(\"pklName = \",self.pklName)\n\n\n #exit(678)\n #break#一个page之后结束\n except Exception(\"又发生了错误\"):\n print(\"先保存下来\")\n break\n\n # if len(self.ResultList) >= 30:\n # break\n\n\n\n if self.ResultNum > 0:\n print(\"the ResultNum = \",self.ResultNum)\n if self.filter_by_comment:\n pickle.dump(self.ResultList, open(os.path.join(self.base_save_dir,\"songWithComment\" + str(self.pklName) + \".pkl\"), \"wb\"))\n json.dump(self.ResultList,open(os.path.join(self.base_save_dir,\"songWithComment\"+str(self.pklName) + \".json\"),\"w\",encoding=\"utf-8\"),ensure_ascii=False)\n else:\n pickle.dump(self.ResultList, open(os.path.join(self.base_save_dir,\"song\" + str(self.pklName) + \".pkl\"), \"wb\"))\n json.dump(self.ResultList,\n open(os.path.join(self.base_save_dir, \"song\" + str(self.pklName) + \".json\"), \"w\",\n encoding=\"utf-8\"), ensure_ascii=False)\n self.ResultList = []\n self.ResultNum = 0\n self.pklName += 1\n print(\"save pkl succeed!\")\n print(\"ResultNum = \", self.ResultNum)\n print(\"pklName = \", self.pklName)\n\n\nclass MergeSongComment:\n def __init__(self,in_file,out_file=\"songComment.pkl\",base_dir = 'E:\\\\PycharmProjects\\\\FirstDayOnMS2\\\\Data\\\\Song'):\n self.in_file = in_file\n self.out_file = out_file\n self.base_dir = base_dir\n self.songs = pickle.load(open(os.path.join(base_dir,in_file),\"rb\"))\n\n def MergeComment(self):\n self.ResultList = []\n for ele in self.songs:\n ori_name, ori_singers, ori_album = ele['music_name']['ori_name'], \\\n ele['singer_info'][0]['ori_name'], \\\n ele['album_info'][0]['ori_name']\n comments, byReplied = CommentProvider.get_comments(ori_name, ori_singers,\n ori_album) # ori_name, ori_singers, ori_album\n if len(comments) == 0:\n continue\n comments = Counter(comments)\n byReplied = Counter(byReplied)\n comments = comments - byReplied\n temp = ele\n temp[\"comments\"] = comments\n self.ResultList.append(temp)\n pickle.dump(self.ResultList,open(os.path.join(self.base_dir,self.out_file),\"wb\"))\n\nif __name__ == \"__main__\":\n # songClawer = songSpider(target=['http://corechat-usermemory-int.trafficmanager.net:19200/'],filter_by_comment=False,pklName=6)\n # songClawer.crawlSong(index='migu_music_merged_current')\n songClawer = songSpider()\n songClawer.crawlSong()\n # merger = MergeSongComment(in_file=\"song4.pkl\")\n # merger.MergeComment()\n\n # a = ['', '', '']#ori_name, ori_singers, ori_album\n # b = [a, a, a, a, a]\n # comments, beReplied = CommentProvider.get_comments(\"\", \"\", \"\")\n # print(len(comments))\n # for content in comments:\n # print(content)\n # print(len(content))\n # comments = list(CommentProvider.batch_get_comments(b))\n # print(\"*\"*100)\n # print(len(comments))\n # for comment in comments:\n # print(len(comment))\n # for content in comment:\n # print(content)\n # print(len(content))","sub_path":"Model/Song/crawlSong.py","file_name":"crawlSong.py","file_ext":"py","file_size_in_byte":13760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"15908942","text":"import tensorflow as tf\nimport numpy as np\n\nclass Converter(object):\n\n def __init__(self, tf_nodes, mx_nodes, mx_params):\n self.tf_nodes = tf_nodes\n self.mx_nodes = mx_nodes\n self.mx_params = mx_params\n\n def to_tuple(self, string, conv_type=str):\n return tuple(map(conv_type, map(str.strip, string[1:-1].split(','))))\n\n def create_var(self, node, shape=None):\n node_name = node['name']\n if shape is None:\n if node_name in self.mx_params:\n shape = self.mx_params[node_name].shape\n else:\n shape = ()\n # print('Creating var with shape:', shape)\n created_node = tf.get_variable(node_name, shape=shape, initializer=tf.zeros_initializer, trainable = False)\n self.tf_nodes[node_name] = created_node\n return created_node\n\n def create_bn(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n\n epsilon = float(node['attrs']['eps'])\n input_shape = input_sym.get_shape()\n axis = list(range(len(input_shape) - 1))\n\n def create_bn_params(i):\n cur_node = self.mx_nodes[node['inputs'][i][0]]\n cur_name = cur_node['name']\n self.create_var(cur_node)\n self.tf_nodes[cur_name].load(self.mx_params[cur_name].asnumpy())\n return self.tf_nodes[cur_name]\n if len(node['inputs']) > 3:\n gamma, beta, mean, var = (create_bn_params(i) for i in range(1, 5))\n else:\n gamma, beta = (create_bn_params(i) for i in range(1, 3))\n mean = tf.get_variable(node_name + '_mean', shape=input_shape[-1], initializer=tf.zeros_initializer, trainable = False)\n mean.load(np.zeros((input_shape[-1],), dtype='float32'))\n var = tf.get_variable(node_name + '_var', shape=input_shape[-1], initializer=tf.ones_initializer, trainable = False)\n var.load(np.ones((input_shape[-1],), dtype='float32'))\n if 'fix_gamma' in node['attrs']:\n if node['attrs']['fix_gamma'] == 'True':\n # print('Fix')\n gamma = tf.get_variable(node_name + '_gamma_fixed', shape=input_shape[-1], initializer=tf.ones_initializer, trainable = False)\n gamma.load(np.ones((input_shape[-1],), dtype='float32'))\n else:\n gamma = tf.get_variable(node_name + '_gamma_fixed', shape=input_shape[-1], initializer=tf.ones_initializer, trainable = False)\n gamma.load(np.ones((input_shape[-1],), dtype='float32'))\n self.tf_nodes[node_name] = tf.nn.batch_normalization(input_sym, mean, var, beta, gamma, epsilon, name=node_name)\n return self.tf_nodes[node_name]\n\n def create_conv(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n num_filters_in = input_sym.get_shape()[-1]\n num_filters_out = int(node['attrs']['num_filter'])\n kernel_size = self.to_tuple(node['attrs']['kernel'], int)\n\n if 'no_bias' in node['attrs']:\n if node['attrs']['no_bias']:\n add_bias = False\n else:\n add_bias = True\n else:\n # by default, bias exists\n add_bias = True\n \n # add bias\n if add_bias:\n bias_node = self.mx_nodes[node['inputs'][2][0]]\n #print(\"-----------------------conv name:\", node_name, \", bias:\", add_bias, \"name:\", bias_node['name'])\n bias = self.create_var(bias_node, shape=(num_filters_out))\n #print(\"bias shape:\", bias.shape)\n bias_numpy = self.mx_params[bias_node['name']].asnumpy()\n bias.load(bias_numpy)\n \n if 'num_group' in node['attrs']:\n num_group = int(node['attrs']['num_group'])\n else:\n num_group = 1\n if 'pad' in node['attrs']:\n padding = self.to_tuple(node['attrs']['pad'], int)\n else:\n padding = (0, 0)\n stride = self.to_tuple(node['attrs']['stride'], int)\n \n weights_node = self.mx_nodes[node['inputs'][1][0]]\n weights_numpy = self.mx_params[weights_node['name']].asnumpy().transpose((2, 3, 1, 0))\n \n if padding[0] > 0 or padding[1] > 0:\n padded_input = tf.pad(input_sym, [[0, 0], [padding[0], padding[0]], [padding[1], padding[1]], [0, 0]], 'CONSTANT')\n else:\n padded_input = input_sym\n convolve = lambda input_sym, kernel, name=None: tf.nn.conv2d(input_sym, kernel, [1, stride[0], stride[1], 1], padding='VALID', name=name)\n \n if num_group > 1:\n #redefine with group conv.\n weights = self.create_var(weights_node,\n shape=(kernel_size[0], kernel_size[1], num_filters_in, 1))\n weights.load(weights_numpy.transpose((0,1,3,2)))\n \n self.tf_nodes[node_name] = tf.nn.depthwise_conv2d(padded_input, weights, strides = [1, stride[0], stride[1], 1], padding='VALID', name = node_name)\n else:\n weights = self.create_var(weights_node,\n shape=(kernel_size[0], kernel_size[1], num_filters_in // num_group, num_filters_out))\n weights.load(weights_numpy)\n if add_bias:\n _tmp_node = convolve(padded_input, weights, name=node_name+\"_before_bias\")\n self.tf_nodes[node_name] = tf.add(_tmp_node, bias, name = node_name)\n else:\n self.tf_nodes[node_name] = convolve(padded_input, weights, name=node_name)\n\n return self.tf_nodes[node_name]\n \n def create_fc(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n #if node is not 1-d vector, we flatten it.\n if len(input_sym.get_shape()) > 2:\n input_sym = tf.layers.flatten(input_sym)\n \n num_units_in = input_sym.get_shape()[1]\n num_units_out = int(node['attrs']['num_hidden'])\n weights_node = self.mx_nodes[node['inputs'][1][0]]\n weights = self.create_var(weights_node, shape=(num_units_in, num_units_out))\n bias_node = self.mx_nodes[node['inputs'][2][0]]\n bias = self.create_var(bias_node, shape=(num_units_out,))\n weights_numpy = self.mx_params[weights_node['name']].asnumpy()\n weights.load(weights_numpy.T)\n bias.load(self.mx_params[bias_node['name']].asnumpy())\n self.tf_nodes[node_name] = tf.nn.xw_plus_b(input_sym, weights, bias, name=node_name)\n return self.tf_nodes[node_name]\n \n def create_pooling(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n pooling_type = node['attrs']['pool_type']\n kernel_size = self.to_tuple(node['attrs']['kernel'], int)\n if 'stride' in node['attrs']:\n stride = self.to_tuple(node['attrs']['stride'], int)\n else:\n stride = (1, 1)\n if 'global_pool' in node['attrs']:\n global_pool = node['attrs']['global_pool'] == 'True'\n else:\n global_pool = False\n if 'pad' in node['attrs']:\n padding = self.to_tuple(node['attrs']['pad'], int)\n else:\n padding = (0, 0)\n if global_pool:\n self.tf_nodes[node_name] = tf.reduce_mean(input_sym, reduction_indices=[1, 2], name=node_name)\n else:\n if padding[0] > 0 or padding[1] > 0:\n padded_input = tf.pad(input_sym,\n [[0, 0], [padding[0], padding[0]], [padding[1], padding[1]], [0, 0]],\n 'CONSTANT')\n else:\n padded_input = input_sym\n if pooling_type == 'max':\n self.tf_nodes[node_name] = tf.nn.max_pool(padded_input,\n ksize=[1, kernel_size[0], kernel_size[1], 1],\n strides=[1, stride[0], stride[1], 1],\n padding='VALID', name=node_name)\n else:\n raise NameError('Unknown pooling type: %s' % pooling_type)\n return self.tf_nodes[node_name]\n\n def create_activation(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n activation_type = node['attrs']['act_type']\n if activation_type == 'relu':\n activation_fn = tf.nn.relu\n else:\n raise NameError('Unknown activation type: %s' % activation_type)\n self.tf_nodes[node_name] = activation_fn(input_sym, name=node_name)\n return self.tf_nodes[node_name]\n \n def create_lrelu(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n \n act_type = node['attrs']['act_type']\n #print(\"act type:\", act_type)\n \n alpha_sym = self.mx_nodes[node['inputs'][1][0]]\n alpha_name = alpha_sym['name']\n alpha = self.mx_params[alpha_name].asnumpy()\n \n pos = tf.nn.relu(input_sym)\n neg = tf.multiply( tf.multiply( tf.subtract(input_sym, abs(input_sym)), 0.5 ), alpha )\n self.tf_nodes[node_name] = tf.add(pos, neg, name=node_name)\n \n return self.tf_nodes[node_name]\n \n def create_concat(self, node):\n node_name = node['name']\n input_nodes = node['inputs']\n input_syms = []\n for i in range(len(node['inputs'])):\n _node = node['inputs'][i][0]\n input_syms.append(self.tf_nodes[self.mx_nodes[node['inputs'][i][0]]['name']])\n self.tf_nodes[node_name] = tf.concat(input_syms, axis = 3, name=node_name)\n return self.tf_nodes[node_name]\n \n def create_reshape(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n #print(\"reshape input:\", input_sym)\n resize_shape = node['attrs']['shape'] #NCHW\n resize_shape = resize_shape.strip(')')\n resize_shape = resize_shape.strip('(')\n resize_shape = resize_shape.split(',')\n for i in range(len(resize_shape)):\n resize_shape[i] = int(resize_shape[i])\n #print(\"arg:\", resize_shape)\n resize_shape_tf = [resize_shape[0], resize_shape[2], resize_shape[3], resize_shape[1]] #NHWC\n\n resize_shape_tf[0] = 1 # batch dimension\n for i in range(1, len(resize_shape_tf)):\n if resize_shape_tf[i] == 0:\n resize_shape_tf[i] = input_sym.shape[i].value #can also work with input_sym.get_shape().as_list()\n #print(\"reshape:\", resize_shape_tf)\n\n self.tf_nodes[node_name] = tf.reshape(input_sym, shape = resize_shape_tf, name=node_name)\n #print(\"resized output:\", self.tf_nodes[node_name])\n return self.tf_nodes[node_name]\n \n def create_softmaxactivation(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n print(\"softmax input:\", input_sym)\n\n self.tf_nodes[node_name] = tf.nn.softmax(input_sym, axis = 3, name=node_name)\n return self.tf_nodes[node_name]\n \n def create_dropout(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n # at test time, we ignore dropout.\n self.tf_nodes[node_name] = tf.identity(input_sym, name=node_name)\n #self.tf_nodes[node_name] = tf.nn.dropout(input_sym, keep_prob = 0.6, name=node_name)\n return self.tf_nodes[node_name]\n\n def create_copy(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.identity(input_sym, name=node_name)\n\n return self.tf_nodes[node_name]\n \n def create_minus(self, node):\n node_name = node['name']\n val = float(node['attrs'][\"scalar\"])\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.add(input_sym, -1*val, name=node_name.strip(\"_\"))\n\n return self.tf_nodes[node_name]\n \n def create_multiply(self, node):\n node_name = node['name']\n val = float(node['attrs'][\"scalar\"])\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.multiply(input_sym, val, name=node_name.strip(\"_\"))\n\n return self.tf_nodes[node_name]\n \n def create_crop(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n target_sym = self.tf_nodes[self.mx_nodes[node['inputs'][1][0]]['name']]\n self.tf_nodes[node_name] = tf.slice(input_sym, [0,0,0,0], [-1,target_sym.shape[1], target_sym.shape[2], -1], name=node_name)\n return self.tf_nodes[node_name]\n \n def create_upsampling(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n scale = node['attrs']['scale']\n new_size = [input_sym.shape[1]*scale, input_sym.shape[2]*scale]\n\n # defined in https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor\n self.tf_nodes[node_name] = tf.image.resize_nearest_neighbor(input_sym, size = new_size, name=node_name)\n return self.tf_nodes[node_name]\n \n def create_upsampling_v2(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n #print(\"input to upsampling:\", input_sym)\n scale = int(node['attrs']['scale'])\n new_size = [1, input_sym.shape[1]*scale, input_sym.shape[2]*scale, input_sym.shape[3]]\n\n \n def tf_repeat(tensor, repeats, name):\n '''\n expanded_tensor = tf.expand_dims(tensor, -1)\n multiples = [1] + repeats\n tiled_tensor = tf.tile(expanded_tensor, multiples = multiples)\n #tiled_tensor = tf.squeeze(tiled_tensor, axis = 4)\n tiled_tensor = tf.squeeze(tiled_tensor)\n repeated_tesnor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats, name = name)\n '''\n\n # this is the revised version that does not require expanding tensor to 5-dim\n tiled_tensor = tf.tile(tensor, multiples = [1]+repeats[0:3])\n repeated_tesnor = tf.reshape(tiled_tensor, tf.shape(tensor) * repeats, name = name)\n\n return repeated_tesnor\n \n self.tf_nodes[node_name] = tf_repeat(input_sym, [1, scale, scale, 1], name=node_name)\n print(\"#######################upsampling out node:\", self.tf_nodes[node_name])\n return self.tf_nodes[node_name]\n \n def create_softmax(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.nn.softmax(input_sym, name=node_name)\n return self.tf_nodes[node_name]\n\n def create_elementwise(self, node, op='sum'):\n node_name = node['name']\n inputs_sym = [self.tf_nodes[self.mx_nodes[n[0]]['name']] for n in node['inputs']]\n\n if op == 'sum':\n if len(inputs_sym) == 2:\n print(\"-------------adding just 2 nodes, replace with tf.add-------------------\")\n self.tf_nodes[node_name] = tf.add(inputs_sym[0], inputs_sym[1], name=node_name.strip(\"_\"))\n else:\n self.tf_nodes[node_name] = tf.add_n(inputs_sym, name=node_name.strip(\"_\"))\n \n else:\n raise NameError('Unknown elementwise type: %s' % op)\n return self.tf_nodes[node_name]\n\n def create_norm(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.nn.l2_normalize(input_sym, dim=1, name=node_name)\n return self.tf_nodes[node_name]\n\n def create_flatten(self, node):\n node_name = node['name']\n input_sym = self.tf_nodes[self.mx_nodes[node['inputs'][0][0]]['name']]\n self.tf_nodes[node_name] = tf.contrib.layers.flatten(input_sym)\n return self.tf_nodes[node_name]\n","sub_path":"converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":16475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"275044432","text":"from authlib.integrations.flask_oauth2 import current_token\nfrom flask_restful import Resource, fields, marshal_with, reqparse, abort\nfrom sqlalchemy import func\n\nfrom covod.models.models import User, Lecture, db, Comment\nfrom covod.oauth2 import require_oauth\n\nuser_fields = {\n \"id\": fields.Integer,\n \"username\": fields.String,\n \"full_name\": fields.String\n}\n\ncomment_fields = {\n \"id\": fields.Integer,\n \"created_at\": fields.DateTime(dt_format=\"iso8601\"),\n \"modified_at\": fields.DateTime(dt_format=\"iso8601\"),\n \"timestamp\": fields.Integer,\n \"text\": fields.String,\n \"path\": fields.String,\n \"user\": fields.Nested(user_fields)\n}\n\ncomment_fields_recursive = comment_fields.copy()\ncomment_fields_recursive[\"replies\"] = fields.List(fields.Nested(comment_fields_recursive))\n\n\nclass CommentsAPI(Resource):\n @require_oauth(\"view\")\n @marshal_with(comment_fields_recursive, envelope=\"comments\")\n def get(self, lecture_id):\n return Comment.query.filter_by(lecture_id=lecture_id).filter(\n func.length(Comment.path) == Comment.get_n()).all()\n\n @require_oauth(\"comment\")\n @marshal_with(comment_fields_recursive, envelope=\"comments\")\n def put(self, lecture_id):\n lecture = Lecture.query.filter_by(id=lecture_id).first_or_404()\n user = User.query.filter_by(id=current_token.user_id).first()\n\n parser = reqparse.RequestParser()\n parser.add_argument(\"text\", required=True, help=\"No comment text provided\")\n parser.add_argument(\"parent\", type=int)\n parser.add_argument(\"timestamp\", type=int)\n args = parser.parse_args()\n\n if args.parent:\n parent = Comment.query.filter_by(id=args.parent).first()\n\n if not parent:\n abort(400)\n else:\n parent = None\n\n comment = Comment(user=user, text=args.text, parent=parent,\n timestamp=args.timestamp, lecture_id=lecture_id\n )\n\n # Use comment.save()to generate path\n comment.save()\n\n lecture.add_comment(comment)\n db.session.commit()\n return comment\n\n\nclass CommentsFlatAPI(Resource):\n @require_oauth(\"view\")\n @marshal_with(comment_fields, envelope=\"comments\")\n def get(self, lecture_id):\n return Comment.query.filter_by(lecture_id=lecture_id).all()\n","sub_path":"covod/resources/comments.py","file_name":"comments.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"99142604","text":"\"\"\"Response body for getting the data related to quest posts.\"\"\"\r\nfrom typing import Any\r\n\r\nfrom controllers import MultilingualGetOneResult, QuestPostKey\r\nfrom .post_base import (\r\n PostEditFailedResponse, PostEditSuccessResponse, PostEditSuccessResponseKey, PostGetFailedResponse,\r\n PostGetSuccessResponse, PostGetSuccessResponseKey, PostIDCheckResponse, PostIDCheckResponseKey, PostListResponse,\r\n PostListResponseKey, PostPublishFailedResponse, PostPublishSuccessResponse, PostPublishSuccessResponseKey,\r\n)\r\n\r\n__all__ = (\"QuestPostPublishSuccessResponse\", \"QuestPostPublishFailedResponse\", \"QuestPostPublishSuccessResponseKey\",\r\n \"QuestPostListResponse\", \"QuestPostListResponseKey\",\r\n \"QuestPostGetSuccessResponse\", \"QuestPostGetFailedResponse\", \"QuestPostGetSuccessResponseKey\",\r\n \"QuestPostEditSuccessResponse\", \"QuestPostEditFailedResponse\", \"QuestPostEditSuccessResponseKey\",\r\n \"QuestPostIDCheckResponseKey\", \"QuestPostIDCheckResponse\")\r\n\r\n\r\n# region Quest Post / Publish\r\n\r\nclass QuestPostPublishSuccessResponseKey(PostPublishSuccessResponseKey):\r\n \"\"\"Response keys of successfully published a quest post.\"\"\"\r\n\r\n\r\nclass QuestPostPublishSuccessResponse(PostPublishSuccessResponse):\r\n \"\"\"Response body of successfully published a quest post.\"\"\"\r\n\r\n\r\nclass QuestPostPublishFailedResponse(PostPublishFailedResponse):\r\n \"\"\"Response body of failed to publish a quest post.\"\"\"\r\n\r\n\r\n# endregion\r\n\r\n\r\n# region Quest Post / List\r\n\r\nclass QuestPostListResponseKey(PostListResponseKey):\r\n \"\"\"\r\n Response keys of getting a quest post list.\r\n\r\n Keys must be consistent with the type ``QuestPostListResponse`` at the front side.\r\n \"\"\"\r\n\r\n # These keys need to be consistent with the definition structure at the front side\r\n # Type name: `PostListEntry`\r\n POSTS_SEQ_ID = \"seqId\"\r\n POSTS_LANG = \"lang\"\r\n POSTS_TITLE = \"title\"\r\n POSTS_VIEW_COUNT = \"viewCount\"\r\n POSTS_LAST_MODIFIED = \"modified\"\r\n POSTS_PUBLISHED = \"published\"\r\n\r\n @classmethod\r\n def convert_posts_key(cls, posts: list[dict[str, Any]]):\r\n \"\"\"Convert the keys in ``posts`` from model key to be the keys for the response.\"\"\"\r\n ret = []\r\n\r\n for post in posts:\r\n ret.append({\r\n cls.POSTS_SEQ_ID: post[QuestPostKey.SEQ_ID],\r\n cls.POSTS_LANG: post[QuestPostKey.LANG_CODE],\r\n cls.POSTS_TITLE: post[QuestPostKey.TITLE],\r\n cls.POSTS_LAST_MODIFIED: post[QuestPostKey.DT_LAST_MODIFIED],\r\n cls.POSTS_PUBLISHED: post[QuestPostKey.DT_PUBLISHED],\r\n cls.POSTS_VIEW_COUNT: post[QuestPostKey.VIEW_COUNT]\r\n })\r\n\r\n return ret\r\n\r\n\r\nclass QuestPostListResponse(PostListResponse):\r\n \"\"\"Response body of getting a quest post list.\"\"\"\r\n\r\n # pylint: disable=too-many-arguments\r\n def __init__(self, is_admin: bool, show_ads: bool, posts: list[dict[str, Any]], start_idx: int, post_count: int):\r\n super().__init__(is_admin, show_ads, start_idx, post_count)\r\n\r\n self._posts = QuestPostListResponseKey.convert_posts_key(posts)\r\n\r\n def serialize(self):\r\n return super().serialize() | {\r\n QuestPostListResponseKey.POSTS: self._posts\r\n }\r\n\r\n\r\n# endregion\r\n\r\n\r\n# region Quest Post / Get\r\n\r\nclass QuestPostGetSuccessResponseKey(PostGetSuccessResponseKey):\r\n \"\"\"\r\n Response keys of getting a quest post.\r\n\r\n Keys must be consistent with the type ``QuestPostGetResponse`` at the front side.\r\n \"\"\"\r\n\r\n TITLE = \"title\"\r\n\r\n GENERAL_INFO = \"general\"\r\n VIDEO = \"video\"\r\n\r\n INFO_PARENT = \"info\"\r\n INFO_POSITION = \"position\"\r\n INFO_BUILDS = \"builds\"\r\n INFO_ROTATIONS = \"rotations\"\r\n INFO_TIPS = \"tips\"\r\n\r\n ADDENDUM = \"addendum\"\r\n\r\n @classmethod\r\n def convert_info_key(cls, pos_info: list[dict[str, Any]]):\r\n \"\"\"Convert the keys in ``pos_info`` from model key to be the keys for the response.\"\"\"\r\n ret = []\r\n\r\n for post in pos_info:\r\n ret.append({\r\n cls.INFO_POSITION: post[QuestPostKey.INFO_POSITION],\r\n cls.INFO_BUILDS: post[QuestPostKey.INFO_BUILDS],\r\n cls.INFO_ROTATIONS: post[QuestPostKey.INFO_ROTATIONS],\r\n cls.INFO_TIPS: post[QuestPostKey.INFO_TIPS]\r\n })\r\n\r\n return ret\r\n\r\n\r\nclass QuestPostGetSuccessResponse(PostGetSuccessResponse):\r\n \"\"\"Response body of getting a quest post.\"\"\"\r\n\r\n # pylint: disable=too-many-instance-attributes\r\n\r\n def __init__(self, is_admin: bool, show_ads: bool, get_result: MultilingualGetOneResult):\r\n super().__init__(is_admin, show_ads, get_result)\r\n\r\n post = get_result.data\r\n\r\n self._title = post[QuestPostKey.TITLE]\r\n self._general = post[QuestPostKey.GENERAL_INFO]\r\n self._video = post[QuestPostKey.VIDEO]\r\n self._info = QuestPostGetSuccessResponseKey.convert_info_key(post[QuestPostKey.INFO_PARENT])\r\n self._addendum = post[QuestPostKey.ADDENDUM]\r\n\r\n def serialize(self):\r\n return super().serialize() | {\r\n QuestPostGetSuccessResponseKey.TITLE: self._title,\r\n QuestPostGetSuccessResponseKey.GENERAL_INFO: self._general,\r\n QuestPostGetSuccessResponseKey.VIDEO: self._video,\r\n QuestPostGetSuccessResponseKey.INFO_PARENT: self._info,\r\n QuestPostGetSuccessResponseKey.ADDENDUM: self._addendum,\r\n }\r\n\r\n\r\nclass QuestPostGetFailedResponse(PostGetFailedResponse):\r\n \"\"\"Response body of failed to get a quest post.\"\"\"\r\n\r\n\r\n# endregion\r\n\r\n\r\n# region Quest Post / Edit\r\n\r\nclass QuestPostEditSuccessResponseKey(PostEditSuccessResponseKey):\r\n \"\"\"Response keys of successfully edited a quest post.\"\"\"\r\n\r\n\r\nclass QuestPostEditSuccessResponse(PostEditSuccessResponse):\r\n \"\"\"Response body of successfully edited a quest post.\"\"\"\r\n\r\n\r\nclass QuestPostEditFailedResponse(PostEditFailedResponse):\r\n \"\"\"Response body of failed to edit a quest post.\"\"\"\r\n\r\n\r\n# endregion\r\n\r\n\r\n# region Quest Post / ID Check\r\n\r\nclass QuestPostIDCheckResponseKey(PostIDCheckResponseKey):\r\n \"\"\"Response keys of a quest post ID check request.\"\"\"\r\n\r\n\r\nclass QuestPostIDCheckResponse(PostIDCheckResponse):\r\n \"\"\"Response body of a quest post ID check request.\"\"\"\r\n\r\n# endregion\r\n","sub_path":"responses/body/post_quest.py","file_name":"post_quest.py","file_ext":"py","file_size_in_byte":6277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"104281261","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul 4 20:14:55 2017\r\n\r\n@author: luoshichao\r\n\"\"\"\r\n\r\n#类别不平衡 3006个正样本（违约），14611个负样本\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom datetime import date\r\nimport xgboost as xgb\r\nfrom sklearn.cross_validation import train_test_split\r\nimport random\r\n\r\ntrain_data_out=pd.read_csv('E:/geo/bank/train_data/train_data_out.csv')\r\ntrain_data_out['age']=[2017-int(item[6:10]) for item in train_data_out['iden_num_x']]\r\ntrain_data_out['loan_dt'] = pd.to_datetime(train_data_out['loan_dt'],dayfirst=True)\r\ntrain_data_out['loan_weekday'] = train_data_out['loan_dt'].apply(lambda x:x.weekday()+1)\r\ntrain_data_out=train_data_out.drop(['iden_num_x','loan_dt'],axis=1)\r\ntrain_data_out=train_data_out[['mbl_num','target','age', 'loan_weekday']]\r\n\r\ntest_data_out=pd.read_csv('E:/geo/bank/test_data/test_data_out.csv')\r\ndef to_age(x):\r\n x=str(x)\r\n if len(x)==18:\r\n return 2017-int(x[6:10])\r\n else:\r\n return np.nan\r\ntest_data_out['age']=test_data_out['iden_num_x'].apply(to_age)\r\ntest_data_out['age'].fillna(int(test_data_out['age'].mean()),inplace=True)\r\ntest_data_out['loan_dt'] = pd.to_datetime(test_data_out['loan_dt'],dayfirst=True)\r\ntest_data_out['loan_weekday'] = test_data_out['loan_dt'].apply(lambda x:x.weekday()+1)\r\ntest_data_out=test_data_out.drop(['iden_num_x','loan_dt'],axis=1)\r\ntest_data_out=test_data_out[['mbl_num','age', 'loan_weekday']]\r\n\r\ntrain_num=pd.read_csv('E:/geo/bank/train_data/train_num.csv')\r\ntrain_int=pd.read_csv('E:/geo/bank/train_data/train_int_in.csv')\r\ntrain_string=pd.read_csv('E:/geo/bank/train_data/train_string_nan.csv')\r\ntrain_original=pd.merge(train_data_out,train_num,on='mbl_num')\r\ntrain_original=pd.merge(train_original,train_int,on='mbl_num')\r\ntrain_original=pd.merge(train_original,train_string,on='mbl_num')\r\ndel train_num,train_int,train_string\r\n\r\ntest_num=pd.read_csv('E:/geo/bank/test_data/test_num.csv')\r\ntest_int=pd.read_csv('E:/geo/bank/test_data/test_int_in.csv')\r\ntest_string=pd.read_csv('E:/geo/bank/test_data/test_string_nan.csv')\r\ntest_original=pd.merge(test_data_out,test_num,on='mbl_num')\r\ntest_original=pd.merge(test_original,test_int,on='mbl_num')\r\ntest_original=pd.merge(test_original,test_string,on='mbl_num')\r\ndel test_num,test_int,test_string\r\n\r\ntrain_colname=list(train_original.columns)\r\ntrain_colname.remove('target')\r\ntest_original=test_original[train_colname]\r\n\r\n\r\ncol_names=['x_ori_'+str(item) for item in range(2,len(train_original.columns))]\r\ncol_names=['mbl_num','target']+col_names\r\ntrain_original.columns=col_names\r\ntrain=train_original\r\ntrain_y = train.target\r\ntrain_x = train.drop(['mbl_num','target'],axis=1)\r\ndtrain = xgb.DMatrix(train_x, label=train_y)\r\n\r\n'''\r\ntest = pd.read_csv('../../data/test/test_x_rank.csv')\r\ntest_Idx = test.Idx\r\ntest = test.drop('Idx',axis=1)\r\ndtest = xgb.DMatrix(test)\r\n'''\r\n\r\ndef pipeline(iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\r\n params={\r\n 'booster':'gbtree',\r\n\t 'objective': 'rank:pairwise',\r\n\t 'scale_pos_weight': float(len(train_y)-sum(train_y))/float(sum(train_y)),\r\n\t 'eval_metric': 'auc',\r\n\t 'gamma':gamma,\r\n\t 'max_depth':max_depth,\r\n\t 'lambda':lambd,\r\n\t 'subsample':subsample,\r\n\t 'colsample_bytree':colsample_bytree,\r\n\t 'min_child_weight':min_child_weight, \r\n\t 'eta': 0.2,\r\n\t 'seed':random_seed,\r\n\t 'nthread':8\r\n\t }\r\n\r\n watchlist = [(dtrain,'train')]\r\n model = xgb.train(params,dtrain,num_boost_round=700,evals=watchlist)\r\n #model.save_model('./model/xgb{0}.model'.format(iteration))\r\n #predict test set\r\n #test_y = model.predict(dtest)\r\n #test_result = pd.DataFrame(test_Idx,columns=[\"Idx\"])\r\n #test_result[\"score\"] = test_y\r\n #test_result.to_csv(\"./preds/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\r\n \r\n #save feature score\r\n feature_score = model.get_fscore()\r\n feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\r\n fs = []\r\n for (key,value) in feature_score:\r\n fs.append(\"{0},{1}\\n\".format(key,value))\r\n \r\n with open('E:/geo/code/ori_feature_select/feature_score/feature_score_{0}.csv'.format(iteration),'w') as f:\r\n f.writelines(\"feature,score\\n\")\r\n f.writelines(fs)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n random_seed = list(range(10000,20000,100))\r\n gamma = [i/1000.0 for i in range(0,300,3)]\r\n max_depth = [5,6,7]\r\n lambd = list(range(400,600,2))\r\n subsample = [i/1000.0 for i in range(500,700,2)]\r\n colsample_bytree = [i/1000.0 for i in range(550,750,4)]\r\n min_child_weight = [i/1000.0 for i in range(250,550,3)]\r\n \r\n random.shuffle(random_seed)\r\n random.shuffle(gamma)\r\n random.shuffle(max_depth)\r\n random.shuffle(lambd)\r\n random.shuffle(subsample)\r\n random.shuffle(colsample_bytree)\r\n random.shuffle(min_child_weight)\r\n \r\n for i in range(5):\r\n pipeline(i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])","sub_path":"ori_feature_select/ordata_select.py","file_name":"ordata_select.py","file_ext":"py","file_size_in_byte":5030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"581771975","text":"# coding=utf-8\npath=\"/Users/ifind/Desktop/python/111.txt\"\nf = open(path, \"r\")\ncontent = f.read()\nf.close()\n\npoint=path.rfind(\".\")\ndestPath=path[0:point]+\"[备份]\"+path[point:]\n\ncopy = open(destPath, \"w\")\ncopy.write(content)\ncopy.close()\n","sub_path":"study_04/文件的备份.py","file_name":"文件的备份.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"619024261","text":"myAge = 19\nmyAge += 1\nprint(myAge)\n\na = 100\nb = 10\na += b \ns = \"the answer is \"\ns += str(a)\nprint(a)\nprint(s)\n\npet = \"dog\"\npet += \" and bunny\"\nprint(pet)","sub_path":"append.py","file_name":"append.py","file_ext":"py","file_size_in_byte":153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"202025840","text":"import glob\n# Initialisation de la variable permettant de trouver le fichier ou est stocke la temperature via la sonde DALLAS\nbase_dir = '/sys/bus/w1/devices/'\ndevice_folder = glob.glob(base_dir + '28*')[0]\ndevice_file = device_folder + '/w1_slave'\n\n# Fonction pour lire le contenu du fichier des temperatures\ndef read_temp_raw():\n f = open(device_file, 'r')\n lines = f.readlines()\n f.close()\n return lines\n\n# Fonction pour lire le contenu et l'analyser pour trouver la valeur de la temperature\ndef read_temp():\n lines = read_temp_raw()\n # Si le fichier ne contient paqs le mot YES, c'est qu'il y a un probleme, on retourne alors -200\n if lines[0].strip()[-3:] != 'YES':\n return -200\n equals_pos = lines[1].find('t=') # On regarde la position du mot t= dans la seconde ligne du fichier\n if equals_pos != -1:\n temp_string = lines[1][equals_pos+2:]\n temp_c = float(temp_string) / 100.0\n return int(temp_c)\n","sub_path":"GestionSonde.py","file_name":"GestionSonde.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"325851277","text":"#Coin toss!!!\n\nimport random\n\nflips = 0\nheads = 0\ntails = 0\nwhile flips < 5000:\n\theadsortails = random.randint(1,2)\n\t\n\tif heads or tails == 1:\n\t\theads = heads + 1\n\n\tif headsortails == 2:\n\t\ttails = tails + 1\n\n\tflips = flips + 1\n\nprint('Its heads you got ' + str(heads), 'so far and ' + str(tails), 'tails so far ')\nprint('Its tails you got ' + str(tails), 'so far and ' + str(heads), 'heads so far ')\nprint('You flipped ' + str(flips), 'times')\n\n","sub_path":"WilliamsCleveland/Assignments/Python/CoinToss/heads_tails.py","file_name":"heads_tails.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"15370552","text":"import torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torchvision import models\n\nfrom ..utils import initialize_weights\n\nfrom .psp_net import _PyramidPoolingModule\n\n\nclass PSPHead(nn.Module):\n def __init__(self, num_classes):\n super().__init__()\n\n self.ppm = _PyramidPoolingModule(2048, 512, (1, 2, 3, 6))\n self.final = nn.Sequential(\n nn.Conv2d(4096, 512, kernel_size=3, padding=1, bias=False),\n nn.BatchNorm2d(512, momentum=.95),\n nn.ReLU(inplace=True),\n nn.Dropout(0.1),\n nn.Conv2d(512, num_classes, kernel_size=1)\n )\n\n initialize_weights(self.ppm, self.final)\n\n def forward(self, features_from_backbone, img_size):\n result = self.final(self.ppm(features_from_backbone))\n return F.interpolate(result, img_size[2:], mode='bilinear')\n\n\nclass PSPNet_Multihead(nn.Module):\n def __init__(self, num_heads, num_classes, pretrained=True):\n super().__init__()\n\n self.init_heads(num_heads, num_classes=num_classes)\n self.init_backbone(pretrained=pretrained)\n\n def init_backbone(self, pretrained):\n resnet = models.resnet101(pretrained=pretrained)\n\n for n, m in resnet.layer3.named_modules():\n if 'conv2' in n:\n m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)\n elif 'downsample.0' in n:\n m.stride = (1, 1)\n for n, m in resnet.layer4.named_modules():\n if 'conv2' in n:\n m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)\n elif 'downsample.0' in n:\n m.stride = (1, 1)\n\n self.backbone = nn.Sequential(\n nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool), # layer 0\n resnet.layer1, \n resnet.layer2,\n resnet.layer3, \n resnet.layer4,\n )\n\n def init_heads(self, num_heads, num_classes):\n\n self.heads = nn.Sequential(\n *[PSPHead(num_classes=num_classes) for i in range(num_heads)]\n )\n\n def forward(self, image):\n img_size = image.size()\n\n backbone_features = self.backbone(image)\n\n return torch.cat([\n head(backbone_features, img_size=img_size)\n for head in self.heads\n ], dim=1)\n\n","sub_path":"models/psp_net_multihead.py","file_name":"psp_net_multihead.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"305875653","text":"# Author Aleksi Hoffman\n# Based on apns-client implementation by Sardar Yumatov\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\n__title__ = 'apns-clerk'\n__version__ = \"0.1.2\"\n__author__ = \"Aleksi Hoffman\"\n__contact__ = \"aleksi@lekksi.com\"\n__license__ = \"Apache 2.0\"\n__homepage__ = \"https://bitbucket.org/aleksihoffman/apns-clerk\"\n__copyright__ = 'Copyright 2014 Aleksi Hoffman'\n\n\nfrom apns_clerk.apns import APNs, Message\nfrom apns_clerk.transport import Session\n","sub_path":"apns_clerk/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"536226574","text":"from ChatBox import *\nfrom UserLoginGUI import *\n\ng = dict( # globals\n #\n screenWidth = 640, # content-width = 560 + 140spacing for 4 columns\n screenHeight = 480, # content-height=400\n\n #COLORS\n mainBackgroundColor = '#eeeeee', # This color should change depending on the current number of turns.\n buttonBackgroundColor = '#dddddd',\n fontColor = '#a0b4a9',\n\n #UTILITIES\n activeWindows = [],\n\n #STATS\n patience = 0,\n turn = 0\n)\n\ndef main():\n\n root = tk.Tk()\n app = UserLoginGUI(root) # Program's Main Entry point\n #app = ChatManagerWidget(root) #testing. comment out later\n #app = ChatBoxGUI(root)\n root.mainloop()\n\n \"Submitting info in UserLoginGUI form launches ChatManagerWidget\"\n\n \"ChatManagerWidget Launched\"\n\n\nif __name__ == \"__main__\": main()\n","sub_path":"WindowsInWindowsDemo/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"236583849","text":"# coding: utf-8\n\nimport pandas as pd\nfrom keras.optimizers import Adam\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Convolution2D, Dropout, Flatten, Lambda\nfrom keras.preprocessing.image import load_img\nimport numpy as np\nimport cv2\n\ncrop_y = [60, 140]\nRESIZE = (64, 64)\n\n\ndef get_one_image(image_file):\n \"\"\"\n Read in one image file\n\n :param image_file: (str) full path of an image\n :return: (numpy.ndarray) image ndarray\n \"\"\"\n\n image = np.asarray(load_img(image_file.strip()))\n return image\n\n\ndef crop_image(image, crop_y):\n \"\"\"\n Crop the image with crop_y range\n\n :param image: (numpy.ndarray) image ndarray\n :param crop_y: (list) two item list or tuple providing the cropping range\n :return: (numpy array) cropped image ndarray\n \"\"\"\n return image[crop_y[0]:crop_y[1], :, :]\n\n\ndef resize_image(image, RESIZE):\n \"\"\"\n resize the image\n\n :param image: (numpy.ndarray) image ndarray\n :param RESIZE: (list) image size, length 2 tuple or list\n :return: (numpy array) image ndarray after resizing\n \"\"\"\n image = cv2.resize(image, RESIZE, cv2.INTER_AREA) \n return image\n\n\ndef get_one_image_crop(image_file, crop_y):\n \"\"\"\n Buddle of two functions\n\n :param image_file: (numpy.ndarray) image file path\n :param crop_y: (list) crop range\n :return: (numpy array) cropped image\n \"\"\"\n return crop_image(get_one_image(image_file), crop_y)\n\n\ndef shift_image(image, steering_angle, magx=120, magy=40):\n \"\"\"\n Shift the image both horizontally and vertically, correspondingly\n the steering angles change. If the image is shifted to the left, we expect\n the new steering angle would be greater. On the other hand, if the image is\n shifted to the right, the angle should be smaller. Here I am using the change\n factor of 0.004/px.\n\n :param image: (numpy.ndarray) image ndarray\n :param steering_angle: (float) steering angle\n :param magx: (float) magnitude of x direction shift\n :param magy: (float) magnitude of y direction shift\n :return: (tuple) the shifted image, new_steering angle\n \"\"\"\n rows, cols, _ = image.shape\n tx = magx*(np.random.rand(1) - 0.5)\n ty = magy*(np.random.rand(1) - 0.5)\n image = cv2.warpAffine(image, np.float32([[1, 0, tx], [0, 1, ty]]), (cols, rows))\n steering_angle = steering_angle + 0.004*(+tx) + 0.004 * ty * np.sign(steering_angle)\n return image, steering_angle\n\n\ndef flip_image(image, steering_angle):\n \"\"\"\n Flip the image horizontally and change the steering angle sign\n\n :param image: (numpy.ndarray) image ndarray\n :param steering_angle: (float) steering angle\n :return: (tuple) flipped image ndarray, new_steering_angle\n \"\"\"\n return image[:, ::-1, :], -steering_angle\n\n\ndef change_brightness(image, steering_angle):\n \"\"\"\n Change the brightness of the road without changing steering angle\n\n :param image: (numpy.ndarray) image ndarray\n :param steering_angle: (float) steering angle\n :return: (tuple), image and new steering angle\n\n \"\"\"\n scale = int((np.random.rand(1) - 0.8)*255)\n image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n image[:, :, 2] = cv2.add(image[:, :, 2], scale)\n image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)\n return image, steering_angle\n\n\ndef random_rotation(image, steering_angle, mag=20):\n \"\"\"\n Randomly rotate the image and change the steering angle correspondingly\n\n :param image: (numpy.ndarray) image ndarray\n :param steering_angle: (float) steering angle\n :param mag: (float) magnitude of rotation\n :return: (tuple) rotated image, new steering angle\n \"\"\"\n rotate_angle = mag * (np.random.rand(1) - 0.5)\n rows, cols, _ = image.shape\n M = cv2.getRotationMatrix2D((cols/2, rows/2), rotate_angle, 1)\n image = cv2.warpAffine(image, M, (cols, rows))\n steering_angle -= rotate_angle / 30.0\n return image, steering_angle\n\n\ndef shear_image(image, steering_angle, mag=80):\n \"\"\"\n Shear the image and change the steering angle\n\n :param image: (numpy.ndarray) image ndarray\n :param steering_angle: (float) steering angle\n :param mag: (float) magnitude of shearing\n :return: (tuple) sheared image, new steering angle\n \"\"\"\n rows, cols, _ = image.shape\n shear_angle = (np.random.rand(1)-0.5)*mag\n pts1 = np.float32([[0, 80], [320, 80], [shear_angle+100, 0], [shear_angle + 320-100, 0]])\n pts2 = np.float32([[0, 80], [320, 80], [100, 0], [220, 0]])\n m = cv2.getPerspectiveTransform(pts1, pts2)\n image = cv2.warpPerspective(image, m, (cols, rows))\n return image, steering_angle - shear_angle / 160. \n \n \ndef image_generator(image_index, batch_size):\n \"\"\"\n Generate images from driving_log.csv and the image file directory\n\n :param image_index: (pandas.DataFrame) image file name and steering angle\n :param batch_size: (int) batch size\n :return: (tuple) image array and steering angle\n \"\"\"\n\n while True:\n \n random_index = np.random.permutation(image_index.index)[:batch_size]\n image_data = image_index.iloc[random_index]\n image_data.reset_index(drop=True, inplace=True)\n steering_angle = np.asarray(image_data['steering'])\n n_sub = image_data.shape[0]\n image_temp = np.zeros(shape=(n_sub, RESIZE[0], RESIZE[1], 3))\n \n for i, j in image_data.iterrows():\n # flip a coin and decide which camera will be used\n # For the center image, the steering angle does not change\n # For the left camera, I correct the angle by +1./25 degrees\n # For the right camera, the angle is corrected by -1/25 degrees\n column_index = np.random.randint(3) # 0: center, 1: left, 2: right\n steering_factor = 0.25 # 180./np.pi*1./15.0/25.0\n\n image = get_one_image(j.iloc[column_index])\n image = crop_image(image, crop_y)\n if column_index == 0:\n dsteering = 0\n elif column_index == 1:\n dsteering = steering_factor\n else:\n dsteering = -steering_factor\n steering_angle[i] += dsteering\n\n # Data augmentation\n is_shift = np.random.choice([0, 1], size=1, p=[0.5, 0.5])\n if is_shift:\n image, new_angle = shift_image(image, steering_angle[i])\n steering_angle[i] = new_angle\n\n # random flip\n is_flip = np.random.choice([0, 1], size=1, p=[0.5, 0.5])\n if is_flip:\n image, new_angle = flip_image(image, steering_angle[i])\n steering_angle[i] = new_angle\n \n # random brightness\n is_bright_change = np.random.choice([0, 1], size=1, p=[0.2, 0.8])\n if is_bright_change:\n image, _ = change_brightness(image, steering_angle[i])\n\n # random rotation\n is_rotate = np.random.choice([0, 1], size=1, p=[0.5, 0.5])\n if is_rotate:\n image, new_angle = random_rotation(image, steering_angle[i])\n steering_angle[i] = new_angle\n\n # random shear\n is_shear = np.random.choice([0, 1], size=1, p=[0.7, 0.3])\n if is_shear:\n image, new_angle = shear_image(image, steering_angle[i])\n steering_angle[i] = new_angle\n\n image = resize_image(image, RESIZE)\n image_temp[i, :, :, :] = image\n\n yield image_temp, steering_angle\n\n\ndef get_validation(image_index):\n \"\"\"\n Get the center image data as the validation.\n\n :param image_index: (pandas.DataFrame) image names and ther\n :return:\n \"\"\"\n N = image_index.shape[0]\n X = np.zeros(shape=(N, 64, 64, 3))\n Y = image_index['steering']\n for i in range(N):\n X[i, :, :, :] = resize_image(get_one_image_crop(image_index.loc[i, 'center'], crop_y), RESIZE)\n return X, np.array(Y)\n\n\nimage_index = pd.read_csv('driving_log.csv')\n\n\ndef nvidia():\n model = Sequential()\n model.add(Lambda(lambda x: x/255. - 0.5, input_shape= (64, 64, 3)))\n model.add(Convolution2D(32, 5, 5, border_mode='valid', subsample=(2,2), activation='relu'))\n model.add(Convolution2D(36, 5, 5, border_mode='valid', subsample=(2,2), activation='relu'))\n model.add(Convolution2D(48, 5, 5, border_mode='valid', subsample=(2,2), activation='relu'))\n model.add(Convolution2D(64, 3, 3, border_mode='valid', subsample=(1,1), activation='relu'))\n model.add(Convolution2D(64, 3, 3, border_mode='valid', subsample=(1,1), activation='relu'))\n model.add(Flatten())\n model.add(Dense(1164, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(100, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(50, activation='relu'))\n model.add(Dense(1))\n return model\n\n\nbatch_size = 200\nnb_epoch = 10\nN = 40000 \n\nmodel = nvidia()\nprint(model.summary())\nadam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\nmodel.compile(optimizer=adam, loss=\"mse\")\ngenerator = image_generator(image_index, batch_size)\nmodel.fit_generator(generator, samples_per_epoch=N, nb_epoch=nb_epoch,\n verbose=1, validation_data=get_validation(image_index))\n\njson_string = model.to_json()\nopen('model.json', 'w').write(json_string)\nmodel.save_weights('model.h5')\n\n","sub_path":"train_steering_angle.py","file_name":"train_steering_angle.py","file_ext":"py","file_size_in_byte":9316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"614752455","text":"from django.db import models\n\n\nclass GameCategory(models.Model):\n name = models.CharField(max_length=200, unique=True)\n\n class Meta:\n ordering = ('name',)\n\n def __str__(self):\n return self.name\n\n\nclass Game(models.Model):\n \"\"\"\n \"related_name\", e.g., on the owner field, creates a backwards relation from the User model to the Games model.\n This value indicates the name to be used for the relation from the related User object back to a Game object.\n This way, we will be able to access all the games owned by a specific user.\n\n \"on_delete\", e.g., on the owner field, enables that when we delete a user, all the user games be deleted too.\n \"\"\"\n\n owner = models.ForeignKey('auth.User', related_name='games', on_delete=models.CASCADE)\n created = models.DateTimeField(auto_now_add=True)\n name = models.CharField(max_length=200, unique=True)\n game_category = models.ForeignKey(GameCategory, related_name='games', on_delete=models.CASCADE)\n release_date = models.DateTimeField()\n played = models.BooleanField(default=False)\n\n class Meta:\n ordering = ('name',)\n\n def __str__(self):\n return self.name\n\n\nclass Player(models.Model):\n MALE = 'M'\n FEMALE = 'F'\n GENDER_CHOICES = (\n (MALE, 'Male'),\n (FEMALE, 'Female')\n )\n\n created = models.DateTimeField(auto_now_add=True)\n name = models.CharField(max_length=50, blank=False, default='', unique=True)\n gender = models.CharField(max_length=2, choices=GENDER_CHOICES, default=MALE)\n\n class Meta:\n ordering = ('name',)\n\n def __str__(self):\n return self.name\n\n\nclass PlayerScore(models.Model):\n player = models.ForeignKey(Player, related_name='scores', on_delete=models.CASCADE)\n game = models.ForeignKey(Game, on_delete=models.CASCADE)\n score = models.IntegerField()\n score_date = models.DateTimeField()\n\n class Meta:\n # Order by score descending\n ordering = ('-score',)\n\n","sub_path":"django/drf_book/gamesapi/games/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"566912042","text":"'''\n 문제설명\n 백준 1916 최소비용 구하기(다익스트라 알고리즘 이용)\n A 도시에서 B 도시로 가는데 드는 최소 비용을 출력한다\n 해결전략\n 다익스트라 알고리즘을 이용해서 한 정점으로부터 다른 모든 정점으로의 최소 비용(거리)를 구한다.\n 현재 정점에서 가장 가까운 정점을 빠르게 찾기 위해 우선순위 큐를 이용해서 다익스트라 알고리즘을 구현한다. \n 그래프에 대해 주어진 입력(시작 정점, 도착 정점, 가중치)를 인접 리스트 형태로 저장한다.\n 우선순위 큐에 출발 정점을 넣고 하나씩 꺼내면서 꺼낸 정점을 거쳐서 갔을 때의 거리가 더 짧으면 \n 출발 정점으로부터 각 정점까지의 최단 거리인 dis 리스트를 갱신한다.\n'''\nimport sys\nimport heapq\ninput = sys.stdin.readline\n\ndef dij(graph, start, end):\n Q = []\n dis = [INF] * (n+1)\n dis[start] = 0\n heapq.heappush(Q, (0, start))\n while Q:\n cost, pos = heapq.heappop(Q)\n for to, wei in graph[pos]:\n wei += cost\n if wei < dis[to]:\n dis[to] = wei\n heapq.heappush(Q, (wei, to))\n return dis[end]\n\nINF = sys.maxsize\nn = int(input())\nm = int(input())\ngraph = [[] for _ in range(n+1)]\nfor _ in range(m):\n fr, to, wei = map(int, input().split())\n graph[fr].append((to, wei))\nstart, end = map(int, input().split())\nprint(dij(graph, start, end))\n","sub_path":"week5/HongheeLee/BOJ_1916_210129.py","file_name":"BOJ_1916_210129.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"616717387","text":"import re\n\nimport lib\nimport item_db\n\nshop_item_re = re.compile(r'''(.*?)(\\d+)(.*?)(.*?)\\n
  • Votes:
    • \\n
  • Choice:
    • \\n
  • Votes:
    • ')\n formset = self.make_choiceformset([('Calexico', '100'), ('', '')], initial_forms=1)\n self.assertTrue(formset.is_valid())\n self.assertEqual([form.cleaned_data for form in formset.forms], [{\n 'votes': 100,\n 'choice': 'Calexico',\n }, {\n \n }])","sub_path":"Data Set/bug-fixing-4/d04b324969e045d1cf2bf7da18ea0497d8066935--bug.py","file_name":"d04b324969e045d1cf2bf7da18ea0497d8066935--bug.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"159680440","text":"\n# Master List\n\nfrom riotwatcher import *\nimport json\n\n# Lane/Role Identifier\n\ndef Lane_ID(match):\n roles = ['','','','','','','','','','']\n positions_parsed = 0\n supp_sightstone=0\n supp_item=0\n p = match['participants']\n for y in p:\n s = y['stats']\n t = y['timeline']\n for i in range(6):\n item = 'item'+str(i)\n if s[item]==3302 or s[item]==3097 or s[item]==3401 or s[item]==3301 or s[item]==3096 or s[item]==3069 or s[item]==3303 or s[item]==3092 or s[item]==3098:\n supp_item+=1\n if s[item]==2049 or s[item]==2045 and t['lane']!='JUNGLE':\n supp_sightstone+=1\n if t['lane']=='BOTTOM':\n # if t['role']=='DUO_CARRY':\n # roles[positions_parsed]=['BOTTOM','DUO_CARRY']\n # positions_parsed+=1\n # supp_sightstone=0\n # supp_item=0\n # elif t['role']=='DUO_SUPPORT':\n # roles[positions_parsed]=['BOTTOM','DUO_SUPPORT']\n # positions_parsed+=1\n # supp_sightstone=0\n # supp_item=0\n # else:\n roles[positions_parsed]=[t['lane'],t['role'],s['minionsKilled'],s['wardsPlaced'],supp_sightstone,supp_item,s['physicalDamageDealt']]\n positions_parsed+=1\n supp_sightstone=0\n supp_item=0\n else:\n roles[positions_parsed]=[t['lane'],t['role']]\n positions_parsed+=1\n supp_sightstone=0\n supp_item=0\n positions_parsed=0\n return roles\n\n# ELO Identifier\n\ndef ELO_ID(match):\n ELO=['','','','','']\n team_ELO=['','','','','']\n index=0\n team_index=0\n p = match[0]['participants']\n for x in p:\n team_ELO[team_index]=x['highestAchievedSeasonTier']\n team_index+=1\n if team_index==5:\n ELO[index]=team_ELO\n index+=2\n team_index=0\n team_ELO=['','','','','']\n# PRELIM MODEL: UNRANKED = 0, BRONZE = 1, SILVER = 2, GOLD = 3, PLAT = 4, DIAMOND = 5, MASTER = 6, CHALLENGER = 7\n# CHANGE SCALE LATER\n for x in [0,2]:\n AVG_Team_ELO=0\n for y in range(len(ELO[x])):\n print(ELO[x][y])\n if ELO[x][y]=='BRONZE':\n AVG_Team_ELO+=1\n print(AVG_Team_ELO)\n elif ELO[x][y]=='SILVER':\n AVG_Team_ELO+=2\n print(AVG_Team_ELO)\n elif ELO[x][y]=='GOLD':\n AVG_Team_ELO+=3\n print(AVG_Team_ELO)\n elif ELO[x][y]=='PLATINUM':\n AVG_Team_ELO+=4\n print(AVG_Team_ELO)\n elif ELO[x][y]=='DIAMOND':\n AVG_Team_ELO+=5\n print(AVG_Team_ELO)\n elif ELO[x][y]=='MASTER':\n AVG_Team_ELO+=6\n print(AVG_Team_ELO)\n elif ELO[x][y]=='CHALLENGER':\n AVG_Team_ELO+=7\n print(AVG_Team_ELO)\n elif ELO[x][y]=='UNRANKED':\n AVG_Team_ELO+=0\n print(AVG_Team_ELO)\n AVG_Team_ELO=float((AVG_Team_ELO))/5\n ELO[x+1]=AVG_Team_ELO\n ELO[4]=(ELO[1]+ELO[3])/2\n return ELO\n \n# CHAMPION ID GRABBER\n\ndef Champion_ID(match):\n team=['','']\n champ=['', '', '', '', '']\n team_index=0\n index=0\n p = match[0]['participants']\n for x in p:\n t=x['timeline']\n champ[index]=[x['championId'],t['lane']]\n index+=1\n if index==5:\n team[team_index]=champ\n team_index+=1\n index=0\n champ=['','','','','']\n return team\n \n# ITEM BUILD TIMELINE\n\ndef Item_Builds(match):\n build_pathes=['','','','','','','','','','']\n player_1ct=0\n player_2ct=0\n player_3ct=0\n player_4ct=0\n player_5ct=0\n player_6ct=0\n player_7ct=0\n player_8ct=0\n player_9ct=0\n player_10ct=0\n t = match[0]['timeline']\n f=t['frames']\n for i in range(1,len(f)):\n e=f[i]['events']\n for j in range(len(e)):\n if 'itemId' in e[j]:\n if e[j]['participantId']==1:\n player_1ct+=1\n if e[j]['participantId']==2:\n player_2ct+=1\n if e[j]['participantId']==3:\n player_3ct+=1\n if e[j]['participantId']==4:\n player_4ct+=1\n if e[j]['participantId']==5:\n player_5ct+=1\n if e[j]['participantId']==6:\n player_6ct+=1\n if e[j]['participantId']==7:\n player_7ct+=1\n if e[j]['participantId']==8:\n player_8ct+=1\n if e[j]['participantId']==9:\n player_9ct+=1\n if e[j]['participantId']==10:\n player_10ct+=1\n player_1_path=['' for i in range(player_1ct)]\n player_2_path=['' for i in range(player_2ct)]\n player_3_path=['' for i in range(player_3ct)]\n player_4_path=['' for i in range(player_4ct)]\n player_5_path=['' for i in range(player_5ct)]\n player_6_path=['' for i in range(player_6ct)]\n player_7_path=['' for i in range(player_7ct)]\n player_8_path=['' for i in range(player_8ct)]\n player_9_path=['' for i in range(player_9ct)]\n player_10_path=['' for i in range(player_10ct)]\n \n return\n","sub_path":"riotmaster.py","file_name":"riotmaster.py","file_ext":"py","file_size_in_byte":5342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"334989476","text":"'''\n\n\tV-Ray/Blender\n\n\thttp://vray.cgdo.ru\n\n\tAuthor: Andrey M. Izrantsev (aka bdancer)\n\tE-Mail: izrantsev@cgdo.ru\n\n\tThis program is free software; you can redistribute it and/or\n\tmodify it under the terms of the GNU General Public License\n\tas published by the Free Software Foundation; either version 2\n\tof the License, or (at your option) any later version.\n\n\tThis program is distributed in the hope that it will be useful,\n\tbut WITHOUT ANY WARRANTY; without even the implied warranty of\n\tMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n\tGNU General Public License for more details.\n\n\tYou should have received a copy of the GNU General Public License\n\talong with this program. If not, see .\n\n\tAll Rights Reserved. V-Ray(R) is a registered trademark of Chaos Software.\n\n'''\n\nbl_info = {\n \"name\" : \"V-Ray For Blender 2.5\",\n \"author\" : \"\",\n \"blender\" : (2, 67, 0),\n \"location\" : \"Info header, render engine menu\",\n \"description\" : \"Exporter to the V-Ray Standalone file format\",\n \"warning\" : \"\",\n \"wiki_url\" : \"https://github.com/bdancer/vb25/wiki\",\n \"tracker_url\" : \"https://github.com/bdancer/vb25/issues\",\n \"support\" : 'COMMUNITY',\n \"category\": \"Learnbgame\",\n}\n\n\nif \"bpy\" in locals():\n\timport imp\n\timp.reload(lib)\n\timp.reload(plugins)\n\timp.reload(ui)\n\timp.reload(preset)\n\timp.reload(render_ops)\n\timp.reload(events)\nelse:\n\timport bpy\n\tfrom vb25 import lib\n\tfrom vb25 import plugins\n\tfrom vb25 import ui\n\tfrom vb25 import preset\n\tfrom vb25 import render_ops\n\tfrom vb25 import events\n\n\ndef register():\n\tui.register()\n\tevents.register()\n\tplugins.add_properties()\n\trender_ops.register()\n\n\ndef unregister():\n\trender_ops.unregister()\n\tplugins.remove_properties()\n\tevents.unregister()\n\tui.unregister()\n","sub_path":"All_In_One/addons/vb25/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"324443345","text":"#!/usr/bin/env python3\n# -*-coding: utf-8-*-\n# Author : Chris\n# Blog : http://blog.chriscabin.com\n# GitHub : https://www.github.com/chrisleegit\n# File : scale.py\n# Date : 16-7-3\n# Version: 0.1\n# Description: ...\nfrom tkinter import *\n\n\nclass ScaleDemo(Frame):\n def __init__(self, master=None, cnf={}, **kw):\n super(ScaleDemo, self).__init__(master, cnf, **kw)\n\n # 这里使用变量是为了同步，实际上scale可以调用get()和set()方法获取或设置值\n self.var = IntVar(self)\n Scale(self, label='Vertical Hours', command=self.on_move, variable=self.var, from_=0, to=23, tickinterval=2,\n showvalue=YES, resolution=1).pack(expand=YES, fill=Y)\n Scale(self, label='Horizontal Hours', command=self.on_move, variable=self.var, from_=0, to=23,\n length=240, tickinterval=2, showvalue=YES, orient='horizontal').pack(expand=YES, fill=X)\n\n self.var.set(12)\n\n self.pack()\n\n def on_move(self, value):\n print('value: ', value)\n\n\ndef main():\n ScaleDemo().pack(fill=BOTH, expand=YES)\n mainloop()\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"ch8/scale.py","file_name":"scale.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"450268620","text":"'''\nToday's challenge is to divide two polynomials. For example,\nlong division can be implemented.\n\nDisplay the quotient and remainder obtained upon division.\nInput Description\n\nLet the user enter two polynomials. Feel free to accept it as\nyou wish to. Divide the first polynomial by the second. For\nthe sake of clarity, I'm writing whole expressions in the\nchallenge input, but by all means, feel free to accept the\ndegree and all the coefficients of a polynomial.\nOutput Description\n\nDisplay the remainder and quotient obtained.\nChallenge Input\n\n1:\n\n 4x3 + 2x2 - 6x + 3\n\n x - 3\n\n2:\n\n 2x4 - 9x3 + 21x2 - 26x + 12\n\n 2x - 3\n\n3:\n\n 10x4 - 7x2 -1\n\n x2 - x + 3\n\nChallenge Output\n\n1:\n\n Quotient: 4x2 + 14x + 36 Remainder: 111\n\n2:\n\n Quotient: x3 - 3x2 +6x - 4 Remainder: 0\n\n3:\n\n Quotient: 10x2 + 10x - 27 Remainder: -57x + 80\n\n'''\n\nimport re\n\n\ndef multipl(s1, s2, s3):\n \"\"\"\n :param s1:str\n :param s2: tuple\n :param s3: tuple\n :return: str\n\n multiply quotient, divisor coefficients, divisor exponents\n \"\"\"\n mult_string = ''\n # co-efficient of quotient\n if 'x' not in s1:\n coeff_q = int(s1)\n else:\n coeff_q = re.findall('^[+-]\\d+|^\\d+', s1)\n coeff_q = int(coeff_q[0])\n\n # exponent part of quotient\n exp_q = re.findall('[\\^](\\d+)', s1)\n if exp_q == []:\n exp_q = 0\n else:\n exp_q = int(exp_q[0])\n\n for x in range(0, len(s2)):\n co_ef = coeff_q * int(s2[x])\n exp = str(exp_q + int(s3[x]))\n if co_ef >= 0:\n mult_string += '+' + str(co_ef) + 'x^' + exp + ' '\n else:\n mult_string += ' ' + str(co_ef) + 'x^' + exp + ' '\n\n return mult_string\n\n\ndef quotient_is(a, b):\n \"\"\"\n :param a: str\n :param b: str\n :return: str\n\n input dividend and divisor stats to calculate the quotient\n \"\"\"\n co_effs_one = re.findall('^[+-]\\d+', a)\n co_effs_one = int(co_effs_one[0])\n exp_one = re.findall('\\^(\\d+)', a)\n if not '0' in exp_one:\n exp_one.append('0')\n exp_one = int(exp_one[0])\n\n divisor_co_eff_one = re.findall('^\\d+', b)\n if len(divisor_co_eff_one) == 0:\n divisor_co_eff_one.append('1')\n divisor_co_eff_one = int(divisor_co_eff_one[0])\n\n divisor_exp = re.findall('[x^](\\d+)', b)\n if len(divisor_exp) == 0:\n divisor_exp.append('1')\n divisor_exp = int(divisor_exp[0])\n\n co_eff_div = str(int(co_effs_one / divisor_co_eff_one))\n if co_eff_div == '0':\n co_eff_div = '1'\n exp = exp_one - divisor_exp\n q = co_eff_div + 'x^' + str(exp) ## / to -\n return q\n\n\ndef subtract(a, b):\n \"\"\"\n :param a: str\n :param b: str\n :return:str\n subtract remainder from the result 'b' from 'a'\n \"a\" = dividend, \"b\" = mult \"\"\"\n sub_output = ''\n # coefficients of the dividend\n co_effs = re.findall('[+-]?\\d+(?=x)|[+-]\\d+', a)\n # here may be the problem!!\n # exponents of the dividend\n exps = re.findall('[x]\\^\\d+', a)\n if len(exps) < len(co_effs):\n exps.append('x^0')\n # coefficients of the result of previous multiplication\n co_eff_mult = re.findall('([+-]\\d+|\\s[+-]\\d+)', b)\n for x in range(0, len(co_effs)):\n # dividend co-efficient\n divi_coeff = co_effs[x]\n try:\n mult_coeff = co_eff_mult[x]\n except IndexError:\n mult_coeff = '0'\n subtracted = int(divi_coeff) - int(mult_coeff)\n try:\n if subtracted == 0:\n sub_output += ''\n else:\n sub_output += str(subtracted) + exps[x] + ' '\n except IndexError:\n sub_output += str(subtracted)\n\n return sub_output\n\n\ndef division(number_one: str, number_two: str) -> str:\n \"\"\"\n divide the leading term of \"a\" by\n the leading term of the divisor\n :param number_one:\n :param number_two:\n :return:\n \"\"\"\n if 'x' not in number_one:\n return ' ' + str(number_one)\n co_eff_sub = re.findall('[+-]?\\d+(?=x)|[+-]\\d+$', number_one)\n co_eff_sub = int(co_eff_sub[0])\n co_exp_sub = re.findall('\\^(\\d+)', number_one)\n co_exp_sub = int(co_exp_sub[0])\n if co_exp_sub < int(number_two[1][0]):\n return number_one + '/' + divisor\n if co_exp_sub == 0:\n return ''\n tail = re.findall('[x]\\^', number_one)\n tail = tail[0]\n div1 = co_eff_sub / int(number_two[0][0])\n div2 = co_exp_sub - int(number_two[1][0])\n if div2 == 0:\n return str(int(div1))\n return str(int(div1)) + tail + str(div2)\n\n\ndef div_stats(a):\n \"\"\"\n :param a: str\n :return: list\n\n develop lists of divisor co- efficients and exponents\n \"\"\"\n div_coeffs = []\n # break 'a' into parts for processing\n div_parts = re.findall('\\S+', a)\n # find co-efficients\n for x in range(0, len(div_parts)):\n co_eff = re.findall('^[+-]\\d+|^\\d+', div_parts[x])\n div_coeffs.append(co_eff[0])\n\n # find exponents\n div_exps = []\n # find exponents\n for x in range(0, len(div_parts)):\n exps = re.findall('\\^(\\d+)', div_parts[x])\n if div_parts[x].endswith('x'):\n exps = '1'\n elif '^' not in div_parts[x]:\n exps = '0'\n div_exps.append(exps[0])\n\n return (div_coeffs, div_exps)\n\n\ndef pad(ztring):\n \"\"\"\n :param ztring: str\n :return: str\n\n Pad out the missing components of the dividend\n \"\"\"\n nums = re.findall('\\^(\\d+)', ztring)\n ztring = ztring.split()\n ztring = list(ztring)\n\n for x in range(int(max(nums)), -1, -1):\n end = x\n if str(end) in nums:\n continue\n else:\n # ztring.append('0x^' + str(x))\n ztring.insert(x, '0x^' + str(x))\n ztring = ' '.join(ztring)\n return ztring\n\n\ndef sort_string(s):\n \"\"\"\n :param s: str\n :return: str\n\n Sort the string for processing.\n \"\"\"\n temp_string = ''\n s = s.split()\n for item in s:\n item = item[::-1]\n temp_string += item + ' '\n temp_list = temp_string.split()\n temp_list = reversed(sorted(temp_list))\n output = ''\n for item in temp_list:\n item = item[::-1]\n output += item + ' '\n return output\n\n\nif __name__ == '__main__':\n\n quotient_string = ''\n isnum = False\n\n candidates = ['+4x^3 +2x^2 -6x^1 +3', '+2x^4 -9x^3 +21x^2 -26x^1 +12x^0',\n '+10x^4 +0x^3 -7x^2 +0x^1 -1x^0']\n\n divisors = ['1x^1 -3', '2x^1 -3', '1x^2 -1x^1 +3x^0']\n\n for x in range(0, len(candidates)):\n\n dividend = candidates[x]\n divisor = divisors[x]\n # == dividend stats ==============================\n\n dividends = re.findall('\\S+|\\S+$', dividend)\n\n # == divisor stats =================================\n\n divisor_stats = div_stats(divisor)\n\n for y in range(0, len(dividends)):\n if y == 0:\n quotient = quotient_is(dividends[y], divisor)\n quotient_string += quotient\n else:\n mult = multipl(quotient, divisor_stats[0], divisor_stats[1])\n mult = pad(mult)\n mult = sort_string(mult)\n sub = subtract(dividend, mult)\n if sub == '':\n break\n\n quotient = division(sub, divisor_stats) # divide\n\n if isnum:\n if co_eff_quotient >= 0:\n quotient_string += ' +' + str(sub) + '/' + divisor\n else:\n quotient_string += ' ' + str(sub) + '/' + divisor\n isnum = False\n break\n\n # is the quotient a digit??\n isnum = quotient.lstrip('+-').isdigit()\n\n co_eff_quotient = re.findall('^[+-]?\\d+|\\d+', quotient)\n try:\n co_eff_quotient = int(co_eff_quotient[0])\n except IndexError:\n quotient_int = re.findall('[+-]\\d+', quotient)\n quotient_int = int(quotient_int[0])\n if quotient_int < 0:\n quotient_string = ' ' + str(quotient)\n else:\n quotient_string = ' +' + str(quotient)\n\n if co_eff_quotient < 0:\n quotient_string += ' ' + str(quotient)\n else:\n quotient_string += ' +' + str(quotient)\n\n print(quotient_string)\n quotient_string = ''","sub_path":"albums/3/challenge342_easy/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":8379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"228624929","text":"# Blender import system clutter\nimport sys\nfrom pathlib import Path\nfrom abc import ABC, abstractmethod\nfrom typing import List, Tuple\nimport numpy as np\n\nUTILS_PATH = Path.home() / \"Documents/python_workspace/data-science-learning\"\nsys.path.append(str(UTILS_PATH))\n\nimport utils.blender_utils\nimport importlib\nimportlib.reload(utils.blender_utils)\n\nfrom math import cos, sin, pi\nimport itertools\n\nfrom utils.blender_utils import init_grease_pencil, draw_line, draw_cube\n\nclass LSystem(ABC):\n def __init__(self):\n self.variables = []\n self.constants = []\n self.axiom = []\n\n @abstractmethod\n def rules(self, val):\n pass\n\n @abstractmethod\n def rec_draw(self, draw_fun, vals: List[str], pos: Tuple[float], angle=0, depth=0, max_depth=3):\n pass\n\nclass DragonCurve(LSystem):\n\n def __init__(self):\n super().__init__()\n self.variables = ['X', 'Y']\n self.constants = ['-', '+', 'F']\n self.axiom = ['F', 'X']\n\n def rules(self, val):\n # verify that given var is in the system alphabet\n if val not in self.variables and val not in self.constants:\n raise Exception(\"{} not in the alphabet\".format(val))\n if val in self.constants:\n return []\n elif val == 'X':\n return list('X+YF+')\n elif val == 'Y':\n return list('-FX-Y')\n\n def rec_draw(self, draw_fun, vals: List[str], pos: Tuple[float], angle=0, depth=0, max_depth=3):\n LINE_LENGTH = 1\n ANGLE_ADD = pi/2\n\n if depth >= max_depth:\n return angle, pos\n\n for val in vals:\n if val == '+':\n angle += ANGLE_ADD\n elif val == '-':\n angle -= ANGLE_ADD\n elif val == 'F':\n new_pos = (\n pos[0] + LINE_LENGTH * cos(angle),\n pos[1] + LINE_LENGTH * sin(angle),\n pos[2]\n )\n draw_fun(pos, new_pos)\n pos = new_pos\n angle, pos = self.rec_draw(draw_fun, self.rules(val), pos, angle, depth=depth + 1, max_depth=max_depth)\n return angle, pos\n\n\nclass KochCurve(LSystem):\n\n def __init__(self):\n super().__init__()\n self.variables = ['F']\n self.constants = ['-', '+']\n self.axiom = ['F']\n\n def rules(self, val):\n # verify that given val is in the system alphabet\n if val not in self.variables and val not in self.constants:\n raise Exception(\"{} not in the alphabet\".format(val))\n if val in self.constants:\n return []\n elif val == 'F':\n return list('F+F-F-F+F')\n\n def rec_draw(self, draw_fun, vals: List[str], pos: Tuple[float], angle=0, depth=0, max_depth=3):\n LINE_LENGTH = 1\n ANGLE_ADD = pi/2\n\n if depth >= max_depth:\n return angle, pos\n\n for val in vals:\n if val == '+':\n angle += ANGLE_ADD\n elif val == '-':\n angle -= ANGLE_ADD\n elif val == 'F':\n new_pos = (\n pos[0] + LINE_LENGTH * cos(angle),\n pos[1] + LINE_LENGTH * sin(angle),\n pos[2]\n )\n draw_fun(pos, new_pos)\n pos = new_pos\n angle, pos = self.rec_draw(draw_fun, self.rules(val), pos, angle, depth=depth + 1, max_depth=max_depth)\n return angle, pos\n\n\nclass FractalPlant:\n\n def __init__(self):\n self.variables = ['X', 'F']\n self.constants = ['-', '+', '[', ']']\n self.axiom = ['X']\n\n def rules(self, val):\n # verify that given val is in the system alphabet\n if val not in self.variables and val not in self.constants:\n raise Exception(\"{} not in the alphabet\".format(val))\n if val in self.constants:\n return [val]\n elif val == 'X':\n return list('F+[[X]-X]-F[-FX]+X')\n elif val == 'F':\n return ['F', 'F']\n\n def rec_draw(self, draw_fun, plant, pos: tuple, angle=0):\n LINE_LENGTH = 1\n\n ANGLE_ADD = 25\n skip = 0\n count = 0\n\n for i, val in enumerate(plant):\n # print(skip)\n count += 1\n if skip > 0:\n skip -= 1\n continue\n elif val not in self.variables and val not in self.constants:\n raise Exception(\"{} not in the alphabet\".format(val))\n elif val in self.constants:\n if val == '+':\n angle += ANGLE_ADD\n elif val == '-':\n angle -= ANGLE_ADD\n elif val == '[':\n skip = self.rec_draw(draw_fun, plant[i + 1:], (pos[0], pos[1], 0), angle)\n elif val == ']':\n return count\n elif val == 'X':\n continue\n elif val == 'F':\n new_pos = (\n pos[0] + LINE_LENGTH * cos(angle * (pi / 180)), pos[1] + LINE_LENGTH * sin(angle * (pi / 180)), 0)\n draw_fun(pos, new_pos)\n # print(new_pos)\n pos = new_pos\n\n\ndef animate_lsystem(system: LSystem, max_depth: int, pos=(0, 0, 0), layer_name='GP_layer'):\n gp_layer = init_grease_pencil(clear_layer=True, gpencil_layer_name=layer_name)\n gp_layer.frames.new(0)\n\n system.rec_draw(lambda x, y: draw(x, y, gp_layer), vals=system.axiom, pos=pos, max_depth=max_depth)\n\n\ndef animate_plant():\n fractal_plant = FractalPlant()\n\n NB_ITERATIONS = 5\n\n res = fractal_plant.axiom\n for i in range(1, NB_ITERATIONS):\n res = list(itertools.chain(*[fractal_plant.rules(x) for x in res]))\n\n gp_layer = init_grease_pencil(clear_layer=True)\n gp_layer.frames.new(0)\n\n fractal_plant.rec_draw(lambda x, y: draw(x, y, gp_layer), plant=res, pos=(0,0,0))\n\n\ndef draw(start: tuple, end: tuple, gp_layer):\n gp_frame = gp_layer.frames[-1]\n\n # Cube Transition\n # from scipy.spatial import distance\n # for i in range(1, 10):\n # anim_frames = gp_layer.frames.copy(gp_frame)\n # draw_cube(anim_frames, start, distance.euclidean(start, end)/i)\n\n # Rotating Line Transition\n # angle = 2 * pi / 10 # angle in radians\n # for i in range(1, 10):\n # anim_frames = gp_layer.frames.copy(gp_frame)\n # # Define stroke geometry\n # radius = distance.euclidean(start, end)\n # x = start[0] + radius * cos(angle * i)\n # y = start[1] + radius * sin(angle * i)\n # z = start[2]\n # anim_end = (x, y, z)\n # draw_line(anim_frames, start, anim_end)\n\n gp_frame = gp_layer.frames.copy(gp_frame)\n if gp_frame.frame_number%100 == 0:\n print(\"Writing to frame {}\".format(gp_frame.frame_number))\n draw_line(gp_frame, start, end)\n\n\n#animate_plant()\nanimate_lsystem(DragonCurve(), 11, layer_name='dragon_curve')\nanimate_lsystem(KochCurve(), 5, layer_name='koch_curve')\n","sub_path":"graphics/blender/l_systems.py","file_name":"l_systems.py","file_ext":"py","file_size_in_byte":6961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"149117660","text":"# -*- coding: utf-8 -*-\n\n\ndef xrange(start_stop, stop=None, step=None):\n \"\"\"\n Funkcja która działa jak funkcja range (wbudowana i z poprzednich zajęć)\n która działa dla liczb całkowitych.\n \"\"\"\n start = start_stop\n\n if step == None:\n step = 1\n if stop == None:\n start = 0\n stop = start_stop\n\n\n i = start\n while (i < stop):\n #print (i)\n yield i\n i = i + step\n\n#xrange(0, 10, 2)\n#xrange(0, 10)\n#xrange(5)\n","sub_path":"tasks/zaj2/zadanie1.py","file_name":"zadanie1.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"46887714","text":"import sys\nimport os\npath = sys.path[0].replace('Script', 'Library')\nif path not in sys.path:\n sys.path.append(path)\nimport pandas as pd\nfrom tkinter import *\nimport graphs as gr\nimport functions as ft\n\n\n\n\ncompanies = pd.read_csv('../Data/Companies.csv', encoding='latin1', index_col='Unnamed: 0')\ncollaborations = pd.read_csv('../Data/Collaboration_Id.csv', encoding='latin1', index_col='Unnamed: 0')\nproducts = pd.read_csv('../Data/Products.csv', encoding='latin1', index_col='Unnamed: 0')\n\n\n\nnotebooks = ft.df_to_dict(companies, collaborations, products)\n\n\ndef UpdateNotebooks():\n lbox.delete(0, END)\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n\n\n\nroot = Tk()\n\nlbox = Listbox(selectmode=EXTENDED, height = 100, width = 140)\nlbox.pack(side=LEFT)\nscroll = Scrollbar(command=lbox.yview)\nscroll.pack(side=LEFT, fill=Y)\nlbox.config(yscrollcommand=scroll.set, bg=\"#ffd8f2\")\n\nfor i in ft.strings(notebooks):\n lbox.insert(0,i)\n lbox.insert(0,'')\n\n\nf = Frame()\nf.pack(side=TOP, padx=10)\n\nCPUlabel = Label(bg = 'black', fg = 'white', width = 100, text = \"CPU\")\nCPUlabel.pack()\n\n\n\nLabelCpu1 = Frame()\nLabelCpu1.pack(side = TOP)\n\nLabelCpu2 = Frame()\nLabelCpu2.pack(side=TOP)\n\nLabelCpu3 = Frame()\nLabelCpu3.pack(side=TOP)\n\nLabelCpu4 = Frame()\nLabelCpu4.pack(side=TOP)\n\nLabelCpu5 = Frame()\nLabelCpu5.pack(side=TOP)\n\nLabelCpu6 = Frame()\nLabelCpu6.pack(side=TOP)\n\n\n\n\ncvar1 = BooleanVar()\ncvar1.set(0)\ncpu1 = Checkbutton(LabelCpu1,text=\"Intel Core i3\", variable=cvar1, onvalue=1, offvalue=0)\n\ncvar2 = BooleanVar()\ncvar2.set(0)\ncpu2 = Checkbutton(LabelCpu1,text=\"Intel Core i5\", variable=cvar2, onvalue=1, offvalue=0)\n\n\ncvar3 = BooleanVar()\ncvar3.set(0)\ncpu3 = Checkbutton(LabelCpu1,text=\"Intel Core i7\", variable=cvar3, onvalue=1, offvalue=0)\n\ncvar4 = BooleanVar()\ncvar4.set(0)\ncpu4 = Checkbutton(LabelCpu2,text=\"Intel Atom\", variable=cvar4, onvalue=1, offvalue=0)\n\ncvar5 = BooleanVar()\ncvar5.set(0)\ncpu5 = Checkbutton(LabelCpu2,text=\"AMD A9-Series\", variable=cvar5, onvalue=1, offvalue=0)\n\ncvar6 = BooleanVar()\ncvar6.set(0)\ncpu6 = Checkbutton(LabelCpu2,text=\"AMD E-Series\", variable=cvar6, onvalue=1, offvalue=0)\n\ncvar7 = BooleanVar()\ncvar7.set(0)\ncpu7 = Checkbutton(LabelCpu3,text=\"AMD A6-Series\", variable=cvar7, onvalue=1, offvalue=0)\n\ncvar8 = BooleanVar()\ncvar8.set(0)\ncpu8 = Checkbutton(LabelCpu3,text=\"Intel Celeron\", variable=cvar8, onvalue=1, offvalue=0)\n\ncvar9 = BooleanVar()\ncvar9.set(0)\ncpu9 = Checkbutton(LabelCpu3,text=\"AMD Ryzen\", variable=cvar9, onvalue=1, offvalue=0)\n\ncvar10 = BooleanVar()\ncvar10.set(0)\ncpu10 = Checkbutton(LabelCpu4,text=\"Intel Pentium\", variable=cvar10, onvalue=1, offvalue=0)\n\ncvar11 = BooleanVar()\ncvar11.set(0)\ncpu11 = Checkbutton(LabelCpu4,text=\"AMD FX\", variable=cvar11, onvalue=1, offvalue=0)\n\ncvar12 = BooleanVar()\ncvar12.set(0)\ncpu12 = Checkbutton(LabelCpu4,text=\"Intel Xeon\", variable=cvar12, onvalue=1, offvalue=0)\n\ncvar13 = BooleanVar()\ncvar13.set(0)\ncpu13 = Checkbutton(LabelCpu5,text=\"AMD A10-Series\", variable=cvar13, onvalue=1, offvalue=0)\n\ncvar14 = BooleanVar()\ncvar14.set(0)\ncpu14 = Checkbutton(LabelCpu5,text=\"AMD A8-Series\", variable=cvar14, onvalue=1, offvalue=0)\n\ncvar15 = BooleanVar()\ncvar15.set(0)\ncpu15 = Checkbutton(LabelCpu5,text=\"AMD A12-Series\", variable=cvar15, onvalue=1, offvalue=0)\n\ncvar16 = BooleanVar()\ncvar16.set(0)\ncpu16 = Checkbutton(LabelCpu6,text=\"AMD A4-Series\", variable=cvar16, onvalue=1, offvalue=0)\n\ncvar17 = BooleanVar()\ncvar17.set(0)\ncpu17 = Checkbutton(LabelCpu6,text=\"Samsung Cortex\", variable=cvar17, onvalue=1, offvalue=0)\n\ncvar18 = BooleanVar()\ncvar18.set(0)\ncpu18 = Checkbutton(LabelCpu6,text=\"Intel Core M\", variable=cvar18, onvalue=1, offvalue=0)\n\n\n\nramlabel = Label(bg = 'black', fg = 'white', width = 100, text = \"Ram\")\nramlabel.pack(side=TOP)\n\n\nLabelCpu1 = Frame()\nLabelCpu1.pack(side = TOP)\n\nLabelCpu2 = Frame()\nLabelCpu2.pack(side=TOP)\n\nLabelCpu3 = Frame()\nLabelCpu3.pack(side=TOP)\n\n\n\nsvar1 = BooleanVar()\nsvar1.set(0)\nspu1 = Checkbutton(LabelCpu1,text=\"2GB\", variable=svar1, onvalue=1, offvalue=0)\n\nsvar2 = BooleanVar()\nsvar2.set(0)\nspu2 = Checkbutton(LabelCpu1,text=\"4GB\", variable=svar2, onvalue=1, offvalue=0)\n\nsvar3 = BooleanVar()\nsvar3.set(0)\nspu3 = Checkbutton(LabelCpu1,text=\"6GB\", variable=svar3, onvalue=1, offvalue=0)\n\nsvar4 = BooleanVar()\nsvar4.set(0)\nspu4 = Checkbutton(LabelCpu2,text=\"8GB\", variable=svar4, onvalue=1, offvalue=0)\n\nsvar5 = BooleanVar()\nsvar5.set(0)\nspu5 = Checkbutton(LabelCpu2,text=\"12GB\", variable=svar5, onvalue=1, offvalue=0)\n\nsvar6 = BooleanVar()\nsvar6.set(0)\nspu6 = Checkbutton(LabelCpu2,text=\"16GB\", variable=svar6, onvalue=1, offvalue=0)\n\nsvar7 = BooleanVar()\nsvar7.set(0)\nspu7 = Checkbutton(LabelCpu3,text=\"24GB\", variable=svar7, onvalue=1, offvalue=0)\n\nsvar8 = BooleanVar()\nsvar8.set(0)\nspu8 = Checkbutton(LabelCpu3,text=\"32GB\", variable=svar8, onvalue=1, offvalue=0)\n\nsvar9 = BooleanVar()\nsvar9.set(0)\nspu9 = Checkbutton(LabelCpu3,text=\"64GB\", variable=svar9, onvalue=1, offvalue=0)\n\n\n\n\nTypeNamelabel = Label(bg = 'black', fg = 'white', width = 100, text = \"TypeName\")\nTypeNamelabel.pack(side=TOP)\n\nLabelCpu1 = Frame()\nLabelCpu1.pack(side = TOP)\n\nLabelCpu2 = Frame()\nLabelCpu2.pack(side=TOP)\n\n\n\n\ntvar1 = BooleanVar()\ntvar1.set(0)\ntpu1 = Checkbutton(LabelCpu1,text=\"Ultrabook\", variable=tvar1, onvalue=1, offvalue=0)\n\ntvar2 = BooleanVar()\ntvar2.set(0)\ntpu2 = Checkbutton(LabelCpu1,text=\"Notebook\", variable=tvar2, onvalue=1, offvalue=0)\n\ntvar3 = BooleanVar()\ntvar3.set(0)\ntpu3 = Checkbutton(LabelCpu1,text=\"Netbook\", variable=tvar3, onvalue=1, offvalue=0)\n\ntvar4 = BooleanVar()\ntvar4.set(0)\ntpu4 = Checkbutton(LabelCpu2,text=\"Gaming\", variable=tvar4, onvalue=1, offvalue=0)\n\ntvar5 = BooleanVar()\ntvar5.set(0)\ntpu5 = Checkbutton(LabelCpu2,text=\"2 in 1 Convertible\", variable=tvar5, onvalue=1, offvalue=0)\n\ntvar6 = BooleanVar()\ntvar6.set(0)\ntpu6 = Checkbutton(LabelCpu2,text=\"Workstation\", variable=tvar6, onvalue=1, offvalue=0)\n\n\nTypeNamelabel = Label(bg = 'black', fg = 'white', width = 100, text = \"Company\")\nTypeNamelabel.pack(side=TOP)\n\nLabelCpu1 = Frame()\nLabelCpu1.pack(side = TOP)\n\nLabelCpu2 = Frame()\nLabelCpu2.pack(side=TOP)\n\nLabelCpu3 = Frame()\nLabelCpu3.pack(side=TOP)\n\nLabelCpu4 = Frame()\nLabelCpu4.pack(side=TOP)\n\nLabelCpu5 = Frame()\nLabelCpu5.pack(side=TOP)\n\nLabelCpu6 = Frame()\nLabelCpu6.pack(side=TOP)\n\nLabelCpu7 = Frame()\nLabelCpu7.pack(side=TOP)\n\ncovar1 = BooleanVar()\ncovar1.set(0)\ncspu1 = Checkbutton(LabelCpu1,text=\"Apple\", variable=covar1, onvalue=1, offvalue=0)\n\ncovar2 = BooleanVar()\ncovar2.set(0)\ncspu2 = Checkbutton(LabelCpu1,text=\"HP\", variable=covar2, onvalue=1, offvalue=0)\n\ncovar3 = BooleanVar()\ncovar3.set(0)\ncspu3 = Checkbutton(LabelCpu1,text=\"Acer\", variable=covar3, onvalue=1, offvalue=0)\n\ncovar4 = BooleanVar()\ncovar4.set(0)\ncspu4 = Checkbutton(LabelCpu2,text=\"Asus\", variable=covar4, onvalue=1, offvalue=0)\n\ncovar5 = BooleanVar()\ncovar5.set(0)\ncspu5 = Checkbutton(LabelCpu2,text=\"Dell\", variable=covar5, onvalue=1, offvalue=0)\n\ncovar6 = BooleanVar()\ncovar6.set(0)\ncspu6 = Checkbutton(LabelCpu2,text=\"Lenovo\", variable=covar6, onvalue=1, offvalue=0)\n\n\ncovar7 = BooleanVar()\ncovar7.set(0)\ncspu7 = Checkbutton(LabelCpu3,text=\"Chuwi\", variable=covar7, onvalue=1, offvalue=0)\n\ncovar8 = BooleanVar()\ncovar8.set(0)\ncspu8 = Checkbutton(LabelCpu3,text=\"MSI\", variable=covar8, onvalue=1, offvalue=0)\n\ncovar9 = BooleanVar()\ncovar9.set(0)\ncspu9 = Checkbutton(LabelCpu3,text=\"Microsoft\", variable=covar9, onvalue=1, offvalue=0)\n\ncovar10 = BooleanVar()\ncovar10.set(0)\ncspu10 = Checkbutton(LabelCpu4,text=\"Toshiba\", variable=covar10, onvalue=1, offvalue=0)\n\ncovar11 = BooleanVar()\ncovar11.set(0)\ncspu11 = Checkbutton(LabelCpu4,text=\"Huawei\", variable=covar11, onvalue=1, offvalue=0)\n\ncovar12 = BooleanVar()\ncovar12.set(0)\ncspu12 = Checkbutton(LabelCpu4,text=\"Xiaomi\", variable=covar12, onvalue=1, offvalue=0)\n\ncovar13 = BooleanVar()\ncovar13.set(0)\ncspu13 = Checkbutton(LabelCpu5,text=\"Vero\", variable=covar13, onvalue=1, offvalue=0)\n\ncovar14 = BooleanVar()\ncovar14.set(0)\ncspu14 = Checkbutton(LabelCpu5,text=\"Razer\", variable=covar14, onvalue=1, offvalue=0)\n\ncovar15 = BooleanVar()\ncovar15.set(0)\ncspu15 = Checkbutton(LabelCpu5,text=\"Mediacom\", variable=covar15, onvalue=1, offvalue=0)\n\ncovar16 = BooleanVar()\ncovar16.set(0)\ncspu16 = Checkbutton(LabelCpu6,text=\"Samsung\", variable=covar16, onvalue=1, offvalue=0)\n\ncovar17 = BooleanVar()\ncovar17.set(0)\ncspu17 = Checkbutton(LabelCpu6,text=\"Google\", variable=covar17, onvalue=1, offvalue=0)\n\ncovar18 = BooleanVar()\ncovar18.set(0)\ncspu18 = Checkbutton(LabelCpu6,text=\"Fujitsu\", variable=covar18, onvalue=1, offvalue=0)\n\ncovar19 = BooleanVar()\ncovar19.set(0)\ncspu19 = Checkbutton(LabelCpu7,text=\"LG\", variable=covar19, onvalue=1, offvalue=0)\nOpSyslabel = Label(bg = 'black', fg = 'white', width = 100, text = \"OpSys\")\nOpSyslabel.pack(side=TOP)\n\n\nLabelCpu1 = Frame()\nLabelCpu1.pack(side = TOP)\n\nLabelCpu2 = Frame()\nLabelCpu2.pack(side=TOP)\n\nLabelCpu3 = Frame()\nLabelCpu3.pack(side=TOP)\n\n\n\nosvar1 = BooleanVar()\nosvar1.set(0)\nospu1 = Checkbutton(LabelCpu1,text=\"MacPs\", variable=osvar1, onvalue=1, offvalue=0)\n\nosvar2 = BooleanVar()\nosvar2.set(0)\nospu2 = Checkbutton(LabelCpu1,text=\"No OS\", variable=osvar2, onvalue=1, offvalue=0)\n\nosvar3 = BooleanVar()\nosvar3.set(0)\nospu3 = Checkbutton(LabelCpu1,text=\"Win 10\", variable=osvar3, onvalue=1, offvalue=0)\n\nosvar4 = BooleanVar()\nosvar4.set(0)\nospu4 = Checkbutton(LabelCpu2,text=\"Mac OS X\", variable=osvar4, onvalue=1, offvalue=0)\n\nosvar5 = BooleanVar()\nosvar5.set(0)\nospu5 = Checkbutton(LabelCpu2,text=\"Linux\", variable=osvar5, onvalue=1, offvalue=0)\n\nosvar6 = BooleanVar()\nosvar6.set(0)\nospu6 = Checkbutton(LabelCpu2,text=\"Android\", variable=osvar6, onvalue=1, offvalue=0)\n\nosvar7 = BooleanVar()\nosvar7.set(0)\nospu7 = Checkbutton(LabelCpu3,text=\"Win 10 S\", variable=osvar7, onvalue=1, offvalue=0)\n\nosvar8 = BooleanVar()\nosvar8.set(0)\nospu8 = Checkbutton(LabelCpu3,text=\"Chrome OS\", variable=osvar8, onvalue=1, offvalue=0)\n\nosvar9 = BooleanVar()\nosvar9.set(0)\nospu9 = Checkbutton(LabelCpu3,text=\"Win 7\", variable=osvar9, onvalue=1, offvalue=0)\n\nPricelabel = Label(bg = 'black', fg = 'white', width = 100, text = \"Price\")\nPricelabel.pack(side=TOP)\n\nLabelCpu1 = Frame()\nLabelCpu1.pack()\nlowest_price = Entry(LabelCpu1,width = 30)\n\nhigher_price = Entry(LabelCpu1,width = 30)\n\nsearchEntry = Entry(width = 100)\nsearchEntry.insert(0,'Search')\nsearchEntry.bind(\"\",lambda args: searchEntry.delete('0','end'))\nsearchEntry.pack()\n\n\ndef DeleteAll():\n memory = []\n ram = []\n company = []\n TypeName = []\n OpSys = []\n\n companies = pd.read_csv('../Data/Companies.csv', encoding='latin1', index_col='Unnamed: 0')\n collaborations = pd.read_csv('../Data/Collaboration_Id.csv', encoding='latin1',\n index_col='Unnamed: 0')\n products = pd.read_csv('../Data/Products.csv', encoding='latin1', index_col='Unnamed: 0')\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n if(cvar1.get() == 1):\n memory.append(\"Intel Core i3\")\n if(cvar2.get() == 1):\n memory.append(\"Intel Core i5\")\n if (cvar3.get() == 1):\n memory.append(\"Intel Core i7\")\n if (cvar4.get() == 1):\n memory.append(\"Intel Core M\")\n if (cvar5.get() == 1):\n memory.append(\"AMD A9-Series\")\n if (cvar6.get() == 1):\n memory.append(\"AMD E-Series\")\n if (cvar7.get() == 1):\n memory.append(\"AMD A6-Series\")\n if (cvar8.get() == 1):\n memory.append(\"Intel Celeron\")\n if (cvar9.get() == 1):\n memory.append(\"AMD Ryzen\")\n if (cvar10.get() == 1):\n memory.append(\"Intel Pentium\")\n if (cvar11.get() == 1):\n memory.append(\"AMD FX\")\n if (cvar12.get() == 1):\n memory.append(\"Intel Xeon\")\n if (cvar13.get() == 1):\n memory.append(\"AMD A10-Series\")\n if (cvar14.get() == 1):\n memory.append(\"AMD A8-Series\")\n if (cvar15.get() == 1):\n memory.append(\"AMD A12-Series\")\n if (cvar16.get() == 1):\n memory.append(\"AMD A4-Series\")\n if (cvar17.get() == 1):\n memory.append(\"Samsung Cortex\")\n if (cvar18.get() == 1):\n memory.append(\"Intel Core M\")\n\n\n if (svar1.get() == 1):\n ram.append('2GB')\n if (svar2.get() == 1):\n ram.append('4GB')\n if (svar3.get() == 1):\n ram.append('6GB')\n if (svar4.get() == 1):\n ram.append('8GB')\n if (svar5.get() == 1):\n ram.append('12GB')\n if (svar6.get() == 1):\n ram.append('16GB')\n if (svar7.get() == 1):\n ram.append('32GB')\n if (svar8.get() == 1):\n ram.append('24GB')\n if (svar9.get() == 1):\n ram.append('64GB')\n\n if (tvar1.get() == 1):\n TypeName.append('Ultrabook')\n if (tvar2.get() == 1):\n TypeName.append('Notebook')\n if (tvar3.get() == 1):\n TypeName.append('Netbook')\n if (tvar4.get() == 1):\n TypeName.append('Gaming')\n if (tvar5.get() == 1):\n TypeName.append('2 in 1 Convertible')\n if (tvar6.get() == 1):\n TypeName.append(\"Workstation\")\n\n if (covar1.get() == 1):\n company.append('Apple')\n if (covar2.get() == 1):\n company.append('HP')\n if (covar3.get() == 1):\n company.append('Acer')\n if (covar4.get() == 1):\n company.append('Asus')\n if (covar5.get() == 1):\n company.append('Dell')\n if (covar6.get() == 1):\n company.append('Lenovo')\n if (covar7.get() == 1):\n company.append('Chuwi')\n if (covar8.get() == 1):\n company.append('MSI')\n if (covar9.get() == 1):\n company.append('Microsoft')\n if (covar10.get() == 1):\n company.append('Toshiba')\n if (covar11.get() == 1):\n company.append('Huawei')\n if (covar12.get() == 1):\n company.append('Xiaomi')\n if (covar13.get() == 1):\n company.append('Vero')\n if (covar14.get() == 1):\n company.append('Razer')\n if (covar15.get() == 1):\n company.append('Mediacom')\n if (covar16.get() == 1):\n company.append('Samsung')\n if (covar17.get() == 1):\n company.append('Google')\n if (covar18.get() == 1):\n company.append('Fujitsu')\n if (covar19.get() == 1):\n company.append('LG')\n\n if(osvar1.get() == 1):\n OpSys.append(\"macOS\")\n if (osvar2.get() == 1):\n OpSys.append(\"No OS\")\n if (osvar3.get() == 1):\n OpSys.append(\"Windows 10\")\n if (osvar4.get() == 1):\n OpSys.append(\"Mac OS X\")\n if (osvar5.get() == 1):\n OpSys.append(\"Linux\")\n if (osvar6.get() == 1):\n OpSys.append(\"Android\")\n if (osvar7.get() == 1):\n OpSys.append(\"Windows 10 S\")\n if (osvar8.get() == 1):\n OpSys.append(\"Chrome OS\")\n if (osvar9.get() == 1):\n OpSys.append(\"Windows 7\")\n\n if (higher_price.get() or lowest_price.get()):\n pricelst = []\n pricelst.append(0.0)\n if(lowest_price.get()):\n pricelst.append(float(lowest_price.get()))\n else:\n pricelst.append(0.0)\n if(higher_price.get()):\n pricelst.append(float(higher_price.get()))\n else:\n pricelst.append(9999999.9)\n notebooks = ft.filter_by_price(notebooks,pricelst)\n if (OpSys):\n notebooks = ft.filter_by_specification(notebooks, 'OpSys', OpSys)\n if (company):\n notebooks = ft.filter_by_specification(notebooks, 'Company', company)\n if(TypeName):\n notebooks = ft.filter_by_specification(notebooks, 'TypeName', TypeName)\n if(ram):\n notebooks = ft.filter_by_specification(notebooks, \"Ram\", ram)\n if(memory):\n notebooks = ft.filter_by_cpu(notebooks, memory)\n if (searchEntry.get() != \"\" and searchEntry.get() != \"Search\"):\n # print(notebooks)\n notebooks = ft.search(searchEntry.get(), notebooks)\n print(searchEntry.get())\n if(ram == [] and memory == [] and TypeName == [] and company == [] and OpSys == [] and higher_price.get() == '' and lowest_price.get() == '' and searchEntry.get() == '' and searchEntry == \"Search\"):\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n lbox.delete(0, END)\n if(searchEntry.get() == \"\" or searchEntry.get() == \"Search\"):\n for i in ft.strings(notebooks):\n lbox.insert(0, i)\n lbox.insert(0, '')\n else:\n for i in notebooks.values():\n lbox.insert(0, i)\n lbox.insert(0, '')\n\n\n\ncpu1.pack(side=LEFT)\ncpu2.pack(side=RIGHT)\ncpu3.pack(side=RIGHT)\ncpu4.pack(side=LEFT)\ncpu5.pack(side=LEFT)\ncpu6.pack(side=RIGHT)\ncpu7.pack(side=LEFT)\ncpu8.pack(side=LEFT)\ncpu9.pack(side=RIGHT)\ncpu10.pack(side=RIGHT)\ncpu11.pack(side=RIGHT)\ncpu12.pack(side=RIGHT)\ncpu13.pack(side=RIGHT)\ncpu14.pack(side=RIGHT)\ncpu15.pack(side=RIGHT)\ncpu16.pack(side=RIGHT)\ncpu17.pack(side=RIGHT)\ncpu18.pack(side=RIGHT)\n\nspu1.pack(side=LEFT)\nspu2.pack(side=RIGHT)\nspu3.pack(side=LEFT)\nspu4.pack(side=RIGHT)\nspu5.pack(side=LEFT)\nspu6.pack(side=RIGHT)\nspu7.pack(side=RIGHT)\nspu8.pack(side=RIGHT)\nspu9.pack(side=RIGHT)\n\ntpu1.pack(side=LEFT)\ntpu2.pack(side=RIGHT)\ntpu3.pack(side=LEFT)\ntpu4.pack(side=RIGHT)\ntpu5.pack(side=LEFT)\ntpu6.pack(side=RIGHT)\n\ncspu1.pack(side=LEFT)\ncspu2.pack(side=RIGHT)\ncspu3.pack(side=LEFT)\ncspu4.pack(side=RIGHT)\ncspu5.pack(side=LEFT)\ncspu6.pack(side=RIGHT)\ncspu7.pack(side=LEFT)\ncspu8.pack(side=RIGHT)\ncspu9.pack(side=LEFT)\ncspu10.pack(side=RIGHT)\ncspu11.pack(side=LEFT)\ncspu12.pack(side=RIGHT)\ncspu13.pack(side=LEFT)\ncspu14.pack(side=RIGHT)\ncspu15.pack(side=LEFT)\ncspu16.pack(side=RIGHT)\ncspu17.pack(side=LEFT)\ncspu18.pack(side=RIGHT)\ncspu19.pack(side=RIGHT)\n\nospu1.pack(side=LEFT)\nospu2.pack(side=RIGHT)\nospu3.pack(side=LEFT)\nospu4.pack(side=RIGHT)\nospu5.pack(side=LEFT)\nospu6.pack(side=RIGHT)\nospu7.pack(side=RIGHT)\nospu8.pack(side=RIGHT)\nospu9.pack(side=RIGHT)\n\nPricelabel.pack(side=TOP)\nLabelCpu1.pack(side=TOP)\nlowest_price.pack(side=LEFT)\nhigher_price.pack(side=RIGHT)\n\ndef add():\n window = Tk()\n window.geometry('1400x1000+200+100')\n\n CompanyLabel = Label(window, text = 'Company')\n CompanyEntry = Entry(window)\n ProductLabel = Label(window, text = 'Product')\n ProductEntry = Entry(window)\n TypeNameLabel = Label(window, text = 'TypeName')\n TypeNameEntry = Entry(window)\n InchesLabel = Label(window, text = 'Inches')\n InchesEntry = Entry(window)\n ScreenResolutionLabel = Label(window, text = 'ScreenResolution')\n ScreenResolutionEntry = Entry(window)\n CpuLabel = Label(window, text = 'Cpu')\n CpuEntry = Entry(window)\n RamLabel = Label(window, text = 'Ram')\n RamEntry = Entry(window)\n MemoryLabel = Label(window, text = 'Memory')\n MemoryEntry = Entry(window)\n GpuLabel = Label(window, text = 'Gpu')\n GpuEntry = Entry(window)\n OpSysLabel = Label(window, text = 'OpSys')\n OpSysEntry = Entry(window)\n WeightLabel = Label(window, text = 'Weight')\n WeightEntry = Entry(window)\n PriceEurosLabel = Label(window, text = 'PriceEuros')\n PriceEurosEntry = Entry(window)\n # CompanyEntry.bind(\"\",lambda args: CompanyEntry.delete('0','end'))\n # ProductEntry.bind(\"\",lambda args: ProductEntry.delete('0','end'))\n # TypeNameEntry.bind(\"\",lambda args: TypeNameEntry.delete('0','end'))\n # InchesEntry.bind(\"\",lambda args: InchesEntry.delete('0','end'))\n # ScreenResolutionEntry.bind(\"\",lambda args: ScreenResolutionEntry.delete('0','end'))\n # CpuEntry.bind(\"\",lambda args: CpuEntry.delete('0','end'))\n # RamEntry.bind(\"\",lambda args: RamEntry.delete('0','end'))\n # MemoryEntry.bind(\"\",lambda args: MemoryEntry.delete('0','end'))\n # GpuEntry.bind(\"\",lambda args: GpuEntry.delete('0','end'))\n # OpSysEntry.bind(\"\",lambda args: OpSysEntry.delete('0','end'))\n # WeightEntry.bind(\"\",lambda args: WeightEntry.delete('0','end'))\n # PriceEurosEntry.bind(\"\",lambda args: PriceEurosEntry.delete('0','end'))\n CompanyLabel.pack()\n CompanyEntry.pack()\n ProductLabel.pack()\n ProductEntry.pack()\n TypeNameLabel.pack()\n TypeNameEntry.pack()\n InchesLabel.pack()\n InchesEntry.pack()\n ScreenResolutionLabel.pack()\n ScreenResolutionEntry.pack()\n CpuLabel.pack()\n CpuEntry.pack()\n RamLabel.pack()\n RamEntry.pack()\n MemoryLabel.pack()\n MemoryEntry.pack()\n GpuLabel.pack()\n GpuEntry.pack()\n OpSysLabel.pack()\n OpSysEntry.pack()\n WeightLabel.pack()\n WeightEntry.pack()\n PriceEurosLabel.pack()\n PriceEurosEntry.pack()\n\n def CreateElement():\n companies = pd.read_csv('../Data/Companies.csv', encoding='latin1', index_col='Unnamed: 0')\n collaborations = pd.read_csv('../Data/Collaboration_Id.csv', encoding='latin1', index_col='Unnamed: 0')\n products = pd.read_csv('../Data/Products.csv', encoding='latin1', index_col='Unnamed: 0')\n\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n ParametresLst = []\n\n ParametresLst.extend(\n [CompanyEntry.get(), ProductEntry.get(), TypeNameEntry.get(), InchesEntry.get(),\n ScreenResolutionEntry.get(),\n CpuEntry.get(), RamEntry.get(), MemoryEntry.get(), GpuEntry.get(), OpSysEntry.get(), WeightEntry.get(),\n PriceEurosEntry.get()])\n ft.create(companies,collaborations,products,notebooks,ft.list_to_dct(ParametresLst))\n\n\n submitButton = Button(window, text = \"submit\", command = CreateElement)\n submitButton.pack()\n\n window.geometry('200x600+100+200')\n window.mainloop()\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n\ndef DeleteElement():\n companies = pd.read_csv('../Data/Companies.csv', encoding='latin1', index_col='Unnamed: 0')\n collaborations = pd.read_csv('../Data/Collaboration_Id.csv', encoding='latin1', index_col='Unnamed: 0')\n products = pd.read_csv('../Data/Products.csv', encoding='latin1', index_col='Unnamed: 0')\n\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n ft.delete(collaborations, companies, products,\n float(lbox.get(lbox.curselection()).split()[len(lbox.get(lbox.curselection()).split())-1]))\n\n\n\n\ndef update():\n notebooks = ft.df_to_dict(companies, collaborations, products)\n window2 = Tk()\n window2.geometry('1400x1000+200+100')\n\n UpdateList = ft.id_to_str_list(notebooks, lbox.get(lbox.curselection()).split()[-1])\n CompanyEntry = Entry(window2)\n CompanyEntry.insert(0, UpdateList[0])\n\n ProductEntry = Entry(window2)\n ProductEntry.insert(0, UpdateList[1])\n\n TypeNameEntry = Entry(window2)\n TypeNameEntry.insert(0, UpdateList[2])\n\n InchesEntry = Entry(window2)\n InchesEntry.insert(0, UpdateList[3])\n\n ScreenResolutionEntry = Entry(window2)\n ScreenResolutionEntry.insert(0, UpdateList[4])\n\n CpuEntry = Entry(window2)\n CpuEntry.insert(0, UpdateList[5])\n\n RamEntry = Entry(window2)\n RamEntry.insert(0, UpdateList[6])\n\n MemoryEntry = Entry(window2)\n MemoryEntry.insert(0, UpdateList[7])\n\n GpuEntry = Entry(window2)\n GpuEntry.insert(0, UpdateList[8])\n\n OpSysEntry = Entry(window2)\n OpSysEntry.insert(0, UpdateList[9])\n\n WeightEntry = Entry(window2)\n WeightEntry.insert(0, UpdateList[10])\n\n PriceEurosEntry = Entry(window2)\n PriceEurosEntry.insert(0, UpdateList[11])\n # CompanyEntry.bind(\"\",lambda args: CompanyEntry.delete('0','end'))\n # ProductEntry.bind(\"\",lambda args: ProductEntry.delete('0','end'))\n # TypeNameEntry.bind(\"\",lambda args: TypeNameEntry.delete('0','end'))\n # InchesEntry.bind(\"\",lambda args: InchesEntry.delete('0','end'))\n # ScreenResolutionEntry.bind(\"\",lambda args: ScreenResolutionEntry.delete('0','end'))\n # CpuEntry.bind(\"\",lambda args: CpuEntry.delete('0','end'))\n # RamEntry.bind(\"\",lambda args: RamEntry.delete('0','end'))\n # MemoryEntry.bind(\"\",lambda args: MemoryEntry.delete('0','end'))\n # GpuEntry.bind(\"\",lambda args: GpuEntry.delete('0','end'))\n # OpSysEntry.bind(\"\",lambda args: OpSysEntry.delete('0','end'))\n # WeightEntry.bind(\"\",lambda args: WeightEntry.delete('0','end'))\n # PriceEurosEntry.bind(\"\",lambda args: PriceEurosEntry.delete('0','end'))\n if(lbox.curselection()):\n CompanyEntry.pack()\n ProductEntry.pack()\n TypeNameEntry.pack()\n InchesEntry.pack()\n ScreenResolutionEntry.pack()\n CpuEntry.pack()\n RamEntry.pack()\n MemoryEntry.pack()\n GpuEntry.pack()\n OpSysEntry.pack()\n WeightEntry.pack()\n PriceEurosEntry.pack()\n\n\n\n def UpdateElement():\n ParametresLst = []\n\n ParametresLst.extend(\n [CompanyEntry.get(), ProductEntry.get(), TypeNameEntry.get(), InchesEntry.get(),\n ScreenResolutionEntry.get(),\n CpuEntry.get(), RamEntry.get(), MemoryEntry.get(), GpuEntry.get(), OpSysEntry.get(), WeightEntry.get(),\n PriceEurosEntry.get()])\n # print(CompanyEntry.get(), '|', ProductEntry.get(), '|', TypeNameEntry.get(), '|', InchesEntry.get(),\n # ScreenResolutionEntry.get(),\n # CpuEntry.get(), '|', RamEntry.get(), '|', MemoryEntry.get(), '|', GpuEntry.get(), '|', OpSysEntry.get(), '|', WeightEntry.get(),\n # PriceEurosEntry.get())\n # print([CompanyEntry.get(), ProductEntry.get(), TypeNameEntry.get(), InchesEntry.get(),\n # ScreenResolutionEntry.get(),\n # CpuEntry.get(), RamEntry.get(), MemoryEntry.get(), GpuEntry.get(), OpSysEntry.get(), WeightEntry.get(),\n # PriceEurosEntry.get()])\n # print(ParametresLst)\n ft.update(collaborations,companies,products,\n float(lbox.get(lbox.curselection()).split()[len(lbox.get(lbox.curselection()).split())-1])\n ,ft.list_to_dct(ParametresLst))\n\n\n submitButton = Button(window2, text = \"submit\", command = UpdateElement)\n submitButton.pack()\n\n window2.geometry('200x300+100+200')\n window2.mainloop()\n\n notebooks = ft.df_to_dict(companies, collaborations, products)\n\n\nbsearch = Button(f, text=\"Search\",command = DeleteAll, width = 100)\nbsearch.pack(side=BOTTOM)\naddbtn = Button(text = \"Add\", command = add, width = 100)\naddbtn.pack()\n\n\nfrbtn = Frame(width=50)\nfrbtn.pack()\n\n\nupdatebtn = Button(frbtn, text = \"update\", command = update, width = 25)\nupdatebtn.grid(row=1, column=1)\n\ndelbtn = Button(frbtn, text = \"Delete\", command = DeleteElement, width =25)\ndelbtn.grid(row=1, column=2)\n\n\ndef comp_gr():\n x = ft.unique_specifications(notebooks, \"Company\")\n y = [ft.aver_prices(notebooks, \"Company\")[j] for j in x]\n gr.create_graphs(x, y, \"Company\")\n\n\ndef typename_gr():\n x = ft.unique_specifications(notebooks, \"TypeName\")\n y = [ft.aver_prices(notebooks, \"TypeName\")[j] for j in x]\n gr.create_graphs(x, y, \"TypeName\")\n\n\ndef inches_gr():\n x = ft.unique_specifications(notebooks, \"Inches\")\n y = [ft.aver_prices(notebooks, \"Inches\")[j] for j in x]\n gr.create_graphs(x, y, \"Inches\")\n\n\ndef ram_gr():\n x = ft.unique_specifications(notebooks, \"Ram\")\n y = [ft.aver_prices(notebooks, \"Ram\")[j] for j in x]\n gr.create_graphs(x, y, \"Ram\")\n\n\ndef opsys_gr():\n x = ft.unique_specifications(notebooks, \"OpSys\")\n y = [ft.aver_prices(notebooks, \"OpSys\")[j] for j in x]\n gr.create_graphs(x, y, \"OpSys\")\n\n\ndef graph_window():\n graph_wind = Toplevel()\n f_gr = Frame(graph_wind, width=100)\n f_gr.pack()\n bcomp = Button(f_gr, text=\"Company\", width=30, command=comp_gr, bg=\"#fdd5b1\")\n bcomp.pack()\n btypename = Button(f_gr, text=\"Typename\", width=30, command=typename_gr, bg=\"#ffebcd\")\n btypename.pack()\n binches = Button(f_gr, text=\"Inches\", width=30, command=inches_gr, bg=\"#ffdab9\")\n binches.pack()\n bram = Button(f_gr, text=\"Ram\", width=30, command=ram_gr, bg=\"#ffebcd\")\n bram.pack()\n bopsys = Button(f_gr, text=\"OpSys\", width=30, command=opsys_gr, bg=\"#fdd5b1\")\n bopsys.pack()\n\n\nbgraph = Button(f, text=\"Graph\", width=100, command=graph_window)\nbgraph.pack(side=BOTTOM)\n\n\nroot.geometry('1200x1000')\nroot.resizable(False, False)\nroot.mainloop()","sub_path":"Script/Gui.py","file_name":"Gui.py","file_ext":"py","file_size_in_byte":28107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"638862476","text":"from .main import Main\r\n\r\nclass CCcn(Main):\r\n def __init__(self, link, posts, handbook):\r\n Main.__init__(self, link, posts, handbook)\r\n\r\n self.menu = ['News','Ethereum','Litecoin','Altcoins']\r\n\r\n self.result = []\r\n\r\n def start(self):\r\n try:\r\n menu = self.get_menu('id', 'menu-main4')\r\n\r\n for page in menu:\r\n self.get_news(page['url'], page['title'])\r\n\r\n self.log.write(\"End: added {0} posts\".format(len(self.result)))\r\n except RuntimeError as error:\r\n self.log.write(\"Error: {0}\".format(error))\r\n\r\n def get_news(self, url, section):\r\n self.set_file(url)\r\n\r\n soup = self.soup()\r\n\r\n wrapper = soup.find('div', {'class': 'posts-row'})\r\n\r\n try:\r\n blocks = wrapper.find_all('article', {'class': 'type-post'})\r\n except AttributeError:\r\n raise RuntimeError(\"structure of the news list has changed\")\r\n\r\n for block in blocks:\r\n h4 = block.find('h4', {'class': 'entry-title'})\r\n\r\n try:\r\n a = h4.find('a')\r\n except AttributeError:\r\n raise RuntimeError(\"structure of the news list has changed\")\r\n\r\n url = self.check_url(a.get('href'))\r\n title = a.text.strip()\r\n date = block.find('time',{'class': 'updated'}).get('datetime')\r\n\r\n if title in self.posts:\r\n continue\r\n\r\n if self.check_date(date) is None:\r\n break\r\n\r\n try:\r\n post = self.get_post(url)\r\n\r\n if post:\r\n handbook = self.check_handbook_post(title, post['content'])\r\n\r\n if handbook:\r\n self.result.append({\r\n 'url': url,\r\n 'title': title,\r\n 'date': date,\r\n 'section': section,\r\n 'text': post['content'],\r\n 'handbook': handbook\r\n })\r\n\r\n self.posts.append(title)\r\n\r\n except Warning as error:\r\n self.log.write(\"Error: {0}\".format(error))\r\n\r\n def get_post(self, url):\r\n if url is None:\r\n return\r\n\r\n self.set_file(url)\r\n\r\n soup = self.soup()\r\n\r\n content = soup.find('div', {'class': 'entry-content'})\r\n\r\n if content is None:\r\n raise Warning(\"structure of the news post has changed. Post link: {0}\".format(url))\r\n\r\n text = []\r\n for div in content.find_all('div'):\r\n if not div is None:\r\n div.extract()\r\n\r\n for p in content.find_all('p'):\r\n script = p.script\r\n\r\n if not script is None:\r\n script.extract()\r\n\r\n text.append(self.clear(p.text))\r\n\r\n return {\r\n 'content': '
    '.join(text),\r\n }","sub_path":"cron/sites/ccn.py","file_name":"ccn.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"344751233","text":"def g():\n print('1')\n x = yield 'hello'\n print('2', 'x=', x)\n y = 5 + (yield x)\n print('3', 'y=', y)\n # yield 11\n\nf = g()\nprint(f)\n\n# 迭代器的 __iter__() 返回它自己\nfiter = f.__iter__()\nprint(fiter) # = print(f)\n\n# 第一次调用时必须先next()或send(None)，否则会报错，\n# send后之所以为None是因为这时候没有上一个yield。可以认为，next()等同于send(None)。\nprint(f.__next__()) \n# f.send(None)\n# print(f.send(5))\nprint('------------------------ separator ---------------')\n\nprint(next(f))\n\nprint(type(g()))\n\n# a = yield 5, 执行完之后 a = None, 返回5\n\n# send可以强行修改**上一个**yield表达式值。比如函数中有一个yield赋值，a = yield 5，\n# 第一次迭代到这里会返回5，a还没有赋值。第二次迭代时，使用.send(10)，那么，就是\n# 强行修改yield 5表达式的值为10，本来是None的，那么a=10\n\n# send(msg)与next()都有返回值，它们的返回值是当前迭代遇到yield时，\n# yield后面表达式的值，其实就是当前迭代中yield后面的参数。\n\n# print(f.send(2))\n\n# 若没有遇到 yield 表达式，生成器函数就已经退出，那么该方法会抛出 StopIterator 异常。\n# print(next(f))\n\n# for i in f:\n# print(i)\n\n\ndef fib():\n a, b = 0, 1\n while True:\n # c = yield (a + b)\n yield a + b\n a, b = b, a + b\n\nfor i in fib():\n print(i)\n if (i > 100):\n break\n\nmygenerator = (x*x for x in range(3))\n\n###############################################################################\ndef jumping_range(up_to):\n \"\"\"Generator for the sequence of integers from 0 to up_to, exclusive.\n\n Sending a value into the generator will shift the sequence by that amount.\n \"\"\"\n index = 0\n while index < up_to:\n jump = yield index\n if jump is None:\n jump = 1\n index += jump\n\n\nif __name__ == '__main__':\n iterator = jumping_range(5)\n print(next(iterator)) \n print(iterator.send(2)) \n print(next(iterator)) \n print(iterator.send(-1)) \n for x in iterator:\n print(x) # 3,4\n","sub_path":"coroutine/yield.py","file_name":"yield.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"60153874","text":"# -*- coding: utf-8 -*-\n\n__author__ = 'whyjay'\nimport csv\nimport functools\nimport sys\nimport excel_manager\nfrom PyQt4 import QtGui, QtCore\nfrom widget_y import WidgetY\nfrom widget_m import WidgetM\nfrom widget_d import WidgetD\nfrom widget_h import WidgetH\nfrom calculator import *\n\n\nclass WidgetMain(QtGui.QMainWindow):\n YEAR1 = 1924\n tmp_widget = None\n tmp_inputs = None\n\n def __init__(self):\n super(WidgetMain, self).__init__()\n\n palette = QtGui.QPalette()\n palette.setColor(QtGui.QPalette.Background,QtCore.Qt.white)\n self.setPalette(palette)\n\n self.central_widget = QtGui.QStackedWidget()\n self.setCentralWidget(self.central_widget)\n\n self.widget_y = WidgetY()\n self.widget_m = WidgetM()\n self.widget_d = WidgetD()\n self.widget_h = WidgetH()\n\n self.central_widget.addWidget(self.widget_y)\n self.central_widget.addWidget(self.widget_m)\n self.central_widget.addWidget(self.widget_d)\n self.central_widget.addWidget(self.widget_h)\n\n # self.init_const()\n self.lb_init = QtGui.QLabel()\n self.lb_init.setText(self.qs(\"갑자를 설정해주세요.\"))\n self.lb_init.setAlignment(QtCore.Qt.AlignCenter)\n\n self.start_code = 0\n self.tmp_cycles = [x for x in CODE2CYCLE]\n\n self.init_ui()\n\n # init\n def init_ui(self):\n self.init_tool_bar()\n self.statusBar()\n\n # window geometry\n self.resize(800, 600)\n self.center()\n self.setWindowTitle(self.qs('주기 데이터베이스'))\n self.showMaximized()\n\n def init_tool_bar(self):\n # 액션 추가\n which = ['y', 'm', 'd', 'h', 'c', 'x']\n titles = {'y': '주기 설정',\n 'm': '월 단위 보기',\n 'd': '일 단위 보기',\n 'h': '시 단위 보기',\n 'c': '시점 이동',\n 'x': 'target.xlsx 파일입력'}\n\n tips = {'y': '주기번호의 기준년도를 설정합니다.',\n 'm': '연, 월 입력',\n 'd': '연, 월, 일 입력',\n 'h': '연, 월, 일, 시 입력',\n 'c': '이동 갯수를 입력',\n 'x': 'target.xlsx 파일입력'}\n\n tb = self.addToolBar(self.qs('Tool Bar'))\n for i in which:\n act = QtGui.QAction(self.qs(titles.get(i)), self)\n act.setStatusTip(self.qs(tips.get(i)))\n act.triggered.connect(functools.partial(\n self.dl_set_page, i\n ))\n tb.addAction(act)\n\n def center(self):\n qr = self.frameGeometry()\n cp = QtGui.QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())\n\n # dialogs\n def dl_set_page(self, which):\n\n titles = {\n 'y': '연도 설정',\n 'm': '월 설정',\n 'd': '일 설정',\n 'h': '시 설정',\n 'c': '시점 이동',\n 'x': 'target.xlsx 파일입력'\n }\n hints = {\n 'y': \"연도와 숫자를 콤마(,)로 구분해서 입력 (예: 1992, 45)\",\n 'm': \"년, 월을 콤마(,)로 구분해서 입력 (예: 1992, 9)\",\n 'd': \"년, 월, 일을 콤마(,)로 구분해서 입력 (예: 1992, 9, 8)\",\n 'h': \"년, 월, 일, 시를 콤마(,)로 구분해서 입력 (예: 1992, 9, 8, 13)\",\n 'c': \"이동한 갯수를 입력 (예: 75)\",\n 'x': \"target.xlsx 파일입력\"\n }\n\n if which == 'x':\n excel_manager.edit_file()\n return\n\n text, ok = QtGui.QInputDialog.getText(self,\n self.qs(titles.get(which)),\n self.qs(hints.get(which)))\n if ok:\n inputs = [int(n.strip()) for n in re.split(\"[,.\\-:]\", str(text))]\n\n if which == 'c':\n which = self.tmp_widget\n\n func = get_next\n if inputs[0] < 0:\n func = get_prev\n\n for i in range(abs(inputs[0])):\n self.tmp_inputs = func(which, *self.tmp_inputs)\n\n if which == 'm':\n self.tmp_inputs = self.tmp_inputs[:2]\n elif which == 'd':\n self.tmp_inputs = self.tmp_inputs[:3]\n elif which == 'h':\n self.tmp_inputs = self.tmp_inputs[:4]\n else:\n self.tmp_widget = which\n self.tmp_inputs = inputs\n\n if which == 'y':\n self.set_cycle(inputs[0])\n self.YEAR1 = inputs[0] - inputs[1] + 1\n\n\n widget = getattr(self, 'widget_'+which)\n widget.init_ui(self.tmp_inputs, self.tmp_cycles, self.YEAR1)\n self.central_widget.setCurrentWidget(widget)\n\n def set_cycle(self, year):\n self.start_code = get_y_code(year)\n for i in range(NUM_CYCLE):\n self.tmp_cycles[i] = CODE2CYCLE[(NUM_CYCLE-self.start_code + i) % NUM_CYCLE]\n\n def qs(self, s):\n return QtCore.QString(unicode(s, 'utf-8'))\n\n\ndef main():\n app = QtGui.QApplication(sys.argv)\n h = WidgetMain()\n sys.exit(app.exec_())\n\nif __name__ == '__main__':\n main()\n","sub_path":"widget_main.py","file_name":"widget_main.py","file_ext":"py","file_size_in_byte":5306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"411235610","text":"from fabric.api import cd, env, execute, run, shell_env, sudo\nfrom fabric.network import ssh\n\nssh.util.log_to_file(\"paramiko.log\", 10)\n\nenv.hosts = [\"linode\"]\nenv.use_ssh_config = True\nROOT = \"/home/david/public_html/django/django_carlog/public\"\n\n\ndef restart():\n with cd(ROOT):\n sudo(\"touch /etc/uwsgi/apps-available/django_carlog.ini\")\n\n\ndef update():\n with cd(ROOT):\n run(\"git pull --rebase\")\n\n\ndef schema():\n with cd(ROOT), shell_env(\n DJANGO_SETTINGS_MODULE=\"django_carlog.settings.production\"\n ):\n run(\"env/bin/python ./manage.py migrate\")\n\n\ndef backupdb():\n with cd(ROOT):\n run(\"./backupLocalDB.sh carlog_django\")\n\n\ndef static():\n with cd(ROOT):\n run(\"rm -rf static/*\")\n run(\n \"env/bin/python ./manage.py collectstatic --settings=django_carlog.settings.production --noinput --link\"\n )\n\n\ndef deploy():\n execute(update)\n execute(env)\n execute(backupdb)\n execute(schema)\n execute(static)\n execute(restart)\n\n\ndef env():\n with cd(ROOT):\n run(\"./createVirtualEnv.sh\")\n","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"505863512","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('product', views.save_product, name='product'),\n path('product/edit/', views.edit_product, name='edit_product'),\n path('product/edit//subtract/', views.edit_product, name='edit_product_subtract'),\n path('update/current_amount/', views.update_current_amount, name='update_current_amount'),\n path('shopping_list', views.shopping_list, name='shopping_list'),\n path('product/delete/', views.product_delete, name='product_delete'),\n]\n","sub_path":"products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"302068285","text":"\"\"\"\nEscreva o jogo do chute.\nNele você deve sortear um número inteiro entre 1 e 100 e pedir\npara o usuário advinhar o número que você escolheu\n\nPara cada chute do usuário você deve imprimir uma dica, se\nele chutou baixo de mais ou alto demais\n\nUma vez que o usuário acerte o chute o programa imprime uma\nmensagem e também o número de chutes que o usuário deu\n\nOBS: Use o statement break\n\nExemplo:\n\n>>>\nTente advinhar o número que eu estou pensando\nSeu Chute: 50\nVocê deve chutar mais alto!\nSeu Chute: 75\nVocê deve chutar mais alto!\nSeu Chute: 87\nVocê deve chutar mais alto!\nSeu Chute: 93\nVocê deve chutar mais alto!\nSeu Chute: 97\nVocê deve chutar mais baixo!\nSeu Chute: 95\nParabens você acertou!!\nVocê chutou 6 vezes\n>>>\n\n\"\"\"\n\n\nfrom random import randint\n\n\ndef main():\n \"\"\"\n Função principal do programa\n \"\"\"\n numero_sorteado = randint(1, 100)\n\n print(\"Tente adivinhar o número que eu estou pensando\")\n cont = 0\n\n while True:\n chute = int(input(\"Seu chute: \"))\n cont += 1\n if chute == numero_sorteado:\n break\n elif chute > numero_sorteado:\n print(\"Você deve chutar mais baixo!\")\n else:\n print(\"Você deve chutar mais alto!\")\n\n print(\"Parabens você acertou!!\")\n print(f\"Você chutou {cont} vezes\")\n\n\nmain()\n","sub_path":"funcoes/exercício2_043.py","file_name":"exercício2_043.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"82775018","text":"# Copyright (c) 2014, Salesforce.com, Inc. All rights reserved.\n# Copyright (c) 2015, Gamelan Labs, Inc.\n# Copyright (c) 2016, Google, Inc.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# - Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# - Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# - Neither the name of Salesforce.com nor the names of its contributors\n# may be used to endorse or promote products derived from this\n# software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS\n# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR\n# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE\n# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nfrom __future__ import division\ntry:\n from itertools import izip as zip\nexcept ImportError:\n pass\nimport numpy\nimport scipy.stats\nfrom numpy import pi\nfrom numpy.testing import rand\nfrom nose import SkipTest\nfrom nose.tools import assert_almost_equal\nfrom nose.tools import assert_equal\nfrom nose.tools import assert_greater\nfrom nose.tools import assert_less\nfrom goftests import seed_all\nfrom goftests import get_dim\nfrom goftests import multinomial_goodness_of_fit\nfrom goftests import discrete_goodness_of_fit\nfrom goftests import auto_density_goodness_of_fit\nfrom goftests import mixed_density_goodness_of_fit\nfrom goftests import split_discrete_continuous\nfrom goftests import volume_of_sphere\n\nNUM_BASE_SAMPLES = 250\n\nNUM_SAMPLES_SCALE = 1000\n\nTEST_FAILURE_RATE = 5e-4\n\n\ndef test_multinomial_goodness_of_fit():\n for dim in range(2, 20):\n yield _test_multinomial_goodness_of_fit, dim\n\n\ndef _test_multinomial_goodness_of_fit(dim):\n seed_all(0)\n sample_count = int(1e5)\n probs = numpy.random.dirichlet([1] * dim)\n\n counts = numpy.random.multinomial(sample_count, probs)\n p_good = multinomial_goodness_of_fit(probs, counts, sample_count)\n assert_greater(p_good, TEST_FAILURE_RATE)\n\n unif_counts = numpy.random.multinomial(sample_count, [1. / dim] * dim)\n p_bad = multinomial_goodness_of_fit(probs, unif_counts, sample_count)\n assert_less(p_bad, TEST_FAILURE_RATE)\n\n\ndef test_volume_of_sphere():\n for r in [0.1, 1.0, 10.0]:\n assert_almost_equal(volume_of_sphere(1, r), 2.0 * r)\n assert_almost_equal(volume_of_sphere(2, r), pi * r ** 2)\n assert_almost_equal(volume_of_sphere(3, r), 4 / 3.0 * pi * r ** 3)\n\n\nsplit_examples = [\n {'mixed': False, 'discrete': False, 'continuous': []},\n {'mixed': 0, 'discrete': 0, 'continuous': []},\n {'mixed': 'abc', 'discrete': 'abc', 'continuous': []},\n {'mixed': 0.0, 'discrete': None, 'continuous': [0.0]},\n {'mixed': (), 'discrete': (), 'continuous': []},\n {'mixed': [], 'discrete': (), 'continuous': []},\n {'mixed': (0,), 'discrete': (0, ), 'continuous': []},\n {'mixed': [0, ], 'discrete': (0, ), 'continuous': []},\n {'mixed': (0.0, ), 'discrete': (None, ), 'continuous': [0.0]},\n {'mixed': [0.0, ], 'discrete': (None, ), 'continuous': [0.0]},\n {\n 'mixed': [True, 1, 'xyz', 3.14, [None, (), ([2.71],)]],\n 'discrete': (True, 1, 'xyz', None, (None, (), ((None,),))),\n 'continuous': [3.14, 2.71],\n },\n {\n 'mixed': numpy.zeros(3),\n 'discrete': (None, None, None),\n 'continuous': [0.0, 0.0, 0.0],\n },\n]\n\n\ndef split_example(i):\n example = split_examples[i]\n discrete, continuous = split_discrete_continuous(example['mixed'])\n assert_equal(discrete, example['discrete'])\n assert_almost_equal(continuous, example['continuous'])\n\n\ndef test_split_continuous_discrete():\n for i in range(len(split_examples)):\n yield split_example, i\n\n\nseed_all(0)\ndefault_params = {\n 'bernoulli': [(0.2,)],\n 'beta': [\n (0.5, 0.5),\n (0.5, 1.5),\n (0.5, 2.5),\n ],\n 'binom': [(40, 0.4)],\n 'dirichlet': [\n ([2.0, 2.5],),\n ([2.0, 2.5, 3.0],),\n ([2.0, 2.5, 3.0, 3.5],),\n ],\n 'erlang': [(7,)],\n 'dlaplace': [(0.8,)],\n 'frechet': [tuple(2 * rand(1)) + (0,) + tuple(2 * rand(2))],\n 'geom': [(0.1,)],\n 'hypergeom': [(40, 14, 24)],\n 'logser': [(0.9,)],\n 'multivariate_normal': [\n (numpy.ones(1), numpy.eye(1)),\n (numpy.ones(2), numpy.eye(2)),\n (numpy.ones(3), numpy.eye(3)),\n ],\n 'nbinom': [(40, 0.4)],\n 'ncf': [(27, 27, 0.415784417992)],\n 'planck': [(0.51,)],\n 'poisson': [(20,)],\n 'reciprocal': [tuple(numpy.array([0, 1]) + rand(1)[0])],\n 'trapz': [(0.333, 0.666)],\n 'triang': [tuple(rand(1))],\n 'truncnorm': [(0.1, 2.0)],\n 'vonmises': [tuple(1.0 + rand(1))],\n 'wrapcauchy': [(0.5,)],\n 'zipf': [(1.2,)],\n}\n\nknown_failures = set([\n 'alpha',\n 'boltzmann',\n 'gausshyper', # very slow\n 'ksone', # ???\n 'levy_stable', # ???\n 'ortho_group', # matrix\n 'randint', # too sparse\n 'random_correlation', # matrix\n 'rv_continuous', # abstract\n 'rv_discrete', # abstract\n 'special_ortho_group', # matrix\n 'zipf', # bug?\n 'invwishart', # matrix\n 'wishart', # matrix\n 'matrix_normal', # matrix\n 'rv_histogram', # TODO Support distributions without .numargs attribute.\n 'multinomial', # numargs\n])\n\n\ndef transform_dirichlet(ps):\n dim = len(ps)\n assert dim > 1\n # return ps[:-1] - ps[-1] * (dim ** 0.5 - 1.0) / (dim - 1.0)\n return ps[:-1]\n\n\ntransforms = {\n 'dirichlet': transform_dirichlet,\n}\n\n\ndef _test_scipy_stats(name):\n if name in known_failures:\n raise SkipTest('known failure')\n dist = getattr(scipy.stats, name)\n try:\n params = default_params[name]\n except KeyError:\n params = [tuple(1.0 + rand(dist.numargs))]\n for param in params:\n print('param = {}'.format(param))\n dim = get_dim(dist.rvs(*param, size=2)[0])\n sample_count = NUM_BASE_SAMPLES + NUM_SAMPLES_SCALE * dim\n samples = list(dist.rvs(*param, size=sample_count))\n if name in transforms:\n transformed = list(map(transforms[name], samples))\n else:\n transformed = samples\n\n if hasattr(dist, 'pmf'):\n probs = [dist.pmf(sample, *param) for sample in samples]\n probs_dict = dict(zip(samples, probs))\n gof = discrete_goodness_of_fit(transformed, probs_dict, plot=True)\n else:\n probs = [dist.pdf(sample, *param) for sample in samples]\n gof = auto_density_goodness_of_fit(transformed, probs, plot=True)\n assert_greater(gof, TEST_FAILURE_RATE)\n\n gof = mixed_density_goodness_of_fit(transformed, probs, plot=True)\n assert_greater(gof, TEST_FAILURE_RATE)\n\n\ndef test_scipy_stats():\n seed_all(0)\n for name in dir(scipy.stats):\n if hasattr(getattr(scipy.stats, name), 'rvs'):\n yield _test_scipy_stats, name\n","sub_path":"goftests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"132935171","text":"from imageai.Prediction.Custom import ModelTraining\n\nPATH_DATA = r\"C:/Users/Moses/Documents/Moses/W7/AI/Custom Datasets/idenprof\"\n\n\ndef main(path_data):\n model_trainer = ModelTraining()\n model_trainer.setModelTypeAsResNet()\n model_trainer.setDataDirectory(path_data)\n model_trainer.trainModel(num_objects=10, num_experiments=20, enhance_data=True,\n batch_size=32, show_network_summary=True)\n\n\nif __name__ == '__main__':\n main(path_data=PATH_DATA)","sub_path":"examples/custom_model_training.py","file_name":"custom_model_training.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"322918336","text":"############################################################\n# -*- coding: utf-8 -*-\n#\n# # # # # # #\n# ## ## # ## # #\n# # # # # # # # # # #\n# # ## # ## ## ######\n# # # # # # #\n#\n# Python-based Tool for interaction with the 10micron mounts\n# GUI with PyQT5 for python\n# Python v3.7.5\n#\n# Michael Würtenberger\n# (c) 2019\n#\n# Licence APL2.0\n#\n###########################################################\n# standard libraries\nimport logging\nimport zlib\nimport os\nfrom datetime import datetime\n# external packages\nimport PyQt5\nimport numpy as np\nimport astropy.io.fits as fits\n# local imports\nfrom mw4.base import indiClass\n\n\nclass CameraSignals(PyQt5.QtCore.QObject):\n \"\"\"\n The CameraSignals class offers a list of signals to be used and instantiated by\n the Mount class to get signals for triggers for finished tasks to\n enable a gui to update their values transferred to the caller back.\n\n This has to be done in a separate class as the signals have to be subclassed from\n QObject and the Mount class itself is subclassed from object\n \"\"\"\n\n __all__ = ['CameraSignals']\n version = '0.100.0'\n\n integrated = PyQt5.QtCore.pyqtSignal()\n saved = PyQt5.QtCore.pyqtSignal(object)\n message = PyQt5.QtCore.pyqtSignal(object)\n\n\nclass Camera(indiClass.IndiClass):\n \"\"\"\n the class Camera inherits all information and handling of the Camera device.\n\n\n >>> Camera(app=None,\n >>> host=host,\n >>> name='',\n >>> )\n \"\"\"\n\n __all__ = ['Camera',\n ]\n\n version = '0.100.0'\n logger = logging.getLogger(__name__)\n\n # update rate to 1000 milli seconds for setting indi server\n UPDATE_RATE = 1000\n\n def __init__(self,\n app=None,\n host=None,\n name='',\n ):\n super().__init__(host=host,\n name=name,\n app=app,\n )\n self.app = app\n\n self.signals = CameraSignals()\n self.imagePath = ''\n self.filterNames = dict()\n self.filterNumber = 0\n\n def setUpdateConfig(self, deviceName):\n \"\"\"\n _setUpdateRate corrects the update rate of camera devices to get an defined\n setting regardless, what is setup in server side.\n\n :param deviceName:\n :return: success\n \"\"\"\n\n if deviceName != self.name:\n return False\n\n if self.device is None:\n return False\n\n # set BLOB mode also\n self.client.setBlobMode(blobHandling='Also',\n deviceName=deviceName)\n # setting a object name\n objectName = self.device.getText('FITS_HEADER')\n objectName['FITS_OBJECT'] = 'skyview'\n self.client.sendNewText(deviceName=deviceName,\n propertyName='FITS_HEADER',\n elements=objectName,\n )\n # setting WCS Control off\n wcs = self.device.getSwitch('WCS_CONTROL')\n wcs['WCS_DISABLE'] = True\n self.client.sendNewSwitch(deviceName=deviceName,\n propertyName='WCS_CONTROL',\n elements=wcs,\n )\n # setting active device for telescope\n telescope = self.device.getText('ACTIVE_DEVICES')\n telescope['ACTIVE_TELESCOPE'] = 'LX200 10micron'\n self.client.sendNewText(deviceName=deviceName,\n propertyName='ACTIVE_DEVICES',\n elements=telescope,\n )\n # setting polling updates in driver\n update = self.device.getNumber('POLLING_PERIOD')\n if 'PERIOD_MS' not in update:\n return False\n if update.get('PERIOD_MS', 0) == self.UPDATE_RATE:\n return True\n update['PERIOD_MS'] = self.UPDATE_RATE\n suc = self.client.sendNewNumber(deviceName=deviceName,\n propertyName='POLLING_PERIOD',\n elements=update,\n )\n\n return suc\n\n def setExposureState(self, propertyName='', value=0):\n \"\"\"\n\n :param propertyName:\n :param value:\n :return: success\n \"\"\"\n\n if propertyName == 'CCD_EXPOSURE':\n if not hasattr(self.device, 'CCD_EXPOSURE'):\n return False\n if self.device.CCD_EXPOSURE['state'] == 'Idle':\n self.signals.message.emit('')\n elif self.device.CCD_EXPOSURE['state'] == 'Busy':\n if value == 0:\n self.signals.integrated.emit()\n self.signals.message.emit('download')\n else:\n self.signals.message.emit(f'expose {value:2.0f} s')\n elif self.device.CCD_EXPOSURE['state'] == 'Ok':\n self.signals.message.emit('')\n return True\n else:\n return False\n\n def updateNumber(self, deviceName, propertyName):\n \"\"\"\n updateNumber is called whenever a new number is received in client. it runs\n through the device list and writes the number data to the according locations.\n\n :param deviceName:\n :param propertyName:\n :return:\n \"\"\"\n\n if self.device is None:\n return False\n if deviceName != self.name:\n return False\n\n for element, value in self.device.getNumber(propertyName).items():\n key = propertyName + '.' + element\n self.data[key] = value\n # print(propertyName, element, value)\n\n self.setExposureState(propertyName=propertyName, value=value)\n\n return True\n\n def updateText(self, deviceName, propertyName):\n \"\"\"\n updateNumber is called whenever a new number is received in client. it runs\n through the device list and writes the number data to the according locations.\n\n :param deviceName:\n :param propertyName:\n :return:\n \"\"\"\n\n if self.device is None:\n return False\n if deviceName != self.name:\n return False\n\n for element, value in self.device.getText(propertyName).items():\n key = propertyName + '.' + element\n self.data[key] = value\n # print(propertyName, element, value)\n\n return True\n\n def updateSwitch(self, deviceName, propertyName):\n \"\"\"\n updateNumber is called whenever a new number is received in client. it runs\n through the device list and writes the number data to the according locations.\n\n :param deviceName:\n :param propertyName:\n :return:\n \"\"\"\n\n if self.device is None:\n return False\n if deviceName != self.name:\n return False\n\n for element, value in self.device.getSwitch(propertyName).items():\n key = propertyName + '.' + element\n self.data[key] = value\n # print(propertyName, element, value)\n return True\n\n def updateLight(self, deviceName, propertyName):\n \"\"\"\n updateNumber is called whenever a new number is received in client. it runs\n through the device list and writes the number data to the according locations.\n\n :param deviceName:\n :param propertyName:\n :return:\n \"\"\"\n\n if self.device is None:\n return False\n if deviceName != self.name:\n return False\n\n for element, value in self.device.getLight(propertyName).items():\n key = propertyName + '.' + element\n self.data[key] = value\n # print(propertyName, element, value)\n return True\n\n def updateBLOB(self, deviceName, propertyName):\n \"\"\"\n updateBLOB is called whenever a new BLOB is received in client. it runs\n through the device list and writes the number data to the according locations.\n\n :param deviceName:\n :param propertyName:\n :return:\n \"\"\"\n\n if self.device is None:\n return False\n if deviceName != self.name:\n return False\n\n data = self.device.getBlob(propertyName)\n\n if 'value' not in data:\n return False\n if data['name'] != 'CCD1':\n return False\n if not self.imagePath:\n return False\n if not os.path.isdir(os.path.dirname(self.imagePath)):\n return False\n\n if data['format'] == '.fits.fz':\n HDU = fits.HDUList.fromstring(data['value'])\n fits.writeto(self.imagePath, HDU[0].data, HDU[0].header, overwrite=True)\n self.logger.debug('Image BLOB is in FPacked format')\n\n elif data['format'] == '.fits.z':\n HDU = fits.HDUList.fromstring(zlib.decompress(data['value']))\n fits.writeto(self.imagePath, HDU[0].data, HDU[0].header, overwrite=True)\n self.logger.debug('Image BLOB is compressed fits format')\n\n elif data['format'] == '.fits':\n HDU = fits.HDUList.fromstring(data['value'])\n fits.writeto(self.imagePath, HDU[0].data, HDU[0].header, overwrite=True)\n self.logger.debug('Image BLOB is uncompressed fits format')\n\n else:\n self.logger.debug('Image BLOB is not supported')\n\n self.signals.saved.emit(self.imagePath)\n return True\n\n def canSubFrame(self, subFrame=100):\n \"\"\"\n canSubFrame checks if a camera supports sub framing and reports back\n\n :param subFrame:\n :return: success\n \"\"\"\n if subFrame > 100:\n return False\n if subFrame < 10:\n return False\n if 'CCD_FRAME.X' not in self.data or 'CCD_FRAME.Y' not in self.data:\n return False\n\n return True\n\n def canBinning(self, binning=1):\n \"\"\"\n canBinning checks if the camera supports that type of binning\n\n :param binning:\n :return: success\n \"\"\"\n if binning < 1:\n return False\n if binning > 4:\n return False\n if 'CCD_BINNING.HOR_BIN' not in self.data:\n return False\n\n return True\n\n def calcSubFrame(self, subFrame=100):\n \"\"\"\n calcSubFrame calculates the subFrame parameters depending on the percentage of\n the reduction. the subFrame will be centered on the image area.\n\n :param subFrame: percentage 0-100 of\n :return:\n \"\"\"\n if subFrame < 10 or subFrame > 100:\n width = self.data['CCD_INFO.CCD_MAX_X']\n height = self.data['CCD_INFO.CCD_MAX_Y']\n posX = 0\n posY = 0\n else:\n width = int(self.data['CCD_INFO.CCD_MAX_X'] * subFrame / 100)\n height = int(self.data['CCD_INFO.CCD_MAX_Y'] * subFrame / 100)\n posX = int((self.data['CCD_INFO.CCD_MAX_X'] - width) / 2)\n posY = int((self.data['CCD_INFO.CCD_MAX_Y'] - height) / 2)\n\n return posX, posY, width, height\n\n def setupFrameCompress(self):\n \"\"\"\n setupFrameCompress prepares the overall INDI setup data for imaging\n\n :return: success\n \"\"\"\n\n # setting compression to on as default\n indiCmd = self.device.getSwitch('CCD_COMPRESSION')\n if 'CCD_COMPRESS' not in indiCmd:\n return False\n indiCmd['CCD_COMPRESS'] = True\n suc = self.client.sendNewSwitch(deviceName=self.name,\n propertyName='CCD_COMPRESSION',\n elements=indiCmd,\n )\n if not suc:\n return False\n\n # setting frame type to light\n indiCmd = self.device.getSwitch('CCD_FRAME_TYPE')\n if 'FRAME_LIGHT' not in indiCmd:\n return False\n indiCmd['FRAME_LIGHT'] = True\n suc = self.client.sendNewSwitch(deviceName=self.name,\n propertyName='CCD_FRAME_TYPE',\n elements=indiCmd,\n )\n return suc\n\n def sendDownloadMode(self, fastReadout=False):\n \"\"\"\n setDownloadMode sets the readout speed of the camera\n\n :return: success\n \"\"\"\n\n # setting fast mode:\n quality = self.device.getSwitch('READOUT_QUALITY')\n self.logger.debug(f'camera has readout quality entry: {quality}')\n quality['QUALITY_LOW'] = fastReadout\n quality['QUALITY_HIGH'] = not fastReadout\n suc = self.client.sendNewSwitch(deviceName=self.name,\n propertyName='READOUT_QUALITY',\n elements=quality,\n )\n\n return suc\n\n def expose(self, imagePath='', expTime=3, binning=1,\n subFrame=100, fastReadout=True):\n \"\"\"\n\n :param imagePath:\n :param expTime:\n :param binning:\n :param subFrame:\n :param fastReadout:\n :return: success\n \"\"\"\n\n if not imagePath:\n return False\n if not self.canSubFrame(subFrame=subFrame):\n return False\n if not self.canBinning(binning=binning):\n return False\n\n self.imagePath = imagePath\n\n suc = self.setupFrameCompress()\n if not suc:\n if not suc:\n self.logger.info('Camera has no compression settings')\n\n suc = self.sendDownloadMode(fastReadout=fastReadout)\n if not suc:\n self.logger.info('Camera has no download quality settings')\n\n # setting binning value for x and y equally\n indiCmd = self.device.getNumber('CCD_BINNING')\n indiCmd['HOR_BIN'] = binning\n indiCmd['VER_BIN'] = binning\n suc = self.client.sendNewNumber(deviceName=self.name,\n propertyName='CCD_BINNING',\n elements=indiCmd,\n )\n if not suc:\n return False\n\n # setting subFrame\n posX, posY, width, height = self.calcSubFrame(subFrame)\n\n indiCmd = self.device.getNumber('CCD_FRAME')\n indiCmd['X'] = posX\n indiCmd['Y'] = posY\n indiCmd['WIDTH'] = width\n indiCmd['HEIGHT'] = height\n suc = self.client.sendNewNumber(deviceName=self.name,\n propertyName='CCD_FRAME',\n elements=indiCmd,\n )\n if not suc:\n return False\n\n # setting and starting exposure\n indiCmd = self.device.getNumber('CCD_EXPOSURE')\n indiCmd['CCD_EXPOSURE_VALUE'] = expTime\n suc = self.client.sendNewNumber(deviceName=self.name,\n propertyName='CCD_EXPOSURE',\n elements=indiCmd,\n )\n return suc\n\n def abort(self):\n \"\"\"\n abort cancels the exposing\n\n :return: success\n \"\"\"\n\n if not self.device:\n return False\n\n indiCmd = self.device.getSwitch('CCD_ABORT_EXPOSURE')\n if 'ABORT' not in indiCmd:\n return False\n indiCmd['ABORT'] = True\n suc = self.client.sendNewSwitch(deviceName=self.name,\n propertyName='CCD_ABORT_EXPOSURE',\n elements=indiCmd,\n )\n\n return suc\n\n def sendCoolerSwitch(self, coolerOn=False):\n \"\"\"\n sendCoolerTemp send the desired cooler temp, but does not switch on / off the cooler\n\n :param coolerOn:\n :return: success\n \"\"\"\n\n # setting fast mode:\n cooler = self.device.getSwitch('CCD_COOLER')\n cooler['COOLER_ON'] = coolerOn\n cooler['COOLER_OFF'] = not coolerOn\n suc = self.client.sendNewSwitch(deviceName=self.name,\n propertyName='CCD_COOLER',\n elements=cooler,\n )\n\n return suc\n\n def sendCoolerTemp(self, temperature=0):\n \"\"\"\n sendCoolerTemp send the desired cooler temp, indi does automatically start cooler\n\n :param temperature:\n :return: success\n \"\"\"\n\n # setting fast mode:\n temp = self.device.getNumber('CCD_TEMPERATURE')\n temp['CCD_TEMPERATURE_VALUE'] = temperature\n suc = self.client.sendNewNumber(deviceName=self.name,\n propertyName='CCD_TEMPERATURE',\n elements=temp,\n )\n\n return suc\n\n def sendFilterNumber(self, filterNumber=1):\n \"\"\"\n sendFilterNumber send the desired filter number\n\n :param filterNumber:\n :return: success\n \"\"\"\n\n # setting fast mode:\n filterNo = self.device.getNumber('FILTER_SLOT')\n filterNo['FILTER_SLOT_VALUE'] = filterNumber\n suc = self.client.sendNewNumber(deviceName=self.name,\n propertyName='FILTER_SLOT',\n elements=filterNo,\n )\n\n return suc\n","sub_path":"mw4/imaging/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":17554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"410331139","text":"import tensorflow as tf\n\nduplicate_rgb = True\n\ndef get_args(read_input: bool = False, duplicate_rgb: bool = False):\n if read_input:\n return [float(input(var + '\\n')) for var in ['tv_eps (0.2)', 'tv_lam (0.3)', 'reg_lam(1.0)']]\n if duplicate_rgb:\n return [200.5, 0.000009, 0.02]\n return [0.2, 0.0, 0.0, 0.0001]\n\ndef _tv_diff(c):\n x_wise = c[:, :, 1:] - c[:, :, :-1]\n y_wise = c[:, 1:, :] - c[:, :-1, :]\n return x_wise, y_wise\n\ndef smooth_tv(c):\n x_wise, y_wise = _tv_diff(c)\n return tf.reduce_sum(tf.multiply(x_wise, x_wise)) + tf.reduce_sum(tf.multiply(y_wise, y_wise))\n\ndef get_tv(t):\n return smooth_tv(t[:, 0]) + smooth_tv(t[:, 1]) + smooth_tv(t[:, 2])\n\ndef get_ct(x):\n t_shape = x[:, :1, :, :].shape\n t = tf.random_uniform(t_shape, minval=0, maxval=1) - 0.5\n repeat_t = tf.keras.backend.repeat_elements(t, 3, axis=1)\n return repeat_t\n\ndef get_tv_loss(x):\n #assumes x is of shape (N,3,b,w), where N is batch size, 3 is num channels & b,w are standard\n ct = get_ct(x)\n tv_eps, tv_lam, reg_lam = get_args(duplicate_rgb=duplicate_rgb)\n tv_loss = tv_lam * get_tv(ct)\n return tv_loss\n\n\nif __name__ == \"__main__\":\n x = tf.random_uniform([4,32,32,3])\n print(x.shape)\n y = get_tv_loss(x)\n print(y)\n","sub_path":"attacks/tv_loss.py","file_name":"tv_loss.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"198811688","text":"import sys\nfrom collections import Counter\nimport re\nimport os\n\n#method to estimate how many named entities an ASR output got correct\n\n#sys.argv[1] = transcript file\n#sys.argv[2] = location of ASR transcripts\n\n#########number of named entities in gold transcripts##################\n\n#counters to hold tokens\nne = Counter()\nnon_ne = Counter()\n\nf = open(sys.argv[1], \"rb\")\n\nid_regex = r'^[0-9A-z]+.*'\n\nbackslash_regex = r'\\\\'\n\nfor line in f:\n line_split = line.rstrip().split(\" \")\n #if first item is utterance ID\n if re.match(id_regex, line):\n sentence = line_split[1:]\n #if there is no utterance ID\n else:\n sentence = line_split\n #for each idx in sentence\n for i in range(len(sentence)):\n word = sentence[i]\n #remove any backslashes\n clean_word = re.sub(backslash_regex, \"\", word)\n #if it's not first word in sentence and is capitalized\n if i != 0 and clean_word[0].isupper():\n ne[clean_word.lower()] += 1\n else:\n non_ne[clean_word.lower()] += 1\n\nne_count = sum(ne.values())\nnon_ne_count = sum(non_ne.values())\ntotal_count = ne_count + non_ne_count\n\nne_rate = float(ne_count) / float(total_count)\n\nprint(\"number of named entities\", ne_count)\nprint(\"number of non named entities\", non_ne_count)\nprint(\"percentage of named entities\", ne_rate)\n\nf.close()\n\n##########named entity rate in ASR transcripts#################\n\n#coarse comparison: see how many of the named entities in `ne` appear\n#in the ASR transcripts\n\n#path\ntranscript_path = sys.argv[2]\n\n#all files in directory (not including path)\nall_ASR_transcripts = os.listdir(transcript_path)\n#all files - including path\nall_ASR_transcripts_fullPath = [transcript_path + \"/\" + all_ASR_transcripts[i] for i in range(len(all_ASR_transcripts))]\n\n#concatenate all transcripts into one list of words\nall_words = Counter()\n\nfor asr_file in all_ASR_transcripts_fullPath:\n f = open(asr_file, \"rb\")\n for line in f:\n line_split = line.split(\" \")\n if re.match(id_regex, line):\n sentence = line_split[1:]\n #if there is no utterance ID\n else:\n sentence = line_split\n #add each word to all_words counter\n for token in sentence:\n all_words[token.lower()] += 1\n f.close()\n\n#all_words counter - non_ne = all_words ne\n #this is an approximation of the words that the ASR system assigned to named entities\nall_words_ne = all_words - non_ne\n\nprint(\"approximate named entities as transcribed by ASR\", all_words_ne)\n\n#real ne - approximate ne = incorrect ne\n #this is an approximation of the named entities that the ASR system got incorrect\nincorrect_ne = ne - all_words_ne\n\nprint(\"incorrect named entities as transcribed by ASR\", incorrect_ne)\n\n#count of incorrect_ne\nincorrect_ne_count = sum(incorrect_ne.values())\n\n#percentage of incorect ne to entire number of words\nincorrect_ne_rate = float(incorrect_ne_count) / float(total_count)\n\nprint(\"percentage of incorrect named entities\", incorrect_ne_rate)\n\n\ncorrect = 0\nincorrect = 0\ntotal = 0\n\nfor word in ne.keys():\n total += 1\n if word in all_words:\n correct += 1\n else:\n incorrect += 1\n\nincorrect_ne_rate_distinct = float(incorrect) / float(total)\n\nprint(\"percentage of distinct incorrect named entities\", incorrect_ne_rate_distinct)\n\n","sub_path":"tools/PVanalysis/NE-rate.py","file_name":"NE-rate.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"65963602","text":"\"\"\"\nDefinition of views.\n\"\"\"\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render\nfrom django.http import HttpRequest\nfrom django.template import RequestContext\nfrom datetime import datetime\n\ndef home(request):\n \"\"\"Renders the home page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/index.html',\n {\n 'title':'Home Page',\n 'year':datetime.now().year,\n }\n )\n\ndef contact(request):\n \"\"\"Renders the contact page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Contact',\n 'message':'Your contact page.',\n 'year':datetime.now().year,\n }\n )\n\ndef about(request):\n \"\"\"Renders the about page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )\ndef registration(request):\n \"\"\"Renders the registration page.\"\"\"\n regform = UserCreationForm (request.POST)\n if request.method == \"POST\": # после отправки формы\n regform = UserCreationForm (request.POST)\n if regform.is_valid(): #валидация полей формы\n reg_f = regform.save(commit=False) # не сохраняем данные формы\n reg_f.is_staff = False # запрещен вход в административный раздел\n reg_f.is_active = True # активный пользователь\n reg_f.is_superuser = False # не является суперпользователем\n reg_f.date_joined = datetime.now() # дата регистрации\n reg_f.last_login = datetime.now() # дата последней авторизации\n\n reg_f.save() # сохраняем изменения после добавления данных (добавление пользователя в БД пользователей)\n\n return redirect('home') # переадресация на главную страницу после регистрации\n else:\n regform = UserCreationForm() # создание объекта формы для ввода данных нового пользователя\n assert isinstance(request, HttpRequest) \n return render(\n request,\n 'app/registration.html',\n {\n\n 'regform': regform, # передача формы в шаблон веб-страницы\n\n 'year':datetime.now().year,\n }\n )\n","sub_path":"DjangoWebProject2/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"47370548","text":"import torch as pt\n\nfrom .. import base\nfrom . import geometric_algebra\n\ndef keepdims_decorator(f):\n def wrapped(*args, **kwargs):\n if 'keepdims' in kwargs:\n kwargs['keepdim'] = kwargs.pop('keepdims')\n return f(*args, **kwargs)\n return wrapped\n\nclass VectorAttention(base.VectorAttention, pt.nn.Module):\n __doc__ = base.VectorAttention.__doc__\n\n algebra = geometric_algebra\n\n math = base.Namespace(\n all=pt.all,\n any=pt.any,\n asarray=pt.as_tensor,\n concat=pt.cat,\n logical_and=pt.logical_and,\n pow=pt.pow,\n product=keepdims_decorator(pt.prod),\n reshape=pt.reshape,\n shape=lambda x: x.shape,\n softmax=pt.softmax,\n sqrt=pt.sqrt,\n sum=keepdims_decorator(pt.sum),\n tensordot=pt.tensordot,\n where=pt.where,\n zeros_like=pt.zeros_like,\n )\n\n def __init__(self, n_dim, *args, **kwargs):\n pt.nn.Module.__init__(self)\n base.VectorAttention.__init__(self, *args, **kwargs)\n\n self.n_dim = n_dim\n\n if type(self) == VectorAttention:\n self.init()\n\n def init(self):\n \"\"\"Initialize the weights for this layer.\"\"\"\n weight_sets = self._build_weight_definitions(self.n_dim)\n for (name, defs) in weight_sets.groups.items():\n weights = pt.nn.ParameterList([\n pt.nn.Parameter(pt.normal(0, pt.ones(*def_.shape)*def_.stdev)) for def_ in defs])\n setattr(self, name, weights)\n\n for (name, def_) in weight_sets.singles.items():\n weight = pt.nn.Parameter(pt.normal(0, pt.ones(*def_.shape)*def_.stdev))\n setattr(self, name, weight)\n\n def _calculate_attention(self, scores, values, old_shape):\n dims, reduce_axes = self._get_reduction()\n\n shape = list(old_shape[:dims]) + [old_shape[dims:].numel()]\n scores = self.math.reshape(scores, shape)\n attention = self.math.reshape(self.math.softmax(scores, -1), old_shape)\n if reduce_axes:\n output = self.math.sum(attention*values, reduce_axes)\n else:\n output = attention*values\n\n return attention, output\n\n def forward(self, inputs):\n \"\"\"Evaluate the attention calculation for this layer.\"\"\"\n return self._evaluate(inputs).output\n","sub_path":"geometric_algebra_attention/pytorch/VectorAttention.py","file_name":"VectorAttention.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"626454002","text":"import sys\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport seaborn as sns\nimport sklearn as sk\n\nfrom sklearn.model_selection import train_test_split\n\n\n\ndef model():\n inputs = tf.keras.layers.Input(shape=(3,))\n outputs = tf.keras.layers.Dense(2, activation='softmax', kernel_initializer='glorot_uniform')(inputs)\n model = tf.keras.Model(inputs=inputs, outputs=outputs)\n \n model.compile(optimizer='adam', loss='categorical_crossentropy',\n metrics=['accuracy'])\n return model\n\n\nif __name__ == '__main__':\n\tdf = pd.read_csv('hospitaldata.csv')\n\n\tprint(df.columns)\n\tlabel=np.array(df.pop('LABEL'))\n\tdf.pop('SL NO.')\n\tdf.pop('NAME')\n\n\n\tdata=np.array(df)\n\n\n\ttrain_data, test_data, train_label, test_label = train_test_split(\n \tdata, label, train_size=0.8,shuffle=True)\n\n\ttrain_data = train_data.astype('float32')\n\ttest_data = test_data.astype('float32')\n\ttrain_label = tf.one_hot(np.array(train_label).reshape(-1,), depth=2)\n\ttest_label = tf.one_hot(np.array(test_label).reshape(-1,), depth=2)\n\n\tprint(train_data)\n\tprint(train_label)\n\n\tprint(test_data)\n\tprint(test_label)\n\n\n\tmodel = model()\n\tmodel.summary()\n\tmodel.fit(train_data, train_label, epochs=100,\n validation_data=(test_data, test_label))\n\t\n\toutput = model.predict(test_data)\n\t\n\tmodel.save(\"model.h5\")\n\n\t\n","sub_path":"model/hackathon.py","file_name":"hackathon.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"262993533","text":"#!/usr/bin/env python\n# -*-coding:utf-8 -*-\n# ** -----------------------------------------------------------------------------------------------\n# ** 文件名称: config.py\n# ** 功能描述: 配置文件\n# ** 创建者: sunwj\n# ** 创建日期: 2017-09-26\n# ** 修改日志:\n# ** 修改日期:\n# ** -----------------------------------------------------------------------------------------------\n\nimport sys\nimport os\nimport logging.config\nfrom os.path import realpath\nfrom os.path import basename, splitext, join\n\n# 工程目录配置\ndirectory = [\"common\", \"utils\", \"custloss\", \"custvalue\", \"offervalue\", \"offerrec\",\"result\",\"userrecm\"]\nreal_path = realpath(sys.argv[0])\ncurDir = os.path.dirname(real_path) # 当前路径\nPROJ_HOME = os.path.dirname(curDir)\nfor curdir in directory:\n sys.path.append(PROJ_HOME + os.sep + curdir)\n\n# 日志文件配置\nbase = splitext(basename(sys.argv[0]))[0]\nlpath = join(PROJ_HOME, 'logs')\nfpath = join(lpath, base + '.log')\n\n# set logger\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n# set console/file handler\ncnslHandler = logging.StreamHandler()\ncnslHandler.setLevel(logging.DEBUG)\nfileHandler = logging.FileHandler(fpath, mode='w', encoding='utf-8')\nfileHandler.setLevel(logging.DEBUG)\n\n# set formatter\n# fmt = logging.Formatter('%(asctime)s [%(levelname)s] {%(filename)s:%(lineno)s, %(processName)s:%(process)d, %(threadName)s:%(thread)d} %(message)s')\nfmt = logging.Formatter('%(asctime)s [%(levelname)s] {%(filename)s:%(lineno)s} %(message)s')\ncnslHandler.setFormatter(fmt)\nfileHandler.setFormatter(fmt)\n\n# add handler\nlogger.addHandler(cnslHandler)\nlogger.addHandler(fileHandler)\n\n#\n# 定义hive仓库路径\ndim_path = '/user/hive/warehouse/test.db'\ndw_path = '/user/hive/warehouse/test.db'\ndwd_path = '/user/hive/warehouse/test.db'\nst_path = '/user/hive/warehouse/test.db'\n#stg_path = '/user/hive/warehouse/test.db'\n# 定义表存放路径\ntmp_path = '/user/hive/TEMP'\n# 定义hive表的存储格式\nstore_fmt = 'RCFILE'","sub_path":"AiInsight/datamining/utils/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"488019112","text":"\"\"\"create delimiter table\n\nRevision ID: 43ce6999f48e\nRevises: 56e5b86b4b40\nCreate Date: 2014-02-10 18:27:15.111090\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '43ce6999f48e'\ndown_revision = '37881a97d680'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n op.create_table(\n 'delimiters',\n sa.Column('id', sa.Integer, primary_key=True),\n sa.Column('url', sa.String(2000), nullable=False),\n sa.Column('url_hash', sa.String(128), nullable=False),\n sa.Column('value', sa.Integer, nullable=False, server_default='1')\n )\n\n op.create_index('idx_url_hash', 'delimiters', ['url_hash'])\n\n\ndef downgrade():\n op.drop_index('idx_url_hash', 'delimiters')\n op.drop_table('delimiters')\n\n","sub_path":"holmes/migrations/versions/43ce6999f48e_create_delimiter_table.py","file_name":"43ce6999f48e_create_delimiter_table.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"454307075","text":"# - *- coding: utf- 8 - *-\nimport os\nimport io\nfrom nltk.tag.stanford import StanfordPOSTagger as POS_Tag\nbangla_postagger = POS_Tag('./bengaliModelFile.tagger', './stanford-postagger.jar')\nsentences = io.open(\"./page_1.txt\",\"r\",encoding='utf-8')\nf=io.open('output.txt','w',encoding='utf-8')\nfor sentence in sentences :\n print(sentence+'\\n')\n print('1')\n temp=bangla_postagger.tag(sentence.split())\n for key , value in temp :\n f.write( key+'\\t\\t'+value+'\\n')\n\n\n","sub_path":"postest_Sag.py","file_name":"postest_Sag.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"346546692","text":"from collections import OrderedDict\nfrom typing import Dict, List, Optional\n\nfrom deepclustering2.utils import nice_dict\nfrom .individual_meters._metric import _Metric, MeterResultDict\n\n_Record_Type = Dict[str, float]\n\n\nclass EpochResultDict(dict):\n \"\"\"\n The dictionary only allows input as an instance of `MeterResult`\n \"\"\"\n\n def __init__(self, *args, **kwargs) -> None:\n\n if len(args):\n for _dict in args:\n for k, v in _dict.items():\n assert isinstance(k, str), k\n assert isinstance(v, MeterResultDict), v\n if len(kwargs):\n for k, v in kwargs.items():\n assert isinstance(k, str), k\n assert isinstance(v, MeterResultDict), v\n super(EpochResultDict, self).__init__(*args, **kwargs)\n\n def __repr__(self):\n string_info = \"\"\n for k, v in self.items():\n string_info += f\"{k}: \\n\"\n string_info += f\"\\t{nice_dict(v)}\\n\"\n return string_info\n\n def __setitem__(self, key, value):\n assert isinstance(key, str), key\n assert isinstance(value, MeterResultDict), value\n super(EpochResultDict, self).__setitem__(key, value)\n\n\nclass MeterInterface:\n \"\"\"\n meter interface only concerns about the situation in one epoch,\n without considering historical record and save/load state_dict function.\n \"\"\"\n\n def __init__(self) -> None:\n \"\"\"\n :param meter_config: a dict of individual meter configurations\n \"\"\"\n self._ind_meter_dicts: Dict[str, _Metric] = OrderedDict()\n self._group_dicts: Dict[str, List[str]] = OrderedDict()\n\n def __getitem__(self, meter_name: str) -> _Metric:\n try:\n return self.meters[meter_name]\n except KeyError as e:\n raise KeyError(e)\n\n def register_meter(self, name: str, meter: _Metric, group_name=None) -> None:\n assert isinstance(name, str), name\n assert isinstance(\n meter, _Metric\n ), f\"{meter.__class__.__name__} should be a subclass of {_Metric.__name__}, given {meter}.\"\n # add meters\n self._ind_meter_dicts[name] = meter\n if group_name is not None:\n if group_name not in self._group_dicts:\n self._group_dicts[group_name] = []\n self._group_dicts[group_name].append(name)\n\n def delete_meter(self, name: str) -> None:\n assert (\n name in self.meter_names\n ), f\"{name} should be in `meter_names`: {self.meter_names}, given {name}.\"\n del self.meters[name]\n for group, meter_namelist in self._group_dicts.items():\n if name in meter_namelist:\n meter_namelist.remove(name)\n\n def delete_meters(self, name_list: List[str]):\n assert isinstance(\n name_list, list\n ), f\" name_list must be a list of str, given {name_list}.\"\n for name in name_list:\n self.delete_meter(name)\n\n @property\n def meter_names(self) -> List[str]:\n if hasattr(self, \"_ind_meter_dicts\"):\n return list(self._ind_meter_dicts.keys())\n\n @property\n def meters(self) -> Optional[Dict[str, _Metric]]:\n if hasattr(self, \"_ind_meter_dicts\"):\n return self._ind_meter_dicts\n raise NotImplementedError(\"_ind_meter_dicts\")\n\n @property\n def group(self) -> List[str]:\n return list(self._group_dicts.keys())\n\n def _tracking_status(\n self, group_name=None, detailed_summary=False\n ) -> EpochResultDict:\n \"\"\"\n return current training status from \"ind_meters\"\n :param group_name:\n :return:\n \"\"\"\n if group_name:\n assert group_name in self.group\n return EpochResultDict(\n **{\n k: v.detailed_summary() if detailed_summary else v.summary()\n for k, v in self.meters.items()\n if k in self._group_dicts[group_name]\n }\n )\n return EpochResultDict(\n **{\n k: v.detailed_summary() if detailed_summary else v.summary()\n for k, v in self.meters.items()\n }\n )\n\n def tracking_status(self, group_name=None, final=False, cache_time=10):\n if final:\n return self._tracking_status(group_name=group_name)\n if not hasattr(self, \"__n__\"):\n self.__n__ = 0\n if not hasattr(self, \"__cache__\"):\n self.__cache__ = self._tracking_status(group_name=group_name)\n\n self.__n__ += 1\n if self.__n__ % cache_time == 0:\n self.__cache__ = self._tracking_status(group_name=group_name)\n return self.__cache__\n\n def add(self, meter_name, *args, **kwargs):\n assert meter_name in self.meter_names\n self.meters[meter_name].add(*args, **kwargs)\n\n def reset(self) -> None:\n \"\"\"\n reset individual meters\n :return: None\n \"\"\"\n for v in self.meters.values():\n v.reset()\n","sub_path":"deepclustering2/meters2/meter_interface.py","file_name":"meter_interface.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"82857896","text":"from .aes_encryption import *\n\n\nclass BaseMiddleware(object):\n\n def __init__(self, get_response):\n self.get_response = get_response\n\n def __call__(self, request):\n self.process_request(request) # Call process_request()\n response = self.get_response(request)\n return response\n\n\nclass decryptUrlMiddleware(BaseMiddleware):\n\n def process_request(self, request):\n # print(\"Path Info\" + request.path_info)\n\n checkForApiUrl = request.path_info[:5]\n # print(\"checkForApiUrl \" + checkForApiUrl)\n\n if (checkForApiUrl == \"/api/\"):\n return None\n\n key = \"abcd\"\n decrypterObj = AESCipher(key)\n url_path = request.path_info\n refined_url_path = url_path[1:] # Removing the forward slash\n decrypted_path = decrypterObj.aesdecrypt(refined_url_path)\n refined_decrypted_path = \"/\" + decrypted_path \n\n request.path_info = refined_decrypted_path\n # print(\"Decryption Done\")\n","sub_path":"video_conferencing/decryptUrlMW.py","file_name":"decryptUrlMW.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"282078110","text":"#!/usr/bin/env python\nimport os\nimport sys\nimport urllib.request\nfrom urllib.parse import urlencode, quote_plus\nimport json\n\ndef main():\n try:\n ghtoken = os.environ[\"GH_TOKEN\"]\n except KeyError:\n print(\"Please set the environment variable GH_TOKEN\")\n sys.exit(1)\n\n try:\n ghserver = os.environ[\"GH_SERVER\"]\n except KeyError:\n print(\"Please set the environment variable GH_SERVER\")\n sys.exit(1)\n\n if len(sys.argv) != 3:\n print(\"No search argument passed to script. Two search arguments required.\")\n sys.exit(1)\n\n repo_search_term = sys.argv[1]\n code_search_term = sys.argv[2]\n per_page = 100\n print(\"Searching in repositiries for: '\" + repo_search_term + \"'\")\n repo_search_params = urlencode({'q': repo_search_term, 'per_page': per_page}, quote_via=quote_plus)\n repo_url = ghserver + '/api/v3/search/code?%s' % repo_search_params\n req = urllib.request.Request(repo_url)\n req.add_header('Authorization', 'token ' + ghtoken)\n req.add_header('Content-Type', 'application/json; charset=utf-8')\n response = urllib.request.urlopen(req)\n try:\n json_response = json.load(response)\n except ValueError as err:\n print('Decoding JSON has failed. Error:', err)\n raise\n repos = []\n for item in json_response['items']:\n repos.append(item['repository']['full_name'])\n\n print(\"Searching for '\" + code_search_term + \"' in the following repos: \" + ','.join(repos))\n code_search_params = urllib.parse.urlencode({'per_page': per_page, 'q': code_search_term + \" repo:\" + ' repo:'.join(repos)}, quote_via=quote_plus)\n code_url = ghserver + '/api/v3/search/code?%s' % code_search_params\n print(code_url)\n req = urllib.request.Request(code_url)\n req.add_header('Authorization', 'token ' + ghtoken)\n req.add_header('Content-Type', 'application/json; charset=utf-8')\n response = urllib.request.urlopen(req)\n json_response = json.load(response)\n if json_response['incomplete_results']:\n print(\"WARN! Not all results was returned (incomplete_results=true)\")\n \n print(\"Results: \")\n for item in json_response['items']:\n print(item['html_url'])\n if json_response['total_count'] == 0:\n print(\"No results found.\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"596550684","text":"# coding=utf-8\nfrom django import forms\nfrom .models import Thread, Post, Category, Section\n\n\nclass PostForm(forms.ModelForm):\n \"\"\"Форма комментария.\"\"\"\n\n class Meta:\n model = Post\n exclude = ['user', 'user_ip', 'thread', 'date_created', 'last_updated']\n\n\nclass ThreadForm(forms.ModelForm):\n \"\"\"Форма темы.\"\"\"\n\n class Meta:\n model = Thread\n exclude = ['section', 'user', 'posts_count', 'last_updated']\n\n\nclass ThreadPageForm(forms.ModelForm):\n \"\"\"Форма темы с выбором категории и раздела.\"\"\"\n qs = Category.objects.filter(is_hidden=False, sections_count__gt=0)\n category = forms.ModelChoiceField(queryset=qs, empty_label=None)\n section = forms.IntegerField(required=False)\n\n def __init__(self, *args, **kwargs):\n super(ThreadPageForm, self).__init__(*args, **kwargs)\n self.fields['category'].widget.attrs.update(\n {\n 'class': 'progect-input',\n 'id': 'category',\n 'required': ''\n }\n )\n self.fields['section'].widget.attrs.update(\n {\n 'class': 'progect-input',\n 'id': 'section',\n 'required': ''\n }\n )\n\n class Meta:\n model = Thread\n exclude = ['section', 'user', 'posts_count', 'last_updated']\n\n","sub_path":"src/gorodkirov/forum/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"341511362","text":"# app/__init__.py\n\nfrom flask import request, jsonify, abort\nfrom flask_api import FlaskAPI\nfrom flask_sqlalchemy import SQLAlchemy\nimport json\n\n# local import\nfrom instance.config import app_config\n\n# initialize sql-alchemy\ndb = SQLAlchemy()\n\n\ndef create_app(config_name):\n from app.models import Cars, Branches, Drivers\n\n app = FlaskAPI(__name__, instance_relative_config=True)\n app.config.from_object(app_config[config_name])\n app.config.from_pyfile('config.py')\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n db.init_app(app)\n\n @app.route('/cars/', methods=['POST', 'GET'])\n def cars_methods():\n if request.method == 'POST':\n make = str(request.data.get('make', ''))\n model = str(request.data.get('model', ''))\n year = request.data.get('year', '')\n if make and model and year:\n car = Cars(make=make, model=model, year=year)\n car.save()\n response: object = jsonify({\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n })\n response.status_code = 201\n return response\n elif request.method == 'GET':\n all_cars = Cars.get_all()\n results = []\n for car in all_cars:\n obj = {\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n }\n results.append(obj)\n response: object = jsonify(results)\n response.status_code = 200\n return response\n\n @app.route('/cars/', methods=['GET', 'PUT'])\n def car_methods(id, **kwargs):\n car = Cars.query.filter_by(id=id).first()\n\n if not car:\n abort(404)\n\n if request.method == 'GET':\n response: object = jsonify({\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n })\n response.status_code = 200\n return response\n elif request.method == 'PUT':\n for key, value in request.data.items():\n setattr(car, key, value)\n car.save()\n response: object = jsonify({\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n })\n response.status_code = 200\n return response\n\n @app.route('/branches/', methods=['POST', 'GET'])\n def branches_methods():\n if request.method == 'POST':\n city = str(request.data.get('city', ''))\n postcode = str(request.data.get('postcode', ''))\n if city and postcode:\n branch = Branches(city=city, postcode=postcode)\n branch.save()\n response: object = jsonify({\n 'id': branch.id,\n 'city': branch.city,\n 'postcode': branch.postcode\n })\n response.status_code = 201\n return response\n elif request.method == 'GET':\n all_branches = Branches.get_all()\n results = []\n for branch in all_branches:\n obj = {\n 'id': branch.id,\n 'city': branch.city,\n 'postcode': branch.postcode\n }\n results.append(obj)\n response: object = jsonify(results)\n response.status_code = 200\n return response\n\n @app.route('/branches/', methods=['GET'])\n def branch_methods(id, **kwargs):\n branch = Branches.query.filter_by(id=id).first()\n\n if not branch:\n abort(404)\n\n if request.method == 'GET':\n response: object = jsonify({\n 'id': branch.id,\n 'city': branch.city,\n 'postcode': branch.postcode\n })\n response.status_code = 200\n return response\n\n @app.route('/drivers/', methods=['POST', 'GET'])\n def drivers_methods():\n if request.method == 'POST':\n name = str(request.data.get('name', ''))\n dob = str(request.data.get('dob', ''))\n if name and dob:\n driver = Drivers(name=name, dob=dob)\n driver.save()\n response: object = jsonify({\n 'id': driver.id,\n 'name': driver.name,\n 'dob': driver.dob\n })\n response.status_code = 201\n return response\n elif request.method == 'GET':\n all_drivers = Drivers.get_all()\n results = []\n for driver in all_drivers:\n obj = {\n 'id': driver.id,\n 'name': driver.name,\n 'dob': driver.dob\n }\n results.append(obj)\n response: object = jsonify(results)\n response.status_code = 200\n return response\n\n @app.route('/drivers/', methods=['GET'])\n def driver_methods(id, **kwargs):\n driver = Drivers.query.filter_by(id=id).first()\n\n if not driver:\n abort(404)\n\n if request.method == 'GET':\n response: object = jsonify({\n 'id': driver.id,\n 'name': driver.name,\n 'dob': driver.dob\n })\n response.status_code = 200\n return response\n\n @app.route('/cars//drivers/', methods=['PUT'])\n def assign_driver(car_id, driver_id):\n car = Cars.query.filter_by(id=car_id).first()\n driver = Drivers.query.filter_by(id=driver_id).first()\n\n if not car and driver:\n abort(404)\n\n car.currently_with = json.dumps(driver.__repr__())\n car.save()\n\n response: object = jsonify({\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n })\n response.status_code = 200\n return response\n\n @app.route('/cars//branches/', methods=['PUT'])\n def assign_branch(car_id, branch_id):\n car = Cars.query.filter_by(id=car_id).first()\n branch = Branches.query.filter_by(id=branch_id).first()\n\n if not car and branch:\n abort(404)\n\n car.currently_with = json.dumps(branch.__repr__())\n car.save()\n\n response: object = jsonify({\n 'id': car.id,\n 'make': car.make,\n 'model': car.model,\n 'year': car.year,\n 'currently_with': car.currently_with\n })\n response.status_code = 200\n return response\n\n\n return app\n","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"293638883","text":"from __future__ import print_function, division\nfrom psychopy import visual, event, core, logging, gui, data\nimport numpy as np\nimport pandas as pd\nimport csv\nimport os\nimport sys\nfrom PIL import Image\nimport scipy.io as sio\nfrom scipy.stats import norm\npd.options.mode.chained_assignment = None # default='warn'\nfrom time import strftime, localtime\n\n\n# Set initial details\nfull = True if sys.argv[1] == '1' else False\nIDnum = sys.argv[2]\nmem = pd.read_csv('Data/{}_testtrials.csv'.format(IDnum))\nnumtrials = mem.shape[0]\nnumcats = np.unique(mem['condition']).shape[0]\npos = [1, 2, 3, 4] * int(numtrials/numcats/4)\nnp.random.shuffle(pos)\npos2 = [1, 2, 3, 4] * int(numtrials/numcats/4)\nnp.random.shuffle(pos2)\npos.extend(pos2)\nmem['corrPos'] = pos\nmem = mem.sample(frac=1).reset_index(drop=True)\nmem.rename(index=str, columns={\"Unnamed: 0\": \"OrigInd\"})\ntrial_list = mem['image']\ntpt1 = 4\ntpt2 = 8\nrefresh = 60\nisi = 1\nfpisi = int(isi * refresh)\nfpt1 = int(tpt1 * refresh)\nfpt2 = int(tpt2 * refresh)\n\n\n# introduce category positions and randomize\n\n# start global timer, generate window, generate empty DataFrame\ntrialTimer = core.Clock()\nmywin = visual.Window([1000, 750], monitor=\"testMonitor\", units=\"pix\", fullscr=full, waitBlanking=False)\ndat = pd.DataFrame(np.zeros(shape=(numtrials, 6)), columns=['resp1', 'RT1', 'frame1',\n 'resp2', 'RT2', 'frame2'])\n\n\ndef instructions(text, resps, wrapup, delay, stems=True):\n # generate instructions\n instruct = visual.TextStim(mywin, text=text, wrapWidth=800,\n alignHoriz='left', pos=(-400, 150))\n posdict1 = dict([(1, (-150, 50)), (2, (-50, 50)), (3, (50, 50)), (4, (150, 50))])\n posdict2 = dict([(1, (-150, -25)), (2, (-50, -25)), (3, (50, -25)), (4, (150, -25))])\n nums = ['1', '2', '3', '4']\n resps = [str(i)+'-' if stems else i for i in resps] \n for i in range(4):\n cuegen1 = preload_text(resps[i], posdict1[i+1], wrap=50, thick=False, height=20)\n cuegen2 = preload_text(nums[i], posdict2[i+1], wrap=50, thick=False, height=25)\n cuegen1.draw()\n cuegen2.draw()\n space = visual.TextStim(mywin, text=wrapup, wrapWidth=800, colorSpace='rgb255',\n alignHoriz='center', color=\"white\", pos=(0, -100), bold=False)\n # show instructions\n instruct.draw()\n space.draw()\n mywin.update()\n # proceed with a delay if SPACE is pressed\n NoKey = True\n while NoKey:\n allKeys = event.getKeys([\"space\", \"escape\"])\n if len(allKeys) > 0:\n resp = allKeys[0]\n if resp == 'space':\n NoKey = False\n else:\n core.quit()\n mywin.flip()\n core.wait(delay)\n\n\n# function to read an image as a uniformly shaped numpy array\ndef read_image(image):\n im = Image.open(image).convert('L')\n im = np.array(np.flipud(np.fliplr(im)) / 255 * 2 - 1)\n im = im[:256, :256]\n return im\n\n\n# Takes an image in as a numpy array and loads it\ndef preload_image(image, position=(0, 50)):\n # size = image.shape\n size = [256, 256]\n curr_stim = visual.ImageStim(mywin,\n image=image,\n pos=position, size=size)\n return curr_stim\n\n\n# Takes an image in as a numpy array and loads it\ndef preload_text(word, position=(0, -125), wrap=100, thick=True, height=40):\n curr_stim = visual.TextStim(mywin, text=word, wrapWidth=wrap, colorSpace='rgb255', height=height,\n alignHoriz='center', color='white', pos=position, bold=thick)\n return curr_stim\n\n\ndef assign_pos(ind, mem, positions):\n PosDict = positions\n Positions = {}\n winPos = mem['corrPos'].iloc[ind]\n OtherPos = [i for i in [1,2,3,4] if i != winPos]\n np.random.shuffle(OtherPos)\n Positions[mem['stem'].iloc[ind]] = PosDict[winPos]\n Positions[mem['lure1'].iloc[ind]] = PosDict[OtherPos[0]]\n Positions[mem['lure2'].iloc[ind]] = PosDict[OtherPos[1]]\n Positions[mem['lure3'].iloc[ind]] = PosDict[OtherPos[2]]\n return Positions\n\n\n# given a trial number, an image, a word DataFrame and the output DataFrame, this runs a trial, and appends the data\ndef trial(tnum, image, mem, dat):\n event.clearEvents()\n next, pressed = False, False\n posdict = dict([(1, (-150, -125)), (2, (-50, -125)), (3, (50, -125)), (4, (150, -125))])\n respCount = 0\n lastResp = 0\n # im = read_image(image)\n imm = preload_image(image)\n _cues = mem['stem'].iloc[tnum], mem['lure1'].iloc[tnum], mem['lure2'].iloc[tnum], mem['lure3'].iloc[tnum]\n _resps = ['definitely new', 'maybe new', 'maybe old', 'definitely old']\n positions = assign_pos(tnum, mem, posdict)\n cues = []\n resps = []\n for cue in _cues:\n cuegen = preload_text(str(cue)+'-', positions[cue])\n cues.append(cuegen)\n for i, resp in enumerate(_resps, 1):\n cuegen = preload_text(resp, posdict[i], wrap=50, thick=False, height=20)\n resps.append(cuegen)\n fix = preload_text('+', position=(0, 0))\n start = trialTimer.getTime() # mark trial start\n for i in range(fpt1): # for each frame in duration\n if not pressed:\n imm.draw() # draw on screen\n for tex in resps:\n tex.draw()\n keys = event.getKeys([\"1\", \"2\", \"3\", \"4\", \"escape\"], timeStamped=trialTimer) # document responses\n if len(keys) != 0 and (i - lastResp) > 5 and respCount < 1:\n if keys[0][0] == 'escape':\n core.quit()\n else:\n dat.iloc[tnum, 0] = keys[0][0]\n dat.iloc[tnum, 1] = keys[0][1] - start\n dat.iloc[tnum, 2] = i\n respCount += 1\n next = True if int(keys[0][0]) > 2 else False\n pressed = True\n lastResp = i\n event.clearEvents()\n mywin.flip()\n if next:\n pressed = False\n for j in range(fpisi):\n imm.draw()\n mywin.flip()\n for k in range(fpt2): # for each frame in duration\n imm.draw() # draw on screen\n for tex in cues:\n tex.draw()\n keys = event.getKeys([\"1\", \"2\", \"3\", \"4\", \"escape\"], timeStamped=trialTimer) # document responses\n if not pressed:\n if len(keys) != 0 and (i - lastResp) > 5 and respCount < 2:\n if keys[0][0] == 'escape':\n core.quit()\n else:\n dat.iloc[tnum, 3] = keys[0][0]\n dat.iloc[tnum, 4] = keys[0][1] - start\n dat.iloc[tnum, 5] = i\n respCount += 1\n pressed = True\n lastResp = i\n event.clearEvents()\n mywin.flip()\n\n for l in range(fpisi):\n fix.draw()\n mywin.flip()\n return dat\n\n\ntext1 = \"In the next block, you will see some images. Please indicate whether you saw the image in the first task. \" \\\n \"Use '1', '2', '3' and '4' to respond.\"\ntext2 = \"If you think an image is old, you will then be shown four word stems. Please indicate which word \" \\\n \"stem belongs to the word that appeared together with the given image. \" \\\n \"Use '1', '2', '3' and '4' to respond.\"\nresps1 = ['definitely new', 'maybe new', 'maybe old', 'definitely old']\nresps2 = ['qu', 'pi', 'lo', 'da']\nwrapup1 = \"Press SPACE to continue to the next page of instructions.\"\nwrapup2 = \"Press SPACE to begin the experiment.\"\n\ninstructions(text1, resps1, wrapup1, 0.5, stems=False)\ninstructions(text2, resps2, wrapup2, 2)\n\n# run experiment\nfor tnum, iter in enumerate(trial_list):\n dat = trial(tnum, iter, mem, dat)\n# fill out DataFrame and save out to csv\nmem = pd.concat([mem, dat], axis=1)\nmem.to_csv('Data/{}_retrieval.csv'.format(IDnum))\n\n\nmywin.close()\ncore.quit()\n","sub_path":"Last.py","file_name":"Last.py","file_ext":"py","file_size_in_byte":7956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"278107140","text":"import frappe\nfrom frappe import _\n\n\ndef salary_slip_save(doc, method):\n _set_ots(doc)\n _set_ot_salary_component(doc)\n\n\ndef _set_ots(doc):\n \"\"\"\n Pre _set_ot_salary_component\n :param doc:\n :return:\n \"\"\"\n def _get_ts_name(timesheet):\n return timesheet.get('time_sheet')\n\n timesheets = list(\n map(_get_ts_name, doc.timesheets)\n )\n\n timesheets_ots = frappe.get_all(\n 'Timesheet',\n fields=['sum(ashbee_ot1) as ashbee_ot1', 'sum(ashbee_ot2) as ashbee_ot2'],\n filters=[['name', 'in', timesheets], ['docstatus', '=', '1']]\n )[0]\n\n doc.ashbee_ot1 = timesheets_ots.get('ashbee_ot1', 0) or 0\n doc.ashbee_ot2 = timesheets_ots.get('ashbee_ot2', 0) or 0\n\n\ndef _set_ot_salary_component(doc):\n overtime = frappe.db.get_single_value('Ashbee Settings', 'overtime')\n\n if not overtime:\n frappe.throw(_('Set Overtime salary component under Ashbee Settings'))\n\n salary_components = map(lambda x: x.salary_component, doc.earnings)\n\n if overtime in salary_components:\n return\n\n ot1_rate = 1.25\n ot2_rate = 1.50\n\n ot1_amount = doc.ashbee_ot1 * (doc.hour_rate * ot1_rate)\n ot2_amount = doc.ashbee_ot2 * (doc.hour_rate * ot2_rate)\n\n doc.append('earnings', {\n 'salary_component': overtime,\n 'amount': ot1_amount + ot2_amount\n })\n\n # Update the net pay from here\n doc.calculate_net_pay()\n","sub_path":"ashbee/ashbee/customs/salary_slip.py","file_name":"salary_slip.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"144402695","text":"#-*-coding:utf-8-*-\nimport time\nimport urllib2\nimport xml.dom.minidom\n\nkayakkey ='YOURKEYHERE'\n\ndef getkayaksession():\n #构造url以开启一个会话\n url = 'http://www.kayak.com/k/ident/apisession?token = %s&version=1'%kayakkey\n #解析返回的XML\n doc = xml.dom.minidom.parseString(urllib2.urlopen(url).read())\n #找到*****的标签\n sid = doc.getElementsByTagName('sid')[0].firstChild.data\n return sid\ndef flightsearch(sid,origin,destination,depart_date):\n #构造搜索用的url\n url = 'http://www.kayak.com/s/apisearch?basicmode=true&oneway=y&origin=%s'%origin\n url+= '&destination=%s&depart_date=%s'%(destination,depart_date)\n url+='&return_date=none&depart_time = a&return_time = a'\n url+= '&travelers=1&cabin=e&action=doFlights&apimode=1'\n url+='&_sid_=%s&version=1'%(sid)\n #得到XML\n doc=xml.dom.minidom.parseString(urllib2.urlopen(url).read())\n #提取搜索用的ID\n searchid=doc.getElemnetsByTagName('searchid')[0].firstChild.data\n return searchid\ndef flightsearchresults(sid,searchid):\n #删除开头的$和逗号，并把数字转化成浮点类型\n def parseprice(p):\n return float(p[1:].replace(',',''))\n #遍历检测\n while 1:\n time.sleep(2)\n #构造检测用的URL\n url= 'http://www.kayak.com/s/basic/flight?'\n url+='searchid=%s&c=5&apimode=1&_sid_=%s&version=1'%(searchid,sid)\n doc=xml.dom.minidom.parseString(urllib2.urlopen(url).read())\n #寻找morepending标签，并等待其不再为True\n morepending=doc.getElementsByTagName('morepending')[0].firstChild\n if morepending==None or morepending.data=='false':break\n #现在下载完整的列表\n url='http://www.kayak.com/s/basic/flight?'\n url+='searchid=%s&c=999&apimode=1&_sid_=%s&version=1'%(searchid,sid)\n doc=xml.dom.minidom.parseString(urllib2.urlopen(url).read())\n #得到不同元素组成的列表\n prices=doc.getElementsByTagName('price')\n departures=doc.getElementsByTagName('depart')\n arrivals=doc.getElementsByTagNamw('arrive')\n #用zip将它们连在一起\n return zip([p.firstChild.data.split('')[1] for p in departures],[p.fistChild.data.split('')[1] for p in arrivals],\n [parseprice(p.firatChild.data) for p in prices])\nsid = getkayaksession()\nsearchid=flightsearch(sid,'BOS','LGA','11/17/2006')\nf=flightsearchresults(sid,searchid)\nprint(f[0:3])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"optimization/kayak.py","file_name":"kayak.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"400118360","text":"from collections import deque\n\nclass Solution(object):\n def updateBoard(self, board, click):\n \"\"\"\n :type board: List[List[str]]\n :type click: List[int]\n :rtype: List[List[str]]\n \"\"\"\n click = tuple(click)\n R, C = len(board), len(board[0])\n\n def neighbors(r, c):\n for dr in range(-1, 2):\n for dc in range(-1, 2):\n if (dr or dc) and 0 <= r + dr < R and 0 <= c + dc < C:\n yield r + dr, c + dc\n\n queue = deque([click])\n seen = {click}\n while queue:\n r, c = queue.popleft()\n if board[r][c] == 'M':\n board[r][c] = 'X'\n else:\n mines_adj = sum( board[nr][nc] in 'MX' for nr, nc in neighbors(r, c))\n if mines_adj:\n board[r][c] = str(mines_adj)\n else:\n board[r][c] = 'B'\n for nei in neighbors(r, c):\n if board[nei[0]][nei[1]] == 'E' and nei not in seen:\n queue.append(nei)\n seen.add(nei)\n return board\n\nif __name__ == '__main__':\n solu = Solution()\n board = [[\"E\",\"E\",\"E\",\"E\",\"E\"],[\"E\",\"E\",\"M\",\"E\",\"E\"],[\"E\",\"E\",\"E\",\"E\",\"E\"],[\"E\",\"E\",\"E\",\"E\",\"E\"]]\n click = [3, 0]\n for item in solu.updateBoard(board, click):\n print(item)","sub_path":"529Minesweeper/529minesweeper.py","file_name":"529minesweeper.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"544738378","text":"import os, sys, time, threading, multiprocessing, shutil\n\nclass my_dictionary(dict):\n \n # __init__ function\n def __init__(self):\n self = dict()\n \n # Function to add key:value\n def add(self, key, value):\n self[key] = value\n\n# Main Function\ntimer = my_dictionary()\n\nactiveThreads = threading.activeCount()\nprint(\"Active Threads = \",activeThreads)\n\nnumberOfCores=multiprocessing.cpu_count()\nprint(\"Number Of Cores = \",numberOfCores)\n\n\n\ntotalFiles = 100\n\n\nfrom PIL import Image\n\ninputDirName='' #enter address for input dir\noutputDirName='' #enter name for output dir\n\ntry:\n # Delete output directory and then create it\n shutil.rmtree(\"./%s/\"%(outputDirName))\n os.mkdir(outputDirName)\nexcept:\n # Create the output directory\n os.mkdir(outputDirName)\n\ndef grayscaleConvert(fileName):\n inputfileName=inputDirName+\"/\"+fileName\n outputFileName=\"./\"+outputDirName+\"/\"+fileName\n img = Image.open(inputfileName)\n img = img.convert(\"L\")\n img.save(outputFileName)\n\ndef timetaken(N):\n startTime=time.time()\n for i in range(totalFiles):\n fN=\"cat.\"+str(i+4001)+\".jpg\" #filename as string\n t = threading.Thread(target=grayscaleConvert , args=(fN,))\n t.start()\n while True:\n if threading.activeCount() - activeThreads + 1 <= N:\n break\n time.sleep(1)\n print (\"thread started for \",fN)\n\n\n while True:\n if threading.activeCount() == activeThreads:\n break\n else:\n print (\"...Thread Left %d...\"%(threading.activeCount() - activeThreads))\n time.sleep(1)\n\n length=str(round(time.time() - startTime,2))\n\n timer.add(N, length)\n\nfor i in range(1,25):\n timetaken(i)\n\nimport matplotlib.pyplot as plt\n\nfor x, y in timer.items():\n print(int(x),float(y))\n plt.bar(int(x),float(y))\n\n# naming the x axis\nplt.xlabel('number of threads')\n# naming the y axis\nplt.ylabel('time in seconds')\n \n# giving a title to my graph\nplt.title('multithreading')\n \n# function to show the plot\n\nplt.show()\n","sub_path":"threading.py","file_name":"threading.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"33605304","text":"from django.contrib import admin\nfrom django.contrib.admin.models import LogEntry\nfrom mptt.admin import MPTTModelAdmin\n\n\nclass FixedMPTTModelAdmin(MPTTModelAdmin):\n\n def __init__(self, *args, **kwargs):\n super(FixedMPTTModelAdmin, self).__init__(*args, **kwargs)\n mptt_opts = self.model._mptt_meta\n # Use mptt default ordering\n self.ordering = (mptt_opts.tree_id_attr, mptt_opts.left_attr)\n if self.list_display and self.sortable not in self.list_display:\n self.list_display = list(self.list_display) + [self.sortable]\n self.list_editable = self.list_editable or []\n if self.sortable not in self.list_editable:\n self.list_editable = list(self.list_editable) + [self.sortable]\n self.exclude = self.exclude or []\n if self.sortable not in self.exclude:\n self.exclude = list(self.exclude) + [self.sortable]\n\n # Return default admin ChangeList\n def get_changelist(self, request, **kwargs):\n return admin.views.main.ChangeList\n\n\n@admin.register(LogEntry)\nclass LogEntryAdmin(admin.ModelAdmin):\n \"\"\" Create an admin view of the history/log table\n \"\"\"\n list_display = (\n 'action_time',\n 'user',\n 'content_type',\n 'change_message',\n 'is_addition',\n 'is_change',\n 'is_deletion'\n )\n list_filter = ['content_type']\n ordering = ('-action_time',)\n\n date_hierarchy = 'action_time'\n\n readonly_fields = [\n 'user',\n 'content_type',\n 'object_id',\n 'object_repr',\n 'action_flag',\n 'change_message'\n ]\n\n def has_add_permission(self, request):\n return False\n\n def has_delete_permission(self, request, obj=None):\n return False\n\n def get_actions(self, request):\n actions = super(LogEntryAdmin, self).get_actions(request)\n del actions['delete_selected']\n return actions\n","sub_path":"apps/main/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"642211779","text":"from __future__ import absolute_import\nimport os\n\nimport six\nimport sh\nfrom sh import git\nfrom docutils.parsers.rst import Directive, directives\nimport sphinx.pycode\n\n\n_DEFAULT_PATH = None\n_RESET_PATHS = {}\n\n\nclass TutDefaults(Directive):\n option_spec = {\n 'path': directives.path,\n }\n\n def run(self):\n\n global _DEFAULT_PATH\n _DEFAULT_PATH = self.options['path']\n\n return []\n\n\nclass TutCheckpoint(Directive):\n\n has_content = False\n required_arguments = 1\n optional_arguments = 0\n final_argument_whitespace = True\n option_spec = {\n 'path': directives.path,\n }\n\n def run(self):\n\n global _DEFAULT_PATH\n global _RESET_PATHS\n\n if 'path' in self.options:\n tut_path = self.options['path']\n elif _DEFAULT_PATH is not None:\n tut_path = _DEFAULT_PATH\n else:\n raise Exception(\"No tut path specified.\")\n\n # paths are relative to the project root\n rel_path, tut_path = self.state.document.settings.env.relfn2path(\n tut_path)\n\n curdir = os.getcwd()\n os.chdir(tut_path)\n\n # if this is the first time visiting this repo\n if tut_path not in _RESET_PATHS:\n # record the current branch\n _RESET_PATHS[tut_path] = \\\n git('name-rev', 'HEAD').strip().split()[-1]\n\n git_ref = self.arguments[0].strip().lower()\n try:\n git.checkout(git_ref)\n\n except sh.ErrorReturnCode_1 as git_error:\n if six.b(\n \"error: pathspec '%s' did not match any \"\n \"file(s) known to git.\\n\" % (\n git_ref,\n )\n ) == git_error.stderr:\n raise ValueError(\n \"git checkpoint '%s' does not exist.\" % (git_ref,)\n )\n\n finally:\n sphinx.pycode.ModuleAnalyzer.cache = {}\n\n os.chdir(curdir)\n\n return []\n\n\ndef initialize(app):\n\n global _RESET_PATHS\n _RESET_PATHS = {}\n\n\ndef cleanup(app, exception):\n\n global _RESET_PATHS\n\n curdir = os.getcwd()\n try:\n for path in _RESET_PATHS:\n os.chdir(path)\n git.checkout(_RESET_PATHS[path])\n finally:\n os.chdir(curdir)\n\n\ndef setup(app):\n\n app.add_directive('tut', TutDefaults)\n app.add_directive('checkpoint', TutCheckpoint)\n\n app.connect('builder-inited', initialize)\n app.connect('build-finished', cleanup)\n","sub_path":"src/tut/sphinx.py","file_name":"sphinx.py","file_ext":"py","file_size_in_byte":2467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"8161423","text":"import argparse\nfrom nmigen import cli\nfrom bellatrix.core import Bellatrix\nimport bellatrix.configuration.configuration as cfg\n\n\ndef main():\n # parser: add options\n parser = argparse.ArgumentParser()\n\n # add (extra) arguments\n parser.add_argument(\"--config-file\", type=str, help=\"configuration file\", required=True)\n\n cli.main_parser(parser)\n args = parser.parse_args()\n\n # load the configuration file\n configuration = cfg.Configuration(args.config_file)\n\n # create the CPU\n cpu = Bellatrix(configuration)\n ports = [\n # instruction port\n cpu.iport.addr,\n cpu.iport.dat_w,\n cpu.iport.sel,\n cpu.iport.we,\n cpu.iport.cyc,\n cpu.iport.stb,\n cpu.iport.cti,\n cpu.iport.bte,\n cpu.iport.dat_r,\n cpu.iport.ack,\n cpu.iport.err,\n # data port\n cpu.dport.addr,\n cpu.dport.dat_w,\n cpu.dport.sel,\n cpu.dport.we,\n cpu.dport.cyc,\n cpu.dport.stb,\n cpu.dport.cti,\n cpu.dport.bte,\n cpu.dport.dat_r,\n cpu.dport.ack,\n cpu.dport.err,\n # exceptions\n cpu.external_interrupt,\n cpu.timer_interrupt,\n cpu.software_interrupt\n ]\n\n # run\n cli.main_runner(parser, args, cpu, name='bellatrix_core', ports=ports)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"225599544","text":"from numpy import log\nimport random\n\n\nclass Event:\n \"\"\" Class object to represent an event that occurs during the simulation \"\"\"\n arrival_rate = 0.1 # Static arrival rate of all events\n departure_rate = 0.01 # Static departure rate of all events\n\n def __init__(self, type, time):\n \"\"\" Initialise the event. Calculate the arrival time and departure time\n of the event, from a given time instance.\n\n Params:\n - type :: Identification of state of event.(start/arrival/departure)\n - time :: A time setting, expected to be the simulation time at the\n point of creation.\n \"\"\"\n self.type = type\n self.arrival_time = time + self.expon(Event.arrival_rate)\n self.departure_time = self.arrival_time + self.expon(Event.departure_rate)\n\n def expon(self, lamda):\n \"\"\" Calculate a random value from a exponential distribution around a\n specified mean.\n\n Params:\n - lamda :: 1/lamda is the mean of the exponential distribution.\n\n Returns:\n - float :: the randomly generated value for time.\n \"\"\"\n return -(log(random.random())/lamda)\n\n def time(self):\n \"\"\" Returns the time of the event depending on its type.\n\n Returns:\n - float :: The time of the event depending on the type.\n \"\"\"\n if self.type == \"arrival\":\n return self.arrival_time\n return self.departure_time\n\n def served_by(self, server_id=None):\n \"\"\" Overloaded function to set the server that has been allocated to the\n event, or to return the server ID. As a server can only be allocated to\n an event that must then depart, the event type is also changed.\n\n Params :\n - server_id :: The id that represents a server\n\n Returns:\n - None :: In the event that a server is being set, None is returned.\n - int :: In the event that no parameter is given, the id of the\n server of this event is given.\n \"\"\"\n\n if server_id:\n self.type = \"departure\"\n self.server_id = server_id\n return\n return self.server_id\n\n def service_time(self):\n \"\"\" Return the service time of the event.\n\n Returns:\n - float :: Difference in time between the arrival and departure of\n the event.\n \"\"\"\n return self.departure_time - self.arrival_time\n\n\nclass M1M2Event(Event):\n \"\"\" Event object responsible for recording the time for the event and the\n type of event.\n \"\"\"\n # Static arrival rates for the handover and new call paths\n priorities = {\"new\": 0.1, \"handover\": 0.01}\n departure_rate = 0.01 # Static departure rates for events\n\n def __init__(self, path, type, time):\n \"\"\" Initialise the values for the event \"\"\"\n self.path = path\n self.type = type\n self.arrival_time = time + self.expon(M1M2Event.priorities[self.path])\n self.departure_time = self.arrival_time + self.expon(M1M2Event.departure_rate)\n\n","sub_path":"wireless_cell_simulation/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":3110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"608904653","text":"# coding: utf8\nimport json\nimport jsonschema\nimport re\nfrom utils.log import log_info, log_debug, log_warning, log_error, log_decorator\n\n\nclass Command_decoder(object):\n\t@log_decorator\n\tdef __init__(self, str_path_to_commands , str_path_to_validator):\n\t\tself.u_path_to_commands = str_path_to_commands.decode(\"utf8\")\n\t\tself.u_path_to_validator = str_path_to_validator.decode(\"utf8\")\n\t\ttry:\n\t\t\twith open(self.u_path_to_commands) as source:\n\t\t\t\tself.json_commands = json.load(source)\n\t\texcept Exception as e:\n\t\t\tlog_error(\"cannot open \" + self.u_path_to_commands.encode(\"utf8\"))\n\t\t\tlog_debug(e.__str__())\n\t\ttry:\n\t\t\twith open(self.u_path_to_validator) as source:\n\t\t\t\tjson_validator = json.load(source)\n\t\t\t\tself.validator = jsonschema.Draft4Validator(json_validator)\n\t\texcept Exception as e:\n\t\t\tlog_error(\"cannot open \" + self.u_path_to_validator.encode(\"utf8\"))\n\t\t\tlog_debug(e.__str__())\n\t\tself.prepare_commands()\n\n\t@log_decorator\n\tdef prepare_commands(self):\n\t\tvalidated = False\n\t\ttry:\n\t\t\tlog_info(\"validating commands \" + self.u_path_to_commands.encode(\"utf8\") + \" against \" + self.u_path_to_validator.encode(\"utf8\"))\n\t\t\tif self.validator.is_valid(self.json_commands):\n\t\t\t\tlog_info(\"Commands format valid\")\n\t\t\t\tvalidated = True\n\t\t\telse:\n\t\t\t\tfor error in sorted(self.validator.iter_errors(self.json_commands), key=str):\n\t\t\t\t\tprint(error.message)\n\t\texcept Exception as e:\n\t\t\tlog_error(\"Error during validating commands\")\n\t\t\tlog_debug(e.__str__())\n\t\tif validated:\n\t\t\tself.compute_mappings()\n\t\t\tself.compute_start_and_stop()\n\n\t@log_decorator\n\tdef compute_mappings(self):\n\t\ttry:\n\t\t\tself.mappings = []\n\t\t\tcommands = self.json_commands[\"commands\"]\n\t\t\tfor command in commands:\n\t\t\t\tmapping={}\n\t\t\t\tregexps_array=[]\n\t\t\t\tmapping[\"id\"] = command[\"id\"]\n\t\t\t\tmapping[\"priority\"] = command[\"priority\"]\n\t\t\t\tregexps = command[\"regexps\"]\n\t\t\t\tfor regexp in regexps:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tcompiled_re = re.compile(regexp[\"re\"], re.UNICODE)\n\t\t\t\t\t\tregexps_array.append(compiled_re)\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tlog_error(\"Error during decoding of regular expression\")\n\t\t\t\t\t\tlog_debug(e.__str__())\n\t\t\t\tmapping[\"regexps\"] = regexps_array\n\t\t\t\tself.mappings.append(mapping)\n\t\t\tself.mappings.sort(lambda x,y : x[\"priority\"]-y[\"priority\"])\n\t\texcept Exception as e:\n\t\t\tlog_error(\"Error during computation of mapping\")\n\t\t\tlog_debug(e.__str__())\n\n\t@log_decorator\n\tdef compute_start_and_stop(self):\n\t\ttry:\n\t\t\tself.re_start=re.compile(self.json_commands[\"wake\"])\n\t\t\tself.re_stop=re.compile(self.json_commands[\"stop\"])\n\t\texcept Exception as e:\n\t\t\tlog_error(\"Error during computation of start and stop triggers\")\n\t\t\tlog_debug(e.__str__())\n\n\t@log_decorator\n\tdef getId(self, str_spoken):\n\t\tu_lower_spoken=str_spoken.lower().decode(\"utf8\")\n\t\tfor mapping in self.mappings:\n\t\t\tfor regexp in mapping[\"regexps\"]:\n\t\t\t\tif regexp.match(u_lower_spoken):\n\t\t\t\t\treturn mapping[\"id\"]\n\t\treturn -1\n\n\t@log_decorator\n\tdef is_start(self, str_spoken):\n\t\tu_lower_spoken=str_spoken.lower().decode(\"utf8\")\n\t\treturn self.re_start.match(u_lower_spoken)\n\n\n\t@log_decorator\n\tdef is_stop(self, str_spoken):\n\t\tu_lower_spoken=str_spoken.lower().decode(\"utf8\")\n\t\treturn self.re_stop.match(u_lower_spoken)","sub_path":"command_decoder.py","file_name":"command_decoder.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"53937495","text":"\n###################\nimport torch.nn as nn\nimport torchvision.models as models\n\nfrom .utils3d import *\n\nResnets = {'resnet18' :{'layers':[2, 2, 2, 2],'filters':[64*4, 128*2, 256//2, 512//4], 'block':residualBlock3D_LOC,'expansion':1}, # pay attension that relut is missed\n 'resnet34' :{'layers':[3, 4, 6, 3],'filters':[64, 128, 256, 512], 'block':residualBlock3D,'expansion':1},\n 'resnet50' :{'layers':[3, 4, 6, 3],'filters':[64, 128, 256, 512], 'block':residualBlock3D,'expansion':4},\n 'resnet101' :{'layers':[3, 4, 23, 3],'filters':[64, 128, 256, 512], 'block':residualBlock3D,'expansion':4},\n 'resnet152':{'layers':[3, 8, 36, 3],'filters':[64, 128, 256, 512], 'block':residualBlock3D,'expansion':4}\n }\n\n\nclass linknet3d_exp(nn.Module):\n\n def __init__(self, resnet='resnet18', feature_scale=4, n_classes=2, is_deconv=True, in_channels=3, is_batchnorm=True, n_macroblocks=None):\n super(linknet3d_exp, self).__init__()\n self.n_classes=n_classes\n self.is_deconv = is_deconv\n self.in_channels = in_channels\n self.is_batchnorm = is_batchnorm\n self.feature_scale = feature_scale\n\n\n assert resnet in Resnets.keys(), 'Not a valid resnet, currently supported resnets are 18, 34, 50, 101 and 152'\n layers = Resnets[resnet]['layers']\n filters = Resnets[resnet]['filters']\n\n\n # filters = [x / self.feature_scale for x in filters]\n expansion =Resnets[resnet]['expansion']\n\n self.inplanes = filters[0]\n\n\n # Encoder\n self.convbnrelu1 = conv3DBatchNormRelu(in_channels=3, k_size=3, n_filters=filters[0],\n padding=1, stride=2, bias=False)\n self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)\n block = Resnets[resnet]['block']\n\n self.encoder1 = self._make_layer(block, filters[0], layers[0])\n self.encoder2 = self._make_layer(block, filters[1], layers[1], stride=2)\n self.encoder3 = self._make_layer(block, filters[2], layers[2], stride=2)\n self.encoder4 = self._make_layer(block, filters[3], layers[3], stride=2)\n\n # Decoder\n self.decoder4 = linknetUp3D(filters[3] * expansion, filters[2])\n self.decoder3 = linknetUp3D(filters[2] * expansion, filters[1])\n self.decoder2 = linknetUp3D(filters[1] * expansion, filters[0])\n self.decoder1 = linknetUp3D(filters[0] * expansion, filters[0])\n\n\n\n # macroblock classification\n self.relu = nn.ReLU(inplace=True)\n self.n_macroblocks = n_macroblocks\n #self.linear = nn.Linear(filters[0]+filters[0]+filters[1]+filters[2], self.n_macroblocks)\n self.linear = nn.Linear(filters[0]+filters[0], self.n_macroblocks)\n self.dropout = nn.Dropout(p=0.3)\n #self.linear = nn.Linear(filters[1]+filters[2]+filters[3], self.n_macroblocks)\n self.downsample1 = conv3DBatchNorm(filters[0], filters[0], k_size=1, stride=1, padding=0, bias=False)\n self.downsample2 = conv3DBatchNorm(filters[0], filters[0], k_size=1, stride=1, padding=0, bias=False)\n self.downsample2_ = conv3DBatchNorm(filters[0], filters[1], k_size=1, stride=2, padding=0, bias=False)\n self.downsample3 = conv3DBatchNorm(filters[1], filters[1], k_size=1, stride=1, padding=0, bias=False)\n self.downsample3_ = conv3DBatchNorm(filters[1], filters[2], k_size=1, stride=2, padding=0, bias=False)\n self.downsample4 = conv3DBatchNorm(filters[2], filters[2], k_size=1, stride=1, padding=0, bias=False)\n self.downsample4_ = conv3DBatchNorm(filters[2], filters[3], k_size=1, stride=2, padding=0, bias=False)\n\n\n # Final Classifier\n self.finaldeconvbnrelu1 = deconv3DBatchNormRelu(filters[0], 32/feature_scale, 2, 2, 0)\n self.finalconvbnrelu2 = conv3DBatchNormRelu(in_channels=32/feature_scale, k_size=3, n_filters=32/feature_scale, padding=1, stride=1)\n self.finalconv3 = nn.Conv3d(int(32/feature_scale), 2, 3, 1, 1)\n\n\n def _make_layer(self, block, planes, blocks, stride=1):\n downsample = None\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = conv3DBatchNorm(self.inplanes, planes*block.expansion, k_size=1, stride=stride, padding=0, bias=False)\n layers = []\n layers.append(block(self.inplanes, planes, stride, downsample))\n self.inplanes = planes * block.expansion\n for i in range(1, blocks):\n layers.append(block(self.inplanes, planes))\n return nn.Sequential(*layers)\n def forward(self, input):\n # Encoder\n network_log('linknet3d=>input.size():{}'.format(input.size()), color_idx=1)\n input1 = self.convbnrelu1(input)\n network_log('[ConvFirst]\\nlinknet3d=>input1.size():{}'.format(input1.size()), color_idx=1)\n input1_maxpool = self.maxpool(input1)\n network_log('linknet3d=>input1_maxpool.size():{}'.format(input1_maxpool.size()), color_idx=1)\n\n loc1_downsample = self.downsample1(input1_maxpool)\n network_log('[EB1]\\nlinknet3d=>loc1_downsample.size():{}'.format(loc1_downsample.size()), color_idx=2)\n e1 = self.relu(self.encoder1(input1_maxpool + loc1_downsample))\n network_log('linknet3d=>e1.size():{}'.format(e1.size()), color_idx=2)\n\n loc2_downsample = self.downsample2(e1)\n network_log('[EB2]\\nlinknet3d=>loc2_downsample.size():{}'.format(loc2_downsample.size()), color_idx=2)\n e2 = self.relu(self.encoder2(e1 + loc2_downsample))\n network_log('linknet3d=>e2.size():{}'.format(e2.size()), color_idx=2)\n\n loc3_downsample = self.downsample3(e2)\n network_log('[EB3]\\nlinknet3d=>loc3_downsample.size():{}'.format(loc3_downsample.size()), color_idx=2)\n e3 = self.relu(self.encoder3(e2 + loc3_downsample))\n network_log('linknet3d=>e3.size():{}'.format(e3.size()), color_idx=2)\n\n '''\n loc4_downsample = self.downsample4(e3_fusion)\n network_log('linknet3d=>loc4_downsample.size():{}'.format(loc4_downsample.size()), color_idx=2)\n e4 = self.relu(self.encoder4(e3_fusion - loc4_downsample))\n network_log('[EB4]\\nlinknet3d=>e4.size():{}'.format(e4.size()), color_idx=2)\n\n\n\n d4 = self.decoder4(e4)\n network_log('linknet3d=>d4.size():{}'.format(d4.size()), color_idx=1)\n\n d4_fusion = self.fusion(d4, e3)\n network_log('linknet3d=>d4_cat.size():{}'.format(d4_fusion.size()), color_idx=1)\n '''\n\n d3 = self.decoder3(e3)\n network_log('linknet3d=>d3.size():{}'.format(d3.size()), color_idx=1)\n\n d3_fusion = d3 + e2\n network_log('linknet3d=>d3_cat.size():{}'.format(d3_fusion.size()), color_idx=1)\n d2 = self.decoder2(d3_fusion)\n network_log('linknet3d=>d2.size():{}'.format(d2.size()), color_idx=1)\n\n d2_fusion = d2 + e1\n network_log('linknet3d=>d2_cat.size():{}'.format(d2_fusion.size()), color_idx=1)\n d1 = self.decoder1(d2_fusion)\n network_log('linknet3d=>d1.size():{}'.format(d1.size()), color_idx=1)\n\n\n f1 = self.finaldeconvbnrelu1(d1)\n network_log('linknet3d=>f1.size():{}'.format(f1.size()), color_idx=2)\n f2 = self.finalconvbnrelu2(f1)\n network_log('linknet3d=>f2.size():{}'.format(f2.size()), color_idx=2)\n f3 = self.finalconv3(f2)\n network_log('linknet3d=>f3.size():{}'.format(f3.size()), color_idx=2)\n\n\n #mb0 = F.max_pool3d(input1_downsample, kernel_size=input1_downsample.size()[2:])\n mb1 = F.max_pool3d(loc1_downsample, kernel_size=loc1_downsample.size()[2:])\n mb2 = F.max_pool3d(loc2_downsample, kernel_size=loc2_downsample.size()[2:])\n #mb3 = F.max_pool3d(loc3_downsample, kernel_size=loc3_downsample.size()[2:])\n #mb4 = F.max_pool3d(loc4_downsample, kernel_size=loc4_downsample.size()[2:])\n #mb_fusion = torch.cat([mb1, mb2, mb3, mb4], dim=1)\n mb_fusion = torch.cat([mb1, mb2], dim=1)\n #mb_fusion = torch.cat([mb1, mb2, mb3], dim=1)\n \n mb_fusion_dropout = self.dropout(mb_fusion)\n mb_fusion_flatten = mb_fusion_dropout.view(-1, mb_fusion_dropout.size()[1])\n mb_final = self.linear(mb_fusion_flatten)\n #print( mb1.size(), mb2.size(), mb3.size(), mb_fusion_flatten.size(), mb_final.size())\n '''\n network_log('linknet3d=>input1_downsample.size():{}'.format(input1_downsample.size()), color_idx=1)\n network_log('linknet3d=>e1_fusion_downsample.size():{}'.format(e1_fusion_downsample.size()), color_idx=1)\n network_log('linknet3d=>e2_fusion_downsample.size():{}'.format(e2_fusion_downsample.size()), color_idx=1)\n network_log('linknet3d=>e3_fusion_downsample.size():{}'.format(e3_fusion_downsample.size()), color_idx=1)\n # splitline\n network_log('linknet3d=>inputs2.size():{}'.format(input2.size()), color_idx=1)\n mb1 = F.max_pool3d(e1, kernel_size=e1.size()[2:])\n network_log('linknet3d=>mb1.size():{}'.format(mb1.size()), color_idx=1)\n mb1_flatten = mb1.view(-1, mb1.size()[1])\n mb2 = F.max_pool3d(e2, kernel_size=e2.size()[2:])\n network_log('linknet3d=>mb2.size():{}'.format(mb2.size()), color_idx=1)\n mb2_flatten = mb2.view(-1, mb2.size()[1])\n mb3 = F.max_pool3d(e3, kernel_size=e3.size()[2:])\n network_log('linknet3d=>mb3.size():{}'.format(mb3.size()), color_idx=1)\n mb3_flatten = mb3.view(-1, mb3.size()[1])\n # mb4 = F.max_pool3d(e4, kernel_size=e4.size()[2:])\n # mb4_flatten = mb4.view(-1, mb4.size()[1])\n '''\n return f3, mb_final","sub_path":"ptsemseg/models/linknet3d_exp.py","file_name":"linknet3d_exp.py","file_ext":"py","file_size_in_byte":9553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"488871696","text":"import pygame as pg\nfrom settings import *\nfrom element import *\nfrom patterns import *\nfrom random import *\nfrom os import path\n\n\n# Classe contenant le jeu dans son ensemble (hors menus)\n\nclass Game:\n # Constructeur de Game, initialise le jeu\n def __init__(self):\n # Initialise pygame, les polices et le son\n pg.init()\n pg.mixer.init()\n pg.font.init()\n\n # Création de la fenêtre\n pg.display.set_caption(TITLE)\n self.screen = pg.display.set_mode((WIDTH,HEIGHT))\n\n\n\n # Création de la clock principale\n self.clock = pg.time.Clock()\n\n # Booléen principal de gestion du jeu\n self.running = True\n\n # Police principale du jeu\n self.font_name = pg.font.match_font(FONT_NAME)\n\n # Chargement des images en mémoires (A NE FAIRE QU'UNE FOIS)\n self.img_background = pg.transform.scale(pg.image.load('./images/Decors/background.png'), [WIDTH,HEIGHT])\n self.img_regles = pg.transform.scale(pg.image.load('./images/regles.png'), [WIDTH,HEIGHT])\n self.img_credits = pg.transform.scale(pg.image.load('./images/credits.png'), [WIDTH,HEIGHT])\n\n self.img_herbe = pg.image.load('./images/Decors/grassMid.png')\n self.img_terre = pg.image.load('./images/Decors/grass.png')\n self.img_ressortCourant = pg.image.load('./images/Decors/trampoBas.png')\n self.img_ressort = [pg.image.load('./images/Decors/trampoBas.png'),pg.image.load('./images/Decors/trampoUp.png')]\n\n self.img_heros = pg.image.load('./images/Droite/faceDroit.png')\n self.img_walkRightHero = [pg.image.load(\"./images/Droite/depDroit1.png\"), pg.image.load(\"./images/Droite/depDroit2.png\"), pg.image.load(\"./images/Droite/depDroit3.png\")]\n self.img_walkLeftHero = [pg.image.load(\"./images/Gauche/depGauche1.png\"), pg.image.load(\"./images/Gauche/depGauche2.png\"), pg.image.load(\"./images/Gauche/depGauche3.png\")]\n self.img_faceDroite = pg.image.load(\"./images/Droite/faceDroit.png\")\n self.img_faceGauche = pg.image.load(\"./images/Gauche/faceGauche.png\")\n self.img_player_hit = pg.image.load(\"./images/Droite/faceDroit_rouge.png\")\n self.img_attackDroite = [pg.image.load(\"./images/Droite/atk1.png\"), pg.image.load(\"./images/Droite/atk2.png\"), pg.image.load(\"./images/Droite/atk3.png\"), pg.image.load(\"./images/Droite/atk4.png\"), pg.image.load(\"./images/Droite/atk5.png\")]\n self.img_attackGauche = [pg.image.load(\"./images/Gauche/atk1Gauche.png\"), pg.image.load(\"./images/Gauche/atk2Gauche.png\"),pg.image.load(\"./images/Gauche/atk3Gauche.png\"),pg.image.load(\"./images/Gauche/atk4Gauche.png\"),pg.image.load(\"./images/Gauche/atk5Gauche.png\")]\n\n self.img_monster_rouge = pg.transform.scale(pg.image.load('./images/Decors/monster_rouge.png'), [70, 64])\n self.img_monster = pg.transform.scale(pg.image.load('./images/Decors/monster.png'), [70, 64])\n self.img_monster_orange = pg.transform.scale(pg.image.load('./images/Decors/monster_orange.png'), [70, 64])\n\n\n self.atk = False;\n self.atkCountHero = 0\n self.walkCountHero = 0;\n self.img_antigravite = pg.image.load('./images/Decors/gravity.png')\n\n self.ressortCount = 0\n self.timerRessort = 30\n self.chute = 0\n\n\n # Lit les datas\n self.load_data()\n\n\n # GENERE LE LEVEL PASSE EN PARAMETRE\n def geneRateLevel(self, level, position=0):\n\n decalage = 0\n nb = 1\n k = 0\n\n while k < len(level): # Parcours des différents pattern\n x = position\n y = 0\n for i in range (0,len(level[k])): # Parcours des lignes\n for j in range (0,len(level[k][i])): # Parcours des colonnes\n # POUR CHAQUE CASE DU PATTERN ON VA CREER L'ELEMENT CORRESPONDANT ET L'AJOUTER AU CONTENEUR ADAPTE\n\n case = level[k][i][j]# On récupère la valeur de la case\n\n # PLATEFORME\n if case == \"P\" or case == \"G\":\n longueur = 0\n if j < NB_BLOCK_LARGEUR-2 and level[k][i][j+2] != \" \" :\n longueur = int(level[k][i][j+1]) * 10 + int(level[k][i][j+2])\n else:\n longueur = int(level[k][i][j+1])\n width = TILE_SIZE*longueur\n if i == len(level[k])-1:\n width += 16\n\n if case == \"G\":\n g = Dirt(x+decalage, y, width, TILE_SIZE, self.img_terre)\n self.all_sprites.add(g)\n self.dirts.add(g)\n if case == \"P\":\n p = Platform(x+decalage, y, width, TILE_SIZE, self.img_herbe)\n self.all_sprites.add(p)\n self.platforms.add(p)\n\n #print(\"je cree du DIRT !\")\n # self.all_sprites.add(g)\n # self.dirts.add(g)\n # self.all_sprites.add(p)\n # self.platforms.add(p)\n\n # HEROS (PERSONNAGE PRINCIPAL)\n if case == \"H\":\n self.player = Hero(x+decalage, y, LARGEUR_HEROS, HAUTEUR_HEROS, self, self.img_heros)\n self.all_sprites.add(self.player)\n\n # RESSORTS\n if case == \"R\":\n self.r = Ressort(x+decalage, y-70/3, 70, 70, self.img_ressortCourant)\n self.all_sprites.add(self.r)\n self.ressorts.add(self.r)\n\n # GRAVITE\n # if level[k][i][j] == \"B\":\n # b = Antigravite(x+decalage, y, TILE_SIZE, TILE_SIZE, self.img_antigravite)\n # self.all_sprites.add(b)\n # self.antigravites.add(b)\n\n # MONSTRES\n if case == \"M\":\n m = Monstre(x+decalage, y, 70, 70, self, self.img_monster)\n self.all_sprites.add(m)\n self.monstres.add(m)\n\n if level[k][i][j] == \"B\":\n b = Antigravite(x+decalage, y, 70, 70, self.img_antigravite)\n self.all_sprites.add(b)\n self.antigravites.add(b)\n\n j += 1\n x += TILE_SIZE\n nb+= 1;\n i += 1\n y += TILE_SIZE\n x = position # on reset la colonne\n decalage += WIDTH # on décale de la taille de la fenetre\n k += 1\n #print(str(len(self.monstres)) + \" monstres ont spawn\")\n\n # charge les données\n def load_data(self):\n #charge le high SCORE\n self.dir = path.dirname(__file__)\n with open(path.join(self.dir, HS_FILE), 'r+') as f:\n try:\n self.highscore = int(f.read())\n except: #si il n'y a rien dans le fichier\n self.highscore = 0\n\n #On se place dans le dossier des sons\n self.snd_dir = path.join(self.dir, 'son')\n\n #On charge le son du menus\n self.son_jeu = pg.mixer.Sound(path.join(self.snd_dir, 'son_jeu.wav'))\n\n #On charge le son du saut\n self.son_saut = pg.mixer.Sound(path.join(self.snd_dir, 'Jump.wav'))\n\n #On charge le son des boutons\n self.son_bouton = pg.mixer.Sound(path.join(self.snd_dir, 'Blip_Select.wav'))\n\n #On charge le son des ressorts\n self.son_ressort = pg.mixer.Sound(path.join(self.snd_dir, 'Ressort.wav'))\n\n # Son blessure joueur\n self.soundHurt = pg.mixer.Sound(path.join(self.snd_dir, 'hurt.wav'))\n\n # BOUCLE DE RUN DU JEU ENTIER\n def run(self):\n\n # On lance l'écran de départ\n self.show_start_screen()\n\n while self.running:\n self.clock.tick(FPS) # gestion durée boucle\n\n # On gère les events des menus\n for event in pg.event.get():\n if event.type == pg.QUIT: # Si on quitte\n self.running = False\n if event.type == pg.KEYDOWN: # Appuie sur une touche\n\n if event.key == pg.K_ESCAPE: # ECHAP = quitte le jeu\n self.running = False\n\n if event.key == pg.K_SPACE: # ESPACE = Lancement d'une partie\n self.play()\n\n elif event.key == pg.K_c: # C = Menu crédits\n self.show_credit_screen()\n self.son_bouton.play()\n\n elif event.key == pg.K_r: # R = Menu règles\n self.show_regle_screen()\n self.son_bouton.play()\n\n elif event.key == pg.K_h: # H = Menu highscore\n self.show_highscore_screen()\n self.son_bouton.play()\n\n elif event.key == pg.K_m: # M = Menu principal\n self.show_start_screen()\n self.son_bouton.play()\n\n elif event.key == pg.K_s: #S = SHOP\n self.show_shop_screen()\n self.son_bouton.play()\n\n\n\n\n # BOUCLE D'UNE PARTIE\n def play(self):\n\n # On lance le son du menus\n pg.mixer.music.load(path.join(self.snd_dir, 'son_jeu.wav'))\n pg.mixer.music.play(loops=-1)\n # self.son_jeu.play()\n\n # Le joueur joue par défaut\n self.playing = True\n\n # Initialisation score à 0\n self.score = 0\n\n self.timerTuto = 14*FPS;\n\n # Création des conteneurs de sprites et d'élement du jeu (ou reset si nouvelle partie)\n self.all_sprites = pg.sprite.Group()\n self.platforms = pg.sprite.Group()\n self.dirts = pg.sprite.Group()\n self.ressorts = pg.sprite.Group()\n self.monstres = pg.sprite.Group()\n self.antigravites = pg.sprite.Group()\n\n # RECUPERATION LEVEL\n level=[startSouley,patternFifth[3]]\n\n nbChoisi = randint(0,NB_PATTERN-1)\n suite = tousLesPatterns[nbChoisi]\n self.finDuPatternCourant = (len(suite)+1) * WIDTH\n for i in range(0,len(suite)):\n level += [suite[i]]\n\n # Génération du level\n self.geneRateLevel(level)\n\n # Reset du TIMER\n self.time = TEMPSMAX;\n self.timePassed = pg.time.get_ticks();\n\n # Boucle principale\n while self.playing:\n\n self.clock.tick(FPS) # Gestion FPS\n\n self.events() # Gestion events\n self.update() # Update (élément, entités, joueur, ...)\n self.draw() # Dessinne à l'écran les éléments\n pg.mixer.music.fadeout(500)\n\n # On affiche l'écran de fin de partie si le joueur n'a pas quitté (running)\n if self.running:\n self.show_go_screen();\n\n\n # GESTION DES EVENEMENTS\n def events(self):\n # Récupération de tous les events\n for event in pg.event.get():\n\n # Evenement de fermeture de la fenêtre\n if event.type == pg.QUIT:\n self.playing = False\n self.running = False\n\n # Evenement touche appuyées\n if event.type == pg.KEYDOWN:\n\n if event.key == pg.K_ESCAPE: # Appuie ECHAP (Quitte la partie)\n self.playing = False\n\n if event.key == pg.K_UP: # Appuie Flèche haut (Le héros saute)\n self.player.jump()\n\n if event.key == pg.K_a: # Appuie Flèche haut (Le héros saute)\n if self.player.peutVoler:\n self.player.gravitation *= INVERSION_GRAVITATION\n\n if event.key == pg.K_SPACE: # Appuie ESPACE (Le héros attaque)\n if self.player.compteurAttack == 0:\n self.player.attack()\n self.atk = True;\n\n # if event.key == pg.K_UP: # Appuie touche flèche haut (test montée jauge espoir)\n # self.player.espoir.setValCourante(self.player.espoir.valCourante + 1)\n\n # if event.key == pg.K_DOWN: # Appuie touche flèche bas (test descente jauge espoir)\n # self.player.espoir.setValCourante(self.player.espoir.valCourante - 1)\n\n\n def animation_attack(self):\n if self.atkCountHero + 1 >= 20:\n self.atkCountHero=0\n self.atk = False\n\n if self.player.direction == DROITE and self.atk == True:\n self.player.image = pg.transform.scale(self.img_attackDroite[self.atkCountHero//4], [70, 64])\n self.atkCountHero +=1;\n elif self.player.direction == GAUCHE and self.atk == True:\n self.player.image = pg.transform.scale(self.img_attackGauche[self.atkCountHero//4], [70, 64])\n self.atkCountHero +=1;\n\n # elif self.player.deplacement == GAUCHE:\n # self.player.image = pg.transform.scale(self.img_walkLeftHero[self.walkCountHero//14], [70, 64])\n # self.walkCountHero +=1;\n # elif self.player.direction == GAUCHE:\n # self.player.image = pg.transform.scale(self.img_faceGauche, [70, 64])\n # else :\n # self.player.image = pg.transform.scale(self.img_faceDroite, [70, 64])\n\n def animation_ressort(self):\n for i in range (0, len(self.ressorts.sprites())):\n if (pg.sprite.collide_rect(self.player, self.ressorts.sprites()[i])):\n self.ressorts.sprites()[i].image = self.img_ressort[1]\n self.timerRessort = 30\n elif self.timerRessort == 0:\n self.ressorts.sprites()[i].image = self.img_ressort[0]\n if self.timerRessort > 0:\n self.timerRessort -= 1\n\n\n # UPDATE LES ELEMENTS DU JEU\n def update(self):\n\n # On update tous les sprites du jeu (appel des fonctions update des classes)\n self.all_sprites.update()\n\n # On update le TIMER\n self.updateTimer()\n\n #print(str(len(self.all_sprites)) + \" entites\")\n\n for element in self.platforms:\n if element.rect.right < -50:\n element.kill()\n self.platforms.remove(element)\n self.all_sprites.remove(element)\n\n for element in self.ressorts:\n if element.rect.right < -50:\n element.kill()\n self.ressorts.remove(element)\n self.all_sprites.remove(element)\n\n for element in self.monstres:\n if element.rect.right < -50:\n element.kill()\n self.monstres.remove(element)\n self.all_sprites.remove(element)\n\n for element in self.all_sprites:\n if element.rect.right < -50:\n element.kill()\n self.all_sprites.remove(element)\n self.all_sprites.remove(element)\n\n # GESTION MECHANIQUES DE JEU`\n if self.player.vel.y<0:\n hits = pg.sprite.spritecollide(self.player, self.platforms, False)\n if hits:\n topest = hits[0]\n\n for hit in hits:\n if hit.rect.bottom > topest.rect.bottom:\n topest = hit;\n\n if topest.rect.bottom-20 < self.player.pos.y and self.player.pos.y < topest.rect.bottom+12:\n self.player.pos.y = topest.rect.bottom;\n self.player.vel.y = 0\n self.player.jumping = 0\n\n #On verifie si le joueur touche la plateforme seulement si il est entrain de tomber\n if self.player.vel.y > 0:\n hits = pg.sprite.spritecollide(self.player,self.platforms, False, pg.sprite.collide_mask)\n if hits:\n lowest = hits[0]\n\n for hit in hits:\n if hit.rect.bottom > lowest.rect.bottom:\n lowest = hit\n self.player.vel.x = 0\n self.player.vel.y = 0\n\n\n\n if lowest.rect.top+20 > self.player.pos.y and self.player.pos.y > lowest.rect.top-12:\n self.player.pos.y = lowest.rect.top\n self.player.vel.y = 0\n\n self.player.jumping = False\n\n #Test de contact avec le dirt pour stopper le perso\n hits = pg.sprite.spritecollide(self.player,self.dirts, False)\n if hits:\n lowest = hits[0]\n rightest = hits[0]\n leftest = hits[0]\n for hit in hits:\n if hit.rect.bottom > lowest.rect.bottom:\n lowest = hit\n self.player.vel.y = 0\n self.player.vel.x = 0\n self.player.acc.y = 3\n self.player.jumping = False\n if hit.rect.left > rightest.rect.left:\n rightest = hit\n self.player.vel.y = 0\n self.player.vel.x = 0\n # self.player.acc.y = 3\n self.player.jumping = False\n elif hit.rect.right > leftest.rect.right:\n leftest = hit\n self.player.vel.y = 0\n self.player.vel.x = 0\n # self.player.acc.y = 7\n self.player.jumping = False\n\n\n if self.player.direction == DROITE and self.player.pos.x + 70 >= rightest.rect.left and self.player.pos.x + 70 <= rightest.rect.left + 100 and self.player.pos.y > rightest.rect.top:\n testVal = self.player.pos.x + self.player.width - rightest.rect.left\n self.player.pos.x = rightest.rect.left - 20;\n\n\n\n elif self.player.pos.x < leftest.rect.right and self.player.pos.x > leftest.rect.right - 10 and self.player.direction == GAUCHE and self.player.pos.y > leftest.rect.top:\n testVal = leftest.rect.right -self.player.pos.x;\n self.player.pos.x = leftest.rect.right + testVal;\n self.player.vel.x = 0\n\n\n #if rightest.rect.left < self.player.pos.x :\n\n # if self.player.pos.y < lowest.rect.bottom:\n # if self.player.pos.x < lowest.rect.right - 5:\n # if self.player.pos.x > lowest.rect.left - 15:\n # self.player.pos.y = lowest.rect.top\n # self.player.vel.y = 0\n\n\n\n keys = pg.key.get_pressed()\n if (keys[pg.K_DOWN]):\n self.chute = 5\n\n elif (self.chute == 0):\n if self.player.vel.y > 0:\n hits = pg.sprite.spritecollide(self.player,self.platforms, False, pg.sprite.collide_mask)\n if hits:\n lowest = hits[0]\n for hit in hits:\n if hit.rect.bottom > lowest.rect.bottom:\n lowest = hit\n\n if lowest.rect.top+20 > self.player.pos.y and self.player.pos.y > lowest.rect.top-12:\n self.player.pos.y = lowest.rect.top\n self.player.vel.y = 0\n self.player.jumping = False\n # if self.player.pos.y < lowest.rect.bottom:\n # if self.player.pos.x < lowest.rect.right - 5:\n # if self.player.pos.x > lowest.rect.left - 15:\n # self.player.pos.y = lowest.rect.top\n # self.player.vel.y = 0\n if (self.chute > 0):\n self.chute -= 1\n\n # if self.player.vel.x:\n # hits = pg.sprite.spritecollide(self.player, self.platforms.Sprites()mask, False, pg.sprite.collide_mask)\n # if hits:\n\n\n\n #Si on touche le Ressort gang !\n Ressort_hits = pg.sprite.spritecollide(self.player,self.ressorts, False, pg.sprite.collide_mask)\n\n if Ressort_hits:\n self.player.vel.y = -POWER_RESSORT\n self.player.jumping = False\n self.son_ressort.play()\n\n #Pour scroll le fond si le joueur atteind 1/2 de l'ecran\n if self.player.rect.right >= WIDTH / 2:\n self.player.pos.x -= max(abs(self.player.vel.x),2)\n self.score += round(max(abs(self.player.vel.x),2))\n #self.posTexte -= max(abs(self.player.vel.x),2)\n for plat in self.platforms:\n plat.rect.right -= max(abs(self.player.vel.x),2)\n for dirt in self.dirts:\n dirt.rect.right -= max(abs(self.player.vel.x),2)\n for ressort in self.ressorts:\n ressort.rect.right -= max(abs(self.player.vel.x),2)\n for monstre in self.monstres:\n monstre.pos.x -= max(abs(self.player.vel.x),2)\n for antigravite in self.antigravites:\n antigravite.rect.right -= max(abs(self.player.vel.x),2)\n\n\n #Génération de la suite de la carte\n\n if self.score > self.finDuPatternCourant - 2000:\n # print( \"score :\" + str(self.score) + \" fin du pattern : \" + str(self.finDuPatternCourant))\n # print(\"on gen²ère\")\n # print(\"YES\")\n nbChoisi = randint(0,NB_PATTERN-1)\n suite = tousLesPatterns[nbChoisi]\n # print(\" choisi \" + str(nbChoisi))\n # print(len(suite)*WIDTH)\n # print(\"manque : \", self.finDuPatternCourant - self.score)\n self.finDuPatternCourant += len(suite)*WIDTH\n self.geneRateLevel(suite,2000)\n # print(\"nouvelle fin :\" +str(self.finDuPatternCourant))\n\n\n #LES MOBS\n for monstre in self.monstres:\n monstre.acc.x = 0\n hits = pg.sprite.spritecollide(monstre,self.platforms, False)\n if hits:\n monstre.pos.y = hits[0].rect.top\n monstre.vel.y = 0\n taille = hits[0].width\n borneplus = hits[0].rect.right - (taille*0.20)\n bornemoins = hits[0].rect.left + (taille*0.20)\n\n\n if monstre.deplacementADroite == [\"False\"]:\n monstre.acc.x = -MONSTRE_ACC\n if monstre.pos.x < bornemoins:\n monstre.deplacementADroite = [\"True\"]\n monstre.direction = DROITE\n\n else:\n monstre.acc.x = MONSTRE_ACC\n if monstre.pos.x > borneplus:\n monstre.deplacementADroite = [\"False\"]\n monstre.direction = GAUCHE\n\n\n\n\n monstre.pos += monstre.acc\n monstre.rect.midbottom = monstre.pos\n # Ressort_hits = pg.sprite.spritecollide(monstre,self.ressorts, False)\n #\n # for ressort in Ressort_hits:\n # if Ressort_hits:\n # monstre.acc.y = -POWER_RESSORT*0.9\n\n # Die!\n # if self.event == \"FIN\":\n # for sprite in self.all_sprites:\n # sprite.rect.y -= max(self.player.vel.y, 10)\n # if sprite.rect.bottom < 0:\n # sprite.kill()\n # if len(self.platforms) == 0:\n # self.playing = False\n\n #Si le joueur touche le declencheur de gravite\n antigravite_hits = pg.sprite.spritecollide(self.player,self.antigravites, False)\n if antigravite_hits:\n for anti in antigravite_hits:\n self.all_sprites.remove(anti)\n self.antigravites.remove(anti)\n self.player.compteurVol = 10 * FPS\n self.player.peutVoler = True\n self.trucGravitation(True)\n\n # Fait des trucs sur la GRAVITATION\n def trucGravitation(self, bool):\n if bool:\n # On fait apparaitre l'icone\n print(\"Debut controle gravitationnel\")\n\n else:\n # On le fait disparaitre\n print(\"Fin controle gravitationnel\")\n\n # AFFICHE TOUS LES ELEMENTS A L'ECRAN\n def draw(self):\n\n # On clear l'écran\n self.screen.blit(self.img_background, (0,0))\n #self.screen.fill(lightblue)\n\n # On dessine tous les sprites (sur la surface self.screen)\n self.ressorts.draw(self.screen)\n self.antigravites.draw(self.screen)\n self.monstres.draw(self.screen)\n self.platforms.draw(self.screen)\n self.dirts.draw(self.screen)\n\n self.screen.blit(self.player.image, (self.player.rect.x, self.player.rect.y))\n\n # On dessine le score (en dessinant un texte à l'écran)\n self.draw_text(str(self.score), 40, WHITE, WIDTH * 23/26, 30)\n\n # On dessine la jauge d'espoir\n self.drawJaugeEspoir()\n\n # On dessine le timer\n self.drawTimer()\n\n self.animation_Hero();\n\n self.animation_attack();\n\n self.animation_ressort()\n\n\n #print(self.timerTuto)\n if self.timerTuto > 12*FPS :\n self.drawTutoFleche();\n\n elif self.timerTuto < 11*FPS and self.timerTuto > 8*FPS:\n self.drawSpaceTuto();\n elif self.timerTuto < 7.5*FPS and self.timerTuto > 5.5*FPS:\n self.drawQTuto()\n elif self.timerTuto > 0 and self.timerTuto < 5 * FPS:\n self.drawTutoJauge()\n\n self.timerTuto -= 1\n # On affiche la surface\n pg.display.flip()\n\n\n\n def drawTutoJauge(self):\n self.draw_text(\"La barre à gauche est votre espoir\", 40, WHITE, 500, 90);\n self.draw_text(\"Votre vitesse augmente avec sa jauge\", 40, WHITE, 500, 120)\n self.draw_text(\"Vous serez bloqué pendant un cours instant si elle ateint zéro\", 40, WHITE, 500, 150)\n\n def drawQTuto(self):\n self.draw_text(\"Appuyer sur A pour activer l'antigravité après avoir mangé le fruit\", 40, WHITE, 500, 150)\n\n def drawTutoFleche(self):\n self.draw_text(\"Déplacez vous avec les flèches directionnelles\", 50, WHITE, 500, 150)\n\n def drawSpaceTuto(self):\n self.draw_text(\"Utilisez espace pour attaquer\", 50, WHITE, 500, 150)\n\n #ANIMATION\n def animation_Hero(self):\n if self.walkCountHero + 1 >= 42:\n self.walkCountHero = 0;\n\n if self.player.deplacement == DROITE:\n self.player.image = pg.transform.scale(self.img_walkRightHero[self.walkCountHero//14], [70, 64])\n self.walkCountHero +=1;\n elif self.player.deplacement == GAUCHE:\n self.player.image = pg.transform.scale(self.img_walkLeftHero[self.walkCountHero//14], [70, 64])\n self.walkCountHero +=1;\n elif self.player.direction == GAUCHE:\n self.player.image = pg.transform.scale(self.img_faceGauche, [70, 64])\n else :\n self.player.image = pg.transform.scale(self.img_faceDroite, [70, 64])\n\n if self.player.gravitation == -1:\n self.player.image = pg.transform.flip(self.player.image, 0, 1)\n\n\n\n\n # FONCTION QUI DESSINNE LA JAUGE D'ESPOIR\n def drawJaugeEspoir(self):\n\n size = 500; # taille de la jauge\n once = 10; # nombre d'onces d'espoir\n\n graduation = self.player.espoir.valMax / once; # nombre de graduations\n espacement = size / graduation # espacement entre deux graduations\n\n # On dessine le rectangle principal de la jauge\n rect = pg.Surface((50,size), pg.SRCALPHA)\n rect.fill((255, 255, 255, 100))\n self.screen.blit(rect, (50,100))\n\n # On dessine le niveau sur la jauge\n jauge = pg.Surface((50, ((size/self.player.espoir.valMax) * self.player.espoir.valCourante)), pg.SRCALPHA)\n jauge.fill((0,255,255, 160));\n self.screen.blit(jauge, ((50,100 + size - size/self.player.espoir.valMax * self.player.espoir.valCourante)))\n #pg.draw.rect(self.screen, (0,255,255), [50 ,(100 + size) - ((size/self.player.espoir.valMax) * self.player.espoir.valCourante), 50, ((size/self.player.espoir.valMax) * self.player.espoir.valCourante)])\n\n # On dessine les graduations\n i = 0;\n while(i<500):\n if not(i == 0):\n tmp = pg.Surface((25, 3), pg.SRCALPHA)\n tmp.fill((0,0,0,150))\n self.screen.blit(tmp, (50, 100+i));\n\n\n #self.screen.blit(grad, (50, i+100))\n #pg.draw.line(self.screen, (0,0,255, 100), (50, i+100), (75, i+100), 3);\n i = i + espacement;\n\n\n # Dessinne du texte à l'écran à la position voulu et de la couleur choisie (en utilisant la police par défaut du jeu)\n def draw_text(self, text, size, color, x, y):\n font = pg.font.Font(self.font_name, size)\n text_surface = font.render(text, True, color)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n self.screen.blit(text_surface, text_rect)\n\n\n # DESSINNE LE TIMER DE JEU\n def drawTimer(self):\n\n # Calcul minutes et secondes\n minute = str(int(str(self.time / 60).split(\".\")[0]))\n sec = str(round(self.time % 60))\n\n if len(sec) == 1:\n sec = \"0\" + sec\n\n # Affichage du timer\n self.draw_text(minute + \":\" + sec, 50, WHITE, WIDTH / 2, 30)\n\n # UPDATE LE TIMER\n def updateTimer(self):\n\n # Différence entre temps actuel et 3 minutes\n self.time = TEMPSMAX - round((pg.time.get_ticks()-self.timePassed) / 1000)\n\n # Quand le compteur est à 0\n if self.time == 0:\n self.event = \"FIN\"\n self.playing = False\n for sprite in self.all_sprites:\n # sprite.rect.y -= max(self.player.vel.y, 20)\n if sprite.rect.bottom < 0:\n self.player.kill()\n\n # if self.playing == False and self.running == True:\n # sprite.rect.y += max(self.player.vel.y, 10)\n # self.player = Hero(10, 10, 10, 10, self)\n # self.all_sprites.add(self.player)\n\n\n # AFFICHE LE SHOP\n def show_shop_screen(self):\n pg.mixer.music.load(path.join(self.snd_dir, 'son_menu.wav'))\n pg.mixer.music.play(loops=-1)\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n # Affichage consigne\n self.draw_text(\"H : HIGH SCORE\", 22, WHITE, 220, 15)\n self.draw_text(\"M : MENU\", 22, WHITE, 420, 15)\n self.draw_text(\"R : REGLES\", 22, WHITE, 620, 15)\n self.draw_text(\"C : CREDITS\", 22, WHITE, 820, 15)\n\n self.draw_text(\"SHOP\", 48, WHITE, WIDTH/2, HEIGHT/4)\n self.draw_text(\" Degat : \", 32, WHITE, 220, 280)\n self.draw_text(\"Espoir Max : \", 32, WHITE, 220, 380)\n self.draw_text(\"Espoir Min : \", 32, WHITE, 220, 480)\n self.draw_text(\"Appuyez sur ESPACE pour jouer une partie\", 22, WHITE, WIDTH / 2, HEIGHT * 3/4 )\n\n pg.display.flip()\n\n\n\n # AFFICHE L'ECRAN DE DEPART\n def show_start_screen(self):\n\n self.voixoff = pg.mixer.Sound(path.join(self.snd_dir, 'voice_menu.wav'))\n self.voixoff.play()\n\n\n # pg.mixer.music.load(path.join(self.snd_dir, 'voice_menu.wav'))\n pg.mixer.music.load(path.join(self.snd_dir, 'son_menu.wav'))\n pg.mixer.music.play(loops=-1)\n\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n # Affichage consigne\n self.draw_text(\"H : HIGH SCORE\", 22, WHITE, 220, 15)\n self.draw_text(\"S : SHOP\", 22, WHITE, 420, 15)\n self.draw_text(\"R : REGLES\", 22, WHITE, 620, 15)\n self.draw_text(\"C : CREDITS\", 22, WHITE, 820, 15)\n\n self.draw_text(TITLE, 48, WHITE, WIDTH/2, HEIGHT/4)\n self.draw_text(\"Partez à la recherche d'espoir !\", 22, WHITE, WIDTH / 2, HEIGHT / 2)\n self.draw_text(\"Appuyez sur ESPACE pour jouer une partie\", 22, WHITE, WIDTH / 2, HEIGHT * 3/4 )\n\n pg.display.flip()\n\n\n # AFFICHE L'ECRAN DE FIN\n def show_go_screen(self):\n\n pg.mixer.music.load(path.join(self.snd_dir, 'son_menu.wav'))\n pg.mixer.music.play(loops=-1)\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n # Affichage consigne\n self.draw_text(\"H : HIGH SCORE\", 22, WHITE, 120, 15)\n self.draw_text(\"M : MENU\", 22, WHITE, 320, 15)\n self.draw_text(\"R : REGLES\", 22, WHITE, 520, 15)\n self.draw_text(\"C : CREDITS\", 22, WHITE, 720, 15)\n self.draw_text(\"S : SHOP\", 22, WHITE, 920, 15)\n\n self.draw_text(\"GAME OVER\", 48, WHITE, WIDTH/2, HEIGHT/4)\n self.draw_text(\"Score: \" + str(self.score), 22, WHITE, WIDTH / 2, HEIGHT / 2)\n self.draw_text(\"Appuyez sur ESPACE pour rejouer\", 22, WHITE, WIDTH / 2, HEIGHT * 3 / 4)\n\n if self.score > self.highscore:\n self.highscore = self.score\n self.draw_text(\"NEW HIGH SCORE !\", 22, WHITE, WIDTH / 2, HEIGHT / 2 + 40)\n with open(path.join(self.dir,HS_FILE), 'w+') as f:\n f.write(str(self.score))\n # print(\"j'ai ecrit\")\n else:\n self.draw_text(\"le highscore : \" + str(self.highscore), 22, WHITE, WIDTH/2, HEIGHT/2+40)\n\n pg.display.flip()\n\n\n # AFFICHE L'ECRAN DE CREDITS\n def show_credit_screen(self):\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n\n # self.screen.blit(self.img_background, (0,0))\n self.screen.blit(self.img_credits, (0,0))\n\n # Affichage consigne\n self.draw_text(\"H : HIGH SCORE\", 22, WHITE, 220, 15)\n self.draw_text(\"M : MENU\", 22, WHITE, 420, 15)\n self.draw_text(\"R : REGLES\", 22, WHITE, 620, 15)\n self.draw_text(\"S : SHOP\", 22, WHITE, 820, 15)\n\n self.draw_text(\"IUT2 - Informatique \", 22, WHITE, 910, 670)\n # self.draw_text(\"Appuyez sur ESPACE pour jouer une partie\", 22, WHITE, WIDTH / 2, HEIGHT * 3/4 )\n\n pg.display.flip()\n\n\n # AFFICHAGE ECRAN HIGH SCORE\n def show_highscore_screen(self):\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n # Affichage consigne\n self.draw_text(\"S : SHOP\", 22, WHITE, 220, 15)\n self.draw_text(\"M : MENU\", 22, WHITE, 420, 15)\n self.draw_text(\"R : REGLES\", 22, WHITE, 620, 15)\n self.draw_text(\"C : CREDITS\", 22, WHITE, 820, 15)\n\n self.draw_text(\"HIGHSCORE :\", 48, WHITE, WIDTH/2, HEIGHT/4)\n self.draw_text(str(self.highscore), 96, WHITE, WIDTH/2, HEIGHT/2 - 50)\n self.draw_text(\"Appuyez sur ESPACE pour jouer une partie\", 22, WHITE, WIDTH / 2, HEIGHT * 3/4 )\n\n pg.display.flip()\n\n\n # AFFICHAGE ECRAN REGLES DU JEU\n def show_regle_screen(self):\n\n # Background\n self.screen.blit(self.img_background, (0,0))\n\n # Instruction\n self.screen.blit(self.img_regles, (0,0))\n self.draw_text(\"H : HIGH SCORE\", 22, WHITE, 220, 15)\n self.draw_text(\"M : MENU\", 22, WHITE, 420, 15)\n self.draw_text(\"S : SHOP\", 22, WHITE, 620, 15)\n self.draw_text(\"C : CREDITS\", 22, WHITE, 820, 15)\n\n self.draw_text(\"REGLE : \", 48, WHITE, WIDTH/2, HEIGHT/8)\n self.draw_text(\"Appuyez sur ESPACE pour jouer une partie\", 22, WHITE, WIDTH / 2, HEIGHT * (90/100))\n\n pg.display.flip()\n\n\n\n\n\n\n# FIN DE FICHIER\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":35068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"214393180","text":"from django.conf.urls import url\r\nfrom . import views\r\nfrom django.contrib.auth.views import login, logout_then_login\r\n\r\n\r\napp_name = 'portal'\r\n\r\nurlpatterns = [\r\n url(r'^$', views.home, name='home'),\r\n # url(r'^portal_login/$', views.portal_login, name='portal_login'),\r\n url(r'^campaigns/$', views.campaigns, name='campaigns'),\r\n url(r'^get_prospect/(?P[0-9]+)/$', views.get_prospect, name='get_prospect'),\r\n # url(r'^updated_prospect/(?P[0-9]+)')\r\n url(r'^campaign/(?P[0-9]+)/$', views.campaign_details, name='campaign_details'),\r\n url(r'^make_lead/(?P[0-9]+)/(?P[0-9]+)/$', views.make_lead, name='make_lead'),\r\n url(r'^make_dnc/(?P[0-9]+)/(?P[0-9]+)/$', views.make_dnc, name='make_dnc'),\r\n url(r'^make_view/(?P[0-9]+)/(?P[0-9]+)/$', views.make_view, name='make_view'),\r\n url(r'^make_changes/(?P[0-9]+)/(?P[0-9]+)/$', views.make_changes, name='make_changes'),\r\n url(r'^my_leads/(?P[0-9]+)/$', views.my_leads, name='my_leads'),\r\n url(r'^my_views/(?P[0-9]+)/$', views.my_views, name='my_views'),\r\n url(r'^my_dncs/(?P[0-9]+)/$', views.my_dncs, name='my_dncs'),\r\n url(r'^login/$', login, {'template_name': 'portal/portal_login.html'}, name='login'),\r\n url(r'^logout/$', logout_then_login, name='logout'),\r\n\r\n]","sub_path":"portal/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"259824664","text":"''' Ram's scenario:\n1) Change the default directory. Hard code now later get it from user\n2) Read the files, directories from the given directory\n3) Get input from User for file extension like (txt, iso, .xlsx, .doc etc..)\n4) Print the count of files with provided file extension\n5) Run it in windows and Linux machine\n'''\n\nimport os\n#print(os.getcwd())\n#os.chdir(\"C:/Ashok/Test\")\n#print(os.getcwd())\nfile_extenstion =input(\"Please Enter the file Extension: \\n\")\ncount = 0\nfor root, dirs, files in os.walk(\"C:\\Ashok\\Test\"):\n for name in files:\n #print(os.path.join(root, name))\n if file_extenstion == name.split(\".\")[-1]:\n count = count+1\nprint (count)\n","sub_path":"3.FilesystemOperations/filesystem_Ashok.py","file_name":"filesystem_Ashok.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"447316339","text":"'''\n\nTITLE: \n Handling Image Histograms (Simple and Cumulative)\n\nDESCRIPTION:\n Use of the main OpenCV histogram features, emphasizing the \n possibility of generating the simple and accumulated graphs\n \nVERSION: \n Author: Leonardo Godói (eng.leonardogodoi@gmail.com)\n Creation date: 14-September-2018\n\nREVISION HISTORY:\n V1.0 | 14-September-2018 | Leonardo Godói | Creation\n\n'''\n\n# -------------------------------------------------------------\n# -------------------------------------------------------------\n# -------------------------------------------------------------\n\n# Importing package for computer vision with Python\nimport cv2 as cv \n\n# Importing package for scientific computing with Python\nimport numpy as np \n \n# Importing package for plotting graphics\t \nfrom matplotlib import pyplot as plt \n\n# Importing the image in grayscale to be processed and resizing it \nimg = cv.imread('imgs/messi.jpg', 0) \nimg_original = cv.resize(img, (0, 0), fx=0.5, fy=0.5) \n \n# Applying equalization in the histogram\nimg_equalized = cv.equalizeHist(img_original)\n\n# Plotting before and after graphics of histogram\nplt.hist(img_original.ravel(),256,[0,256], color='red', label='Original') \nplt.legend() \nplt.show() \nplt.hist(img_equalized.ravel(),256,[0,256], color='green', label='equalizada') \nplt.legend() \nplt.show() \n \n# Plotting before and after graphics of cumulative histogram \nplt.hist(img_original.ravel(),256,[0,256], color='red', label='Original', cumulative=True) \nplt.legend() \nplt.show() \nplt.hist(img_equalized.ravel(),256,[0,256], color='green', label='equalizada', cumulative=True) \nplt.legend() \nplt.show() \n \n# Saving original and equalized images side by side for comparison\nimg_original_equalized = np.hstack((img_original, img_equalized)) \ncv.imwrite('imgs/messi_original_equalized.jpg', img_original_equalized) \n\n# -------------------------------------------------------------\n# -------------------------------------------------------------\n# -------------------------------------------------------------","sub_path":"python/handling_histograms.py","file_name":"handling_histograms.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"261795127","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 19 16:16:33 2021\n\n@author: masum\n\"\"\"\n\n\nfrom pycocotools.coco import COCO\nimport requests\n\ncoco = COCO('/home/masum/3017/lab/cattle/code/Data/raw/coco/annotations_trainval2017/annotations/instances_val2017.json')\n\ncats = coco.loadCats(coco.getCatIds())\nnms=[cat['name'] for cat in cats]\nprint('COCO categories: \\n{}\\n'.format(' '.join(nms)))\n\ncatIds = coco.getCatIds(catNms=['cow'])\nimgIds = coco.getImgIds(catIds=catIds )\nimages = coco.loadImgs(imgIds)\nprint(\"imgIds: \", imgIds)\nprint(\"images: \", images)\n\nfor im in images:\n print(\"im: \", im)\n img_data = requests.get(im['coco_url']).content\n with open('val/' + im['file_name'], 'wb') as handler:\n handler.write(img_data)","sub_path":"coco.py","file_name":"coco.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"619248200","text":"\"\"\"\nSource: `whitebox/refact/test/pyro_example/br_original.py`.\nChanges from the source: marked as `# WL: ...`.\n\"\"\"\n\nimport logging\nimport os\n# WL: edited. =====\nimport sys, argparse, time\n# =================\n\nimport torch\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom torch.distributions import constraints\n\nimport pyro\nimport pyro.distributions as dist\nimport pyro.optim as optim\n\nassert pyro.__version__.startswith('1.3.0')\n\nplt.style.use('default')\n\n# WL: edited. =====\n# logging.basicConfig(format='%(message)s', level=logging.INFO)\nlogging.basicConfig(format='%(message)s', level=logging.INFO, stream=sys.stdout)\n# =================\n# Enable validation checks\npyro.enable_validation(True)\nsmoke_test = ('CI' in os.environ)\n# WL: edited. =====\n# pyro.set_rng_seed(1)\n# =================\nDATA_URL = \"https://d2hg8soec8ck9v.cloudfront.net/datasets/rugged_data.csv\"\nrugged_data = pd.read_csv(DATA_URL, encoding=\"ISO-8859-1\")\n\ndef model(is_cont_africa, ruggedness, log_gdp):\n a = pyro.sample(\"a\", dist.Normal(0., 10.))\n b_a = pyro.sample(\"bA\", dist.Normal(0., 1.))\n b_r = pyro.sample(\"bR\", dist.Normal(0., 1.))\n b_ar = pyro.sample(\"bAR\", dist.Normal(0., 1.))\n sigma = pyro.sample(\"sigma\", dist.Uniform(0., 10.))\n mean = a + b_a * is_cont_africa + b_r * ruggedness + b_ar * is_cont_africa * ruggedness\n with pyro.plate(\"data\", len(ruggedness)):\n pyro.sample(\"obs\", dist.Normal(mean, sigma), obs=log_gdp)\n\ndef guide(is_cont_africa, ruggedness, log_gdp):\n a_loc = pyro.param('a_loc', torch.tensor(0.))\n a_scale = pyro.param('a_scale', torch.tensor(1.),\n constraint=constraints.positive)\n sigma_loc = pyro.param('sigma_loc', torch.tensor(1.),\n constraint=constraints.positive)\n weights_loc = pyro.param('weights_loc', torch.randn(3))\n weights_scale = pyro.param('weights_scale', torch.ones(3),\n constraint=constraints.positive)\n a = pyro.sample(\"a\", dist.Normal(a_loc, a_scale))\n b_a = pyro.sample(\"bA\", dist.Normal(weights_loc[0], weights_scale[0]))\n b_r = pyro.sample(\"bR\", dist.Normal(weights_loc[1], weights_scale[1]))\n b_ar = pyro.sample(\"bAR\", dist.Normal(weights_loc[2], weights_scale[2]))\n sigma = pyro.sample(\"sigma\", dist.Normal(sigma_loc, torch.tensor(0.05)))\n mean = a + b_a * is_cont_africa + b_r * ruggedness + b_ar * is_cont_africa * ruggedness\n\n# Utility function to print latent sites' quantile information.\ndef summary(samples):\n site_stats = {}\n for site_name, values in samples.items():\n marginal_site = pd.DataFrame(values)\n describe = marginal_site.describe(percentiles=[.05, 0.25, 0.5, 0.75, 0.95]).transpose()\n site_stats[site_name] = describe[[\"mean\", \"std\", \"5%\", \"25%\", \"50%\", \"75%\", \"95%\"]]\n return site_stats\n\n# WL: added. =====\nparser = argparse.ArgumentParser(description=\"bayesian regression\")\nparser.add_argument('-s', '--seed', type=int, default=None)\nparser.add_argument('-n', '--num-steps', type=int, default=5000)\nparser.add_argument('-ef', '--eval-frequency', type=int, default=100)\nparser.add_argument('-lr', '--learning-rate', type=float, default=0.05)\nargs = parser.parse_args()\n\nlogging.info(args)\nif args.seed is not None: pyro.set_rng_seed(args.seed)\n# ================\n\n# Prepare training data\ndf = rugged_data[[\"cont_africa\", \"rugged\", \"rgdppc_2000\"]]\ndf = df[np.isfinite(df.rgdppc_2000)]\ndf[\"rgdppc_2000\"] = np.log(df[\"rgdppc_2000\"])\ntrain = torch.tensor(df.values, dtype=torch.float)\n\nfrom pyro.infer import SVI, Trace_ELBO\n\n# WL: edited. =====\n# svi = SVI(model, guide, optim.Adam({\"lr\": .05}), loss=Trace_ELBO())\nsvi = SVI(model, guide, optim.Adam({\"lr\": args.learning_rate}), loss=Trace_ELBO())\n# =================\n\nis_cont_africa, ruggedness, log_gdp = train[:, 0], train[:, 1], train[:, 2]\npyro.clear_param_store()\n\n# WL: edited. =====\ntimes = [time.time()]\nlogging.info(\"\\nstep\\t\"+\"elbo\\t\"+\"time(sec)\")\n\n# num_iters = 5000 if not smoke_test else 2\n# for i in range(num_iters):\nfor i in range(1, args.num_steps+1):\n# =================\n elbo = svi.step(is_cont_africa, ruggedness, log_gdp)\n # WL: edited. =====\n # if i % 500 == 0:\n # logging.info(\"Elbo loss: {}\".format(elbo))\n if (args.eval_frequency > 0 and i % args.eval_frequency == 0) or (i == 1):\n times.append(time.time())\n logging.info(f\"{i:06d}\\t\"\n f\"{-elbo:.4f}\\t\"\n f\"{times[-1]-times[-2]:.3f}\")\n # =================\n","sub_path":"srepar/srepar/examples/br/orig/br.py","file_name":"br.py","file_ext":"py","file_size_in_byte":4527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"516879855","text":"# encoding: utf-8\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, JsonResponse, Http404\nfrom wechatpy.utils import check_signature\nfrom wechatpy import parse_message\nfrom wechatpy.replies import TextReply\nfrom django.views.decorators.csrf import csrf_exempt\nfrom adWrist.models import userlist, sportrecords\nfrom datetime import *\nfrom urllib.request import *\nimport json\nimport config\n# Create your views here.\n\n\nserverIP = config.serverIP\nAPI_ID = config.API_ID\nAPI_SECRET = config.API_SECRET\nTOKEN = config.TOKEN\n\n\n@csrf_exempt\ndef handle_msg(request):\n if request.method == 'GET':\n signature = request.GET.get('signature')\n timestamp = request.GET.get('timestamp')\n nonce = request.GET.get('nonce')\n echo_str = request.GET.get('echostr')\n check_signature(TOKEN, signature, timestamp, nonce)\n return HttpResponse(echo_str)\n elif request.method == 'POST':\n body = request.body\n msg = parse_message(body)\n rep = TextReply()\n rep.source = msg.target\n rep.target = msg.source\n if msg.type == 'event':\n if msg.event == 'click':\n print(msg.key)\n if msg.key == 'sports_advice':\n rep.content = recommend_plan(msg.source)\n elif msg.key == 'view_info':\n rep.content = get_info(msg.source)\n elif msg.key == 'add_test':\n rep.content = add_test(msg.source)\n elif msg.key == 'show_today':\n rep.content = get_datatoday(msg.source)\n elif msg.event == 'subscribe':\n rep.content = create_newuser(msg.source)\n else:\n rep.content = '!!!'\n else:\n rep.content = '你好'\n repxml = rep.render()\n return HttpResponse(repxml)\n\n\ndef create_newuser(openID):\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n new_user = userlist(\n user_open_id = openID\n )\n new_user.save()\n return '欢迎你，新用户'\n else:\n return '欢迎你，老朋友'\n\n\ndef show_info_page(request):\n if request.method == 'GET':\n code = request.GET.get('code')\n openID = get_openid(code)\n return render_to_response('personalInfo.html',{'openID':openID },context_instance=RequestContext(request))\n else:\n raise Http404\n\n\ndef add_test(openID):\n try:\n cur_user = userlist.objects.get(user_open_id = openID)\n except userlist.DoesNotExist:\n rep = '好像出问题了，请填写信息'\n else:\n print(openID)\n date = datetime.now()\n for i in range(7):\n new_record1 = sportrecords(\n sportrecords_person_id=cur_user,\n sportrecords_sport_type='慢跑',\n sportrecords_quantity=2000,\n sportrecords_calorie=500\n )\n new_record2 = sportrecords(\n sportrecords_person_id=cur_user,\n sportrecords_sport_type='走路',\n sportrecords_quantity=1000,\n sportrecords_calorie=200\n )\n new_record1.save()\n new_record2.save()\n new_record1.sportrecords_end_time = date + timedelta(days=-i)\n new_record2.sportrecords_end_time = date + timedelta(days=-i)\n new_record1.save()\n new_record2.save()\n rep = '添加成功'\n return rep\n\n\ndef get_datatoday(openID):\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n rep = '好像出问题了，请填写信息'\n else:\n cur_data = sportrecords.objects.filter(sportrecords_person_id=cur_user,\n sportrecords_end_time__startswith=datetime.today().date())\n if cur_data :\n walk_quantity = 0\n slow_run_quantity = 0\n walk_calorie = 0\n slow_run_calorie = 0\n for single_data in cur_data:\n if single_data.sportrecords_sport_type == '慢跑':\n slow_run_quantity += single_data.sportrecords_quantity\n slow_run_calorie += single_data.sportrecords_calorie\n elif single_data.sportrecords_sport_type == '走路':\n walk_quantity += single_data.sportrecords_quantity\n walk_calorie += single_data.sportrecords_calorie\n rep = '您今天慢跑:\\n'+str(slow_run_quantity)+'步\\n消耗卡路里:\\n'+str(slow_run_calorie)+'大卡\\n您今天走路:\\n'+str(walk_quantity)+'步\\n消耗卡路里:\\n'+str(walk_calorie)+'大卡'\n else:\n rep = '没查到数据'\n return rep\n\n\ndef get_info(openID):\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n rep = '您还没有填写个人信息'\n else:\n if cur_user.user_confirmed:\n age = cur_user.user_age\n if cur_user.user_sex:\n sex = '男'\n else:\n sex = '女'\n weight = cur_user.user_weight\n height = cur_user.user_height\n rep = '您的年龄:'+str(age)+'\\n您的性别:'+sex+'\\n您的身高:'+str(height)+'cm\\n您的体重:'+str(weight)+'kg'\n else:\n rep = '您还没有填写个人信息'\n return rep\n\n\ndef recommend_plan(openID):\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n plan = '您还没有填写个人信息'\n else:\n if cur_user.user_confirmed:\n if cur_user.user_sex:\n bmr = 66 + 13.7*cur_user.user_weight + 5*cur_user.user_height - 6.8*cur_user.user_age\n else:\n bmr = 655 + 9.6*cur_user.user_weight + 1.8*cur_user.user_height - 4.7*cur_user.user_age\n bmi = cur_user.user_weight*10000/(cur_user.user_height*cur_user.user_height)\n if bmi < 18.5:\n fatflag = 0\n msg = \"体重过小，不宜减肥\"\n elif bmi>=18.5 and bmi<24.5:\n msg = \"体重正常，建议维持\"\n fatflag = 1\n elif bmi>25 and bmi<32:\n msg = \"超重，注意减肥\"\n fatflag = 2\n else:\n msg = \"严重超重\"\n fatflag = 3\n if fatflag >= 2:\n caladvise = bmr * 0.55\n else:\n caladvise = bmr * 0.375\n rundistance = caladvise*1.6/100\n walkdistance = caladvise/52\n plan = \"您的身体状况是：\" + msg + \"\\n每天建议额外消耗:\" + str(int(caladvise)) + \"大卡\\n即慢跑：\" + \\\n str(int(rundistance)) + \"公里\\n或走路：\" + str(int(walkdistance)) + \"公里\\n\"\n else:\n plan = '您还没有填写个人信息'\n return plan\n\n\ndef change_info(request):\n if request.method == 'GET':\n openID = request.GET.get('openID')\n type = request.GET.get('type')\n if type == 'init':\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n new_user = userlist(\n user_open_id = openID\n )\n new_user.save()\n return JsonResponse({\"age\": new_user.user_age, \"sex\": new_user.user_sex, \"weight\": new_user.user_weight,\n \"height\": new_user.user_height})\n else:\n return JsonResponse({\"age\": cur_user.user_age, \"sex\": cur_user.user_sex, \"weight\": cur_user.user_weight,\n \"height\": cur_user.user_height})\n elif type == 'confirm':\n sex = request.GET.get('sex')\n if sex == 'true':\n sex = True\n else:\n sex = False\n age = request.GET.get('age')\n height = request.GET.get('height')\n weight = request.GET.get('weight')\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n newuser = userlist(\n user_open_id=openID,\n user_age=age,\n user_sex=sex,\n user_height=height,\n user_weight=weight,\n user_confirmed=True\n )\n newuser.save()\n else:\n cur_user.user_age = age\n cur_user.user_sex = sex\n cur_user.user_height = height\n cur_user.user_weight = weight\n cur_user.user_confirmed = True\n cur_user.save()\n return HttpResponse('success')\n else:\n raise Http404\n\ndef show_history(request):\n if request.method == 'GET':\n code = request.GET.get('code')\n openID = get_openid(code)\n return render_to_response('sportdata_check_previous.html', {'openID': openID},\n context_instance=RequestContext(request))\n else:\n raise Http404\n\n\ndef check_previous(request):\n if request.method == 'GET':\n openID = request.GET.get('openID')\n year = request.GET.get('year')\n month = request.GET.get('month')\n day = request.GET.get('day')\n try:\n cur_user = userlist.objects.get(user_open_id=openID)\n except userlist.DoesNotExist:\n rep = {\"flag\": 'failed'}\n else:\n cur_data = sportrecords.objects.filter(sportrecords_person_id=cur_user,\n sportrecords_end_time__startswith=date(int(year), int(month), int(day)))\n if cur_data:\n walk_quantity = 0\n slow_run_quantity = 0\n walk_calorie = 0\n slow_run_calorie = 0\n for single_data in cur_data:\n if single_data.sportrecords_sport_type == '慢跑':\n slow_run_quantity += single_data.sportrecords_quantity\n slow_run_calorie += single_data.sportrecords_calorie\n elif single_data.sportrecords_sport_type == '走路':\n walk_quantity += single_data.sportrecords_quantity\n walk_calorie += single_data.sportrecords_calorie\n rep = {\"flag\": 'success', \"sporttype\": '走路', \"quantity\": walk_quantity, \"calorie\": walk_calorie}\n else:\n rep = {\"flag\": 'failed'}\n return JsonResponse(rep)\n\n\ndef oauth_test(request):\n if request.method == 'GET':\n code = request.GET.get('code')\n openID = get_openid(code)\n return HttpResponse(openID)\n else:\n raise Http404\n\n\ndef show_chart(request):\n if request.method == 'GET':\n code = request.GET.get('code')\n openID = get_openid(code)\n return render_to_response('pedometer.html', {'openID': openID},\n context_instance=RequestContext(request))\n else:\n raise Http404\n\n\ndef get_openid(code):\n url = 'https://api.weixin.qq.com/sns/oauth2/access_token?appid=' + API_ID + '&secret=' + API_SECRET + \\\n '&code=' + code + '&grant_type=authorization_code'\n res_data = urlopen(url)\n res = res_data.read()\n resj = json.loads(res.decode('utf-8'))\n openID = resj['openid']\n return openID\n\ndef get_steps(request):\n if request.method == 'GET':\n openID = request.GET.get('openID')\n type = request.GET.get('type')\n if type == 'init':\n cur_date = datetime.today().date()\n print(cur_date)\n else:\n datestr = request.GET.get('date')\n datelist = datestr.split('-')\n cur_date = date(int(datelist[0]),int(datelist[1]),int(datelist[2]))\n if type == 'next':\n cur_date += timedelta(days=1)\n elif type == 'previous':\n cur_date += timedelta(days=-1)\n try:\n cur_user = userlist.objects.get(user_open_id = openID)\n except userlist.DoesNotExist:\n rep = {\"goal\": 0, \"steps\": 0, \"distance\": 0, \"cal\": 0}\n else:\n cur_data = sportrecords.objects.filter(sportrecords_person_id=cur_user,\n sportrecords_end_time__startswith=cur_date)\n if cur_data:\n print(cur_data)\n walk_quantity = 0\n walk_calorie = 0\n for single_data in cur_data:\n if single_data.sportrecords_sport_type == '走路':\n walk_quantity += single_data.sportrecords_quantity\n walk_calorie += single_data.sportrecords_calorie\n dist = float(str('%.2f' % (walk_quantity*0.5/1000)))\n goal = 20000\n rep = {\"goal\": goal, \"steps\": walk_quantity, \"distance\": dist, \"cal\": walk_calorie}\n else:\n rep = {\"goal\": 0, \"steps\": 0, \"distance\": 0, \"cal\": 0}\n if type != 'someday':\n rep['date'] = str(cur_date)\n print(rep)\n return JsonResponse(rep)\n else:\n raise Http404\n\n\n","sub_path":"myadmilk/adWrist/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"504582391","text":"import sys\nimport csv\nimport ntpath\nimport os.path\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QInputDialog, QLineEdit, QFileDialog, QListWidgetItem\nfrom PyQt5.QtCore import (QCoreApplication, Qt, QEvent)\nfrom mainUI import Ui_MainWindow\nfrom data_extractor import *\n\n\"\"\"\nC&W Networks\nJuan Diego\nJorge Ortega\n\"\"\"\n\n#pyuic5 mainWindow.ui -o mainUI.py\n\n\nclass Qfile(QListWidgetItem):\n def __init__(self, path, parent=None):\n self.path = path\n self.filename = os.path.basename(self.path)\n super().__init__(self.filename)\n\nclass AppWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n self.listIndex = -1\n\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n self.ui.open_fileB.triggered.connect(self.openFileNameDialog)\n self.ui.pushButton.clicked.connect(self.extract_data)\n self.ui.listWidget.itemSelectionChanged.connect(self.test)\n\n def test(self):\n self.listIndex = self.ui.listWidget.currentRow()\n print(self.ui.listWidget.currentRow())\n\n def keyPressEvent(self, event):\n key = event.key()\n\n if key == Qt.Key_Escape:\n QCoreApplication.quit()\n elif key == Qt.Key_Delete:\n print(self.ui.listWidget.currentRow())\n if self.listIndex>=0:\n item = self.ui.listWidget.takeItem(self.ui.listWidget.currentRow())\n item = None\n for i in range(self.ui.listWidget.count()):\n self.ui.listWidget.item(i).setSelected(False)\n self.listIndex = -1\n self.ui.pushButton.setFocus()\n\n def mousePressEvent(self, event):\n #print(\"X:\",event.x(),\", Y:\",event.y())\n for i in range(self.ui.listWidget.count()):\n self.ui.listWidget.item(i).setSelected(False)\n self.listIndex = -1\n self.ui.pushButton.setFocus()\n\n def openFileNameDialog(self):\n options = QFileDialog.Options()\n options |= QFileDialog.DontUseNativeDialog\n fileNames, _ = QFileDialog.getOpenFileNames(self,\"Abrir TXT\", \"\",\"TXT (*.txt)\", options=options)\n if fileNames:\n for i in fileNames:\n self.ui.listWidget.addItem(Qfile(i))\n self.ui.pushButton.setFocus()\n\n def extract_data(self):\n outFile = 'Resultado_SAPs'\n final_data = [[\"Name\", \"Service\", \"Saps\", \"Ports\"]]\n\n if os.path.isfile(outFile +'.csv'):\n index = 0\n print(\"Problem\")\n while True:\n if os.path.isfile(outFile + '_' + str(index) + '.csv'):\n index = index + 1\n else:\n outFile = outFile + '_' + str(index)\n break;\n print( outFile + '_' + str(index) + '.csv')\n\n\n with open(outFile +'.csv', 'w', newline='') as f:\n for i in range(self.ui.listWidget.count()):\n #print(self.ui.listWidget.item(i).text())\n data = extract(self.ui.listWidget.item(i).path)\n final_data = final_data + data + [[\"\",\"\",\"\",\"\"]]\n\n writer = csv.writer(f)\n writer.writerows(final_data)\n\n self.ui.listWidget.clear()\n\n\napp = QApplication(sys.argv)\nw = AppWindow()\nw.show()\n\nsys.exit(app.exec_())\nw.end()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"88632980","text":"# coding=utf-8\r\n# Copyright (C) 2019 ATHENA AUTHORS; Xiangang Li; Shuaijiang Zhao\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n# ==============================================================================\r\n# pylint: disable=no-member, invalid-name\r\n\"\"\" audio dataset \"\"\"\r\n\r\nfrom absl import logging\r\nimport tensorflow as tf\r\nfrom .base import SpeechBaseDatasetBuilder\r\n\r\n\r\nclass SpeechDatasetBuilder(SpeechBaseDatasetBuilder):\r\n \"\"\"SpeechDatasetBuilder\r\n \"\"\"\r\n\r\n default_config = {\r\n \"audio_config\": {\"type\": \"Fbank\"},\r\n \"num_cmvn_workers\": 1,\r\n \"cmvn_file\": None,\r\n \"input_length_range\": [20, 50000],\r\n \"data_csv\": None,\r\n }\r\n\r\n def __init__(self, config=None):\r\n super().__init__(config=config)\r\n if self.hparams.data_csv is not None:\r\n self.preprocess_data(self.hparams.data_csv)\r\n\r\n def preprocess_data(self, file_path):\r\n \"\"\"generate a list of tuples (wav_filename, wav_length_ms, speaker).\"\"\"\r\n logging.info(\"Loading data from {}\".format(file_path))\r\n with open(file_path, \"r\", encoding='utf-8') as file:\r\n lines = file.read().splitlines()\r\n headers = lines[0]\r\n lines = lines[1:]\r\n\r\n self.entries = []\r\n for line in lines:\r\n items = line.split(\"\\t\")\r\n wav_filename, length, speaker = items[0], items[1], 'global'\r\n if 'speaker' in headers.split(\"\\t\"):\r\n speaker = items[-1]\r\n self.entries.append(tuple([wav_filename, length, speaker]))\r\n self.entries.sort(key=lambda item: float(item[1]))\r\n\r\n self.speakers = []\r\n for _, _, speaker in self.entries:\r\n if speaker not in self.speakers:\r\n self.speakers.append(speaker)\r\n\r\n self.entries = list(filter(lambda x: int(x[1]) in\r\n range(self.hparams.input_length_range[0],\r\n self.hparams.input_length_range[1]), self.entries))\r\n return self\r\n\r\n def __getitem__(self, index):\r\n \"\"\"get a sample\r\n\r\n Args:\r\n index (int): index of the entries\r\n\r\n Returns:\r\n dict: sample::\r\n\r\n {\r\n \"input\": input_data,\r\n \"input_length\": input_data.shape[0],\r\n \"output\": output_data,\r\n \"output_length\": output_data.shape[0],\r\n }\r\n \"\"\"\r\n audio_file, _, speaker = self.entries[index]\r\n feat = self.audio_featurizer(audio_file)\r\n feat = self.feature_normalizer(feat, speaker)\r\n input_data = feat\r\n output_data = tf.reshape(\r\n feat, [-1, self.audio_featurizer.dim * self.audio_featurizer.num_channels]\r\n )\r\n\r\n return {\r\n \"input\": input_data,\r\n \"input_length\": input_data.shape[0],\r\n \"output\": output_data,\r\n \"output_length\": output_data.shape[0],\r\n }\r\n\r\n @property\r\n def num_class(self):\r\n \"\"\":obj:`@property`\r\n\r\n Returns:\r\n int: the target dim\r\n \"\"\"\r\n target_dim = self.audio_featurizer.dim * self.audio_featurizer.num_channels\r\n return target_dim\r\n\r\n @property\r\n def sample_type(self):\r\n \"\"\":obj:`@property`\r\n\r\n Returns:\r\n dict: sample_type of the dataset::\r\n\r\n {\r\n \"input\": tf.float32,\r\n \"input_length\": tf.int32,\r\n \"output\": tf.float32,\r\n \"output_length\": tf.int32,\r\n }\r\n \"\"\"\r\n return {\r\n \"input\": tf.float32,\r\n \"input_length\": tf.int32,\r\n \"output\": tf.float32,\r\n \"output_length\": tf.int32,\r\n }\r\n\r\n @property\r\n def sample_shape(self):\r\n \"\"\":obj:`@property`\r\n\r\n Returns:\r\n dict: sample_shape of the dataset::\r\n\r\n {\r\n \"input\": tf.TensorShape(\r\n [None, self.audio_featurizer.dim, self.audio_featurizer.num_channels]\r\n ),\r\n \"input_length\": tf.TensorShape([]),\r\n \"output\": tf.TensorShape([None, None]),\r\n \"output_length\": tf.TensorShape([]),\r\n }\r\n \"\"\"\r\n return {\r\n \"input\": tf.TensorShape(\r\n [None, self.audio_featurizer.dim, self.audio_featurizer.num_channels]\r\n ),\r\n \"input_length\": tf.TensorShape([]),\r\n \"output\": tf.TensorShape([None, None]),\r\n \"output_length\": tf.TensorShape([]),\r\n }\r\n\r\n @property\r\n def sample_signature(self):\r\n \"\"\":obj:`@property`\r\n\r\n Returns:\r\n dict: sample_signature of the dataset::\r\n\r\n {\r\n \"input\": tf.TensorSpec(\r\n shape=(None, None, None, None), dtype=tf.float32\r\n ),\r\n \"input_length\": tf.TensorSpec(shape=([None]), dtype=tf.int32),\r\n \"output\": tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\r\n \"output_length\": tf.TensorSpec(shape=([None]), dtype=tf.int32),\r\n }\r\n \"\"\"\r\n return (\r\n {\r\n \"input\": tf.TensorSpec(\r\n shape=(None, None, None, None), dtype=tf.float32\r\n ),\r\n \"input_length\": tf.TensorSpec(shape=([None]), dtype=tf.int32),\r\n \"output\": tf.TensorSpec(shape=(None, None, None), dtype=tf.float32),\r\n \"output_length\": tf.TensorSpec(shape=([None]), dtype=tf.int32),\r\n },\r\n )\r\n","sub_path":"athena/data/datasets/speech_set.py","file_name":"speech_set.py","file_ext":"py","file_size_in_byte":6068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"213132282","text":"# clear; clc;close all;\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef wgn(x, snr):\n P_signal = np.sum(abs(x) ** 2) / len(x)\n P_noise = P_signal / 10 ** (snr / 10.0)\n return np.random.randn(len(x)) * np.sqrt(P_noise)\n\n\nN = 1024 # 采样点\nFS = 5000 # 采样频率\nSNR = 20 # 信噪比设置\n\nn = np.arange(0, N - 1)\nd = 6\nx1 = 5 * np.cos(2 * np.pi * 10 * n / FS) # 输入函数1\n# noise1 = wgn(x1, SNR)\n# s1 = x1 + noise1\nx2 = 5 * np.cos(2 * np.pi * 10 * (n + d) / FS) # 输入函数2\n# noise2 = wgn(x2, SNR)\n# s2 = x2 + noise2\n\nx1 = list(x1)\nx2 = list(x2)\nTs = 1 / FS\nTa = np.arange(0, Ts * N, Ts / 10)\n\n\nfa = list(range(0 , len(Ta)))\ni = 0\nfor i in fa:\n fa[i] = 0\n\nfb = list(range(0 , len(Ta)))\ni = 0\nfor i in fb:\n fb[i] = 0\n\nT = np.arange(0, len(Ta) - 1)\nk = np.arange(0, Ts * N, Ts)\nt = np.arange(0, len(k) - 1)\n\nfor Tx in T:\n for tx in t:\n fa[Tx] = fa[Tx] + x1[tx] * np.sinc((Tx / 10 - tx))\n\nfor Tx in T:\n for tx in t:\n fb[Tx] = fb[Tx] + x2[tx] * np.sinc((Tx / 10 - d - tx))\n\nfa = np.array(fa)\nfb = np.array(fb)\n\nnoise1 = wgn(fa, SNR)\nnoise2 = wgn(fb, SNR)\ns1 = fa + noise1\ns2 = fb + noise2\n\nX1 = np.fft.fft(s1, 2 * N - 1) # 快速傅里叶正变换处理\nX2 = np.fft.fft(s2, 2 * N - 1)\nSxy = X1 * np.conj(X2) # 取x1和x2的互相关函数\n\nPA = np.fft.fftshift(np.fft.ifft(Sxy / (abs(Sxy))))\nPA = PA.real\n\nt1 = np.arange(-N + 1, N) / FS\n\n# 以下为画图操作\nplt.plot(t1, PA, 'r')\nplt.title('PHAT')\nplt.xlabel('t/s')\nplt.ylabel('PA(t)')\n\nRH_list = list(PA)\ndis2 = RH_list.index(max(RH_list)) # 取峰值\nk = max(RH_list)\n\nd2 = dis2 - N\ndelay = d2 / FS # 时延估计\n\nprint(delay)\nplt.show()\n","sub_path":"时差提取/ROTH.py","file_name":"ROTH.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"398044440","text":"\nimport asyncio\nimport logging\nimport itertools\nfrom six import string_types\n\nfrom transitions.core import (Condition, Event, EventData, Machine,\n MachineError, State, Transition)\nfrom .nesting import (HierarchicalMachine, NestedState,\n NestedTransition, NestedEvent)\n\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n\nclass AsyncNestedState(NestedState):\n\n async def enter(self, event_data):\n \"\"\" Triggered when a state is entered. \"\"\"\n logger.debug(\"%sEntering state %s. Processing callbacks...\", event_data.machine.name, self.name)\n for oe in self.on_enter:\n await event_data.machine._callback(oe, event_data)\n logger.info(\"%sEntered state %s\", event_data.machine.name, self.name)\n\n async def exit(self, event_data):\n \"\"\" Triggered when a state is exited. \"\"\"\n logger.debug(\"%sExiting state %s. Processing callbacks...\", event_data.machine.name, self.name)\n for oe in self.on_exit:\n await event_data.machine._callback(oe, event_data)\n logger.info(\"%sExited state %s\", event_data.machine.name, self.name)\n\n async def exit_nested(self, event_data, target_state):\n if self.level > target_state.level:\n await self.exit(event_data)\n return (await self.parent.exit_nested(event_data, target_state))\n elif self.level <= target_state.level:\n tmp_state = target_state\n while self.level != tmp_state.level:\n tmp_state = tmp_state.parent\n tmp_self = self\n while tmp_self.level > 0 and tmp_state.parent.name != tmp_self.parent.name:\n await tmp_self.exit(event_data)\n tmp_self = tmp_self.parent\n tmp_state = tmp_state.parent\n if tmp_self != tmp_state:\n await tmp_self.exit(event_data)\n return tmp_self.level\n else:\n return tmp_self.level + 1\n\n async def enter_nested(self, event_data, level=None):\n if level is not None and level <= self.level:\n if level != self.level:\n await self.parent.enter_nested(event_data, level)\n await self.enter(event_data)\n\n\nclass AsyncNestedCondition(Condition):\n\n async def check(self, event_data):\n return (await super().check(event_data))\n\n async def _condition_check(self, statement):\n if asyncio.iscoroutine(statement):\n statement = await statement\n\n return statement == self.target\n\n\nclass AsyncNestedTransition(NestedTransition):\n\n condition_cls = AsyncNestedCondition\n\n async def execute(self, event_data):\n\n logger.debug(\"%sInitiating transition from state %s to state %s...\",\n event_data.machine.name, self.source, self.dest)\n machine = event_data.machine\n\n for func in self.prepare:\n await machine._callback(func, event_data)\n logger.debug(\"Executed callback '%s' before conditions.\" % func)\n\n for c in self.conditions:\n if not (await c.check(event_data)):\n logger.warning(\"%sTransition condition failed: %s() does not \" +\n \"return %s. Transition halted.\", event_data.machine.name, c.func, c.target)\n return False\n for func in itertools.chain(machine.before_state_change, self.before):\n await machine._callback(func, event_data)\n logger.debug(\"%sExecuted callback '%s' before transition.\", event_data.machine.name, func)\n\n await self._change_state(event_data)\n\n for func in itertools.chain(self.after, machine.after_state_change):\n await machine._callback(func, event_data)\n logger.debug(\"%sExecuted callback '%s' after transition.\", event_data.machine.name, func)\n return True\n\n async def _change_state(self, event_data):\n\n machine = event_data.machine\n model = event_data.model\n dest_state = machine.get_state(self.dest)\n source_state = machine.get_state(model.state)\n lvl = await source_state.exit_nested(event_data, dest_state)\n event_data.machine.set_state(self.dest, model)\n event_data.update(model)\n await dest_state.enter_nested(event_data, lvl)\n machine.state_change.set()\n machine.state_change.clear()\n\n\nclass AsyncNestedEvent(NestedEvent):\n\n lock = asyncio.Lock() # Global lock to be acquired to start transitions\n\n async def trigger(self, *args, **kwargs):\n \"\"\"To start a transition we will have to await this function\n and the next to come. The goal is to have just one task\n that switches ON on entering a state and off when exiting.\n We must be sure though, that no other transitions are going to occour\n otherwise we risk a data race condition.\n \"\"\"\n with (await self.lock):\n return (await super().trigger(*args, **kwargs))\n\n async def _trigger(self, model, *args, **kwargs):\n state = self.machine.get_state(model.state)\n while state.parent and state.name not in self.transitions:\n state = state.parent\n if state.name not in self.transitions:\n msg = \"%sCan't trigger event %s from state %s!\" % (self.machine.name, self.name,\n model.state)\n if self.machine.get_state(model.state).ignore_invalid_triggers:\n logger.warning(msg)\n else:\n raise MachineError(msg)\n event_data = EventData(self.machine.get_state(model.state), self, self.machine,\n model, args=args, kwargs=kwargs)\n\n for func in self.machine.prepare_event:\n await self.machine._callback(func, event_data)\n logger.debug(\"Executed machine preparation callback '%s' before conditions.\" % func)\n\n try:\n for t in self.transitions[state.name]:\n event_data.transition = t\n transition_result = await t.execute(event_data)\n if transition_result:\n event_data.result = True\n break\n except Exception as e:\n event_data.error = e\n raise\n finally:\n for func in self.machine.finalize_event:\n await self.machine._callback(func, event_data)\n logger.debug(\"Executed machine finalize callback '%s'.\" % func)\n return event_data.result\n\n\nclass AsyncHierarchicalMachine(HierarchicalMachine):\n\n state_cls = AsyncNestedState\n transition_cls = AsyncNestedTransition\n event_cls = AsyncNestedEvent\n\n def __init__(self, *args, **kwargs):\n self.state_change = asyncio.Event()\n self.state_change.clear()\n super().__init__(*args, **kwargs)\n\n async def _callback(self, func, event_data):\n if isinstance(func, string_types):\n func = getattr(event_data.model, func)\n\n if self.send_event:\n callback = func(event_data)\n else:\n callback = func(*event_data.args, **event_data.kwargs)\n\n if asyncio.iscoroutine(callback):\n await callback\n\n async def wait_state(self, state):\n while self.state != state:\n await self.state_change.wait()\n\n async def _process(self, trigger):\n if not self.has_queue:\n if not self._transition_queue:\n return (await trigger())\n else:\n raise MachineError(\n \"Attempt to process events synchronously while transition queue is not empty!\"\n )\n\n self._transition_queue.append(trigger)\n if len(self._transition_queue) > 1:\n return True\n\n while self._transition_queue:\n try:\n callback = self._transition_queue[0]()\n\n if asyncio.iscoroutine(callback):\n await callback\n\n self._transition_queue.popleft()\n except Exception:\n self._transition_queue.clear()\n raise\n return True\n","sub_path":"transitions/extensions/asynchronous.py","file_name":"asynchronous.py","file_ext":"py","file_size_in_byte":8162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"450778833","text":"# -*- coding: utf-8 -*-\nimport time\nimport logging # 引入logging模块\nimport os.path\nfrom utils.net import get_local_ip\n\nBASIC_FORMAT = '%(levelname)s %(asctime)s %(pathname)s:%(lineno)d %(ip)s ' \\\n 'process=%(process)d thread=%(thread)d data=%(message)s'\n\n\nclass Formatter(logging.Formatter):\n def __init__(self, fmt=None, datefmt=None):\n if fmt is None:\n fmt = BASIC_FORMAT\n datefmt = datefmt\n logging.Formatter.__init__(self, fmt, datefmt)\n\n def format(self, record):\n try:\n record.ip = getattr(record, 'ip', None) or get_local_ip()\n except:\n record.ip = '127.0.0.1'\n\n return logging.Formatter.format(self, record)\n\n\n_logger = None\n\n\ndef basic_config():\n global _logger\n print(id(_logger))\n if _logger:\n return _logger\n # 第一步，创建一个logger\n logger = logging.getLogger()\n logger.setLevel(logging.INFO) # Log等级总开关\n # 第二步，创建一个handler，用于写入日志文件\n rq = time.strftime('spyder_%Y%m%d', time.localtime(time.time()))\n # log_path = os.path.expanduser('~') + '/log/python/'\n log_path = '/home/log/'\n log_name = log_path + rq + '.log'\n logfile = log_name\n fh = logging.FileHandler(logfile)\n fh.setLevel(logging.DEBUG) # 输出到file的log等级的开关\n # 第三步，定义handler的输出格式\n formatter = Formatter(BASIC_FORMAT)\n fh.setFormatter(formatter)\n # 第四步，将logger添加到handler里面\n logger.addHandler(fh)\n _logger = logger\n\n return logger\n\n\nlogger = basic_config()\n","sub_path":"logs/slogging.py","file_name":"slogging.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"531520630","text":"from PIL import Image, ImageTk\nimport abc\n\n\nclass Piece:\n def __init__(self, board, pieceName, color, size):\n self.name = color + '_' + pieceName\n size = int(size * 0.5)\n img = Image.open('resources/pieces/' + self.name + '.png').convert(\"RGBA\")\n oldSize = img.size\n\n widthPerc = (size / float(oldSize[0]))\n newHeight = int((float(oldSize[1]) * float(widthPerc)))\n\n img = img.resize((size, newHeight), Image.ANTIALIAS)\n self.imgSize = img.size\n self.image = ImageTk.PhotoImage(img)\n self.board = board\n self.gridXY = (-1, -1)\n self._position = None\n self._moveCounter = 0\n self._playable = False\n\n def move(self, pos, XY, diffGrid):\n self.gridXY = XY\n self._position = pos\n\n if diffGrid:\n self._moveCounter += 1\n\n # Change the playability status of this piece.\n # Playable pieces belong to the player.\n def setPlayability(self, flag):\n self._playable = flag\n\n # Check if this piece belongs to the player or rather to the opponent.\n def isFriendly(self):\n return self._playable\n\n # Get the minimal area of the piece that the grid should contain.\n def getAnchor(self):\n anchor = (self._position[0], self._position[1] - self.board.topY)\n width, height = self.imgSize\n A = (anchor[0] - width / 4, anchor[1] + height / 4)\n B = (anchor[0] + width / 4, anchor[1] + height / 4)\n C = (anchor[0], anchor[1] + height / 2)\n return A, B, C\n\n def _scanGrid(self, grid):\n self.board.highlightGrid(grid)\n gridObj = self.board.getGrid(grid)\n\n if gridObj is not None and gridObj.isOccupied():\n self._tryTarget(grid)\n return False # encountered blockage\n\n return True\n\n def _scanVerticalMoves(self, yRange):\n for i in yRange:\n if not self._scanGrid((self.gridXY[0], i)):\n break\n\n def _scanHorizontalMoves(self, xRange):\n for i in xRange:\n if not self._scanGrid((i, self.gridXY[1])):\n break\n\n def _scanDiagonalMoves(self, xRange, yRange):\n for i, j in zip(xRange, yRange):\n if not self._scanGrid((i, j)):\n break\n\n def _tryTarget(self, grid):\n gridObj = self.board.getGrid(grid)\n\n if gridObj is not None:\n targetPiece = gridObj.getOccupation()\n if targetPiece is not None and not targetPiece.isFriendly():\n self.board.targetGrid(grid)\n\n @abc.abstractmethod\n def highlightPossibleMoves(self):\n pass\n","sub_path":"game_pieces/Piece.py","file_name":"Piece.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"141537654","text":"\n\n#calss header\nclass _GRANDMOTHER():\n\tdef __init__(self,): \n\t\tself.name = \"GRANDMOTHER\"\n\t\tself.definitions = [u\"the mother of a person's father or mother: \"]\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_grandmother.py","file_name":"_grandmother.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"285184791","text":"import time\nimport socket\nimport socketserver\nimport threading\nimport os\nimport pickle\nfrom threading import Timer\n\n# Flag of making decision which router will be used to send packet\nrouterAlter = True\n\n# These are the variables that are used to handle Go-Back-N algorithm\n# Base is the expected Ack packet order\n# Window size is maximum packet amount can be send at the same time\n# Next Sequence number is the packet order which will be send next\nbase = 0\nwindowSize = 10\nnextSeqNum = 0\npacketCount = 0\n\n# First all packets are received by broker than sent to destination.\n# Therefore we need to store packets inside global array\nallPackets = []\n\n# These are the variables that are used to handle calculation of time-out.\ntimeoutInterval = 0.0\ntimeStart = 0.0\ntimeEnd = 0.0\nbeta = 0.25\nalfa = 0.125\nestimatedRTT = 0.0\nsampleRTT = 0.0\ntimeoutlnterval = 0.2\ndevRTT = 0.0\n\n\n# Simple lock in order to prevent race condition.\nlock = threading.Lock()\n\n\n# This function is used to calculate final time-out interval\ndef calculateInterval():\n global timeoutInterval, estimatedRTT, devRTT\n timeoutInterval = estimatedRTT + 4 * devRTT\n\n\n# This function is used to calculate deviation between estimated RTT and sample RTT\ndef calculateDevRTT():\n global beta, devRTT, sampleRTT, estimatedRTT\n devRTT = (1 - beta) * devRTT + beta * (sampleRTT - estimatedRTT)\n\n# This function simply calculates the estimated RTT\ndef calculateEstimated():\n global estimatedRTT, sampleRTT, alfa\n estimatedRTT = (1 - alfa) * estimatedRTT + alfa * sampleRTT\n\n\n# When the time-out occurs, this function will be called by new thread.\n# Starts new timer and make the next sequential number equal to the base.\n# Simply makes the system sends the packets again\ndef timeoutCallback():\n global nextSeqNum, base, packetCount\n lock.acquire()\n packetCount = base\n nextSeqNum = base\n global s\n try:\n s.cancel()\n except:\n print(\"\")\n calculateEstimated()\n calculateDevRTT()\n calculateInterval()\n s = Timer(timeoutlnterval , timeoutCallback)\n s.start()\n lock.release()\n\ns = Timer(timeoutlnterval, timeoutCallback)\n\n\n# This is the class that contains data, hashValue of data and sequence number and is transferred through sockets.\nclass DataPacket:\n def __init__(self, byteData, seqNumber, checksum):\n self.byteData = byteData\n self.seqNumber = seqNumber\n self.checksum = checksum\n\n\n# This function is called by new thread itself whenever a UDP connection is established.\n# Receiving acknowledge packets triggers this function.\n# This is the place where decision of whether packets are sent successful or not is done.\nclass udpHandler(socketserver.BaseRequestHandler):\n def handle(self):\n global base, windowSize, nextSeqNum, allPackets, lock\n ackedNo = int(self.request[0].decode(\"UTF-8\")[3:])\n\n # Locking is done in order to make operations in critical sections.\n # Other threads cannot reach below.\n lock.acquire()\n\n # If packed is successfully received, move the base by one so that new packets can be send to the destination.\n if base < ackedNo + 1:\n base = ackedNo + 1\n\n # If base and next sequence number are equal, stop the timer.\n if base == nextSeqNum:\n global s\n s.cancel()\n else:\n try:\n s.cancel()\n except:\n print(\"\")\n\n # Calculate the time-out before starting timer.\n calculateEstimated()\n calculateDevRTT()\n calculateInterval()\n s = Timer(timeoutlnterval, timeoutCallback)\n s.start()\n\n print(\" NextSeq-> \", nextSeqNum, \" Base -> \", base, \"AckedNo -> \", ackedNo)\n\n if base == 5917:\n os._exit(0)\n\n # Releasing the lock is done in order to make operations in critical sections by other threads.\n # Other threads can reach below from now on.\n lock.release()\n\n# Class of the UDP Server inherits from two class\nclass ThreadedUDPServer(socketserver.ThreadingMixIn,socketserver.UDPServer):\n pass\n\n\n# This function is called by new thread itself whenever a TCP connection is established.\n# Receiving packets from source triggers this function.\n# This is the place where send the packets to the destination.\nclass tcpHandler(socketserver.BaseRequestHandler):\n\n def handle(self):\n\n global allPackets, nextSeqNum, base, windowSize, lock, packetCount, routerAlter\n # flag = True\n packetCount = 0\n while True:\n # If last packet is received, break.\n # 5918 is the last packet's sequence number in our system.\n # And it only works for 5mb files.\n if packetCount == 5918:\n break\n else :\n data = self.request.recv(985)\n if len(data) == 0:\n continue\n elif len(data) == 463 and packetCount != 5917:\n data += self.request.recv(522)\n print(\"Received data length = \", len(data))\n\n # Adding the packet to the global array.\n allPackets.append(data)\n packetCount += 1\n print(packetCount)\n\n # Create new UDP socket in order to create connection between destination and itself.\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n # Initial sequence number.\n packetCount = 0\n\n while True:\n\n # Checking if broker is able to send packet while not exceeding the window size.\n if nextSeqNum - base >= windowSize:\n continue\n\n # Locking is done in order to make operations in critical sections.\n # Other threads cannot reach below.\n lock.acquire()\n\n\n if packetCount >= 5918:\n packetCount = 5917\n\n\n print(\" Sent Packet Seq Num : \", packetCount, \" Base -> \", base, \" Next Number : \", nextSeqNum)\n\n # If base and next sequence number are equal, stop the timer.\n if base == nextSeqNum:\n global s\n try:\n s.cancel()\n except:\n print(\"\")\n\n # Calculate the time-out before starting timer.\n calculateEstimated()\n calculateDevRTT()\n calculateInterval()\n s = Timer(timeoutlnterval, timeoutCallback)\n s.start()\n\n\n print(\"Length of packet : \", len(allPackets[packetCount]))\n\n global timeStart\n\n # Start the time\n timeStart = time.time()\n\n # Sending packets are done in here\n if routerAlter:\n routerAlter = not routerAlter\n # sock.sendto(allPackets[packetCount], (\"127.0.0.1\", 5005))\n sock.sendto(allPackets[packetCount], (\"10.10.3.2\", 5005))\n else:\n routerAlter = not routerAlter\n sock.sendto(allPackets[packetCount], (\"10.10.5.2\", 5005))\n packetCount += 1\n nextSeqNum += 1\n\n # Releasing the lock is done in order to make operations in critical sections by other threads.\n # Other threads can reach below from now on.\n lock.release()\n\n # Sleeping is necessary in order to not to overload the destination's socket.\n time.sleep(0.01)\n\n\n# Class of the TCP Server inherits from two class\nclass ThreadedTCPServer(socketserver.ThreadingMixIn,socketserver.TCPServer):\n pass\n\n\nif __name__ == \"__main__\":\n\n # udpServer = ThreadedUDPServer((\"127.0.0.1\", 5006), udpHandler)\n\n # Creating Threaded UDP server is done here\n udpServer1 = ThreadedUDPServer((\"10.10.4.1\", 5006), udpHandler)\n udpServerThread1 = threading.Thread(target=udpServer1.serve_forever)\n udpServerThread1.daemon = True\n udpServerThread1.start()\n\n # Creating Threaded second UDP server is done here in order to handle two router\n udpServer2 = ThreadedUDPServer((\"10.10.2.1\", 5006), udpHandler)\n udpServerThread2 = threading.Thread(target=udpServer2.serve_forever)\n udpServerThread2.daemon = True\n udpServerThread2.start()\n\n\n\n # Creating Threaded TCP server is done here\n # tcpServer = ThreadedTCPServer((\"127.0.0.1\", 5002),tcpHandler)\n tcpServer = ThreadedTCPServer((\"10.10.1.2\", 5002),tcpHandler)\n tcpServerThread = threading.Thread(target=tcpServer.serve_forever)\n tcpServerThread.daemon = True\n tcpServerThread.start()\n\n input()\n\n # Closing the servers.\n tcpServer.shutdown()\n udpServer1.shutdown()\n udpServer2.shutdown()\n tcpServer.server_close()\n udpServer1.server_close()\n udpServer2.server_close()\n","sub_path":"CENG_435_DATA_COMMUNICATIONS_AND_NETWORKING/Phase2/broker.py","file_name":"broker.py","file_ext":"py","file_size_in_byte":8738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"493957","text":"from node import Node\n\nclass LinkedList:\n def __init__(self, node=None):\n self.head = node\n self.tail = node\n self.len = 0\n\n def add_to_head(self, data):\n node = Node(data)\n if self.len == 0:\n self.head = node\n self.tail = node\n else:\n node.set_next_node(self.head)\n self.head = node\n self.len += 1\n\n def remove_head(self):\n if self.len == 0:\n return None\n value = self.head.get_value()\n if self.len == 1:\n self.head = None\n self.tail = None\n self.len = 0\n if self.len > 1:\n self.head = self.head.get_next_node()\n self.len -= 1\n return value\n\n def add_to_tail(self, data):\n new_node = Node(data)\n if self.len == 0:\n self.head = new_node\n else:\n self.tail.set_next_node(new_node)\n self.tail = new_node\n self.len += 1\n\n def contains(self, data):\n if self.len == 0:\n return False\n node = self.head\n for i in range(self.len):\n if node.get_value() == data:\n return True\n node = node.get_next_node()\n return False\n\n def get_max(self):\n if self.len == 0:\n return None\n node = self.head\n max_value = node.get_value()\n for i in range(self.len):\n if node.get_value() > max_value:\n max_value = node.get_value()\n node = node.get_next_node()\n return max_value\n\n def __len__(self):\n return self.len\n","sub_path":"stack/singly_linked_list.py","file_name":"singly_linked_list.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"592804312","text":"import string\nimport sys\nimport json\nimport time\n\nfrom filelock import FileLock\n\nif __name__ == '__main__':\n with FileLock(\"LOCK\"):\n f = open('/home/jenkins/workspace/ansible/hosts', 'r')\n contents = f.readlines()\n f.close()\n\n found = False\n contents_string = ''\n ips = []\n for cont in contents:\n if found == True:\n if len(cont.split('.')) == 4:\n ips.append(cont.strip('\\n'))\n if cont == '\\n':\n found = False\n continue\n if (found == False) and (sys.argv[1] in cont):\n found = True\n continue\n contents_string += cont\n \n print(contents_string)\n \n f = open('/home/jenkins/workspace/ansible/hosts', 'w')\n f.write(contents_string)\n f.close() \n\n f = open('/home/jenkins/workspace/Clone_VM/ip_mapping', 'r')\n mappings = f.readlines()\n f.close()\n\n mapping_string = ''\n for m in mappings:\n if m.split(':')[1].strip('\\n') not in ips:\n mapping_string += m \n\n print(mapping_string)\n\n f = open('/home/jenkins/workspace/Clone_VM/ip_mapping', 'w')\n f.write(mapping_string)\n f.close() \n \n \n","sub_path":"remove_hosts.py","file_name":"remove_hosts.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"328328530","text":"### This Python code receives several articles about Hollywood actors who play action and non-action movies (Selected theme is \"action\").\r\n### The goal is to build a model that can detect the contents of new unseen articles and predict which articles are for action actors.\r\n\r\nimport pandas as pd\r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\nimport re\r\nimport numpy as np\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\r\nfrom sklearn.metrics import roc_auc_score\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn import svm\r\nfrom sklearn.metrics import confusion_matrix\r\nimport matplotlib.pyplot as plt\r\nimport itertools\r\n######################## read the articles of Hollywood actors########################################\r\nActors = pd.read_excel(\"Articles.xlsx\", dtype='unicode')\r\n##--------------------------- Labeling ------------------------------------------------------\r\ndef targetLable(dataframe):\r\n dataframe['target'] = 0\r\n for index, item in dataframe.iterrows():\r\n # convert text to lowercase\r\n dataframe.at[index, 'Description'] = item['Description'].strip().lower()\r\n if item['Description'].rfind('action') != -1:\r\n dataframe.at[index, 'target'] = 1\r\n return dataframe\r\n##------------------------ Removing numbers from text ---------------------------------------------------\r\ndef removeDigit(list):\r\n pattern = '[0-9]'\r\n list = re.sub(pattern, '', list)\r\n return list\r\n##------- Removing punctuation and stopwords from text and making clean tokens to have a clean text ------\r\ndef transformation(input):\r\n array = []\r\n for text in input:\r\n # replace punctuation characters with spaces\r\n filters = '!\"\\'#$%&()*+,./:;<=>?@[\\\\]^_`{|}~\\t\\n–'\r\n translate_dict = dict((c, \"\") for c in filters)\r\n translate_map = str.maketrans(translate_dict)\r\n text = text.translate(translate_map)\r\n text = removeDigit(text)\r\n token = [t for t in text.split()]\r\n token = [w for w in token if len(w) > 2]\r\n array.append(token)\r\n\r\n Extra = set(stopwords.words('english'))\r\n # clean_item = []\r\n cleanFinalToken = []\r\n for item in array:\r\n clean_item = item[:]\r\n for t in item:\r\n if t in Extra:\r\n clean_item.remove(t)\r\n cleanFinalToken.append(' '.join(clean_item))\r\n return cleanFinalToken\r\n##-------------------------------------------------------------------------------------------------\r\ndef plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=None, normalize=True):\r\n accuracy = np.trace(cm) / float(np.sum(cm))\r\n misclass = 1 - accuracy\r\n if cmap is None:\r\n cmap = plt.get_cmap('Blues')\r\n plt.figure(figsize=(8, 6))\r\n plt.title(title)\r\n if target_names is not None:\r\n tick_marks = np.arange(len(target_names))\r\n plt.xticks(tick_marks, target_names, rotation=45)\r\n plt.yticks(tick_marks, target_names)\r\n if normalize:\r\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\r\n thresh = cm.max() / 1.5 if normalize else cm.max() / 2\r\n\r\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n if normalize:\r\n plt.text(j, i, \"{:0.0f}%\".format(cm[i, j] * 100), horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n else:\r\n plt.text(j, i, \"{:,}\".format(cm[i, j]), horizontalalignment=\"center\",\r\n color=\"white\" if cm[i, j] > thresh else \"black\")\r\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n plt.colorbar()\r\n plt.tight_layout()\r\n plt.ylabel('True label')\r\n plt.xlabel('Predicted label\\naccuracy={:0.2f}'.format(100 * accuracy))\r\n plt.show()\r\n\r\ntargetLable(Actors)\r\nActors['Cleaned_Description'] = transformation(Actors['Description'])\r\n##-------------------- Split data into train and test sets -------------------------------------------------\r\nX_train, X_test, y_train, y_test = train_test_split(Actors['Cleaned_Description'], Actors['target'], random_state=35)\r\nvect = CountVectorizer().fit(X_train)\r\nX_train_vectorized = vect.transform(X_train)\r\n\r\n##---------------------------- Naive Bayes -----------------------------------------------------------------\r\nNBclf = MultinomialNB(alpha=0.1).fit(X_train_vectorized, y_train)\r\npreds_NB = NBclf.predict(vect.transform(X_test))\r\ntarin_preds_NB = NBclf.predict(X_train_vectorized)\r\nplot_confusion_matrix(cm=confusion_matrix(y_train, tarin_preds_NB), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"NB Confusion Matrix for Train-set\")\r\nplot_confusion_matrix(cm=confusion_matrix(y_test, preds_NB), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"NB Confusion Matrix for Test-set\")\r\n\r\n##--------------------------------- Random Forest ----------------------------------------------------------\r\n\r\nRFclf = RandomForestClassifier(max_depth=4, n_estimators=2).fit(X_train_vectorized, y_train)\r\ntarin_preds_RF = RFclf.predict(X_train_vectorized)\r\npreds_RF = RFclf.predict(vect.transform(X_test))\r\nplot_confusion_matrix(cm=confusion_matrix(y_train, tarin_preds_RF), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"RFC Confusion Matrix for Train-set\")\r\nplot_confusion_matrix(cm=confusion_matrix(y_test, preds_RF), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"RFC Confusion Matrix for Test-set\")\r\n\r\n##------------------------------------ SVM ----------------------------------------------------------------\r\n\r\nSVMclf = svm.SVC(kernel='linear').fit(X_train_vectorized, y_train) # Linear Kernel\r\ntarin_preds_SVM = SVMclf.predict(X_train_vectorized)\r\npreds_SVM = SVMclf.predict(vect.transform(X_test))\r\nplot_confusion_matrix(cm=confusion_matrix(y_train, tarin_preds_SVM), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"SVM Confusion Matrix for Train-set\")\r\nplot_confusion_matrix(cm=confusion_matrix(y_test, preds_SVM), normalize=True, target_names=['NotAction', 'Action'],\r\n title=\"SVM Confusion Matrix for Test-set\")\r\n\r\ndef Output(predictedValue, realTest):\r\n df = pd.DataFrame(columns=realTest.columns)\r\n for i in range(len(predictedValue)):\r\n if predictedValue[i] == 1:\r\n df = df.append(realTest.loc[i, :], ignore_index=True)\r\n return df\r\n###################################################################################################\r\n########################### Test with New Data ###################################################\r\n\r\nNewdata = pd.read_excel(\"RealTestArticles.xlsx\", dtype='unicode')\r\nprint('New unseen data')\r\nprint(Newdata)\r\ntargetLable(Newdata)\r\nNewdata['Cleaned_Description'] = transformation(Newdata['Description'])\r\n\r\n################################## Outputs ########################################################\r\n\r\n##### Prediction using Naive Bayes Classifier ######################\r\npreds1 = NBclf.predict(vect.transform(Newdata['Cleaned_Description']))\r\n\r\nprint(\"Classified New Articles with Naive Bayes Classifier: (0: Non-Action , 1:Action) \", preds1)\r\nprint(\"Articles Related to 'Action':\", Output(preds1, Newdata))\r\n##### Prediction using SVM Classifier #############################\r\npreds2 = SVMclf.predict(vect.transform(Newdata['Cleaned_Description']))\r\n\r\nprint(\"Classified New Articles with SVM: (0: Non-Action , 1:Action) \", preds2)\r\nprint(\"Articles Related to 'Action':\", Output(preds2, Newdata))\r\n######## Prediction using Random Forest Classifier ################\r\npreds3 = RFclf.predict(vect.transform(Newdata['Cleaned_Description']))\r\n\r\nprint(\"Classified New Articles with RF: (0: Non-Action , 1:Action) \", preds3)\r\nprint(\"Articles Related to 'Action':\", Output(preds3, Newdata))\r\nprint(\"Based on the contents of new articles, the first two articles are \"\r\n \"for non-action actors (Adam Sandler and Julia Roberts) and the third \"\r\n \"one is for an action actor (Ben Affleck). You may tend to test the model with your articles,\"\r\n \"so just place your file name instead of RealTestArticles.xlsx in outputs section of the code!\")","sub_path":"TechnicalTest_BenjaminKh2.py","file_name":"TechnicalTest_BenjaminKh2.py","file_ext":"py","file_size_in_byte":8343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"64774513","text":"import numpy as np\n\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport tkinter as tk\n\nfrom neu_ro_arm.constants import GRIPPER_CLOSED, GRIPPER_OPENED\nfrom neu_ro_arm.robot.motion_planner import (MotionPlanner, UnsafeTrajectoryError,\n UnsafeJointPosition, ProbitedHandPosition)\nfrom neu_ro_arm.robot.simulator_controller import SimulatorController\nfrom neu_ro_arm.robot.xarm_controller import XArmController\n\nclass RobotArm:\n def __init__(self, controller_type='real'):\n '''Real or simulated xArm robot interface for safe, high-level motion commands\n\n Parameters\n ----------\n controller_type : str, {'real','sim'}, default to 'real'\n Indicate whether motor control be sent to a simulator or the real robot\n\n Attrbutes\n ---------\n joint_names : :tuple: str\n names of the joints in the arm\n controller : BaseController\n Controller used to execute motion commands\n mp : MotionPlanner\n Internal simulator of robot used to perform IK and collision\n detection\n '''\n self.joint_names = ('base', 'shoulder','elbow', 'wrist','wristRotation')\n\n if controller_type == 'real':\n self.controller = XArmController()\n elif controller_type == 'sim':\n self.controller = SimulatorController()\n else:\n raise TypeError('invalid controller type')\n self.controller_type = controller_type\n\n self.mp = MotionPlanner()\n self._mirror_planner()\n\n def home(self):\n '''Moves to home arm positions\n '''\n self.move_arm_jpos(self.controller.arm_jpos_home)\n self._mirror_planner()\n\n def passive_mode(self):\n if self.controller_type == 'real':\n self.controller.power_off()\n else:\n print('WARNING: passive mode does not exist for simulated robot')\n\n def active_mode(self):\n if self.controller_type == 'real':\n self.controller.power_on()\n self.mp.mirror(arm_jpos=self.get_arm_jpos(),\n gripper_state=self.get_gripper_state())\n else:\n print('WARNING: active mode does not exist for simulated robot')\n\n\n def add_camera(self, pose_mtx):\n '''Add camera object to motion planner's internal simulator so that it is\n included in collision checking\n\n Parameters\n ----------\n pose_mtx : ndarray\n pose matrix of camera in world coordinate frame; shape=(4,4);\n dtype=float\n '''\n self.mp.add_camera(pose_mtx)\n\n if self.controller_type == 'sim':\n self.controller.add_camera(pose_mtx)\n\n def get_arm_jpos(self):\n '''Get positions of the arm joints\n\n Returns\n -------\n ndarray\n joint angles in radians; shape=(5,); dtype=float\n '''\n arm_jpos = self.controller.read_command(self.controller.arm_joint_idxs)\n return arm_jpos\n\n def move_arm_jpos(self, jpos, verbose=True):\n '''Moves arm joints to specific positions\n\n Parameters\n ----------\n jpos : ndarray\n Desired joint angles in radians for each joint in the arm;\n shape=(5,); dtype=float\n verbose : bool\n Whether to print error messages in case of an issue\n\n Returns\n -------\n bool\n True if joint angles returned from IK were achieved\n '''\n try:\n self.mp.check_arm_trajectory(jpos)\n except UnsafeTrajectoryError as e:\n if verbose:\n print(f\"[MOVE FAILED] Trajectory would result in collision\"\n \" of robot:{e.robot_link} and {e.other_body}.\")\n return False\n\n self.controller.move_command(self.controller.arm_joint_idxs, jpos)\n success, achieved_jpos = self.controller.monitor(self.controller.arm_joint_idxs,\n jpos)\n self.mp.mirror(arm_jpos=achieved_jpos)\n return success\n\n def get_hand_pose(self):\n return self.mp.get_hand_pose()\n\n def move_hand_to(self, pos, rot=None, verbose=True):\n '''Moves end effector to desired pose in world\n\n Parameters\n ----------\n pos : ndarray\n desired 3d position of end effector; shape=(3,); dtype=float\n rot : ndarray\n desired euler angles of end effector; shape=(3,); dtype=float\n verbose : bool\n Whether to print error messages in case of an issue\n\n Raises\n ------\n UnsafeJointPosition\n If desired pose results in joint configuration that is in collision\n with the world\n UnsafeTrajectoryError\n If trajectory to reach desired pose will cause a collision. See\n move_arm_jpos for details\n\n Returns\n -------\n bool\n True if joint angles returned from IK were achieved\n '''\n try:\n jpos, data = self.mp.calculate_ik(pos, rot)\n except ProbitedHandPosition as e:\n if verbose:\n print(f\"[MOVE FAILED] {e}\")\n return False\n except UnsafeJointPosition as e:\n if verbose:\n print(f\"[MOVE FAILED] Target configuration would result in collision\"\n \" of robot:{e.robot_link} and {e.other_body}.\")\n return False\n\n return self.move_arm_jpos(jpos)\n\n def open_gripper(self):\n '''Opens gripper completely\n\n Returns\n -------\n bool\n True if desired gripper state was achieved\n '''\n return self.set_gripper_state(GRIPPER_OPENED)\n\n def close_gripper(self):\n '''Closes gripper completely\n\n Returns\n -------\n bool\n True if desired gripper state was achieved\n '''\n return self.set_gripper_state(GRIPPER_CLOSED)\n\n def set_gripper_state(self, state):\n '''Get state of gripper\n\n Parameters\n ----------\n state : float\n gripper state to move to; must be in range [0,1]\n\n Returns\n -------\n bool\n True if desired gripper state was achieved\n '''\n assert 0 <= state <= 1\n jpos = self.controller.gripper_state_to_jpos(state)\n\n self.controller.move_command(self.controller.gripper_joint_idxs, jpos)\n success, achieved_jpos = self.controller.monitor(self.controller.gripper_joint_idxs,\n jpos)\n\n achieved_gripper_state = self.controller.gripper_jpos_to_state(achieved_jpos)\n self.mp.mirror(gripper_state=achieved_gripper_state)\n return success\n\n def get_gripper_state(self):\n '''Get state of gripper\n\n Returns\n -------\n float\n value in range [0,1] describing how close to open(1) or closed(0)\n the gripper is\n '''\n jpos = self.controller.read_command(self.controller.gripper_joint_idxs)\n jpos = np.mean(jpos)\n state = self.controller.gripper_jpos_to_state(jpos)\n return state\n\n def move_with_gui(self):\n '''Use interface to control positions of robot's joints\n\n Gui does not implement collision checking at the moment\n '''\n def move_joint_fn_generator(j_idx):\n def move_joint_fn(jpos):\n jpos = float(jpos)\n self.controller.move_command([j_idx], [jpos], speed='normal')\n return move_joint_fn\n\n def move_gripper_fn(state):\n state = float(state)\n gripper_jpos = self.controller.gripper_state_to_jpos(state)\n self.controller.move_command(self.controller.gripper_joint_idxs,\n gripper_jpos,\n speed='normal')\n\n def go_home_fn():\n self.controller.home()\n [scl.set(jp) for scl, jp in zip(scales, self.controller.arm_jpos_home)]\n\n H,W = 500, 300\n window = tk.Tk()\n heading = tk.Label(text=\"CAUTION!\\nCollision detection is not running.\\nMotors are active so do not move by hand.\",\n fg=\"#FF0000\")\n heading.pack()\n\n main_frame = tk.Frame(master=window, width=W, height=H)\n main_frame.pack(fill=tk.BOTH)\n\n # add scales for arm joints\n scales = []\n for j_idx, j_name in zip(self.controller.arm_joint_idxs, self.joint_names):\n row_frame = tk.Frame(master=main_frame, width=W,\n height=H//7, borderwidth=1)\n row_frame.pack(fill=tk.X)\n row_frame.pack_propagate(0)\n\n col_frame_left = tk.Frame(master=row_frame, width=W//3)\n col_frame_left.pack(side=tk.LEFT)\n\n col_frame_right = tk.Frame(master=row_frame, width=2*W//3)\n col_frame_right.pack(side=tk.RIGHT)\n\n lbl_joint = tk.Label(master=col_frame_left, text=j_name)\n lbl_joint.pack()\n\n move_joint_fn = move_joint_fn_generator(j_idx)\n scl_joint = tk.Scale(master=col_frame_right,\n from_=self.controller.joint_limits[j_idx][0],\n to=self.controller.joint_limits[j_idx][1],\n resolution=self.controller.joint_precision,\n orient=tk.HORIZONTAL,\n length=2*W//3,\n command=move_joint_fn)\n scl_joint.pack()\n scl_joint.pack_propagate(0)\n scales.append(scl_joint)\n\n arm_jpos = self.get_arm_jpos()\n [scl.set(jp) for scl, jp in zip(scales, arm_jpos)]\n\n #gripper\n row_frame = tk.Frame(master=main_frame, width=W,\n height=H//7, borderwidth=1)\n row_frame.pack(fill=tk.X)\n row_frame.pack_propagate(0)\n col_frame_left = tk.Frame(master=row_frame, width=W//3)\n col_frame_left.pack(side=tk.LEFT)\n col_frame_right = tk.Frame(master=row_frame, width=2*W//3)\n col_frame_right.pack(side=tk.RIGHT)\n\n tk.Label(master=col_frame_left, text=\"gripper state\").pack()\n\n scl_gripper = tk.Scale(master=col_frame_right,\n from_=0,\n to=1,\n resolution=0.1,\n orient=tk.HORIZONTAL,\n length=2*W//3,\n command=move_gripper_fn)\n scl_gripper.pack()\n scl_gripper.pack_propagate(0)\n scl_gripper.set(self.get_gripper_state())\n\n # home button\n row_frame = tk.Frame(master=main_frame, width=W,\n height=H//7, borderwidth=1)\n row_frame.pack(fill=tk.X)\n btn_go_home = tk.Button(row_frame, text=\"Go to HOME position\",\n fg=\"#0000FF\", command = go_home_fn)\n btn_go_home.pack()\n\n window.mainloop()\n\n def _mirror_planner(self):\n self.mp.mirror(arm_jpos=self.get_arm_jpos(),\n gripper_state=self.get_gripper_state())\n","sub_path":"neu_ro_arm/robot/robot_arm.py","file_name":"robot_arm.py","file_ext":"py","file_size_in_byte":11249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"308473854","text":"from flask import Blueprint\nfrom flask import Flask, request, jsonify\nimport bcrypt\nfrom flask_cors import CORS\nfrom .. import models\nfrom datetime import datetime, timedelta\nimport json\nfrom flask_jwt_extended import (JWTManager, jwt_required, create_access_token,\n get_jwt_identity, unset_jwt_cookies, create_refresh_token)\nfrom ast import literal_eval\n\nbp = Blueprint('user_relations', __name__, url_prefix='/')\n\nmydb = models.client['medical'] # db name\n\n@bp.route('/user-relations', methods=['POST'])\ndef graphSend():\n\n body = literal_eval(request.get_json()['body'])\n userid = body['userid']\n\n x_list = [userid]\n m_list = []\n y_list = []\n result = {'nodes': [], 'links': []}\n\n for m in mydb['community_post'].find({'userid': userid}):\n m_list.append(m['postingid'])\n for x in mydb['comments_post'].find({'postingid': m['postingid']}):\n x_list.append(x['userid'])\n for x in x_list:\n for y in mydb['community_post'].find({'userid': x}):\n for t in mydb['comments_post'].find({\"postingid\": y['postingid']}):\n result['links'].append({ 'source': x, 'target': t['userid'] })\n\n \n\n for x in x_list:\n usercheck=models.User.query.filter_by(id=x).first()\n userprofilecheck=models.Userprofile.query.filter_by(userid=x).first()\n result['nodes'].append({ 'id': x , 'nickname':usercheck.nickname, 'src':userprofilecheck.profilephotourl})\n result['links'].append({ 'source': userid, 'target': x })\n \n\n\n\n return jsonify({\"data\": result})\n\n\ndef forPostAlgorithm():\n body = literal_eval(request.get_json()['body'])\n userid = body['userid']\n\n x_list = [userid]\n m_list = []\n\n for m in mydb['community_post'].find({'userid': userid}):\n m_list.append(m['postingid'])\n for x in mydb['comments_post'].find({'postingid': m['postingid']}):\n x_list.append(x['userid'])\n\n return x_list","sub_path":"medical/views/user_relations.py","file_name":"user_relations.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"559338968","text":"from tkinter import *\r\nimport sqlite3\r\nimport os\r\nimport tkinter as tk\r\nfrom tkinter import filedialog\r\nfrom tkinter import messagebox\r\nimport csv\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\nimport nltk\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.datasets import load_files\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.neural_network import MLPClassifier\r\nnltk.download('stopwords')\r\nimport pickle\r\nfrom nltk.corpus import stopwords\r\n##########################################################################################################\r\n### Machine Learning Algos\r\n\r\ndef naive_bayes_final():\r\n print(\"Naive Bayes Solution\")\r\n print(\"\\n\")\r\n movie_data = load_files(r\"D:\\Data\\Desktop\\ptechnosoft projects\\txt_sentoken\")\r\n X, y = movie_data.data, movie_data.target\r\n documents = []\r\n\r\n from nltk.stem import WordNetLemmatizer\r\n\r\n stemmer = WordNetLemmatizer()\r\n\r\n for sen in range(0, len(X)):\r\n # Remove all the special characters\r\n document = re.sub(r'\\W', ' ', str(X[sen]))\r\n\r\n # remove all single characters\r\n document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Remove single characters from the start\r\n document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Substituting multiple spaces with single space\r\n document = re.sub(r'\\s+', ' ', document, flags=re.I)\r\n\r\n # Removing prefixed 'b'\r\n document = re.sub(r'^b\\s+', '', document)\r\n\r\n # Converting to Lowercase\r\n document = document.lower()\r\n\r\n # Lemmatization\r\n document = document.split()\r\n\r\n document = [stemmer.lemmatize(word) for word in document]\r\n document = ' '.join(document)\r\n\r\n documents.append(document)\r\n from sklearn.feature_extraction.text import CountVectorizer\r\n\r\n vectorizer = CountVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = vectorizer.fit_transform(documents).toarray()\r\n\r\n from sklearn.feature_extraction.text import TfidfTransformer\r\n tfidfconverter = TfidfTransformer()\r\n X = tfidfconverter.fit_transform(X).toarray()\r\n from sklearn.feature_extraction.text import TfidfVectorizer\r\n\r\n tfidfconverter = TfidfVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = tfidfconverter.fit_transform(documents).toarray()\r\n from sklearn.model_selection import train_test_split\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\r\n classifier = GaussianNB()\r\n classifier.fit(X_train, y_train)\r\n y_pred = classifier.predict(X_test)\r\n from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\r\n print(\"Printing the results of the Naive Bayes\")\r\n print(\"\\n\")\r\n print(confusion_matrix(y_test, y_pred))\r\n print(classification_report(y_test, y_pred))\r\n print(accuracy_score(y_test, y_pred))\r\n with open('text_classifier', 'wb') as picklefile:\r\n pickle.dump(classifier, picklefile)\r\n with open('text_classifier', 'rb') as training_model:\r\n model = pickle.load(training_model)\r\n y_pred2 = model.predict(X_test)\r\n\r\n\r\ndef ANN_final():\r\n print(\"ANN Solution\")\r\n print(\"\\n\")\r\n movie_data = load_files(r\"D:\\Data\\Desktop\\ptechnosoft projects\\txt_sentoken\")\r\n X, y = movie_data.data, movie_data.target\r\n documents = []\r\n\r\n from nltk.stem import WordNetLemmatizer\r\n\r\n stemmer = WordNetLemmatizer()\r\n\r\n for sen in range(0, len(X)):\r\n # Remove all the special characters\r\n document = re.sub(r'\\W', ' ', str(X[sen]))\r\n\r\n # remove all single characters\r\n document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Remove single characters from the start\r\n document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Substituting multiple spaces with single space\r\n document = re.sub(r'\\s+', ' ', document, flags=re.I)\r\n\r\n # Removing prefixed 'b'\r\n document = re.sub(r'^b\\s+', '', document)\r\n\r\n # Converting to Lowercase\r\n document = document.lower()\r\n\r\n # Lemmatization\r\n document = document.split()\r\n\r\n document = [stemmer.lemmatize(word) for word in document]\r\n document = ' '.join(document)\r\n\r\n documents.append(document)\r\n from sklearn.feature_extraction.text import CountVectorizer\r\n\r\n vectorizer = CountVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = vectorizer.fit_transform(documents).toarray()\r\n\r\n from sklearn.feature_extraction.text import TfidfTransformer\r\n tfidfconverter = TfidfTransformer()\r\n X = tfidfconverter.fit_transform(X).toarray()\r\n from sklearn.feature_extraction.text import TfidfVectorizer\r\n\r\n tfidfconverter = TfidfVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = tfidfconverter.fit_transform(documents).toarray()\r\n from sklearn.model_selection import train_test_split\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\r\n classifier = MLPClassifier(alpha=1, max_iter=1000)\r\n classifier.fit(X_train, y_train)\r\n y_pred = classifier.predict(X_test)\r\n from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\r\n print(\"Printing the results of the ANN\")\r\n print(\"\\n\")\r\n print(confusion_matrix(y_test, y_pred))\r\n print(classification_report(y_test, y_pred))\r\n print(accuracy_score(y_test, y_pred))\r\n #def text_box():\r\n #from tkinter import *\r\n #root = Tk() \r\n #T = Text(root, height=2, width=30) \r\n #T.pack() \r\n #T.insert(END,accuracy_score(y_test, y_pred) ) \r\n #mainloop() \r\n\r\n with open('text_classifier', 'wb') as picklefile:\r\n pickle.dump(classifier, picklefile)\r\n with open('text_classifier', 'rb') as training_model:\r\n model = pickle.load(training_model)\r\n y_pred2 = model.predict(X_test)\r\n\r\n\r\ndef SVM_final():\r\n print(\"SVM Solution\")\r\n print(\"\\n\")\r\n movie_data = load_files(r\"D:\\Data\\Desktop\\ptechnosoft projects\\txt_sentoken\")\r\n X, y = movie_data.data, movie_data.target\r\n documents = []\r\n\r\n from nltk.stem import WordNetLemmatizer\r\n\r\n stemmer = WordNetLemmatizer()\r\n\r\n for sen in range(0, len(X)):\r\n # Remove all the special characters\r\n document = re.sub(r'\\W', ' ', str(X[sen]))\r\n\r\n # remove all single characters\r\n document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Remove single characters from the start\r\n document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document)\r\n\r\n # Substituting multiple spaces with single space\r\n document = re.sub(r'\\s+', ' ', document, flags=re.I)\r\n\r\n # Removing prefixed 'b'\r\n document = re.sub(r'^b\\s+', '', document)\r\n\r\n # Converting to Lowercase\r\n document = document.lower()\r\n\r\n # Lemmatization\r\n document = document.split()\r\n\r\n document = [stemmer.lemmatize(word) for word in document]\r\n document = ' '.join(document)\r\n\r\n documents.append(document)\r\n from sklearn.feature_extraction.text import CountVectorizer\r\n\r\n vectorizer = CountVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = vectorizer.fit_transform(documents).toarray()\r\n\r\n from sklearn.feature_extraction.text import TfidfTransformer\r\n tfidfconverter = TfidfTransformer()\r\n X = tfidfconverter.fit_transform(X).toarray()\r\n from sklearn.feature_extraction.text import TfidfVectorizer\r\n\r\n tfidfconverter = TfidfVectorizer(max_features=1500, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\r\n X = tfidfconverter.fit_transform(documents).toarray()\r\n from sklearn.model_selection import train_test_split\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\r\n classifier = MLPClassifier(alpha=1, max_iter=1000)\r\n classifier.fit(X_train, y_train)\r\n y_pred = classifier.predict(X_test)\r\n from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\r\n print(\"Printing the results of the SVM\")\r\n print(\"\\n\")\r\n print(confusion_matrix(y_test, y_pred))\r\n print(classification_report(y_test, y_pred))\r\n print(accuracy_score(y_test, y_pred))\r\n with open('text_classifier', 'wb') as picklefile:\r\n pickle.dump(classifier, picklefile)\r\n with open('text_classifier', 'rb') as training_model:\r\n model = pickle.load(training_model)\r\n y_pred2 = model.predict(X_test)\r\n \r\n\r\n\r\n\r\n\r\n\r\n##########################################################################################################\r\n\r\n\r\nroot = Tk()\r\nroot.title(\"Log in or Register to Perpetuuiti\")\r\n\r\nwidth = 640\r\nheight = 480\r\nscreen_width = root.winfo_screenwidth()\r\nscreen_height = root.winfo_screenheight()\r\nx = (screen_width/2) - (width/2)\r\ny = (screen_height/2) - (height/2)\r\nroot.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\nroot.resizable(0, 0)\r\n\r\n################################################################################################################################\r\n##MY CODE\r\n\r\n#global filename\r\n#global string\r\n\r\n#from tkMessageBox import *\r\n#global flag\r\n\r\n##def create_list(filename):\r\n## top = Tk() \r\n## Lb = Listbox(top) \r\n## Lb.insert(1, 'Python') \r\n## Lb.insert(2, 'Java') \r\n## Lb.insert(3, 'C++') \r\n## Lb.insert(4, 'Any other') \r\n## Lb.pack() \r\n## top.mainloop() \r\n\r\n#root.geometry(\"100x100\")\r\ndef open_screen_allowed():\r\n global screen10\r\n screen10 = Tk()\r\n screen10.title(\"Open screen allowed\")\r\n frame = Frame(screen10) \r\n frame.pack() \r\n bottomframe = Frame(screen10) \r\n bottomframe.pack( side = BOTTOM )\r\n ourMessage ='You are allowed to select the file'\r\n messageVar = Message(screen10, text = ourMessage) \r\n messageVar.config(bg='lightgreen') \r\n messageVar.pack(side = BOTTOM)\r\n\r\n\r\ndef open_screen_notallowed():\r\n global screen11\r\n screen11 = Tk()\r\n screen11.title(\"Open screen not allowed\")\r\n frame = Frame(screen11) \r\n frame.pack() \r\n bottomframe = Frame(screen11) \r\n bottomframe.pack( side = BOTTOM )\r\n ourMessage ='You are not allowed to select the file'\r\n messageVar = Message(screen11, text = ourMessage) \r\n messageVar.config(bg='lightgreen') \r\n messageVar.pack(side = BOTTOM)\r\n\r\n\r\ndef file_display_csv():\r\n delete_screen_10()\r\n messagebox.showinfo(\"File selection\", \"You have selected a .CSV file\")\r\n apply_solution()\r\n\r\ndef file_display_xlsx():\r\n delete_screen_10()\r\n messagebox.showinfo(\"File selection\", \"You have selected a .XLSX file\")\r\n apply_solution()\r\n\r\ndef file_display_txt():\r\n delete_screen_10()\r\n messagebox.showinfo(\"File selection\", \"You have selected a .TXT file\")\r\n apply_solution()\r\n\r\ndef file_not_allowed_csv():\r\n delete_screen_11()\r\n messagebox.showinfo(\"File selection\", \"You have not selected a .CSV file\")\r\n \r\ndef file_not_allowed_xlsx():\r\n delete_screen_11()\r\n messagebox.showinfo(\"File selection\", \"You have not selected a .XLSX file\")\r\n\r\ndef file_not_allowed_txt():\r\n delete_screen_11()\r\n messagebox.showinfo(\"File selection\", \"You have not selected a .TXT file\")\r\n\r\n\r\n#def delete_screen_8():\r\n #screen8.destroy()\r\n\r\ndef delete_screen_9():\r\n screen9.destroy()\r\n\r\ndef delete_screen_10():\r\n screen10.destroy()\r\n\r\ndef delete_screen_11():\r\n screen11.destroy()\r\n\r\ndef delete_root():\r\n root.destroy()\r\n \r\ndef file_type_csv():\r\n #delete_root()\r\n root.destroy()\r\n delete_screen_9()\r\n #Tk.destroy()\r\n filename = filedialog.askopenfilename()\r\n print('Selected:', filename)\r\n #print(len(filename))\r\n length = len(filename)\r\n str1 = \"csv\"\r\n len2 = length-3\r\n flag = 0\r\n for i in range(length-3,length,1):\r\n if(str1[i-len2]==filename[i]):\r\n #print(\"Allowed\")\r\n flag = flag+1\r\n else:\r\n flag = 0\r\n if(flag == 3):\r\n print(\"Allowed\")\r\n open_screen_allowed()\r\n file_display_csv()\r\n print('Selected:', filename)\r\n #global screen11\r\n #screen_final = Tk()\r\n #screen_final.title(\"Your selected .CSV file is:-\")\r\n## with open(filename, newline=\"\") as file:\r\n## reader = csv.reader(file)\r\n##\r\n## # r and c tell us where to grid the labels\r\n## r = 0\r\n## for col in reader:\r\n## c = 0\r\n## for row in col:\r\n## # i've added some styling\r\n## label = Label(screen_final, width=10, height=2, text=row, relief=RIDGE)\r\n## label.grid(row=r, column=c)\r\n## c += 1\r\n## r += 1\r\n #delete_root()\r\n else:\r\n print(\"Not allowed\")\r\n open_screen_notallowed()\r\n #delete_root()\r\n file_not_allowed_csv()\r\n #print(string)\r\n\r\ndef file_type_xlsx():\r\n #delete_root()\r\n #Tk.destroy()\r\n root.destroy()\r\n delete_screen_9()\r\n filename = filedialog.askopenfilename()\r\n print('Selected:', filename)\r\n length = len(filename)\r\n str1 = \"xlsx\"\r\n len2 = length-4\r\n flag = 0\r\n for i in range(length-4,length,1):\r\n if(str1[i-len2]==filename[i]):\r\n # print(\"Allowed\")\r\n flag = flag+1\r\n else:\r\n flag = 0\r\n if(flag == 4):\r\n print(\"Allowed\")\r\n open_screen_allowed()\r\n file_display_xlsx()\r\n \r\n #global screen11\r\n #screen_final = Tk()\r\n #screen_final.title(\"Your selected .XLSX file is:-\")\r\n## with open(filename, newline=\"\") as file:\r\n## reader = csv.reader(file)\r\n##\r\n## # r and c tell us where to grid the labels\r\n## r = 0\r\n## for col in reader:\r\n## c = 0\r\n## for row in col:\r\n## # i've added some styling\r\n## label = Label(screen_final, width=10, height=2, text=row, relief=RIDGE)\r\n## label.grid(row=r, column=c)\r\n## c += 1\r\n## r += 1\r\n else:\r\n print(\"Not allowed\")\r\n open_screen_notallowed()\r\n file_not_allowed_xlsx()\r\n\r\ndef file_type_txt():\r\n #delete_root()\r\n root.destroy()\r\n delete_screen_9()\r\n filename = filedialog.askopenfilename()\r\n print('Selected:', filename)\r\n length = len(filename)\r\n str1 = \"txt\"\r\n len2 = length-3\r\n flag = 0\r\n for i in range(length-3,length,1):\r\n if(str1[i-len2]==filename[i]):\r\n #print(\"Allowed\")\r\n flag = flag+1\r\n else:\r\n flag = 0\r\n if(flag == 3):\r\n print(\"Allowed\")\r\n open_screen_allowed()\r\n file_display_txt()\r\n #open_screen_allowed()\r\n## global screen11\r\n## screen_final = Tk()\r\n## screen_final.title(\"Your selected .TXT file is:-\")\r\n #f = open(filename,'r')\r\n \r\n## with open(filename, newline=\"\") as file:\r\n## reader = csv.reader(file)\r\n##\r\n## # r and c tell us where to grid the labels\r\n## r = 0\r\n## for col in reader:\r\n## c = 0\r\n## for row in col:\r\n## # i've added some styling\r\n## label = Label(screen_final, width=10, height=2, text=row, relief=RIDGE)\r\n## label.grid(row=r, column=c)\r\n## c += 1\r\n## r += 1\r\n## \r\n else:\r\n print(\"Not allowed\")\r\n open_screen_notallowed()\r\n file_not_allowed_txt()\r\n\r\ndef exit_menu():\r\n messagebox.showinfo(\"Exit Message\", \"Do you want to really exit\")\r\n exit();\r\n\r\n\r\ndef newListbox():\r\n global screen9\r\n #delete_screen_8()\r\n #delete_root()\r\n screen9 = Tk()\r\n screen9.title(\"File Selection\")\r\n v = IntVar(value = 1)\r\n label4 = Label(root, text=\"Choose type of File\", width=20, font=(\"bold\", 10))\r\n label4.pack()\r\n Radiobutton(screen9, text='.csv File', variable=v, value=1,tristatevalue=0, command = file_type_csv).pack() \r\n Radiobutton(screen9, text='.xlsx File', variable=v, value=1,tristatevalue=0, command = file_type_xlsx).pack()\r\n Radiobutton(screen9, text='.txt File', variable=v, value=1,tristatevalue=0, command = file_type_txt).pack()\r\n button = tk.Button(screen9,text = 'EXIT',height=\"2\", width=\"30\", command = exit_menu)\r\n button.pack()\r\n #print(value)\r\n #root.destroy()\r\n mainloop()\r\n\r\n \r\n \r\n\r\n\r\ndef main():\r\n #delete_root()\r\n global screen8\r\n screen8 = Tk()\r\n screen8.title(\"Project selection\")\r\n #button = tk.Button(screen8, text = 'Open Project', height=\"2\", width=\"30\",command = newListbox)\r\n #button.pack(side = LEFT)\r\n #Label(text=\" amit \").pack()\r\n root.mainloop()\r\n #w = RadioButton()\r\n\r\n\r\n#main()\r\n#root.destroy()\r\n\r\n\r\n\r\n\r\ndef apply_solution():\r\n action()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n################################################################################################################################\r\n\r\n#=======================================VARIABLES=====================================\r\nUSERNAME = StringVar()\r\nPASSWORD = StringVar()\r\nFIRSTNAME = StringVar()\r\nLASTNAME = StringVar()\r\n\r\n#=======================================METHODS=======================================\r\ndef UploadAction():\r\n\r\n global filename\r\n filename = filedialog.askopenfilename()\r\n print('Selected:', filename)\r\n\r\ndef algorithm_selection_messsage():\r\n screen7.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the algorithm based solution\")\r\n\r\n\r\ndef neural_network_selection_message():\r\n screen7.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the neural networks based solution\")\r\n\r\ndef algo_type_NaiveBayes():\r\n screen_algo.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the Naive Bayes based Algorithm\")\r\n naive_bayes_final()\r\n\r\n\r\ndef algo_type_SVM():\r\n screen_algo.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the SVM based Algorithm\")\r\n SVM_final()\r\n\r\n\r\ndef ann():\r\n screen_nn.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the ANN\")\r\n ANN_final()\r\n \r\n\r\ndef cnn():\r\n screen_nn.destroy()\r\n messagebox.showinfo(\"Solution selection\", \"You have selected the CNN\")\r\n \r\ndef radio_for_algo():\r\n global screen_algo\r\n screen_algo = Tk()\r\n screen_algo.title(\"Algorithm Selection\")\r\n v = IntVar(value = 1)\r\n label4 = Label(screen_algo, text=\"Choose type of Algorithm\", width=20, font=(\"bold\", 10))\r\n label4.pack()\r\n Radiobutton(screen_algo, text='NAIVE BAYES', variable=v, value=1,tristatevalue=0, command = algo_type_NaiveBayes).pack() \r\n Radiobutton(screen_algo, text='SVM', variable=v, value=1,tristatevalue=0, command = algo_type_SVM).pack()\r\n #Radiobutton(screen9, text='.txt File', variable=v, value=1,tristatevalue=0, command = file_type_txt).pack()\r\n button = tk.Button(screen_algo,text = 'EXIT',height=\"2\", width=\"30\", command = exit_menu)\r\n button.pack()\r\n #print(value)\r\n #root.destroy()\r\n mainloop()\r\n \r\n\r\ndef radio_for_nn():\r\n global screen_nn\r\n screen_nn = Tk()\r\n screen_nn.title(\"Neural Networks Selection\")\r\n v = IntVar(value = 1)\r\n label4 = Label(screen_nn, text=\"Choose type of Neural Network\", width=20, font=(\"bold\", 10))\r\n label4.pack()\r\n Radiobutton(screen_nn, text='ANN', variable=v, value=1,tristatevalue=0, command = ann).pack() \r\n Radiobutton(screen_nn, text='CNN', variable=v, value=1,tristatevalue=0, command = cnn).pack()\r\n #Radiobutton(screen9, text='.txt File', variable=v, value=1,tristatevalue=0, command = file_type_txt).pack()\r\n button = tk.Button(screen_algo,text = 'EXIT',height=\"2\", width=\"30\", command = exit_menu)\r\n button.pack()\r\n #print(value)\r\n #root.destroy()\r\n mainloop()\r\n \r\n\r\n\r\ndef action():\r\n global screen7\r\n screen7 = Tk()\r\n screen7.title(\"ACTION\")\r\n screen7.geometry(\"1920x1080\")\r\n button = tk.Button(screen7, text=\"1.Algorithm based solution\", command=radio_for_algo)\r\n button.pack()\r\n button = tk.Button(screen7, text=\"2.Neural Network based solution\", command=radio_for_nn)\r\n button.pack()\r\n #global word\r\n #global svm\r\n #word = str(input(\"Enter whether 1.Algorithm based solution or 2.Neural Network based solution\"))\r\n #if word == \"1\":\r\n # word2 = str(input(\"Enter whether 1.SVM or 2.NAIVE BAYES\"))\r\n # if word2 == \"1\":\r\n## # IMPORTING DATASET\r\n## dataset = pd.read_csv(filename)\r\n## x = dataset.iloc[:, [2, 3]].values\r\n## y = dataset.iloc[:, 4].values\r\n##\r\n## # Splitting Data into Training & Testing\r\n## from sklearn.cross_validation import train_test_split\r\n## x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=0)\r\n##\r\n## # Feature Scaling\r\n## from sklearn.preprocessing import StandardScaler\r\n## sc_x = StandardScaler()\r\n## x_train = sc_x.fit_transform(x_train)\r\n## x_test = sc_x.transform(x_test)\r\n##\r\n## # Fitting SVM to the Training set\r\n## from sklearn.svm import SVC\r\n## classifier = SVC(kernel='rbf', random_state=0)\r\n## # C = Penalty Parameter\r\n## # Kernel = RBF, LINEAR, POLY, SIGMOID\r\n## # Degree = If you choose POLY\r\n## classifier.fit(x_train, y_train)\r\n##\r\n## # Predicting the Test set results\r\n## y_pred = classifier.predict(x_test)\r\n##\r\n## # for i in range(len(y_pred)):\r\n## # print(x_test[i, :], y_pred[i])\r\n##\r\n## else:\r\n## # IMPORTING DATASET\r\n## dataset = pd.read_csv(filename)\r\n## x = dataset.iloc[:, [2, 3]].values\r\n## y = dataset.iloc[:, 4].values\r\n##\r\n## # Splitting Data into Training & Testing\r\n## from sklearn.cross_validation import train_test_split\r\n## x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=0)\r\n##\r\n## # Feature Scaling\r\n## from sklearn.preprocessing import StandardScaler\r\n## sc_x = StandardScaler()\r\n## x_train = sc_x.fit_transform(x_train)\r\n## x_test = sc_x.transform(x_test)\r\n##\r\n## # Fitting Logistic Regression to the Training set\r\n## from sklearn.naive_bayes import GaussianNB\r\n## classifier = GaussianNB()\r\n## classifier.fit(x_train, y_train)\r\n##\r\n## # Predicting the Test set results\r\n## y_pred = classifier.predict(x_test)\r\n##\r\n## # for i in range(len(y_pred)):\r\n## # print(x_test[i, :], y_pred[i])\r\n##\r\n# else:\r\n # print(\"go again\")\r\n\r\n\r\ndef new_window():\r\n #screen.destroy()\r\n screen3.destroy()\r\n #home.destroy()\r\n Label(text=\"\").pack()\r\n global root\r\n root = Tk()\r\n root.title(\"PERPETUUITI MAIN PAGE\")\r\n Label(text=\"\").pack()\r\n width = 700\r\n height = 700\r\n screen_width = root.winfo_screenwidth()\r\n screen_height = root.winfo_screenheight()\r\n x = (screen_width / 2) - (width / 2)\r\n y = (screen_height / 2) - (height / 2)\r\n root.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n root.resizable(0, 0)\r\n label4 = Label(root, text=\"Choose File\", width=20, font=(\"bold\", 10))\r\n label4.pack()\r\n button = tk.Button(root, text='NEW PROJECT', height=\"2\", width=\"30\", command=newListbox)\r\n button.pack()\r\n Label(text=\"\").pack()\r\n\r\n \r\ndef delete1():\r\n screen1.destroy()\r\n\r\n\r\ndef delete2():\r\n screen3.destroy()\r\n\r\n\r\ndef delete3():\r\n screen4.destroy()\r\n\r\n\r\ndef delete4():\r\n screen5.destroy()\r\n\r\n\r\ndef login_success():\r\n #screen2.destroy()\r\n # log_btn.pack_forget()\r\n # reg_btn.pack_forget()\r\n global screen3\r\n screen3 = Toplevel(root)\r\n screen3.title(\"Success\")\r\n screen3.geometry(\"800x600\")\r\n Label(screen3, text=\"Login Success\").pack()\r\n # Button(screen3, text=\"OK\", command=delete2).pack()\r\n Button(screen3, text=\"Continue\", command=new_window).pack()\r\n\r\n\r\ndef Database():\r\n global conn, cursor\r\n conn = sqlite3.connect(\"db_member.db\")\r\n cursor = conn.cursor()\r\n cursor.execute(\"CREATE TABLE IF NOT EXISTS `member` (mem_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, username TEXT, password TEXT, firstname TEXT, lastname TEXT)\")\r\n\r\n\r\ndef Exit():\r\n result = tk.MessageBox.askquestion('System', 'Are you sure you want to exit?', icon=\"warning\")\r\n if result == 'yes':\r\n root.destroy()\r\n exit()\r\n\r\n\r\ndef LoginForm():\r\n global LoginFrame, lbl_result1\r\n LoginFrame = Frame(root)\r\n LoginFrame.pack(side=TOP, pady=80)\r\n lbl_username = Label(LoginFrame, text=\"Username:\", font=('arial', 25), bd=18)\r\n lbl_username.grid(row=1)\r\n lbl_password = Label(LoginFrame, text=\"Password:\", font=('arial', 25), bd=18)\r\n lbl_password.grid(row=2)\r\n lbl_result1 = Label(LoginFrame, text=\"\", font=('arial', 18))\r\n lbl_result1.grid(row=3, columnspan=2)\r\n username = Entry(LoginFrame, font=('arial', 20), textvariable=USERNAME, width=15)\r\n username.grid(row=1, column=1)\r\n password = Entry(LoginFrame, font=('arial', 20), textvariable=PASSWORD, width=15, show=\"*\")\r\n password.grid(row=2, column=1)\r\n btn_login = Button(LoginFrame, text=\"Login\", font=('arial', 18), width=35, command=Login)\r\n btn_login.grid(row=4, columnspan=2, pady=20)\r\n lbl_register = Label(LoginFrame, text=\"Register\", fg=\"Blue\", font=('arial', 12))\r\n lbl_register.grid(row=0, sticky=W)\r\n lbl_register.bind('', ToggleToRegister)\r\n\r\ndef RegisterForm():\r\n global RegisterFrame, lbl_result2\r\n RegisterFrame = Frame(root)\r\n RegisterFrame.pack(side=TOP, pady=40)\r\n lbl_username = Label(RegisterFrame, text=\"Username:\", font=('arial', 18), bd=18)\r\n lbl_username.grid(row=1)\r\n lbl_password = Label(RegisterFrame, text=\"Password:\", font=('arial', 18), bd=18)\r\n lbl_password.grid(row=2)\r\n lbl_firstname = Label(RegisterFrame, text=\"Firstname:\", font=('arial', 18), bd=18)\r\n lbl_firstname.grid(row=3)\r\n lbl_lastname = Label(RegisterFrame, text=\"Lastname:\", font=('arial', 18), bd=18)\r\n lbl_lastname.grid(row=4)\r\n lbl_result2 = Label(RegisterFrame, text=\"\", font=('arial', 18))\r\n lbl_result2.grid(row=5, columnspan=2)\r\n username = Entry(RegisterFrame, font=('arial', 20), textvariable=USERNAME, width=15)\r\n username.grid(row=1, column=1)\r\n password = Entry(RegisterFrame, font=('arial', 20), textvariable=PASSWORD, width=15, show=\"*\")\r\n password.grid(row=2, column=1)\r\n firstname = Entry(RegisterFrame, font=('arial', 20), textvariable=FIRSTNAME, width=15)\r\n firstname.grid(row=3, column=1)\r\n lastname = Entry(RegisterFrame, font=('arial', 20), textvariable=LASTNAME, width=15)\r\n lastname.grid(row=4, column=1)\r\n btn_login = Button(RegisterFrame, text=\"Register\", font=('arial', 18), width=35, command=Register)\r\n btn_login.grid(row=6, columnspan=2, pady=20)\r\n lbl_login = Label(RegisterFrame, text=\"Login\", fg=\"Blue\", font=('arial', 12))\r\n lbl_login.grid(row=0, sticky=W)\r\n lbl_login.bind('', ToggleToLogin)\r\n\r\ndef ToggleToLogin(event=None):\r\n RegisterFrame.destroy()\r\n LoginForm()\r\n\r\ndef ToggleToRegister(event=None):\r\n LoginFrame.destroy()\r\n RegisterForm()\r\n\r\ndef Register():\r\n Database()\r\n if USERNAME.get == \"\" or PASSWORD.get() == \"\" or FIRSTNAME.get() == \"\" or LASTNAME.get == \"\":\r\n lbl_result2.config(text=\"Please complete the required field!\", fg=\"orange\")\r\n else:\r\n cursor.execute(\"SELECT * FROM `member` WHERE `username` = ?\", (USERNAME.get(),))\r\n if cursor.fetchone() is not None:\r\n lbl_result2.config(text=\"Username is already taken\", fg=\"red\")\r\n else:\r\n cursor.execute(\"INSERT INTO `member` (username, password, firstname, lastname) VALUES(?, ?, ?, ?)\", (str(USERNAME.get()), str(PASSWORD.get()), str(FIRSTNAME.get()), str(LASTNAME.get())))\r\n conn.commit()\r\n USERNAME.set(\"\")\r\n PASSWORD.set(\"\")\r\n FIRSTNAME.set(\"\")\r\n LASTNAME.set(\"\")\r\n lbl_result2.config(text=\"Successfully Created!\", fg=\"black\")\r\n cursor.close()\r\n conn.close()\r\ndef Login():\r\n Database()\r\n if USERNAME.get == \"\" or PASSWORD.get() == \"\":\r\n lbl_result1.config(text=\"Please complete the required field!\", fg=\"orange\")\r\n else:\r\n cursor.execute(\"SELECT * FROM `member` WHERE `username` = ? and `password` = ?\", (USERNAME.get(), PASSWORD.get()))\r\n if cursor.fetchone() is not None:\r\n lbl_result1.config(text=\" Successful Login\", fg=\"blue\")\r\n login_success()\r\n else:\r\n lbl_result1.config(text=\"Invalid Username or password\", fg=\"red\")\r\nLoginForm()\r\n\r\n#========================================MENUBAR WIDGETS==================================\r\nmenubar = Menu(root)\r\nfilemenu = Menu(menubar, tearoff=0)\r\nfilemenu.add_command(label=\"Exit\", command=Exit)\r\nmenubar.add_cascade(label=\"File\", menu=filemenu)\r\nroot.config(menu=menubar)\r\n\r\n\r\n#========================================INITIALIZATION===================================\r\nif __name__ == '__main__':\r\n \r\n root.mainloop()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"projectFinalInitial.py","file_name":"projectFinalInitial.py","file_ext":"py","file_size_in_byte":29413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"226708906","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 11 08:11:12 2019\n\n@author: Stefan Draghici\n\"\"\"\n\nfrom threading import Thread, Lock\n\nclass BookMyBus:\n def __init__(self, available_seats):\n self. available_seats= available_seats\n self.lock=Lock()\n \n def buy(self, seats_requested):\n self.lock.acquire()\n if(self.available_seats>seats_requested):\n print('Confirming a seat')\n print('Processing payment')\n print('Printing a ticket')\n self.available_seats-=seats_requested\n else:\n print('No sets available')\n self.lock.release()\n \nobj=BookMyBus(5)\n\nt1=Thread(target=obj.buy, args=(3,))\nt2=Thread(target=obj.buy, args=(1,))\nt3=Thread(target=obj.buy, args=(4,))\nt1.start()\nt2.start()\nt3.start()","sub_path":"threads/threads2.py","file_name":"threads2.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"8951419","text":"#\n# Project: MXCuBE\n# https://github.com/mxcube.\n#\n# This file is part of MXCuBE software.\n#\n# MXCuBE 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 3 of the License, or\n# (at your option) any later version.\n#\n# MXCuBE 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 MXCuBE. If not, see .\n\nimport os\nimport logging\n\nfrom PyQt4 import QtCore\nfrom PyQt4 import QtGui\nfrom PyQt4 import uic\n\nfrom widgets.Qt4_data_path_widget import DataPathWidget\nfrom widgets.Qt4_acquisition_widget import AcquisitionWidget\nfrom widgets.Qt4_widget_utils import DataModelInputBinder\nfrom widgets.Qt4_processing_widget import ProcessingWidget\n\nfrom BlissFramework.Utils import Qt4_widget_colors\nfrom BlissFramework import Qt4_Icons\n\n\n__category__ = 'Qt4_TaskToolbox_Tabs'\n\n\nclass DCParametersWidget(QtGui.QWidget):\n def __init__(self, parent = None, name = \"parameter_widget\"):\n\n QtGui.QWidget.__init__(self, parent)\n if name is not None:\n self.setObjectName(name) \n\n # Properties ----------------------------------------------------------\n\n # Signals -------------------------------------------------------------\n\n # Slots ---------------------------------------------------------------\n\n # Hardware objects ----------------------------------------------------\n self._beamline_setup_hwobj = None\n\n # Internal variables --------------------------------------------------\n self._data_collection = None\n self.add_dc_cb = None\n self._tree_view_item = None\n\n # Graphic elements ----------------------------------------------------\n _dc_parameters_widget = QtGui.QWidget(self)\n self._data_path_widget = DataPathWidget(_dc_parameters_widget)\n self._acq_widget = AcquisitionWidget(_dc_parameters_widget, \n layout = 'horizontal')\n #self._acq_widget.setFixedHeight(170)\n self._processing_widget = ProcessingWidget(_dc_parameters_widget)\n _snapshot_widget = QtGui.QWidget(self)\n self.position_widget = uic.loadUi(os.path.join(os.path.dirname(__file__),\n 'ui_files/Qt4_snapshot_widget_layout.ui'))\n # LNLS\n self.position_widget.setFixedSize(450, 340)\n\n # Layout --------------------------------------------------------------\n _dc_parameters_widget_layout = QtGui.QVBoxLayout(_dc_parameters_widget)\n _dc_parameters_widget_layout.addWidget(self._data_path_widget)\n _dc_parameters_widget_layout.addWidget(self._acq_widget)\n _dc_parameters_widget_layout.addWidget(self._processing_widget)\n _dc_parameters_widget_layout.setContentsMargins(0, 0, 0, 0)\n _dc_parameters_widget_layout.setSpacing(2)\n _dc_parameters_widget_layout.addStretch(0)\n\n _snapshots_vlayout = QtGui.QVBoxLayout(_snapshot_widget)\n _snapshots_vlayout.addWidget(self.position_widget)\n _snapshots_vlayout.setContentsMargins(0, 0, 0, 0)\n _snapshots_vlayout.setSpacing(2)\n # LNLS\n # _snapshots_vlayout.addStretch(10)\n _snapshots_vlayout.addStretch(0)\n _snapshot_widget.setLayout(_snapshots_vlayout)\n # ----\n\n _main_hlayout = QtGui.QHBoxLayout(self)\n _main_hlayout.addWidget(_dc_parameters_widget)\n _main_hlayout.addWidget(_snapshot_widget)\n _main_hlayout.setContentsMargins(0, 0, 0, 0)\n _main_hlayout.setSpacing(2)\n _main_hlayout.addStretch(0)\n\n # SizePolicies --------------------------------------------------------\n \n\n # Qt signal/slot connections ------------------------------------------\n self._data_path_widget.data_path_layout.prefix_ledit.textChanged.connect(\n self._prefix_ledit_change)\n self._data_path_widget.data_path_layout.run_number_ledit.textChanged.connect( \n self._run_number_ledit_change)\n\n self._acq_widget.madEnergySelectedSignal.connect(self.mad_energy_selected)\n self._acq_widget.acqParametersChangedSignal.connect(\\\n self.acq_parameters_changed)\n\n # Other ---------------------------------------------------------------\n\n def set_beamline_setup(self, bl_setup):\n self._acq_widget.set_beamline_setup(bl_setup)\n self._beamline_setup_hwobj = bl_setup\n\n def _prefix_ledit_change(self, new_value):\n prefix = self._data_collection.acquisitions[0].\\\n path_template.get_prefix()\n self._data_collection.set_name(prefix)\n self._tree_view_item.setText(0, self._data_collection.get_name())\n\n def _run_number_ledit_change(self, new_value):\n if str(new_value).isdigit():\n self._data_collection.set_number(int(new_value))\n self._tree_view_item.setText(0, self._data_collection.get_name())\n\n def acq_parameters_changed(self):\n if self._tree_view_item is None:\n #TODO fix this\n return \n dc_tree_widget = self._tree_view_item.listView().parent()\n dc_tree_widget.check_for_path_collisions()\n path_template = self._data_collection.acquisitions[0].path_template\n path_conflict = self.queue_model_hwobj.\\\n check_for_path_collisions(path_template)\n if new_value != '':\n if path_conflict:\n logging.getLogger(\"user_level_log\").\\\n error('The current path settings will overwrite data' +\\\n ' from another task. Correct the problem before collecting')\n\n widget.setPaletteBackgroundColor(widget_colors.LIGHT_RED)\n else:\n widget.setPaletteBackgroundColor(widget_colors.WHITE)\n\n def __add_data_collection(self):\n return self.add_dc_cb(self._data_collection, self.collection_type)\n \n def mad_energy_selected(self, name, energy, state):\n path_template = self._data_collection.acquisitions[0].path_template\n\n if state:\n path_template.mad_prefix = name\n else:\n path_template.mad_prefix = ''\n\n run_number = self._beamline_setup_hwobj.queue_model_hwobj.\\\n get_next_run_number(path_template)\n\n self._data_path_widget.set_run_number(run_number)\n self._data_path_widget.set_prefix(path_template.base_prefix)\n model = self._tree_view_item.get_model()\n model.set_name(path_template.get_prefix())\n self._tree_view_item.setText(0, model.get_name())\n \n def tab_changed(self):\n if self._tree_view_item:\n self.populate_parameter_widget(self._tree_view_item)\n\n def set_enabled(self, state):\n self._acq_widget.setEnabled(state)\n self._data_path_widget.setEnabled(state)\n self._processing_widget.setEnabled(state)\n\n def populate_widget(self, item):\n data_collection = item.get_model()\n self._tree_view_item = item\n self._data_collection = data_collection\n self._acquisition_mib = DataModelInputBinder(self._data_collection.\\\n acquisitions[0].acquisition_parameters)\n # The acq_widget sends a signal to the path_widget, and it relies\n # on that both models upto date, we need to refactor this part\n # so that both models are set before taking ceratin actions.\n # This workaround, works for the time beeing.\n self._data_path_widget._data_model = data_collection.acquisitions[0].path_template\n\n self._acq_widget.set_energies(data_collection.crystal.energy_scan_result)\n self._acq_widget.update_data_model(data_collection.acquisitions[0].\\\n acquisition_parameters,\n data_collection.acquisitions[0].\\\n path_template)\n self._data_path_widget.update_data_model(data_collection.\\\n acquisitions[0].path_template)\n \n self._processing_widget.update_data_model(data_collection.\\\n processing_parameters)\n\n if data_collection.acquisitions[0].acquisition_parameters.\\\n centred_position.snapshot_image:\n image = data_collection.acquisitions[0].\\\n acquisition_parameters.centred_position.snapshot_image\n # LNLS\n #ration = image.height() / float(image.width())\n #image = image.scaled(450, 360 * ration, QtCore.Qt.KeepAspectRatio)\n image = image.scaled(450, 360, QtCore.Qt.KeepAspectRatio)\n self.position_widget.svideo.setPixmap(QtGui.QPixmap(image))\n\n invalid = self._acquisition_mib.validate_all()\n\n if invalid:\n msg = \"This data collection has one or more incorrect parameters,\"+\\\n \" correct the fields marked in red to solve the problem.\"\n\n logging.getLogger(\"user_level_log\").\\\n warning(msg)\n","sub_path":"Bricks/widgets/Qt4_dc_parameters_widget.py","file_name":"Qt4_dc_parameters_widget.py","file_ext":"py","file_size_in_byte":9417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"174579502","text":"# -*- coding: utf-8 -*- \nimport unicodecsv\nimport codecs\nfrom itertools import takewhile, izip\n\n# def removeNonAscii(s): return \"\".join(filter(lambda x: ord(x)<128, s))\ndef get_common_prefix(strings):\n b = zip(*strings)\n c = [x[0] for x in b if x==(x[0],)*len(x)]\n result = \"\".join(c)\n # result = ''.join(c[0] for c in takewhile(lambda x: all(x[0] == y for y in x), izip(*strings)))\n return result\n\ndata_file = u'data/Chandas छन्दः - अर्धसम.csv'\nout_file = u'data/ardhasama_prefix.csv'\n\nwith open(data_file, 'r') as csvfile, codecs.open(out_file, 'w', 'utf-8') as outfile:\n chandas_reader = unicodecsv.reader(csvfile, encoding='utf-8')\n # chandas_reader = csvfile.readlines()\n for chandas in chandas_reader:\n # chandas = chandas.decode(\"utf-8\").split(',')\n # chandas = chandas.split(',')\n # print chandas\n # prefix = get_common_prefix([chandas[3].decode(\"utf-8\"), chandas[5].decode(\"utf-8\")])\n prefix = get_common_prefix([chandas[3], chandas[5]])\n outfile.write(prefix + '\\n')\n","sub_path":"src/main/python/chandas_relation/ardhasama_common_suffix.py","file_name":"ardhasama_common_suffix.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"193790800","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 1 13:56:33 2020\n\n@author: hossein\n\"\"\"\n\nfrom torchreid import models , data , optim , engine\nimport torch\nfrom collections import OrderedDict\nimport time\nimport numpy as np\n# from sklearn.metrics.pairwise import cosine_similarity\n\n#%%\ndef my_load_pretrain(model1 , pretrain_path):\n state_dict = torch.load(pretrain_path)\n model_dict = model1.state_dict()\n new_state_dict = OrderedDict()\n \n matched_layers, discarded_layers = [], []\n for k, v in state_dict.items(): # state dict is our loaded weights\n if k.startswith('module.'):\n k = k[7:] # discard module.\n if k in model_dict and model_dict[k].size() == v.size():\n new_state_dict[k] = v\n matched_layers.append(k)\n else:\n discarded_layers.append(k)\n \n model_dict.update(new_state_dict)\n model1.load_state_dict(model_dict) \n \n if len(matched_layers) == 0:\n print(\n 'The pretrained weights from \"{}\" cannot be loaded, '\n 'please check the key names manually '\n '(** ignored and continue **)'\n ) \n return model1\n\n#%%\ndatamanager = data.ImageDataManager(\n root='reid-data',\n sources='market1501',\n targets='market1501',\n height=256,\n width=128,\n batch_size_train=10,\n batch_size_test=2,\n transforms=['random_flip', 'random_crop']\n)\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n#%%\npretrain_path = '/home/hossein/anaconda3/envs/torchreid/osnet_ain_x1_0_msmt17_256x128_amsgrad_ep50_lr0.0015_coslr_b64_fb10_softmax_labsmth_flip_jitter.pth'\n\nmodel = models.build_model(\n name='osnet_ain_x1_0',\n num_classes=datamanager.num_train_pids,\n loss='softmax',\n pretrained=False\n)\nnew_model = my_load_pretrain(model , pretrain_path = pretrain_path)\nnew_model.to(device)\n\nmodel2 = models.build_model(\n name='osnet_ain_x1_0',\n num_classes=datamanager.num_train_pids,\n loss='softmax',\n pretrained=True\n)\n\nnew_model2 = my_load_pretrain(model2 , pretrain_path = pretrain_path)\nnew_model2.to(device)\n#%%\n\npretrain_path = '/home/hossein/anaconda3/envs/torchreid/osnet_ain_x1_0_msmt17_256x128_amsgrad_ep50_lr0.0015_coslr_b64_fb10_softmax_labsmth_flip_jitter.pth'\n\nmodel = models.build_model(\n name='osnet_ain_x1_0',\n num_classes=datamanager.num_train_pids,\n loss='softmax',\n pretrained=False\n)\nnew_model = my_load_pretrain(model , pretrain_path = pretrain_path)\nnew_model.to(device)\n \n#%%\nnew_state_dict = new_model.state_dict()\nkeys = []\nfor name in new_state_dict:\n keys.append(name)\nfor idx, m in enumerate(new_model.children()):\n print(idx, '->', m) \n\n#%%\ndef feature_model(model):\n new_model1 = torch.nn.Sequential(*list(model.children())[:-2])\n return new_model1\n\nfeat_model = feature_model(new_model)\nfeat_model2 = feature_model(model2)\nfeat_model1 = torch.nn.Sequential(*list(new_model.children())[:-4])\nfeat_model.eval()\nfeat_model2.eval()\nfeat_model1.eval()\n#%%\nnew_state_dict = feat_model1.state_dict()\nkeys = []\nfor name in new_state_dict:\n keys.append(name)\nfor idx, m in enumerate(new_model.children()):\n print(idx, '->', m) \n\n#%%\n'''\n testing our feature model that does give us \n feature vectors (stright from convolution network)\n'''\n\ntest_dataset = datamanager.test_dataset['market1501']['query'] \ntest_loader = datamanager.test_loader['market1501']['query'] \nfor img in test_loader:\n break # img is an dictionary that camera id image and people ids for 100 people (batch size)\nstart = time.time()\ninput_images = img['img']\nout_put = feat_model(input_images) # pretrained on Market1501\nfinish = time.time()\nprint('the time of getting output for batch size of {} is:'.format(input_images.size()),finish - start) # 3 seconds for batch size of 20\nprint(out_put.size())\n\nstart = time.time()\nout_put2 = feat_model2(input_images) # pretrained for Image-Net\nfinish = time.time()\nprint('the time of getting output for batch size of {} is:'.format(input_images.size()[0]),finish - start) # 3 seconds for batch size of 20\nprint(out_put2.size())\n#%%\n\n\nprint(out_put.size()) # (5,512,1,1)\nout_put = torch.Tensor.squeeze(out_put)\nprint(out_put.size()) # (5,512,1,1) (5,512)\nb = out_put[:,4:]\nprint(b.size()) # (5,508)\npids = img['pid'][:5]\nprint(pids) #(1255 , 1438 , 319 , 1347 , 96)\n#some how eliminating two last layer worked and now we have feature extractor output\n\n#%%\n'''\nextracting features for training set to be as source data\n\n'''\ntrain_loader = datamanager.train_loader\nparser = engine.Engine\ndef _feature_extraction(data_loader):\n start = time.time()\n f_, pids_ = [], []\n for batch_idx, data1 in enumerate(train_loader):\n imgs, pids = data1['img'] , data1['pid'] \n\n features = feat_model(imgs)\n features = torch.Tensor.squeeze(features)\n features = features.data.cpu()\n f_.append(features)\n pids_.extend(pids)\n f_ = torch.cat(f_, 0)\n pids_ = np.asarray(pids_)\n finish = time.time()\n print('the time of getting output whole training_set:',finish - start) \n return f_, pids_\n\nf, pids = _feature_extraction(train_loader)\nfeates = np.asarray(f)\nfeatures_save_path = '/home/hossein/anaconda3/envs/torchreid/train_market1501_osnet_ain_x1_0_features.npy'\npids_save_path = '/home/hossein/anaconda3/envs/torchreid/train_market1501_pids.npy'\nnp.save(features_save_path , feates)\nnp.save(pids_save_path , pids)\n#%%\ntest_loader = datamanager.test_loader['market1501']['query'] \nsource_features = np.load(features_save_path )\nfor query in test_loader:\n \n start = time.time()\n img , pids = query['img'] , query['pid']\n features = feat_model(img)\n features = torch.Tensor.squeeze(features)\n features = features.data.cpu()\n features = np.asarray(features)\n similarity = cosine_similarity(source_features,features)\n \n \n \n \n\n#%%\n'''\n calculating SI for each subset of output features\n \n'''\n\n#%%\noptimizer = optim.build_optimizer(\n model,\n optim='adam',\n lr=0.0003\n)\n\n#%%\nscheduler = optim.build_lr_scheduler(\n optimizer,\n lr_scheduler='single_step',\n stepsize=20\n)\n\n#%%\nengine = engine.ImageSoftmaxEngine(\n datamanager,\n new_model,\n optimizer=optimizer,\n scheduler=scheduler,\n label_smooth=True\n)\n\n#%%\nengine.run(\n save_dir='log/resnet50',\n max_epoch=60,\n eval_freq=10,\n print_freq=10,\n test_only=True,\n visrank = True\n)\n\n#%%\nimport torch.nn as nn\nm = nn.AdaptiveAvgPool2d(7)\ninput = torch.randn(1, 64, 10, 9)\noutput = m(input)\nprint(input.size(),output.size())\n\n#%%\ninput1 = torch.randn(5,20,1)\ninput2 = torch.randn(5,20,1)\no = torch.cat((input1,input2),1)","sub_path":"my_osnet/my_load_model.py","file_name":"my_load_model.py","file_ext":"py","file_size_in_byte":6706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"25249829","text":"import sys\nsys.path.append(\"/Users/mirali/dev/CS101\")\n\nfrom cs101_libraries_py35.cs1graphics import *\n# from cs1graphics import *\nfrom time import sleep\n\n_scene = None\n_world = None\n\nt = 0.2\n\ndef create_world():\n global _scene, _world\n if _scene:\n raise RuntimeError(\"A world already exists!\")\n _world = _World(500, 300)\n _scene = Canvas(_world.width, _world.height)\n _scene.setTitle(\"Mario World\")\n _world.draw_scene()\n\nclass _World(object):\n def __init__(self, width, height):\n self.width = width\n self.height = height\n\n def draw_scene(self):\n \"\"\"\n draw background here\n Don't forget _scene.add(name)\n \"\"\"\n grass = Rectangle(1000, 150, Point(250, 250))\n grass.setFillColor('green')\n grass.setDepth(100)\n _scene.add(grass)\n\n #blocks\n block = Rectangle(40, 40, Point(200, 100))\n block.setFillColor('brown')\n\n qmark = Text(\"?\", 20, Point(200, 100))\n qmark.setFontColor('Yellow')\n qmark.setDepth(48)\n _scene.add(qmark)\n\n block2 = block.clone()\n block2.move(40, 0)\n block.setDepth(50)\n _scene.add(block)\n _scene.add(block2)\n\n #pipe\n pipe = Polygon(Point(400, 150), Point(400, 160), Point(410, 160), Point(410, 320), Point(470, 320), Point(470, 160), Point(480, 160), Point(480, 150))\n pipe.setFillColor('lightgreen')\n pipe.setDepth(10)\n pipe.move(-10, 0)\n _scene.add(pipe)\n\n\nclass Mushroom(object):\n def __init__(self, x=200, y=92):\n mushroom = Layer()\n uppermush = Ellipse(38, 18, Point(x, y))\n uppermush.setFillColor('red')\n uppermush.setDepth(52)\n lowermush = Ellipse(35, 25, Point(x, y+8))\n lowermush.setFillColor('beige')\n lowermush.setDepth(53)\n mushroom.add(lowermush)\n mushroom.add(uppermush)\n mushroom.setDepth(52)\n\n self.layer = mushroom\n _scene.add(self.layer)\n\n def diappear(self):\n self.layer.scale(0.001)\n\n def move(self, x, y):\n self.layer.move(x, y)\n\n def arise(self):\n self.layer.setDepth(45)\n self.layer.move(0, -20)\n\n\n\nCOLOR = ['Red', 'Blue']\nTYPE = ['super', 'normal']\n\nclass Mario(object):\n def __init__(self, color='Blue', type='normal'):\n assert type in TYPE and color in COLOR\n self.color = color\n self.type = type\n self.step_size = 3\n\n # Constructing Mario\n mario = Layer()\n # body\n body = Rectangle(33, 22, Point(200, 200))\n body.setFillColor(color)\n body.setDepth(50)\n mario.add(body)\n\n # face\n face = Ellipse(30, 20, Point(200, 180))\n face.setFillColor('beige')\n face.setDepth(40)\n mario.add(face)\n\n #hat\n hat = Polygon(Point(185, 175), Point(220, 175), Point(220, 173), Point(215, 173), Point(212, 168), Point(188, 168))\n hat.setFillColor(color)\n hat.setDepth(39)\n mario.add(hat)\n\n #beard\n beard = Polygon(Point(207, 183), Point(217, 183), Point(215, 180), Point(209, 180))\n beard.setFillColor('Brown')\n beard.setDepth(38)\n mario.add(beard)\n\n shoe = Layer()\n #left shoe\n lshoe = Rectangle(15, 6, Point(191, 215))\n lshoe.setFillColor('black')\n lshoe.setDepth(52)\n shoe.add(lshoe)\n\n #right shoe\n rshoe = lshoe.clone()\n rshoe.move(17, 0)\n shoe.add(rshoe)\n mario.add(shoe)\n\n # save alias of moveable parts\n self.layer = mario\n self.body = body\n self.hat = hat\n self.shoe = shoe\n _scene.add(self.layer)\n\n self.moving_part_count = 0\n\n if type == 'super':\n self.supermario()\n\n\n def shoe_move(self):\n\n if self.moving_part_count % 3 == 0:\n self.shoe.move(3, 0)\n elif self.moving_part_count % 3 == 1:\n self.shoe.move(-5,0)\n else: self.shoe.move(2,0)\n self.moving_part_count += 1\n if self.moving_part_count % 3 == 0: self.moving_part_count = 0\n\n def move(self,x=10,y=0):\n self.layer.move(x,y)\n\n\n def supermario(self):\n tempPt = self.body.getReferencePoint()\n self.layer.adjustReference(tempPt.getX(), tempPt.getY())\n for i in range(3):\n self.layer.scale(1.3)\n sleep(t/2)\n self.layer.scale(0.9)\n sleep(t/2)\n\n def walk(self,x=20):\n assert x > 0\n total_step = int(x / self.step_size)\n for i in range(total_step):\n sleep(t/4)\n self.move(self.step_size, 0)\n self.shoe_move()\n\ndef show_animation():\n sleep(t)\n mario.move(0, -50)\n mushroom.arise()\n\n sleep(t)\n mario.move(0, 50)\n mushroom.move(0, 8)\n\n for i in range(7):\n sleep(t/2)\n mushroom.move(10, 0)\n mario.move(10, 0)\n mario.shoe_move()\n sleep(t/2)\n mario.shoe_move()\n\n sleep(t/2)\n mushroom.move(0, 50)\n mario.move(10, 0)\n mario.shoe_move()\n sleep(t/2)\n mario.shoe_move()\n\n sleep(t)\n mushroom.move(0, 50)\n\n sleep(t/2)\n mushroom.diappear()\n sleep(t/2)\n mario.supermario()\n\n for i in range(6):\n sleep(t/2)\n mario.move(10, 0)\n mario.shoe_move()\n sleep(t/2)\n mario.shoe_move()\n\n for i in range(2):\n sleep(t)\n mario.move(28, -60)\n\n for i in range(1):\n sleep(t)\n mario.move(32, 40)\n\n sleep(2*t)\n for i in range(4):\n sleep(t)\n mario.move(0, 25)\n \ndef interactive_example():\n while True:\n e = _scene.wait()\n d = e.getDescription()\n if d == \"keyboard\":\n k = e.getKey()\n if k == \"q\":\n _scene.close()\n break\n elif k == \"w\":\n mario.walk(20)\n elif k == \"r\":\n mario.walk(40)\n elif k == \"j\":\n mario.move(0, -50)\n sleep(t)\n mario.move(0, 50)\n\ncreate_world()\nmario = Mario('Blue', 'normal')\nmushroom = Mushroom(200, 92)\n\nshow_animation()\n# interactive_example()\n\n","sub_path":"lab10/marioworld.py","file_name":"marioworld.py","file_ext":"py","file_size_in_byte":6162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"174317610","text":"from smtplib import SMTP\nfrom email.message import Message\n\ndef send_mail(from_addr, to_addr, subject, body):\n\n msg = Message()\n msg['Subject'] = subject\n msg['From'] = from_addr\n msg['To'] = to_addr if isinstance(to_addr, str) else ', '.join(to_addr)\n msg.add_header('Content-Type', 'text/html')\n msg.set_payload(body)\n\n smtp = SMTP('localhost')\n smtp.sendmail(from_addr, to_addr, msg.as_string())\n smtp.quit()\n","sub_path":"python/send_mail.py","file_name":"send_mail.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"592985473","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nZetCode PyQt4 tutorial \n\nIn this example, we create a simple\nwindow in PyQt4.\n\nauthor: Jan Bodnar\nwebsite: zetcode.com \nlast edited: October 2011\n\"\"\"\n\nimport sys\nfrom PyQt4 import QtGui, QtCore\n\ndef changeBtn(btn):\n\tbtn.setText(\"hello\")\n\nclass application(QtGui.QWidget):\n\t\n\tdef __init__(self):\n\t\tsuper(application, self).__init__()\n\t\tqbtn = QtGui.QPushButton('Quit', self)\n\t\tqbtn.clicked.connect(lambda: changeBtn(qbtn))\n\t\tqbtn.resize(qbtn.sizeHint())\n\t\tqbtn.move(50, 50) \n\t\tself.setWindowTitle('Icon')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t \n\t\tself.show()\n\n\ndef main():\n \n app = QtGui.QApplication(sys.argv)\n\n w = application()\n w.setWindowTitle('Simple')\n \n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"basestation/qt-example.py","file_name":"qt-example.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"602013672","text":"from keras.preprocessing.image import ImageDataGenerator\nfrom build.randomNamemaker import randomName\nfrom tkinter import filedialog, messagebox\nfrom keras import preprocessing as pre\nfrom imutils.paths import list_images\nfrom PIL import Image, ImageTk\nimport tkinter.ttk as ttk\nimport tkinter as tk\nimport numpy as np\nimport pickle\nimport shutil\nimport random\nimport time\nimport PIL\nimport cv2\nimport os\nimport re\n\nclass canvas:\n def __init__(self):\n self.window = tk.Tk()\n self.window.title('augGo2')\n self.window.geometry('900x430+70+70')\n\n title = tk.Label(self.window, text = 'go2 with data augmentation')\n\n self.imgText = tk.StringVar()\n self.imgText.set('Loaded image')\n imageLb = tk.Label(self.window, textvariable = self.imgText, relief = 'solid')\n\n imageLb.place(y = 40, x = 460, width = 420, height = 20)\n title.place(y = 10, x = 150)\n\n self.genBtn = tk.Button(self.window, text = 'generate', command = self.generate)\n self.exitBtn = tk.Button(self.window, text = 'exit', command = self.EXIT)\n self.loadBtn = tk.Button(self.window, text = 'load files', command = self.loadImgs)\n self.applyBtn = tk.Button(self.window, text = 'apply', command = self.apply)\n\n self.loadBtn.place(y = 160, x = 20, width = 100)\n self.applyBtn.place(y = 160 ,x = 120, width = 100)\n self.genBtn.place(y = 160, x = 220, width = 100)\n self.exitBtn.place(y = 160, x = 320, width = 100)\n\n self.loadedPath = tk.StringVar()\n self.loadedPath.set('Image not loaded')\n loadPathlb = tk.Label(self.window, textvariable = self.loadedPath, relief = 'solid')\n loadPathlb.place(y = 200, x = 20, width = 400, height = 20)\n\n self.frame = tk.Frame(self.window, width = 270, height = 160, relief = 'solid', bd = 2)\n self.imgFrame = tk.Frame(self.window, width = 420, height = 350, relief = 'solid', bd = 2)\n self.frame.place(y = 230, x = 20)\n self.imgFrame.place(y = 70, x = 460)\n\n self.fileScrollH = tk.Scrollbar(self.frame)\n self.fileScrollH.pack(side = 'right', fill = 'y')\n self.fileListbox = tk.Listbox(self.frame, selectmode = 'single', width = 270, height = 160, yscrollcommand = self.fileScrollH.set)\n self.fileListbox.place(y = 220, x = 10)\n\n self.horizonCheck = tk.BooleanVar()\n self.verticalCheck = tk.BooleanVar()\n\n self.horizonCheck.set(False)\n self.verticalCheck.set(False)\n\n #* horizonCheck.get -> horizontalCheck flag recieve \n\n rotText = tk.StringVar()\n zoomText = tk.StringVar()\n widthText = tk.StringVar()\n heightText = tk.StringVar()\n shearText = tk.StringVar()\n noiText = tk.StringVar()\n\n self.horizon = tk.Checkbutton(self.window, text = ' horizontal flip', var = self.horizonCheck)\n self.vertical = tk.Checkbutton(self.window, text = ' vertical flip', var = self.verticalCheck)\n self.rotationRange = tk.Entry(self.window, justify = 'center', textvariable = rotText)\n self.zoomRange = tk.Entry(self.window, justify = 'center', textvariable = zoomText)\n self.widthRange = tk.Entry(self.window, justify = 'center', textvariable = widthText)\n self.heightRange = tk.Entry(self.window, justify = 'center', textvariable = heightText)\n self.shearRange = tk.Entry(self.window, justify = 'center', textvariable = shearText)\n self.numofImg = tk.Entry(self.window, justify = 'center', textvariable = noiText)\n\n rotationLb = tk.Label(self.window, text = 'rotation range')\n zoomLb = tk.Label(self.window, text = 'zoom range')\n widthLb = tk.Label(self.window, text = 'width shift range')\n heightLb = tk.Label(self.window, text = 'height shift range')\n shearLb = tk.Label(self.window, text = 'shear range')\n saveImgLb = tk.Label(self.window, text = '# of save image')\n\n rotText.set(0)\n zoomText.set(0.0)\n widthText.set(0.0)\n heightText.set(0.0)\n shearText.set(0.0)\n noiText.set(100)\n\n self.rotationRange.place(y = 70, x = 165, width = 50)\n rotationLb.place(y = 70, x = 30)\n\n self.shearRange.place(y = 70, x = 365, width = 50)\n shearLb.place(y = 70, x = 225)\n\n self.widthRange.place(y = 100, x = 165, width = 50)\n widthLb.place(y = 100, x = 30)\n\n self.heightRange.place(y = 100, x = 365, width = 50)\n heightLb.place(y = 100, x = 225)\n\n self.zoomRange.place(y = 130, x = 165, width = 50)\n zoomLb.place(y = 130, x = 30)\n\n self.numofImg.place(y = 130, x = 365, width = 50)\n saveImgLb.place(y = 130, x = 225)\n\n self.vertical.place(y = 40, x = 50)\n self.horizon.place(y = 40, x = 250)\n self.window.mainloop()\n\n def apply(self):\n try:\n testImg = self.imgs[0]\n print(type(testImg))\n\n except Exception as e:\n text = f'[ERR 2] {e} - images load first... :('\n self.log(text)\n\n print(text)\n messagebox.showerror('error occured', 'please load images')\n pass\n\n try:\n hori, ver, rot, zoom, width, height, shear = self.getParams()\n generator = ImageDataGenerator(rotation_range = rot, width_shift_range = width, height_shift_range= height,\n zoom_range=zoom, horizontal_flip=hori, vertical_flip=ver, shear_range=shear)\n\n testImg = pre.image.img_to_array(testImg)\n testImg = np.expand_dims(testImg, axis = 0)\n\n for (idx, batch) in enumerate(generator.flow(testImg, batch_size=1)):\n img = pre.image.array_to_img(batch[0])\n print(type(img))\n img.save(f'applied_{idx}.jpg')\n if idx % 1 == 0:\n break\n\n # image = PIL.Image.fromarray(img)\n image = ImageTk.PhotoImage(image = img)\n self.imgText.set('Applied image')\n\n firstImg = tk.Label(self.imgFrame, image = image)\n firstImg.pack(fill = 'both')\n\n text = '[INFO] apply comlete! :D'\n self.log(text)\n print(text)\n\n except Exception as e:\n text = f'[ERR 4] {e} - apply failed... :('\n self.log(text)\n\n print(text)\n pass\n\n def loadImgs(self):\n try:\n imgPath = filedialog.askdirectory(title = 'Select your Images', initialdir = './')\n self.imgPaths = list(sorted(list_images(imgPath)))\n self.showimgs = []\n self.imgs = []\n\n for img in self.imgPaths:\n print(img)\n image = cv2.imread(img)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n npimg = image.copy()\n\n image = PIL.Image.fromarray(image)\n image = ImageTk.PhotoImage(image = image)\n self.imgs.append(npimg)\n self.showimgs.append(image)\n\n firstImg = tk.Label(self.imgFrame, image = self.showimgs[0])\n firstImg.pack(fill = 'both')\n\n text = '[INFO] images load complete! :D'\n self.log(text)\n print(text)\n\n self.loadedPath.set(imgPath)\n except Exception as e:\n text = f'[ERR 1] {e} - invalid image directory... :('\n self.log(text)\n\n print(text)\n messagebox.showerror('error occured', 'invalid image directory')\n pass\n\n def generate(self):\n # try:\n savePath = filedialog.askdirectory(title = 'Select your save path')\n text = f'[INFO] selected {savePath} as savepath'\n print(f'[INFO] selected {savePath} as savepath')\n self.log(text)\n\n text = '[INFO] save path ready! :D'\n self.log(text)\n print(text)\n # try:\n noi = int(self.numofImg.get())\n # print(self.imgPaths)\n\n for imgPath in self.imgPaths:\n imPath = imgPath.split(os.path.sep)[:-1]\n imPath = '/'.join(imPath)\n\n nof = len(os.listdir(imPath))\n diff = noi - nof\n\n if diff < 0:\n try:\n print(diff)\n os.makedirs(f'./residue/{imgPath.split(os.path.sep)[-2]}', exist_ok=True)\n for _ in range(abs(diff)):\n random.seed(time.ctime())\n random.shuffle(self.imgPaths)\n shutil.move(imgPath, f'./residue/{imgPath.split(os.path.sep)[-2]}/{imgPath.split(os.path.sep)[-1]}')\n except:\n pass\n # except Exception as e:\n # text = f'[ERR 3] {e} - invalid parameters... :('\n # self.log(text)\n\n # print(text)\n # messagebox.showerror('error occured', 'invalid parameters included')\n # pass\n\n try:\n hori, ver, rot, zoom, width, height, shear = self.getParams()\n generator = ImageDataGenerator(rotation_range = rot, width_shift_range = width, height_shift_range= height,\n zoom_range=zoom, horizontal_flip=hori, vertical_flip=ver, shear_range=shear)\n\n for (idx, oriImg) in enumerate(self.imgs):\n\n imgPath = self.imgPaths[idx]\n dirName = imgPath.split(os.path.sep)[-2]\n os.makedirs(f'{savePath}/{dirName}', exist_ok=True)\n\n dirs = \"/\".join(imgPath.split(os.path.sep)[:-1])\n nof = len(os.listdir(dirs))\n print(dirName, nof)\n print(int(noi/nof))\n\n oriImg = pre.image.img_to_array(oriImg)\n oriImg = np.expand_dims(oriImg, axis = 0)\n cnt = int(noi / nof) + int(noi % nof) if (idx + 1) == nof else int(noi / nof)\n\n for (idx2, batch) in enumerate(generator.flow(oriImg, batch_size=1)):\n genImg = pre.image.array_to_img(batch[0])\n genImg.save(f'{savePath}/{dirName}/{randomName(13)}_{time.time()}_{randomName(12)}{idx2}.jpg')\n\n if (idx2+1) % cnt == 0:\n break\n \n text = '[INFO] generate ok! :D'\n self.log(text)\n print(text)\n\n except Exception as e:\n text = f'[ERR 5] {e} - generate failed... :('\n self.log(text)\n messagebox.showerror('error occured', 'generate failed...')\n\n print(text)\n pass\n\n # except Exception as e:\n # text = f'[ERR 6] {e} - invalid save path... :('\n # self.log(text)\n\n # print(text)\n # pass\n\n def getParams(self):\n try:\n hori = self.horizonCheck.get()\n ver = self.verticalCheck.get()\n\n rot = int(self.rotationRange.get())\n zoom = float(self.zoomRange.get())\n width = float(self.widthRange.get())\n height = float(self.heightRange.get())\n shear = float(self.shearRange.get())\n\n return hori, ver, rot, zoom, width, height, shear\n\n except Exception as e:\n text = f'[ERR 3] {e} - get params failed... :('\n self.log(text)\n\n print(text)\n messagebox.showerror('error occured', 'invalid argument included')\n pass\n \n def log(self, text):\n TIME = time.localtime(time.time())\n YYYY = TIME.tm_year\n MM = TIME.tm_mon\n DD = TIME.tm_mday\n H = TIME.tm_hour\n M = TIME.tm_min\n S = TIME.tm_sec\n nowTime = f'[{YYYY:04d}-{MM:02d}-{DD:02d} {H:02d}:{M:02d}:{S:02d}] - '\n\n os.makedirs('build/log', exist_ok=True)\n with open('build/log/logs.txt', 'a') as f:\n text = nowTime + text + '\\n'\n f.write(text)\n \n def EXIT(self):\n if messagebox.askokcancel('Quit', 'Do you want to quit?'):\n exit()\n\nif __name__ == '__main__':\n canvas()\n \n","sub_path":"UTILS/CLS/augImgsaver.py","file_name":"augImgsaver.py","file_ext":"py","file_size_in_byte":12006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"100211342","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\n\nimport MySQLdb as msd\nimport config as cf\n\nfrom items import CrawlspokeintelItem\n\n\nDB = cf.DATABASE\nTABLES = cf.TABLES\nTC = cf.TABLE_COMPANIES_COLS\n# HOST = 'localhost'\n# USER = 'root'\n# PASSWD = '976269'\n# DATABASE = 'companies'\n\n# TABLE_COMPANY = 'si_companies'\n# TABLE_MEMBERS = 'si_members'\n# TABLE_FUNDINGS = 'si_fundings'\n# TABLE_ACQUISITION = 'si_acquisitions'\n# TABLE_INVESTORS = 'si_investors'\n\n\nclass CrawlspokeintelPipeline(object):\n def __init__(self):\n self.conn = msd.connect(host=DB['host'], user=DB['user'], passwd=DB['passwd'], db=DB['database'])\n self.cur = self.conn.cursor()\n self._create_tables(self.cur, TABLES)\n\n\n def _create_tables(self, cur=None, tables=None):\n ct_dict = self._create_tables_str(tables)\n for k, v in ct_dict.iteritems():\n cur.execute(self._gen_drop_table_str(k))\n cur.execute(self._gen_create_table_str(k, v.keys(), v.values()))\n return\n\n\n def _create_tables_str(self, tables=None):\n tables_dict = {}\n excluded_keys = ('members',\n 'funding_history',\n 'funding_investors',\n 'investments')\n # create table si_companies\n col_names = [k for k in TC.values() if k not in excluded_keys]\n col_types_dict = dict.fromkeys(col_names, 'text')\n tables_dict[tables['table_companies_name']] = col_types_dict\n\n # map other 4 tables to their column names\n tables_dict[tables['table_members_name']] = dict.fromkeys(cf.TABLE_MEMBERS_COLS.values(), 'text')\n tables_dict[tables['table_fundings_name']] = dict.fromkeys(cf.TABLE_FUNDINGS_COLS.values(), 'text')\n tables_dict[tables['table_acquisitions_name']] = dict.fromkeys(cf.TABLE_ACQUISITIONS_COLS.values(), 'text')\n tables_dict[tables['table_investors_name']] = dict.fromkeys(cf.TABLE_INVESTORS_COLS.values(), 'text')\n\n return tables_dict\n\n\n def _gen_drop_table_str(self, table_name):\n ''' Generalized string generator for table dropping. '''\n return 'drop table if exists %s;'%table_name\n\n\n def _gen_create_table_str(self, table_name, col_names, col_types):\n ''' Generalized string generator for table creating. '''\n assert len(col_names) == len(col_types)\n name_type_str = ', '.join([' '.join(row) for row in zip(col_names, col_types)])\n new_str = 'create table %(tname)s (%(nstr)s);'%{'tname': table_name, 'nstr': name_type_str}\n return new_str\n\n def process_item(self, item, spider):\n return item\n\n\ndef main():\n ''' For testing. '''\n obj = CrawlspokeintelPipeline()\n # print obj._gen_create_table_str(TABLES['table_companies_name'], ('name', 'price'), ('text', 'text'))\n # print obj._gen_drop_table_str(TABLES['table_companies_name'])\n\n\nif __name__ == '__main__':\n main()","sub_path":"CrawlSpokeIntel/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"303880067","text":"from core import admin, teacher, student\n\ntag = True\n\nchoice_dict = {\n '1': admin.admin_main,\n '2': teacher.teacher_main,\n '3': student.student_main\n}\n\n\ndef run():\n global tag\n while tag:\n print(\n '''\n 1. 管理员视图\n 2. 教师视图\n 3. 学生视图\n q. 退出\n '''\n )\n choice = input('请选择：').strip()\n if choice == 'q':\n tag = False\n break\n if choice not in choice_dict: continue\n choice_dict[choice]()","sub_path":"core/src.py","file_name":"src.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"535781901","text":"#Luca Pinello 2018 @lucapinello\n\nfrom freshroastsr700 import *\nimport sys\nimport time\nimport logging\n\nfrom multiprocessing import Process, Value, Array\nfrom ctypes import c_bool\n\n\nfrom Phidget22.Devices.TemperatureSensor import *\nfrom Phidget22.PhidgetException import *\nfrom Phidget22.Phidget import *\nfrom freshroastsr700_phidget.PhidgetHelperFunctions import *\n\ntry:\n from freshroastsr700_phidget import max31865\n max31865_available=True\nexcept:\n max31865_available=False\n\n\nclass PhidgetTemperature(object):\n def __init__(self,hub_port=0,hub_channel=1,serial_number=-1,use_hub=False):\n\n self.use_hub=use_hub\n self.hub_port=hub_port\n self.hub_channel=hub_channel\n\n try:\n self.ch = TemperatureSensor()\n self.ch.setDeviceSerialNumber(serial_number)\n if use_hub:\n self.ch.setHubPort(hub_port)\n self.ch.setChannel(hub_channel)\n except PhidgetException as e:\n sys.stderr.write(\"Runtime Error -> Creating TemperatureSensor: \\n\\t\")\n DisplayError(e)\n self.ch.close()\n raise\n except RuntimeError as e:\n sys.stderr.write(\"Runtime Error -> Creating TemperatureSensor: \\n\\t\" + e)\n self.ch.close()\n raise\n\n logging.info(\"Phidget: Opening and Waiting for Attachment...\")\n\n try:\n self.ch.openWaitForAttachment(5000)\n except PhidgetException as e:\n PrintOpenErrorMessage(e, self.ch)\n self.ch.close()\n raise EndProgramSignal(\"Program Terminated: Open Failed\")\n time.sleep(1)\n logging.info(\"Phidget: Ready!\")\n\n def getTemperature(self,fahrenheit=False):\n\n\n if fahrenheit:\n return ( self.ch.getTemperature() * 9/5.0) + 32\n\n else:\n return self.ch.getTemperature()\n\n\n def closeConnection(self):\n return self.ch.close()\n\nclass max31865Temp(object):\n def __init__(self, **kwargs):\n self.max = max31865.max31865(**kwargs)\n\n def temp_f(self):\n return (self.max.readTemp() * 9.0/5.0) + 32.0\n\nclass SR700Phidget(freshroastsr700):\n\n def __init__(self,use_phidget_temp,\n phidget_use_hub=False,\n phidget_hub_port=0,\n phidget_hub_channel=4,\n use_max31865=False,\n max_31865_gpio_cs=8,\n max_31865_gpio_miso=9,\n max_31865_gpio_mosi=10,\n max_31865_gpio_clk=11,\n *args, **kwargs):\n\n self._current_temp_phidget=Value('d', 0.0)\n self._use_phidget_temp=Value(c_bool,use_phidget_temp)\n self._phidget_use_hub=Value(c_bool,phidget_use_hub)\n self._phidget_error=Value(c_bool,False)\n self._phidget_hub_channel=Value('i', phidget_hub_channel)\n self._phidget_hub_port=Value('i', phidget_hub_port)\n self._log_info=True\n self.bttemp = None\n self._use_max31865=Value(c_bool, use_max31865)\n self._current_temp_max31865=Value('d', 0.0)\n if use_max31865 and not max31865_available:\n raise Exception(\"Could not import max31865 from freshroastsr700_phidget, so max31865 not available\")\n if use_max31865:\n self.bttemp = max31865Temp(csPin=max_31865_gpio_cs,\n misoPin=max_31865_gpio_miso,\n mosiPin=max_31865_gpio_mosi,\n clkPin=max_31865_gpio_clk)\n\n try:\n super(SR700Phidget, self).__init__(*args, **kwargs)\n except:\n raise\n\n @property\n def log_info(self):\n return self._log_info\n\n @log_info.setter\n def log_info(self, value):\n self._log_info = value\n\n @property\n def target_temp(self):\n \"\"\"Get/Set the target temperature for this package's built-in software\n PID controler. Only used when freshroastsr700 is instantiated with\n thermostat=True.\n Args:\n Setter: value (int): a target temperature in degF between 120\n and 551.\n Returns:\n Getter: (int) target temperature in degF between 120\n and 551\n \"\"\"\n return self._target_temp.value\n\n @target_temp.setter\n def target_temp(self, value):\n if value not in range(120, 551):\n raise exceptions.RoasterValueError\n\n self._target_temp.value = value\n\n @property\n def phidget_error(self):\n\n return self._phidget_error.value\n\n @property\n def current_temp_max31865(self):\n\n return self._current_temp_max31865.value\n\n @current_temp_max31865.setter\n def current_temp_max31865(self, value):\n\n self._current_temp_max31865.value=value\n\n @property\n def current_temp_phidget(self):\n\n return self._current_temp_phidget.value\n\n @current_temp_phidget.setter\n def current_temp_phidget(self, value):\n\n self._current_temp_phidget.value=value\n\n def _create_update_data_system(\n self, update_data_func, setFunc=True, createThread=False):\n # these callbacks cannot be called from another process in Windows.\n # Therefore, spawn a thread belonging to the calling process\n # instead.\n # the comm and timer processes will set events that the threads\n # will listen for to initiate the callbacks\n\n # only create the mp.Event once -\n # to mimic create_state_transition_system, for future-proofing\n # (in this case, currently, this is only called at __init__() time)\n if not hasattr(self, 'update_data_event'):\n self.update_data_event = mp.Event()\n # only create the thread.Event once - this is used to exit\n # the callback thread\n if not hasattr(self, 'update_data_callback_kill_event'):\n self.update_data_callback_kill_event = mp.Event()\n # destroy an existing thread if we had created one previously\n if(hasattr(self, 'update_data_thread') and\n self.update_data_thread is not None):\n # let's tear this down. To kill it, two events must be set...\n # in the right sequence!\n self.update_data_callback_kill_event.set()\n self.update_data_event.set()\n self.update_data_thread.join()\n if setFunc:\n self.update_data_func = update_data_func\n if self.update_data_func is not None:\n if createThread:\n self.update_data_callback_kill_event.clear()\n self.update_data_thread = threading.Thread(\n name='sr700_update_data',\n target=self.update_data_run,\n args=(self.update_data_event,)\n )\n self.update_data_thread.daemon=True\n else:\n self.update_data_thread = None\n\n\n def _create_state_transition_system(\n self, state_transition_func, setFunc=True, createThread=False):\n # these callbacks cannot be called from another process in Windows.\n # Therefore, spawn a thread belonging to the calling process\n # instead.\n # the comm and timer processes will set events that the threads\n # will listen for to initiate the callbacks\n\n # only create the mp.Event once - this fn can get called more\n # than once, by __init__() and by set_state_transition_func()\n if not hasattr(self, 'state_transition_event'):\n self.state_transition_event = mp.Event()\n # only create the thread.Event once - this is used to exit\n # the callback thread\n if not hasattr(self, 'state_transition_callback_kill_event'):\n self.state_transition_callback_kill_event = mp.Event()\n # destroy an existing thread if we had created one previously\n if(hasattr(self, 'state_transition_thread') and\n self.state_transition_thread is not None):\n # let's tear this down. To kill it, two events must be set...\n # in the right sequence!\n self.state_transition_callback_kill_event.set()\n self.state_transition_event.set()\n self.state_transition_thread.join()\n if setFunc:\n self.state_transition_func = state_transition_func\n if self.state_transition_func is not None:\n if createThread:\n self.state_transition_callback_kill_event.clear()\n self.state_transition_thread = threading.Thread(\n name='sr700_state_transition',\n target=self.state_transition_run,\n args=(self.state_transition_event,)\n )\n self.state_transition_thread.daemon=True\n else:\n self.state_transition_thread = None\n\n def _comm(self, thermostat=False,\n kp=0.4, ki=0.0075, kd=0.9,\n heater_segments=8, ext_sw_heater_drive=False,\n update_data_event=None):\n \"\"\"Do not call this directly - call auto_connect(), which will spawn\n comm() for you.\n\n This is the main communications loop to the roaster.\n whenever a valid packet is received from the device, if an\n update_data_event is available, it will be signalled.\n\n Args:\n thermostat (bool): thermostat mode.\n if set to True, turns on thermostat mode. In thermostat\n mode, freshroastsr700 takes control of heat_setting and does\n software PID control to hit the demanded target_temp.\n\n ext_sw_heater_drive (bool): enable direct control over the internal\n heat_controller object. Defaults to False. When set to True, the\n thermostat field is IGNORED, and assumed to be False. Direct\n control over the software heater_level means that the\n PID controller cannot control the heater. Since thermostat and\n ext_sw_heater_drive cannot be allowed to both be True, this arg\n is given precedence over the thermostat arg.\n\n kp (float): Kp value to use for PID control. Defaults to 0.06.\n\n ki (float): Ki value to use for PID control. Defaults to 0.0075.\n\n kd (float): Kd value to use for PID control. Defaults to 0.01.\n\n heater_segments (int): the pseudo-control range for the internal\n heat_controller object. Defaults to 8.\n\n update_data_event (multiprocessing.Event): If set, allows the\n comm_process to signal to the parent process that new device data\n is available.\n\n Returns:\n nothing\n \"\"\"\n # since this process is started with daemon=True, it should exit\n # when the owning process terminates. Therefore, safe to loop forever.\n\n\n use_phidget_temp=self._use_phidget_temp.value\n use_max31865=self._use_max31865.value\n\n if use_phidget_temp:\n try:\n\n logging.info('Phidget: Inizializing Phidget...')\n\n if self._phidget_use_hub.value:\n logging.info('Phidget: Enabling hub mode.')\n\n ph=PhidgetTemperature(use_hub=self._phidget_use_hub.value,\n hub_port=self._phidget_hub_port.value,\n hub_channel=self._phidget_hub_channel.value)\n\n phidget_available=True\n logging.info('Using Phidget to control the roaster temp.')\n logging.info('SR700: PID - kp: %f ki: %f kd: %f)' % (kp,ki,kd))\n self._phidget_error.value=False\n except Exception as e:\n logging.error('Phidget: I cannot communicate with the Phidget device.')\n logging.error('Phidget: Try to reboot your machine and try again.')\n logging.error(e)\n self._phidget_error.value=True\n self._teardown.value=1\n\n elif use_max31865:\n phidget_available=False\n logging.info('Using max31865 to control the roaster temp.')\n logging.info('SR700: PID - kp: %f ki: %f kd: %f)' % (kp,ki,kd))\n\n else:\n phidget_available=False\n logging.info('Not using Phidget to control the roaster temp')\n logging.info('SR700: PID settings - kp: %f ki: %f kd: %f)' % (kp,ki,kd))\n\n while not self._teardown.value:\n\n logging.info('SR700: Starting SR700 Comm Process...')\n\n # waiting for command to attempt connect\n # print( \"waiting for command to attempt connect\")\n while self._attempting_connect.value == self.CA_NONE:\n time.sleep(0.25)\n if self._teardown.value:\n break\n # if we're tearing down, bail now.\n if self._teardown.value:\n break\n\n # we got the command to attempt to connect\n # change state to 'attempting_connect'\n self._connect_state.value = self.CS_ATTEMPTING_CONNECT\n # attempt connection\n if self.CA_AUTO == self._attempting_connect.value:\n # this call will block until a connection is achieved\n # it will also set _connect_state to CS_CONNECTING\n # if appropriate\n if self._auto_connect():\n # when we unblock, it is an indication of a successful\n # connection\n self._connected.value = 1\n self._connect_state.value = self.CS_CONNECTED\n else:\n # failure, normally due to a timeout\n self._connected.value = 0\n self._connect_state.value = self.CS_NOT_CONNECTED\n # we failed to connect - start over from the top\n # reset flag\n self._attempting_connect.value = self.CA_NONE\n continue\n\n elif self.CA_SINGLE_SHOT == self._attempting_connect.value:\n # try once, now, if failure, start teh big loop over\n try:\n self._connect()\n self._connected.value = 1\n self._connect_state.value = self.CS_CONNECTED\n except exceptions.RoasterLookupError:\n self._connected.value = 0\n self._connect_state.value = self.CS_NOT_CONNECTED\n if self._connect_state.value != self.CS_CONNECTED:\n # we failed to connect - start over from the top\n # reset flag\n self._attempting_connect.value = self.CA_NONE\n continue\n else:\n # shouldn't be here\n # reset flag\n self._attempting_connect.value = self.CA_NONE\n continue\n\n # We are connected!\n # print( \"We are connected!\")\n # reset flag right away\n self._attempting_connect.value = self.CA_NONE\n\n # Initialize PID controller if thermostat function was specified at\n # init time\n pidc = None\n heater = None\n if(thermostat):\n\n pidc = pid.PID(kp, ki, kd,\n Output_max=heater_segments,\n Output_min=0\n )\n if thermostat or ext_sw_heater_drive:\n heater = heat_controller(number_of_segments=heater_segments)\n\n read_state = self.LOOKING_FOR_HEADER_1\n r = []\n write_errors = 0\n read_errors = 0\n while not self._disconnect.value:\n start = datetime.datetime.now()\n # write to device\n if not self._write_to_device():\n logging.error('SR700: comm - _write_to_device() failed!')\n write_errors += 1\n if write_errors > 3:\n # it's time to consider the device as being \"gone\"\n logging.error('SR700: comm - 3 successive write '\n 'failures, disconnecting.')\n self._disconnect.value = 1\n continue\n else:\n # reset write_errors\n write_errors = 0\n\n # read from device\n try:\n while self._ser.in_waiting:\n _byte = self._ser.read(1)\n read_state, r, err = (\n self._process_reponse_byte(\n read_state, _byte, r, update_data_event))\n except IOError:\n # typically happens when device is suddenly unplugged\n logging.error('SR700: comm - read from device failed!')\n read_errors += 1\n if write_errors > 3:\n # it's time to consider the device as being \"gone\"\n logging.error('SR700: comm - 3 successive read '\n 'failures, disconnecting.')\n self._disconnect.value = 1\n continue\n else:\n read_errors = 0\n\n # next, drive SW heater when using\n # thermostat mode (PID controller calcs)\n # or in external sw heater drive mode,\n # when roasting.\n if thermostat or ext_sw_heater_drive:\n\n if phidget_available:\n self.current_temp_phidget=int( ph.getTemperature(fahrenheit=True))\n elif use_max31865:\n self.current_temp_max31865=int(self.bttemp.temp_f())\n\n if 'roasting' == self.get_roaster_state():\n if heater.about_to_rollover():\n # it's time to use the PID controller value\n # and set new output level on heater!\n if ext_sw_heater_drive:\n # read user-supplied value\n heater.heat_level = self._heater_level.value\n else:\n # thermostat\n\n #this will use the phidget\n if phidget_available and use_phidget_temp:\n #logging.info('Using Phidget')\n output = pidc.update(\n self.current_temp_phidget,self.target_temp )\n elif use_max31865:\n logging.info('Using max31865')\n output = pidc.update(\n self.current_temp_max31865, self.target_temp)\n else:\n #logging.info('SR700: Using Internal Temp')\n output = pidc.update(\n self.current_temp, self.target_temp)\n\n #logging.info('SR700 temp: %d Phidget Temp:%d Target Temp:%d Heat:%d Using Phidget Temp:%d' % (self.current_temp,\n # self.current_temp_phidget,\n # self.target_temp,\n # output, use_phidget_temp))\n #logging.info('SR700 temp: %d max31865 Temp:%d Target Temp:%d Heat:%d Using max31865 Temp:%d' % (self.current_temp,\n # self.current_temp_max31865,\n # self.target_temp,\n # output, use_max31865))\n\n heater.heat_level = output\n # make this number visible to other processes...\n self._heater_level.value = heater.heat_level\n # read bang-bang heater output array element & apply it\n if heater.generate_bangbang_output():\n # ON\n self.heat_setting = 3\n else:\n # OFF\n self.heat_setting = 0\n else:\n # for all other states, heat_level = OFF\n heater.heat_level = 0\n # make this number visible to other processes...\n self._heater_level.value = heater.heat_level\n self.heat_setting = 0\n\n # calculate sleep time to stick to 0.25sec period\n comp_time = datetime.datetime.now() - start\n sleep_duration = 0.25 - comp_time.total_seconds()\n if sleep_duration > 0:\n time.sleep(sleep_duration)\n\n self._ser.close()\n # reset disconnect flag\n self._disconnect.value = 0\n # reset connection values\n self._connected.value = 0\n self._connect_state.value = self.CS_NOT_CONNECTED\n print(\"We are disconnected.\")\n\n if phidget_available:\n ph.closeConnection()\n","sub_path":"freshroastsr700_phidget/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":21335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"441618198","text":"from rest_framework.test import APITestCase\n\n\nclass BaseApiTest(APITestCase):\n def request(self, method='get', url_name=None, data=None,\n status_code=None):\n \"\"\"\n :type method: str\n :type url_name: str\n :type data: dict\n :rtype: rest_framework.response.Response\n \"\"\"\n fn = getattr(self.client, method)\n urls = self.request_urls()\n url = urls if url_name is None else urls[url_name]\n r = fn(url, data, format='json')\n if status_code is not None:\n self.assertEqual(r.status_code, status_code, r.content)\n return r\n\n def request_urls(self):\n raise NotImplementedError()\n","sub_path":"api/v1/tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"539383405","text":"from rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import viewsets\nfrom rest_framework import generics\nfrom projects.serializers import ProjectsSerializer, UploadedFilesSerializer, WorkFilesSerializer\nfrom projects.models import Projects, UploadedFiles, WorkFiles, Tables\nfrom rest_framework import permissions\nfrom tempfile import mkdtemp\nfrom multiprocessing import Process, Pool\nfrom os.path import join\nfrom projects.parser import Parser\nimport pandas as pd\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.decorators import api_view, permission_classes\nfrom django.shortcuts import HttpResponseRedirect\nfrom django.views.decorators.gzip import gzip_page\nfrom pyexcelerate import Workbook\n\n\n\npool = Pool(processes=1)\n\nclass ProjectsViewSet(viewsets.ModelViewSet):\n queryset = Projects.objects.all()\n serializer_class = ProjectsSerializer\n permission_classes = (permissions.IsAuthenticatedOrReadOnly,)\n def perform_create(self, serializer):\n serializer.save(user=self.request.user)\n\nclass UploadedFilesViewSet(viewsets.ModelViewSet):\n queryset = UploadedFiles.objects.all()\n serializer_class = UploadedFilesSerializer\n permission_classes = (permissions.IsAuthenticatedOrReadOnly,)\n\n def get_queryset(self):\n return UploadedFiles.objects.filter(project=self.request.project)\n\n def perform_create(self, serializer):\n serializer.save(project=self.request.project.name)\n\nclass WorkFilesViewSet(viewsets.ModelViewSet):\n serializer_class = WorkFilesSerializer\n permission_classes = (permissions.IsAuthenticatedOrReadOnly,)\n\n def get_queryset(self):\n return WorkFiles.objects.filter(project=self.request.project)\n\n def perform_create(self, serializer):\n serializer.save(project=self.request.project.name)\n\n\n@api_view(['POST', ])\ndef change_pass(request):\n user = request.user\n old_pass = request.data.get('old_pass')\n new_pass = request.data.get('new_pass')\n if user.check_password(old_pass):\n user.set_password(new_pass)\n user.save()\n return Response([])\n\n@api_view(['POST', ])\ndef upload_file(request):\n path = mkdtemp(suffix='_xmart')\n uploaded_file = request.FILES['file']\n filename = join(path, uploaded_file.name)\n with open(filename, 'wb+') as f:\n for chunk in uploaded_file.chunks():\n f.write(chunk)\n UploadedFiles.objects.create(\n project = request.project,\n filename = filename,\n description = request.POST.get('description'),\n network = request.POST.get('network'),\n filetype = request.POST.get('file_type'),\n vendor = request.POST.get('vendor')\n )\n return Response([])\n\n\n@api_view(['POST', ])\ndef process_all(request):\n for uf in UploadedFiles.objects.filter(project=request.project):\n pool.apply_async(Parser().parse_file, (uf, )) \n return Response([])\n\n\n@api_view(['GET', ])\ndef by_technology(request, vendor, network):\n result = set()\n project = request.project\n for t in Tables.objects.filter(vendor=vendor, network=network, workfile__project=project):\n result.add(t.table)\n result = list(result)\n result.sort()\n return Response(result)\n\n@api_view(['GET', ])\n@gzip_page\ndef table(request, table):\n data = []\n for table in Tables.objects.filter(table=table, workfile__project=request.project):\n data.extend(table.data)\n columns = list(data[0].keys())\n columns.sort()\n return Response({'data': data, 'columns': columns})\n\n@api_view(['GET', ])\n@permission_classes((AllowAny, ))\ndef get_excel(request): \n tables = request.GET.getlist('table') \n wb = Workbook()\n for table in tables:\n data = []\n columns = []\n for t in Tables.objects.filter(table=table, workfile__project=request.project):\n if len(t.data) > 0:\n columns.extend(t.data[0].keys())\n data.extend(t.data)\n columns = list(set(columns))\n columns.sort()\n excel_data = [columns, ]\n for row in data:\n r = []\n for col in columns:\n r.append(row.get(col))\n excel_data.append(r)\n wb.new_sheet(table, data=excel_data)\n wb.save('frontend/static/report.xlsx') \n return HttpResponseRedirect('/static/report.xlsx')\n","sub_path":"projects/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"637440464","text":"# coding=utf-8\nfrom reportlab.lib import colors\nfrom reportlab.lib.pagesizes import letter, inch, landscape\nfrom reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph\nfrom reportlab.lib.styles import getSampleStyleSheet\nfrom reportlab.lib.units import cm\nfrom reportlab.platypus import BaseDocTemplate, Frame, PageTemplate\nfrom reportlab.pdfgen import canvas\nfrom reportlab.lib.enums import TA_JUSTIFY, TA_LEFT, TA_CENTER\n\ndoc = SimpleDocTemplate(\"test.pdf\", pagesize=letter, rightMargin=30, leftMargin=30, topMargin=30, bottomMargin=18)\n\ndoc.pagesize = letter\nelements = []\n\n\n### Page title\ntitleStyle = getSampleStyleSheet()\n\nelements.append(Paragraph(\"NC DWQ Stream Identification Form Version 4.1\", titleStyle[\"Title\"]))\n\n### Header table\nh_style = getSampleStyleSheet()\nh_style = h_style[\"BodyText\"]\nh_style.wordWrap=None\n\nheader_data=[\n [\"Date:\",\"Project/Site:\",\"Latitude:\"],\n [\"Evaluator: \",\"County: \",\"Longitude:\"],\n [\"Total Points: \\n Stream is at least intermittent \\n if ≥ 19 or perennial if ≥ 30\",\"Stream Determination (circle one) \\n Ephemeral Intermittent Perennial\",\"Stream \\n Ephemeral Intermittent Perennial\"]\n]\n\nheader_data = [[Paragraph(cell, h_style) for cell in row] for row in header_data]\n\nh_style = TableStyle([\n ('ALIGN',(0,0),(0,-1),'CENTER'),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'),\n ('INNERGRID', (0, 0), (-1, -1), 0.25, colors.black),\n ('BOX', (0, 0), (-1, -1), 0.25, colors.black),\n])\nh_table = Table(header_data,colWidths=(7 * cm, None, None))\nh_table.setStyle(h_style)\nelements.append(h_table)\nelements.append(Paragraph(\"\\n\", titleStyle['BodyText']))\n\n\n### geomorphology table\ngeomorph_style = getSampleStyleSheet()\ngeomorph_style = geomorph_style[\"BodyText\"]\ngeomorph_style.wordWrap='LRT'\n\ngeomorph_data = [\n [\"A. Geomorphology (Subtotal=______)\", \"Absent\", \"Weak\", \"Moderate\", \"Strong\"],\n [\"1ª. Continuity of bed and bank\", \"0\", \"1\", \"2\", \"3\"],\n [\"2. Sinuosity of channel along thalweg\", \"0\", \"1\", \"2\", \"3\"],\n [\"3. In-channel structure: ex. riffle-pool, step-pool, ripple-pool sequence\", \"0\", \"1\", \"2\", \"3\"],\n [\"4. Particle size of stream substrate\", \"0\", \"1\", \"2\", \"3\"],\n [\"5. Active/relict floodplain\", \"0\", \"1\", \"2\", \"3\"],\n [\"6. Depositional bars or benches\", \"No=0\", \"Yes=3\"],\n]\n\ngeomorph_style = TableStyle([\n # ('BACKGROUND', (1, 1), (-1, -1), colors.green),\n # ('TEXTCOLOR', (1, 1), (-2, -2), colors.red),\n # ('VALIGN', (0, 0), (0, -1), 'TOP'),\n # ('TEXTCOLOR', (0, 0), (0, -1), colors.blue),\n\n ('VALIGN', (0, 0), (-1, -1), 'MIDDLE'),\n ('INNERGRID', (0, 0), (-1, -1), 0.25, colors.black),\n # ('SPAN', (1, 5), (-1, -1)),\n ('BOX', (0, 0), (-1, -1), 0.25, colors.black),\n ('ALIGN', (3,1), (3,1), 'CENTER'),\n\n ])\n# testing geomorph_style add\n# geomorph_style.add('BACKGROUND', (0, 0), (-1, 0), colors.Color(0, 0.7, 0.7))\n\n# Configure geomorph_style and word wrap\ns = getSampleStyleSheet()\ns = s[\"BodyText\"]\ns.wordWrap = None\n\ndata2 = [[Paragraph(cell, s) for cell in row] for row in geomorph_data]\nt = Table(data2, colWidths=(7 * cm, None, None, None, None))\nt.setStyle(geomorph_style)\n\n# Send the geomorph_data and build the file\nelements.append(t)\n\ndata= [['00', '01', '02', '03', '04'],\n['10', '11', '12', '13', '14'],\n['20', '21', '22', '23', '24'],\n['30', '31', '32', '33', '34']]\nt1=Table(data,5*[0.4*inch], 4*[0.4*inch])\nt1.setStyle(TableStyle([('ALIGN',(1,1),(-2,-2),'RIGHT'),\n('TEXTCOLOR',(1,1),(-2,-2),colors.red),\n('VALIGN',(0,0),(0,-1),'TOP'),\n('TEXTCOLOR',(0,0),(0,-1),colors.blue),\n('ALIGN',(0,-1),(-1,-1),'CENTER'),\n('ALIGN',(0,0),(-1,-1),'LEFT'),\n('VALIGN',(0,-1),(-1,-1),'MIDDLE'),\n('TEXTCOLOR',(0,-1),(-1,-1),colors.green),\n('INNERGRID', (0,0), (-1,-1), 0.25, colors.black),\n('BOX', (0,0), (-1,-1), 0.25, colors.black),\n]))\n\nelements.append(t1)\n\n\n\ndoc.build(elements)\n\n","sub_path":"table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":3982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"123768066","text":"import sys\n\nsys.path.append('singly_linked_list')\nfrom singly_linked_list import SinglyLinkedList\n\ndef reverse(head):\n reverse_head = None\n while head:\n next = head.next\n head.next = reverse_head\n reverse_head = head\n head = next\n return reverse_head\n\ndef is_palindrome(l):\n l.print_all()\n slow, fast = l.head, l.head\n pos = 0\n while fast and fast.next:\n slow, fast = slow.next, fast.next.next\n pos += 1\n\n reverse_node = reverse(slow)\n head_node = l.head\n is_palin = True\n while (head_node and reverse_node):\n if (head_node.data == reverse_node.data):\n head_node = head_node.next\n reverse_node = reverse_node.next\n else:\n is_palin = False\n break\n return is_palin\n\nif __name__ == \"__main__\":\n test_str_arr = ['ab','aa','aba','abba','abcba']\n for str in test_str_arr:\n l = SinglyLinkedList()\n for i in str:\n l.insert_value_to_head(i)\n\n print(is_palindrome(l))\n","sub_path":"python/linked_list/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"577259744","text":"# =============================================================================\n#\n# EZID :: config_loader.py\n#\n# Low-level configuration file loader, for use by config.py and Django\n# configuration files only. Regarding the latter, note that this\n# module does *not* import any Django classes, and hence can itself be\n# imported by Django to obtain database passwords and such without\n# encountering circular import problems.\n#\n# Author:\n# Greg Janee \n#\n# License:\n# Copyright (c) 2015, Regents of the University of California\n# http://creativecommons.org/licenses/BSD/\n#\n# -----------------------------------------------------------------------------\n\nimport ConfigParser\nimport os.path\n\n\nclass Config(object):\n \"\"\"\n Holds the contents of the EZID configuration files.\n \"\"\"\n\n def __init__(\n self, siteRoot, projectRoot, configFile, shadowConfigFile, deploymentLevel\n ):\n self._config = ConfigParser.ConfigParser(\n {\"SITE_ROOT\": siteRoot, \"PROJECT_ROOT\": projectRoot}\n )\n f = open(configFile)\n self._config.readfp(f)\n f.close()\n self._shadowConfig = ConfigParser.ConfigParser()\n if os.path.exists(shadowConfigFile):\n f = open(shadowConfigFile)\n self._shadowConfig.readfp(f)\n f.close()\n self._level = \"{%s}\" % deploymentLevel\n\n def getOption(self, option):\n \"\"\"\n Returns the value of a configuration option. The option name\n should be specified in section.option syntax, e.g.,\n \"datacite.username\".\n \"\"\"\n s, o = option.split(\".\")\n if self._shadowConfig.has_option(s, self._level + o):\n return self._shadowConfig.get(s, self._level + o)\n elif self._config.has_option(s, self._level + o):\n return self._config.get(s, self._level + o)\n elif self._shadowConfig.has_option(s, o):\n return self._shadowConfig.get(s, o)\n else:\n return self._config.get(s, o)\n","sub_path":"impl/config_loader.py","file_name":"config_loader.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"405873483","text":"# === Parameters ====== ========================================================\n#--corpus_dir /home/olga/Schreibtisch/MASTER__ARBEIT/RESOURCES/URL_CATEGORIZATION_multilanguage__TRAIN_TEST_SAMPLES/train1__random_split\n#--log_dir /home/olga/Schreibtisch/WEB_PAGES_CATEGORIZATION/logger/logs\n#--temp_dir /home/olga/Schreibtisch\n#--true_lang_path /home/olga/Schreibtisch/WEB_PAGES_CATEGORIZATION/resources/url_true_languages.txt\n# =============================================================================\nimport os.path\nimport logging\nimport argparse\nfrom time import time\nfrom logger.logging import init_logging\nfrom time import gmtime, strftime\nfrom loaders.doc_corpus_loader import DocCorpusLoader\n\n\ndef config_arg_parser():\n parser = argparse.ArgumentParser(description='Split dataset on train and test samples')\n parser.add_argument('--corpus_dir', required=True, help='a directory path to corpus of Urls') #input from test_ train\n parser.add_argument('--log_dir', required=True, help='path to log-directory')\n parser.add_argument('--temp_dir', required=True, help=\"path to dir to save splitted datasets\")\n parser.add_argument('--true_lang_path', required=True, help='')\n\n return parser.parse_args()\n\n\nlogger = logging.getLogger()\nloader = DocCorpusLoader()\n\n\ndef copy_files(source_path, target_path):\n with open(source_path, encoding='utf-8') as f:\n source_file_text = ' '.join(f.readlines())\n target_file = open(target_path, 'a', encoding='utf-8')\n target_file.write(source_file_text)\n target_file.close()\n\n\ndef create_dir(directory):\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n\ndef get_true_languages():\n lang_true = {}\n with open(arguments.true_lang_path) as f:\n lines = f.readlines()\n for line in lines:\n split_line = line.split(',')\n url = split_line[0]\n language = split_line[1].replace('\\n', '')\n lang_true.update({url: language})\n return lang_true\n\n\n\nclass DatasetSplitting():\n def __init__(self):\n self.corpus_dir = arguments.corpus_dir\n self.true_languages = get_true_languages()\n\n\n def split(self):\n self.sample = self.get_sample(arguments.corpus_dir) #url, label,language, file_path\n unique_languages = self.get_unique_languages()\n dir_name = self.parse_file_path()\n dir_path = os.path.join(arguments.temp_dir, dir_name)\n create_dir(dir_path)\n #\n for language in unique_languages:\n print(language)\n language_dir_path = os.path.join(dir_path, language)\n create_dir(language_dir_path)\n\n language_dataset_urls = self.get_language_dataset(language) #paths\n for url in language_dataset_urls:\n for item in self.sample:\n if url == item[0]:\n source_path = item[3]\n target_path = os.path.join(language_dir_path, self.get_file_name(source_path))\n copy_files(source_path, target_path)\n\n def get_sample(self, corpus_path):\n data_set = []\n for filename in os.listdir(corpus_path):\n file_path = os.path.join(corpus_path, filename)\n url, label = self.parse_url_cat_filename(filename)\n language = self.true_languages[url]\n data_set.append((url, label, language, file_path))\n return data_set\n\n\n def parse_url_cat_filename(self, filename):\n result = filename.split('___')\n url = result[0]\n label = result[1]\n return url, label\n\n\n def get_file_name(self, path):\n splitted_path = path.split('/')\n return splitted_path[len(splitted_path)-1]\n\n\n def get_language_dataset(self, language):\n language_dataset = []\n for item in self.sample:\n if language == item[2]:\n language_dataset.append(item[0])\n return language_dataset\n\n\n def get_unique_languages(self):\n unique_languages = []\n for item in self.sample :\n if item[2] not in unique_languages:\n unique_languages.append(item[2])\n return unique_languages\n\n\n def parse_file_path(self):\n splitted_path = arguments.corpus_dir.split('/')\n path = splitted_path[len(splitted_path)-1]\n print(path)\n return path\n\n\n\n\nif __name__ == \"__main__\":\n start_time = time()\n arguments = config_arg_parser()\n logger.info('Started splitting sample by language')\n file_name = os.path.join(arguments.log_dir, '__log__split_sample_by_lang__{0}.log'.format(strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())))\n init_logging(arguments.log_dir, file_name)\n ds = DatasetSplitting()\n ds.split()\n logger.info(\"Total execution time is %0.4fs\" % (time() - start_time))\n logger.info(\"=\"*60)\n","sub_path":"scripts/dataset_processing/split_sample_by_language.py","file_name":"split_sample_by_language.py","file_ext":"py","file_size_in_byte":4838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"238987755","text":"# Посчитать четные и нечетные цифры введенного натурального числа.\n# Например, если введено число 34560, то у него 3 четные цифры (4, 6 и 0) и 2 нечетные (3 и 5).\n\nn = int(input('Введите число > 0: '))\nif n > 0:\n l = list(str(n))\n L_even = []\n L_odd = []\n even = 0\n odd = 0\n print(f'В вашем числе = {n}:')\n for item in l:\n if int(item) % 2 == 0:\n even += 1\n L_even.append(int(item))\n else:\n odd += 1\n L_odd.append(int(item))\n if even == 0:\n print(f'нет четных чисел')\n else:\n print(f'{even} четн. чис. {L_even}')\n if odd == 0:\n print(f'и нет нечетных чисел')\n else:\n print(f'и {odd} нечетн. чис. {L_odd}')\nelse:\n print('Число д.б. > 0')","sub_path":"hw_algorithms_2/even_odd.py","file_name":"even_odd.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"580972875","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nniftidataset.dataset\n\nthe actual dataset classes of niftidataset\n\nAuthor: Jacob Reinhold (jacob.reinhold@jhu.edu)\n\nCreated on: Oct 24, 2018\n\"\"\"\n\n__all__ = ['NiftiDataset']\n\nfrom typing import Optional, Callable\n\nimport nibabel as nib\nfrom torch.utils.data.dataset import Dataset\n\nfrom .utils import glob_nii\n\n\nclass NiftiDataset(Dataset):\n \"\"\"\n create a dataset class in PyTorch for reading NIfTI files\n\n Args:\n source_dir (str): path to source images\n target_dir (str): path to target images\n transform (Callable): transform to apply to both source and target images\n \"\"\"\n\n def __init__(self, source_dir: str, target_dir: str, transform: Optional[Callable]=None):\n self.source_dir, self.target_dir = source_dir, target_dir\n self.source_fns, self.target_fns = glob_nii(source_dir), glob_nii(target_dir)\n self.transform = transform\n if len(self.source_fns) != len(self.target_fns) or len(self.source_fns) == 0:\n raise ValueError(f'Number of source and target images must be equal and non-zero')\n\n def __len__(self):\n return len(self.source_fns)\n\n def __getitem__(self, idx: int):\n src_fn, tgt_fn = self.source_fns[idx], self.target_fns[idx]\n sample = (nib.load(src_fn).get_data(), nib.load(tgt_fn).get_data())\n if self.transform is not None:\n sample = self.transform(sample)\n return sample\n","sub_path":"niftidataset/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"312576900","text":"\"\"\"\n@author: roycek\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef center_mean(array):\n return array - array.mean()\n\n\ndef covariance_matrix(matrix):\n return np.dot(matrix.T, matrix)\n\n\ndef compute_eigen(reshaped_matrix):\n return np.linalg.eig(reshaped_matrix)\n\n\ndef select_eigen(eigen_val, eigen_vecs):\n index = -1\n max_val = max(eigen_val)\n for i in range(len(eigen_val)):\n if eigen_val[i] == max_val:\n index = i\n vec = eigen_vecs[index]\n normalised_eigen_vec = vec / np.sqrt((np.linalg.norm(vec)))\n return max_val, normalised_eigen_vec\n\n\ncol_1, col_2 = np.loadtxt(\"pca_toy.txt\", unpack=True)\nmc_col_1, mc_col_2 = center_mean(col_1), center_mean(col_2)\nmatrix = np.vstack((mc_col_1, mc_col_2))\ncov_matrix = covariance_matrix(np.vstack((mc_col_1, mc_col_2)).T)\nprint(f'Covariance Matrix: {cov_matrix}, \\nShape: {cov_matrix.shape}')\neigen_value, eigen_vectors = compute_eigen(cov_matrix)\nprint(f'Eigen Value: {eigen_value}, Eigen Vector: {eigen_vectors}')\nmax_eigen_val, max_eigen_vector = select_eigen(eigen_value, eigen_vectors.T)\n\nZ = np.dot(max_eigen_vector, matrix) * max_eigen_val\nz_x = Z * max_eigen_vector[0]\nz_y = Z * max_eigen_vector[1]\n\norigin = [0, 0]\nplt.scatter(col_1, col_2, color=['b'])\nplt.scatter(mc_col_1, mc_col_2, color=['r'])\nplt.quiver(*origin, *eigen_vectors[:, 0], color=['g'], scale=4)\nplt.quiver(*origin, *eigen_vectors[:, 1], color=['g'], scale=4)\nplt.scatter(z_x, z_y, color=['g'])\nplt.show()\n","sub_path":"PCA/pca.py","file_name":"pca.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"314213318","text":"#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport sys\nimport rospy\n\nfrom collections import deque\nimport numpy as np\nimport cv2\nimport imutils\n\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\n\n\n# define the lower and upper boundaries of the \"green\"\n# ball in the HSV color space\n\n\n\nclass image_converter:\n def __init__(self):\n\n self.image_pub = rospy.Publisher(\"opencv_camera\",Image, queue_size=10)\n self.bridge = CvBridge()\n self.image_sub = rospy.Subscriber(\"/gimbal/camera/image_raw\",Image,self.callback)\n counter = 0\n (dX, dY) = (0, 0)\n direction = \"\"\n\n def callback(self,data):\n greenLower = (29, 86, 6)\n greenUpper = (64, 255, 255)\n pts = deque(maxlen=32)\n try:\n frame = self.bridge.imgmsg_to_cv2(data, \"bgr8\")\n except CvBridgeError as e:\n print(e)\n\n # frame = imutils.resize(frame, width=600)\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n\n # cv2.imshow(\"Blurred\", blurred) #present blurred filter\n # cv2.imshow(\"HSV\", hsv) #present inverted colors\n\n # construct a mask for the color \"green\", then perform\n # a series of dilations and erosions to remove any small\n # blobs left in the mask\n mask0 = cv2.inRange(hsv, greenLower, greenUpper)\n mask1 = cv2.erode(mask0, None, iterations=2)\n mask = cv2.dilate(mask1, None, iterations=2)\n\n # find contours in the mask and initialize the current\n # (x, y) center of the ball\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n cnts = imutils.grab_contours(cnts)\n center = None\n\n\n # only proceed if at least one contour was found\n if len(cnts) > 0:\n # find the largest contour in the mask, then use\n # it to compute the minimum enclosing circle and centroid\n c = max(cnts, key=cv2.contourArea)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n M = cv2.moments(c)\n center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\n\n # only proceed if the radius meets a minimum size\n if radius > 10:\n # draw the circle and centroid on the frame,\n # then update the list of tracked points\n cv2.circle(frame, (int(x), int(y)), int(radius),\n (0, 255, 255), 2)\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\n pts.appendleft(center)\n # loop over the set of tracked points\n\n for i in np.arange(1, len(pts)):\n # if either of the tracked points are None, ignore\n # them\n if pts[i - 1] is None or pts[i] is None:\n continue\n\n # check to see if enough points have been accumulated in\n # the buffer\n if counter >= 10 and i == 1 and pts[-10] is not None:\n # compute the difference between the x and y\n # coordinates and re-initialize the direction\n # text variables\n dX = pts[-10][0] - pts[i][0]\n dY = pts[-10][1] - pts[i][1]\n (dirX, dirY) = (\"\", \"\")\n\n # ensure there is significant movement in the\n # x-direction\n if np.abs(dX) > 20:\n dirX = \"East\" if np.sign(dX) == 1 else \"West\"\n\n # ensure there is significant movement in the\n # y-direction\n if np.abs(dY) > 20:\n dirY = \"North\" if np.sign(dY) == 1 else \"South\"\n\n # handle when both directions are non-empty\n if dirX != \"\" and dirY != \"\":\n direction = \"{}-{}\".format(dirY, dirX)\n\n # otherwise, only one direction is non-empty\n else:\n direction = dirX if dirX != \"\" else dirY\n\n # otherwise, compute the thickness of the line and\n # draw the connecting lines\n thickness = int(np.sqrt(args[\"buffer\"] / float(i + 1)) * 2.5)\n cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)\n\n # print (\"dx: \", dirX, \"dy: \", dirY)\n frame1 = cv2.resize(frame, (800,600), interpolation = cv2.INTER_AREA)\n cv2.imshow(\"Image window\", frame1)\n cv2.waitKey(3)\n\n\ndef main(args):\n ic = image_converter()\n rospy.init_node('image_converter', anonymous=True)\n try:\n rospy.spin()\n except KeyboardInterrupt:\n print(\"Shutting down\")\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main(sys.argv)","sub_path":"src/opencv/track_object.py","file_name":"track_object.py","file_ext":"py","file_size_in_byte":4417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"233372469","text":"# -*- coding: utf-8 -*-\n\nimport src.game_gui as game_gui\n\nif __name__ == '__main__':\n try:\n file = open('../logs/all.log', 'w')\n file.write('')\n file.close()\n except FileNotFoundError:\n print('无需清理日志文件')\n game_gui.create()\n","sub_path":"src/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"93705468","text":"from django.test import TestCase\nfrom django.conf import settings\nfrom mirage.crypto import Crypto\n\n\nclass TestCrypto(TestCase):\n\n def setUp(self):\n self.crypto = Crypto()\n self.value = 'hello,text'\n if getattr(settings, \"MIRAGE_SECRET_KEY\", None):\n self.encrypted = \"4DIIbNsZPqO1DuXX1GjpkQ==\"\n else:\n self.encrypted = 'pyy1FL2ftjBjUrJlGjgl3g=='\n\n def test_encrypt(self):\n self.assertEqual(self.crypto.encrypt(self.value), self.encrypted)\n\n def test_decrypt(self):\n self.assertEqual(self.crypto.decrypt(self.encrypted), self.value)\n","sub_path":"tests/test_crypto.py","file_name":"test_crypto.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"740389","text":"import numpy as nppr\nimport math\nimport pandas as pd\nfrom LayerClass import Layer, NeuralNetwork\nfrom random import randrange, uniform\nfrom functions import *\nimport get_dataset\nimport sys\n\n##\n## REDE NEURAL COM UMA CAMADA ESCONDIDA\n##\n\ndef new_neuralnet(train_set):\n neural_net = NeuralNetwork()\n\n # Adicionando a camada de input no camada 0\n neural_net.camadas.append(Layer(False, train_set.shape[1], train_set.shape[1]))\n neural_net.functions.append(identidade)\n neural_net.derivatives.append(identidade)\n\n # Adicionando a camada escondida com 342 neurônios na camada 1\n neural_net.camadas.append(Layer(True, train_set.shape[1], 342))\n neural_net.functions.append(leakyrelu)\n neural_net.derivatives.append(leakyreluDerivative)\n\n # Adicionando a camada escondida com 180 neurônios na camada 2\n neural_net.camadas.append(Layer(True, 342, 180))\n neural_net.functions.append(leakyrelu)\n neural_net.derivatives.append(leakyreluDerivative)\n\n # Adicionando a camada de saída com 10 neurônios na camada 3\n neural_net.camadas.append(Layer(True, 180, 10))\n neural_net.functions.append(softmax)\n neural_net.derivatives.append(softmax_derivative)\n\n return neural_net\n\ndef main():\n train_set, valid_set, train_labels, valid_labels = get_dataset.main()\n\n neural_net = new_neuralnet(train_set)\n batch_size = 256\n iteracoes_grid = int(sys.argv[1])\n iteracoes_train = int(sys.argv[2])\n # Treinando\n learning_rate, lamb = grid_search(new_neuralnet, train_set, train_labels, iteracoes_grid)\n print_acuracia = True\n neural_net.train_neuralnet(train_set, train_labels, valid_set, valid_labels, lamb, learning_rate,batch_size,iteracoes_train, print_acuracia, 'nn_twohidden_leaky')\n neural_net.save_model(\"two_hidden_leaky.npy\")\n \nif __name__ == \"__main__\":\n main()\n","sub_path":"experiments/exp5_twohidden_leakyrelu.py","file_name":"exp5_twohidden_leakyrelu.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"12852055","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom operations import *\r\nfrom torch.autograd import Variable\r\nfrom genotypes import PRIMITIVES\r\nfrom genotypes import Genotype\r\nimport math\r\nimport numpy as np\r\nfrom config import config\r\nimport copy\r\nfrom utils import check_cand\r\n\r\nclass MixedOp(nn.Module):\r\n\r\n def __init__(self, C, stride):\r\n super(MixedOp, self).__init__()\r\n self._ops = nn.ModuleList()\r\n for idx, primitive in enumerate(PRIMITIVES):\r\n op = OPS[primitive](C, stride, True)\r\n op.idx = idx\r\n if 'pool' in primitive:\r\n op = nn.Sequential(op, nn.BatchNorm2d(C, affine=True))\r\n self._ops.append(op)\r\n\r\n def forward(self, x, rng):\r\n return self._ops[rng](x)\r\n\r\n\r\nclass Cell(nn.Module):\r\n\r\n def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev):\r\n super(Cell, self).__init__()\r\n if reduction_prev:\r\n self.preprocess0 = FactorizedReduce(C_prev_prev, C, affine=True)\r\n else:\r\n self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=True)\r\n self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=True)\r\n self._steps = steps\r\n self._multiplier = multiplier\r\n self._C = C\r\n self.out_C = self._multiplier * C\r\n self.reduction = reduction\r\n\r\n self._ops = nn.ModuleList()\r\n self._bns = nn.ModuleList()\r\n self.time_stamp = 1 \r\n\r\n for i in range(self._steps):\r\n for j in range(2+i):\r\n stride = 2 if reduction and j < 2 else 1\r\n op = MixedOp(C, stride)\r\n self._ops.append(op)\r\n\r\n def forward(self, s0, s1, rngs):\r\n s0 = self.preprocess0(s0)\r\n s1 = self.preprocess1(s1)\r\n states = [s0, s1]\r\n offset = 0\r\n for i in range(self._steps):\r\n s = sum(self._ops[offset+j](h, rngs[offset+j]) for j, h in enumerate(states))\r\n offset += len(states)\r\n states.append(s)\r\n return torch.cat(states[-self._multiplier:], dim=1)\r\n\r\nclass Network(nn.Module):\r\n def __init__(self, C=16, num_classes=10, layers=8, steps=4, multiplier=4, stem_multiplier=3):\r\n super(Network, self).__init__()\r\n self._C = C\r\n self._num_classes = num_classes\r\n self._layers = layers\r\n self._steps = steps\r\n self._multiplier = multiplier\r\n\r\n C_curr = stem_multiplier * C\r\n\r\n self.stem = nn.Sequential(\r\n nn.Conv2d(3, C_curr, 3, padding=1, bias=False),\r\n nn.BatchNorm2d(C_curr)\r\n )\r\n\r\n C_prev_prev, C_prev, C_curr = C_curr, C_curr, C\r\n\r\n self.cells = nn.ModuleList()\r\n reduction_prev = False\r\n\r\n for i in range(layers):\r\n if i in [layers // 3, 2 * layers // 3]:\r\n C_curr *= 2\r\n reduction = True\r\n else:\r\n reduction = False\r\n cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)\r\n reduction_prev = reduction\r\n self.cells += [cell]\r\n C_prev_prev, C_prev = C_prev, multiplier * C_curr\r\n\r\n self.global_pooling = nn.AdaptiveAvgPool2d(1)\r\n self.classifier = nn.Linear(C_prev, num_classes)\r\n\r\n def forward(self, input, rng):\r\n s0 = s1 = self.stem(input)\r\n for i, cell in enumerate(self.cells):\r\n s0, s1 = s1, cell(s0, s1, rng)\r\n out = self.global_pooling(s1)\r\n logits = self.classifier(out.view(out.size(0),-1))\r\n return logits\r\n\r\nif __name__ == '__main__':\r\n from copy import deepcopy\r\n model = Network()\r\n operations = []\r\n for _ in range(config.edges):\r\n operations.append(list(range(config.op_num)))\r\n rng = [np.random.randint(len(config.blocks_keys)) for i in range(config.edges)]\r\n\r\n rngs = check_cand(rng, operations)\r\n x = torch.rand(4,3,32,32)\r\n logit = model(x, rngs)\r\n print('logit:{0}'.format(logit))","sub_path":"darts_search_space/cifar10/rlnas/evolution_search/super_model.py","file_name":"super_model.py","file_ext":"py","file_size_in_byte":3839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"577623805","text":"import socket\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n ip = '127.0.0.1'\n port = 50000\n server = (ip, port)\n sock.connect(server)\n msg = ''\n while msg != 'exit':\n msg = input('->')\n sock.send(msg.encode('utf-8'))\n data = sock.recv(1024).decode('utf-8')\n print('Received from server: ' + str(data))\n","sub_path":"TCP_IP/TCP3/tcp_client.py","file_name":"tcp_client.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"425046336","text":"import os\nimport xlrd\nimport openpyxl\n\n\n# 保存相应文件夹下一级文件夹名称\ndef save_folders(a):\n\tb = [] # 创建空列表\n\tfor root, dirs, file in os.walk(a, topdown=False):\n\t\tb = dirs # 保存文件夹名称\n\treturn b # 返回文件夹列表\n\n\n# 保存相应文件夹下一级文件名称\ndef save_file(a):\n\tc = [] # 创建空列表\n\tfor root, dirs, file in os.walk(a, topdown=False):\n\t\tc = file # 保存文件名称\n\t\tbreak # 跳出循环\n\treturn c # 返回文件名称\n\n\n# 对ROI或RES进行排序\ndef sort(file, folder):\n\tlist_number = [] # 文件夹中的数字列表\n\tlist = [] # 最终返回的列表\n\t# 判断需要使用哪个部分\n\tif 'C Mode' in folder:\n\t\tfor item in file:\n\t\t\titem1 = item.replace('ROI', '') # 删除文件夹名称中的ROI\n\t\t\tlist_number.append(int(item1)) # 提取数字并增加到列表中\n\t\tlist_number.sort() # 对数字进行排序\n\t\tfor number in list_number:\n\t\t\tfile_name = 'ROI' + str(number) # 文件夹名称还原\n\t\t\tlist.append(file_name) # 增加文件夹名称\n\t\treturn list # 返回最终排序好的文件夹名称列表\n\telif 'B Mode' in folder:\n\t\tfor item in file:\n\t\t\titem1 = item.replace('RES', '') # 删除文件夹名称中的RES\n\t\t\tlist_number.append(int(item1)) # 提取数字并增加到列表中\n\t\tlist_number.sort() # 对数字进行排序\n\t\tfor number in list_number:\n\t\t\tfile_name = 'RES' + str(number) # 文件夹名称还原\n\t\t\tlist.append(file_name) # 增加文件夹名称\n\t\treturn list # 返回最终排序好的文件夹名称列表\n\telse:\n\t\treturn file # 返回文件夹名称\n\n\n# 对文件名进行排序\ndef sort_file(file):\n\tlist_number = [] # 文件夹中的数字列表\n\tlist = [] # 最终返回的列表\n\tstring1 = file[0][::-1] # 对文件名称进行颠倒\n\tstring2 = string1.split('_', 1)[1] # 提取文件名称需要的字符串\n\tstring3 = string2[::-1] # 最终需要的字符串\n\n\tfor item in file:\n\t\titem1 = item.replace('%.xls', '') # 删除文件名称中的%.xls\n\t\tnumber_file = item1.split('_')[-1] # 提取文件名称中的最后数字\n\t\tlist_number.append(int(number_file)) # 将数字增加到数字列表中\n\n\tlist_number.sort() # 对数字进行排序\n\tfor number in list_number:\n\t\tfile_name = string3 + '_' + str(number) + '%.xls' # 将文件名称进行复原\n\t\tlist.append(file_name) # 增加文件名称\n\treturn list # 返回最终排序好的文件名称列表\n\n\n# 提取excel文件中MI/TIS的值\ndef extract_MI_TIS(path):\n\twb = xlrd.open_workbook(path) # 打开相应的excle表\n\tws = wb.sheet_by_name('Output') # 需要提取的sheet表名称\n\tMI = ws.cell(54, 2).value # 提取MI数据\n\tTIS = ws.cell(55, 2).value # 提取TIS数据\n\treturn MI, TIS # 返回MI,TIS的值\n\n\n# 创建excel表\ndef creat_excel(data1, data2, data3, count, sheet_name, file_string):\n\t# 判断创建新的excle表还是创建新的sheet表\n\tif count <= 0:\n\t\twb = openpyxl.Workbook() # 创建excel表\n\t\tws = wb.create_sheet(sheet_name, count) # 创建新的sheet表\n\t\tdel wb['Sheet'] # 删除sheet\n\n\t# 表\n\telse:\n\t\twb = openpyxl.load_workbook(file_string) # 读取excel文件\n\t\tws = wb.create_sheet(sheet_name, count) # 创建新的sheet表\n\n\trow = 3 # 行数\n\tcolumn = 1 # 列数\n\tbold = openpyxl.styles.Font(bold=True) # 设置字体加粗\n\tcenter = openpyxl.styles.Alignment(horizontal='center', vertical='center') # 设置垂直居中和水平居中\n\n\tws['A1'] = 'powerlevel' # A1表格输入powerlevel\n\tws['A1'].font = bold # 设置字体加粗\n\tws['A1'].alignment = center # 设置垂直居中和水平居中\n\tws['B1'] = 'MI' # B1表格输入MI\n\tws['B1'].font = bold # 设置字体加粗\n\tws['B1'].alignment = center # 设置垂直居中和水平居中\n\tws['C1'] = 'TIS' # C1表格输入TIS\n\tws['C1'].font = bold # 设置字体加粗\n\tws['C1'].alignment = center # 设置垂直居中和水平居中\n\tws['A2'] = 0\n\tws['B2'] = 0\n\tws['C2'] = 0\n\n\t# 循环输入powerlever值\n\tfor cost in data3:\n\t\tws.cell(row, column).value = cost\n\t\trow += 1\n\n\trow = 3 # 重置行数\n\n\t# 循环输入MI值\n\tfor item in data1:\n\t\tws.cell(row, column + 1).value = item\n\t\trow += 1\n\n\trow = 3 # 重置行数\n\n\t# 循环输入TIS值\n\tfor item in data2:\n\t\tws.cell(row, column + 2).value = item\n\t\trow += 1\n\n\twb.save(file_string) # excel保存路径\n\n\n# 创建文件夹\ndef creat_folder(path, folder):\n\tfolder_path = path + folder # 文件夹路径\n\ta = os.path.exists(folder_path) # 返回文件夹是否存在\n\tif a:\n\t\ta = 1\n\telse:\n\t\tos.mkdir(folder_path) # 不存在文件夹创建新的文件夹\n","sub_path":"MI-TIS/code/read_function.py","file_name":"read_function.py","file_ext":"py","file_size_in_byte":6410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"204610170","text":"\nfrom Node import Node\nimport serial\nimport json\nfrom numpy import interp\nimport time\n\nser = serial.Serial()\nser.port = \"/dev/ttyACM1\"\nser.baudrate = 115200\nser.write_timeout = 1\n\nser.open()\n\nnode = Node(\"test_node.json\")\n\nwhile True:\n msg = node.recv_simple(\"inputs-out\")\n dmsg = json.loads(msg[11:])\n out = [bytes([int(interp(dmsg[1], [-32768, 32768], [255, 0]))])[0], bytes([int(interp(dmsg[4], [-32768, 32768], [255,0]))])[0], bytes([int(dmsg[7])])[0]]\n \n try:\n ser.write(bytearray(out))\n ser.write(bytearray('\\n'))\n except Exception:\n print(\"timeout\")\n pass\n time.sleep(0.01)\n\n print(bytearray(out))\n \n \n\n","sub_path":"test_node.py","file_name":"test_node.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"350636030","text":"#!/usr/bin/python\n# encoding=utf-8\n\n\"\"\"\n@Author : Don\n@Date : 7/25/2020 2:02 PM\n@Desc :\n\"\"\"\n\nimport decimal\nimport json\nimport time\n\nimport allure\nimport requests\nimport urllib3\nfrom loguru import logger\nfrom requests import sessions\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n\ndef request_encapsulate(req):\n def send(*args, **kwargs):\n start = time.process_time()\n response = req(*args, **kwargs)\n end = time.process_time()\n elapsed = str(decimal.Decimal(\"%.3f\" % float(end - start))) + \"s\"\n log4a = \"{}{} status:{} response:{} elapsed:{}\"\n try:\n kv = \"\"\n for k, v in kwargs.items():\n # if not json, str()\n try:\n v = json.dumps(v, ensure_ascii=False)\n except TypeError:\n v = str(v)\n kv += f\" {k}:{v} \"\n if args:\n method = f'method:\"{args[0]}\" '\n else:\n method = \"\"\n request_response = log4a.format(method, kv, response.status_code, response.text, elapsed)\n logger.info(request_response)\n allure.attach(request_response, 'request & response', allure.attachment_type.TEXT)\n except AttributeError:\n logger.error(\"request failed\")\n except TypeError:\n logger.warning(log4a)\n return response\n\n return send\n\n\n@request_encapsulate\ndef request(method, url, **kwargs):\n \"\"\"Constructs and sends a :class:`Request `.\n\n :param method: method for the new :class:`Request` object: ``GET``, ``OPTIONS``, ``HEAD``, ``POST``, ``PUT``, ``PATCH``, or ``DELETE``.\n :param url: URL for the new :class:`Request` object.\n :param params: (optional) Dictionary, list of tuples or bytes to send\n in the query string for the :class:`Request`.\n :param data: (optional) Dictionary, list of tuples, bytes, or file-like\n object to send in the body of the :class:`Request`.\n :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.\n :param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.\n :param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.\n :param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.\n ``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``\n or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string\n defining the content type of the given file and ``custom_headers`` a dict-like object containing additional headers\n to add for the file.\n :param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.\n :param timeout: (optional) How many seconds to wait for the server to send data\n before giving up, as a float, or a :ref:`(connect timeout, read\n timeout) ` tuple.\n :type timeout: float or tuple\n :param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.\n :type allow_redirects: bool\n :param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.\n :param verify: (optional) Either a boolean, in which case it controls whether we verify\n the server's TLS certificate, or a string, in which case it must be a path\n to a CA bundle to use. Defaults to ``True``.\n :param stream: (optional) if ``False``, the response content will be immediately downloaded.\n :param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.\n :return: :class:`Response ` object\n :rtype: requests.Response\n\n Usage::\n\n >>> import requests\n >>> req = requests.request('GET', 'https://httpbin.org/get')\n >>> req\n \n \"\"\"\n\n # By using the 'with' statement we are sure the session is closed, thus we\n # avoid leaving sockets open which can trigger a ResourceWarning in some\n # cases, and look like a memory leak in others.\n with sessions.Session() as session:\n return session.request(method=method, url=url, **kwargs)\n","sub_path":"tep/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"204238825","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.selector import Selector\nfrom sachin.items import SachinItem\n\n\nclass SachincrawlSpider(scrapy.Spider):\n name = 'sachincrawl'\n # allowed_domains = ['http://www.howstat.com']\n start_urls = ['http://www.howstat.com/cricket/Statistics/Players/PlayerProgressBat_ODI.asp?PlayerID=3600']\n\n def parse(self, response):\n # take a list of dom elements to iterate into\n rowlist = Selector(response).xpath('//tr[contains(@bgcolor,\"#\")]')\n # for each dom element in the list obtained above,\n # create a new instance of the class created in the item.py file\n # and store values derived from the xpath into item class attributes\n for row in rowlist:\n item = SachinItem()\n item[\"match_seq\"] = row.xpath('td[1]/text()').extract()[0].strip()\n item[\"match_date\"] = row.xpath('td[2]/a[@class=\"LinkNormal\"]/text()').extract()[0].strip()\n item[\"match_versus\"] = row.xpath('td[3]/text()').extract()[0].strip()\n item[\"match_ground\"] = row.xpath('td[4]/text()').extract()[0].strip()\n item[\"match_dismissal\"] = row.xpath('td[5]/text()').extract()[0].strip().replace(u\"\\u2020\",\"\")\n item[\"match_runs\"] = row.xpath('td[6]/text()').extract()[0].strip().replace(\"*\",\"\").replace(\"-\",\"\")\n item[\"match_balls_faced\"] = row.xpath('td[7]/text()').extract()[0].strip().replace(\"-\",\"\")\n # yield or produce the item\n yield item\n","sub_path":"Automation/scrapy-project/sachin/sachin/spiders/sachincrawl.py","file_name":"sachincrawl.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"286722745","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib\r\nimport os\r\n\r\ntf.set_random_seed(777)\r\n\r\nif \"DISPLAY\" not in os.environ:\r\n matplotlib.use('Agg')\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\ndef MinMaxScaler(data):\r\n numerator = data - np.min(data,0)\r\n denomiator = np.max(data,0) - np.min(data,0)\r\n return numerator / (denominator + 1e-7)\r\n\r\n\r\nseq_length = 7\r\ndata_dim =5\r\nhidden_dim = 10\r\noutput_dim = 1\r\nlearning_rate = 0.01\r\nireations= 500\r\n\r\nxy = np.loadtxt('https://github.com/hunkim/DeepLearningZeroToAll/blob/master/data-02-stock_daily.csv', delimiter=',')\r\nxy = xy[::-1]\r\n\r\ntrain_size =int(len(xy) * 0.7)\r\ntrain_set = xy[0:train_size]\r\ntest_set = xy[train_size - seq_length]\r\n\r\ntrain_set = MinMaxScaler(train_set)\r\ntest_set = MinMaxScaler(test_set)\r\n\r\ndef build_dataset(time_series, seq_length):\r\n dataX = []\r\n dataY = []\r\n for i in range(0, len(time_series) - seq_length):\r\n _x = time_series[i:i+swq_length, :]\r\n _y = time_series[i+seq_length, [-1]]\r\n\r\n print(_x,\"->\",_y)\r\n dataX.append(_x)\r\n dataY.append(-y)\r\n return np.array(dataX), np.array(dataY)\r\n\r\ntrainX, trainY = build_dataset(train_set, seq_length)\r\ntestX, testY = build_dataset(test_set, seq_length)\r\n\r\nX = tf.placeholder(tf.float32, [None, seq_length, data_dim])\r\nY = tf.placeholder(tf.float32, [None, 1])\r\n\r\ncell = tf.contrib.rnn.BacsicLSTMCell(\r\n num_units=hidden_dim, state_is_tuple=True, activation=tf.tanh)\r\noutputs, _states = tf.nn.dynamic_rnn(cell,X,dtype=tf.float32)\r\nY_pred = tf.contrib.layers.fully_connected(outputs[:-1], output_dim, activation_fn=None)\r\n\r\nloss =tf.reduce_sum(tf.square(Y_pred -Y))\r\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\r\ntrain =optimizer.minimize(loss)\r\n\r\ntargets = tf.placeholder(tf.float32, [None,1])\r\nrmse = tf.sqrt(tf.reduce_mean(tf.square(targets - prediction)))\r\n\r\nwith tf.Session() as sess:\r\n init = tf.globale_variables_initializer()\r\n sess.run(init)\r\n\r\n for i in range(iterations):\r\n _,step_loss = sess.run([train, loss], feed_dict={X:trainX, Y:trainY})\r\n print(\"[step: {}] loss: {}\".format(i, step_loss))\r\n\r\n test_predict = sess.run(Y_pred, feed_dict={X:testX})\r\n rmse_val = sess.run(rmse, feed_dict={\r\n targets: testY, predictions: test_predict})\r\n print(\"RMSE: {}\".format(rmse_val))\r\n\r\n plt.plot(TestY)\r\n plt,plot(test_predict)\r\n plt.xlabel(\"time perid\")\r\n plt.ylabel(\"stock price\")\r\n plt.show()\r\n","sub_path":"tensorflow/Rnn/12-5 stock.py","file_name":"12-5 stock.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"476528657","text":"from sklearn.model_selection import train_test_split\nimport pandas as pd\nimport numpy as np\nimport os\nimport time\nimport sys\nimport json\n\n\"\"\"\nconvert table to one hot encoding, save as numpy array.\n\"\"\"\n\n\ndef watson_crick(x_nt, y_nt, alphabet=None):\n \"\"\"\n fun assigns 1 if input string\n is in alphabet, otherwise\n it returns 0.\n parameters:\n x_nt = nucleotide on x axis\n y_nt = nucleotide on y axis\n alphabet = dict of nt_pair:score\n \"\"\"\n if not alphabet:\n alphabet = {\"AT\": 1, \"TA\": 1, \"GC\": 1, \"CG\": 1}\n pair = x_nt + y_nt\n return alphabet.get(pair, 0)\n\n\ndef one_hot_encoding(df, tensor_dim):\n \"\"\"\n fun transform input database to\n one hot encoding array.\n\n paramenters:\n df=input dataset\n tensor_dim=tensors shapes\n \"\"\"\n\n samples = df.shape[0]\n # matrix of 4D with samples, nucleotide\n # binding length, miRNA length,\n # channels (dot matrix and conservation)\n shape_matrix_2d = (samples, *tensor_dim)\n ohe_matrix_2d = np.zeros(shape_matrix_2d, dtype=\"float32\")\n multichannel = tensor_dim[-1]\n\n start = time.time()\n\n for index, row in df.iterrows():\n if multichannel > 1:\n sample_bind_score = list(map(float, row.binding_cons_score.split(\",\")))\n sample_mirna_score = list(map(float, row.mirna_cons_score.split(\",\")))\n\n for bind_index, bind_nt in enumerate(row.binding_sequence):\n if multichannel > 1:\n nt_bind_cons_score = sample_bind_score[bind_index]\n\n for mirna_index, mirna_nt in enumerate(row.mirna_binding_sequence):\n\n ohe_matrix_2d[index, bind_index, mirna_index, 0] = watson_crick(\n bind_nt, mirna_nt\n )\n if multichannel == 2:\n cons_score = nt_bind_cons_score * sample_mirna_score[mirna_index]\n ohe_matrix_2d[index, bind_index, mirna_index, 1] = cons_score\n elif multichannel == 3:\n ohe_matrix_2d[\n index, bind_index, mirna_index, 1\n ] = nt_bind_cons_score\n ohe_matrix_2d[\n index, bind_index, mirna_index, 2\n ] = sample_mirna_score[mirna_index]\n if index % 1000 == 0:\n end = time.time()\n print(\n \"rows:\\t%s\" % (index),\n \"elapsed (sec):\\t%s\" % (end - start),\n \"multichannel:\\t%s\" % (multichannel),\n sep=\" | \",\n )\n return ohe_matrix_2d\n\n\ndef make_sets_ohe(samples, labels, tensor_dim):\n \"\"\"\n fun converts input batch into \n one hot encoding of features\n and labels. output a tuple of \n train test ohe dataframes and train test label dataframes.\n\n paramenters:\n batch=mini-batch as Pandas df\n \"\"\"\n X = samples.reset_index(drop=True).copy()\n y = labels.reset_index(drop=True).copy()\n\n X_ohe = one_hot_encoding(X, tensor_dim)\n y_ohe = pd.get_dummies(y).to_numpy()\n\n return X_ohe, y_ohe\n\n\ndef load_dataset(OPTIONS, main=False):\n \"\"\"\n fun loads connection table as pandas df to\n one hot encoding array.\n\n parameters:\n OPTIONS=input custard options (dict)\n \"\"\"\n\n infiles = OPTIONS[\"input_file\"]\n tensor_dim = OPTIONS[\"tensor_dim\"]\n restore_dataset = OPTIONS[\"load_dataset\"]\n save_datasets = OPTIONS[\"save_ohe\"]\n output_dataset_filename = OPTIONS[\"output_ohe_datasets_name\"]\n make_validation = OPTIONS[\"validation\"]\n train = OPTIONS[\"flags\"][\"train\"]\n eval = OPTIONS[\"flags\"][\"evaluate\"]\n\n if eval:\n datasets = [0, 0]\n elif train:\n datasets = [0, 0, 0, 0]\n elif main:\n datasets = [0, 0, 0, 0]\n print(\"running as stand-alone script\")\n else:\n print(\"nothing to do, exit...\")\n sys.exit()\n\n if restore_dataset:\n print(\"load dataset from file:\", infiles, sep=\"\\t\")\n if train:\n with np.load(infiles) as data:\n datasets = [\n data[\"X_train\"],\n data[\"y_train\"],\n data[\"X_val\"],\n data[\"y_val\"],\n ]\n elif eval:\n with np.load(infiles) as data:\n datasets = [data[\"X_test\"], data[\"y_test\"]]\n else:\n print(\"converting files to ohe datasets:\", infiles, sep=\"\\t\")\n\n try:\n df = pd.read_csv(infiles, sep=\"\\t\")\n except Exception as e:\n if not main:\n logging.error(\"Exception occured\", exc_info=True)\n raise SystemExit(\"Failed to load dataset as pandas DataFrame\")\n\n if make_validation:\n y_samples = df.label\n X_samples = df.drop([\"label\"], axis=1)\n X_train, X_val, y_train, y_val = train_test_split(\n X_samples, y_samples, test_size=0.2, random_state=1989\n )\n else:\n print(\"training with no validation is not implemented yet, exit...\")\n sys.exit()\n\n datasets[0], datasets[1] = make_sets_ohe(X_train, y_train, tensor_dim)\n datasets[2], datasets[3] = make_sets_ohe(X_val, y_val, tensor_dim)\n\n if save_datasets:\n print(\"saving ohe datasets at location:\", output_dataset_filename, sep=\"\\t\")\n if len(datasets) == 4:\n np.savez(\n output_dataset_filename,\n X_train=datasets[0],\n X_val=datasets[2],\n y_train=datasets[1],\n y_val=datasets[3],\n )\n elif len(datasets) == 2:\n np.savez(\n output_dataset_filename, X_test=datasets[0], y_test=datasets[1],\n )\n return datasets\n\n\nif __name__ == \"__main__\":\n try:\n with open(sys.argv[1], \"r\") as fp:\n OPTIONS = json.load(fp)\n except:\n with open(\n \"/home/grioni_andrea/loft/custard/AG_table2ohe_confing.json\", \"r\"\n ) as fp:\n OPTIONS = json.load(fp)\n\n load_dataset(OPTIONS, main=True)\n","sub_path":"table2ohe.py","file_name":"table2ohe.py","file_ext":"py","file_size_in_byte":6062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"515279951","text":"# coding: utf-8\nimport os\nimport codecs\nimport shutil\nimport json\n\nkss_path = '../../../data/speech/korean-single-speaker-speech-dataset'\n\nwith open(os.path.join(kss_path, 'transcript.v.1.4.txt'), 'r', encoding='utf-8') as t:\n\traw_text = t.readlines()\n\naudio_list = []\ntext_list = []\nfor r in raw_text:\n\tsplit_raw = r.split('|')\n\taudio_list.append(split_raw[0])\n\ttext_list.append(split_raw[2])\n\nprint(audio_list[0:3])\nprint(text_list[0:3])\n\ntacotron_path = 'datasets/kss'\n\nif not os.path.exists(tacotron_path):\n\tos.mkdir(tacotron_path)\n\tos.mkdir(os.path.join(tacotron_path, 'audio'))\n\nwith open(os.path.join(tacotron_path, 'kss-recognition-All.json'), 'w') as f:\n\tf.write('{\\n')\t\n\tfor audio, text in zip(audio_list, text_list):\n\t\tshutil.copy(os.path.join(kss_path,'kss', audio),\n\t\t\t\tos.path.join(tacotron_path, 'audio', audio[2:]))\n\t\tf.write('\\t'+'\"'+'./'+os.path.join(tacotron_path,'audio',audio[2:])+'\"'\\\n\t\t\t\t+': '+'\"'+text+'\"'+',\\n')\n\tf.write('}')\n\nprint('Finsh')\n","sub_path":"kss2tacotron.py","file_name":"kss2tacotron.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"398941968","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom datetime import datetime\r\nfrom PyQt5.QtWidgets import QApplication, QTableView, QFileDialog\r\nimport os\r\nimport os.path\r\nfrom os.path import expanduser\r\nimport matplotlib.pyplot as plt\r\nimport pandas\r\nfrom PyQt5.QtCore import QAbstractTableModel, Qt\r\n\r\nGERMAN_VERSION = True\r\n\r\nclass PandasModel(QtCore.QAbstractTableModel): \r\n def __init__(self, df = pandas.DataFrame(), parent=None): \r\n QtCore.QAbstractTableModel.__init__(self, parent=parent)\r\n self._df = df\r\n\r\n def headerData(self, section, orientation, role=QtCore.Qt.DisplayRole):\r\n if role != QtCore.Qt.DisplayRole:\r\n return QtCore.QVariant()\r\n\r\n if orientation == QtCore.Qt.Horizontal:\r\n try:\r\n return self._df.columns.tolist()[section]\r\n except (IndexError, ):\r\n return QtCore.QVariant()\r\n elif orientation == QtCore.Qt.Vertical:\r\n try:\r\n # return self.df.index.tolist()\r\n return self._df.index.tolist()[section]\r\n except (IndexError, ):\r\n return QtCore.QVariant()\r\n\r\n def data(self, index, role=QtCore.Qt.DisplayRole):\r\n if role != QtCore.Qt.DisplayRole:\r\n return QtCore.QVariant()\r\n\r\n if not index.isValid():\r\n return QtCore.QVariant()\r\n\r\n return QtCore.QVariant(str(self._df.iloc[index.row(), index.column()]))\r\n\r\n def setData(self, index, value, role):\r\n row = self._df.index[index.row()]\r\n col = self._df.columns[index.column()]\r\n if hasattr(value, 'toPyObject'):\r\n # PyQt4 gets a QVariant\r\n value = value.toPyObject()\r\n else:\r\n # PySide gets an unicode\r\n dtype = self._df[col].dtype\r\n if dtype != object:\r\n value = None if value == '' else dtype.type(value)\r\n self._df.set_value(row, col, value)\r\n return True\r\n\r\n def rowCount(self, parent=QtCore.QModelIndex()): \r\n return len(self._df.index)\r\n\r\n def columnCount(self, parent=QtCore.QModelIndex()): \r\n return len(self._df.columns)\r\n\r\n def sort(self, column, order):\r\n colname = self._df.columns.tolist()[column]\r\n self.layoutAboutToBeChanged.emit()\r\n self._df.sort_values(colname, ascending= order == QtCore.Qt.AscendingOrder, inplace=True)\r\n self._df.reset_index(inplace=True, drop=True)\r\n self.layoutChanged.emit()\r\n\r\n\r\nclass Show_data(QtWidgets.QWidget):\r\n def __init__(self, parent=None):\r\n QtWidgets.QWidget.__init__(self, parent=None)\r\n vLayout = QtWidgets.QVBoxLayout(self)\r\n hLayout = QtWidgets.QHBoxLayout()\r\n self.loadBtn = QtWidgets.QPushButton(\"Save File\", self)\r\n vLayout.addLayout(hLayout)\r\n self.pandasTv = QtWidgets.QTableView(self)\r\n hLayout.addWidget(self.pandasTv)\r\n #self.pandasTv.setEnabled(False)\r\n vLayout.addWidget(self.loadBtn)\r\n self.loadBtn.clicked.connect(self.saveFile)\r\n self.pandasTv.setSortingEnabled(True)\r\n model = PandasModel(ui.data_)\r\n self.pandasTv.setModel(model)\r\n\r\n def saveFile(self):\r\n fileName = QtWidgets.QFileDialog.getSaveFileName(None, \"Save Data to file\", \".\", \"CSV file (*.csv)\")[0]\r\n if fileName:\r\n try:\r\n ui.data_.to_csv(fileName,index=False,sep='\\t',encoding=\"ISO-8859-1\")\r\n ui.textBrowser.append('File Saved Successfully !\\n')\r\n except:\r\n ui.textBrowser.append('File is Already open by another Process or Permission Denied to location : Unable to save !\\n')\r\n else:\r\n ui.textBrowser.append('No file chosen : Unable to save !\\n') \r\n\r\n\r\nclass Show_info(object):\r\n def setupUi(self, Dialog):\r\n Dialog.setObjectName(\"Dialog\")\r\n Dialog.resize(700, 500)\r\n Dialog.setFixedSize(700,500)\r\n icon = QtGui.QIcon()\r\n icon.addPixmap(QtGui.QPixmap(\"ico.ico\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n Dialog.setWindowIcon(icon)\r\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(Dialog)\r\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout()\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.textBrowser = QtWidgets.QTextBrowser(Dialog)\r\n self.textBrowser.setOpenExternalLinks(True)\r\n self.textBrowser.setOpenLinks(True)\r\n self.textBrowser.setObjectName(\"textBrowser\")\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n self.textBrowser.setFont(font)\r\n self.textBrowser.setText('\\nQuick Look :\\n\\n'+ui.info+'\\n')\r\n self.verticalLayout.addWidget(self.textBrowser)\r\n self.verticalLayout_2.addLayout(self.verticalLayout)\r\n self.Open_Button = QtWidgets.QPushButton(Dialog)\r\n self.Open_Button.setObjectName(\"Open_Button\")\r\n self.verticalLayout_2.addWidget(self.Open_Button)\r\n self.Open_Button.clicked.connect(self.save_info)\r\n\r\n self.retranslateUi(Dialog)\r\n QtCore.QMetaObject.connectSlotsByName(Dialog) \r\n\r\n def retranslateUi(self, Dialog):\r\n _translate = QtCore.QCoreApplication.translate\r\n Dialog.setWindowTitle(_translate(\"Dialog\", \"Dialog\"))\r\n self.Open_Button.setText(_translate(\"Dialog\", \"Save\"))\r\n\r\n def save_info(self):\r\n fileName = QtWidgets.QFileDialog.getSaveFileName(None, \"Save Info to file\", \".\", \"Text file (*.txt)\")[0]\r\n if fileName:\r\n try:\r\n fhandle = open(fileName,'w')\r\n fhandle.write(ui.info)\r\n fhandle.close()\r\n ui.textBrowser.append('File Saved Successfully !\\n')\r\n except Exception as e:\r\n ui.textBrowser.append(\"ERROR : \"+str(e)+'File is Already open by another Process or permission denied : Unable to save !\\n')\r\n else:\r\n ui.textBrowser.append('No file chosen : Unable to save !\\n')\r\n\r\n\r\nclass Ui_Dialog(object):\r\n\r\n def setupUi(self, Dialog):\r\n Dialog.setObjectName(\"Dialog\")\r\n Dialog.resize(1079, 590)\r\n self.label = QtWidgets.QLabel(Dialog)\r\n self.label.setGeometry(QtCore.QRect(20, 50, 91, 31))\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n self.label.setFont(font)\r\n self.label.setObjectName(\"label\")\r\n self.Open_Button = QtWidgets.QPushButton(Dialog)\r\n self.Open_Button.setGeometry(QtCore.QRect(690, 20, 111, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.Open_Button.setFont(font)\r\n self.Open_Button.setObjectName(\"Open_Button\")\r\n self.Default_Button = QtWidgets.QPushButton(Dialog)\r\n self.Default_Button.setGeometry(QtCore.QRect(820, 20, 111, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.Default_Button.setFont(font)\r\n self.Default_Button.setObjectName(\"Default_Button\")\r\n self.Copyright_label = QtWidgets.QLabel(Dialog)\r\n self.Copyright_label.setGeometry(QtCore.QRect(880, 560, 181, 21))\r\n font = QtGui.QFont()\r\n font.setPointSize(9)\r\n self.Copyright_label.setFont(font)\r\n self.Copyright_label.setObjectName(\"Copyright_label\")\r\n self.line = QtWidgets.QFrame(Dialog)\r\n self.line.setGeometry(QtCore.QRect(20, 130, 1041, 20))\r\n self.line.setFrameShape(QtWidgets.QFrame.HLine)\r\n self.line.setFrameShadow(QtWidgets.QFrame.Sunken)\r\n self.line.setObjectName(\"line\")\r\n self.DateSelection_GroupBox = QtWidgets.QGroupBox(Dialog)\r\n self.DateSelection_GroupBox.setGeometry(QtCore.QRect(20, 150, 391, 161))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.DateSelection_GroupBox.setFont(font)\r\n self.DateSelection_GroupBox.setObjectName(\"DateSelection_GroupBox\")\r\n self.dateEdit = QtWidgets.QDateEdit(self.DateSelection_GroupBox)\r\n self.dateEdit.setGeometry(QtCore.QRect(210, 40, 131, 31))\r\n self.dateEdit.setCalendarPopup(True)\r\n self.dateEdit.setObjectName(\"dateEdit\")\r\n self.dateEdit_2 = QtWidgets.QDateEdit(self.DateSelection_GroupBox)\r\n self.dateEdit_2.setGeometry(QtCore.QRect(210, 100, 131, 31))\r\n self.dateEdit_2.setCalendarPopup(True)\r\n self.dateEdit_2.setObjectName(\"dateEdit_2\")\r\n self.label_3 = QtWidgets.QLabel(self.DateSelection_GroupBox)\r\n self.label_3.setGeometry(QtCore.QRect(80, 40, 91, 31))\r\n self.label_3.setObjectName(\"label_3\")\r\n self.label_4 = QtWidgets.QLabel(self.DateSelection_GroupBox)\r\n self.label_4.setGeometry(QtCore.QRect(80, 100, 81, 21))\r\n self.label_4.setObjectName(\"label_4\")\r\n self.GroupBy_GroupBox = QtWidgets.QGroupBox(Dialog)\r\n self.GroupBy_GroupBox.setGeometry(QtCore.QRect(440, 150, 211, 241))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.GroupBy_GroupBox.setFont(font)\r\n self.GroupBy_GroupBox.setObjectName(\"GroupBy_GroupBox\")\r\n self.year_radioButton = QtWidgets.QRadioButton(self.GroupBy_GroupBox)\r\n self.year_radioButton.setGeometry(QtCore.QRect(70, 40, 121, 21))\r\n self.year_radioButton.setLayoutDirection(QtCore.Qt.LeftToRight)\r\n self.year_radioButton.setObjectName(\"radioButton\")\r\n self.month_radioButton = QtWidgets.QRadioButton(self.GroupBy_GroupBox)\r\n self.month_radioButton.setGeometry(QtCore.QRect(70, 90, 121, 21))\r\n self.month_radioButton.setChecked(True)\r\n self.month_radioButton.setObjectName(\"month_radioButton\")\r\n self.week_radioButton = QtWidgets.QRadioButton(self.GroupBy_GroupBox)\r\n self.week_radioButton.setGeometry(QtCore.QRect(70, 140, 121, 21))\r\n self.week_radioButton.setObjectName(\"week_radioButton\")\r\n self.day_radioButton = QtWidgets.QRadioButton(self.GroupBy_GroupBox)\r\n self.day_radioButton.setGeometry(QtCore.QRect(70, 190, 121, 21))\r\n self.day_radioButton.setObjectName(\"day_radioButton\")\r\n self.DataSelection_GroupBox = QtWidgets.QGroupBox(Dialog)\r\n self.DataSelection_GroupBox.setGeometry(QtCore.QRect(740, 160, 221, 141))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.DataSelection_GroupBox.setFont(font)\r\n self.DataSelection_GroupBox.setObjectName(\"DataSelection_GroupBox\")\r\n self.label_5 = QtWidgets.QLabel(self.DataSelection_GroupBox)\r\n self.label_5.setGeometry(QtCore.QRect(30, 40, 111, 21))\r\n self.label_5.setObjectName(\"label_5\")\r\n self.label_6 = QtWidgets.QLabel(self.DataSelection_GroupBox)\r\n self.label_6.setGeometry(QtCore.QRect(20, 90, 141, 21))\r\n self.label_6.setObjectName(\"label_6\")\r\n self.spinBox = QtWidgets.QSpinBox(self.DataSelection_GroupBox)\r\n self.spinBox.setGeometry(QtCore.QRect(170, 40, 42, 22))\r\n self.spinBox.setObjectName(\"spinBox\")\r\n self.spinBox_2 = QtWidgets.QSpinBox(self.DataSelection_GroupBox)\r\n self.spinBox_2.setGeometry(QtCore.QRect(170, 90, 42, 22))\r\n self.spinBox_2.setObjectName(\"spinBox_2\")\r\n self.label_7 = QtWidgets.QLabel(Dialog)\r\n self.label_7.setGeometry(QtCore.QRect(170, 340, 91, 21))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.label_7.setFont(font)\r\n self.label_7.setObjectName(\"label_7\")\r\n self.Calculate_Button = QtWidgets.QPushButton(Dialog)\r\n self.Calculate_Button.setGeometry(QtCore.QRect(480, 460, 141, 51))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.Calculate_Button.setFont(font)\r\n self.Calculate_Button.setObjectName(\"Calculate_Button\")\r\n self.Graph_Button = QtWidgets.QPushButton(Dialog)\r\n self.Graph_Button.setGeometry(QtCore.QRect(710, 510, 141, 51))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.Graph_Button.setFont(font)\r\n self.Graph_Button.setObjectName(\"Graph_Button\")\r\n self.summary_Button = QtWidgets.QPushButton(Dialog)\r\n self.summary_Button.setGeometry(QtCore.QRect(710, 330, 141, 51))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.summary_Button.setFont(font)\r\n self.summary_Button.setObjectName(\"summary_Button\")\r\n self.details_Button = QtWidgets.QPushButton(Dialog)\r\n self.details_Button.setGeometry(QtCore.QRect(710, 420, 141, 51))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.details_Button.setFont(font)\r\n self.details_Button.setObjectName(\"details_Button\")\r\n self.clear_Button = QtWidgets.QPushButton(Dialog)\r\n self.clear_Button.setGeometry(QtCore.QRect(950, 20, 111, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.clear_Button.setFont(font)\r\n self.clear_Button.setObjectName(\"clear_Button\")\r\n self.credit_Checkbox = QtWidgets.QCheckBox(Dialog)\r\n self.credit_Checkbox.setGeometry(QtCore.QRect(920, 400, 81, 20))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.credit_Checkbox.setFont(font)\r\n self.credit_Checkbox.setChecked(True)\r\n self.credit_Checkbox.setObjectName(\"checkBox\")\r\n self.debit_Checkbox = QtWidgets.QCheckBox(Dialog)\r\n self.debit_Checkbox.setGeometry(QtCore.QRect(920, 450, 81, 20))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.debit_Checkbox.setFont(font)\r\n self.debit_Checkbox.setChecked(True)\r\n self.debit_Checkbox.setObjectName(\"debit_Checkbox\")\r\n self.savings_Checkbox = QtWidgets.QCheckBox(Dialog)\r\n self.savings_Checkbox.setGeometry(QtCore.QRect(920, 500, 81, 20))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.savings_Checkbox.setFont(font)\r\n self.savings_Checkbox.setObjectName(\"savings_Checkbox\")\r\n self.textBrowser = QtWidgets.QTextBrowser(Dialog)\r\n self.textBrowser.setGeometry(QtCore.QRect(30, 370, 361, 192))\r\n font = QtGui.QFont()\r\n font.setPointSize(9)\r\n self.textBrowser.setFont(font)\r\n self.textBrowser.setObjectName(\"textBrowser\")\r\n self.textBrowser_2 = QtWidgets.QTextBrowser(Dialog)\r\n self.textBrowser_2.setGeometry(QtCore.QRect(130, 20, 531, 100))\r\n font = QtGui.QFont()\r\n font.setPointSize(9)\r\n self.textBrowser_2.setFont(font)\r\n self.textBrowser_2.setObjectName(\"textBrowser_2\")\r\n self.grid_Checkbox = QtWidgets.QCheckBox(Dialog)\r\n self.grid_Checkbox.setGeometry(QtCore.QRect(920, 350, 81, 20))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.grid_Checkbox.setFont(font)\r\n self.grid_Checkbox.setChecked(True)\r\n self.grid_Checkbox.setObjectName(\"grid_Checkbox\")\r\n self.other_bank_Checkbox = QtWidgets.QCheckBox(Dialog)\r\n self.other_bank_Checkbox.setGeometry(QtCore.QRect(690, 90, 121, 21))\r\n font = QtGui.QFont()\r\n font.setPointSize(9)\r\n self.other_bank_Checkbox.setFont(font)\r\n self.other_bank_Checkbox.setObjectName(\"other_bank_Checkbox\")\r\n self.convert_Button = QtWidgets.QPushButton(Dialog)\r\n self.convert_Button.setGeometry(QtCore.QRect(820, 80, 111, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.convert_Button.setFont(font)\r\n self.convert_Button.setObjectName(\"convert_Button\")\r\n self.Open_Button.clicked.connect(self.get_file_name)\r\n self.Default_Button.clicked.connect(self.find_default)\r\n self.Calculate_Button.clicked.connect(self.calculate)\r\n self.Graph_Button.clicked.connect(self.graph_plot)\r\n self.summary_Button.clicked.connect(self.show_summary_table)\r\n self.details_Button.clicked.connect(self.show_details)\r\n self.clear_Button.clicked.connect(self.clear)\r\n self.convert_Button.clicked.connect(self.convert_german)\r\n self.other_bank_Checkbox.stateChanged.connect(self.check_german)\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.textBrowser.setFont(font)\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n self.textBrowser_2.setFont(font)\r\n\r\n self.retranslateUi(Dialog)\r\n QtCore.QMetaObject.connectSlotsByName(Dialog)\r\n\r\n def retranslateUi(self, Dialog):\r\n _translate = QtCore.QCoreApplication.translate\r\n Dialog.setWindowTitle(_translate(\"Dialog\", \"Bank Balance Analyser (German version)\"))\r\n self.label.setText(_translate(\"Dialog\", \"File Name\"))\r\n self.Open_Button.setText(_translate(\"Dialog\", \"Open\"))\r\n self.Default_Button.setText(_translate(\"Dialog\", \"Default\"))\r\n self.Copyright_label.setText(_translate(\"Dialog\", \"Developed by Arpan Ghosh\"))\r\n self.DateSelection_GroupBox.setTitle(_translate(\"Dialog\", \"Date Selection\"))\r\n self.label_3.setText(_translate(\"Dialog\", \"Start Date\"))\r\n self.label_4.setText(_translate(\"Dialog\", \"End Date\"))\r\n self.GroupBy_GroupBox.setTitle(_translate(\"Dialog\", \" Group By\"))\r\n self.year_radioButton.setText(_translate(\"Dialog\", \"Year\"))\r\n self.month_radioButton.setText(_translate(\"Dialog\", \"Month\"))\r\n self.week_radioButton.setText(_translate(\"Dialog\", \"Week\"))\r\n self.day_radioButton.setText(_translate(\"Dialog\", \"Day\"))\r\n self.DataSelection_GroupBox.setTitle(_translate(\"Dialog\", \"Data Selection\"))\r\n self.label_5.setText(_translate(\"Dialog\", \"Skip Top Rows\"))\r\n self.label_6.setText(_translate(\"Dialog\", \"Skip Bottom Rows\"))\r\n self.label_7.setText(_translate(\"Dialog\", \"Log Record\"))\r\n self.Calculate_Button.setText(_translate(\"Dialog\", \"Calculate\"))\r\n self.Graph_Button.setText(_translate(\"Dialog\", \"Graph\"))\r\n self.summary_Button.setText(_translate(\"Dialog\", \"Summary\"))\r\n self.details_Button.setText(_translate(\"Dialog\", \"Details\"))\r\n self.clear_Button.setText(_translate(\"Dialog\", \"Clear\"))\r\n self.credit_Checkbox.setText(_translate(\"Dialog\", \" Credit\"))\r\n self.debit_Checkbox.setText(_translate(\"Dialog\", \"Debit\"))\r\n self.savings_Checkbox.setText(_translate(\"Dialog\", \"Savings\"))\r\n self.grid_Checkbox.setText(_translate(\"Dialog\", \"Grid\"))\r\n self.other_bank_Checkbox.setText(_translate(\"Dialog\", \" Sparda Bank\"))\r\n self.convert_Button.setText(_translate(\"Dialog\", \"Convert\"))\r\n\r\n def get_file_name(self):\r\n self.clear()\r\n self.filename = QFileDialog.getOpenFileName(None, 'Open file', expanduser('~'),\"Excel files (*.xls , *.csv)\")\r\n self.textBrowser_2.setText(self.filename[0])\r\n if self.filename[0]:\r\n self.textBrowser.append(\"successfully Located File : \"+self.filename[0]+'\\n')\r\n self.Default_Button.setEnabled(True)\r\n if self.other_bank_Checkbox.isChecked():\r\n f_handle = open(self.filename[0],'r')\r\n raw_data = f_handle.read()\r\n if raw_data.startswith('Txn Date\tValue Date\tDescription\tCredit\tDebit') == False:\r\n self.auto_convert()\r\n else:\r\n self.textBrowser.append(\"Converted file Identified !\\n\")\r\n if self.filename[0]:\r\n if self.other_bank_Checkbox.isChecked():\r\n if raw_data.startswith('Txn Date\tValue Date\tDescription\tCredit\tDebit'):\r\n self.textBrowser_2.setText(self.filename[0])\r\n else:\r\n self.textBrowser_2.setText(\" [CONVERTED] \"+self.filename[0])\r\n self.find_default()\r\n else:\r\n self.textBrowser.append(\"No File Chosen !\\n\")\r\n self.Default_Button.setEnabled(False)\r\n self.year_radioButton.setEnabled(False)\r\n self.month_radioButton.setEnabled(False)\r\n self.week_radioButton.setEnabled(False)\r\n self.day_radioButton.setEnabled(False)\r\n self.Calculate_Button.setEnabled(False)\r\n self.Graph_Button.setEnabled(False)\r\n self.summary_Button.setEnabled(False)\r\n self.details_Button.setEnabled(False)\r\n self.credit_Checkbox.setEnabled(False)\r\n self.debit_Checkbox.setEnabled(False)\r\n self.savings_Checkbox.setEnabled(False)\r\n self.dateEdit.setEnabled(False)\r\n self.dateEdit_2.setEnabled(False)\r\n #self.spinBox.setEnabled(False)\r\n #self.spinBox_2.setEnabled(False)\r\n\r\n def to_float(self,value):\r\n try:\r\n if type(value) == str:\r\n value = value.replace(',','')\r\n f_ = float(value)\r\n return f_\r\n except Exception:\r\n return 0.0\r\n \r\n\r\n\r\n def find_default(self):\r\n try:\r\n file_path = self.filename[0]\r\n f = open(file_path,'r')\r\n my_list = f.read().split('\\n')\r\n skip_r = 18\r\n for text in my_list:\r\n if text.startswith('Txn Date'):\r\n skip_r = my_list.index(text)\r\n break\r\n if skip_r == 0:\r\n skip_f = 0\r\n else:\r\n skip_f = 3\r\n\r\n data = pandas.read_csv(file_path,sep = '\\t',skiprows=skip_r,skipfooter=skip_f,engine='python',encoding=\"ISO-8859-1\")\r\n #data = pandas.read_excel(file_path,skiprows=skip_r,skipfooter=skip_f)\r\n try:\r\n data['Debit'] = data[' Debit']\r\n except KeyError:\r\n self.textBrowser.append('Debit Spacing Already fixed\\n')\r\n try:\r\n data['Txn Date'] = data['Txn Date'].apply(lambda x: datetime.strptime(x, '%d %b %Y'))\r\n data['Value Date'] = data['Value Date'].apply(lambda x: datetime.strptime(x, '%d %b %Y'))\r\n except:\r\n data['Txn Date'] = data['Txn Date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\r\n data['Value Date'] = data['Value Date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\r\n start_date = str(data['Txn Date'].min().date())\r\n end_date = str(data['Txn Date'].max().date())\r\n data['Credit'] = data['Credit'].apply(lambda x: self.to_float(x))\r\n data['Debit'] = data['Debit'].apply(lambda x: self.to_float(x))\r\n #self.dateEdit.setDateTime(QtCore.QDateTime.fromString(start_date, 'yyyy-mm-dd'))\r\n #self.dateEdit_2.setDateTime(QtCore.QDateTime.fromString(end_date, \"yyyy-mm-dd\"))\r\n l = start_date.split('-')\r\n self.dateEdit.setDate(QtCore.QDate(int(l[0]),int(l[1]),int(l[2])))\r\n l = end_date.split('-')\r\n self.dateEdit_2.setDate(QtCore.QDate(int(l[0]),int(l[1]),int(l[2])))\r\n self.month_radioButton.setChecked(True)\r\n self.spinBox.setValue(skip_r)\r\n self.spinBox_2.setValue(skip_f)\r\n self.textBrowser.append('Default Values are now set \\n')\r\n \r\n except Exception as e:\r\n self.textBrowser.append(\"ERROR : \"+str(e)+'\\n')\r\n self.textBrowser.append('Warning : Default File format is not matching [may need conversion to proper format] !\\n\\nUnable to set Default Values ! Try Manual Adjustments \\n\\n(Make sure you give a valid date range and proper top and bottom skip row values including newline characters.)\\n\\nThen Try the Calculate button\\n')\r\n\r\n self.year_radioButton.setEnabled(True)\r\n self.month_radioButton.setEnabled(True)\r\n self.week_radioButton.setEnabled(True)\r\n self.day_radioButton.setEnabled(True)\r\n self.dateEdit.setEnabled(True)\r\n self.dateEdit_2.setEnabled(True)\r\n self.spinBox.setEnabled(True)\r\n self.spinBox_2.setEnabled(True)\r\n self.Calculate_Button.setEnabled(True)\r\n self.credit_Checkbox.setEnabled(True)\r\n self.debit_Checkbox.setEnabled(True)\r\n self.savings_Checkbox.setEnabled(True)\r\n \r\n\r\n def clear(self):\r\n self.Default_Button.setEnabled(False)\r\n self.year_radioButton.setEnabled(False)\r\n self.month_radioButton.setEnabled(False)\r\n self.week_radioButton.setEnabled(False)\r\n self.day_radioButton.setEnabled(False)\r\n self.Calculate_Button.setEnabled(False)\r\n self.Graph_Button.setEnabled(False)\r\n self.summary_Button.setEnabled(False)\r\n self.details_Button.setEnabled(False)\r\n self.credit_Checkbox.setEnabled(False)\r\n self.debit_Checkbox.setEnabled(False)\r\n self.savings_Checkbox.setEnabled(False)\r\n self.grid_Checkbox.setEnabled(False)\r\n self.dateEdit.setEnabled(False)\r\n self.dateEdit_2.setEnabled(False)\r\n #self.spinBox.setEnabled(False)\r\n #self.spinBox_2.setEnabled(False)\r\n self.textBrowser.setText('')\r\n self.textBrowser_2.setText('')\r\n self.check_german()\r\n\r\n def calculate(self):\r\n skip_r = self.spinBox.value()\r\n skip_f = self.spinBox_2.value()\r\n try:\r\n data = pandas.read_csv(self.filename[0],sep = '\\t',skiprows=skip_r,skipfooter=skip_f,engine='python',encoding=\"ISO-8859-1\")\r\n try:\r\n data['Debit'] = data[' Debit']\r\n except KeyError:\r\n self.textBrowser.append(\"Looks like Debit spacing already adjusted !\\n\")\r\n data['Credit'] = data['Credit'].apply(lambda x: self.to_float(x))\r\n data['Debit'] = data['Debit'].apply(lambda x: self.to_float(x))\r\n data.sort_values(\"Value Date\",ascending = True, inplace = True) \r\n data.reset_index(drop = True)\r\n try:\r\n data['Txn Date'] = data['Txn Date'].apply(lambda x: datetime.strptime(x, '%d %b %Y'))\r\n data['Value Date'] = data['Value Date'].apply(lambda x: datetime.strptime(x, '%d %b %Y'))\r\n except:\r\n data['Txn Date'] = data['Txn Date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\r\n data['Value Date'] = data['Value Date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\r\n start_date = str(self.dateEdit.date().toPyDate())\r\n end_date = str(self.dateEdit_2.date().toPyDate())\r\n data['year_month_date'] = data['Value Date'].dt.to_period(\"D\")\r\n data['year_month'] = data['Value Date'].dt.to_period(\"M\")\r\n data['year'] = data['Value Date'].dt.to_period(\"Y\")\r\n data['week'] = data['Value Date'].dt.year.apply(str)+'_'+data['Value Date'].dt.week.apply(str)\r\n condition_1 = data['year_month_date']>=pandas.Period(start_date,'D')\r\n condition_2 = data['year_month_date']<=pandas.Period(end_date,'D')\r\n data = data[condition_1 & condition_2]\r\n if self.year_radioButton.isChecked():\r\n data_gb = data.groupby('year')\r\n self.choice = 'Y'\r\n elif self.month_radioButton.isChecked():\r\n data_gb = data.groupby('year_month')\r\n self.choice = 'M'\r\n elif self.week_radioButton.isChecked():\r\n data_gb = data.groupby('week')\r\n self.choice = 'W'\r\n else:\r\n data_gb = data.groupby('year_month_date')\r\n self.choice = 'D'\r\n credit,debit,saving,key_list = [],[],[],[]\r\n Total_C = 0\r\n Total_D = 0\r\n count = 0\r\n for key,df in data_gb:\r\n C = sum(df['Credit'].apply(lambda x: 0 if (x == ' ') else x).tolist())\r\n D = sum(df['Debit'].apply(lambda x: 0 if (x == ' ') else x).tolist())\r\n Total_C += C\r\n Total_D += D\r\n count += 1\r\n credit.append(C)\r\n debit.append(D)\r\n saving.append(C-D)\r\n key_list.append(key)\r\n summary = pandas.DataFrame({'key':key_list,'credit':credit,'debit':debit,'saving':saving})\r\n if self.choice == 'W':\r\n summary['key_year'] = summary['key']\r\n summary['key_week'] = summary['key']\r\n summary['key_year'] = summary['key_year'].apply(lambda x : int(x.split('_')[0]))\r\n summary.key_year = summary.key_year.astype(int)\r\n summary['key_week'] = summary['key_week'].apply(lambda x : int(x.split('_')[1]))\r\n summary.key_week = summary.key_week.astype(int)\r\n summary = summary.sort_values(['key_year','key_week'])\r\n summary = summary.drop(columns=['key_week', 'key_year'])\r\n else:\r\n summary = summary.sort_values('key',ascending = False)\r\n\r\n if self.choice == 'W':\r\n summary['key_'] = summary['key']\r\n what = 'week'\r\n elif self.choice == 'M':\r\n summary['key_'] = summary['key'].apply(lambda x : x.strftime(\"%Y-%b\"))\r\n what = 'month'\r\n elif self.choice == 'Y':\r\n summary['key_'] = summary['key'].apply(lambda x : x.strftime(\"%Y\"))\r\n what = 'year'\r\n elif self.choice == 'D':\r\n summary['key_'] = summary['key'].apply(lambda x : x.strftime(\"%Y-%b-%d\"))\r\n what = 'day'\r\n self.summary = summary\r\n self.Max_C = data[data.Credit == data.Credit.max()]\r\n self.Max_D = data[data.Debit == data.Debit.max()]\r\n self.info = \"Total Credit over the given Period : \"+str(round(Total_C,3))+'\\n'+\"Total Debit over the given Period : \"+str(round(Total_D,3))+'\\n'+\"Average Credit per \"+what+\" Over the given period : \"+str(round((Total_C/count),3))+'\\n'+\"Average Debit per \"+what+\" Over the given period :\"+str(round(Total_D/count,3))+'\\n'+\"Average Retention per \"+what+\" Over the given period : \"+str(round((Total_C-Total_D)/count,3))+'\\n'+\"Standard Deviation of Credit : \"+str(round(summary.credit.std(ddof=0),4))+'\\n'+\"Standard Deviation of Debit : \"+str(round(summary.debit.std(ddof=0),4))+'\\n'\r\n self.info += \"Maximum Credit of \"+str(self.Max_C['Credit'].to_list()[0])+\" was caused on \"+str(self.Max_C['Value Date'].to_list()[0])+\" because \"+str(self.Max_C['Description'].to_list()[0])+'\\n'+\"Maximum Debit of \"+str(self.Max_D['Debit'].to_list()[0])+\" was caused on \"+str(self.Max_D['Value Date'].to_list()[0])\r\n self.info += \" because \"+str(self.Max_D['Description'].to_list()[0])+'\\n'\r\n self.summary = summary\r\n self.Graph_Button.setEnabled(True)\r\n self.grid_Checkbox.setEnabled(True)\r\n self.summary_Button.setEnabled(True)\r\n self.details_Button.setEnabled(True)\r\n self.textBrowser.append('Calculations Successfully Performed\\n')\r\n\r\n except Exception as e:\r\n self.textBrowser.append(\"ERROR : \"+str(e)+'\\nYour data is not Supported for calculation !\\n')\r\n\r\n def graph_plot(self):\r\n self.calculate()\r\n sort_out = ['key','key_']\r\n if self.credit_Checkbox.isChecked():\r\n sort_out.append('credit')\r\n if self.debit_Checkbox.isChecked():\r\n sort_out.append('debit')\r\n if self.savings_Checkbox.isChecked():\r\n sort_out.append('saving')\r\n if self.choice != 'W':\r\n ax = self.summary[sort_out].plot(kind = 'bar',x = 'key',title = 'Bank data Analyser by Arpan Ghosh',logy = False)\r\n else:\r\n ax = self.summary[sort_out].plot(kind = 'bar',title = 'Bank data Analyser by Arpan Ghosh',logy = False)\r\n ax.set_xticklabels(self.summary['key_'])\r\n if self.choice == 'W':\r\n ax.set_xlabel(\"Year_Week-number\")\r\n elif self.choice == 'M':\r\n ax.set_xlabel(\"Year_Month-name\")\r\n elif self.choice == 'Y':\r\n ax.set_xlabel(\"Year\")\r\n elif self.choice == 'D':\r\n ax.set_xlabel(\"Date\")\r\n ax.set_ylabel(\"Amount\")\r\n if self.grid_Checkbox.isChecked():\r\n plt.grid()\r\n plt.show()\r\n self.textBrowser.append('Graph Displayed.\\n')\r\n #self.Graph_Button.setEnabled(False)\r\n #self.grid_Checkbox.setEnabled(False)\r\n \r\n def show_summary_table(self):\r\n self.calculate()\r\n sort_out = ['key','key_']\r\n if self.credit_Checkbox.isChecked():\r\n sort_out.append('credit')\r\n if self.debit_Checkbox.isChecked():\r\n sort_out.append('debit')\r\n if self.savings_Checkbox.isChecked():\r\n sort_out.append('saving')\r\n self.data_ = self.summary[sort_out]\r\n self.data_window = Show_data()\r\n self.data_window.show()\r\n self.textBrowser.append('Summary Table Displayed.\\n')\r\n #self.summary_Button.setEnabled(False)\r\n return self.data_window\r\n\r\n def show_details(self):\r\n self.calculate()\r\n self.info_Dialog = QtWidgets.QDialog()\r\n self.info_window = Show_info()\r\n self.info_window.setupUi(self.info_Dialog)\r\n self.info_Dialog.show()\r\n return self.info_window\r\n\r\n def check_german(self):\r\n if self.other_bank_Checkbox.isChecked():\r\n #self.convert_Button.setEnabled(True)\r\n self.spinBox.setValue(11)\r\n self.spinBox_2.setValue(1)\r\n self.spinBox_2.setEnabled(True)\r\n self.spinBox.setEnabled(True)\r\n self.textBrowser.append(\"Sparda Bank is checked : open button expects sparda bank satatement format.\\n\\nYou may need to adjust the skip row values before pressing 'Open' button otherwise auto-conversion may fail.\\n\")\r\n else:\r\n self.convert_Button.setEnabled(False)\r\n self.spinBox.setValue(0)\r\n self.spinBox_2.setValue(0)\r\n #self.spinBox_2.setEnabled(False)\r\n #self.spinBox.setEnabled(False)\r\n self.textBrowser.append(\"Sparda Bank is Unchecked : open button expects SBI bank statement format\\n\")\r\n\r\n def convert_german(self):\r\n\r\n def fix_date(string):\r\n lst = string.split('.')\r\n return pandas.Timestamp(int(lst[2]),int(lst[1]),int(lst[0]))\r\n\r\n def amount_splitter(value,credit = True):\r\n value = value.replace('.','')\r\n value = value.replace(',','.')\r\n value = float(value)\r\n if credit:\r\n if value > 0:\r\n return value\r\n else:\r\n return 0.00\r\n else:\r\n if value > 0:\r\n return 0.00\r\n else:\r\n return -value\r\n\r\n \r\n try:\r\n filename_1 = QFileDialog.getOpenFileName(None, 'Open file', expanduser('~'),\"CSV files (*.csv )\")[0]\r\n if filename_1:\r\n self.data_ = pandas.read_csv(filename_1,sep=';',engine='python',names = ['Txn Date','Value Date','Description','amount','EUR','NONE'],skiprows = self.spinBox.value(),skipfooter = self.spinBox_2.value())\r\n del self.data_['NONE']\r\n del self.data_['EUR']\r\n self.data_['Txn Date'] = self.data_['Txn Date'].apply(lambda x : fix_date(x))\r\n self.data_['Value Date'] = self.data_['Value Date'].apply(lambda x : fix_date(x))\r\n self.data_['Credit'] = self.data_['amount'].apply(lambda x : amount_splitter(x,True))\r\n self.data_['Debit'] = self.data_['amount'].apply(lambda x : amount_splitter(x,False))\r\n del self.data_['amount']\r\n self.textBrowser.append('\\nConversion Successful !\\n')\r\n self.data_window = Show_data()\r\n self.data_window.show()\r\n return self.data_window\r\n else:\r\n self.textBrowser.append(\"No file chosen !\\n\")\r\n except Exception as e:\r\n self.textBrowser.append(\"ERROR : \"+str(e)+'\\nEither your file is not Supported for Convertion or skip row values are not correct (Default values are not being supported) !\\n')\r\n\r\n def auto_convert(self):\r\n def fix_date(string):\r\n lst = string.split('.')\r\n return pandas.Timestamp(int(lst[2]),int(lst[1]),int(lst[0]))\r\n\r\n def amount_splitter(value,credit = True):\r\n value = value.replace('.','')\r\n value = value.replace(',','.')\r\n value = float(value)\r\n if credit:\r\n if value > 0:\r\n return value\r\n else:\r\n return 0.00\r\n else:\r\n if value > 0:\r\n return 0.00\r\n else:\r\n return -value\r\n\r\n \r\n try:\r\n filename_1 = self.filename[0]\r\n self.data_ = pandas.read_csv(filename_1,sep=';',engine='python',encoding=\"ISO-8859-1\",names = ['Txn Date','Value Date','Description','amount','EUR','NONE'],skiprows = self.spinBox.value(),skipfooter = self.spinBox_2.value())\r\n del self.data_['NONE']\r\n del self.data_['EUR']\r\n self.data_['Txn Date'] = self.data_['Txn Date'].apply(lambda x : fix_date(x))\r\n self.data_['Value Date'] = self.data_['Value Date'].apply(lambda x : fix_date(x))\r\n self.data_['Credit'] = self.data_['amount'].apply(lambda x : amount_splitter(x,True))\r\n self.data_['Debit'] = self.data_['amount'].apply(lambda x : amount_splitter(x,False))\r\n del self.data_['amount']\r\n self.textBrowser.append('Attempting to convert file autometically !\\n')\r\n save_name = os.path.split(filename_1)[0]+'\\\\'+os.path.split(filename_1)[1].split('.')[0]+'_converted'+'.'+os.path.split(filename_1)[1].split('.')[1]\r\n if os.path.exists(save_name):\r\n self.textBrowser.append('Unable to save '+save_name+\" : FILE ALREADY EXISTS !\\n\\nAborting Auto conversion , please try convert button to convert and save manually then open that file usinf 'open' button.\\n\")\r\n self.textBrowser_2.setText('')\r\n self.filename = ['','']\r\n else:\r\n self.data_.to_csv(save_name,encoding=\"ISO-8859-1\",index=False,sep='\\t')\r\n self.textBrowser.append('File written as \"'+save_name+' And Loading from that file\\n')\r\n self.filename = [save_name,'']\r\n\r\n except Exception as e:\r\n self.textBrowser.append(\"ERROR : \"+str(e)+\"\\nAuto-conversion is not supporting ( Already converted or Unknown format ) \\n\\nPlease try manual conversion button, save and then open it IN CASE OF FURTHUS ERRORS ONLY\\n\")\r\n #raise e\r\n self.convert_Button.setEnabled(True)\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n Dialog = QtWidgets.QDialog()\r\n ui = Ui_Dialog()\r\n ui.setupUi(Dialog)\r\n ui.Default_Button.setEnabled(False)\r\n ui.year_radioButton.setEnabled(False)\r\n ui.month_radioButton.setEnabled(False)\r\n ui.week_radioButton.setEnabled(False)\r\n ui.day_radioButton.setEnabled(False)\r\n ui.Calculate_Button.setEnabled(False)\r\n ui.Graph_Button.setEnabled(False)\r\n ui.summary_Button.setEnabled(False)\r\n ui.details_Button.setEnabled(False)\r\n ui.credit_Checkbox.setEnabled(False)\r\n ui.debit_Checkbox.setEnabled(False)\r\n ui.savings_Checkbox.setEnabled(False)\r\n ui.dateEdit.setEnabled(False)\r\n ui.dateEdit_2.setEnabled(False)\r\n #ui.spinBox.setEnabled(False)\r\n #ui.spinBox_2.setEnabled(False)\r\n ui.grid_Checkbox.setEnabled(False)\r\n ui.convert_Button.setEnabled(False)\r\n ui.other_bank_Checkbox.setChecked(GERMAN_VERSION)\r\n Dialog.show()\r\n sys.exit(app.exec_())","sub_path":"myprojects-Python_3/Bank Balance Analyser/Bank_GUI_clean.py","file_name":"Bank_GUI_clean.py","file_ext":"py","file_size_in_byte":39550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"139416785","text":"import os\n\nimport pandas as pd\nimport pytest\n\nfrom pandas_datareader.compat import PY3\nfrom pandas_datareader.tiingo import TiingoDailyReader, TiingoMetaDataReader, \\\n TiingoQuoteReader, get_tiingo_symbols\n\nTEST_API_KEY = os.getenv('TIINGO_API_KEY')\n# Ensure blank TEST_API_KEY not used in pull request\nTEST_API_KEY = None if not TEST_API_KEY else TEST_API_KEY\n\nsyms = ['GOOG', ['GOOG', 'XOM']]\nids = list(map(str, syms))\n\n\n@pytest.fixture(params=syms, ids=ids)\ndef symbols(request):\n return request.param\n\n\n@pytest.mark.skipif(TEST_API_KEY is None, reason=\"TIINGO_API_KEY not set\")\ndef test_tiingo_quote(symbols):\n df = TiingoQuoteReader(symbols=symbols).read()\n assert isinstance(df, pd.DataFrame)\n if isinstance(symbols, str):\n symbols = [symbols]\n assert df.shape[0] == len(symbols)\n\n\n@pytest.mark.skipif(TEST_API_KEY is None, reason=\"TIINGO_API_KEY not set\")\ndef test_tiingo_historical(symbols):\n df = TiingoDailyReader(symbols=symbols).read()\n assert isinstance(df, pd.DataFrame)\n if isinstance(symbols, str):\n symbols = [symbols]\n assert df.index.levels[0].shape[0] == len(symbols)\n\n\n@pytest.mark.skipif(TEST_API_KEY is None, reason=\"TIINGO_API_KEY not set\")\ndef test_tiingo_metadata(symbols):\n df = TiingoMetaDataReader(symbols=symbols).read()\n assert isinstance(df, pd.DataFrame)\n if isinstance(symbols, str):\n symbols = [symbols]\n assert df.shape[1] == len(symbols)\n\n\n@pytest.mark.skipif(not PY3, reason='test.support missing on Python 2')\ndef test_tiingo_no_api_key(symbols):\n from test.support import EnvironmentVarGuard\n env = EnvironmentVarGuard()\n env.unset('TIINGO_API_KEY')\n with env:\n with pytest.raises(ValueError):\n TiingoMetaDataReader(symbols=symbols)\n\n\ndef test_tiingo_stock_symbols():\n sym = get_tiingo_symbols()\n assert isinstance(sym, pd.DataFrame)\n","sub_path":"book_env/Lib/site-packages/pandas_datareader/tests/test_tiingo.py","file_name":"test_tiingo.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"607683762","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom bp import get_fich\nfrom models import Pages\nfrom django.core.exceptions import ObjectDoesNotExist\n# Create your views here.\ndef listar(request):\n Lista = Pages.objects.all()\n response = \"
      \"\n for page in Lista:\n response += \"
    1. \"+page.name + \"\"\n response += \"
    \"\n return HttpResponse(response)\n\ndef insertar(request,name,page):\n newpage = Pages(name = name, page=page)\n newpage.save()\n response=\"200 OK\"\n return HttpResponse(response)\n\ndef mostrar(request, identificador):\n try:\n\n barrapunto = get_fich()\n page = Pages.objects.get(id=identificador)\n response = page.page +'
    '+ barrapunto\n except ObjectDoesNotExist:\n response = \"Does not exist\"\n return HttpResponse(response)\n","sub_path":"contentAppBarraPunto/barrapunto/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"508181506","text":"# This is zju_psytest for collecting testing reservation data.\n# By Pengyu CHEN (cpy.prefers.you[at]gmail.com)\n# COPY LEFT, ALL WRONGS RESERVED.\n\nimport os\nimport misc\n\n# Paths\n# For use of OpenShift\ndata_path = os.getenv('OPENSHIFT_DATA_DIR', '~/app-root/data/')\nrepo_path = os.getenv('OPENSHIFT_REPO_DIR', '~/app-root/runtime/repo/')\ntemp_path = os.getenv('OPENSHIFT_TMP_DIR', '/tmp/')\n# For local testing\ntry:\n os.environ['OPENSHIFT_DATA_DIR']\nexcept KeyError:\n data_path = '../data/'\n repo_path = '../'\n temp_path = '/tmp/'\nwsgi_path = repo_path + './wsgi/'\ndb_path = data_path + './dbfile'\ndb_schema_path = wsgi_path + './db_schema.sql'\nstatic_path = wsgi_path + './static/'\nview_path = wsgi_path + './view/'\n\n# Misc\ndebug = False\nsecret = misc.md5('Aperture Science Stringified Secret Container')\nsalt = misc.md5('Aperture Science Stringified Salt Container')\nsession_key = misc.md5('session_id')\n\n","sub_path":"wsgi/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"93625296","text":"#!/usr/bin/env python\n# coding=utf-8\n# author: Tianfeng Shi\n# 2020/2/13 22:58\n#\n\nfrom data import get_raw_data, imputed_file_writer, data_generator, data_generator_using_matrix, getLDMatrix, getGMatrix, multi_process_imputed_file_writer\nfrom keras import models\nfrom keras import layers\nimport numpy as np\nimport time\nimport sys\nfrom keras.preprocessing.text import Tokenizer\n# from keras.utils import to_categorical\nfrom miss_generator import make_missing_data\nimport matplotlib.pyplot as plt\nimport pickle\nimport os\nfrom sklearn.metrics import classification_report\nfrom keras.utils.np_utils import to_categorical\nfrom contextlib import contextmanager\n@contextmanager\ndef timer(name=\"time\", level=\"normal\"):\n start_time = time.time()\n yield\n end_time = time.time()\n if level == \"important\":\n print('\\033[1;31;40m{}:{}\\033[0m'.format(name, end_time - start_time))\n else:\n print('{}:{}'.format(name, end_time - start_time))\n\n\nseq_size = 100000\nnum_epochs = 50\ntrain_rate = 0.8\nval_rate = 0.1\ntest_rate = 0.1\n\n# window_size = 2 * N\n#N_cross_snp = 5 # the number of different snp alleles besides the loci\n#N_cross_ind = 0 # the number of different ind alleles besides the loci\n\n\ndef build_model():\n \"\"\"\n 构建网络\n :return:\n \"\"\"\n model = models.Sequential()\n \n model.add(layers.LSTM(32, return_sequences=True))\n model.add(layers.LSTM(64, return_sequences=True))\n model.add(layers.LSTM(64, return_sequences=True))\n model.add(layers.LSTM(32, return_sequences=True))\n # 分成0 1 2三类 多分类的激活函数使用softmax\n model.add(layers.Dense(3, activation='softmax'))\n\n # 多分类的损失函数使用categorical_crossentropy\n model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n return model\n\n\ndef load_data(train_data, train_label, test_data):\n m_d = np.reshape(train_data, (len(train_data), 1, len(train_data[0])))\n\n d = np.reshape(train_label, (len(train_label), 1, 1))\n x = np.array(test_data)\n test_d = np.reshape(x, (len(test_data), 1, len(test_data[0])))\n return m_d, d, test_d\n\n\ndef matrixToT(data):\n data_array = np.array(data)\n # 元素是每个ind\n return data_array.T\n\n\ndef train_test_file_decoder(single_line):\n samples = []\n s = single_line.strip('\\n').split('\\n')\n for item in s:\n samples += item.split('\\t')\n #samples = single_line.strip('\\n').split('\\t')\n\n results = []\n for sample in samples:\n #results.append(list(map(float, sample.split())))\n results.append(sample.split())\n return results\n\ndef test_file_decoder(single_line):\n samples = []\n s = single_line.strip('\\n').split(':')\n for item in s:\n temp = item.strip('\\n').split('\\t')\n if temp[0]:\n samples += temp\n\n results = []\n for sample in samples:\n results.append(sample.split())\n return results\n\n\ndef train_label_decoder(single_line):\n results = []\n s = single_line.strip('\\n').split('\\n')\n for item in s:\n results += item.split('\\t')\n #results = single_line.strip('\\n').split('\\t')\n\n return results\n\n\ndef main_func(snp_num, data_dir, train_file_num):\n \"\"\"\n 数据预处理\n \"\"\"\n\n # 读取数据\n raw_file_name = data_dir + \"sub_geno_file\" + str(train_file_num) + \".txt\"\n\n with timer(name='get geno data', level='important'):\n # raw_geno_data_with_miss, header_data, prev_data = get_raw_data(file=file_name) # 得到0125序列 5代表缺失\n with open(raw_file_name, 'r') as f:\n data_list = []\n prev_data_list = []\n for l in f:\n data_line = l.strip('\\n').split('\\t')\n prev_data_list.append(data_line[:9])\n data_list.append(data_line[9:])\n\n raw_geno_data_with_miss = []\n for data in data_list:\n line = []\n for allele in data:\n final_allele = sum(list(map(int, allele.split(\"|\"))))\n line.append(5 if final_allele >= 5 else final_allele)\n raw_geno_data_with_miss.append(line)\n\n geno_data_with_miss_array_snps = np.array(raw_geno_data_with_miss)\n\n # 填充好的数据\n imputed_data = []\n\n # 训练数据文件\n train_data_file_name = data_dir + \"sub_train_file\" + str(train_file_num) + \".txt\"\n\n # 训练标签文件\n train_label_file_name = data_dir + \"sub_train_label_file\" + str(train_file_num) + \".txt\"\n\n # 测试数据文件\n test_data_file_name = data_dir + \"sub_test_file\" + str(train_file_num) + \".txt\"\n\n '''\n 读取训练数据\n '''\n with open(train_data_file_name, 'r') as f:\n train_seq = f.readlines()\n\n with open(train_label_file_name, 'r') as f:\n train_label_seq = f.readlines()\n\n with open(test_data_file_name, 'r') as f:\n test_seq = f.readlines()\n\n '''\n 构建并训练模型\n '''\n # 针对每个marker\n for snp in range(0, int(snp_num)):\n print(\"----------------------------------------------------------------\")\n print(\"MODEL\", str(snp + 1), \":\")\n with timer():\n '''\n 读取该maker的数据\n '''\n # print(\"DATA LOADING......\")\n #with timer(\"读取该marker的数据\"):\n train_data_list = train_test_file_decoder(train_seq[snp])\n train_label_list = train_label_decoder(train_label_seq[snp])\n test_data_list = test_file_decoder(test_seq[snp])\n\n '''\n 预处理成张量\n '''\n # print(\"DATA PREPROCESSING......\")\n #with timer(\"预处理成张量\"):\n train_data, train_labels, test_data = load_data(train_data_list, train_label_list, test_data_list)\n\n # one-hot编码\n train_labels = to_categorical(train_labels, num_classes=3)\n\n '''\n 构建模型\n '''\n # print(\"MODEL BUILDING......\")\n #with timer(\"构建模型\"):\n model = build_model()\n\n '''\n 训练模型\n '''\n #with timer(\"训练模型\", \"important\"):\n history = model.fit(train_data, train_labels, epochs=num_epochs, batch_size=128, validation_split=0.15,\n verbose=False)\n\n '''\n 输入缺失位点数据到模型，生成预测\n '''\n #with timer(\"生成预测\"):\n pred = model.predict(test_data)\n\n pred_list = [np.argmax(item) for item in pred]\n \n '''\n 将预测结果加入到原数据中\n '''\n raw_seq = geno_data_with_miss_array_snps[snp]\n \n raw_seq[np.where(raw_seq == 5)[0]] = pred_list\n\n imputed_seq = raw_seq\n\n imputed_data.append(imputed_seq)\n '''\n 写入新文件\n '''\n #with timer(name='writing result file', level='important'):\n new_file_name = data_dir+\"sub_result\" + str(train_file_num) + \".txt\"\n\n multi_process_imputed_file_writer(new_file_name, prev_data_list, imputed_data)\n\n return\n\n\nif __name__ == '__main__':\n snp_num = sys.argv[1]\n data_dir = sys.argv[2]\n train_file_num = sys.argv[3]\n print('This_is_file ' + train_file_num)\n main_func(snp_num=snp_num, data_dir=data_dir, train_file_num=train_file_num)\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"models/multi_process_train_program_LSTM_LSTM_LSTM_LSTM.py","file_name":"multi_process_train_program_LSTM_LSTM_LSTM_LSTM.py","file_ext":"py","file_size_in_byte":7382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"378670667","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 8 22:20:33 2019\n@author: saurabh2\n\"\"\"\n\n#API request to get data\n#You can find details about the code in jupyter notebook folder of the repo\ndef nearest_neighbours(show_id):\n import requests as req\n url = 'https://api.tvmaze.com/schedule?country=US'\n resp = req.get(url)\n response = resp.text\n\n import json\n data = json.loads(response)\n\n from pandas.io.json import json_normalize\n df = json_normalize(data)\n df_first = df.copy()\n df_first['id']\n df_relevant = df[['name','id','show.name','show.genres','show.rating.average','show.type']]\n df_relevant[\"full_name\"] = df_relevant[\"show.name\"].map(str) +\"-\"+ df_relevant[\"name\"]\n df_relevant = df_relevant.drop(['name','show.name'], axis=1)\n renamed_columns_dictionary = {'show.genres': 'genres',\n 'show.language': 'language',\n 'show.rating.average':'rating',\n 'show.type':'type'\n }\n df_relevant.rename(columns=renamed_columns_dictionary, inplace=True)\n df_relevant = df_relevant.drop(df_relevant[df_relevant['genres'].str.len() == 0].index)\n df = df_relevant.copy()\n df[\"rating\"].fillna(df[\"rating\"].median(), inplace = True)\n df['genres'] = df['genres'].apply(lambda x: \",\".join(x))\n import pandas as pd\n tv_show_features = pd.concat([df[\"genres\"].str.get_dummies(sep=\",\"),\n pd.get_dummies(df[[\"type\"]]),\n df[[\"rating\"]]],axis=1)\n from sklearn.preprocessing import MinMaxScaler\n min_max_scaler = MinMaxScaler()\n tv_show_features = min_max_scaler.fit_transform(tv_show_features)\n from sklearn.neighbors import NearestNeighbors\n nbrs = NearestNeighbors(n_neighbors=6, algorithm='ball_tree').fit(tv_show_features)\n distances, indices = nbrs.kneighbors(tv_show_features)\n indices\n\n def get_index_from_name(name):\n return df[df[\"full_name\"]==name].index.tolist()[0]\n\n def get_index_from_id(id):\n return df_first[df_first[\"id\"]==id].index.tolist()[0]\n\n all_show_names = list(df.full_name.values)\n\n def get_id_from_partial_name(partial):\n for name in all_show_names:\n if partial in name:\n print(name,all_show_names.index(name))\n\n def print_similar_tvshows(id=None):\n related_showids=[]\n if id:\n for iditer in indices[get_index_from_id(id)][1:]:\n related_showids.append(df_first.iloc[iditer][\"id\"])\n return related_showids\n\n return print_similar_tvshows(show_id)\n","sub_path":"serverfiles/rec_using_knn.py","file_name":"rec_using_knn.py","file_ext":"py","file_size_in_byte":2667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"291075343","text":"#!/usr/bin/env python\n# coding=utf-8\n\n\nimport requests\nimport re\nimport datetime\nimport calendar\nfrom pymongo import MongoClient\nfrom pyquery import PyQuery as pq\nfrom multiprocessing import Pool\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n\nclient = MongoClient()\ndb = client['todaydb']\n\nhistory_url = 'http://m.lssdjt.com/'\n# params = {\"date\": \"2017-11-7\"}\nheaders = {'content-type': 'application/json',\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:22.0) Gecko/20100101 Firefox/22.0'}\n\n\ndef get_date(month):\n year = \"2017\"\n num = calendar.monthrange(int(year), month)[1] + 1\n for date in range(1, num):\n date = year + \"-\" + str(month) + \"-\" + str(date)\n params = {\"date\": date}\n yield params\n\n\ndef get_link(params):\n dict_link = {}\n resp = requests.get(history_url, params=params, headers=headers)\n if resp.status_code == requests.codes.ok:\n resp.encoding = \"utf-8\"\n doc = pq(resp.text)(\"div\")(\"li\")(\"a\")\n for i in doc.items():\n dict_link[i.text()] = i.attr('href')\n else:\n pass\n return dict_link\n\n\ndef catch_contents(params):\n dl = get_link(params)\n # print(dl)\n for key in dl.keys():\n today_url = history_url + dl[key]\n resp = requests.get(today_url)\n resp.encoding = 'utf-8'\n if resp.status_code == requests.codes.ok:\n doc = pq(resp.text.replace(\" \", \"%\"))(\"ul\")\n doc(\"img\").filter(\".hcode\").remove()\n doc(\"img\").filter(\".hidcode\").remove()\n doc(\"img\").filter(\".hicode\").remove()\n doc(\"img\").filter(\".hidecode\").remove()\n pattern = re.compile('<[\"/a\"a][^>]+>')\n text = re.sub(pattern, \"\", doc.html().replace(\"%\", \" \"))\n t_date = datetime.datetime.strptime(params['date'], \"%Y-%m-%d\").strftime(\"%m%d\")\n post = {\n \"title\": key,\n \"t_date\": t_date,\n \"text\": text,\n \"date\": datetime.datetime.utcnow()\n }\n posts = db.posts\n posts.insert(post)\n\n\nif __name__ == \"__main__\":\n pool = ThreadPool(4)\n pool.map(catch_contents, get_date(10))\n pool.close()\n pool.join()\n\n","sub_path":"site/Code/History/today.py","file_name":"today.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"193145873","text":"\n\nfrom xai.brain.wordbase.nouns._voter import _VOTER\n\n#calss header\nclass _VOTERS(_VOTER, ):\n\tdef __init__(self,): \n\t\t_VOTER.__init__(self)\n\t\tself.name = \"VOTERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"voter\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_voters.py","file_name":"_voters.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"454387304","text":"from django.shortcuts import render, HttpResponse\nfrom cmdb.models import Asset, Server, CPU, RAM, Disk, NIC ,Manufactory\n\n\n# Create your views here.\n\n\ndef index(request):\n serverinfo = {\n \"cpu_information\": {\n \"manufactory\": \"GenuineIntel\",\n \"cpu_count\": 1,\n \"cpu_core_count\": 1,\n \"cpu_model\": \"Intel(R) Xeon(R) CPU E5-2682 v4 @ 2.50GHz\"\n },\n \"essential_information\": {\n \"manufactory\": \"Alibaba Cloud\",\n \"os_type\": \"Ubuntu\",\n \"kernel_release\": \"4.4.0-63-generic\",\n \"SN\": \"5ad48cfe-3cbf-4110-ac46-95d697e213bf\",\n \"os_release\": \"14.04\",\n \"os_distribution\": \"Ubuntu 14.04.5 LTS\",\n \"model\": \"Alibaba Cloud ECS\",\n \"hostname\": \"iZ2ze4cwodiz4t64z887v4Z\"\n },\n \"disk_information\": [\n {\n \"capacity\": 40960,\n \"name\": \"vda\"\n },\n {\n \"capacity\": 20480,\n \"name\": \"vdb\"\n },\n {\n \"capacity\": 1024,\n \"name\": \"sr0\"\n }\n ],\n \"interfaces_information\": [\n {\n \"ip_address\": \"127.0.0.1\",\n \"netmask\": \"255.0.0.0\",\n \"name\": \"lo\",\n \"macaddress\": \"\"\n },\n {\n \"ip_address\": \"172.17.199.190\",\n \"netmask\": \"255.255.240.0\",\n \"name\": \"eth0\",\n \"macaddress\": \"00:16:3e:06:3e:76\"\n }\n ],\n \"memory_information\": {\n \"capacity\": 992\n }\n }\n # factory_obj = Manufactory()\n # factory_obj.save()\n\n\n # print(factory_obj)\n # factory_obj.save()\n # asset_obj = Asset(asset_type='server',\n # name=serverinfo['essential_information']['hostname'],\n # sn=serverinfo['essential_information']['SN'],\n # manufactory=factory_obj,\n # status=0\n # )\n # asset_obj.save()\n # server_obj = Server(asset=asset_obj,\n # created_by='auto',\n # model=serverinfo['essential_information']['model'],\n # os_type=serverinfo['essential_information']['os_type'],\n # os_distribution=serverinfo['essential_information']['os_distribution'],\n # os_release=serverinfo['essential_information']['os_release'],\n # kernel_release=serverinfo['essential_information']['kernel_release'],\n # )\n # server_obj.save()\n\n # factory_obj = Manufactory.objects.filter(name=serverinfo['cpu_information']['manufactory'], ).first()\n # if not factory_obj:\n # factory_obj = Manufactory(name=serverinfo['cpu_information']['manufactory'])\n # factory_obj.save()\n # asset_obj = Asset.objects.filter(name=serverinfo['essential_information']['hostname']).first()\n # print(asset_obj)\n # cpu_obj = CPU(asset=asset_obj,\n # model=serverinfo['cpu_information']['cpu_model'],\n # count=serverinfo['cpu_information']['cpu_count'],\n # core_count=serverinfo['cpu_information']['cpu_core_count'],\n # )\n # cpu_obj.save()\n\n # asset_obj = Asset.objects.filter(name=serverinfo['essential_information']['hostname']).first()\n # print(asset_obj)\n # for diskinfo in serverinfo['disk_information']:\n # disk_obj = Disk(name=diskinfo['name'],\n # capacity=diskinfo['capacity'],\n # asset=asset_obj\n # )\n # disk_obj.save()\n\n\n # asset_obj = Asset.objects.filter(name=serverinfo['essential_information']['hostname']).first()\n # print(asset_obj)\n #\n # for nic_info in serverinfo['interfaces_information']:\n # nic_obj = NIC(asset=asset_obj,\n # name=nic_info['name'],\n # ip_address=nic_info['ip_address'],\n # netmask=nic_info['netmask'],\n # mac_address=nic_info['macaddress']\n # )\n # nic_obj.save()\n\n\n # asset_obj = Asset.objects.filter(name=serverinfo['essential_information']['hostname']).first()\n # print(asset_obj)\n #\n # ram_obj = RAM(asset=asset_obj,capacity=serverinfo['memory_information']['capacity'])\n # ram_obj.save()\n return render(request, 'index.html', {})\n\n\ndef get_server_list(request):\n import json\n server_objs = Asset.objects.filter(asset_type='server').all()\n server_list = []\n for server_obj in server_objs:\n server_list.append(server_obj.get_base_info())\n return HttpResponse(json.dumps(server_list))\n","sub_path":"cmdb/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"500306908","text":"#\n# Copyright 2012 Quantopian, Inc.\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\nimport pytz\nimport numbers\n\nfrom hashlib import md5\nfrom datetime import datetime\nfrom itertools import izip_longest\nfrom zipline.protocol import (\n DATASOURCE_TYPE,\n Event\n)\n\n\ndef mock_raw_event(sid, dt):\n event = {\n 'sid': sid,\n 'dt': dt,\n 'price': 1.0,\n 'volume': 1\n }\n return event\n\n\ndef alternate(g1, g2):\n \"\"\"Specialized version of roundrobin for just 2 generators.\"\"\"\n for e1, e2 in izip_longest(g1, g2):\n if e1 is not None:\n yield e1\n if e2 is not None:\n yield e2\n\n\ndef hash_args(*args, **kwargs):\n \"\"\"Define a unique string for any set of representable args.\"\"\"\n arg_string = '_'.join([str(arg) for arg in args])\n kwarg_string = '_'.join([str(key) + '=' + str(value)\n for key, value in kwargs.iteritems()])\n combined = ':'.join([arg_string, kwarg_string])\n\n hasher = md5()\n hasher.update(combined)\n return hasher.hexdigest()\n\n\ndef create_trade(sid, price, amount, datetime, source_id=\"test_factory\"):\n\n trade = Event()\n\n trade.source_id = source_id\n trade.type = DATASOURCE_TYPE.TRADE\n trade.sid = sid\n trade.dt = datetime\n trade.price = price\n trade.close = price\n trade.open = price\n trade.low = price * .95\n trade.high = price * 1.05\n trade.volume = amount\n\n return trade\n\n\ndef assert_datasource_protocol(event):\n \"\"\"Assert that an event meets the protocol for datasource outputs.\"\"\"\n\n assert isinstance(event.source_id, basestring)\n assert event.type in DATASOURCE_TYPE\n\n # Done packets have no dt.\n if not event.type == DATASOURCE_TYPE.DONE:\n assert isinstance(event.dt, datetime)\n assert event.dt.tzinfo == pytz.utc\n\n\ndef assert_trade_protocol(event):\n \"\"\"Assert that an event meets the protocol for datasource TRADE outputs.\"\"\"\n assert_datasource_protocol(event)\n\n assert event.type == DATASOURCE_TYPE.TRADE\n assert isinstance(event.sid, int)\n assert isinstance(event.price, numbers.Real)\n assert isinstance(event.volume, numbers.Integral)\n assert isinstance(event.dt, datetime)\n\n\ndef assert_datasource_unframe_protocol(event):\n \"\"\"Assert that an event is valid output of zp.DATASOURCE_UNFRAME.\"\"\"\n assert isinstance(event.source_id, basestring)\n assert event.type in DATASOURCE_TYPE\n\n\ndef assert_sort_protocol(event):\n \"\"\"Assert that an event is valid input to zp.FEED_FRAME.\"\"\"\n assert isinstance(event.source_id, basestring)\n assert event.type in DATASOURCE_TYPE\n\n\ndef assert_sort_unframe_protocol(event):\n \"\"\"Same as above.\"\"\"\n assert isinstance(event.source_id, basestring)\n assert event.type in DATASOURCE_TYPE\n\n\ndef assert_merge_protocol(tnfm_ids, message):\n \"\"\"Merge should output an ndict with a field for each id\n in its transform set.\"\"\"\n assert set(tnfm_ids) == set(message.keys())\n","sub_path":"zipline/gens/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"202036031","text":"# coding: utf-8\n\n\"\"\"\n Cloudbreak API\n\n Cloudbreak is a powerful left surf that breaks over a coral reef, a mile off southwest the island of Tavarua, Fiji. Cloudbreak is a cloud agnostic Hadoop as a Service API. Abstracts the provisioning and ease management and monitoring of on-demand clusters. SequenceIQ's Cloudbreak is a RESTful application development platform with the goal of helping developers to build solutions for deploying Hadoop YARN clusters in different environments. Once it is deployed in your favourite servlet container it exposes a REST API allowing to span up Hadoop clusters of arbitary sizes and cloud providers. Provisioning Hadoop has never been easier. Cloudbreak is built on the foundation of cloud providers API (Amazon AWS, Microsoft Azure, Google Cloud Platform, Openstack), Apache Ambari, Docker lightweight containers, Swarm and Consul. For further product documentation follow the link: http://hortonworks.com/apache/cloudbreak/\n\n OpenAPI spec version: 2.9.0\n \n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nfrom pprint import pformat\nfrom six import iteritems\nimport re\n\n\nclass ClusterRepairRequest(object):\n \"\"\"\n NOTE: This class is auto generated by the swagger code generator program.\n Do not edit the class manually.\n \"\"\"\n\n\n \"\"\"\n Attributes:\n swagger_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n swagger_types = {\n 'host_groups': 'list[str]',\n 'remove_only': 'bool'\n }\n\n attribute_map = {\n 'host_groups': 'hostGroups',\n 'remove_only': 'removeOnly'\n }\n\n def __init__(self, host_groups=None, remove_only=False):\n \"\"\"\n ClusterRepairRequest - a model defined in Swagger\n \"\"\"\n\n self._host_groups = None\n self._remove_only = None\n\n self.host_groups = host_groups\n if remove_only is not None:\n self.remove_only = remove_only\n\n @property\n def host_groups(self):\n \"\"\"\n Gets the host_groups of this ClusterRepairRequest.\n List of hostgroups where the failed nodes will be repaired\n\n :return: The host_groups of this ClusterRepairRequest.\n :rtype: list[str]\n \"\"\"\n return self._host_groups\n\n @host_groups.setter\n def host_groups(self, host_groups):\n \"\"\"\n Sets the host_groups of this ClusterRepairRequest.\n List of hostgroups where the failed nodes will be repaired\n\n :param host_groups: The host_groups of this ClusterRepairRequest.\n :type: list[str]\n \"\"\"\n if host_groups is None:\n raise ValueError(\"Invalid value for `host_groups`, must not be `None`\")\n\n self._host_groups = host_groups\n\n @property\n def remove_only(self):\n \"\"\"\n Gets the remove_only of this ClusterRepairRequest.\n If true, the failed nodes will only be removed, otherwise the failed nodes will be removed and new nodes will be started.\n\n :return: The remove_only of this ClusterRepairRequest.\n :rtype: bool\n \"\"\"\n return self._remove_only\n\n @remove_only.setter\n def remove_only(self, remove_only):\n \"\"\"\n Sets the remove_only of this ClusterRepairRequest.\n If true, the failed nodes will only be removed, otherwise the failed nodes will be removed and new nodes will be started.\n\n :param remove_only: The remove_only of this ClusterRepairRequest.\n :type: bool\n \"\"\"\n\n self._remove_only = remove_only\n\n def to_dict(self):\n \"\"\"\n Returns the model properties as a dict\n \"\"\"\n result = {}\n\n for attr, _ in iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"\n Returns the string representation of the model\n \"\"\"\n return pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"\n For `print` and `pprint`\n \"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"\n Returns true if both objects are equal\n \"\"\"\n if not isinstance(other, ClusterRepairRequest):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"\n Returns true if both objects are not equal\n \"\"\"\n return not self == other\n","sub_path":"whoville/cloudbreak/models/cluster_repair_request.py","file_name":"cluster_repair_request.py","file_ext":"py","file_size_in_byte":5200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"649511905","text":"import re\nfrom collections import Counter, defaultdict\nfrom functools import lru_cache\nfrom glob import glob\nfrom pathlib import Path\nfrom typing import Collection, Dict, List, Iterable\nfrom warnings import warn\n\nfrom pandas import DataFrame\n\n\nclass GeneSet:\n\n def __init__(self, name: str, genes: Collection[str], description: str = None, warn_if_empty=True):\n self.name = name\n self.genes = frozenset(genes)\n self.description = description\n\n if warn_if_empty and self.is_empty:\n warn(f'GeneSet {repr(name)} is empty')\n\n redundant_genes = None\n\n if len(genes) != len(self.genes):\n redundant_genes = {gene: count for gene, count in Counter(genes).items() if count > 1}\n\n warn(f'GeneSet {repr(name)} received a non-unique collection of genes; redundant genes: {redundant_genes}')\n\n self.redundant_genes = redundant_genes\n\n @classmethod\n def from_gmt_line(cls, line, **kwargs):\n name, description, *ids = line.strip().split('\\t')\n return cls(name, ids, description, **kwargs)\n\n @property\n def is_empty(self):\n return len(self.genes) == 0\n\n def __repr__(self):\n genes = ': ' + (', '.join(sorted(self.genes))) if len(self.genes) < 5 else ''\n return f''\n\n def __eq__(self, other: 'GeneSet'):\n return (\n self.name == other.name\n and\n self.genes == other.genes\n )\n\n def __hash__(self):\n return hash((self.name, self.genes))\n\n\nclass GeneSets:\n\n def __init__(self, gene_sets: Collection[GeneSet], name='', allow_redundant=False, remove_empty=True, path=None):\n self.gene_sets = tuple(gene_sets) # NOTE: this is not final\n self.name = name\n self.path = path\n if not allow_redundant:\n redundant = self.find_redundant()\n if any(redundant):\n message = 'Provided gene sets are not redundant; '\n if len(redundant) > 3:\n message += (\n f'there are {len(redundant)} gene sets having more than one name assigned; '\n 'use `find_redundant()` to investigate further.'\n )\n else:\n identical = ', '.join(\n ' and '.join(map(repr, pathways)) + f' ({len(gene_set)} genes)'\n for gene_set, pathways in redundant.items()\n )\n message += f'following gene sets are identical: {identical}'\n warn(message)\n\n empty_gene_sets = {gene_set for gene_set in gene_sets if gene_set.is_empty}\n\n if len(empty_gene_sets) != 0:\n empty_message = (\n ', '.join(gene_set.name for gene_set in empty_gene_sets)\n if len(empty_gene_sets) <= 5 else\n 'use `empty_gene_sets` property to investigate further.'\n )\n warn(f'There are {len(empty_gene_sets)} empty gene sets: {empty_message}')\n\n if remove_empty:\n gene_sets = set(gene_sets) - empty_gene_sets\n warn(f'{len(empty_gene_sets)} empty gene sets were removed.')\n\n self.empty_gene_sets = empty_gene_sets\n self.gene_sets = tuple(gene_sets)\n\n def group_identical(self, key='name') -> Dict[frozenset, List[str]]:\n pathways_by_gene_set = defaultdict(list)\n for pathway in self.gene_sets:\n pathways_by_gene_set[pathway.genes].append(getattr(pathway, key))\n return pathways_by_gene_set\n\n def find_redundant(self, key='name', min_duplicates=1) -> Dict[frozenset, List[str]]:\n return {\n gene_set: pathways\n for gene_set, pathways in self.group_identical(key=key).items()\n if len(pathways) > min_duplicates\n }\n\n @classmethod\n def from_gmt(cls, path, name=None, **kwargs):\n with open(path) as f:\n return cls(\n {\n GeneSet.from_gmt_line(line, warn_if_empty=False)\n for line in f\n },\n name=name or Path(path).name,\n path=path,\n **kwargs\n )\n\n def trim(self, min_genes: int = 0, max_genes: int = float('Inf')):\n return GeneSets({\n gene_set\n for gene_set in self.gene_sets\n if min_genes <= len(gene_set.genes) <= max_genes\n })\n\n def format_names(self, formatter):\n for gene_set in self.gene_sets:\n gene_set.name = formatter(gene_set.name)\n return self\n\n def _to_gmt(self, f):\n for gene_set in self.gene_sets:\n f.write(gene_set.name + '\\t' + '\\t'.join(gene_set.genes) + '\\n')\n\n def to_gmt(self, path):\n if isinstance(path, str):\n with open(path, mode='w') as f:\n self._to_gmt(f)\n else:\n self._to_gmt(path)\n\n def subset(self, genes: Iterable[str]):\n if not isinstance(genes, set):\n genes = set(genes)\n return GeneSets({\n GeneSet(name=gene_set.name, genes=gene_set.genes & genes, warn_if_empty=False)\n for gene_set in self.gene_sets\n })\n\n @property\n @lru_cache()\n def all_genes(self):\n all_genes = set()\n\n for gene_set in self.gene_sets:\n all_genes.update(gene_set.genes)\n\n return all_genes\n\n def to_frame(self) -> DataFrame:\n all_genes = self.all_genes\n return DataFrame(\n [\n [\n gene in gene_set.genes\n for gene in all_genes\n ]\n for gene_set in self.gene_sets\n ],\n index=[gene_set.name for gene_set in self.gene_sets],\n columns=all_genes\n )\n\n @property\n @lru_cache()\n def gene_sets_by_name(self):\n by_name = {\n gene_set.name: gene_set\n for gene_set in self.gene_sets\n }\n assert len(self.gene_sets) == len(by_name)\n return by_name\n\n def __len__(self):\n return len(self.gene_sets)\n\n def __repr__(self):\n name = ' ' + repr(self.name) if self.name else ''\n return f''\n\n def __eq__(self, other: 'GeneSets'):\n return (\n set(self.gene_sets) == set(other.gene_sets)\n and\n self.name == other.name\n )\n\n def __hash__(self):\n return hash((self.name, self.gene_sets))\n\n\n# for backwards compatibility\nGeneMatrixTransposed = GeneSets\n\n\nclass MolecularSignaturesDatabase:\n def __init__(self, path, version='7.1'):\n self.path = Path(path)\n if not self.path.exists():\n raise ValueError(f'Could not find MSigDB: {self.path} does not exist')\n self.version = str(version)\n wildcard_path = str((self.path / f'*.v{self.version}.*.gmt').resolve())\n self.gene_sets = [\n self.parse_name(Path(p).name)\n for p in glob(wildcard_path)\n ]\n\n def parse_name(self, name):\n parsed = re.match(rf'(?P.*?)\\.v{self.version}\\.(?P(entrez|symbols)).gmt', name)\n return parsed.groupdict()\n\n def resolve(self, gene_sets, id_type):\n path = self.path / f'{gene_sets}.v{self.version}.{id_type}.gmt'\n if path.exists():\n return str(path)\n else:\n raise ValueError(f'Unknown library: {path}!')\n\n def load(self, gene_sets, id_type) -> GeneSets:\n path = self.resolve(gene_sets=gene_sets, id_type=id_type)\n\n return GeneSets.from_gmt(path, name=gene_sets)\n","sub_path":"gsea_api/molecular_signatures_db.py","file_name":"molecular_signatures_db.py","file_ext":"py","file_size_in_byte":7691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"307086885","text":"#!/usr/bin/python3\n\"\"\"new view for State objects that handles all\ndefault RestFul API actions\n\"\"\"\nfrom flask import Flask, jsonify, make_response, abort, request\nfrom api.v1.views import app_views\nfrom models import storage\nfrom models.state import State\n\n\n@app_views.route(\"/states\", methods=['GET'], strict_slashes=False)\n@app_views.route(\"/states/\", methods=['GET'], strict_slashes=False)\ndef state_view(state_id=None):\n \"\"\"\n Retrieves the list of all State objects\n \"\"\"\n if state_id:\n st = storage.get(State, state_id)\n if st is None:\n abort(404)\n return jsonify(st.to_dict())\n else:\n states = [value.to_dict() for value in storage.all(State).values()]\n return jsonify(states)\n\n\n@app_views.route(\n \"/states/\", methods=['DELETE'], strict_slashes=False)\ndef delete_state(state_id):\n \"\"\"Deletes a State object\"\"\"\n st = storage.get(State, state_id)\n if st is None:\n abort(404)\n storage.delete(st)\n storage.save()\n return make_response(jsonify({}), 200)\n\n\n@app_views.route(\"/states\", methods=['POST'], strict_slashes=False)\ndef post_state():\n \"\"\"Create a State\"\"\"\n content = request.get_json()\n if content:\n if content.get('name'):\n new_state = State(**content)\n new_state.save()\n return jsonify(new_state.to_dict()), 201\n abort(400, \"Missing name\")\n else:\n abort(400, \"Not a JSON\")\n\n\n@app_views.route(\"/states/\", methods=['PUT'], strict_slashes=False)\ndef update_state(state_id):\n \"\"\"Updates a State object\"\"\"\n st = storage.get(State, state_id)\n if st is None:\n abort(404)\n else:\n content = request.get_json()\n if content:\n keys_ignored = ['id', 'created_at', 'updated_at']\n for key, value in content.items():\n if key not in keys_ignored:\n setattr(st, key, value)\n st.save()\n return jsonify(st.to_dict()), 200\n else:\n abort(400, \"Not a JSON\")\n","sub_path":"api/v1/views/states.py","file_name":"states.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"451482522","text":"from matplotlib.pyplot import *\n# from PyQt5 import *\n\nfrom numpy import linspace, pi, sin\n\n# plot sin(x) for some interval\nx = linspace(-2 * pi, 2 * pi, 200)\n\n# To get a new clean figure window:\nfigure(1)\ntitle('any title can be applied under function figure(1)')\nplot(x, sin(x))\n\n# plot marker for every 4th point\nsamples = x[::4]\n\n# To get a new clean figure window:\nfigure(2)\nplot(samples, sin(samples), 'r*')\n\n# add title and grid lines\ntitle('Function sin(x) and some points plotted')\ngrid()\n","sub_path":"plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"632760826","text":"'''\nAuthor: Audrey Mbogho\nDate: 20/01/2021\nPurpose: Calculates the area of a\ncircle using a radius provide by\nth user\n'''\n#from math import pi\n\nPI = 3.14\nr = float(input(\"Enter a radius: \")) # r is the radius\narea1 = PI * r * r\n\nprint(\"The area of your circle is \" + str(area1))\n\n# print(\"The area of your circle is\", area1)\n\n# builtin functions are automaticall available.\n# your don't have to import any library to use them\n# examples are: len, max, min, abs\n# write a program that asks the user for a length and a width\n# and prints out the area of the rectangle.\n","sub_path":"exam/jan20.py","file_name":"jan20.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"268808023","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Event, MeetingMinutes, Meeting, Resource\nfrom .forms import ResourceForm, MeetingForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.urls import reverse_lazy\n\n# Create your views here.\ndef index(request):\n return render(request, 'Club/index.html')\n\ndef getResources(request):\n resources_list=Resource.objects.all()\n context={'resources_list' : resources_list }\n return render(request, 'Club/resources.html', context=context)\n\ndef getMeetings(request):\n meetings_list=Meeting.objects.all()\n return render(request, 'Club/meetings.html',{'meetings_list' : meetings_list})\n\ndef getMeetingsDetail(request, id):\n meetdetail_list=get_object_or_404(Meeting, pk=id)\n context={\n 'meetdetail_list' : meetdetail_list,\n }\n return render (request, 'Club/meetingsdetails.html', context=context)\n\n#Forms Views\n@login_required\ndef newResource(request):\n form=ResourceForm\n if request.method == 'POST':\n form = ResourceForm(request.POST)\n if form.is_valid():\n post=form.save(commit=True)\n post.save()\n form=ResourceForm()\n else:\n form=ResourceForm()\n return render(request, 'Club/newresource.html', {'form': form})\n\n\n@login_required\ndef newMeeting(request):\n form=MeetingForm\n if request.method == 'POST':\n form = MeetingForm(request.POST)\n if form.is_valid():\n post=form.save(commit=True)\n post.save()\n form=MeetingForm()\n else:\n form=MeetingForm()\n return render(request, 'Club/newmeeting.html', {'form': form})\n\n\ndef loginmessage(request):\n return render(request, 'Club/loginmessage.html')\n\ndef logoutmessage(request):\n return render(request, 'Club/logoutmessage.html')\n","sub_path":"PythonClub/Club/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"571139785","text":"import rospy\nfrom sensor_msgs.msg import Image\nimport cv2\nimport thread\nimport numpy as np\n\nimage = None\n\n\n\ndef image_callback(data):\n # print(data)\n # print(\"receiving\")\n global image\n tmp = np.fromstring(data.data, np.uint8)\n image = np.reshape(tmp, (data.height, data.width, 3))\n pass\n\ndef node():\n rospy.init_node('image')\n _sub_image = rospy.Subscriber('/qhd/image_color_rect', Image, image_callback)\n rospy.spin()\n\n\n\ndef thd():\n\n while True:\n if not image is None:\n cv2.imshow(\"x\", image)\n # print(\"..\")\n cv2.waitKey(10)\n\nthread.start_new_thread(thd, ())\nnode()\n","sub_path":"src/test5.py","file_name":"test5.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"419310945","text":"from __future__ import division\nimport math \nimport numpy as np\nimport tensorflow as tf\ndef int_shape(x):\n return list(map(int, x.get_shape()))\ndef log_sum_exp(x):\n \"\"\"\n Numerically stable log_sum_exp implementation that prevents overflow\n log_sum_exp: log(sum(exp(x), axis=-1))\n \n \"\"\"\n axis = len(x.get_shape()) - 1\n m = tf.reduce_max(x, axis)\n m2 = tf.reduce_max(x, axis, keepdims = True)\n return m + tf.log(tf.reduce_sum(tf.exp(x - m2),axis))\n\ndef log_prob_from_logits(x):\n \"\"\"\n Numerically stable log_softmax implementation that prevents overflow\n \"\"\"\n axis = len(x.get_shape()) - 1\n m = tf.reduce_max(x, axis, keepdims = True)\n return x - m - tf.log(tf.reduce_sum(tf.exp(x -m), axis, keepdims = True))\n\ndef discretized_mix_logistic_loss(y_hat, y, num_classes=256,\n\t\tlog_scale_min=-7.0, reduce=True,nr_mix = 10):\n\t'''Discretized mix of logistic distributions loss.\n\tNote that it is assumed that input is scaled to [-1, 1]\n\tArgs:\n\t\ty_hat: Tensor [batch_size, channels, time_length], predicted output.\n\t\ty: Tensor [batch_size, time_length, 1], Target.\n\tReturns:\n\t\tTensor loss\n\t'''\n\n\n\t#[Batch_size, time_length, channels]\n\t# y_hat = tf.transpose(y_hat, [0, 2, 1])\n \n\t#unpack parameters. [batch_size, time_length, num_mixtures] x 3\n \n\tlogit_probs = y_hat[:, :, :nr_mix]\n\tmeans = y_hat[:, :, nr_mix:2 * nr_mix]\n\tlog_scales = tf.maximum(y_hat[:, :, 2* nr_mix: 3 * nr_mix], log_scale_min)\n\n\t#[batch_size, time_length, 1] -> [batch_size, time_length, num_mixtures]\n\ty = y * tf.ones(shape=[1, 1, nr_mix], dtype=tf.float32)\n\n\tcentered_y = y - means\n\tinv_stdv = tf.exp(-log_scales)\n\tplus_in = inv_stdv * (centered_y + 0.5)\n\tcdf_plus = tf.nn.sigmoid(plus_in)\n\tmin_in = inv_stdv * (centered_y - 0.5)\n\tcdf_min = tf.nn.sigmoid(min_in)\n\n\tlog_cdf_plus = plus_in - tf.nn.softplus(plus_in) # log probability for edge case of 0 (before scaling)\n\tlog_one_minus_cdf_min = -tf.nn.softplus(min_in) # log probability for edge case of 255 (before scaling)\n\n\t#probability for all other cases\n\tcdf_delta = cdf_plus - cdf_min\n\n\tmid_in = inv_stdv * centered_y\n\t#log probability in the center of the bin, to be used in extreme cases\n\t#(not actually used in this code)\n\tlog_pdf_mid = mid_in - log_scales - 2. * tf.nn.softplus(mid_in)\n\n\tlog_probs = tf.where(y < -0.999, log_cdf_plus,\n\t\ttf.where(y > 0.999, log_one_minus_cdf_min,\n\t\t\ttf.where(cdf_delta > 1e-5,\n\t\t\t\ttf.log(tf.maximum(cdf_delta, 1e-12)),\n\t\t\t\tlog_pdf_mid - np.log((num_classes - 1) / 2))))\n\t#log_probs = log_probs + tf.nn.log_softmax(logit_probs, -1)\n\n\tlog_probs = log_probs + tf.nn.log_softmax(logit_probs, axis=-1)\n\n\tif reduce:\n\t\treturn -tf.reduce_sum(log_sum_exp(log_probs))\n\telse:\n\t\treturn -tf.expand_dims(log_sum_exp(log_probs), [-1])\n\ndef sample_from_discretized_mix_logistic(y, log_scale_min = -32.23619130191664):\n \n \"\"\"\n Sample from discretized mixture of logistic distributions\n Args:\n y(Tensor): B x T x C\n log_scale (float): log scale minimum value\n \"\"\"\n log_scale_min = float(np.log(1e-14))\n y_shape = y.get_shape().as_list()\n \n nr_mix = y_shape[2] // 3\n logit_probs = y[:, :, :nr_mix] # [B, T, nr_mix]\n # a = tf.random_uniform(tf.shape(logit_probs), minval=1e-5, maxval=1. - 1e-5)\n # print(a)\n # sample mixture indicator from softmax\n temp = tf.random_uniform(tf.shape(logit_probs), minval=1e-5, maxval=1. - 1e-5)\n temp = logit_probs - tf.log(-tf.log(temp))\n argmax = tf.argmax(temp, 2)\n sel = tf.one_hot(argmax,depth=nr_mix, dtype=tf.float32 )\n # sel = tf.one_hot(\n # tf.argmax(\n # logit_probs - tf.log(-tf.log(\n # tf.random_uniform(\n # tf.shape(logit_probs), minval=1e-5, maxval=1. - 1e-5))), 2),\n # depth=nr_mix, dtype=tf.float32)\n\n # select logistic parameters\n means = tf.reduce_sum(y[:, :, nr_mix: 2 * nr_mix] * sel, 2)\n\n log_scales = tf.maximum(tf.reduce_sum(\n y[:, :, 2 * nr_mix: 3 * nr_mix] * sel, 2), log_scale_min)\n\n # sample from logistic & clip to interval\n # we don't actually round to the nearest 8bit value when sampling\n u = tf.random_uniform(tf.shape(means), minval=1e-5, maxval=1. - 1e-5)\n x = means + tf.exp(log_scales) * (tf.log(u) - tf.log(1. - u)) # inverse of sigmoid, [B, T]\n # x = tf.clip_by_value(x, -0.9999999, 0.9999999) # ITU-Ts it necessary?\n x = tf.minimum(tf.maximum(x, -1.), 1.)\n \n # negative log-likelihood\n # z = (x - means) * tf.exp(-log_scales) # z = (x - u) / S\n # log_likelihood = z - log_scales - 2. * tf.nn.softplus(z)\n return x\n","sub_path":"wavenet/mixture.py","file_name":"mixture.py","file_ext":"py","file_size_in_byte":4584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"489804373","text":"import cv2\nimport numpy as np\n\nfrom time import time\nimport datetime\nfrom detector import MotionDetector\nfrom packer import pack_images\nfrom numba import jit\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\n\n@jit(nopython=True)\ndef filter_fun(b):\n return ((b[2] - b[0]) * (b[3] - b[1])) > 300\n\n\nif __name__ == \"__main__\":\n\n cap = cv2.VideoCapture(-1)\n cap.set(cv2.CAP_PROP_FRAME_WIDTH, 960)\n cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 540)\n # cap = PiCamera()\n # cap.resolution = (1280, 720)\n # cap.framerate = 30\n\n detector = MotionDetector(bg_history=10,\n bg_skip_frames=1,\n movement_frames_history=2,\n brightness_discard_level=5,\n bg_subs_scale_percent=0.2,\n pixel_compression_ratio=0.1,\n group_boxes=True,\n expansion_step=5)\n\n # group_boxes=True can be used if one wants to get less boxes, which include all overlapping boxes\n\n b_height = 320\n b_width = 320\n\n res = []\n fc = dict()\n ctr = 0\n # used to record the time when we processed last frame\n prev_frame_time = 0\n\n while True:\n # Capture frame-by-frame\n ret, frame = cap.read()\n if frame is None:\n break\n\n begin = time()\n\n boxes, frame = detector.detect(frame)\n # boxes hold all boxes around motion parts\n\n ## this code cuts motion areas from initial image and\n ## fills \"bins\" of 320x320 with such motion areas.\n ##\n results = []\n if boxes:\n results, box_map = pack_images(frame=frame, boxes=boxes, width=b_width, height=b_height,\n box_filter=filter_fun)\n # box_map holds list of mapping between image placement in packed bins and original boxes\n\n ## end\n\n for b in boxes:\n cv2.rectangle(frame, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 1)\n\n end = time()\n it = (end - begin) * 1000\n\n res.append(it)\n print(\"StdDev: %.4f\" % np.std(res), \"Mean: %.4f\" % np.mean(res), \"Last: %.4f\" % it,\n \"Boxes found: \", len(boxes))\n\n if len(res) > 10000:\n res = []\n\n # idx = 0\n # for r in results:\n # idx += 1\n # cv2.imshow('packed_frame_%d' % idx, r)\n\n ctr += 1\n nc = len(results)\n if nc in fc:\n fc[nc] += 1\n else:\n fc[nc] = 0\n\n if ctr % 100 == 0:\n print(\"Total Frames: \", ctr, \"Packed Frames:\", fc)\n\n # time when we finish processing for this frame\n\n # Calculating the fps\n\n # fps will be number of frame processed in given time frame\n # since their will be most of time error of 0.001 second\n # we will be subtracting it to get more accurate result\n fps = 1 / (begin - prev_frame_time)\n prev_frame_time = begin\n\n # converting the fps into integer\n fps = int(fps)\n\n # converting the fps to string so that we can display it on frame\n # by using putText function\n\n display_text = str(fps)\n\n # Use putText() method for\n # inserting text on video\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.putText(frame,\n display_text,\n (20, 50),\n font, 1,\n (0, 255, 255),\n 2,\n cv2.LINE_4)\n cv2.imshow('last_frame', frame)\n #cv2.imshow('detect_frame', detector.detection_boxed)\n #cv2.imshow('diff_frame', detector.color_movement)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n print(fc, ctr)\n","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":3789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"653815329","text":"from random import choice, randint\nimport numpy as np\nfrom main import *\n\ndef cal_pop_fitness(pop):\n\n # fitness = []\n score = []\n for i in range(pop.shape[0]):\n # fit, sc = run_game_with_ml(display,clock,pop[i])\n scc, sc = main_menu(pop[i])\n print('fitness value of chromosome '+ str(i) +' : ', \"score: \", scc, \" reward: \", sc)\n # fitness.append(fit)\n score.append(sc)\n return np.array(score)\n\ndef select_mating_pool(pop, fitness, num_parents):\n\n # parents = np.empty((num_parents, pop.shape[1]))\n parents = []\n parents_fitness = []\n # print(pop[0, :])\n for parent_num in range(num_parents):\n max_fitness_idx = np.where(fitness == np.max(fitness))\n max_fitness_idx = max_fitness_idx[0][0]\n parents_fitness.append(np.max(fitness))\n\n parents.append(pop[max_fitness_idx, :])\n fitness[max_fitness_idx] = -99999999\n return np.array(parents), parents_fitness\n\ndef getParentIndex(cummFitness, random_value):\n i = -1\n for value in cummFitness:\n if value < random_value:\n i += 1\n else:\n return i\n\ndef uniformCrossover(parents, offspring_size, fitness):\n offspring = np.zeros(offspring_size)\n\n cummFitness = []\n cummFitness.append(fitness[0])\n for i in range(1,len(fitness)):\n cummFitness.append(cummFitness[i-1] + fitness[i])\n\n cummFitness = cummFitness/np.max(cummFitness)\n\n for k in range(offspring_size[0]):\n\n random_value = random.uniform(0, 1)\n parent1_idx = getParentIndex(cummFitness, random_value)\n\n while True:\n random_value = random.uniform(0, 1)\n parent2_idx = getParentIndex(cummFitness, random_value)\n\n if parent1_idx != parent2_idx:\n for j in range(offspring_size[1]):\n if random.uniform(0, 1) < 0.5:\n offspring[k][j] = parents[parent1_idx][j]\n else:\n offspring[k][j] = parents[parent2_idx][j]\n break\n return offspring\n\n\ndef mutation(offspring_crossover):\n\n for idx in range(offspring_crossover.shape[0]):\n for _ in range(25):\n i = randint(0,offspring_crossover.shape[1]-1)\n\n random_value = np.random.choice(np.arange(-1,1,step=0.001),size=(1),replace=False)\n offspring_crossover[idx, i] = offspring_crossover[idx, i] + random_value\n\n return offspring_crossover\n","sub_path":"GA.py","file_name":"GA.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"271590617","text":"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n# In[1]:\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport nltk\r\nimport string\r\nimport sklearn\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\nfrom sklearn import metrics\r\n\r\n\r\n\r\n# In[2]:\r\n\r\n\r\ndef READ (d,D) : \r\n df = pd.read_csv(\"train.csv\",sep = ',', names = [\"Tweet\",\"Target\",\"Stance\",\"Opinion\",\"Sentiment\"],engine = 'python')\r\n data = np.array(df)\r\n data = data[1:,:]\r\n\r\n cnt = 0\r\n for a in data :\r\n if a[1] not in d :\r\n d[a[1]] = cnt\r\n cnt += 1\r\n\r\n for i in range (cnt) :\r\n D.append ([])\r\n\r\n for a in data :\r\n D [d[a[1]]].append ([a[0],a[2]])\r\n\r\ndef READ2 (d,D) : \r\n df = pd.read_csv(\"test.csv\",sep = ',', names = [\"Tweet\",\"Target\",\"Stance\",\"Opinion\",\"Sentiment\"],engine = 'python')\r\n data = np.array(df)\r\n data = data[1:,:]\r\n\r\n cnt = 0\r\n for a in data :\r\n if a[1] not in d :\r\n d[a[1]] = cnt\r\n cnt += 1\r\n\r\n for i in range (cnt) :\r\n D.append ([])\r\n\r\n for a in data :\r\n D [d[a[1]]].append ([a[0],a[2]])\r\n# In[3]:\r\n\r\n\r\ndef clean(text) :\r\n tokens = nltk.word_tokenize(text)\r\n table = str.maketrans('', '', string.punctuation)\r\n ptokens = [w.translate(table) for w in tokens]\r\n non_blank_tokens = [s.lower() for s in ptokens if s]\r\n return nltk.pos_tag(non_blank_tokens)\r\n\r\n\r\n# In[ ]:\r\n\r\n\r\n\r\n\r\n\r\n# In[4]:\r\n\r\n\r\ndef CHANGE(d,D) :\r\n for i in range (len(D)) :\r\n for j in range (len(D[i])) :\r\n D[i][j][0] = clean (D[i][j][0]) \r\n\r\n\r\n# In[ ]:\r\n\r\n\r\n\r\n\r\n\r\n# In[ ]:\r\n\r\n\r\n\r\n\r\n\r\n# In[6]:\r\n\r\n\r\nD = []\r\nd = {}\r\nREAD(d,D)\r\nCHANGE(d,D)\r\n\r\nD2=[]\r\nd2={}\r\nREAD2(d2,D2)\r\nCHANGE(d2,D2)\r\n\r\n\r\n\r\n\r\n\r\n# In[7]:\r\n\r\n\r\nprint (d)\r\nprint(d2)\r\n\r\n\r\n# In[30]:\r\n\r\n\r\ntagset = {'NN','NNS','NNP','VB','VBD','VBJ','VBN','VBP','VBZ','JJ','JJR','JJS'}\r\n\r\n\r\n# In[36]:\r\n\r\n\r\nFEATURES = []\r\nPositions = []\r\nfor i in range (len(d)) :\r\n Features = []\r\n for a in D[i] :\r\n for b in a[0] :\r\n if b[1] in tagset:\r\n Features.append(b[0])\r\n \r\n Features = list(set(Features))\r\n \r\n Pos = {}\r\n for a in range (len(Features)) :\r\n Pos[Features[a]] = a\r\n Positions.append(Pos)\r\n FEATURES.append(Features)\r\n\r\n\r\n# In[39]:\r\n\r\n\r\nOBS = []\r\nfor i in range (len(d)) :\r\n observations = [] \r\n for a in D[i] :\r\n obs = [0] * len(FEATURES[i])\r\n for b in a[0] :\r\n if b[0] in Positions[i] :\r\n obs [Positions[i][b[0]]]= 1\r\n observations.append (obs)\r\n OBS.append (observations)\r\n \r\n\r\nrt={0:3,1:4,2:0,3:1,4:2}\r\nxx={0:\"Hillary\",1:\"Abortion\",2:\"Atheism\",3:\"Climate\",4:\"feminism\"}\r\ns=0\r\n\r\nn2ov = []\r\nypredov = []\r\n\r\nfor ll in range(5):\r\n\r\n n1=[]\r\n n2=[]\r\n for a in D[ll]:\r\n if (a[1]==\"FAVOR\"):\r\n n1.append(0)\r\n elif (a[1]==\"AGAINST\"):\r\n n1.append(1)\r\n elif (a[1]==\"NONE\"):\r\n n1.append(2)\r\n\r\n for a in D2[rt[ll]]:\r\n if (a[1]==\"FAVOR\"):\r\n n2.append(0)\r\n elif (a[1]==\"AGAINST\"):\r\n n2.append(1)\r\n elif (a[1]==\"NONE\"):\r\n n2.append(2)\r\n print (len(n1))\r\n observations2 = [] \r\n for a in D2[rt[ll]] :\r\n obs = [0] * len(FEATURES[ll])\r\n for b in a[0] :\r\n if b[0] in Positions[ll] :\r\n obs [Positions[ll][b[0]]]= 1\r\n observations2.append (obs)\r\n rf = RandomForestClassifier(n_estimators = 100)\r\n# Train the model on training data\r\n rf.fit(OBS[ll], n1);\r\n ypred=rf.predict(observations2)\r\n c=0\r\n t=len(n2)\r\n for j in range(len(n2)):\r\n if (ypred[j]==n2[j]):\r\n c+=1\r\n\r\n n2ov.extend(n2)\r\n ypredov.extend(ypred)\r\n \r\n accuracy=rf.score(observations2,n2)\r\n s+=accuracy\r\n print (xx[ll],accuracy)\r\n\r\n\r\nprint(metrics.classification_report(n2ov, ypredov, labels=[0, 1, 2]))\r\n\r\nprint (\"avg f1 score rand_forest_classifier:\",s/5)","sub_path":"NLP Project/CODES/rf.py","file_name":"rf.py","file_ext":"py","file_size_in_byte":4047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"429604275","text":"from django.urls import include, path\nfrom . import views\n\n\nurlpatterns = [\n path('ingrediente', views.lista_ingredientes),\n path('ingrediente/', views.detalle_ingrediente),\n path('hamburguesa', views.lista_hamburguesas),\n path('hamburguesa/', views.detalle_hamburguesa),\n path('hamburguesa//ingrediente/', views.preparacion),\n path(\"\", views.home)\n]","sub_path":"hamburgueseria/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"97996589","text":"# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.\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\nfrom __future__ import print_function\nimport sys\nimport paddle\nfrom paddle.optimizer import Optimizer\nfrom paddle.fluid.clip import ClipGradByGlobalNorm\nfrom ...utils.hybrid_parallel_util import fused_allreduce_gradients, sharding_reduce_gradients\nfrom ...base.topology import ParallelMode\nfrom paddle.fluid.dygraph import base as imperative_base\nfrom paddle.fluid import framework\nfrom paddle.fluid.framework import Variable\nfrom ...utils.log_util import logger\nfrom paddle.fluid import core\nfrom paddle.fluid import layers\n\n__all__ = []\n\n\nclass HybridParallelClipGrad:\n def __init__(self, clip, hcg):\n self._clip = clip\n self._hcg = hcg\n\n @imperative_base.no_grad\n def _dygraph_clip(self, params_grads):\n params_and_grads = []\n sum_square_list = []\n for p, g in params_grads:\n if g is None:\n continue\n if getattr(p, 'need_clip', True) is False:\n continue\n merge_grad = g\n if g.type == core.VarDesc.VarType.SELECTED_ROWS:\n merge_grad = layers.merge_selected_rows(g)\n merge_grad = layers.get_tensor_from_selected_rows(merge_grad)\n square = layers.square(merge_grad)\n sum_square = layers.reduce_sum(square)\n sum_square_list.append(sum_square)\n\n # all parameters have been filterd out\n if len(sum_square_list) == 0:\n return params_grads\n\n global_norm_var = layers.concat(sum_square_list)\n global_norm_var = layers.reduce_sum(global_norm_var)\n # add all reduce to get global norm in world size\n paddle.distributed.all_reduce(global_norm_var,\n self._hcg.get_check_parallel_group())\n global_norm_var = layers.sqrt(global_norm_var)\n\n max_global_norm = layers.fill_constant(\n shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)\n clip_var = layers.elementwise_div(\n x=max_global_norm,\n y=layers.elementwise_max(\n x=global_norm_var, y=max_global_norm))\n for p, g in params_grads:\n if g is None:\n continue\n if getattr(p, 'need_clip', True) is False:\n params_and_grads.append((p, g))\n continue\n new_grad = layers.elementwise_mul(x=g, y=clip_var)\n params_and_grads.append((p, new_grad))\n\n return params_and_grads\n\n def __getattr__(self, item):\n return getattr(self._clip, item)\n\n def __call__(self, params_grads):\n return self._clip(params_grads)\n\n\nclass HybridParallelOptimizer:\n # adapter wrapper for optimizer\n def __init__(self, optimizer, hcg, strategy):\n self._inner_opt = optimizer\n self._strategy = strategy\n self._hcg = hcg\n\n self._use_dp_mode = (\n self._hcg.get_parallel_mode() == ParallelMode.DATA_PARALLEL)\n\n self._need_dp = (self._hcg.get_data_parallel_world_size() > 1)\n\n self._sharding_enable = (\n self._hcg.get_sharding_parallel_world_size() > 1)\n\n if isinstance(self._inner_opt._grad_clip,\n ClipGradByGlobalNorm) and not self._use_dp_mode:\n logger.warning(\"using ClipGradByGlobalNorm in TensorParallel, the origin \" \\\n \"optmizer'grad clip will be changed.\")\n self._inner_opt._grad_clip = HybridParallelClipGrad(\n self._inner_opt._grad_clip, hcg)\n\n @imperative_base.no_grad\n @framework.dygraph_only\n def step(self):\n # Here should use global parameter list \n if self._sharding_enable:\n sharding_reduce_gradients(\n list(self._inner_opt._parameter_list), self._hcg)\n\n if not self._use_dp_mode and self._need_dp:\n fused_allreduce_gradients(\n list(self._inner_opt._parameter_list), self._hcg)\n self._inner_opt.step()\n\n @imperative_base.no_grad\n def minimize(self,\n loss,\n startup_program=None,\n parameters=None,\n no_grad_set=None):\n\n parameter_list = parameters if parameters \\\n else self._inner_opt._parameter_list\n\n # Here should use global parameter list \n if self._sharding_enable:\n sharding_reduce_gradients(\n list(self._inner_opt._parameter_list), self._hcg)\n\n if not self._use_dp_mode and self._need_dp:\n fused_allreduce_gradients(list(parameter_list), self._hcg)\n\n return self._inner_opt.minimize(loss, startup_program, parameter_list,\n no_grad_set)\n\n def __getattr__(self, item):\n return getattr(self._inner_opt, item)\n","sub_path":"python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py","file_name":"hybrid_parallel_optimizer.py","file_ext":"py","file_size_in_byte":5355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"165641347","text":"# Python's Libraries\nimport os\n\n# Third-party Libraries\nfrom unipath import Path\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import TimeoutException\nfrom bs4 import BeautifulSoup\n\n\nBASE_DIR = Path(__file__).ancestor(1)\nrelative_path = os.path.join(\n BASE_DIR,\n 'chromedriver',\n '75.0.3770.90',\n 'chromedriver'\n)\nabs_path = os.path.abspath(relative_path)\ndriver = webdriver.Chrome(executable_path=abs_path)\ndriver.get('http://scrapping-site.s3-website-us-east-1.amazonaws.com')\ndelay = 10\n\ntry:\n element = WebDriverWait(driver, delay).until(\n EC.presence_of_element_located((By.CLASS_NAME, 'owl-stage'))\n )\n game_components = driver.find_elements(By.CLASS_NAME, \"owl-stage\")\n\n for component in game_components:\n items = component.find_elements(By.CLASS_NAME, \"product-item__details\")\n if len(items):\n print(\"--------------\")\n count = 0\n for item in items:\n raw_html = item.get_attribute(\"innerHTML\")\n beauty_html = BeautifulSoup(raw_html, 'html.parser')\n title_tag = beauty_html.find(\"h4\")\n title = title_tag.get_text()\n print(title)\n\n count += 1\n if count == 10:\n break\n\nexcept TimeoutException:\n print(\"!!\")\n","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"517685742","text":"from datetime import datetime\nfrom datetime import timedelta\n\n\ndef get_call_price(call_start, call_end):\n\n start_call = call_start.timestamp.time()\n end_call = call_end.timestamp.time()\n start_timedelta = convert_to_timedelta(start_call)\n end_timedelta = convert_to_timedelta(end_call)\n\n start_time_to_charge = datetime.strptime(\"06:00:00\", \"%H:%M:%S\").time()\n end_time_to_charge = datetime.strptime(\"22:00:00\", \"%H:%M:%S\").time()\n start_timedelta_charge = convert_to_timedelta(start_time_to_charge)\n end_timedelta_charge = convert_to_timedelta(end_time_to_charge)\n\n standing_charge = 0.36\n charge_per_minute = 0.09\n\n if(\n start_call < start_time_to_charge\n and end_call < start_time_to_charge\n or start_call > end_time_to_charge\n and end_call > end_time_to_charge\n ):\n call_price = standing_charge\n elif(\n start_call >= start_time_to_charge\n and end_call <= end_time_to_charge\n ):\n diff_time = end_timedelta - start_timedelta\n minutes_between = int(diff_time.seconds/60)\n call_price = (minutes_between * charge_per_minute) + standing_charge\n\n elif(\n start_call < start_time_to_charge\n and end_call >= start_time_to_charge\n and end_call <= end_time_to_charge\n ):\n diff_time = end_timedelta - start_timedelta_charge\n minutes_between = int(diff_time.seconds/60)\n call_price = (minutes_between * charge_per_minute) + standing_charge\n elif(\n start_call >= start_time_to_charge\n and start_call <= end_time_to_charge\n and end_call >= start_time_to_charge\n ):\n diff_time = end_timedelta_charge - start_timedelta\n minutes_between = int(diff_time.seconds/60)\n call_price = (minutes_between * charge_per_minute) + standing_charge\n elif(\n start_call < start_time_to_charge\n and end_call > end_time_to_charge\n ):\n diff_time = end_timedelta_charge - start_timedelta_charge\n minutes_between = int(diff_time.seconds/60)\n call_price = (minutes_between * charge_per_minute) + standing_charge\n\n return call_price\n\n\ndef convert_to_timedelta(date1):\n data = {'hours': date1.hour,\n 'minutes': date1.minute,\n 'seconds': date1.second}\n\n return timedelta(**data)\n","sub_path":"bill/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"140456647","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\n\nimport plotly.graph_objs as go \n\napp = dash.Dash()\napp.css.append_css({'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'})\n\nx = [1, 2, 3, 4, 5]\ny = [10, 15, 12, 13]\n\ntrace = go.Scatter(x=x, y=y)\ndata = [trace]\nmy_figure = dict(data = data)\n\napp.layout = html.Div([\n\thtml.H1(\"Dash App\"),\n\thtml.Div([dcc.Dropdown(options = [\n\t\t{\"label\":\"one\", 'value':1},\n\t\t{\"label\":\"two\", \"value\":2}], value = \"one\"\n\n\t\t)]),\n\thtml.Div([html.Div([dcc.Graph(figure = my_figure)], className = \"six columns\"),\n\t\t\t\thtml.Div([dcc.Graph(figure = my_figure)], className = \"six columns\")\n\n\n\t\t], className = \"row\"),\n\thtml.Div([html.P(\"Some text goes here\")])\n\t\n\t], className = 'container')\n\nif __name__ == '__main__':\n app.run_server(debug=True)","sub_path":"Lectures/Lecture_7/ddxk/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"569796229","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('app', '0010_auto_20200219_2238'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='userprofile',\n name='color',\n field=models.CharField(max_length=200, verbose_name=b'color', blank=True),\n ),\n ]\n","sub_path":"app/migrations/0011_userprofile_color.py","file_name":"0011_userprofile_color.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"487906756","text":"from curva_calibracao import Curva\n\nclass Solucoes(Curva):\n def __init__(self, vidraria_estoque, vidraria_padrao, pipeta):\n self.vidraria_estoque = vidraria_estoque\n self.vidraria_padrao = vidraria_padrao\n self.pipeta = pipeta\n \n def preparo_solucoes(self, curva):\n \n list = [] \n for i in range(curva.pontos):\n list.append(i)\n return list\n\n\nglicose = Curva(10,1,10,\"g/ml\")\nprint(glicose)\nprint(glicose.curva_calib())\n\nsolucoes = Solucoes(1000,100,1)\n\nprint(solucoes.preparo_solucoes(glicose))","sub_path":"preparo_solucoes.py","file_name":"preparo_solucoes.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"520292891","text":"import entities\nimport worldmodel\nimport pygame\nimport math\nimport random\nimport point\nimport image_store\n\nBLOB_RATE_SCALE = 4\nBLOB_ANIMATION_RATE_SCALE = 50\nBLOB_ANIMATION_MIN = 1\nBLOB_ANIMATION_MAX = 3\n\nORE_CORRUPT_MIN = 20000\nORE_CORRUPT_MAX = 30000\n\nQUAKE_STEPS = 10\nQUAKE_DURATION = 1100\nQUAKE_ANIMATION_RATE = 100\n\nVEIN_SPAWN_DELAY = 500\nVEIN_RATE_MIN = 8000\nVEIN_RATE_MAX = 17000\n\n\ndef sign(x):\n if x < 0:\n return -1\n elif x > 0:\n return 1\n else:\n return 0\n\n\ndef adjacent(pt1, pt2):\n return ((pt1.x == pt2.x and abs(pt1.y - pt2.y) == 1) or\n (pt1.y == pt2.y and abs(pt1.x - pt2.x) == 1))\n\n\n","sub_path":"actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"285716291","text":"# -*- coding: utf-8 -*-\r\n###############################################################################\r\n# Extract Features from the Image Data using the Pre-trained YOLOv3 Network\r\n###############################################################################\r\n\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport pickle\r\nfrom numpy import expand_dims\r\nfrom keras.preprocessing.image import load_img\r\nfrom keras.preprocessing.image import img_to_array\r\nfrom keras.models import load_model\r\n\r\n\r\n#------------------------------------------------------------------------------\r\n# Load the YOLOv3 Model (created using YOLOv3Model.py)\r\n#------------------------------------------------------------------------------\r\nmodel = load_model('./Output/modelRGB.h5')\r\nmodel.summary()\r\n\r\n#------------------------------------------------------------------------------\r\n# Define the required input shape for the YOLOv3 model\r\n#------------------------------------------------------------------------------\r\n_width, _height = 416, 416\r\n\r\n#------------------------------------------------------------------------------\r\n# Load and Process an image\r\n#------------------------------------------------------------------------------\r\ndef processImage(file, size):\r\n \r\n # Load the image\r\n image = load_img(file)\r\n \r\n # Get the shape of the image\r\n w, h = image.size\r\n \r\n # Load the Image with the target size\r\n image = load_img(file, target_size = size)\r\n \r\n # Convert the Image to a Numpy Array\r\n image = img_to_array(image)\r\n \r\n # Normalize the Values by scaling it to [0, 1])\r\n image = image.astype('float32')\r\n image /= 255.0\r\n \r\n # Add a dimension to output one sample\r\n image = expand_dims(image, 0)\r\n \r\n # Return the result\r\n return image, w, h\r\n\r\n#------------------------------------------------------------------------------\r\n# Extract Faetures of an Image using the pre-trained YOLOv3 Model\r\n#------------------------------------------------------------------------------\r\ndef extractFeatures(image):\r\n\r\n # Define the Filename of the Photo\r\n filename = image\r\n \r\n # Load and process the image\r\n image, image_w, image_h = processImage(filename, (_width, _height))\r\n \r\n prediction = model.predict(image)[0]\r\n \r\n prediction = prediction.reshape(prediction.shape[0], -1)\r\n \r\n return prediction\r\n\r\n#------------------------------------------------------------------------------\r\n# Convert the images to a numpy array\r\n#------------------------------------------------------------------------------\r\ndef images2Numpy(image):\r\n\r\n # Define the Filename of the Photo\r\n filename = image\r\n \r\n # Load and process the image\r\n image, image_w, image_h = processImage(filename, (150, 150))\r\n \r\n return image\r\n\r\n#------------------------------------------------------------------------------\r\n# Set the Folder Paths\r\n#------------------------------------------------------------------------------\r\npath = \"D:/CITDissertation/Input/\"\r\n\r\nfolder1 = \"01_TUMOR\"\r\nfolder2 = \"02_STROMA\"\r\nfolder3 = \"03_COMPLEX\"\r\nfolder4 = \"04_LYMPHO\"\r\nfolder5 = \"05_DEBRIS\"\r\nfolder6 = \"06_MUCOSA\"\r\nfolder7 = \"07_ADIPOSE\"\r\nfolder8 = \"08_EMPTY\"\r\n\r\n#------------------------------------------------------------------------------\r\n# Perform Features Extraction of 5,000 images stored in folder1 - folder8\r\n#------------------------------------------------------------------------------\r\ndata = []\r\nlabel = []\r\nindex = 0\r\n\r\nfor folder in [folder1, folder2, folder3, folder4, folder5, folder6, folder7, folder8]:\r\n pathIn = path + folder\r\n for filename in os.listdir(pathIn):\r\n \r\n image = os.path.join(pathIn,filename)\r\n \r\n extractedFeatures = extractFeatures(image)\r\n extractedFeatures = np.append(extractedFeatures, index)\r\n \r\n data.append(extractedFeatures)\r\n label.append(folder)\r\n \r\n index += 1\r\n\r\ndataFinal = np.vstack(data)\r\n\r\n#df = pd.DataFrame(data=dataFinal)\r\n#df.columns = [str(col) + '_x' for col in df.columns]\r\n#for i in df.columns:\r\n# print(i)\r\n#class_counts = df.groupby('43095_x').size() \r\n#print(class_counts)\r\n\r\n#------------------------------------------------------------------------------\r\n# save to npy file\r\n#------------------------------------------------------------------------------\r\nnp.save('./Data/dataWithTL.npy', dataFinal)\r\npickle.dump(label, file = open(\"./Data/label.pickle\", \"wb\"))\r\n\r\n#------------------------------------------------------------------------------\r\n# Convert the 5,000 images stored in folder1 - folder8 into a numpy array\r\n#------------------------------------------------------------------------------\r\ndata1 = []\r\nlabel1 = []\r\nindex1 = 0\r\n\r\nfor folder in [folder1, folder2, folder3, folder4, folder5, folder6, folder7, folder8]:\r\n pathIn = path + folder\r\n for filename in os.listdir(pathIn):\r\n \r\n image = os.path.join(pathIn,filename) \r\n processedImage = images2Numpy(image)\r\n processedImage = np.append(processedImage, index1)\r\n \r\n data1.append(processedImage)\r\n label1.append(folder)\r\n \r\n index1 += 1\r\n\r\ndataFinal1 = np.vstack(data1)\r\n\r\n#df = pd.DataFrame(data=dataFinal1)\r\n#df.columns = [str(col) + '_x' for col in df.columns]\r\n#for i in df.columns:\r\n# print(i)\r\n#class_counts = df.groupby('67500_x').size() \r\n#print(class_counts)\r\n\r\n#------------------------------------------------------------------------------\r\n# save to npy file\r\n#------------------------------------------------------------------------------\r\nnp.save('./Data/dataWithoutTL.npy', dataFinal1)\r\npickle.dump(label1, file = open(\"./Data/label1.pickle\", \"wb\"))\r\n\r\n\r\n","sub_path":"02 ExtractFeatures.py","file_name":"02 ExtractFeatures.py","file_ext":"py","file_size_in_byte":5712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"175555974","text":"## Script (Python) \"getCaptchaImage\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=\n##title=\nfrom quintagroup.captcha.core.utils import gen_captcha, decrypt, getWord, parseKey\nfrom Products.CMFCore.utils import getToolByName\nimport random\npropTool = getToolByName(context, 'portal_properties')\ncaptchaProps = propTool['qPloneCaptchas']\n\nhk = context.REQUEST.traverse_subpath[0]\ndk = decrypt(context.captcha_key, hk)\nkey = parseKey(dk)['key']\n\ntext = getWord(int(key))\nsize = captchaProps.getProperty('image_size')\nbkground = captchaProps.getProperty('background')\nfont_color = captchaProps.getProperty('font_color')\nkwargs = {'text': text,\n 'size': size,\n 'bkground': bkground,\n 'font_color': font_color}\nif captchaProps.getProperty('random_params', 'False'):\n period = random.uniform(0.05, 0.12)\n amplitude = random.uniform(3.0, 6.5)\nelse:\n period = captchaProps.getProperty('period')\n amplitude = captchaProps.getProperty('amplitude')\n\nkwargs['distortion'] = [period, amplitude, (0.0, 0.0)]\n\nim = gen_captcha(**kwargs)\ncontext.REQUEST.RESPONSE.setHeader('Content-Type', 'image/jpeg')\ncontext.REQUEST.RESPONSE.setHeader('Content-Length', im['size'])\ncontext.REQUEST.RESPONSE.setHeader('Accept-Ranges', 'bytes')\nreturn im['src']\n","sub_path":"quintagroup/captcha/core/skins/captcha_core/dynamic/getCaptchaImage.py","file_name":"getCaptchaImage.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"507953451","text":"from django import forms\nfrom django.contrib.auth.models import User\n\nfrom projects.models import Project, UserProject\n\nclass AddVolunteerForm(forms.ModelForm):\n \n user = forms.CharField(max_length = 30, required = True)\n \n def clean_user(self):\n data = self.cleaned_data\n if 'user' in data:\n try:\n user = User.objects.get(username = data['user'])\n except User.DoesNotExist:\n raise forms.ValidationError('This user does not exist')\n return user\n\n def clean(self):\n data = self.cleaned_data\n if 'project' in data and 'user' in data:\n try:\n UserProject.objects.get(user = data['user'], project = data['project'])\n raise forms.ValidationError('This user is already a part of this project')\n except UserProject.DoesNotExist:\n pass\n return data\n\n class Meta:\n model = UserProject\n fields = ('user', 'project')\n widgets = {\n 'project' : forms.HiddenInput(),\n 'user' : forms.TextInput(),\n }\n\n'''\n def clean_project(self):\n data = self.cleaned_data\n if 'project' in data:\n try:\n Project.objects.get(pk = data['project'])\n except Project.DoesNotExist:\n raise forms.ValidationError('This project does not exist')\n return data['project']\n'''\n\n","sub_path":"applications/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"545553374","text":"import requests, bs4, os\n\nrepo = 'c:/images'\n\nif not os.path.exists(repo):\n os.makedirs(repo)\n\nsearch_word = 'space'\nurl = 'https://www.google.com.br/search?q=' + search_word + '&source=lnms&tbm=isch'\ngoogle = 'https://www.google.com.br'\n\nres = requests.get(url)\nres.raise_for_status()\n\nsoup = bs4.BeautifulSoup(res.text, \"lxml\")\n\nall_images = soup.select('') #HERE IS THE PROBLEM! CAN'T FIND THE PROPER SELECTOR\n\nif all_images == []:\n print('Image link not found!')\nelse:\n download_image = min(4, len(all_images))\n\n for image in download_image:\n actual_image_link = google + download_image[image].get('href')\n\n res = requests.get(actual_image_link)\n res.raise_for_status()\n\n image_file = open(repo+'/pic.png', 'wb')\n\n for chunk in res.iter_content(100000):\n image_file.write(chunk)\n image_file.close()\n\n print('Done! Images saved in ' + repo)\n","sub_path":"testingCrawler.py","file_name":"testingCrawler.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"605185369","text":"import logging\nfrom threading import Thread\nimport time\nimport threading\nfrom twisted.internet import reactor\nfrom pyupnp.event import EventProperty\nfrom pyupnp.device import Device, DeviceIcon\nfrom pyupnp.logr import Logr\nfrom pyupnp.services import register_action, Service, ServiceActionArgument, ServiceStateVariable\nfrom pyupnp.ssdp import SSDP\nfrom pyupnp.upnp import UPnP\n\nimport os\nimport time\nimport grovepi\n\nled_chain = 7 # D7\nnbLeds = 9 # Number of led on the chain\n\ngrovepi.pinMode(led_chain,\"OUTPUT\")\ngrovepi.chainableRgbLed_init(led_chain, nbLeds)\n\nthisLedOnly = 0\nallLedsExceptThis = 1\nthisLedAndInwards = 2\nthisLedAndOutwards = 3\n\n# definition du service\nclass LedService(Service):\n\t# identifiants\n\tversion = (1, 0)\n\tserviceType = \"urn:schemas-upnp-org:service:LedService:1\"\n\tserviceId = \"urn:upnp-org:serviceId:LedService\"\n\n\t# actions gerees par le service\n\tactions = {\n\t\t'GetLed': [\n\t\t\tServiceActionArgument('Led','in','Led')\n\t\t]\n\t}\n\n\t# definition des variables\n\tstateVariables = [\n\t\tServiceStateVariable('Led','string',sendEvents=True)\n\t]\n\n\t#Turns off the led specified by led_num\n\tdef turn_off_led(self,led_num):\n\t grovepi.storeColor(0,0,0)\n\t grovepi.chainableRgbLed_pattern(led_chain, thisLedOnly, led_num)\n\t \n\t#Turns on the led specified by led_num to color set by r,g,b\n\t#def turn_on_led(self,led_num):\n\t# grovepi.storeColor(int(self.ledr),int(self.ledg),int(self.ledb))\n\t# grovepi.chainableRgbLed_pattern(led_chain, thisLedOnly, led_num)\n\tdef turn_on_led(self,led_num,param):\n\t\tparams = param.split(',', 2 )\n\t\tgrovepi.storeColor(int(params[0]),int(params[1]),int(params[2]))\n\t\tgrovepi.chainableRgbLed_pattern(led_chain, thisLedOnly, led_num)\n\n\t@register_action('GetLed')\n\tdef getLed(self,param):\n\t\tself.turn_on_led(0,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(1,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(2,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(3,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(4,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(5,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(6,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(7,param)\n\t\ttime.sleep(0.5)\n\t\tself.turn_on_led(8,param)\n\t\ttime.sleep(0.5)\n\n","sub_path":"network/led_service.py","file_name":"led_service.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"17201028","text":"import pygame\nimport os\nimport sys\n\n# プログラミングは、思った通りに動けばオッケー！！！\n# スプライトを動かしてみよう！\n\n# MARIOMAKER\nWIDTH = 300\nHEIGHT = 200\nFPS = 60\nTITLE = \"NANCHATTE MARIO MAKER\"\nSC = (10,20,40)\n\n# MARIO\nSPEED_LR = 10\nSPEED_UD = 15\nMC = (0,100,100)\n\n\nclass MarioMaker:\n def __init__(self):\n pygame.init()\n self.screen = pygame.display.set_mode((WIDTH, HEIGHT))\n pygame.display.set_caption(TITLE)\n clock = pygame.time.Clock()\n self.all = pygame.sprite.Group()\n mario = Mario()\n self.all.add(mario)\n \n while True:\n clock.tick(FPS)\n self.key_handler()\n self.update()\n self.draw()\n \n def key_handler(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE:\n pygame.quit()\n sys.exit()\n\n def update(self):\n self.all.update()\n\n def draw(self):\n self.screen.fill(SC)\n self.all.draw(self.screen)\n pygame.display.flip()\n\n\nclass Mario(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.Surface((30,30))\n self.image.fill(MC)\n self.rect = self.image.get_rect()\n self.rect.x = 0\n self.rect.y = 0\n self.vx = 0\n self.vy = 0\n\"\"\"\n def update(self):\n pk = pygame.key.get_pressed()\n\n if pk[pygame.K_RIGHT]:\n self.vx = SPEED_LR\n elif pk[pygame.K_LEFT]:\n self.vx = -SPEED_LR\n elif pk[pygame.K_UP]:\n self.vy = -SPEED_UD\n elif pk[pygame.K_DOWN]:\n self.vy = SPEED_UD\n else:\n self.vx = 0\n self.vy = 0\n\n self.rect.x += self.vx\n self.rect.y += self.vy\n\"\"\"\n\nMarioMaker()","sub_path":"part_1.py","file_name":"part_1.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"454099772","text":"from urllib.parse import urljoin\nimport json\nimport os\nimport requests\nimport sys ,getopt\nimport time\n##API地址\nweb=\"http://obs.casearth.cn\"\ntest_API=\"/api/v1/auth-token/\"\n\n##获取token\ndef get_token(username, password):\n post_text=requests.post(urljoin(web,test_API),data={'version':'v1','username':username,'password':password})\n text=json.loads(post_text.text)\n token1=text['token']\n token=token1['key']\n return token\n\n\n#创建一个桶\ndef create_bucket(bucket_name,token):\n bucket_post=requests.post(urljoin(web,'/api/v1/buckets/'),data={'name':bucket_name},headers= {'Authorization':'Token '+token})\n print(bucket_post.text)\n\n \n#上传文件\ndef upload_file(filename,filedir,bucket,dir,chunk_size, chunk_offset,token,t):\n time2=time.asctime(time.localtime(time.time()))\n f = open(filedir,'rb')\n print('文件打开了',time2)\n fsize = os.path.getsize(filedir) #上传文件的大小\n chunk_count = 0 #分片计数，初始为0\n while chunk_offset < fsize:\n rest_size = fsize - chunk_offset # 文件上传剩余量\n if rest_size < chunk_size:\n #上传剩余量大小的块\n print('最后一块了')\n put_obj = requests.put(urljoin(web, '/api/v1/obj/'+ bucket + dir + '/' + filename+t + '/'), files = {'chunk': f.read(rest_size)}, data = {'chunk_offset': chunk_offset, 'chunk_size': rest_size, 'overwrite': True}, headers= {'Authorization':'Token '+token})\n #偏移量更新\n chunk_offset = chunk_offset + rest_size\n chunk_count = chunk_count + 1\n print('分片成功')\n\n else:\n print('第',chunk_count+1,'块开始上传',time.asctime(time.localtime(time.time())))\n #上传分块大小的块\n put_obj = requests.put(urljoin(web, '/api/v1/obj/'+ bucket + dir + '/' + filename+t + '/'), files = {'chunk': f.read(chunk_size)}, data = {'chunk_offset': chunk_offset, 'chunk_size': chunk_size, 'overwrite': True}, headers= {'Authorization':'Token '+token})\n #偏移量更新\n chunk_offset = chunk_offset + chunk_size\n chunk_count = chunk_count + 1\n print('第',chunk_count,'块上传成功',time.asctime(time.localtime(time.time())))\n f.close()\n print('文件成功')\n time1=time.asctime(time.localtime(time.time()))\n print(time1)\n #返回总分片数\n return chunk_count\n## python upload_dir_parallel.py -s E:/test -d win -k 97d1cff95c36804c740eb781c17f1446b01528f2 \ndef GetOpt(argv):\n try:\n \n opts,args=getopt.getopt(argv,\"s:d:k:h\",[\"src=\",\"dst=\",\"token=\",\"help\"])\n for opt,value in opts:\n if opt in (\"-s\" , \"--src\"):\n global src;\n src = value;\n if opt in (\"-d\" , \"--dst\"):\n global dst;\n dst = value;\n if opt in (\"-k\" , \"--token\"):\n global token;\n token = value;\n if opt in (\"-h\" , \"--help\"):\n usage();\n sys.exit(-1)\n except getopt.GetoptError as e:\n print (e.msg)\n sys.exit(-1)\n\ndef usage():\n print (''' 程序使用说明如下：\n 00_single_big_parallel.py [option][value]...\n Example: ./00_single_big_parallel.py -s \"/root/1.file\" -d \"/bucket_name/objstorepath/1.file\" -k \"xxxxxxxx\"\n -h or --help\n -s or --src=\"文件源路径\"，\n -d or --dst=\"桶名称\",\n -k or --token=\"认证token\"\n ''')\n\n\n\ndef main(filedir,dst,token,t):\n #请输入用户名，密码\n## username = ''\n## password = ''\n\n\n## filedir='e:/test/333.txt' #文件路径＋名字\n global filename\n filename=os.path.basename(filedir)\n chunk_size=1024*1024*100 #文件分块大小（1M）\n chunk_offset=0 #偏移量\n dir=''\n## token = get_token(username, password)\n \n upload_file(filename,filedir,dst,dir,chunk_size, chunk_offset,token,t)\n\n\n\nif __name__ == '__main__':\n\n GetOpt(sys.argv[1:])\n create_bucket(dst,token)##创建桶的名字\n for i in range(100):\n r=\"%d\" %i\n main(src,dst,token,r)\n \n","sub_path":"upload_download_file/upload_for.py","file_name":"upload_for.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"394395487","text":"import json\nimport copy\nimport operator\n\nfrom data.fixtures.test_data import JudgmentsTestData\nfrom acj.models import PostsForAnswers, Posts, Judgements\nfrom acj.tests.test_acj import ACJAPITestCase\nfrom acj.judgement import AnswerPairGenerator\n\n\nclass JudgementAPITests(ACJAPITestCase):\n def setUp(self):\n super(JudgementAPITests, self).setUp()\n self.data = JudgmentsTestData()\n self.course = self.data.get_course()\n self.question = self.data.get_questions()[0]\n self.base_url = self._build_url(self.course.id, self.question.id)\n self.answer_pair_url = self.base_url + '/pair'\n\n def _build_url(self, course_id, question_id, tail=\"\"):\n url = \\\n '/api/courses/' + str(course_id) + '/questions/' + str(question_id) + '/judgements' + \\\n tail\n return url\n\n def _build_judgement_submit(self, answerpair_id, winner_id):\n submit = {\n 'answerpair_id': answerpair_id,\n 'judgements': [\n {\n 'question_criterion_id': self.question.criteria[0].id,\n 'answer_id_winner': winner_id\n }\n ]\n }\n return submit\n\n def test_get_answer_pair_access_control(self):\n # test login required\n rv = self.client.get(self.answer_pair_url)\n self.assert401(rv)\n # test deny access to unenroled users\n with self.login(self.data.get_unauthorized_student().username):\n rv = self.client.get(self.answer_pair_url)\n self.assert403(rv)\n\n with self.login(self.data.get_unauthorized_instructor().username):\n rv = self.client.get(self.answer_pair_url)\n self.assert403(rv)\n\n # enroled user from this point on\n with self.login(self.data.get_authorized_student().username):\n # test non-existent course\n rv = self.client.get(self._build_url(9993929, self.question.id, '/pair'))\n self.assert404(rv)\n # test non-existent question\n rv = self.client.get(self._build_url(self.course.id, 23902390, '/pair'))\n self.assert404(rv)\n # no judgements has been entered yet, question is not in judging period\n rv = self.client.get(self._build_url(\n self.course.id, self.data.get_question_in_answer_period().id, '/pair'))\n self.assert403(rv)\n\n def test_get_answer_pair_basic(self):\n with self.login(self.data.get_authorized_student().username):\n # no judgements has been entered yet\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n actual_answer_pair = rv.json\n actual_answer1 = actual_answer_pair['answers'][0]\n actual_answer2 = actual_answer_pair['answers'][1]\n expected_answer_ids = [answer.id for answer in self.data.get_student_answers()]\n # make sure that we actually got answers for the question we're targetting\n self.assertIn(actual_answer1['id'], expected_answer_ids)\n self.assertIn(actual_answer2['id'], expected_answer_ids)\n\n def test_get_answer_pair_answer_exclusions_for_answers_with_no_scores(self):\n \"\"\"\n The user doing judgements should not see their own answer in a judgement.\n Instructor and TA answers should not show up.\n Answers cannot be paired with itself.\n For answers that don't have a score yet, which means they're randomly matched up.\n \"\"\"\n with self.login(self.data.get_authorized_student().username):\n excluded_student_answer = PostsForAnswers.query.join(Posts).filter(\n Posts.users_id == self.data.get_authorized_student().id,\n PostsForAnswers.questions_id == self.question.id).first()\n self.assertTrue(excluded_student_answer, \"Missing authorized student's answer.\")\n excluded_instructor_answer = PostsForAnswers.query.join(Posts).filter(\n Posts.users_id == self.data.get_authorized_instructor().id,\n PostsForAnswers.questions_id == self.question.id).first()\n self.assertTrue(excluded_instructor_answer, \"Missing instructor answer\")\n excluded_ta_answer = PostsForAnswers.query.join(Posts).filter(\n Posts.users_id == self.data.get_authorized_ta().id,\n PostsForAnswers.questions_id == self.question.id).first()\n self.assertTrue(excluded_ta_answer, \"Missing TA answer\")\n # no judgements has been entered yet, this tests the randomized pairing when no answers has\n # scores, since it's randomized though, we'll have to run it lots of times to be sure\n for i in range(50):\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n actual_answer_pair = rv.json\n actual_answer1 = actual_answer_pair['answers'][0]\n actual_answer2 = actual_answer_pair['answers'][1]\n # exclude student's own answer\n self.assertNotEqual(actual_answer1['id'], excluded_student_answer.id)\n self.assertNotEqual(actual_answer2['id'], excluded_student_answer.id)\n # exclude instructor answer\n self.assertNotEqual(actual_answer1['id'], excluded_instructor_answer.id)\n self.assertNotEqual(actual_answer2['id'], excluded_instructor_answer.id)\n # exclude ta answer\n self.assertNotEqual(actual_answer1['id'], excluded_ta_answer.id)\n self.assertNotEqual(actual_answer2['id'], excluded_ta_answer.id)\n\n # need a user with no answers submitted, otherwise pairs with the same answers\n # won't be generated since we have too few answers\n with self.login(self.data.get_authorized_student_with_no_answers().username):\n for i in range(50):\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n # answer cannot be paired with itself\n self.assertNotEqual(rv.json['answers'][0]['id'], rv.json['answers'][1]['id'])\n\n def test_submit_judgement_access_control(self):\n # test login required\n rv = self.client.post(\n self.base_url,\n data=json.dumps({}),\n content_type='application/json')\n self.assert401(rv)\n\n # establish expected data by first getting an answer pair\n with self.login(self.data.get_authorized_student().username):\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n # expected_answer_pair = rv.json\n judgement_submit = self._build_judgement_submit(rv.json['id'], rv.json['answers'][0]['id'])\n\n # test deny access to unenroled users\n with self.login(self.data.get_unauthorized_student().username):\n rv = self.client.post(\n self.base_url,\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert403(rv)\n\n with self.login(self.data.get_unauthorized_instructor().username):\n rv = self.client.post(\n self.base_url,\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert403(rv)\n\n # test deny access to non-students\n with self.login(self.data.get_authorized_instructor().username):\n rv = self.client.post(\n self.base_url,\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert403(rv)\n\n # authorized user from this point\n with self.login(self.data.get_authorized_student().username):\n # test non-existent course\n rv = self.client.post(\n self._build_url(9999999, self.question.id),\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert404(rv)\n # test non-existent question\n rv = self.client.post(\n self._build_url(self.course.id, 9999999),\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert404(rv)\n # test reject missing criteria\n faulty_judgements = copy.deepcopy(judgement_submit)\n faulty_judgements['judgements'] = []\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert400(rv)\n # test reject missing course criteria id\n faulty_judgements = copy.deepcopy(judgement_submit)\n del faulty_judgements['judgements'][0]['question_criterion_id']\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert400(rv)\n # test reject missing winner\n faulty_judgements = copy.deepcopy(judgement_submit)\n del faulty_judgements['judgements'][0]['answer_id_winner']\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert400(rv)\n # test invalid criteria id\n faulty_judgements = copy.deepcopy(judgement_submit)\n faulty_judgements['judgements'][0]['question_criterion_id'] = 3930230\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert400(rv)\n # test invalid winner id\n faulty_judgements = copy.deepcopy(judgement_submit)\n faulty_judgements['judgements'][0]['answer_id_winner'] = 2382301\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert400(rv)\n # test invalid answer pair\n faulty_judgements = copy.deepcopy(judgement_submit)\n faulty_judgements['answerpair_id'] = 2382301\n rv = self.client.post(\n self.base_url,\n data=json.dumps(faulty_judgements),\n content_type='application/json')\n self.assert404(rv)\n\n def test_submit_judgement_basic(self):\n with self.login(self.data.get_authorized_student().username):\n # calculate number of judgements to do before user has judged all the pairs it can\n num_eligible_answers = -1 # need to minus one to exclude the logged in user's own answer\n for answer in self.data.get_student_answers():\n if answer.question.id == self.question.id:\n num_eligible_answers += 1\n # n - 1 possible pairs before all answers have been judged\n num_possible_judgements = num_eligible_answers - 1\n winner_ids = []\n for i in range(num_possible_judgements):\n # establish expected data by first getting an answer pair\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n expected_answer_pair = rv.json\n judgement_submit = self._build_judgement_submit(rv.json['id'], rv.json['answers'][0]['id'])\n winner_ids.append(rv.json['answers'][0]['id'])\n # test normal post\n rv = self.client.post(\n self.base_url,\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert200(rv)\n actual_judgements = rv.json['objects']\n self._validate_judgement_submit(judgement_submit, actual_judgements, expected_answer_pair)\n # Resubmit of same judgement should fail\n rv = self.client.post(\n self.base_url,\n data=json.dumps(judgement_submit),\n content_type='application/json')\n self.assert400(rv)\n # all answers has been judged by the user, errors out when trying to get another pair\n rv = self.client.get(self.answer_pair_url)\n self.assert400(rv)\n\n def _validate_judgement_submit(self, judgement_submit, actual_judgements, expected_answer_pair):\n self.assertEqual(\n len(actual_judgements), len(judgement_submit['judgements']),\n \"The number of judgements saved does not match the number sent\")\n for actual_judgement in actual_judgements:\n self.assertEqual(\n expected_answer_pair['answers'][0]['id'],\n actual_judgement['answerpairing']['answers_id1'],\n \"Expected and actual judgement answer1 id did not match\")\n self.assertEqual(\n expected_answer_pair['answers'][1]['id'],\n actual_judgement['answerpairing']['answers_id2'],\n \"Expected and actual judgement answer2 id did not match\")\n found_judgement = False\n for expected_judgement in judgement_submit['judgements']:\n if expected_judgement['question_criterion_id'] != \\\n actual_judgement['question_criterion']['id']:\n continue\n self.assertEqual(\n expected_judgement['answer_id_winner'],\n actual_judgement['answers_id_winner'],\n \"Expected and actual winner answer id did not match.\")\n found_judgement = True\n self.assertTrue(\n found_judgement,\n \"Actual judgement received contains a judgement that was not sent.\")\n\n def test_get_answer_pair_answer_exclusion_with_scored_answers(self):\n \"\"\"\n The user doing judgements should not see their own answer in a judgement.\n Instructor and TA answers should not show up.\n Answers cannot be paired with itself.\n Scored answer pairing means answers should be matched up to similar scores.\n \"\"\"\n # Make sure all answers are judged first\n self._submit_all_possible_judgements_for_user(\n self.data.get_authorized_student().id)\n self._submit_all_possible_judgements_for_user(\n self.data.get_secondary_authorized_student().id)\n\n with self.login(self.data.get_authorized_student_with_no_answers().username):\n excluded_instructor_answer = PostsForAnswers.query.join(Posts).filter(\n Posts.users_id == self.data.get_authorized_instructor().id,\n PostsForAnswers.questions_id == self.question.id).first()\n self.assertTrue(excluded_instructor_answer, \"Missing instructor answer\")\n excluded_ta_answer = PostsForAnswers.query.join(Posts).filter(\n Posts.users_id == self.data.get_authorized_ta().id,\n PostsForAnswers.questions_id == self.question.id).first()\n self.assertTrue(excluded_ta_answer, \"Missing TA answer\")\n # no judgements has been entered yet, this tests the randomized pairing when no answers has\n # scores, since it's randomized though, we'll have to run it lots of times to be sure\n for i in range(50):\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n actual_answer_pair = rv.json\n actual_answer1 = actual_answer_pair['answers'][0]\n actual_answer2 = actual_answer_pair['answers'][1]\n # exclude instructor answer\n self.assertNotEqual(actual_answer1['id'], excluded_instructor_answer.id)\n self.assertNotEqual(actual_answer2['id'], excluded_instructor_answer.id)\n # exclude ta answer\n self.assertNotEqual(actual_answer1['id'], excluded_ta_answer.id)\n self.assertNotEqual(actual_answer2['id'], excluded_ta_answer.id)\n # answer cannot be paired with itself\n self.assertNotEqual(actual_answer1['id'], actual_answer2['id'])\n\n def _submit_all_possible_judgements_for_user(self, user_id):\n # self.login(username)\n # calculate number of judgements to do before user has judged all the pairs it can\n num_eligible_answers = -1 # need to minus one to exclude the logged in user's own answer\n for answer in self.data.get_student_answers():\n if answer.question.id == self.question.id:\n num_eligible_answers += 1\n # n - 1 possible pairs before all answers have been judged\n num_possible_judgements = num_eligible_answers - 1\n winner_ids = []\n loser_ids = []\n for i in range(num_possible_judgements):\n pair_generator = AnswerPairGenerator(self.course.id, self.question, user_id)\n answerpairing = pair_generator.get_pair()\n # answer_pair = AnswerPairings.query.get(answerpairing.id)\n # establish expected data by first getting an answer pair\n # rv = self.client.get(self.answer_pair_url)\n # self.assert200(rv)\n # expected_answer_pair = rv.json\n min_id = min([answerpairing.answers_id1, answerpairing.answers_id2])\n max_id = max([answerpairing.answers_id1, answerpairing.answers_id2])\n judgement_submit = self._build_judgement_submit(answerpairing.id, min_id)\n winner_ids.append(min_id)\n loser_ids.append(max_id)\n Judgements.create_judgement(judgement_submit, answerpairing, user_id)\n Judgements.calculate_scores(self.question.id)\n # test normal post\n # rv = self.client.post(self.base_url, data=json.dumps(judgement_submit),\n # \t\t\t\t\t content_type='application/json')\n # self.assert200(rv)\n # self.logout()\n\n return {'winners': winner_ids, 'losers': loser_ids}\n\n def test_score_calculation(self):\n \"\"\"\n This is just a rough check on whether score calculations are correct. Answers\n that has more wins should have the highest scores.\n \"\"\"\n # Make sure all answers are judged first\n winner_ids = self._submit_all_possible_judgements_for_user(\n self.data.get_authorized_student().id)['winners']\n winner_ids.extend(self._submit_all_possible_judgements_for_user(\n self.data.get_secondary_authorized_student().id)['winners'])\n\n # Count the number of wins each answer has had\n num_wins_by_id = {}\n for winner_id in winner_ids:\n num_wins = num_wins_by_id.setdefault(winner_id, 0)\n num_wins_by_id[winner_id] = num_wins + 1\n\n # Get the actual score calculated for each answer\n answers = self.data.get_student_answers()\n answer_scores = {}\n for answer in answers:\n if answer.question.id == self.question.id:\n answer_scores[answer.id] = answer.scores[0].score\n\n # Check that ranking by score and by wins match, this only works for low number of\n # judgements\n expected_ranking_by_wins = [answer_id for (answer_id, wins) in sorted(\n num_wins_by_id.items(),\n key=operator.itemgetter(1))]\n actual_ranking_by_scores = [answer_id for (answer_id, score) in sorted(\n answer_scores.items(),\n key=operator.itemgetter(1)) if score > 0]\n self.assertSequenceEqual(actual_ranking_by_scores, expected_ranking_by_wins)\n\n def test_comparison_count_matched_pairing(self):\n # Make sure all answers are judged first\n answer_ids = self._submit_all_possible_judgements_for_user(\n self.data.get_authorized_student().id)\n answer_ids2 = self._submit_all_possible_judgements_for_user(\n self.data.get_secondary_authorized_student().id)\n compared_ids = \\\n answer_ids['winners'] + answer_ids2['winners'] + \\\n answer_ids['losers'] + answer_ids2['losers']\n\n # Just a simple test for now, make sure that answers with the smaller number of\n # comparisons are matched up with each other\n # Count number of comparisons done for each answer\n num_comp_by_id = {}\n for answer_id in compared_ids:\n num_comp = num_comp_by_id.setdefault(answer_id, 0)\n num_comp_by_id[answer_id] = num_comp + 1\n\n comp_groups = {}\n for answerId in num_comp_by_id:\n count = num_comp_by_id[answerId]\n comp_groups.setdefault(count, [])\n comp_groups[count].append(answerId)\n counts = sorted(comp_groups)\n # get the answerIds with the lowest count of comparisons\n possible_answer_ids = comp_groups[counts[0]]\n if len(possible_answer_ids) < 2:\n # if the lowest count group does not have enough to create a pair - add the next group\n possible_answer_ids += comp_groups[counts[1]]\n\n # Check that the 2 answers with 1 win gets returned\n with self.login(self.data.get_authorized_student_with_no_answers().username):\n rv = self.client.get(self.answer_pair_url)\n self.assert200(rv)\n self.assertIn(rv.json['answers'][0]['id'], possible_answer_ids)\n self.assertIn(rv.json['answers'][1]['id'], possible_answer_ids)\n\n def test_get_judgement_count(self):\n url = self._build_url(self.data.get_course().id, self.question.id)\n\n # test login required\n tail = '/users/' + str(self.data.get_authorized_student().id) + '/count'\n rv = self.client.get(url + tail)\n self.assert401(rv)\n\n # test unauthorized user\n with self.login(self.data.get_unauthorized_student().username):\n tail = '/users/' + str(self.data.get_unauthorized_student().id) + '/count'\n rv = self.client.get(url + tail)\n self.assert403(rv)\n\n with self.login(self.data.get_authorized_instructor().username):\n tail = '/users/' + str(self.data.get_authorized_instructor().id) + '/count'\n # test invalid course id\n invalid_url = self._build_url(999, self.question.id)\n rv = self.client.get(invalid_url + tail)\n self.assert404(rv)\n\n # test invalid question id\n invalid_url = self._build_url(self.data.get_course().id, 999)\n rv = self.client.get(invalid_url + tail)\n self.assert404(rv)\n\n # test authorized instructor\n rv = self.client.get(url + tail)\n self.assert200(rv)\n self.assertEqual(rv.json['count'], 0)\n\n # test authorized student\n winners = self._submit_all_possible_judgements_for_user(\n self.data.get_authorized_student().id)['winners']\n tail = '/users/' + str(self.data.get_authorized_student().id) + '/count'\n with self.login(self.data.get_authorized_student().username):\n rv = self.client.get(url + tail)\n self.assert200(rv)\n self.assertEqual(rv.json['count'], len(winners))\n\n def test_get_all_judgement_count(self):\n url = '/api/courses/' + str(self.data.get_course().id) + '/judgements/count'\n\n # test login required\n rv = self.client.get(url)\n self.assert401(rv)\n\n # test unauthorized user\n with self.login(self.data.get_unauthorized_instructor().username):\n rv = self.client.get(url)\n self.assert403(rv)\n\n with self.login(self.data.get_authorized_instructor().username):\n # test invalid course id\n rv = self.client.get('/api/courses/999/judgements/count')\n self.assert404(rv)\n\n questions = self.data.get_questions()\n # test authorized instructor\n rv = self.client.get(url)\n self.assert200(rv)\n count = rv.json['judgements']\n\n for ques in questions:\n question_id = str(ques.id)\n self.assertTrue(question_id in count)\n self.assertEqual(count[question_id], 0)\n\n # test authorized student\n winners = self._submit_all_possible_judgements_for_user(\n self.data.get_authorized_student().id)['winners']\n judgement_count = len(winners)\n with self.login(self.data.get_authorized_student().username):\n rv = self.client.get(url)\n self.assert200(rv)\n count = rv.json['judgements']\n\n for ques in questions:\n question_id = str(ques.id)\n self.assertTrue(question_id in count)\n jcount = judgement_count if ques.id == self.question.id else 0\n self.assertEqual(count[question_id], jcount)\n\n def test_get_all_availPair_logic(self):\n url = '/api/courses/' + str(self.data.get_course().id) + '/judgements/availpair'\n\n # test login required\n rv = self.client.get(url)\n self.assert401(rv)\n\n # test unauthorized user\n with self.login(self.data.get_unauthorized_student().username):\n rv = self.client.get(url)\n self.assert403(rv)\n\n with self.login(self.data.get_authorized_student().username):\n # test invalid course id\n invalid_url = '/api/courses/999/judgements/availpair'\n rv = self.client.get(invalid_url)\n self.assert404(rv)\n\n first_ques = self.data.get_questions()[0]\n last_ques = self.data.get_questions()[-1]\n expected = {ques.id: True for ques in self.data.get_questions()}\n expected[last_ques.id] = False\n # test authorized student - when haven't judged\n rv = self.client.get(url)\n self.assert200(rv)\n logic = rv.json['availPairsLogic']\n for ques in self.data.get_questions():\n self.assertEqual(logic[str(ques.id)], expected[ques.id])\n\n self._submit_all_possible_judgements_for_user(self.data.get_authorized_student().id)\n with self.login(self.data.get_authorized_student().username):\n # test authorized student - when have judged all\n rv = self.client.get(url)\n self.assert200(rv)\n logic = rv.json['availPairsLogic']\n expected[first_ques.id] = False\n for ques in self.data.get_questions():\n self.assertEqual(logic[str(ques.id)], expected[ques.id])\n\n def test_get_availPair_logic(self):\n url = self._build_url(self.data.get_course().id, self.question.id)\n\n tail = '/users/' + str(self.data.get_unauthorized_student().id) + '/availpair'\n # test login required\n rv = self.client.get(url + tail)\n self.assert401(rv)\n\n # test unauthorized user\n with self.login(self.data.get_unauthorized_student().username):\n rv = self.client.get(url + tail)\n self.assert403(rv)\n\n # test invalid course id\n tail = '/users/' + str(self.data.get_authorized_student().id) + '/availpair'\n with self.login(self.data.get_authorized_student().username):\n invalid_url = self._build_url(999, self.question.id)\n rv = self.client.get(invalid_url + tail)\n self.assert404(rv)\n\n # test invalid question id\n invalid_url = self._build_url(self.data.get_course().id, 999)\n rv = self.client.get(invalid_url + tail)\n self.assert404(rv)\n\n with self.login(self.data.get_authorized_student().username):\n # test authorized student - when haven't judged\n rv = self.client.get(url + tail)\n self.assert200(rv)\n self.assertTrue(rv.json['availPairsLogic'])\n\n self._submit_all_possible_judgements_for_user(self.data.get_authorized_student().id)\n # test authorized student - when have judged all\n self.login(self.data.get_authorized_student().username)\n rv = self.client.get(url + tail)\n self.assert200(rv)\n self.assertFalse(rv.json['availPairsLogic'])\n","sub_path":"acj/tests/api/test_judgements.py","file_name":"test_judgements.py","file_ext":"py","file_size_in_byte":28303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"} +{"seq_id":"600018107","text":"#encoding =utf-8\nfrom socket import *\nimport os,sys\nfrom time import sleep\nimport getpass \nfrom view import View\n\ndef do_child(s):\n data = s.recv(1024).decode()\n print(data)\n \ndef do_parent(s,name):\n view = View()\n while True:\n view.sysFunctionView()\n try:\n msg = int(input(\"请输入您要的选项(1or2or3or4or5or6):\"))\n except Exception:\n print('您输入的选项有误:')\n continue\n if msg not in [1,2,3,4,5,6,7]:\n print('请输入正确选项:')\n sys.stdin.flush()\n continue\n elif msg == 1: \n s.send('T'.encode())\n if do_tickets(s,name) !=0:\n print(\"购票失败\")\n else:\n print(\"购票成功\")\n elif msg == 2:\n if do_Recharge(s,name) == 0:\n print(\"充值成功\")\n else:\n print(\"充值失败\")\n elif msg == 3:\n if quiry_ticket(s,name) != 0:\n print(\"没有购票记录\")\n else:\n print(\"购票记录如上\")\n elif msg == 4: \n if quiry_money(s,name) != 0:\n print(\"没有余额\")\n else:\n print(\"余额如上\")\n elif msg == 5:\n if change_pwd(s,name) !=0:\n print(\"修改失败\")\n else:\n print(\"密码修改成功\")\n elif msg == 6:\n if c_records(s,name) == 1:\n print(\"没有消费记录\")\n else:\n print(\"消费记录如上\")\n elif msg == 7:\n return\n\n\n\ndef c_records(s,name):\n while True:\n option = input(\"请输入您需要的服务，输入1查询消费记录，输入2查询充值记录\"\n)\n data = \"D {} {}\".format(option,name)\n s.send(data.encode())\n sleep(0.5)\n msg = s.recv(1024).decode()\n if msg ==\"没有消费记录\":\n return 1\n else:\n print(msg)\n return 0\n\n\ndef change_pwd(s,name):\n while True:\n passwd = getpass.getpass(\"请输入您要修改的密码:\")\n passwd1 = getpass.getpass(\"请确认您要修改的密码:\")\n if passwd != passwd1 or len(passwd)<6:\n continue\n data = 'P {} {}'.format(name,passwd)\n s.send(data.encode())\n msg = s.recv(1024).decode()\n if msg ==\"修改失败\":\n return 1 \n else:\n return 0 \n\n \n\ndef quiry_ticket(s,name):\n while True:\n data = \"Q {}\".format(name)\n s.send(data.encode())\n sleep(1)\n msg = s.recv(1024).decode()\n if msg == \"没有购票记录\":\n return 1\n else:\n print(msg)\n return 0\n\ndef quiry_money(s,name):\n while True:\n data = \"M {}\".format(name)\n s.send(data.encode())\n msg = s.recv(1024).decode()\n if msg ==\"没有充值记录\":\n return 1\n else:\n print(msg)\n return 0\n\ndef do_tickets(s,name):\n while True:\n sleep(1)\n data = s.recv(1024).decode()\n if not data:\n break\n print(data)\n # choice = input(\"请选择影院or退出：\")\n # msg = \n num = input(\"请选择电影序号or退出：\")\n sleep(1)\n t_num = input(\"您要购买几张票:\")\n if num.isdigit() and 0