code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
def avg_dissim_within_group_element(node, element_list):
max_diameter = -np.inf
sum_dissm = 0
for i in element_list:
sum_dissm += dissimilarity_matrix[node][i]
if dissimilarity_matrix[node][i] > max_diameter:
max_diameter = dissimilarity_matrix[node... | flexible | {
"blob_id": "267695555e876dc2fe5820dc194490aad9e5e344",
"index": 1361,
"step-1": "<mask token>\n\n\ndef avg_dissim_within_group_element(node, element_list):\n max_diameter = -np.inf\n sum_dissm = 0\n for i in element_list:\n sum_dissm += dissimilarity_matrix[node][i]\n if dissimilarity_mat... | [
4,
5,
6,
8,
9
] |
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | normal | {
"blob_id": "86b24ddaae0d3477a3f82295224b7e84805eed91",
"index": 1413,
"step-1": "<mask token>\n\n\nclass AuthenticatorTest(absltest.TestCase):\n <mask token>\n\n def testGetGoogleSheetsServiceByCred_badFilePath_raisesFileNotFoundError(\n self):\n bad_file_path = './credential.json'\n ... | [
2,
3,
4,
5,
6
] |
'''
THROW with or without parameters
Which of the following is true about the THROW statement?
Answer the question
50XP
Possible Answers
- The THROW statement without parameters should be placed within a CATCH block.
- The THROW statement with parameters can only be placed within a CATCH block.
- The... | normal | {
"blob_id": "75023c7600fcceda0dc225992e7c433291b1a190",
"index": 7254,
"step-1": "<mask token>\n",
"step-2": "'''\nTHROW with or without parameters\n\n\nWhich of the following is true about the THROW statement?\n\nAnswer the question\n50XP\n\nPossible Answers\n\n - The THROW statement without parameters sho... | [
0,
1
] |
from django.db import models
class Author(models.Model):
author = models.CharField(
"Author",
max_length=30,
blank=False,
null=False
)
biography = models.TextField(
"About author",
max_length=500,
blank=True,
null=True
)
def __str__... | normal | {
"blob_id": "b34ad8d7fc8df0ab86c5930ab2b5aa1f86d13ae3",
"index": 7580,
"step-1": "<mask token>\n\n\nclass Series(models.Model):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Genre(models.Model):\n genre = models.CharField('Genre', max_length=50, blank=False, null=False)\n description = m... | [
7,
9,
11,
12,
14
] |
from flask import Flask, render_template , request
import joblib
# importing all the important libraires
import numpy as np
import pandas as pd
import nltk
import string
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfV... | normal | {
"blob_id": "df92166378c8a8cc0ba02d0ba33d75bbd94510a7",
"index": 4754,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return render_template('form.html')\n\n\n@app.route('/submit', methods=['POST'])\ndef form_data():\n user_data = request.form.get('user_data')\n user_data1 = [user_data]\n ... | [
2,
3,
4,
5,
6
] |
'''
def Sort(a):
i=1
while i<len(a):
j=i
while j>0 and a[j-1] > a[j]:
temp = a[j-1]
a[j-1] = a[j]
a[j] = temp
j-=1
i+=1
return a
'''
def Sort(a):
i=1
n=len(a)
while i<len(a):
j=i
print(i-1,'\t',i)
whi... | normal | {
"blob_id": "3f8b8b8cfbe712f09734d0fb7302073187d65a73",
"index": 982,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Sort(a):\n i = 1\n n = len(a)\n while i < len(a):\n j = i\n print(i - 1, '\\t', i)\n while a[j - 1] > a[j] and j >= 0:\n j -= 1\n pr... | [
0,
1,
2
] |
import numpy as np, argparse, sys, itertools, os, errno, warnings
from mpi4py import MPI
from enlib import enmap as en, powspec, utils
from enlib.degrees_of_freedom import DOF, Arg
from enlib.cg import CG
warnings.filterwarnings("ignore")
#from matplotlib.pylab import *
parser = argparse.ArgumentParser()
parser.add_ar... | normal | {
"blob_id": "6a601d1c7c3c162c0902d03e6c39f8d75d4bcaf0",
"index": 798,
"step-1": "import numpy as np, argparse, sys, itertools, os, errno, warnings\nfrom mpi4py import MPI\nfrom enlib import enmap as en, powspec, utils\nfrom enlib.degrees_of_freedom import DOF, Arg\nfrom enlib.cg import CG\nwarnings.filterwarning... | [
0
] |
# 나의 풀이
def solution(prices):
# 초 단위로 기록된 주식가격이 담긴 배열 prices # 가격이 떨어지지 않은 기간을 리턴
answer = [0]*len(prices)
for i in range(len(prices)-1):
for j in range(i+1, len(prices)):
answer[i] += 1
# 가격이 떨어졌을 경우
if prices[i] > prices[j]:
break
retur... | normal | {
"blob_id": "23b6d754adf1616bc6ea1f8c74984fbd8dade6dd",
"index": 4238,
"step-1": "<mask token>\n",
"step-2": "def solution(prices):\n answer = [0] * len(prices)\n for i in range(len(prices) - 1):\n for j in range(i + 1, len(prices)):\n answer[i] += 1\n if prices[i] > prices[j... | [
0,
1,
2
] |
'''Autogenerated by xml_generate script, do not edit!'''
from OpenGL import platform as _p, arrays
from OpenGL.constant import Constant as _C
# End users want this...
from OpenGL.raw.GLES2 import _errors
# Code generation uses this
from OpenGL.raw.GLES2 import _types as _cs
_EXTENSION_NAME = 'GLES2_NV_viewport_array'
... | normal | {
"blob_id": "9535973f9714926269490b8550a67c74d04d8f0a",
"index": 834,
"step-1": "<mask token>\n\n\n@_f\n@_p.types(None, _cs.GLuint, _cs.GLsizei, arrays.GLfloatArray)\ndef glDepthRangeArrayfvNV(first, count, v):\n pass\n\n\n@_f\n@_p.types(None, _cs.GLuint, _cs.GLfloat, _cs.GLfloat)\ndef glDepthRangeIndexedfNV(... | [
11,
12,
13,
14,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "68f3d3fce52d08381adc522ee032ef3181aec82a",
"index": 400,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('beerFriends'... | [
0,
1,
2,
3,
4
] |
from machine import Pin, PWM
import time
# externe LED zit op pin D1 (GPIO5)
PinNum = 5
# pwm initialisatie
pwm1 = PWM(Pin(PinNum))
pwm1.freq(60)
pwm1.duty(0)
step = 100
for i in range(10):
# oplichten
while step < 1000:
pwm1.duty(step)
time.sleep_ms(500)
step+=100
# uitdoven
... | normal | {
"blob_id": "9f31694d80f2dcc50a76b32aa296871694d3644d",
"index": 7838,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npwm1.freq(60)\npwm1.duty(0)\n<mask token>\nfor i in range(10):\n while step < 1000:\n pwm1.duty(step)\n time.sleep_ms(500)\n step += 100\n while step > 0:\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class MongoDB:
<|reserved_special_token_0|>
def get_one(self, query):
return self.table.find_one(query, property={'_id': False})
<|reserved_special_token_0|>
def add(self, kv_dict):
return self.table.insert_one(kv_dict)
def delete(self, query):
... | flexible | {
"blob_id": "b5f88a6d119f2c3ce8fb77cf8c45b6c9252f5128",
"index": 7619,
"step-1": "<mask token>\n\n\nclass MongoDB:\n <mask token>\n\n def get_one(self, query):\n return self.table.find_one(query, property={'_id': False})\n <mask token>\n\n def add(self, kv_dict):\n return self.table.ins... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(s)
<|reserved_special_token_1|>
lista = [2, 3.2, 4, 52, 6.25]
s = sum(lista)
print(s)
| flexible | {
"blob_id": "05aa8eac846154024d25d639da565135e41403c2",
"index": 9611,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(s)\n",
"step-3": "lista = [2, 3.2, 4, 52, 6.25]\ns = sum(lista)\nprint(s)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
# Generated by Django 3.2.5 on 2021-08-05 07:19
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('organization', '0010_aut... | normal | {
"blob_id": "f2c53efa4b7c2df592582e3093ff269b703be1e0",
"index": 3054,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [migrations.sw... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import numpy as np
import urllib.request
import urllib.parse
import json
def predict(input_text):
URL = "http://127.0.0.1:8000/api/v1/predict/"
values = {
"format": "json",
"input_text": input_text,
}
data = urllib.parse.urlencode({'input_text': i... | normal | {
"blob_id": "b7632cc7d8fc2f9096f7a6bb61c471dc61689f70",
"index": 8342,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef predict(input_text):\n URL = 'http://127.0.0.1:8000/api/v1/predict/'\n values = {'format': 'json', 'input_text': input_text}\n data = urllib.parse.urlencode({'input_text'... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class PolicyEstimator:
<|reserved_special_token_0|>
class ValueEstimator:
def __init__(self, reuse=False, trainable=True):
self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.
uint8, name='X')
self.targets = tf.placeholder(shape=[None], dt... | flexible | {
"blob_id": "0fbf8efd39f583581c46fcd3f84c65a7787145cd",
"index": 847,
"step-1": "<mask token>\n\n\nclass PolicyEstimator:\n <mask token>\n\n\nclass ValueEstimator:\n\n def __init__(self, reuse=False, trainable=True):\n self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.\n uint... | [
3,
4,
5,
6,
7
] |
import pandas as pd
import numpy as np
import json
from pprint import pprint
from shapely.geometry import shape, Point
from geopy.geocoders import Nominatim
from geopy.exc import GeocoderTimedOut
from geopy.exc import GeocoderServiceError
import collections
from matplotlib import pyplot as plt
import time
import csv
... | normal | {
"blob_id": "c1bb2052b3f623c6787ba080dff2dc81f4d6f55e",
"index": 1818,
"step-1": "import pandas as pd\nimport numpy as np\nimport json\nfrom pprint import pprint\nfrom shapely.geometry import shape, Point\nfrom geopy.geocoders import Nominatim\nfrom geopy.exc import GeocoderTimedOut\nfrom geopy.exc import Geocod... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
consonant += consonant.upper()
<|reserved_special_token_0|>
for c in message:
if c in vowel:
vowel_count += 1
elif c in consonant:
consonant_count += 1
print(vowel_count, consonant_count)
<|reserved_speci... | flexible | {
"blob_id": "edf704d720abdb09d176937664c9ba98bcd253a5",
"index": 8320,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nconsonant += consonant.upper()\n<mask token>\nfor c in message:\n if c in vowel:\n vowel_count += 1\n elif c in consonant:\n consonant_count += 1\nprint(vowel_count, c... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def trigLSQ(data):
noPoints = len(data['x'])
order = int(noPoints / 2) if int(noPoints / 2) < noPoints / 2 else int(
noPoints / 2) - 1
c = lambda a: np.array([np.cos(a * float(data['x'][i])) for i in range(
... | flexible | {
"blob_id": "98c2fdf0dfc9a660a3eb9a359aa9ca14d83c60ce",
"index": 4588,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef trigLSQ(data):\n noPoints = len(data['x'])\n order = int(noPoints / 2) if int(noPoints / 2) < noPoints / 2 else int(\n noPoints / 2) - 1\n c = lambda a: np.array([... | [
0,
1,
2,
3,
4
] |
a = []
for i in range((2 * int(input()))):
a.append(int(input()))
if 1 in a:
c = a.index(max(a))
if a[c + 1] == 1:
print(c)
else:
del a[c]
s = a.index(max(a))
if a[s + 1] == 1:
print(s)
else:
print('-1')
| normal | {
"blob_id": "e3e50df47ef074f13382e249832c065ebdce18a6",
"index": 8406,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(2 * int(input())):\n a.append(int(input()))\nif 1 in a:\n c = a.index(max(a))\n if a[c + 1] == 1:\n print(c)\n else:\n del a[c]\n s = a.ind... | [
0,
1,
2,
3
] |
from plone import api
from plone.app.robotframework.testing import AUTOLOGIN_LIBRARY_FIXTURE
from plone.app.testing import applyProfile
from plone.app.testing import FunctionalTesting
from plone.app.testing import IntegrationTesting
from plone.app.testing import PLONE_FIXTURE
from plone.app.testing import PloneSandboxL... | normal | {
"blob_id": "eec2b818ea9d50161bad60e8bf83dcb7ce9bf9fa",
"index": 7428,
"step-1": "<mask token>\n\n\nclass OiRAFixture(PloneSandboxLayer):\n <mask token>\n\n def setUpZope(self, app, configurationContext):\n z2.installProduct(app, 'Products.membrane')\n z2.installProduct(app, 'Products.statusm... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def precision_recall_fscore(nodes, y_true, y_pred):
df = pd.DataFrame({'true': y_true, 'pred': y_pred}, index=nodes)
n_predicted_nodes = len(df[df['pred'] != 0])
n_corrects = len(df[df['pred'] == df['true']])
n_test = len(nodes)
p = n_corrects / n_predicted_nodes
r... | flexible | {
"blob_id": "79c7a2f2e5f0301c15efe1b26a7839a12098f793",
"index": 6618,
"step-1": "<mask token>\n\n\ndef precision_recall_fscore(nodes, y_true, y_pred):\n df = pd.DataFrame({'true': y_true, 'pred': y_pred}, index=nodes)\n n_predicted_nodes = len(df[df['pred'] != 0])\n n_corrects = len(df[df['pred'] == df... | [
2,
3,
4,
5,
6
] |
import os
import argparse
import torch
import model.model as module_arch
from utils.util import remove_weight_norms
from train import get_instance
from librosa import load
from librosa.output import write_wav
from time import time
def main(config, resume, infile, outfile, sigma, dur, half):
# build model architec... | normal | {
"blob_id": "a2421a8673a524c32539555596711a71a8e00dbf",
"index": 439,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main(config, resume, infile, outfile, sigma, dur, half):\n model = get_instance(module_arch, 'arch', config)\n model.summary()\n checkpoint = torch.load(resume)\n state... | [
0,
1,
2,
3,
4
] |
from valuate.predict import *
def get_profit_rate(intent, popularity):
"""
获取畅销系数
"""
# 按畅销程度分级,各交易方式相比于标价的固定比例
profits = gl.PROFITS
profit = profits[popularity]
# 计算各交易方式的价格相比于标价的固定比例
if intent == 'sell':
# 商家收购价相比加权平均价的比例
profit_rate = 1 - profit[0] - profit[1]
el... | normal | {
"blob_id": "1f01989f10be5404d415d4abd1ef9ab6c8695aba",
"index": 6069,
"step-1": "<mask token>\n\n\ndef process_mile(price, use_time, mile):\n \"\"\"\n mile处理\n \"\"\"\n mile_per_month = mile / use_time\n if mile_per_month < gl.MILE_THRESHOLD_2_5:\n return price + 0.035 * (1 - mile_per_mont... | [
12,
15,
16,
18,
25
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def html_print(text, title=''):
from IPython.core.display import display, HTML
display(HTML('<h4>' + str(title) + '</h4>'))
html = display(HTML('<font size=2 face=Verdana>' + text + '</font>'))
return html
<|reserved_special_token_1|>
def h... | flexible | {
"blob_id": "84a63f60a45f1f8fc1efec8f30345a43c3c30c63",
"index": 7332,
"step-1": "<mask token>\n",
"step-2": "def html_print(text, title=''):\n from IPython.core.display import display, HTML\n display(HTML('<h4>' + str(title) + '</h4>'))\n html = display(HTML('<font size=2 face=Verdana>' + text + '</f... | [
0,
1,
2
] |
from django.contrib import admin
from django.contrib.admin.sites import AdminSite
from obp.models import *
from django.utils.html import format_html
from jet.admin import CompactInline
#from django.utils.translation import ugettext_lazy as _
from jet.dashboard import modules
from jet.dashboard.dashboard import Dashboa... | normal | {
"blob_id": "d301ffa790d6444519e354a2b6f8d65f67d380c0",
"index": 1739,
"step-1": "<mask token>\n\n\nclass Client_OrderInline(admin.TabularInline):\n <mask token>\n\n\nclass MyAdminSite(AdminSite):\n site_header = 'Pizza-Day'\n index_template = 'admin/index.html'\n\n\n@admin.register(Product)\nclass Prod... | [
16,
17,
18,
24,
25
] |
club_info = {'club_url':
'https://www.futbin.com///18/leagues/Major%20League%20Soccer?page=1&club=101112'
, 'club_logo':
'https://cdn.futbin.com/content/fifa18/img/clubs/101112.png',
'club_name': 'Vancouver Whitecaps FC'}
players = {}
players['Waston'] = {'player_url':
'https://www.futbin.com//18/pl... | normal | {
"blob_id": "35c4e26acbe99ca7f37b63b67f38d5c40fbf0ea4",
"index": 2503,
"step-1": "<mask token>\n",
"step-2": "club_info = {'club_url':\n 'https://www.futbin.com///18/leagues/Major%20League%20Soccer?page=1&club=101112'\n , 'club_logo':\n 'https://cdn.futbin.com/content/fifa18/img/clubs/101112.png',\n ... | [
0,
1
] |
def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):
node = hou.pwd()
def getNaskCasting():
path = "E:/WIP/Work/casting-nask.csv"
file = open(path, "r")
fileText = file.readlines()
file.close()
fileText.pop(0)
assetDic = {}
for line ... | normal | {
"blob_id": "3073850890eb7a61fb5200c5ab87c802cafe50bb",
"index": 7229,
"step-1": "<mask token>\n",
"step-2": "def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):\n node = hou.pwd()\n\n def getNaskCasting():\n path = 'E:/WIP/Work/casting-nask.csv'\n file = open(path, 'r')\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TestPool2D(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestPool2D(unittest.TestCase):
@staticmethod
def _build_model(input_shape, ... | flexible | {
"blob_id": "1af9fb91e69ea78709c47fca6b12e4f7a6fd17a8",
"index": 7392,
"step-1": "<mask token>\n\n\nclass TestPool2D(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestPool2D(unittest.TestCase):\n\n @staticmethod\n def _build_model(input_s... | [
1,
3,
4,
5,
6
] |
import mmap;
import random;
def shuffle():
l_digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'];
random.shuffle(l_digits);
return "".join(l_digits);
with open("hello.txt", "r+") as f:
map = mmap.mmap(f.fileno(), 1000);
l_i = 0;
for l_digit in shuffle():
map[l_i] = l_digit;
... | normal | {
"blob_id": "b0468e58c4d0387a92ba96e8fb8a876ece256c78",
"index": 6507,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef shuffle():\n l_digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n random.shuffle(l_digits)\n return ''.join(l_digits)\n\n\n<mask token>\n",
"step-3": "<mask ... | [
0,
1,
2,
3,
4
] |
'''
8-6. 도시 이름
도시와 국가 이름을 받는 city_country() 함수를 만드세요. 이 함수는 다음과 같은 문자열을 반환해야 합니다.
'Santiago, Chile'
- 최소한 세 개의 도시-국가 쌍으로 함수를 호출하고 반환값을 출력하세요.
Output:
santiago, chile
ushuaia, argentina
longyearbyen, svalbard
'''
| normal | {
"blob_id": "2d5abcd75dcbeb1baa3f387035bdcc3b7adbfe3f",
"index": 7856,
"step-1": "<mask token>\n",
"step-2": "'''\n8-6. 도시 이름\n도시와 국가 이름을 받는 city_country() 함수를 만드세요. 이 함수는 다음과 같은 문자열을 반환해야 합니다.\n'Santiago, Chile'\n- 최소한 세 개의 도시-국가 쌍으로 함수를 호출하고 반환값을 출력하세요.\n\nOutput:\nsantiago, chile\nushuaia, argentina\nlongye... | [
0,
1
] |
#!/usr/bin/env python3
import click
@click.command()
@click.option("--name", prompt = "Your name")
def hello(name):
print("hello", name)
if __name__ == '__main__':
hello()
| normal | {
"blob_id": "19c1a50cf19f04a9e0d0163a9383cb900bca1d38",
"index": 9862,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@click.command()\n@click.option('--name', prompt='Your name')\ndef hello(name):\n print('hello', name)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\n@click.command()\n@click... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class UploaderThread(object):
<|reserved_special_token_0|>
def is_uploading_tourney(self, tourney):
return tourney in self.uploading_tourneys
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def get_last_successful_upload_time(self, tourney_name):
... | flexible | {
"blob_id": "bd202e18cb98efc2b62ce4670fadcf70c35a33cb",
"index": 2529,
"step-1": "<mask token>\n\n\nclass UploaderThread(object):\n <mask token>\n\n def is_uploading_tourney(self, tourney):\n return tourney in self.uploading_tourneys\n <mask token>\n <mask token>\n\n def get_last_successful... | [
19,
21,
26,
31,
34
] |
import pandas as pd
import re
import sqlite3 as lite
import os
from pybedtools import BedTool
import django
from checkprimers import CheckPrimers
from pandas import ExcelWriter
import datetime
os.environ['DJANGO_SETTINGS_MODULE'] = 'mysite.settings'
django.setup()
class GetPrimers(object):
"""Extracts data from e... | normal | {
"blob_id": "1d817ee09705301b574c421a9ff716748c146fdd",
"index": 9591,
"step-1": "import pandas as pd\nimport re\nimport sqlite3 as lite\nimport os\nfrom pybedtools import BedTool\nimport django\nfrom checkprimers import CheckPrimers\nfrom pandas import ExcelWriter\nimport datetime\nos.environ['DJANGO_SETTINGS_M... | [
0
] |
"""@brief the routes for Flask application
"""
import hashlib
import json
import time
import requests
from flask import render_template, url_for
from soco import SoCo
from app import app
app.config.from_pyfile("settings.py")
sonos = SoCo(app.config["SPEAKER_IP"])
def gen_sig():
"""@brief return the MD5 checksum... | normal | {
"blob_id": "86f33895e9ae0e026d7d6e40e611796b2dc2c713",
"index": 8394,
"step-1": "<mask token>\n\n\ndef gen_sig():\n \"\"\"@brief return the MD5 checksum \"\"\"\n return hashlib.md5((app.config['ROVI_API_KEY'] + app.config[\n 'ROVI_SHARED_SECRET'] + repr(int(time.time()))).encode('utf-8')\n )... | [
16,
17,
18,
20,
23
] |
"""1) Написать бота-консультанта, который будет собирать информацию с
пользователя (его ФИО, номер телефона, почта, адресс, пожелания).
Записывать сформированную заявку в БД (по желанию SQl/NOSQL).)."""
import telebot
from .config import TOKEN
from telebot.types import ReplyKeyboardMarkup, KeyboardButton, InlineKeybo... | normal | {
"blob_id": "dcb2351f9489815fbec8694b446d0a93972a6590",
"index": 6388,
"step-1": "<mask token>\n\n\nclass User(Document):\n surname = StringField(required=True)\n name = StringField(required=True)\n middle_name = StringField(required=True)\n phone = StringField(required=True)\n email = StringField... | [
10,
12,
13,
14,
16
] |
# This handle the url for routing
from django.urls import path
from . import views
# Defines views to pass dynamic data to listings page
urlpatterns = [
path('', views.index, name='listings'),
path('<int:listing_id>', views.listing, name='listing'),
path('search', views.search, name='search')
] | normal | {
"blob_id": "be894830bb0dde6bacaea6be823391e0445603c3",
"index": 1192,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.index, name='listings'), path(\n '<int:listing_id>', views.listing, name='listing'), path('search',\n views.search, name='search')]\n",
"step-3": "fr... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i_angle in np.arange(0, 360, 45):
ri_filenames.append('r%di%d.csv' % (i_angle, i_angle))
ri_filenames.append('r%di%d.csv' % (i_angle + 45, i_angle))
ri_filenames.append('r360i360.csv')
<|reserved_special_token_0|>
for ... | flexible | {
"blob_id": "3d3b9956a98f11a170d66280abe7f193cef9ccfb",
"index": 808,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i_angle in np.arange(0, 360, 45):\n ri_filenames.append('r%di%d.csv' % (i_angle, i_angle))\n ri_filenames.append('r%di%d.csv' % (i_angle + 45, i_angle))\nri_filenames.append('r36... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@app.route('/')
def root():
return 'Test!'
@app.route('/federal/geographic')
def federal_geographic():
pass
<|reserved_special_token_0|>
@app.route('/state/geographic')
def state_geographic():
pass
@app.route('/local/temporal')
def local_temporal():
pass
<|reserv... | flexible | {
"blob_id": "cc094f8aeff3b52bd9184f7b815320529ecb4550",
"index": 9928,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef root():\n return 'Test!'\n\n\n@app.route('/federal/geographic')\ndef federal_geographic():\n pass\n\n\n<mask token>\n\n\n@app.route('/state/geographic')\ndef state_geographic():\n pas... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class TestValidate(unittest.TestCase):
@db_session
def setUp(self):
db.execute('delete from Person')
registry = getattr(core, '__warningregistry__', {})
for key in list(registry):
if type(key) is not tuple:
continue
... | flexible | {
"blob_id": "33c39b098cb9d3368b8f74a7433e0943fe252da5",
"index": 5672,
"step-1": "<mask token>\n\n\nclass TestValidate(unittest.TestCase):\n\n @db_session\n def setUp(self):\n db.execute('delete from Person')\n registry = getattr(core, '__warningregistry__', {})\n for key in list(regis... | [
6,
8,
9,
10,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ROOT_URL = 'https://api.twitter.com'
UPLOAD_URL = 'https://upload.twitter.com'
REQUEST_TOKEN_URL = f'{ROOT_URL}/oauth/request_token'
AUTHENTICATE_URL = f'{ROOT_URL}/oauth/authenticate'
ACCESS_TOKEN_URL = f'{ROOT_URL}/oauth/access_... | flexible | {
"blob_id": "c907f6b954aa3eae21a54eba9d54c116576bd40a",
"index": 5848,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nROOT_URL = 'https://api.twitter.com'\nUPLOAD_URL = 'https://upload.twitter.com'\nREQUEST_TOKEN_URL = f'{ROOT_URL}/oauth/request_token'\nAUTHENTICATE_URL = f'{ROOT_URL}/oauth/authenticate'... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
authors.append(text[start + length + len('<![CDATA['):end - len(']]>')])
while text.find('<wp:author_display_name>', start + 1) != -1:
start = text.find('<wp:author_display_name>', start + 1)
end = text.find('</wp:author_d... | flexible | {
"blob_id": "cf5062c999c6c29f103428c247d8d1a4550f9d75",
"index": 8086,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nauthors.append(text[start + length + len('<![CDATA['):end - len(']]>')])\nwhile text.find('<wp:author_display_name>', start + 1) != -1:\n start = text.find('<wp:author_display_name>', ... | [
0,
1,
2,
3
] |
import krait
from ctrl import ws
krait.mvc.set_init_ctrl(ws.WsPageController())
| normal | {
"blob_id": "da2b946238b429188fe3fa50286658d4b5cdbf41",
"index": 5752,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nkrait.mvc.set_init_ctrl(ws.WsPageController())\n",
"step-3": "import krait\nfrom ctrl import ws\nkrait.mvc.set_init_ctrl(ws.WsPageController())\n",
"step-4": null,
"step-5": null,
... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class LogPlugin(Plugin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def search(self, message, query, *additional_queries):
chat_history = read_lines_from_file('chatlog.log')
chat_history.reverse()
found_line = None
for line in ch... | flexible | {
"blob_id": "d932ab84848c9a8ca8bb23a57424b8f6190b6260",
"index": 2563,
"step-1": "<mask token>\n\n\nclass LogPlugin(Plugin):\n <mask token>\n <mask token>\n\n def search(self, message, query, *additional_queries):\n chat_history = read_lines_from_file('chatlog.log')\n chat_history.reverse(... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class Methylation(object):
def __init__(self, table, data_type, name, called_sites):
self.table = table
self.data_type = data_type
self.name = name
self.called_sites = called_sites
<|reserved_special_token_0|>
def parse_nanopolish(filename, file_ty... | flexible | {
"blob_id": "d654aea3da3e36ccde8a5f4e03798a0dea5aad8a",
"index": 510,
"step-1": "<mask token>\n\n\nclass Methylation(object):\n\n def __init__(self, table, data_type, name, called_sites):\n self.table = table\n self.data_type = data_type\n self.name = name\n self.called_sites = cal... | [
5,
8,
10,
11,
12
] |
# Title: K번째 수
# Link: https://www.acmicpc.net/problem/11004
import sys
sys.setrecursionlimit(10 ** 6)
def read_list_int():
return list(map(int, sys.stdin.readline().strip().split(' ')))
def read_single_int():
return int(sys.stdin.readline().strip())
def selection_sort(nums, k):
sor... | normal | {
"blob_id": "f4ab6df8efc334fa338ade7deecd36d8cd859e96",
"index": 4174,
"step-1": "<mask token>\n\n\ndef read_list_int():\n return list(map(int, sys.stdin.readline().strip().split(' ')))\n\n\n<mask token>\n\n\ndef selection_sort(nums, k):\n sorted_index = 0\n while True:\n minimum = 9999999999\n ... | [
5,
6,
7,
8,
9
] |
"""Produce a multi-panel figure of each output lead time in a forecast
"""
import matplotlib.pyplot as plt
import iris.plot as iplt
from irise import convert
from irise.plot.util import add_map
from myscripts import plotdir
from myscripts.models.um import case_studies
columns = 3
def main(forecast, name, levels, *a... | normal | {
"blob_id": "310e6e693cdce6ff71d06eac86214a21bef236d4",
"index": 7425,
"step-1": "<mask token>\n\n\ndef main(forecast, name, levels, *args, **kwargs):\n nt = len(forecast)\n rows = nt / columns + 1\n fig = plt.figure(figsize=(18, 10 * float(rows) / columns))\n for n, cubes in enumerate(forecast):\n ... | [
1,
2,
3,
4,
5
] |
import os, glob
import numpy as np
from ..algorithms.utils import get_file_manager
from ..algorithms.clustered_writes import *
from ..exp_utils import create_empty_dir
def test_get_entity_sizes():
# in C order
bytes_per_voxel = 1
R = (10,9,10)
cs = (5,3,2)
partition = (2,3,5)
bs, brs, bss = g... | normal | {
"blob_id": "6dd11f71e514a46462bf0b97ddac9ea474e86ad0",
"index": 366,
"step-1": "<mask token>\n\n\ndef test_get_strategy():\n bytes_per_voxel = 1\n R = 20, 9, 10\n cs = 5, 3, 2\n partition = 4, 3, 5\n bs, brs, bss = get_entity_sizes(cs, bytes_per_voxel, partition)\n test_case = {(5 * 2 * 3): 0,... | [
1,
3,
4,
5,
6
] |
from django.apps import AppConfig
class DojoBookAppConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'dojo_book_app'
| normal | {
"blob_id": "314f6cc97f53fa5bd8bf0ec0e1e305ca6384f1a2",
"index": 1559,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass DojoBookAppConfig(AppConfig):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass DojoBookAppConfig(AppConfig):\n default_auto_field = 'django.db.mod... | [
0,
1,
2,
3
] |
import numpy as np
'''
1. Create 0-D array, 1-D array, 2-D array, 3-D array with following value
0-D: [2]
1-D: [3, 4, 5, 6, 7]
2-D: [[8, 1, 3], [2, 3, 4], [6, 2, 5]]
3-D: [[[1, 2, 4], [3, 3, 2], [1, 9, 1]], [[6, 8, 7], [9, 1, 0], [8, 2, 3]], [[5, 4, 1], [5, 7, 2], [3, 5, 9]]]
print them
'''
D0 = np.... | normal | {
"blob_id": "a868ecb6ea6a5c7a186ddd8fa4fb76d96efeb21d",
"index": 4140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('D0')\nprint(D0)\nprint('D1')\nprint(D1)\nprint('D2')\nprint(D2)\nprint('D3')\nprint(D3)\n<mask token>\nprint('D2')\nprint(D2)\n<mask token>\nprint('D3')\nprint(D3)\n<mask token>\np... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# encoding: utf-8
from rest_client import PY2
from tornado.testing import gen_test
from tornado.web import Application, RequestHandler
from .server import AsyncRESTTestCase
class Handler(RequestHandler):
if PY2:
S = '\xd0\x9f\xd1\x80\xd0\xb8\xd0\xb2\xd0\xb5\xd1\x82 \xd0\xbc\xd0\xb8\x... | normal | {
"blob_id": "4bbd97942023370e053ccf4b5c1496c7247c7bf2",
"index": 9026,
"step-1": "<mask token>\n\n\nclass TestCopy(AsyncRESTTestCase):\n <mask token>\n\n @gen_test\n def test_get(self):\n response = yield self.http_client.get(self.api_url.format('/'))\n assert response.body == Handler.S\n"... | [
2,
3,
4,
6,
7
] |
<|reserved_special_token_0|>
def loop_rec(n, m, mapCoords, dims, data, tple):
if n >= m:
for x in range(dims[m]):
loop_rec(n, m + 1, mapCoords, dims, data, tple + (x,))
else:
temp = loop_access(len(dims) - 1, 0, data, tple)
print(temp)
if temp in mapCoords:
... | flexible | {
"blob_id": "55b4448caa73bcb50a15eb46d07328934fce72c8",
"index": 7029,
"step-1": "<mask token>\n\n\ndef loop_rec(n, m, mapCoords, dims, data, tple):\n if n >= m:\n for x in range(dims[m]):\n loop_rec(n, m + 1, mapCoords, dims, data, tple + (x,))\n else:\n temp = loop_access(len(dim... | [
1,
2,
3,
4,
5
] |
# link https://deeplizard.com/learn/video/QK_PP_2KgGE
import gym
import numpy as np
import random
import time
from IPython.display import clear_output
# setup the env
env = gym.make("FrozenLake8x8-v0", is_slippery=False)
observation = env.reset()
# setup the q-table
action_space_size = env.action_space.n
state_space_... | normal | {
"blob_id": "b791afec1c9fb214d1f3b4ec0ec67f905d96aabf",
"index": 3249,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor episode in range(num_episodes):\n state = env.reset()\n done = False\n rewards_current_episode = 0\n for step in range(steps_per_episodes):\n exploration_rate_thres... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
pandas2ri.activate()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ts = robjects.r('ts')
forecast = importr('forecast', lib_loc=
'C:/Users/sand9888/Documents/sand9888/R/win-library/3... | flexible | {
"blob_id": "e00cbe6e177ee841c6e64de842e5b8f95463b3a8",
"index": 2169,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npandas2ri.activate()\n<mask token>\n",
"step-3": "<mask token>\nts = robjects.r('ts')\nforecast = importr('forecast', lib_loc=\n 'C:/Users/sand9888/Documents/sand9888/R/win-library/3... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Cell(object):
def __init__(self, cellId, top_left_cell, top_right_cell,
bottom_right_cell, bottom_left_cell):
self.cellId = cellId
self.top_left_cell = top_left_cell
self.top_right_cell = top_right_cell
self.bottom_right_cell = bottom_rig... | flexible | {
"blob_id": "01f0ad8746ed9a9941faa699b146625ad3a0b373",
"index": 4289,
"step-1": "<mask token>\n\n\nclass Cell(object):\n\n def __init__(self, cellId, top_left_cell, top_right_cell,\n bottom_right_cell, bottom_left_cell):\n self.cellId = cellId\n self.top_left_cell = top_left_cell\n ... | [
6,
7,
8,
9,
11
] |
from django.core.validators import RegexValidator
from django.db import models
from .image import Image
class AffiliatedStoreManager(models.Manager):
def get_queryset(self):
return super().get_queryset() \
.select_related('icon') \
.select_related('icon__image_type')
def fin... | normal | {
"blob_id": "e2b439974b66e45a899605bc7234850783c3dfb0",
"index": 2231,
"step-1": "<mask token>\n\n\nclass AffiliatedStoreManager(models.Manager):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass AffiliatedStore(models.Model):\n\n\n class Meta:\n db_table = 'affiliated_store'\n object... | [
5,
6,
7,
9,
10
] |
# Percy's playground.
from __future__ import print_function
import sympy as sp
import numpy as np
import BorderBasis as BB
np.set_printoptions(precision=3)
from IPython.display import display, Markdown, Math
sp.init_printing()
R, x, y = sp.ring('x,y', sp.RR, order=sp.grevlex)
I = [ x**2 + y**2 - 1.0, x + y ]
R, x, y... | normal | {
"blob_id": "88af8b4eeb40ecf19622ecde1a5dea9a078bb66c",
"index": 8817,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.set_printoptions(precision=3)\n<mask token>\nsp.init_printing()\n<mask token>\nI.extend([(v ** 2 - 1) for v in vs])\nprint('Generating')\n<mask token>\nprint('Done')\nprint('=== Genera... | [
0,
1,
2,
3,
4
] |
import string
fhand = open("romeo-full.txt")
counts = dict()
for line in fhand:
line.tranc | normal | {
"blob_id": "5493887e32dbe7ae27eca79d28da8488183b37a3",
"index": 8792,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in fhand:\n line.tranc\n",
"step-3": "<mask token>\nfhand = open('romeo-full.txt')\ncounts = dict()\nfor line in fhand:\n line.tranc\n",
"step-4": "import string\nfhand... | [
0,
1,
2,
3,
4
] |
people = 20
cats = 30
dogs = 15
if people < cats:
print("Too many cats")
elif people > cats:
print("Not many cats")
else:
print("we cannnot decide") | normal | {
"blob_id": "0465e33d65c2ce47ebffeec38db6908826bf4934",
"index": 299,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif people < cats:\n print('Too many cats')\nelif people > cats:\n print('Not many cats')\nelse:\n print('we cannnot decide')\n",
"step-3": "people = 20\ncats = 30\ndogs = 15\nif... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class IssuerWalletInMemory(IssuerWallet, WalletInMemory):
def __init__(self, claimDefId, repo: PublicRepo):
WalletInMemory.__init__(self, claimDefId, repo)
self._sks = {}
self._skRs = {}
self... | flexible | {
"blob_id": "890841c8892e89375bb022f0d469fefc27414a2b",
"index": 5823,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass IssuerWalletInMemory(IssuerWallet, WalletInMemory):\n\n def __init__(self, claimDefId, repo: PublicRepo):\n WalletInMemory.__init__(self, claimDefId, repo)\n se... | [
0,
2,
4,
5,
6
] |
# ----------------------------------------------------------------------------
# Written by Khanh Nguyen Le
# May 4th 2019
# Discord: https://discord.io/skyrst
# ----------------------------------------------------------------------------
import operator
def validInput(x):
if x=="a": return True
elif x=... | normal | {
"blob_id": "5209638ec97a666783c102bec7a2b00991c41a08",
"index": 5438,
"step-1": "<mask token>\n\n\ndef takeInput():\n x = input()\n while not validInput(x):\n print('Invalid input. Try another one:')\n x = input()\n return x\n\n\ndef main():\n stats = {'Council': 0, 'United': 0, 'Facel... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Locale(MainHandler):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class MainPage(MainHandler):
def get(self):
self.render('home.html')
def post(self):
pw = self.request.get('pw')
<|reserved_special_token_1|>
<|reserved_special_toke... | flexible | {
"blob_id": "bdcbb946dadf168149342c651ad03eaf4b748401",
"index": 6803,
"step-1": "<mask token>\n\n\nclass Locale(MainHandler):\n <mask token>\n <mask token>\n\n\nclass MainPage(MainHandler):\n\n def get(self):\n self.render('home.html')\n\n def post(self):\n pw = self.request.get('pw')\... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def findNonPrefixes(prefix, array):
result = []
prefixLength = len(prefix)
for string in array:
if string[0:prefixLength] != prefix:
result.append(string)
return result
def run():
r = re... | flexible | {
"blob_id": "8419aee5dbc64b51f3c0f364716aad1630f00fe9",
"index": 7173,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef findNonPrefixes(prefix, array):\n result = []\n prefixLength = len(prefix)\n for string in array:\n if string[0:prefixLength] != prefix:\n result.append... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class MovieRankings(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Movie(models.Model):
"""
电影的数据库表格
"""
movie_name = models.CharField(max_length=64, blank=True)
douban_link = models.CharField(m... | flexible | {
"blob_id": "449ae193f8817d4ee2fe67eadf72d9c19b2c5e53",
"index": 1319,
"step-1": "<mask token>\n\n\nclass MovieRankings(models.Model):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Movie(models.Model):\n \"\"\"\n 电影的数据库表格\n \"\"\"\n movie_name = models.CharField(max_length=64, blan... | [
8,
10,
11,
12,
13
] |
# -*- coding: utf-8 -*-
"""
Created on Thu May 3 09:12:11 2018
@author: shen1994
"""
import codecs
import numpy as np
def create_documents():
""" 按标点符号或是空格存储文件 """
documents_length = 0
chars,labels = [],[]
chars_file = codecs.open("data/data.data", 'w', 'utf-8')
labels_file = codecs.open("data/... | normal | {
"blob_id": "f22836fc4fed22d833755db0ff34502170260766",
"index": 9260,
"step-1": "<mask token>\n\n\ndef create_documents():\n \"\"\" 按标点符号或是空格存储文件 \"\"\"\n documents_length = 0\n chars, labels = [], []\n chars_file = codecs.open('data/data.data', 'w', 'utf-8')\n labels_file = codecs.open('data/lab... | [
4,
7,
8,
10,
11
] |
from config import Config
from flask import Flask
from flask_cors import CORS
from flask_migrate import Migrate
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
CORS(app)
app.config.from_object(Config)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///ws.db'
# app.config['SQLALCHEMY_DATABASE_URI'] = 'mys... | normal | {
"blob_id": "f494d8aeee8c72cce8fc14e44ca896bcf30c100a",
"index": 5627,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nCORS(app)\napp.config.from_object(Config)\n<mask token>\n",
"step-3": "<mask token>\napp = Flask(__name__)\nCORS(app)\napp.config.from_object(Config)\napp.config['SQLALCHEMY_DATABASE_UR... | [
0,
1,
2,
3,
4
] |
from ...routes import Route
from .providers import SQSProvider
from .message_translators import SQSMessageTranslator, SNSMessageTranslator
class SQSRoute(Route):
def __init__(self, provider_queue, provider_options=None, *args, **kwargs):
provider_options = provider_options or {}
provider = SQSPro... | normal | {
"blob_id": "041f1d7c482fe4f65e8cc5a508da62ee6ccf59ff",
"index": 6686,
"step-1": "<mask token>\n\n\nclass SNSQueueRoute(Route):\n\n def __init__(self, provider_queue, provider_options=None, *args, **kwargs):\n provider_options = provider_options or {}\n provider = SQSProvider(provider_queue, **p... | [
2,
3,
4,
5
] |
'''
sin(x) = x^1/1! - x^3/3! + x^5/5! - x^7/7! + …..
Input : x, n ( No. of terms I want in series )
Input : 3.14, 10
Output : sin(3.14) = sin(180) = 0
Radians vs Degrees
( 0, 30, 60, 90 ….)
2pi = 360
Pi = 180
Pseudo code :
1.Take input variables radians,num
2. sin = 0
3. Indices = 1
4. odd = 1
4... | normal | {
"blob_id": "a99426c0751885f17078e709fd523cf3a26f5286",
"index": 5533,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef exponent(base, index):\n if index == 0 and base == 0:\n return -1\n elif index == 0:\n return 1\n elif base == 0:\n return 0\n else:\n prod... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class CnnArticleItem(scrapy.Item):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class GoogleArticleItem(scrapy.Item):
title = scrapy.Fi... | flexible | {
"blob_id": "cf0eb9685cdfc412871d3b36270ddab3e520bb8f",
"index": 104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass CnnArticleItem(scrapy.Item):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass GoogleArticleItem(scrapy.Item):\n title = scrapy.Field()\n d... | [
0,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class BatchGenerator(object):
def __init__(self, data, batch_size=1):
self.inputs, self.labels = data
self.batch_size = batch_size
self.data_length = len(self.inputs)
self.sequence_length = np.array([x.shape[0] for x in self.inputs])
def next_batc... | flexible | {
"blob_id": "912928cea0f96e601eecfcb6dba695ef26a3c6e2",
"index": 9618,
"step-1": "<mask token>\n\n\nclass BatchGenerator(object):\n\n def __init__(self, data, batch_size=1):\n self.inputs, self.labels = data\n self.batch_size = batch_size\n self.data_length = len(self.inputs)\n sel... | [
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
fig.gca().plot(x, y, 'k.')
<|reserved_special_token_0|>
canvas.draw()
<|reserved_special_token_0|>
fig.gca().plot([bbox.x0, bbox.x1, bbox.x1, bbox.x0, bbox.x0], [bbox.y0,
bbox.y0, bbox.y1, bbox.y1, bbox.y0], 'k:', transform=No... | flexible | {
"blob_id": "c87f9885e96abdd32df68f9fe1942b2782bd5b96",
"index": 8149,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfig.gca().plot(x, y, 'k.')\n<mask token>\ncanvas.draw()\n<mask token>\nfig.gca().plot([bbox.x0, bbox.x1, bbox.x1, bbox.x0, bbox.x0], [bbox.y0,\n bbox.y0, bbox.y1, bbox.y1, bbox.y0], 'k... | [
0,
1,
2,
3,
4
] |
from django.db import models
# Create your models here.
STATUS_CHOICES=(
('Pending','Pending'),
('Completed','Completed'))
class Appointment(models.Model):
first_name=models.CharField(max_length=100)
last_name=models.CharField(max_length=100)
phone_number=models.CharField(max_length=12,null=False... | normal | {
"blob_id": "3343844bf49cb3f4d655613475e44a140ac3106d",
"index": 4505,
"step-1": "<mask token>\n\n\nclass Appointment(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.first_n... | [
2,
3,
4,
5,
6
] |
import drawSvg
import noise
import random
import math
import numpy as np
sizex = 950
sizey = 500
noisescale = 400
persistence = 0.5
lacunarity = 2
seed = random.randint(0, 100)
actorsnum = 1000
stepsnum = 50
steplenght = 2
noisemap = np.zeros((sizex, sizey))
for i in range(sizex):
for j in range(sizey):
noi... | normal | {
"blob_id": "68c9944c788b9976660384e5d1cd0a736c4cd0e6",
"index": 3826,
"step-1": "<mask token>\n\n\nclass Actor:\n\n def __init__(self):\n self.x = random.random() * sizex\n self.y = random.random() * sizey\n self.xn = self.x\n self.yn = self.y\n\n def step(self):\n t = g... | [
3,
4,
6,
7
] |
<|reserved_special_token_0|>
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
... | flexible | {
"blob_id": "1a7e83fe9528b177246d6374ddaf2a76a0046e83",
"index": 200,
"step-1": "<mask token>\n\n\ndef cos_dist(a, b):\n if len(a) != len(b):\n return None\n part_up = 0.0\n a_sq = 0.0\n b_sq = 0.0\n for a1, b1 in zip(a, b):\n part_up += a1 * b1\n a_sq += a1 ** 2\n b_sq... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Implements the webservice calls of the command
like rest apis or other network related methods
""" | flexible | {
"blob_id": "48369e1ed826a9a50c0fd9f63b7cc10b8225ce2b",
"index": 8760,
"step-1": "<mask token>\n",
"step-2": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nImplements the webservice calls of the command\nlike rest apis or other network related methods\n\"\"\"",
"step-3": null,
"step-4": null,
... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while len(s) != 1:
count += 1
a = 0
for i in range(len(s)):
a += int(s[i])
s = str(a)
print(count)
<|reserved_special_token_1|>
s = input()
count = 0
while len(s) != 1:
count += 1
a = 0
for i... | flexible | {
"blob_id": "638e21e1eb1e2e14244628260d9c7ac179983721",
"index": 2541,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile len(s) != 1:\n count += 1\n a = 0\n for i in range(len(s)):\n a += int(s[i])\n s = str(a)\nprint(count)\n",
"step-3": "s = input()\ncount = 0\nwhile len(s) != 1... | [
0,
1,
2,
3
] |
#Copyright ReportLab Europe Ltd. 2000-2017
#see license.txt for license details
__version__='3.3.0'
__doc__="""
The Canvas object is the primary interface for creating PDF files. See
doc/reportlab-userguide.pdf for copious examples.
"""
__all__ = ['Canvas']
ENABLE_TRACKING = 1 # turn this off to do profile testing w/... | normal | {
"blob_id": "7d6e8e6142184a1540daa29dac802fe75bd93d8e",
"index": 4428,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nc.translate(inch, inch)\nc.setFont('Helvetica', 80)\nc.setStrokeColorRGB(0.2, 0.5, 0.3)\nc.setFillColorRGB(1, 0, 1)\nc.rect(inch, inch, 6 * inch, 9 * inch, fill=1)\nc.rotate(90)\nc.setFil... | [
0,
1,
2,
3,
4
] |
"""Test Spotify module"""
from spoetify.spotify import Spotify
from nose.tools import assert_equal
def test_search_track():
sp = Spotify()
t = sp.search_track("avocado")
assert_equal(t.id, "1UyzA43l3OIcJ6jd3hh3ac")
| normal | {
"blob_id": "337309da79ce9d90010fef5c171b6b344e6dc63f",
"index": 5937,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_search_track():\n sp = Spotify()\n t = sp.search_track('avocado')\n assert_equal(t.id, '1UyzA43l3OIcJ6jd3hh3ac')\n",
"step-3": "<mask token>\nfrom spoetify.spotify... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
log.fit(X_train, y_train)
log.predict(sc.transform([[45, 87000]]))
<|reserved_special_token_0|>
np.set_printoptions(precision=2)
np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test),
1)), 1)
<|reserved_sp... | flexible | {
"blob_id": "149f8b453786ec54668a55ec349ac157d2b93b5d",
"index": 2397,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlog.fit(X_train, y_train)\nlog.predict(sc.transform([[45, 87000]]))\n<mask token>\nnp.set_printoptions(precision=2)\nnp.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_t... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def rows(**ro):
print(ro)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(id(a))
<|reserved_special_token_0|>
print('hello.....')
print(type(a))
print(id(a))
<|reserved_special_token_0|>
print(id(b))
b.append(10)
print(id(b))
<|reserve... | flexible | {
"blob_id": "95b75395cafc6ba9f75ecf48157421e37ced2518",
"index": 815,
"step-1": "<mask token>\n\n\ndef rows(**ro):\n print(ro)\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(id(a))\n<mask token>\nprint('hello.....')\nprint(type(a))\nprint(id(a))\n<mask token>\nprint(id(b))\nb.append(10)\nprint(id(b))\... | [
1,
3,
4,
5,
6
] |
import json
import struct
import pymel.core as pmc
import os.path
def exportVSSD(path, camName, wantTris=False, renderdata=None):
mainFileDict = {}
mainFilePath = path
mainFileStem = os.path.basename(path)[:-5]
mainFileDir = os.path.dirname(path)
resolution = pmc.ls('defaultResolution')[0]
ren... | normal | {
"blob_id": "004a9cd0e459116bf3f88f3546ff4eded3dfb2a8",
"index": 2512,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef exportVSSD(path, camName, wantTris=False, renderdata=None):\n mainFileDict = {}\n mainFilePath = path\n mainFileStem = os.path.basename(path)[:-5]\n mainFileDir = os.p... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class SpiderMain(object):
<|reserved_special_token_0|>
def craw(self, root_url):
count = 1
self.urls.add_new_url(root_url)
while self.urls.has_new_url():
try:
new_url = self.urls.get_new_url()
print('craw %d : %s... | flexible | {
"blob_id": "e99a81a5600aad6111bb2694cbda02021ccfd71c",
"index": 2817,
"step-1": "<mask token>\n\n\nclass SpiderMain(object):\n <mask token>\n\n def craw(self, root_url):\n count = 1\n self.urls.add_new_url(root_url)\n while self.urls.has_new_url():\n try:\n n... | [
2,
3,
4,
5,
6
] |
line_numbers = input().split(", ")
print("Positive:", ", ".join(list(filter((lambda x: int(x) > -1), line_numbers))))
print("Negative:", ", ".join((list(filter((lambda x: int(x) < 0), line_numbers)))))
print("Even:", ", ".join((list(filter((lambda x: int(x) % 2 == 0), line_numbers)))))
print("Odd:", ", ".join((list(fil... | normal | {
"blob_id": "e4845e5aa949ec523515efc4d7996d647fddabdb",
"index": 7060,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Positive:', ', '.join(list(filter(lambda x: int(x) > -1, line_numbers)))\n )\nprint('Negative:', ', '.join(list(filter(lambda x: int(x) < 0, line_numbers))))\nprint('Even:', ', ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def latex_formula(form):
latex = form.simplify().to_latex(outer=True)
if latex:
display(Math(latex))
display(Markdown('<details><pre>$' + latex + '$</pre></details>'))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def... | flexible | {
"blob_id": "7f7bd2e9ec1932ccfd8aa900956ce85473ee8dbd",
"index": 4668,
"step-1": "<mask token>\n\n\ndef latex_formula(form):\n latex = form.simplify().to_latex(outer=True)\n if latex:\n display(Math(latex))\n display(Markdown('<details><pre>$' + latex + '$</pre></details>'))\n\n\n<mask token>... | [
1,
5,
7,
9,
10
] |
import json
import os
from subprocess import PIPE, Popen as popen
from unittest import TestCase
from substra.commands import Config
objective = [[{
'descriptionStorageAddress': 'http://chunantes.substrabac:8001/objective/d5002e1cd50bd5de5341df8a7b7d11b6437154b3b08f531c9b8f93889855c66f/description/',
'key': 'd... | normal | {
"blob_id": "c55b768466309d2e655c9222e0674a6bc2a958b3",
"index": 9899,
"step-1": "<mask token>\n\n\nclass TestList(TestCase):\n <mask token>\n <mask token>\n\n def test_list_objective(self):\n output = popen(['substra', 'list', 'objective',\n '--config=/tmp/.substra_e2e'], stdout=PIPE)... | [
4,
6,
8,
9,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class WebApiAppConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class WebApiAppConfig(AppConfig):
name = 'WebApiApp'
<|reserved_special_token_1|>
from djan... | flexible | {
"blob_id": "cc97f70b9d41357f020ea9c59d8b149392a336cc",
"index": 9656,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n name = 'WebApiApp'\n",
"step-4": "from django.apps impo... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def check_version(ctx, _, value):
"""
Print current version, and check for latest version.
Called via 'geocube --version'
:param ctx: Application context object (click.Context)
:param value: Passed in by Click
:return None
"""
if not value or ctx.resilien... | flexible | {
"blob_id": "0964121d88fad2906311de7532eac52ff784fff6",
"index": 8306,
"step-1": "<mask token>\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :par... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Test_PW_Functions(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_pw_long_enough_min(self):
sample_pass = 'abcdadcaabc'
expected_result = False
result = validate_pw_long(sample_pass)
self.assertEqual... | flexible | {
"blob_id": "dc7d75bf43f1ba55673a43f863dd08e99a1c0e0f",
"index": 4820,
"step-1": "<mask token>\n\n\nclass Test_PW_Functions(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_pw_long_enough_min(self):\n sample_pass = 'abcdadcaabc'\n expected_result = False\n result = val... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Communication(Module):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def receive(self, length):
if not isinstance(length, int):
raise Exception('Receive length must be an integer.')
return self.port.read(length)
<|reserved_spe... | flexible | {
"blob_id": "eab5bf4776582349615ad56ee1ed93bc8f868565",
"index": 768,
"step-1": "<mask token>\n\n\nclass Communication(Module):\n <mask token>\n <mask token>\n\n def receive(self, length):\n if not isinstance(length, int):\n raise Exception('Receive length must be an integer.')\n ... | [
3,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(f'The apporximate volume is {approxVolume:.2f} liters')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
width = float(input('Enter the width of the tire in mm (ex 205): '))
aspectRatio = float(input('Enter the a... | flexible | {
"blob_id": "65752c8ac50205df0fea105123935110e4a30aba",
"index": 7913,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'The apporximate volume is {approxVolume:.2f} liters')\n",
"step-3": "<mask token>\nwidth = float(input('Enter the width of the tire in mm (ex 205): '))\naspectRatio = float(inpu... | [
0,
1,
2,
3,
4
] |
import glob
import os
import partition
import pickle
import matplotlib.pyplot as plt
import numpy as np
from Cluster import fishermans_algorithm
import argparse
parser = argparse.ArgumentParser()
plt.ion()
parser.add_argument("--fish", help="flag for using fisherman's algorithm")
parser.add_argument("--heat", help="... | normal | {
"blob_id": "805bc144a4945b46b398853e79ded17370ada380",
"index": 3940,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.ion()\nparser.add_argument('--fish', help=\"flag for using fisherman's algorithm\")\nparser.add_argument('--heat', help='flag for using heatmap')\nparser.add_argument('--object', help... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class MovingAverage(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class MovingAverage(object):
def __init__(self, size):
"""
Initialize your data structure here.
:type s... | flexible | {
"blob_id": "9e37b728d8045726aef7625fccc14111ecb0e1c8",
"index": 5578,
"step-1": "<mask token>\n",
"step-2": "class MovingAverage(object):\n <mask token>\n <mask token>\n",
"step-3": "class MovingAverage(object):\n\n def __init__(self, size):\n \"\"\"\n Initialize your data structure h... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while abs(t0 - time.time() < 60):
pass
<|reserved_special_token_1|>
<|reserved_special_token_0|>
t0 = time.time()
while abs(t0 - time.time() < 60):
pass
<|reserved_special_token_1|>
import time
t0 = time.time()
while... | flexible | {
"blob_id": "9a0e37aaa41f3b21ed7ad11096cd6c5dd0bb8564",
"index": 5608,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile abs(t0 - time.time() < 60):\n pass\n",
"step-3": "<mask token>\nt0 = time.time()\nwhile abs(t0 - time.time() < 60):\n pass\n",
"step-4": "import time\nt0 = time.time()\nwh... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
@pytest.mark.usefixtures('driver')
class Test_001_ShedulePage:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_001_elements_exists(self, driver):
"""тапнуть на фичерс,
тапнуть на смотреть расписание,
найти кнопку отмены, кнопку карты... | flexible | {
"blob_id": "c7c412fe4e2d53af1b4f2a55bd3453496767890d",
"index": 975,
"step-1": "<mask token>\n\n\n@pytest.mark.usefixtures('driver')\nclass Test_001_ShedulePage:\n <mask token>\n <mask token>\n\n def test_001_elements_exists(self, driver):\n \"\"\"тапнуть на фичерс,\n тапнуть на смотреть ... | [
4,
5,
6,
7
] |
from .models import Owner, Vehicle
from rest_framework import viewsets, permissions
from .serializers import OwnerSerializer, VehicleSerializer
class OwnerViewSet(viewsets.ModelViewSet):
queryset = Owner.objects.all().order_by('id')
serializer_class = OwnerSerializer
permission_classes = [permissions.IsAu... | normal | {
"blob_id": "9290294b5df081ef0cae5450a9ea3baef789c041",
"index": 6421,
"step-1": "<mask token>\n\n\nclass VehicleViewSet(viewsets.ModelViewSet):\n queryset = Vehicle.objects.all().order_by('id')\n serializer_class = VehicleSerializer\n permission_classes = [permissions.IsAuthenticated]\n",
"step-2": "... | [
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class DuplicatedBlockException(Exception):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class DuplicatedBlockException(Exception):
code = 'duplicated_block_exception'
| flexible | {
"blob_id": "983e3b2902fe3bc701167da2f308fdaed612ae84",
"index": 1784,
"step-1": "<mask token>\n",
"step-2": "class DuplicatedBlockException(Exception):\n <mask token>\n",
"step-3": "class DuplicatedBlockException(Exception):\n code = 'duplicated_block_exception'\n",
"step-4": null,
"step-5": null,... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.asyncio
async def test_ping_once():
async with pair_of_connected_hosts() as (host_a, host_b):
stream = await host_b.new_stream(host_a.get_id(), (ID,))
some_ping = secrets.token_bytes(PING_LENGTH)... | flexible | {
"blob_id": "0233b46da3b9351f110ffc7f8622ca8f9ee9944d",
"index": 3000,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.asyncio\nasync def test_ping_once():\n async with pair_of_connected_hosts() as (host_a, host_b):\n stream = await host_b.new_stream(host_a.get_id(), (ID,))\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
carpeta = Carpeta(settings.folder_sat)
sentinela = SentinelSat(carpeta)
sentinela.start_Monitoring()
<|reserved_special_token_1|>
from LibTools.filesystem import Carpeta
from slaves import... | flexible | {
"blob_id": "9e3f4484542c2629d636fcb4166584ba52bebe21",
"index": 2196,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n carpeta = Carpeta(settings.folder_sat)\n sentinela = SentinelSat(carpeta)\n sentinela.start_Monitoring()\n",
"step-3": "from LibTools.filesystem im... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# @File : config.py
# @Author: TT
# @Email : tt.jiaqi@gmail.com
# @Date : 2018/12/4
# @Desc : config file
from utils.general import getchromdriver_version
from chromedriver.path import path
import os
import sys
chromedriver = os.path.abspath(os.path.dirname(__file__)) + "\\chromedriver\\"+ g... | normal | {
"blob_id": "5b4a196de60a3a30bc571c559fe5f211563b8999",
"index": 5449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nchromedriver = os.path.abspath(os.path.dirname(__file__)\n ) + '\\\\chromedriver\\\\' + getchromdriver_version()\ndownload_path = os.path.abspath(os.path.dirname(__file__)) + '\\\\'\nS... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Window:
def __init__(self, width, height, title='MyWindow', resizable=(False,
False), icon='resources/feather.ico'):
self.root = Tk()
self.root.title(title)
self.root.geometry('+600+300')
if icon:
self.root.iconbitmap(icon)
... | flexible | {
"blob_id": "02d4e1ddb0b4cf75c9902e13263c5a80417de01b",
"index": 6530,
"step-1": "<mask token>\n\n\nclass Window:\n\n def __init__(self, width, height, title='MyWindow', resizable=(False, \n False), icon='resources/feather.ico'):\n self.root = Tk()\n self.root.title(title)\n self.r... | [
8,
9,
10,
12,
14
] |
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