code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
"""
Forms and validation code for user registration.
Note that all of these forms assume your user model is similar in
structure to Django's default User class. If your user model is
significantly different, you may need to write your own form class;
see the documentation for notes on custom user models with
django-re... | normal | {
"blob_id": "3b959481f7c818ec35b8af174b1982954b4c72eb",
"index": 1208,
"step-1": "<mask token>\n\n\nclass RegistrationFormCaseInsensitive(RegistrationForm):\n <mask token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields[User.USERNAME_FIELD].validator... | [
8,
12,
14,
15,
16
] |
import os
import math
def get_datas():
filename = None
while True:
filename = input('Please enter filename:')
if not filename.strip():
print('Filename is empty!')
continue
if not os.path.exists(filename):
print('File is not exists!')
conti... | normal | {
"blob_id": "6829f7bcbc1b12500795eec19829ff077502e270",
"index": 3260,
"step-1": "<mask token>\n\n\ndef get_datas():\n filename = None\n while True:\n filename = input('Please enter filename:')\n if not filename.strip():\n print('Filename is empty!')\n continue\n ... | [
6,
7,
8,
10,
12
] |
from matplotlib import pyplot as plt
dev_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
dev_y = [4000, 45000, 50000, 55000, 60000,
56000, 62316, 64928, 67317, 68748, 73752]
plt.plot(dev_x, dev_y, label='All Devs')
#dev_x and dev_y are respectively x-axis and y-axis
# Median Python Developer Salari... | normal | {
"blob_id": "796a13de72c2879956c5f9c9c9bdef7253760c9d",
"index": 9895,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.plot(dev_x, dev_y, label='All Devs')\n<mask token>\nplt.plot(dev_x, py_dev_y, label='Python')\nplt.xlabel('Ages')\nplt.ylabel('Median Salary')\nplt.title('Median Salary (USD) by Age')... | [
0,
1,
2,
3,
4
] |
import asyncio
def callback():
print('callback invoked')
def stopper(loop):
print('stopper invoked')
loop.stop()
event_loop = asyncio.get_event_loop()
try:
print('registering callbacks')
# the callbacks are invoked in the order they are scheduled
event_loop.call_soon(callback)
event_loop.... | normal | {
"blob_id": "3b96cc4ef538a06251958495e36fe5dbdf80c13d",
"index": 4952,
"step-1": "<mask token>\n\n\ndef callback():\n print('callback invoked')\n\n\ndef stopper(loop):\n print('stopper invoked')\n loop.stop()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef callback():\n print('callback invok... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python3
"""City Module"""
from models.base_model import BaseModel
class City(BaseModel):
"""City Class
Public class attributes:
state_d: type string
name: type string
"""
state_id = ""
name = ""
| normal | {
"blob_id": "3f2c1a83ae0dfdba202038a209b90162ccddee36",
"index": 6115,
"step-1": "<mask token>\n\n\nclass City(BaseModel):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass City(BaseModel):\n <mask token>\n state_id = ''\n name = ''\n",
"step-3": "<mask tok... | [
1,
2,
3,
4,
5
] |
from django.contrib import admin
# Register your models here.
from blog.models import Post,Category,Profile
admin.site.register(Profile)
admin.site.register(Category)
admin.site.register(Post) | normal | {
"blob_id": "20f0de097fdd8f2a435c06a73c6a90cc7ebc69ad",
"index": 4014,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Profile)\nadmin.site.register(Category)\nadmin.site.register(Post)\n",
"step-3": "from django.contrib import admin\nfrom blog.models import Post, Category, Profile\n... | [
0,
1,
2,
3
] |
# Constructor without arguments
class Demo:
def __init__(self):
print("\nThis is constructor")
obj = Demo()
# Constructor with arguments
class Demo2:
def __init__(self, number1, number2):
sumOfNumbers = number1 + number2
print(sumOfNumbers)
obj2 = Demo2(50,75... | normal | {
"blob_id": "b005f4657a1036044c2e6051207641fe621eb17e",
"index": 8861,
"step-1": "<mask token>\n\n\nclass Demo2:\n\n def __init__(self, number1, number2):\n sumOfNumbers = number1 + number2\n print(sumOfNumbers)\n\n\n<mask token>\n",
"step-2": "class Demo:\n <mask token>\n\n\n<mask token>\n... | [
2,
3,
4,
5,
6
] |
from flask import abort
from flask_restx import Resource, Namespace, Model, fields, reqparse
from infraestructura.lineas_repo import LineasRepo
from infraestructura.equipos_repo import EquiposRepo
from infraestructura.clientes_lep_repo import ClientesLepRepo
from infraestructura.lineaequipoplan_repo import LineaEquipoP... | normal | {
"blob_id": "821e89730fde2e12b24b52b04701c1f3501e0d57",
"index": 8771,
"step-1": "<mask token>\n\n\n@nsLinea.route('/<int:id>')\nclass LineasResource(Resource):\n <mask token>\n <mask token>\n\n\n@nsLinea.route('/baja/<int:id>')\nclass LineasResource(Resource):\n\n def put(self, id):\n if repo.ba... | [
3,
7,
8,
9,
12
] |
## SOLVED
## the possible way of solving this is to make a scoring of the hand
## of each player, by encoding the category of winning and the cards
import csv
value = ['2','3','4','5','6','7','8','9','T','J','Q','K','A']
val_order = {k:v for v,k in enumerate(value)}
def compute():
poker_hand = load_data()
ans... | normal | {
"blob_id": "480e595c54da7426951d750187712fecdcb6d8c7",
"index": 9081,
"step-1": "<mask token>\n\n\ndef compute():\n poker_hand = load_data()\n ans = sum(1 for x in poker_hand if p1_wins(x))\n return ans\n\n\ndef p1_wins(hands):\n p1 = [(a[0], a[1]) for a in hands[:5]]\n p2 = [(a[0], a[1]) for a i... | [
6,
7,
8,
9,
11
] |
import pygame
import sys
# класс для хранения настроек
class Settings():
"""docstring for Setting"""
def __init__(self):
# параметры экрана
self.colour = (230, 230, 230)
self.screen_width = 1200
self.screen_height = 800
# параметры коробля
self.ship_speed = 1.5
# параметры пули
self.bullet_speed = ... | normal | {
"blob_id": "2402188380bc0189b88e3cfcbaabf64a9919b3d5",
"index": 8810,
"step-1": "<mask token>\n\n\nclass Settings:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Settings:\n <mask token>\n\n def __init__(self):\n self.colour = 230, 230, 230\n self.screen_width =... | [
1,
2,
3,
4,
5
] |
from django.urls import path
from .views import PostListView, PostDetailView
urlpatterns = [
path('blog/', PostListView.as_view()),
path('blog/<pk>/', PostDetailView.as_view()),
] | normal | {
"blob_id": "be7fb94c3c423b67aa917a34328acda5926cf78a",
"index": 3133,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('blog/', PostListView.as_view()), path('blog/<pk>/',\n PostDetailView.as_view())]\n",
"step-3": "from django.urls import path\nfrom .views import PostListView, Po... | [
0,
1,
2,
3
] |
from collections import deque
for case in xrange(input()):
cards = input()
indexes = map(int, raw_input().split())
deck = [0 for i in xrange(cards)]
index = -1
for i in xrange(1, cards + 1):
while True:
index = (index + 1)%cards
if deck[index] == 0:
break
for j in xrange(i - 1):
... | normal | {
"blob_id": "c7ecf8ada74b3e401c2144457d4fa1050f598727",
"index": 8787,
"step-1": "from collections import deque\n\nfor case in xrange(input()):\n cards = input()\n indexes = map(int, raw_input().split())\n\n deck = [0 for i in xrange(cards)]\n index = -1\n for i in xrange(1, cards + 1):\n while True:\n ... | [
0
] |
# -*- coding: utf-8 -*-
##################################################
# GNU Radio Python Flow Graph
# Title: channel
# Author: Maria Camila Herrera Ramos
# Generated: Thu Aug 2 18:09:17 2018
##################################################
from gnuradio import analog
from gnuradio import blocks
from gnuradio ... | normal | {
"blob_id": "8adf25fbffc14d6927d665931e54a7d699a3b439",
"index": 6202,
"step-1": "<mask token>\n\n\nclass channel(gr.hier_block2):\n <mask token>\n <mask token>\n\n def set_k(self, k):\n self.k = k\n self.channels_fading_model_0.set_K(self.k)\n\n def get_tchannel(self):\n return ... | [
5,
6,
7,
8,
10
] |
from .models import Video, VideoClass
from rest_framework import serializers
# Video 정보
class VideoSerializer(serializers.ModelSerializer):
class Meta:
model = Video
fields = ['videoURL','subTitle', 'numOfLike', 'numOfPlay']
# Video 분류
class VideoClassSerializer(serializers.ModelSerializer):
... | normal | {
"blob_id": "b20a8160ba455a39e990b8b37c5017645530ced3",
"index": 1545,
"step-1": "<mask token>\n\n\nclass VideoClassSerializer(serializers.ModelSerializer):\n <mask token>\n\n\n class Meta:\n model = VideoClass\n fields = 'title', 'video_set'\n\n def get_video_set(self, instance):\n ... | [
2,
3,
4,
5,
6
] |
from django.test import TestCase
from django.urls import reverse
from django.utils import timezone
from recensioni_site import settings
from django.contrib.auth.models import User
from forum.models import Sezione,Post,UserDataReccomandation
class testRegistrazione(TestCase):
def setUp(self):
self.credent... | normal | {
"blob_id": "cf9339659f49b4093c07e3723a2ede1543be41b8",
"index": 4900,
"step-1": "<mask token>\n\n\nclass testRegistrazione(TestCase):\n <mask token>\n\n def tearDown(self):\n self.proprietario1.delete()\n self.proprietario2.delete()\n self.user1.delete()\n self.user2.delete()\n... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 2 13:34:19 2020
@author: ShihaoYang
"""
from pyltp import SentenceSplitter
from pyltp import Segmentor
from pyltp import Postagger
from pyltp import Parser
from pyltp import NamedEntityRecognizer
import os
import jieba
import re
os.getcwd()
os.ch... | normal | {
"blob_id": "dc41c64d09e5fdd0e234f516eeec0cbd2433876c",
"index": 8507,
"step-1": "<mask token>\n\n\nclass Sentence(object):\n\n def __init__(self, text):\n self.text = text\n self.data = dict()\n\n def SentS(self):\n sents = SentenceSplitter.split(self.text)\n return sents\n\n ... | [
9,
11,
12,
14,
16
] |
from Receiver import Receiver
import time
import Image
class Sender:
ACK = []
size = None
windowSize = None
tableOfFrames = []
ChosenSumAlgorithm = None
def __init__(self, receiver):
self.receiver = receiver
pass
def send_frame(self, frame):
self.receiver.receiver_... | normal | {
"blob_id": "ecbcd023b8fec5763c6ff7f4cd0999426fae4a50",
"index": 9093,
"step-1": "<mask token>\n\n\nclass Sender:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def send_frame(self, frame):\n self.receiver.receiver_frame(frame)\n p... | [
5,
7,
8,
10,
11
] |
#!/use/bin/python
import os, sys
from io import BytesIO
from pathlib import Path
from flask_config import app
from flask import send_file
from PyPDF2 import PdfFileReader, PdfFileWriter
def rotate_pdf(working_dir, filename, rotation):
os.chdir(working_dir)
output_name = 'pages'
rotate_pdf_pages(filename, rotati... | normal | {
"blob_id": "624027373f53f62ededc40bfc859f28b5a83ca04",
"index": 3266,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef rotate_pdf_pages(filename, rotation, output_name):\n pdf_reader = PdfFileReader('{}.pdf'.format(filename))\n pdf_writer = PdfFileWriter()\n for page in range(pdf_reader.g... | [
0,
1,
2,
3,
4
] |
def assert_shapes(shape, other):
assert len(shape) == len(other), "Dimensions are different"
for s, o in zip(shape, other):
if s is not None and o is not None:
assert s == o, "Shapes {} and {} are not equal".format(shape, other)
| normal | {
"blob_id": "337311c3fbb6a8baab7a237d08152f0db9822527",
"index": 2931,
"step-1": "<mask token>\n",
"step-2": "def assert_shapes(shape, other):\n assert len(shape) == len(other), 'Dimensions are different'\n for s, o in zip(shape, other):\n if s is not None and o is not None:\n assert s ... | [
0,
1,
2
] |
import netCDF4 as nc
import numpy as np
import os
def RangeExtender(filename,directory):
fileNC=nc.Dataset(directory+filename,'r')
nu=fileNC['nu'][:]
filename,ext=os.path.splitext(filename)
fileOut=nc.Dataset(directory+filename+"_50000cm-1.nc",'w')
nu_orig_length=len(nu)
step=abs(nu[1]... | normal | {
"blob_id": "f3527185117fd7205f55f47f2f08448a7d7b0100",
"index": 8143,
"step-1": "<mask token>\n\n\ndef RangeExtender(filename, directory):\n fileNC = nc.Dataset(directory + filename, 'r')\n nu = fileNC['nu'][:]\n filename, ext = os.path.splitext(filename)\n fileOut = nc.Dataset(directory + filename ... | [
1,
2,
3,
4,
5
] |
import cv2
import numpy as np
if __name__ == "__main__":
cap = cv2.VideoCapture()
while True:
ret, frame = cap.read()
cv2.imshow(frame)
| normal | {
"blob_id": "14f309d478de6de5a0b493503176941fdfa8b702",
"index": 110,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n cap = cv2.VideoCapture()\n while True:\n ret, frame = cap.read()\n cv2.imshow(frame)\n",
"step-3": "import cv2\nimport numpy as np\nif __... | [
0,
1,
2,
3
] |
# animation2.py
# multiple-shot cannonball animation
from math import sqrt, sin, cos, radians, degrees
from graphics import *
from projectile import Projectile
from button import Button
class Launcher:
def __init__(self, win):
"""Create inital launcher with angle 45 degrees and velocity 40
win i... | normal | {
"blob_id": "09aedd6cab0b8c6a05bbee5b336fcd38aea1f7b9",
"index": 3202,
"step-1": "<mask token>\n\n\nclass Launcher:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass ShotTracker:\n \"\"\" Graphical depiction of a projectile flight using a Circle \"\"\"\n\n ... | [
12,
14,
15,
17,
20
] |
#This is a module which implements Naive Set Theory in Python.
#It will be useful for Unions, Intersections, Mutual Exclusion, and more.
#ideas: print(sum([[[1],[2]], [[3],[4]], [[5],[6]]], [])) Monoid - abstraction on +
trial = [1, 2, 3]
trial2 = [3, 4, 5]
def recursiveUnioniser(set):
if isinstance(set[0], int)... | normal | {
"blob_id": "c632c50028fee2f19fb65458f0b55ec228b8006f",
"index": 2137,
"step-1": "<mask token>\n\n\ndef intersection(set_a, set_b):\n res = [i for i in set_a if i in set_b]\n return res\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef recursiveUnioniser(set):\n if isinstance(set[0], int):\n ... | [
1,
4,
5,
6,
7
] |
from ._sinAction import *
from ._sinActionFeedback import *
from ._sinActionGoal import *
from ._sinActionResult import *
from ._sinFeedback import *
from ._sinGoal import *
from ._sinResult import *
| normal | {
"blob_id": "c6b261a09b2982e17704f847586bbf38d27cb786",
"index": 353,
"step-1": "<mask token>\n",
"step-2": "from ._sinAction import *\nfrom ._sinActionFeedback import *\nfrom ._sinActionGoal import *\nfrom ._sinActionResult import *\nfrom ._sinFeedback import *\nfrom ._sinGoal import *\nfrom ._sinResult impor... | [
0,
1
] |
import math
def sieve(n):
sieve = [1] * (n+1)
sieve[1] = 0
sieve[0] = 0
for i in range(2, int(math.sqrt(n) + 1)):
if sieve[i] == 1:
for j in range(i*i, n + 1, i):
sieve[j] = 0
return sieve
def odd_prime(a):
while a != 0:
y = a % 10
if y == 3 ... | normal | {
"blob_id": "60617ff6eda880e5467b3b79d3df13a7147f5990",
"index": 3329,
"step-1": "<mask token>\n\n\ndef sieve(n):\n sieve = [1] * (n + 1)\n sieve[1] = 0\n sieve[0] = 0\n for i in range(2, int(math.sqrt(n) + 1)):\n if sieve[i] == 1:\n for j in range(i * i, n + 1, i):\n ... | [
2,
3,
4,
5,
6
] |
'''
Copyright (c) 2021, Štěpán Beneš
The purpose of this script it to take the 5 BSE and 5 SE hand-picked prototype
images and turn them into the same shape and format as the rest of the data.
Prototype images are resized to 768x768, the info bar is cropped off. Afterwards
the images are normalized to float32 in ran... | normal | {
"blob_id": "af7af5d1048d2b0968e831aad89d5baf30cab608",
"index": 3210,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(proto_images_se_list.shape)\nprint(proto_images_bse_list.shape)\nnp.save('Data/SE_prototypes.npy', proto_images_se_list)\nnp.save('Data/BSE_prototypes.npy', proto_images_bse_list)\n... | [
0,
1,
2,
3,
4
] |
from django.apps import AppConfig
class AttendaceConfig(AppConfig):
name = 'attendace'
| normal | {
"blob_id": "d5d61b23dc14ffdfe7fe6f983164916863928eaf",
"index": 3685,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass AttendaceConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass AttendaceConfig(AppConfig):\n name = 'attendace'\n",
"step-4": "from django.apps impo... | [
0,
1,
2,
3
] |
# import the necessary packages
from .pigear import PiGear
from .camgear import CamGear
from .videogear import VideoGear
__all__ = ["PiGear", "CamGear", "VideoGear"] | normal | {
"blob_id": "3431e342c940b0d91f817c3e583728e55e305210",
"index": 8940,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['PiGear', 'CamGear', 'VideoGear']\n",
"step-3": "from .pigear import PiGear\nfrom .camgear import CamGear\nfrom .videogear import VideoGear\n__all__ = ['PiGear', 'CamGear', '... | [
0,
1,
2,
3
] |
import numpy
import yfinance as yf
import pandas as pd
import path
import math
pd.options.mode.chained_assignment = None # default='warn'
all_tickers = ['2020.OL',
'ABG.OL',
'ADE.OL',
'AFG.OL',
'AKAST.OL',
'AKER.OL',
'AKBM.OL',
... | normal | {
"blob_id": "22ffda3b2d84218af22bad7835689ec3d4959ab2",
"index": 3660,
"step-1": "<mask token>\n\n\ndef calculate_returns(ticker_data):\n returns_list = list()\n previous_ticker_day = None\n for ticker_day in ticker_data.itertuples():\n if previous_ticker_day == None:\n returns_list.ap... | [
6,
8,
9,
10,
11
] |
num_str = "1"
num_str1 = "\u00b2"
num_str2 = "一千零一"
# 判断字符串是否只包含数字
# 1.三种方法都不能判断小数
# 2.isdigit 和 isnumeric 比 isdecimal 强大一些，后者只能判断正常数字，前两者可以判断带有数字的符号，如平方
# isnumeric 还可以判断中文数字
print(num_str)
print(num_str1)
print(num_str.isdecimal())
print(num_str1.isdecimal())
print(num_str.isdigit())
print(num_str1.isdigit())
print(n... | normal | {
"blob_id": "a7be2f43c6ec8d1576ed194a75762a36089cb052",
"index": 4195,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(num_str)\nprint(num_str1)\nprint(num_str.isdecimal())\nprint(num_str1.isdecimal())\nprint(num_str.isdigit())\nprint(num_str1.isdigit())\nprint(num_str.isnumeric())\nprint(num_str1.i... | [
0,
1,
2,
3
] |
"""to get the all the module and its location"""
import sys
print(sys.modules)
| normal | {
"blob_id": "20637e41df8a33e3837905a4729ae0b4a9f94dbb",
"index": 3128,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(sys.modules)\n",
"step-3": "<mask token>\nimport sys\nprint(sys.modules)\n",
"step-4": "\"\"\"to get the all the module and its location\"\"\"\r\nimport sys\r\nprint(sys.modules... | [
0,
1,
2,
3
] |
from datetime import datetime
from app.commands import backfill_performance_platform_totals, backfill_processing_time
# This test assumes the local timezone is EST
def test_backfill_processing_time_works_for_correct_dates(mocker, notify_api):
send_mock = mocker.patch("app.commands.send_processing_time_for_start_... | normal | {
"blob_id": "fcb1285648f6728e3dad31ad4b602fa4e5c5b422",
"index": 9230,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_backfill_totals_works_for_correct_dates(mocker, notify_api):\n send_mock = mocker.patch(\n 'app.commands.send_total_sent_notifications_to_performance_platform')\n ... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
"""Supporting model logic for predicting emotional content of user input.
"""
import pandas as pd
import gensim
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
#load data for emo2vec
loc = 'https://s3-us-west-1.a... | normal | {
"blob_id": "f5f26819be4b98fab3d46e57e1a5431e54342aed",
"index": 414,
"step-1": "<mask token>\n\n\ndef dropper():\n for ex in affected['word']:\n if ex not in model.vocab:\n idx_to_drop.append(affected.loc[affected.word == ex].index[0])\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(... | [
1,
2,
3,
4,
5
] |
def get_all_lefts(word,substring):
if len(substring) == 0:
yield ((len(word),word),)
else:
if substring[0] not in word:
yield (-1,)
else:
for i in range(len(word)):
if word[i] == substring[0]:
for sub_sequance in get_all_lefts(w... | normal | {
"blob_id": "8c0377b70b902e6e61351869a4378b4c2c50a3a7",
"index": 2478,
"step-1": "<mask token>\n",
"step-2": "def get_all_lefts(word, substring):\n if len(substring) == 0:\n yield (len(word), word),\n elif substring[0] not in word:\n yield -1,\n else:\n for i in range(len(word)):\... | [
0,
1,
2,
3
] |
from django.db import models
# Create your models here.
class Orders(models.Model):
customer_name = models.CharField(max_length=80)
customer_email = models.CharField(max_length=120)
customer_mobile = models.CharField(max_length=40)
status = models.CharField(max_length=20)
process_url = models.Cha... | normal | {
"blob_id": "bc7a7b9ba4b3277c862aadb57b56661c24efc6e5",
"index": 5577,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Orders(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step... | [
0,
1,
2,
3,
4
] |
import numpy as np
import math
import datetime
def multi_strassen(A,B, check_ig = True, check_quad = True, check_pot = True, check_time = True):
def Strassen(matriz_1,matriz_2): # Função do algoritmo de Strassen para multiplicação de matrizes do tipo 2x2
if (matriz_1.shape[0] != 2) or (matriz_1.shape[... | normal | {
"blob_id": "6707723b3d0b42271e49c08c639afc9103066dc7",
"index": 4679,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef multi_strassen(A, B, check_ig=True, check_quad=True, check_pot=True,\n check_time=True):\n\n def Strassen(matriz_1, matriz_2):\n if matriz_1.shape[0] != 2 or matriz_1... | [
0,
1,
2,
3
] |
class person(object):
population = 50
def __init__(self, name, age):
self.name = name
self.age = age
@classmethod
def getpopulation(cls):
return cls.population
@staticmethod
def isadult(age=17):
return age >= 18
def display(self):
print(self.name, ... | normal | {
"blob_id": "a21942a835f7b2ea70e9dd7b26285ea2dd411750",
"index": 1205,
"step-1": "class person(object):\n <mask token>\n\n def __init__(self, name, age):\n self.name = name\n self.age = age\n\n @classmethod\n def getpopulation(cls):\n return cls.population\n\n @staticmethod\n ... | [
5,
6,
7,
8
] |
import pytest
import os
import pandas as pd
import numpy as np
import math
import scipy
from scipy import stats
from sklearn import metrics, linear_model
from gpmodel import gpkernel
from gpmodel import gpmodel
from gpmodel import gpmean
from gpmodel import chimera_tools
n = 200
d = 10
X = np.random.random(size=(n, ... | normal | {
"blob_id": "62c28b5eb31b90191dfbab4456fc5373ba51bf64",
"index": 8869,
"step-1": "<mask token>\n\n\ndef test_normalize():\n model = gpmodel.GPRegressor(kernel)\n m, s, normed = model._normalize(Y)\n assert np.isclose(m, Y.mean())\n assert np.isclose(s, Y.std())\n assert np.allclose(normed, (Y - m)... | [
6,
7,
8,
9,
11
] |
import os
import time
import torch
from torch.utils.data import DataLoader
from torchvision.datasets import SVHN
from torchvision.transforms import ToTensor
from lib.utils import Logger, normal_logpdf, sumflat, print_model_info, tanh_to_uint8, get_optimizer
from lib.vae import VAE
def train(hp):
os.makedirs(hp.o... | normal | {
"blob_id": "43db8ed10face1c668aeadd3cbc5b13f87fb0126",
"index": 4997,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef train(hp):\n os.makedirs(hp.out_dir, exist_ok=True)\n device = torch.device('cuda' if hp.use_cuda else 'cpu')\n dataset = SVHN(root='svhn', split='train', download=True, ... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from celery import Celery
app = Celery('task', include=['task.tasks'])
app.config_from_object('task.config')
if __name__ == '__main__':
app.start()
| normal | {
"blob_id": "68d9f77f91a13c73373c323ef0edbe18af9990a3",
"index": 4321,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.config_from_object('task.config')\nif __name__ == '__main__':\n app.start()\n",
"step-3": "<mask token>\napp = Celery('task', include=['task.tasks'])\napp.config_from_object('tas... | [
0,
1,
2,
3,
4
] |
from abc import ABC, abstractmethod
from datetime import datetime, timedelta, date
import os
import housekeeper
import yfinance as yf
import pandas as pd
class DataManager(ABC):
def __init__(self):
self.__myHousekeeper = housekeeper.instance_class()
self.__config_filename = "tickers... | normal | {
"blob_id": "e77e0791ddf211807566528e9532eebb54db43b5",
"index": 5550,
"step-1": "<mask token>\n\n\nclass DataManager(ABC):\n\n def __init__(self):\n self.__myHousekeeper = housekeeper.instance_class()\n self.__config_filename = 'tickers_config.json'\n self.__dir_list = ['Data', 'Tickers'... | [
30,
34,
35,
40,
41
] |
from pathlib import Path
import eyed3
import csv
import sys
import filetype
import os
pathFile = Path(
'C:\\Users\\JORGE\\Music\\Vicente Garcia - Te Soñé (Lyric Video)(MP3_160K).mp3'
)
audiofile = eyed3.load(pathFile)
with open('loveMusic.csv', 'w', newline='') as csvFile:
fieldsName = ['nameFile', 'tittle... | normal | {
"blob_id": "629649abe9d855122a5db6d61a20735ceb89c5cf",
"index": 6426,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('loveMusic.csv', 'w', newline='') as csvFile:\n fieldsName = ['nameFile', 'tittle', 'artist', 'gender', 'path']\n writer = csv.DictWriter(csvFile, fieldnames=fieldsName)\n... | [
0,
1,
2,
3
] |
from RestClient4py.client import RestClient
from API_Wrap import util
import os
import json
kakao_native_app_key, kakao_rest_api_key, kakao_javascript_key, kakao_admin_key = util.kakao_auth()
client = RestClient()
client.set_header("Authorization", "KakaoAK {}".format(kakao_rest_api_key))
client.set_header("Accept", ... | normal | {
"blob_id": "7f58179efecd5a0d691a5c6d83b808f2cd2fcba3",
"index": 5332,
"step-1": "<mask token>\n\n\ndef translation(query, src_lang, target_lang):\n if type(query) != str:\n raise AttributeError('[ERROR] query parameter should be string type')\n elif len(query) > 5000:\n raise AttributeError(... | [
1,
2,
3,
4,
5
] |
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def getIntersectionNode(self, headA, headB):
"""
:type head1, head1: ListNode
:rtype: ListNode
"""
if not hea... | normal | {
"blob_id": "66f60eb86137203a74656be13b631384eba30c84",
"index": 1681,
"step-1": "class Solution(object):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "class Solution(object):\n\n def getIntersectionNode(self, headA, headB):\n \"\"\"\n :type head1, head1: ListNode\n ... | [
1,
2,
3,
4,
5
] |
# This source code is part of the Biotite package and is distributed
# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further
# information.
import warnings
from tempfile import TemporaryFile
import glob
from os.path import join
import pytest
import numpy as np
import biotite.structure as struc
import bi... | normal | {
"blob_id": "cc637d14ce2106fcc3b8bbb54e497691e72a3f65",
"index": 2858,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.parametrize('path', glob.glob(join(data_dir('structure'),\n '*.cif')))\ndef test_array_conversion(path):\n pdbx_file = pdbx.PDBxFile.read(path)\n ref_structure =... | [
0,
1,
2,
3
] |
from django.contrib.auth.models import BaseUserManager
class MyUserManager(BaseUserManager):
def create_user(self, email, password, full_name, national_code, mobile, address):
if not email :
raise ValueError('ایمیل الزامی است')
if not full_name :
raise ValueError('نام و نام... | normal | {
"blob_id": "f5f14e4d114855b7eef555db182ee991bdf26c39",
"index": 8832,
"step-1": "<mask token>\n\n\nclass MyUserManager(BaseUserManager):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass MyUserManager(BaseUserManager):\n <mask token>\n\n def create_superuser(self, email, passwor... | [
1,
2,
3,
4,
5
] |
from microbit import *
import music
while True:
if button_a.is_pressed():
music.pitch(400, 500)
| normal | {
"blob_id": "356c817e254d8885beb447aa10759fff6a45ca25",
"index": 9454,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n if button_a.is_pressed():\n music.pitch(400, 500)\n",
"step-3": "from microbit import *\nimport music\nwhile True:\n if button_a.is_pressed():\n music.... | [
0,
1,
2
] |
from .base import *
RAVEN_CONFIG = {}
ALLOWED_HOSTS = ['*']
| normal | {
"blob_id": "eee60a6f46549ededfbc7b0b294ab723e2e73f7e",
"index": 4490,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nRAVEN_CONFIG = {}\nALLOWED_HOSTS = ['*']\n",
"step-3": "from .base import *\nRAVEN_CONFIG = {}\nALLOWED_HOSTS = ['*']\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.11 on 2018-03-31 17:58
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('words', '0004_auto_20180330_0647'),
]
operations = [
migrations.AddField(
... | normal | {
"blob_id": "e6884afaae15e903c62eecb3baec868548998080",
"index": 2106,
"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 = [('words', '00... | [
0,
1,
2,
3,
4
] |
import unittest, warnings
from pony.orm import *
from pony.orm import core
from pony.orm.tests.testutils import raises_exception
db = Database('sqlite', ':memory:')
class Person(db.Entity):
id = PrimaryKey(int)
name = Required(str)
tel = Optional(str)
db.generate_mapping(check_tables=False)... | normal | {
"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
] |
#MenuTitle: Find and Replace Corner Components at Certain Angles
# -*- coding: utf-8 -*-
from __future__ import division, print_function, unicode_literals
__doc__="""
Replace Corner Components at blunt or acute angles.
"""
import vanilla, math
from Foundation import NSPoint
class ReplaceCornersAtCertainAngles( object... | normal | {
"blob_id": "540ae4be6a41d52d9c803f829fc8b13b523b31bc",
"index": 116,
"step-1": "<mask token>\n\n\nclass ReplaceCornersAtCertainAngles(object):\n\n def __init__(self):\n windowWidth = 250\n windowHeight = 140\n windowWidthResize = 100\n windowHeightResize = 0\n self.w = vani... | [
8,
9,
10,
11,
12
] |
import numpy as np
import random
import sys
import canton as ct
from canton import *
import tensorflow as tf
time_steps = 16
def get_text_data(filename):
import codecs
with open(filename,'rb') as f:
text = f.read()
length = len(text)
print('got corpus length:', length)
return text
def mod... | normal | {
"blob_id": "0016e38d39ed2a4c7a75bed103bc47a5b6fd0e8c",
"index": 2538,
"step-1": "<mask token>\n\n\ndef r(ep=100):\n length = len(corpus)\n batch_size = 256\n mbl = time_steps * batch_size\n sr = length - mbl - time_steps - 2\n for i in range(ep):\n print('---------------------iter', i, '/'... | [
6,
9,
12,
13,
14
] |
#!/usr/bin/env python
"""
Use version of DriverSlave that has pixmap and pixheights
"""
import threading
# import base classes and driver
from bibliopixel import LEDStrip, LEDMatrix
# from bibliopixel.drivers.LPD8806 import DriverLPD8806, ChannelOrder
from bibliopixel.drivers.visualizer import DriverVisualizer, Channel... | normal | {
"blob_id": "307e7a059f9b0b1131f8a57d0f55cf0ee05173e8",
"index": 9822,
"step-1": "#!/usr/bin/env python\n\"\"\"\nUse version of DriverSlave that has pixmap and pixheights\n\"\"\"\nimport threading\n# import base classes and driver\nfrom bibliopixel import LEDStrip, LEDMatrix\n# from bibliopixel.drivers.LPD8806 i... | [
0
] |
questions = ('Какой язык мы учим?', 'Какой тип данных имеет целая переменная?', 'Какой тип данных имеет вещественная переменная?', 'Какой тип данных имеет логическая переменная?', 'Какой тип данных имеет символьная переменная?')
answers = ('Python', 'Integer', 'Float', 'Bool', 'String')
i = 0
count_answers = 0
while i ... | normal | {
"blob_id": "dd936839d71b97b3a21115498092d8984de0e3f1",
"index": 7445,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile i < len(questions):\n user_answers = input('{}...'.format(questions[i]))\n if user_answers.capitalize() == answers[i]:\n count_answers = count_answers + 1\n i += 1\n... | [
0,
1,
2,
3
] |
import time
import pytest
from pytest_bdd import scenarios, given, when, then
from conf import Constants
from page_components.page import PageComponent
from page_components.overall import OverallPage
# Scenarios
scenarios('overall_rating.feature', features_base_dir=Constants.FEATURE_FILES_BASE_DIR)
# Fixtures
... | normal | {
"blob_id": "2809ed3a5ea1e527609e169bca1440e0db2761b9",
"index": 8408,
"step-1": "<mask token>\n\n\n@pytest.fixture\ndef home_page(getBrowser):\n aHome = HomePage(getBrowser)\n return aHome\n\n\n@pytest.fixture\ndef overall_page(getBrowser):\n aOverall = OverallPage(getBrowser)\n return aOverall\n\n\... | [
14,
15,
17,
18,
19
] |
import time
import os
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from app.wechat_subscription.object_page.home_page import HomePage
from conf.decorator import teststep, teststeps
from conf.base_page import BasePage
from selenium.webdriver... | normal | {
"blob_id": "600b49c7884f8b6e3960549702a52deb20089f5a",
"index": 3503,
"step-1": "<mask token>\n\n\nclass LoginPage(BasePage):\n <mask token>\n\n @teststeps\n def __init__(self):\n self.home = HomePage()\n self.toast = Toast()\n <mask token>\n\n @teststeps\n def wait_check_test1(s... | [
17,
18,
20,
21,
22
] |
sentence = input()
check_list = ["U", "C", "P", "C"]
check = True
for i in range(len(check_list)):
if check_list[i] in sentence:
check = True
idx = sentence.find(check_list[i])
sentence = sentence[idx+1:]
else:
check = False
break
if check == True:
print("I love UCP... | normal | {
"blob_id": "4545d9756d1f396ead0b0c75d319fb6a718375cd",
"index": 2108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(len(check_list)):\n if check_list[i] in sentence:\n check = True\n idx = sentence.find(check_list[i])\n sentence = sentence[idx + 1:]\n else:\n ... | [
0,
1,
2,
3
] |
from cobra.model.fabric import HIfPol
from createMo import *
DEFAULT_AUTO_NEGOTIATION = 'on'
DEFAULT_SPEED = '10G'
DEFAULT_LINK_DEBOUNCE_INTERVAL = 100
AUTO_NEGOTIATION_CHOICES = ['on', 'off']
SPEED_CHOICES = ['100M', '1G', '10G', '40G']
def input_key_args(msg='\nPlease Specify Link Level Policy:'):
print msg
... | normal | {
"blob_id": "36ab827b889adcd4d54296e7da432d3b39d5a2e6",
"index": 2246,
"step-1": "from cobra.model.fabric import HIfPol\n\nfrom createMo import *\n\nDEFAULT_AUTO_NEGOTIATION = 'on'\nDEFAULT_SPEED = '10G'\nDEFAULT_LINK_DEBOUNCE_INTERVAL = 100\n\nAUTO_NEGOTIATION_CHOICES = ['on', 'off']\nSPEED_CHOICES = ['100M', '... | [
0
] |
from django.db import models
class TestModel(models.Model):
name = models.CharField(max_length=15)
surname = models.CharField(max_length=10)
age = models.IntegerField()
class Example(models.Model):
integer_field = models.IntegerField()
positive_field = models.PositiveIntegerField()
positive_... | normal | {
"blob_id": "8afce5b47c7c9c67a8be493f7f4de1510352b1c7",
"index": 4559,
"step-1": "<mask token>\n\n\nclass Place(models.Model):\n name = models.CharField(max_length=50)\n address = models.CharField(max_length=80)\n\n def __str__(self):\n return self.name\n\n\nclass Restaurant(models.Model):\n p... | [
10,
12,
16,
17,
21
] |
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
] |
def multiplica():
one = int(input('1º: '))
two = int(input('2º: '))
print('a multiplicação é: ', one*two)
def soma():
one = int(input('1º: '))
two = int(input('2º: '))
print('a soma é: ', one+two)
def subtra():
one = int(input('1º: '))
two = int(input('2º: '))
... | normal | {
"blob_id": "414fa4021b21cea0dc49380aebfe67f0204f0574",
"index": 5994,
"step-1": "def multiplica():\n one = int(input('1º: '))\n two = int(input('2º: '))\n print('a multiplicação é: ', one * two)\n\n\ndef soma():\n one = int(input('1º: '))\n two = int(input('2º: '))\n print('a soma é: ', one + ... | [
2,
3,
4,
5,
6
] |
import math
import torch
from torch import nn
from d2l import torch as d2l
def masked_softmax(X, valid_lens):
"""通过在最后一个轴上掩蔽元素来执行softmax操作"""
# X:3D张量，valid_lens:1D或2D张量
if valid_lens is None:
return nn.functional.softmax(X, dim=-1)
else:
shape = X.shape
if valid_lens.dim() == ... | normal | {
"blob_id": "cda01bc7b0ebcfaf010bb87e7d9be34fd310d7a7",
"index": 9626,
"step-1": "<mask token>\n\n\nclass AdditiveAttention(nn.Module):\n <mask token>\n\n def __init__(self, key_size, query_size, num_hiddens, dropout, **kwargs):\n super(AdditiveAttention, self).__init__(**kwargs)\n self.W_k =... | [
7,
9,
10,
11,
13
] |
from django.db import models
from django.contrib.gis.db import models
from django.contrib.auth.models import User
from django.urls import reverse
class Project(models.Model):
actual_developer = models.ForeignKey(User,null = True,blank=True, on_delete=models.CASCADE)
# actual_developer = models.CharField(User,null ... | normal | {
"blob_id": "ac1d38f550e548dff6ba226dbfc3dd1e5ff876a8",
"index": 5563,
"step-1": "<mask token>\n\n\nclass Project(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n ... | [
3,
4,
5,
6,
7
] |
import unittest
import sys
import os
#Add project root to path
sys.path.append('../..')
from speckle.SpeckleClient import SpeckleApiClient
class TestSpeckleStream(unittest.TestCase):
def setUp(self):
self.s = SpeckleApiClient()
self.user = {'email':'testuser@arup.com','password':'testpassword',... | normal | {
"blob_id": "b39403171ed264c8fae5ea4ae9d17f77cfcab497",
"index": 9122,
"step-1": "<mask token>\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n self.s = SpeckleApiClient()\n self.user = {'email': 'testuser@arup.com', 'password':\n 'testpassword', 'username': 'te... | [
7,
11,
12,
13,
14
] |
def equals(left, right, tol=0.001):
"""
Tests equality of left and right
Rosalind allows for a default [absolute] error of 0.001 in decimal
answers unless otherwise stated.
"""
try:
left = left.strip()
right = right.strip()
except AttributeError:
pass
try:
... | normal | {
"blob_id": "b137fc40a5b2dec63c7abb6953664a969f5c126f",
"index": 8022,
"step-1": "<mask token>\n",
"step-2": "def equals(left, right, tol=0.001):\n \"\"\"\n Tests equality of left and right\n\n Rosalind allows for a default [absolute] error of 0.001 in decimal\n answers unless otherwise stated.\n ... | [
0,
1,
2
] |
import os
templateFile = 'crab_template.py'
samples=[\
#"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
#"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v2/MINIAODSIM", #Identical? Same event count #miniAO... | normal | {
"blob_id": "184b850e85b523f22a44cfde698efd96b94d819d",
"index": 2095,
"step-1": "import os\ntemplateFile = 'crab_template.py'\nsamples=[\\\n#\"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM\", \n#\"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-t... | [
0
] |
import matplotlib.pyplot as plt
import sys
sys.path.append('coin_flipping_src')
from monte_carlo import monte_carlo
from probability import probability
plt.style.use('bmh')
x_coords = range(10)
probablility_results = [probability(x,10) for x in x_coords]
plt.plot(x_coords,probablility_results,linewidth = 2.5)
# plt.plo... | normal | {
"blob_id": "124d7da330aa7c869320e10f4f89cc1c872f85f2",
"index": 430,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('coin_flipping_src')\n<mask token>\nplt.style.use('bmh')\n<mask token>\nplt.plot(x_coords, probablility_results, linewidth=2.5)\nfor _ in range(5):\n plt.plot(x_coords, ... | [
0,
1,
2,
3,
4
] |
"""
@file
@brief Various function to clean files.
"""
from __future__ import print_function
import os
import re
def clean_exts(folder=".", fLOG=print, exts=None, fclean=None):
"""
Cleans files in a folder and subfolders with a given extensions.
@param folder folder to clean
@param fLOG... | normal | {
"blob_id": "57972e6368aa5749edeab94e45d84f7897ca14ab",
"index": 8751,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef clean_files(folder='.', posreg='.*[.]((py)|(rst))$', negreg=\n '.*[.]git/.*', op='CR', fLOG=print):\n \"\"\"\n Cleans ``\\\\r`` in files a folder and subfolders with a gi... | [
0,
1,
2,
3,
4
] |
#Nianzu Wang
#Email: wangn89@gmail.com
#for_while.py: demonstrates some fun things with for and while loops
def starsFor(x):
array = range(x, 0, -1)
array2 = range(1, x)
for num in array2:
print "*" * num
for num in array:
print "*" * num
def starsWhile(n):
a = 1
while a < n:
... | normal | {
"blob_id": "7e287eca041cf27d99292a331604fef9e9f90fc2",
"index": 7268,
"step-1": "#Nianzu Wang\n#Email: wangn89@gmail.com\n\n#for_while.py: demonstrates some fun things with for and while loops\n\ndef starsFor(x):\n array = range(x, 0, -1)\n array2 = range(1, x)\n for num in array2:\n print \"*\"... | [
0
] |
#
# @lc app=leetcode id=14 lang=python3
#
# [14] Longest Common Prefix
#
# @lc code=start
class Solution:
def longestCommonPrefix(self, strs: List[str]) -> str:
pass
# At the moment I just wanna test my workspace so it's working tomorrow it's time for the problems
# @lc code=end
| normal | {
"blob_id": "401c6b09edf593e00aecf5bbb1b2201effc9e78c",
"index": 7384,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def longestCommonPrefix(self, strs: List[str]) ->str:\n pass\n",
"step-4": "#\n# @lc app=leetcode id=14 lang=python3\n... | [
0,
1,
2,
3
] |
def TriSelection(S):
""" Tri par sélection
Le tableau est constitué de deux parties : la 1ère constituée des éléments triés
(initialisée avec seulement le 1er élément) et la seconde constituée des éléments
non triés (initialisée du 2ème au dernier élément) """
for i in range(0, len(S)-1):
... | normal | {
"blob_id": "4c752c96b7e503ae5c9bc87a038fcf6dc176b776",
"index": 5830,
"step-1": "def TriSelection(S):\r\n \"\"\" Tri par sélection\r\n\r\n Le tableau est constitué de deux parties : la 1ère constituée des éléments triés\r\n (initialisée avec seulement le 1er élément) et la seconde constituée des éléme... | [
0
] |
""".. Ignore pydocstyle D400."""
from rolca.payment.api.views import (
PaymentViewSet,
)
routeList = ((r'payment', PaymentViewSet),)
| normal | {
"blob_id": "2bfdc259bcd5ff058ee8661a14afd8a915b8372b",
"index": 7020,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrouteList = ('payment', PaymentViewSet),\n",
"step-3": "<mask token>\nfrom rolca.payment.api.views import PaymentViewSet\nrouteList = ('payment', PaymentViewSet),\n",
"step-4": "\"\"\... | [
0,
1,
2,
3
] |
# Copyright (c) Facebook, Inc. and its affiliates.
from .build import build_backbone, BACKBONE_REGISTRY # noqa F401 isort:skip
from .backbone import Backbone
from .fpn import FPN
from .resnet import ResNet, ResNetBlockBase, build_resnet_backbone, make_stage
__all__ = [k for k in globals().keys() if not k.star... | normal | {
"blob_id": "502f405f48df92583757ebc9edb4b15910c1f76a",
"index": 2305,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = [k for k in globals().keys() if not k.startswith('_')]\n",
"step-3": "from .build import build_backbone, BACKBONE_REGISTRY\nfrom .backbone import Backbone\nfrom .fpn import FP... | [
0,
1,
2,
3
] |
# Background: The Fibonacci numbers are defined by F(n) = F(n-1) + F(n-2).
# There are different conventions on whether 0 is a Fibonacci number,
# and whether counting starts at n=0 or at n=1. Here, we will assume that
# 0 is not a Fibonacci number, and that counting starts at n=0,
# so F(0)=F(1)=1, and F(2)=2. Wit... | normal | {
"blob_id": "40744a8530df28f0bd8648900beb8a66e2d44cd0",
"index": 7730,
"step-1": "<mask token>\n",
"step-2": "def fun_nthfibonaccinumber(n):\n n1 = 1\n n2 = 1\n if n == 0:\n return n2\n else:\n for i in range(0, n - 1):\n sum = n1 + n2\n n1 = n2\n n2 =... | [
0,
1,
2
] |
""" A set of constants to describe the package.
Don't put any code in here, because it must be safe to execute in setup.py. """
__title__ = 'space_tracer' # => name in setup.py
__version__ = '4.10.2'
__author__ = "Don Kirkby"
__author_email__ = "donkirkby@gmail.com"
__description__ = "Trade time for space when debug... | normal | {
"blob_id": "6cb29ebd9c0f2660d0eb868bec87ffd97cf4d198",
"index": 6262,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__title__ = 'space_tracer'\n__version__ = '4.10.2'\n__author__ = 'Don Kirkby'\n__author_email__ = 'donkirkby@gmail.com'\n__description__ = 'Trade time for space when debugging your code.'... | [
0,
1,
2
] |
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions,SetupOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import StandardOptions
dataflow_options = ['--project=lofty-shine-248403', '--job_name=newjob', '--temp_... | normal | {
"blob_id": "93a2385d9ebdbc1a7a88185c0a0d5d1f227e46a3",
"index": 8159,
"step-1": "<mask token>\n\n\ndef MLmodel(data):\n import pickle\n import numpy as np\n from google.cloud import storage\n storage_client = storage.Client()\n bucket = storage_client.get_bucket('testing-gcp-mandar')\n blob = ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python3
"""Test telegram_menu package."""
| normal | {
"blob_id": "8d4ffed90e103e61a85a54d6163770966fb2e5c9",
"index": 5049,
"step-1": "<mask token>\n",
"step-2": "#!/usr/bin/env python3\n\n\"\"\"Test telegram_menu package.\"\"\"\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
letters = ['a', 'b', 'c']
def delete_head(letters):
del letters[0]
print letters
print delete_head(letters)
| normal | {
"blob_id": "e0c10dfa4074b0de4d78fc78a6f373074ef4dadd",
"index": 3971,
"step-1": "letters = ['a', 'b', 'c']\ndef delete_head(letters):\n\tdel letters[0]\n\tprint letters\nprint delete_head(letters)\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
# Generated by Django 3.1.2 on 2020-10-21 21:00
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('monitoring', '0002_auto_20201021_0027'),
]
operations = [
migrations.AlterField(
model_name='endpoint',
name='freque... | normal | {
"blob_id": "20f56ff484321a7d623cead4315e5a6b3b0653a7",
"index": 2720,
"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 = [('monitoring'... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.1.2 on 2020-10-17 15:46
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('story1', '0006_visitor'),
]
operations = [
migrations.RenameField(
model_name='visitor',
old_name='identitiy_n... | normal | {
"blob_id": "1aaace83af0235341d10b8ac3b47d00a944dac37",
"index": 1422,
"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 = [('story1', '0... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# Script to time convolution using different number of processors.
# Jason Neal
# December 2016
from __future__ import division, print_function
import datetime
from eniric.nIRanalysis import convolve_spectra
spectrum_name = "lte03900-4.50-0.0.PHOENIX-ACES-AGSS-COND-2011-HiRes_wave.dat"
data_rep = ... | normal | {
"blob_id": "2105619102de0d4d976c7bdfc839ee08058b7ab5",
"index": 2713,
"step-1": "<mask token>\n\n\ndef time_diff_procs(numprocs):\n \"\"\"Time the convolution with different number of processors\"\"\"\n conv_times = dict()\n for proc in numprocs:\n start_time = datetime.datetime.now()\n c... | [
1,
2,
3,
4,
5
] |
from ad_api.base import Client, sp_endpoint, fill_query_params, ApiResponse
class CampaignNegativeKeywords(Client):
@sp_endpoint('/v2/sp/campaignNegativeKeywords/{}', method='GET')
def get_campaign_negative_keyword(self, keywordId, **kwargs) -> ApiResponse:
r"""
get_campaign_negative_keyword(... | normal | {
"blob_id": "f6e0215f9992ceab51887aab6a19f58a5d013eb4",
"index": 7829,
"step-1": "<mask token>\n\n\nclass CampaignNegativeKeywords(Client):\n <mask token>\n\n @sp_endpoint('/v2/sp/campaignNegativeKeywords/{}', method='DELETE')\n def delete_campaign_negative_keyword(self, keywordId, **kwargs\n ) -... | [
5,
6,
8,
9,
10
] |
import argparse
from ags_save_parser import saved_game
def report_mismatch(compare_result_list):
report = []
for i in range(len(compare_result_list)):
value = compare_result_list[i]
if value != '_':
report.append((i, value))
return report
def report_mismatch_for_module(
... | normal | {
"blob_id": "329451a3d3fa95f5572dc1701d1adbf4aaa72628",
"index": 8521,
"step-1": "<mask token>\n\n\ndef report_mismatch_for_module(modules_1, modules_2, index):\n module_1 = modules_1[index]\n module_2 = modules_2[index]\n if len(module_1) != 2 or len(module_2) != 2:\n raise AssertionError('Modul... | [
3,
5,
6,
7,
8
] |
##
#Author: Stephen
##
import socket
import select
import sys, os
from contextlib import contextmanager
hostip = 'localhost'
hostport = 8089
def connect(hostip=hostip,hostport=hostport):
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
IP_address = hostip
Port = hostport
server.connect((IP_a... | normal | {
"blob_id": "5cdf8cd4bfebb9aab2e8f421047fc1ba3190d566",
"index": 3451,
"step-1": "<mask token>\n\n\ndef connect(hostip=hostip, hostport=hostport):\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n IP_address = hostip\n Port = hostport\n server.connect((IP_address, Port))\n return serv... | [
5,
6,
7,
8,
9
] |
# Write a class to hold player information, e.g. what room they are in
# currently.
class Player():
def __init__(self, name, location, items=[]):
self.name = name
self.location = location
self.items = items
# def try_direction(self, user_action):
# attribute = user_action + '_... | normal | {
"blob_id": "b355bd5a519d65ea35d4e8d5e6a384424d79130a",
"index": 3620,
"step-1": "<mask token>\n",
"step-2": "class Player:\n <mask token>\n\n def pick_up_item(self, item):\n if len(self.items) <= 3:\n self.items.append(item)\n print(\n f\"\"\"\n\nNOW YOU HAVE ... | [
0,
2,
3,
4,
5
] |
import rospy
#: the parameter namespace for the arni_countermeasure node
ARNI_CTM_NS = "arni/countermeasure/"
#: the parameter namespace for configuration files
#: of the arni_countermeasure node
ARNI_CTM_CFG_NS = ARNI_CTM_NS + "config/"
def get_param_num(param):
#dummy val
value = 1
try:
value... | normal | {
"blob_id": "70c9d75dabfa9eac23e34f94f34d39c08e21b3c0",
"index": 6070,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_param_num(param):\n value = 1\n try:\n value = rospy.get_param(param)\n if not isinstance(value, (int, float, long)):\n err_msg = 'Param %s is n... | [
0,
2,
3,
4,
5
] |
# encoding: utf-8
from GlyphsApp.plugins import *
from outlineTestPenGlyphs import OutlineTestPenGlyphs
from string import strip
plugin_id = "de.kutilek.RedArrow"
class RedArrow(ReporterPlugin):
def settings(self):
self.menuName = "Red Arrows"
self.keyboardShortcut = 'a'
self.keyboardShortcutModifier = N... | normal | {
"blob_id": "229d7378695f7e00176eb7c3962519af3db1b7e1",
"index": 4461,
"step-1": "<mask token>\n\n\nclass RedArrow(ReporterPlugin):\n <mask token>\n\n def start(self):\n self.addMenuItem()\n self.options = {'extremum_calculate_badness': False,\n 'extremum_ignore_badness_below': 0,\... | [
7,
10,
12,
13,
14
] |
print("calificacion de los alumnos")
lista2_calificaciones=[]
for i in range (0,5):
lista2_calificaciones.append(int(input(f"ingrese la calificacion corresponfiente al alumno")))
print(lista2_calificaciones)
for n in range(0,len(lista2_calificaciones)):
if lista2_calificaciones[i] >=0 and lista2_calific... | normal | {
"blob_id": "1cc9c89182f69a5f1eb9a0e7f3433dc30c8d7035",
"index": 2938,
"step-1": "<mask token>\n",
"step-2": "print('calificacion de los alumnos')\n<mask token>\nfor i in range(0, 5):\n lista2_calificaciones.append(int(input(\n f'ingrese la calificacion corresponfiente al alumno')))\n print(lista2... | [
0,
1,
2,
3
] |
def lucas():
yield 2
a = 2
b = 1
while True:
yield b
a, b = b, a + b
l = lucas()
for i in range(10):
print('{}: {}'.format(i, next(l)))
| normal | {
"blob_id": "4745c00ca0f3ca4316117228a9d44bdb5df02877",
"index": 7799,
"step-1": "<mask token>\n",
"step-2": "def lucas():\n yield 2\n a = 2\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\n\n<mask token>\n",
"step-3": "def lucas():\n yield 2\n a = 2\n b = 1\n while ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
#
# This will take a snapshot and convert it into a volume. To create a volume
# without any links to the old snapshot you need to convert it to a temporary
# volume first, convert that into an image and convert the image back into
# your final volume. Once this is all done, the temporary volume a... | normal | {
"blob_id": "aebe749a20482636d7ed508f9cbd9cde56656b73",
"index": 6236,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main(args):\n openstack.enable_logging(debug=False)\n api = openstack.connect(cloud=args.cloud)\n snapshot_id = args.snapshot\n server = args.volume\n try:\n ... | [
0,
1,
2,
3,
4
] |
from .__main__ import datajson_write, datajson_read
| normal | {
"blob_id": "2269e74c006833976c3a28cd52c238e2dde20051",
"index": 5871,
"step-1": "<mask token>\n",
"step-2": "from .__main__ import datajson_write, datajson_read\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# 出现频率特别高的和频率特别低的词对于文本分析帮助不大，一般在预处理阶段会过滤掉。
# 在英文里，经典的停用词为 “The”, "an"....
# 方法1： 自己建立一个停用词词典
stop_words = ["the", "an", "is", "there"]
# 在使用时： 假设 word_list包含了文本里的单词
word_list = ["we", "are", "the", "students"]
filtered_words = [word for word in word_list if word not in stop_words]
print (filtered_words)
# ... | normal | {
"blob_id": "d14937aaa7a80d6b95825afa2a2d6ff8202e5f5c",
"index": 2498,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(filtered_words)\n<mask token>\nprint(' '.join(singles))\n",
"step-3": "stop_words = ['the', 'an', 'is', 'there']\nword_list = ['we', 'are', 'the', 'students']\nfiltered_words = [w... | [
0,
1,
2,
3,
4
] |
"""
Массив размером 2m + 1, где m — натуральное число, заполнен случайным образом. Найдите в массиве медиану.
Медианой называется элемент ряда, делящий его на две равные части:
в одной находятся элементы, которые не меньше медианы, в другой — не больше медианы.
Примечание: задачу можно решить без сортировки исходного м... | normal | {
"blob_id": "fbcbad9f64c0f9b68e29afde01f3a4fdba012e10",
"index": 4868,
"step-1": "<mask token>\n\n\ndef heapify(array, size, ind):\n largest = ind\n left = 2 * ind + 1\n right = 2 * ind + 2\n if left < size and array[left] > array[largest]:\n largest = left\n if right < size and array[right... | [
2,
3,
4,
5,
6
] |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size, encoder_layer=2, step=4, is_bidir=False, **kw):
super(Model, self).__init__()
fc_embedding = []
# First, we should convert the 1 dim data to ... | normal | {
"blob_id": "188f82b0fb04d6814d77617fa9148113d0e6ef01",
"index": 2170,
"step-1": "<mask token>\n\n\nclass Model(nn.Module):\n <mask token>\n\n def forward(self, input_seq, target_seq=None):\n input_seq = self.fc_embedding(input_seq.unsqueeze(-1))\n _, encoding_result = self.encoder(input_seq)... | [
4,
5,
6,
7,
8
] |
from data import constants
from data.action import Action
from data.point import Point
class MoveActorsAction(Action):
"""A code template for moving actors. The responsibility of this class of
objects is move any actor that has a velocity more than zero.
Stereotype:
Controller
Attributes:... | normal | {
"blob_id": "3be7183b5c1d86ee0ebfdea89c6459efe89510f8",
"index": 6103,
"step-1": "<mask token>\n\n\nclass MoveActorsAction(Action):\n <mask token>\n\n def execute(self, cast):\n \"\"\"Executes the action using the given actors.\n\n Args:\n cast (dict): The game actors {key: tag, va... | [
2,
3,
4,
5,
6
] |
n_m_q=input().split(" ")
n=int(n_m_q[0])
m=int(n_m_q[1])
q=int(n_m_q[2])
dcc=[]
for i in range(n):
a=[]
dcc.append(a)
available=[]
for i in range(m):
x=input().split(" ")
a=int(x[0])
b=int(x[1])
available.append([a,b])
dcc[a-1].append(b)
dcc[b-1].append(a)
for i in range(q):
x=input(... | normal | {
"blob_id": "062b6133ba4de24f7eaf041e4b6c039501b47b9a",
"index": 8873,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n a = []\n dcc.append(a)\n<mask token>\nfor i in range(m):\n x = input().split(' ')\n a = int(x[0])\n b = int(x[1])\n available.append([a, b])\n dc... | [
0,
1,
2,
3
] |
from config import Config
def test_stf_3_2_1_pos(fixture):
seed = fixture.common.get_seed()
fixture.stf.open_stf_exercise('3-2-1', seed)
fixture.stf.open_solution_url(seed)
assert fixture.stf.get_solution() == Config.test_pass_text
fixture.common.back_to_main_page()
def test_stf_3_2_1_neg(fixtur... | normal | {
"blob_id": "028b38a07c71232eb42bedecd734cf7188550239",
"index": 9602,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_stf_3_2_1_neg(fixture):\n seed = fixture.common.get_seed()\n fixture.stf.open_stf_exercise('3-2-1', seed)\n fixture.stf.open_solution_url('test')\n assert fixture... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python3
"""Prints the first State object from the database specified
"""
from sys import argv
import sqlalchemy
from sqlalchemy import create_engine, orm
from model_state import Base, State
if __name__ == "__main__":
engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'
... | normal | {
"blob_id": "1f3e20e7fe597a88cddacf6813250f1ede6c6ee0",
"index": 6595,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(*\n argv[1:4]), pool_pre_ping=True)\n Base.metadata.create_all(engine)\n se... | [
0,
1,
2,
3
] |
import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.select import Select
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions
class TestTa... | normal | {
"blob_id": "777dc2056443f0404ccb75d570f2ddc3a3aa747b",
"index": 6669,
"step-1": "<mask token>\n\n\nclass TestTaniHub:\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestTaniHub:\n <mask token>\n <mask token>\n\n def test_tanihub_number_2... | [
1,
2,
4,
6,
8
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script reads in video information frame-by-frame, and then calculates
visual edge information for each frame, storing the information in a vector.
This can be averaged within TRs in an fMRI analysis to 'regress out'
high-frequency visual information in the video.
... | normal | {
"blob_id": "d70d3d8eef711441ac89c2d98c72a5f95e0ab20d",
"index": 5261,
"step-1": "<mask token>\n\n\ndef AnalyzeFrames(vidpath):\n print('\\nGetting video info & writing out image files for each frame...\\n')\n vidObj = cv2.VideoCapture(vidpath)\n fps = vidObj.get(cv2.CAP_PROP_FPS)\n print('Frames per... | [
1,
2,
3,
4,
5
] |
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