code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
# -*- coding: utf-8 -*-
from rest_framework import serializers
from django.contrib.auth.models import User
from core.models import Detalhe, Viagem, Hospital, Equipamento, Caixa
class UserSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = User
fields = '__all__'
class CaixaSeri... | normal | {
"blob_id": "b5c68211cfa255e47ee316dc5b0627719eacae78",
"index": 8504,
"step-1": "<mask token>\n\n\nclass ViagemSerializer(serializers.ModelSerializer):\n detalhes = DetalheSerializer(many=True, read_only=True)\n caixa = CaixaSerializer(read_only=True)\n localPartida = HospitalSerializer(read_only=True)... | [
2,
6,
7,
8,
9
] |
# -*- coding: utf-8 -*-
c = int(input())
t = input()
m = []
for i in range(12):
aux = []
for j in range(12):
aux.append(float(input()))
m.append(aux)
aux = []
soma = 0
for i in range(12):
soma += m[i][c]
resultado = soma / (t == 'S' and 1 or 12)
print('%.1f' % resultado)
| normal | {
"blob_id": "6edb1f99ca9af01f28322cbaf13f278e79b94e92",
"index": 5882,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\n<mask token>\nfor i in range(12):\n soma += m[i][c]\n<m... | [
0,
1,
2,
3
] |
'''
'Daniel Moulton
'3/24/15
'Implementation of the mergesort sorting algorithm in python.
'Utilizes a series of random numbers as the initial input
'Uses a top down approach to recursively sort the original list and output the final result.
'''
from random import randrange
def mergeSort(original_list):
#initiali... | normal | {
"blob_id": "294229849dcfac8d4afeab79dae3c652c853fc47",
"index": 1924,
"step-1": "<mask token>\n\n\ndef mergeSort(original_list):\n return subSort(original_list)\n\n\ndef subSort(sub_list):\n if len(sub_list) < 2:\n return sub_list\n index = len(sub_list) // 2\n left_list = sub_list[0:index]\n... | [
4,
5,
6,
7,
8
] |
# settings
import config
# various modules
import sys
import time
import multiprocessing
import threading
from queue import Queue
import time
import os
import signal
import db
import time
from random import randint
# telepot's msg loop & Bot
from telepot.loop import MessageLoop
from telepot import Bot
import asyncio... | normal | {
"blob_id": "315fed1806999fed7cf1366ef0772318a0baa84d",
"index": 8789,
"step-1": "# settings\nimport config\n\n# various modules\nimport sys\nimport time\nimport multiprocessing\nimport threading\nfrom queue import Queue\nimport time\nimport os\nimport signal\nimport db\nimport time\nfrom random import randint\n... | [
0
] |
from typing import Dict, Tuple
import torch
from tqdm import tqdm
import schnetpack.properties as structure
from schnetpack.data import AtomsLoader
__all__ = ["calculate_stats"]
def calculate_stats(
dataloader: AtomsLoader,
divide_by_atoms: Dict[str, bool],
atomref: Dict[str, torch.Tensor] = None,
) ->... | normal | {
"blob_id": "b2944a95dbe25057155aaf6198a97d85b3bb620b",
"index": 6436,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef calculate_stats(dataloader: AtomsLoader, divide_by_atoms: Dict[str,\n bool], atomref: Dict[str, torch.Tensor]=None) ->Dict[str, Tuple[torch.\n Tensor, torch.Tensor]]:\n \... | [
0,
1,
2,
3,
4
] |
import tensorflow as tf
class Config(object):
# Source and Target files
from_train_file='data/dev.en'
to_train_file='data/dev.vi'
# Special characters and ID's
_PAD = b"_PAD"
_GO = b"_GO"
_EOS = b"_EOS"
_UNK = b"_UNK"
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_I... | normal | {
"blob_id": "c27c2df1830f066ca4f973c46967722869090d05",
"index": 1373,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Config(object):\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>... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 9 16:22:21 2018
@author: SDis
"""
#import Code.Members_module
class Resources:
""" Parent class for Books and eResources containg the main data fields and related setters and getters"""
def __init__(self, title, author, publisher, year):
self.title = tit... | normal | {
"blob_id": "0709d413ddbe41a0c97f94b7819fdfded241d3fc",
"index": 691,
"step-1": "<mask token>\n\n\nclass Resources:\n <mask token>\n\n def __init__(self, title, author, publisher, year):\n self.title = title\n self.author = author\n self.publisher = publisher\n self.year = year\... | [
4,
8,
9,
10,
13
] |
import socket
import json
from typing import Dict
listadionica = ["GS", "MS", "WFC", "VALBZ", "BOND", "VALE", "XLF"]
class Burza:
def __init__ (self, test):
if test:
host_name = "test-exch-partitivnisumari"
port = 25000
else:
host_name = "production"
... | normal | {
"blob_id": "5a895c864c496e1073d75937909c994432a71d75",
"index": 9760,
"step-1": "import socket\nimport json\n\nfrom typing import Dict\n\nlistadionica = [\"GS\", \"MS\", \"WFC\", \"VALBZ\", \"BOND\", \"VALE\", \"XLF\"]\n\nclass Burza:\n def __init__ (self, test):\n\n if test:\n host_name = ... | [
0
] |
import cv2
import numpy as np
img = cv2.imread('Scan1.jpg')
img_height, img_width, dim = img.shape
cv2.imshow('image1', img[0:int(img_height / 2), 0:int(img_width / 2)])
cv2.imshow('image2', img[int(img_height / 2):img_height, 0:int(img_width / 2)])
cv2.imshow('image3', img[0:int(img_height / 2), int(img_width / 2):img... | normal | {
"blob_id": "8c6f890631e9696a7907975b5d0bb71d03b380da",
"index": 839,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('image1', img[0:int(img_height / 2), 0:int(img_width / 2)])\ncv2.imshow('image2', img[int(img_height / 2):img_height, 0:int(img_width / 2)])\ncv2.imshow('image3', img[0:int(img_... | [
0,
1,
2,
3
] |
import random
from .action import Action
from ..transition.step import Step
from ..value.estimators import ValueEstimator
def greedy(steps: [Step], actions: [Action], value_estimator: ValueEstimator) -> int:
estimations = [value_estimator(steps, action) for action in actions]
return actions[estimations.index... | normal | {
"blob_id": "eab45dafd0366af8ab904eb33719b86777ba3d65",
"index": 2925,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef e_greedy(steps: [Step], actions: [Action], value_estimator:\n ValueEstimator, e: float) ->int:\n return random.sample(actions, 1) if random.uniform(0, 1) < e else greedy(\n ... | [
0,
1,
2,
3,
4
] |
import numpy as np
import tensorflow as tf
K_model = tf.keras.models.load_model('K_model.h5')
K_model.summary()
features, labels = [], []
# k_file = open('dataset_20200409.tab')
k_file = open('ts.tab')
for line in k_file.readlines():
line = line.rstrip()
contents = line.split("\t")
label = contents.pop()... | normal | {
"blob_id": "1c2a862f995869e3241dd835edb69399141bfb64",
"index": 8926,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nK_model.summary()\n<mask token>\nfor line in k_file.readlines():\n line = line.rstrip()\n contents = line.split('\\t')\n label = contents.pop()\n labels.append([float(label)])... | [
0,
1,
2,
3,
4
] |
# len(): tamanho da string
# count(): conta quantas vezes um caractere aparece
# lower(), upper()
# replace(): substitui as letras por outra
# split(): quebra uma string a partir dos espacos em branco
a = len('Karen')
print(a)
b = 'Rainha Elizabeth'.count('a')
print(b)
c = 'karen nayara'.replace('a','@')
print(c)
d = ... | normal | {
"blob_id": "3079fdbe6319454ad166d06bda5670554a5746ee",
"index": 1004,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(a)\n<mask token>\nprint(b)\n<mask token>\nprint(c)\n<mask token>\nprint(d)\n",
"step-3": "a = len('Karen')\nprint(a)\nb = 'Rainha Elizabeth'.count('a')\nprint(b)\nc = 'karen nayar... | [
0,
1,
2,
3
] |
# line_count.py
import sys
count = 0
for line in sys.stdin:
count += 1
# print goes to sys.stdout
print count | normal | {
"blob_id": "46194829fc54c2f3e51febde572e05bcff261fb2",
"index": 7126,
"step-1": "# line_count.py\nimport sys\ncount = 0\nfor line in sys.stdin:\n\tcount += 1\n# print goes to sys.stdout\nprint count",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
# Libraries
from sqlalchemy import Column, ForeignKey, Integer, String
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.orm import relationship
# Taskobra
from taskobra.orm.base import ORMBase
from taskobra.orm.relationships import SystemComponent
class System(ORMBase):
__tablename__ ... | normal | {
"blob_id": "2fc2fd6631cee5f3737dadaac1a115c045af0986",
"index": 5058,
"step-1": "<mask token>\n\n\nclass System(ORMBase):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def add_component(self, component):\n for system_component in self.s... | [
3,
4,
5,
6,
8
] |
from abstract_class_V import V
import torch
import torch.nn as nn
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self,y,X,nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self... | normal | {
"blob_id": "27e9e63338d422b5fca6f7a67fa3d255602a3358",
"index": 225,
"step-1": "<mask token>\n\n\nclass V_test_abstract(V):\n\n def __init__(self):\n super(V_test_abstract, self).__init__()\n <mask token>\n\n def forward(self):\n z = self.beta[:self.dim]\n r1_local = self.beta[self... | [
3,
4,
5,
6,
7
] |
# day one question 1 solution
# find product of two numbers in input.txt list that sum to 2020
# pull everything out of input file
nums = []
with open('input.txt', 'r') as file:
for line in file:
nums.append(int(line))
target = 0
product = 0
# for each number in the input, figure out what it's complement... | normal | {
"blob_id": "38504dae7b010c2df8c16b752c2179b6b3561c0e",
"index": 7770,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('input.txt', 'r') as file:\n for line in file:\n nums.append(int(line))\n<mask token>\nfor ini in nums:\n target = 2020 - ini\n for chk in nums:\n if chk ... | [
0,
1,
2,
3
] |
import array as arr
# from array import * # To remove use of 'arr' everytime.
studentMarks = arr.array('i', [2,30,45,50,90]) # i represnts datatype of array which is int here.
# accessing array
print(studentMarks[3])
studentMarks.append(95)
# using for loop
for i in studentMarks:
print(i)
# usin... | normal | {
"blob_id": "d442d5c7afd32dd149bb47fc9c4355409c53dab8",
"index": 6719,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(studentMarks[3])\nstudentMarks.append(95)\nfor i in studentMarks:\n print(i)\n<mask token>\nwhile i < len(studentMarks):\n print(studentMarks[i])\n i += 1\n",
"step-3": "... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# This file just executes its arguments, except that also adds OUT_DIR to the
# environ. This is for compatibility with cargo.
import subprocess
import sys
import os
os.environ["OUT_DIR"] = os.path.abspath(".")
assert os.path.isdir(os.environ["OUT_DIR"])
sys.exit(subprocess.call(sys.argv[1:], env... | normal | {
"blob_id": "be238268b9fdd565f3cb0770839789b702940ef9",
"index": 8248,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nassert os.path.isdir(os.environ['OUT_DIR'])\nsys.exit(subprocess.call(sys.argv[1:], env=os.environ))\n",
"step-3": "<mask token>\nos.environ['OUT_DIR'] = os.path.abspath('.')\nassert os... | [
0,
1,
2,
3,
4
] |
"""
This handy script will download all wallpapears from simpledesktops.com
Requirements
============
BeautifulSoup - http://www.crummy.com/software/BeautifulSoup/
Python-Requests - http://docs.python-requests.org/en/latest/index.html
Usage
=====
cd /path/to/the/script/
python simpledesktops.py
"""
from StringIO imp... | normal | {
"blob_id": "452d5d98b6c0b82a1f4ec18f29d9710a8c0f4dc9",
"index": 7371,
"step-1": "\"\"\"\nThis handy script will download all wallpapears from simpledesktops.com\n\nRequirements\n============\nBeautifulSoup - http://www.crummy.com/software/BeautifulSoup/\nPython-Requests - http://docs.python-requests.org/en/late... | [
0
] |
"""
OO 05-18-2020
Task
----------------------------------------------------------------------------------------------------------
Your company needs a function that meets the following requirements:
- For a given array of 'n' integers, the function returns the index of the element with the minimum value
... | normal | {
"blob_id": "8fdc9a52b00686e10c97fa61e43ddbbccb64741b",
"index": 8946,
"step-1": "<mask token>\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n ... | [
9,
10,
11,
13,
14
] |
#coding=utf-8
import pandas as pd
# 学生成绩表
df_grade = pd.read_excel("学生成绩表.xlsx")
df_grade.head()
# 学生信息表
df_sinfo = pd.read_excel("学生信息表.xlsx")
df_sinfo.head()
# 只筛选第二个表的少量的列
df_sinfo = df_sinfo[["学号", "姓名", "性别"]]
df_sinfo.head()
# join
df_merge = pd.merge(left=df_grade, right=df_sinfo, left_on="学号", right_on="学... | normal | {
"blob_id": "f6c48731b2a4e0a6f1f93034ee9d11121c2d0427",
"index": 6810,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndf_grade.head()\n<mask token>\ndf_sinfo.head()\n<mask token>\ndf_sinfo.head()\n<mask token>\ndf_merge.head()\n<mask token>\nfor name in ['姓名', '性别'][::-1]:\n new_columns.remove(name)\n... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainMenu.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWind... | normal | {
"blob_id": "f4094a81f90cafc9ae76b8cf902221cbdbc4871a",
"index": 6711,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_MainWindow(object):\n <mask token>\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_... | [
0,
2,
3,
4,
5
] |
from igbot import InstaBot
from settings import username, pw
from sys import argv
def execute_script(InstaBot):
InstaBot.get_unfollowers()
#InstaBot.unfollow()
#InstaBot.follow()
#InstaBot.remove_followers()
def isheadless():
if len(argv) > 1:
if argv[1] == 'head':
return False
else:
raise ValueError("... | normal | {
"blob_id": "f379092cefe83a0a449789fbc09af490081b00a4",
"index": 3818,
"step-1": "<mask token>\n\n\ndef isheadless():\n if len(argv) > 1:\n if argv[1] == 'head':\n return False\n else:\n raise ValueError(\"optional arg must be : 'head'\")\n return True\n\n\n<mask token>\... | [
1,
2,
3,
4,
5
] |
print(4 / 2, 4 / 3, 4 / 4)
print(5 / 2, 5 / 3, 5 / 4)
print(4 // 2, 4 // 3, 4 // 4)
print(5 // 2, 5 // 3, 5 // 4)
print(4.0 / 2, 4 / 3.0, 4.0 / float(4))
print(5.0 / 2, 5 / 3.0, 5.0 / float(4))
print(4.0 // 2, 4 // 3.0, 4.0 // float(4))
print(5.0 // 2, 5 // 3.0, 5.0 // float(4))
| normal | {
"blob_id": "988e1f0631c434cbbb6d6e973792a65ebbd9405e",
"index": 9474,
"step-1": "<mask token>\n",
"step-2": "print(4 / 2, 4 / 3, 4 / 4)\nprint(5 / 2, 5 / 3, 5 / 4)\nprint(4 // 2, 4 // 3, 4 // 4)\nprint(5 // 2, 5 // 3, 5 // 4)\nprint(4.0 / 2, 4 / 3.0, 4.0 / float(4))\nprint(5.0 / 2, 5 / 3.0, 5.0 / float(4))\np... | [
0,
1
] |
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
import tensorflow as tf
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import u... | normal | {
"blob_id": "28c4c09b81d63785750cee36a8efd77760cac451",
"index": 7231,
"step-1": "<mask token>\n\n\ndef rotate_img(image, angle, color, filter=Image.NEAREST):\n if image.mode == 'P' or filter == Image.NEAREST:\n matte = Image.new('1', image.size, 1)\n else:\n matte = Image.new('L', image.size... | [
5,
8,
9,
11,
12
] |
import librosa
import soundfile
import os, glob
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
emotionsRavdessData = {
'01': 'neutral',
'02': 'calm',
'03': 'happy',
'04': 'sad',
'05'... | normal | {
"blob_id": "8cd54362680aa3a96babe100b9231f6f16b3f577",
"index": 6670,
"step-1": "<mask token>\n\n\ndef extract_feature(file_name, mfcc, chroma, mel):\n with soundfile.SoundFile(file_name) as file:\n X = file.read(dtype='float32')\n sample_rate = file.samplerate\n if chroma:\n ... | [
3,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 28 16:36:56 2018
@author: Alex
"""
#%% Import packages
import pickle
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import os
os.chdir('C:\\Users\\Alex\\Documents\\GitHub\\insight-articles-project\\src\\topic modeling\\')
from plotly_network ... | normal | {
"blob_id": "d98db745be2ab9c506a98539b25e9b46e4997136",
"index": 3422,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.chdir(\n 'C:\\\\Users\\\\Alex\\\\Documents\\\\GitHub\\\\insight-articles-project\\\\src\\\\topic modeling\\\\'\n )\n<mask token>\nwith open(filename, 'rb') as fp:\n topic_assi... | [
0,
1,
2,
3,
4
] |
from tempfile import mkdtemp
from shutil import rmtree
from os.path import join
import os
MAX_UNCOMPRESSED_SIZE = 100e6 # 100MB
# Extracts a zipfile into a directory safely
class ModelExtractor(object):
def __init__(self, modelzip):
self.modelzip = modelzip
def __enter__(self):
if not self._... | normal | {
"blob_id": "04670041dab49f8c2d4a0415030356e7ea92925f",
"index": 902,
"step-1": "<mask token>\n\n\nclass ModelExtractor(object):\n\n def __init__(self, modelzip):\n self.modelzip = modelzip\n\n def __enter__(self):\n if not self.__is_model_good():\n raise ValueError('Invalid model ... | [
5,
6,
7,
8,
9
] |
# Check given matrix is valid sudoku or Not.
| normal | {
"blob_id": "502da0f0dafe42d3464fabb1d92ae1b0d7ef11f3",
"index": 431,
"step-1": "# Check given matrix is valid sudoku or Not.\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
1
]
} | [
1
] |
from queue import Queue
class Stack:
def __init__(self):
self.q1 = Queue()
self.q2 = Queue()
def empty(self):
return self.q1.empty()
def push(self, element):
if self.empty():
self.q1.enqueue(element)
else:
self.q2.enqueue(element)
... | normal | {
"blob_id": "4f5f4aadfeabb13790b417b334c5f73c6d0345a7",
"index": 9256,
"step-1": "<mask token>\n\n\nclass Stack:\n\n def __init__(self):\n self.q1 = Queue()\n self.q2 = Queue()\n\n def empty(self):\n return self.q1.empty()\n\n def push(self, element):\n if self.empty():\n ... | [
5,
7,
8,
9,
11
] |
from flask import Blueprint, render_template, request, session, url_for, redirect
from flask_socketio import join_room, leave_room, send, emit
from models.game.game import Game
from models.games.games import Games
from decorators.req_login import requires_login
game_blueprint = Blueprint('game', __name__)
@game_bluep... | normal | {
"blob_id": "1ccb23435d8501ed82debf91bd6bf856830d01cb",
"index": 6063,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@game_blueprint.route('/<string:game_id>')\n@requires_login\ndef game_index(game_id):\n return render_template('game/game.html')\n",
"step-3": "<mask token>\ngame_blueprint = Blu... | [
0,
1,
2,
3
] |
from collections import Counter
N = int(input())
lst = list(map(int, input().split()))
ans = []
for i in range(N):
ans.append(abs(i + 1 - lst[i]))
s = Counter(ans)
rst = []
for i in s:
rst.append([i, s[i]])
rst.sort(key=lambda x: x[0], reverse=True)
for i in rst:
if i[1] > 1:
print(i[0], i[1])
| normal | {
"blob_id": "decd5d50025fc3b639be2f803d917ff313cf7219",
"index": 8838,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(N):\n ans.append(abs(i + 1 - lst[i]))\n<mask token>\nfor i in s:\n rst.append([i, s[i]])\nrst.sort(key=lambda x: x[0], reverse=True)\nfor i in rst:\n if i[1] > 1:\... | [
0,
1,
2,
3
] |
'''
Temperature Container
'''
class TempHolder:
range_start = 0
range_end = 0
star_count_lst = [0,0,0,0,0,0]
counter = 0
def __init__(self, in_range_start, in_range_end):
self.range_start = in_range_start
self.range_end = in_range_end
self.counter = 0
self.s... | normal | {
"blob_id": "330b843501e0fdaff21cc4eff1ef930d54ab6e8d",
"index": 747,
"step-1": "<mask token>\n\n\nclass FRSHTTHolder:\n frshtt_code = ''\n star_count_lst = [0, 0, 0, 0, 0, 0]\n counter = 0\n\n def __init__(self, in_frshtt_code):\n self.frshtt_code = in_frshtt_code\n self.counter = 0\n ... | [
11,
13,
15,
19,
23
] |
# Create a program that will ask the users name, age, and reddit username.
# Have it tell them the information back, in the format:
#
# Your name is (blank), you are (blank) years old, and your username is (blank)
#
# For extra credit, have the program log this information in a file to be accessed later.
#
name = ... | normal | {
"blob_id": "00531c5a7fdcd24204b0546c081bbe7d63d0a6b2",
"index": 1520,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Your name is ' + name + ', you are ' + age +\n ' years old, and your username is ' + reddit)\n",
"step-3": "name = input('What is your name? ')\nage = input('How old are you? ... | [
0,
1,
2,
3
] |
# This program just for testing push from Mac.
def subset2(num):
mid_result = []
result = []
subset2_helper(num, mid_result, result, 0)
print(result)
def subset2_helper(num, mid_result, result, position):
result.append(mid_result[:])
for i in range(position, len(num)):
mid_result.append... | normal | {
"blob_id": "829910af55ca84838537a2e1fa697713c7a6c6ca",
"index": 8400,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef subset2_helper(num, mid_result, result, position):\n result.append(mid_result[:])\n for i in range(position, len(num)):\n mid_result.append(num[i])\n subset2_h... | [
0,
1,
2,
3,
4
] |
"""Test suite for phlsys_tryloop."""
from __future__ import absolute_import
import datetime
import itertools
import unittest
import phlsys_tryloop
# =============================================================================
# TEST PLAN
# -----------------------------------------... | normal | {
"blob_id": "87130c2bbf919cacd3d5dd823cd310dcad4dc790",
"index": 8157,
"step-1": "\"\"\"Test suite for phlsys_tryloop.\"\"\"\n\nfrom __future__ import absolute_import\n\nimport datetime\nimport itertools\nimport unittest\n\nimport phlsys_tryloop\n\n# ==============================================================... | [
0
] |
from odoo import models, fields, api, _
import odoo.addons.decimal_precision as dp
class netdespatch_config(models.Model):
_name = 'netdespatch.config'
name = fields.Char(String='Name')
url = fields.Char(string='URL')
# Royal Mail
rm_enable = fields.Boolean('Enable Royal Mail')
domestic_name =... | normal | {
"blob_id": "407f549cf68660c8f8535ae0bed373e2f54af877",
"index": 5731,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass netdespatch_config(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>... | [
0,
1,
2,
3,
4
] |
# Copyright 2014 Rackspace Hosting
# All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | normal | {
"blob_id": "120021e44f6df9745db35ea2f38f25acecca9252",
"index": 3201,
"step-1": "<mask token>\n\n\n@test(depends_on_classes=[AfterConfigurationsCreation], groups=[tests.\n DBAAS_API_CONFIGURATIONS])\nclass ListConfigurations(ConfigurationsTestBase):\n\n @test\n def test_configurations_list(self):\n ... | [
29,
40,
43,
52,
53
] |
# This module is used to load pascalvoc datasets (2007 or 2012)
import os
import tensorflow as tf
from configs.config_common import *
from configs.config_train import *
from configs.config_test import *
import sys
import random
import numpy as np
import xml.etree.ElementTree as ET
# Original dataset organisation.
DIR... | normal | {
"blob_id": "c33d625ebd6a40551d2ce0393fd78619601ea7ae",
"index": 5834,
"step-1": "<mask token>\n\n\nclass Dataset(object):\n\n def __init__(self):\n self.items_descriptions = {'image':\n 'A color image of varying height and width.', 'shape':\n 'Shape of the image', 'object/bbox':\... | [
11,
12,
13,
14,
16
] |
#!/usr/bin/env python3.4
# -*- coding: utf-8 -*-
"""
Das Pong-Spielfeld wird simuliert.
Court moduliert ein anpassbares Spielfeld für Pong mit einem standardmäßigen Seitenverhältnis von 16:9.
Jenes Spielfeld verfügt über einen Ball und zwei Schläger, jeweils links und rechts am Spielfeldrand,
sowie einen Punktestand ... | normal | {
"blob_id": "5485a1210a0c0361dbb000546ee74df725fad913",
"index": 5647,
"step-1": "<mask token>\n\n\nclass court:\n <mask token>\n\n def __init__(self):\n \"\"\"\n Initialisiert ein court-Objekt.\n Hierzu zählen Spielfeld, Spieler sowie die Startposition des Balles.\n\n :return v... | [
20,
21,
23,
26,
29
] |
from django.shortcuts import get_object_or_404, render
from django.http import Http404
from django.urls import reverse
# Create your views here.
from django.template import loader
from django.http import HttpResponse, HttpResponseRedirect
from .models import Categories, News, SalesSentences
from .models_gfl import Info... | normal | {
"blob_id": "531d1cab3d0860de38f8d1fefee28f10fc018bdb",
"index": 9005,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef index(request):\n listTopNews = News.objects.filter(category__category='topside-news')\n listBottomNews = News.objects.filter(category__category='bottomside-news')\n list... | [
0,
1,
2,
3
] |
#딕셔너리로 데이터 표현
# sales = {'hong':0,'lee':0,'park':0}
# d = {'z':10, 'b':20,'c':30}
# print(d)
# d.pop('b')
# print(d)
# d['f']=40
# print(d)
# d.pop('z')
# d['z'] = 40
# print(d.keys())
#반복문(while)
#조건이 참일동안 수행
#while True:
# print('python!!!')
# a = 0
# while a < 10:
# a += 1
# print(a)
# a = 0
# while Tr... | normal | {
"blob_id": "38bd18e9c1d17f25c10321ab561372eed58e8abc",
"index": 4243,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor x in data:\n if x < min:\n min = x\nprint(min)\n",
"step-3": "data = [5, 6, 2, 8, 9, 1]\nmin = 10\nfor x in data:\n if x < min:\n min = x\nprint(min)\n",
"step... | [
0,
1,
2,
3
] |
from app import db, session, Node_Base, Column, relationship
from datetime import datetime
import models
import os
import json
| normal | {
"blob_id": "1711f74fae36ba761a7c0d84b95271b4e5043d27",
"index": 6312,
"step-1": "<mask token>\n",
"step-2": "from app import db, session, Node_Base, Column, relationship\nfrom datetime import datetime\nimport models\nimport os\nimport json\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids"... | [
0,
1
] |
from django.shortcuts import render, redirect
from .game import run
from .models import Match
from team.models import Team, Player
from django.urls import reverse
# Create your views here.
def startgame(request):
match = Match(team1_pk = 1, team2_pk = 2)
team1 = Team.objects.get(pk = match.team1_pk)
team... | normal | {
"blob_id": "e1829904cea51909b3a1729b9a18d40872e7c13c",
"index": 6163,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef results(request):\n team1damage = 0\n team2damage = 0\n winner = run(1, 2)\n team1 = Team.objects.get(pk=1)\n team2 = Team.objects.get(pk=2)\n player1 = Player.o... | [
0,
1,
2,
3,
4
] |
from states.state import State
class MoveDigState(State):
#init attributes of state
def __init__(self):
super().__init__("MoveDig", "ScanDig")
self.transitionReady = False
self.digSiteDistance = 0
#implementation for each state: overridden
def run(self, moveInstructions):
... | normal | {
"blob_id": "ce4ecff2012cfda4a458912713b0330a218fa186",
"index": 873,
"step-1": "<mask token>\n\n\nclass MoveDigState(State):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass MoveDigState(State):\n\n def __init__(self):\n super().__init__('MoveDig', 'ScanDi... | [
1,
2,
4,
5,
6
] |
'''
Utility functions to do get frequencies of n-grams
Author: Jesus I. Ramirez Franco
December 2018
'''
import nltk
import pandas as pd
from nltk.stem.snowball import SnowballStemmer
from pycorenlp import StanfordCoreNLP
import math
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import st... | normal | {
"blob_id": "367c3b4da38623e78f2853f9d3464a414ad049c2",
"index": 9596,
"step-1": "<mask token>\n\n\ndef clean_doc(text, language='english'):\n \"\"\"\n\tRemoves unknown characters and punctuation, change capital to lower letters and remove\n\tstop words. If stem=False\n\tInputs:\n\tsentence (string): a sting ... | [
3,
8,
10,
11,
12
] |
# SPDX-FileCopyrightText: 2019-2021 Python201 Contributors
# SPDX-License-Identifier: MIT
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path ... | normal | {
"blob_id": "1ead23c6ea4e66b24e60598ae20606e24fa41482",
"index": 1024,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nyear = datetime.datetime.now().year\nproject = 'python201'\ncopyright = f'2019-{year} Geoffrey Lentner, 2018 Ashwin Srinath'\nauthor = 'Geoffrey Lentner, Ashwin Srinath'\nversion = '0.0.1... | [
0,
1,
2,
3
] |
import configparser
import sqlite3
import time
import uuid
from duoquest.tsq import TableSketchQuery
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.ex... | normal | {
"blob_id": "54ec1961f4835f575e7129bd0b2fcdeb97be2f03",
"index": 93,
"step-1": "<mask token>\n\n\ndef input_db_name(conn):\n while True:\n db_name = input('Database name (default: concert_singer) > ')\n if not db_name:\n db_name = 'concert_singer'\n cur = conn.cursor()\n ... | [
6,
7,
8,
11,
12
] |
import json
import os
import ipdb
from tqdm import tqdm
import argparse
from os import listdir
from os.path import isfile, join
import pickle
import joblib
from collections import Counter
from shutil import copyfile
import networkx as nx
import spacy
import nltk
import numpy as np
nltk.download('stopwords')
nltk_stopw... | normal | {
"blob_id": "2da7892722afde5a6f87e3bd6d5763c895ac96c9",
"index": 284,
"step-1": "<mask token>\n\n\nclass Lang:\n\n def __init__(self):\n super(Lang, self).__init__()\n self.word2index = {}\n self.word2count = {}\n self.index2word = {}\n self.n_words = 0\n\n def index_word... | [
5,
8,
9,
11,
13
] |
version https://git-lfs.github.com/spec/v1
oid sha256:0c22c74b2d9d62e2162d2b121742b7f94d5b1407ca5e2c6a2733bfd7f02e3baa
size 5016
| normal | {
"blob_id": "c5f0b1dde320d0042a1bf4de31c308e18b53cbeb",
"index": 6766,
"step-1": "version https://git-lfs.github.com/spec/v1\noid sha256:0c22c74b2d9d62e2162d2b121742b7f94d5b1407ca5e2c6a2733bfd7f02e3baa\nsize 5016\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
... | [
0
] |
# Mezzanine Django Framework createdb error on Max OSX 10.9.2
import django
django.version
| normal | {
"blob_id": "56afde2a31ad9dddee35e84609dff2eb0fc6fe1a",
"index": 9438,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndjango.version\n",
"step-3": "import django\ndjango.version\n",
"step-4": "# Mezzanine Django Framework createdb error on Max OSX 10.9.2\nimport django\ndjango.version\n",
"step-5":... | [
0,
1,
2,
3
] |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License
from .attack_models import (DriftAttack, AdditiveGaussian, RandomGaussian,
BitFlipAttack, RandomSignFlipAttack)
from typing import Dict
def get_attack(attack_config: Dict):
if attack_config["attack_model"] == 'drif... | normal | {
"blob_id": "11320922d24b27c5cfa714f88eb0a757deef987f",
"index": 8546,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_attack(attack_config: Dict):\n if attack_config['attack_model'] == 'drift':\n return DriftAttack(attack_config=attack_config)\n elif attack_config['attack_model']... | [
0,
1,
2,
3,
4
] |
def heapify(lst, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and lst[left_index] > lst[largest]:
largest = left_index
if right_index < heap_size and lst[right_index] > lst[largest]:
largest = right_index
if l... | normal | {
"blob_id": "d8ea396ff8514cc10e02072ea478f0276584153d",
"index": 3274,
"step-1": "<mask token>\n",
"step-2": "def heapify(lst, index, heap_size):\n largest = index\n left_index = 2 * index + 1\n right_index = 2 * index + 2\n if left_index < heap_size and lst[left_index] > lst[largest]:\n lar... | [
0,
1,
2
] |
#5.8-5.9
users = ['user1', 'user2', 'user3', 'user4', 'admin']
#users = []
if users:
for user in users:
if user == 'admin':
print(f"Hello, {user}, would you like to see a status report?")
else:
print(f"Hello, {user}, thank you for logging in again")
else:
print("We need t... | normal | {
"blob_id": "c355be4e05d1df7f5d6f2e32bbb5a8086babe95b",
"index": 7946,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif users:\n for user in users:\n if user == 'admin':\n print(f'Hello, {user}, would you like to see a status report?')\n else:\n print(f'Hello, {use... | [
0,
1,
2,
3
] |
def raizCubica(numero):
r = pow(numero,(1/3))
return r
numeros = []
raices = []
for x in range(5):
numeros.insert(x, float(input("Ingrese Numero: ")))
raices.insert(x, round(raizCubica(numeros[x]),3))
print("Numeros: ", numeros)
print("Raices: ", raices) | normal | {
"blob_id": "180f7f0ade9770c6669680bd13ac8f2fd55cc8c7",
"index": 357,
"step-1": "<mask token>\n",
"step-2": "def raizCubica(numero):\n r = pow(numero, 1 / 3)\n return r\n\n\n<mask token>\n",
"step-3": "def raizCubica(numero):\n r = pow(numero, 1 / 3)\n return r\n\n\n<mask token>\nfor x in range(5... | [
0,
1,
2,
3,
4
] |
n = int(input())
num = list(map(int, input().split()))
plus_cnt = 0
div_max = 0
for i in num:
div = 0
while i > 0:
if i % 2 == 0:
i //= 2
div += 1
else:
i -= 1
plus_cnt += 1
div_max = max(div_max, div)
print(plus_cnt + div_max)
| normal | {
"blob_id": "9247896850e5282265cd08240f6f505e675ce5f0",
"index": 5904,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in num:\n div = 0\n while i > 0:\n if i % 2 == 0:\n i //= 2\n div += 1\n else:\n i -= 1\n plus_cnt += 1\n div_max ... | [
0,
1,
2
] |
import os
from setuptools import setup
from django_spaghetti import __version__
with open(os.path.join(os.path.dirname(__file__), 'README.rst')) as readme:
README = readme.read()
# allow setup.py to be run from any path
os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))
setup(
nam... | normal | {
"blob_id": "6e557c2b85031a0038afd6a9987e3417b926218f",
"index": 6184,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(os.path.join(os.path.dirname(__file__), 'README.rst')) as readme:\n README = readme.read()\nos.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))\nse... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'PhoneUser.last_contacted'
db.add_column(u'smslink_phoneuser', 'last_contacted',
... | normal | {
"blob_id": "2c1de638ac25a9f27b1af94fa075b7c1b9df6884",
"index": 993,
"step-1": "<mask token>\n\n\nclass Migration(SchemaMigration):\n\n def forwards(self, orm):\n db.add_column(u'smslink_phoneuser', 'last_contacted', self.gf(\n 'django.db.models.fields.DateTimeField')(null=True, blank=True)... | [
2,
3,
4,
5,
6
] |
def _get_single_variable(self, name, shape=None, dtype=dtypes.float32, initializer=None, regularizer=None, partition_info=None, reuse=None, trainable=True, collections=None, caching_device=None, validate_shape=True, use_resource=None):
'Get or create a single Variable (e.g. a shard or entire variable).\n\n See t... | normal | {
"blob_id": "51ef1c0f6a17e12b2324a80f962b2ce47cc05bcc",
"index": 1348,
"step-1": "<mask token>\n",
"step-2": "def _get_single_variable(self, name, shape=None, dtype=dtypes.float32,\n initializer=None, regularizer=None, partition_info=None, reuse=None,\n trainable=True, collections=None, caching_device=No... | [
0,
1,
2
] |
import os
def take_shot(filename):
os.system("screencapture "+filename+".png")
| normal | {
"blob_id": "f4c90a6d6afdcf78ec6742b1924a5c854a5a4ed6",
"index": 1825,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef take_shot(filename):\n os.system('screencapture ' + filename + '.png')\n",
"step-3": "import os\n\n\ndef take_shot(filename):\n os.system('screencapture ' + filename + '.p... | [
0,
1,
2,
3
] |
'''引入数据，并对数据进行预处理'''
# step 1 引入数据
import pandas as pd
with open('D:\\Desktop\西瓜数据集3.0.csv', 'r', encoding='utf-8') as data_obj:
df = pd.read_csv(data_obj)
# Step 2 对数据进行预处理
# 对离散属性进行独热编码，定性转为定量，使每一个特征的取值作为一个新的特征
# 增加特征量 Catagorical Variable -> Dummy Variable
# 两种方法：Dummy Encoding VS One Hot Encoding
# 相同点：将Cat... | normal | {
"blob_id": "682b3e1d6d40f4b279052ac27df19268d227fef8",
"index": 6899,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('D:\\\\Desktop\\\\西瓜数据集3.0.csv', 'r', encoding='utf-8') as data_obj:\n df = pd.read_csv(data_obj)\n<mask token>\npd.set_option('display.max_columns', 1000)\n<mask token>\nfor... | [
0,
1,
2,
3,
4
] |
import urllib.request
import urllib.parse
import json
content = input("请输入需要翻译的内容：")
url = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule'
data = {}
data['action'] = 'FY_BY_CLICKBUTTION'
data['bv'] = '1ca13a5465c2ab126e616ee8d6720cc3'
data['client'] = 'fanyideskweb'
data['doctype'] = 'json'
dat... | normal | {
"blob_id": "e01b1f57a572571619d6c0981370030dc6105fd2",
"index": 8636,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('翻译结果：%s' % target['translateResult'][0][0]['tgt'])\n",
"step-3": "<mask token>\ncontent = input('请输入需要翻译的内容：')\nurl = 'http://fanyi.youdao.com/translate?smartresult=dict&smartres... | [
0,
1,
2,
3,
4
] |
# POST API for Red Alert project - NLP and Metalearning components
# Insikt Intelligence S.L. 2019
import pandas as pd
import pickle
from flask import Flask, render_template, request, jsonify
from utilities import load_data, detect_language
from preprocessing import preprocess, Tagger, remove_stopwords
import json
fro... | normal | {
"blob_id": "b51e0ee80a2488197470627821204d1f74cd62a1",
"index": 5437,
"step-1": "<mask token>\n\n\n@app.route('/probability', methods=['POST'])\ndef make_probability():\n try:\n data = request.get_json()\n except Exception as e:\n raise e\n if data == {}:\n return bad_request()\n ... | [
7,
8,
10,
11,
12
] |
#Use bisection search to determine square root
def square_calculator(user_input):
"""
accepts input from a user to determine the square root
returns the square root of the user input
"""
precision = .000000000001
counter = 0
low = 0
high = user_input
guess = (low + high) / 2.0
w... | normal | {
"blob_id": "2bc20f3410d068e0592c8a45e3c13c0559059f24",
"index": 4498,
"step-1": "<mask token>\n",
"step-2": "def square_calculator(user_input):\n \"\"\"\n accepts input from a user to determine the square root\n returns the square root of the user input\n \"\"\"\n precision = 1e-12\n counter... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for the storage format CLI arguments helper."""
import argparse
import unittest
from plaso.cli import tools
from plaso.cli.helpers import storage_format
from plaso.lib import errors
from tests.cli import test_lib as cli_test_lib
class StorageFormatArgumentsHe... | normal | {
"blob_id": "2075e7e05882524c295c8542ca7aefae2cf3e0fc",
"index": 5951,
"step-1": "<mask token>\n\n\nclass StorageFormatArgumentsHelperTest(cli_test_lib.CLIToolTestCase):\n <mask token>\n <mask token>\n\n def testAddArguments(self):\n \"\"\"Tests the AddArguments function.\"\"\"\n argument_... | [
3,
5,
6,
7,
8
] |
#####################将政策文件中的内容抽取出来：标准、伦理、 3部分内容##########################
###########step 1：把3部分内容找到近义词，组成一个词表######
###########step 2：把文件与词表相匹配，判断文件到底在讲啥######
from nltk.corpus import wordnet as wn
import os
import codecs
# goods = wn.synsets('beautiful')
# beautifuls = wn.synsets('pretty')
# bads = wn.synsets... | normal | {
"blob_id": "caca4309034f08874e1e32828a601e7e3d4d3efd",
"index": 2058,
"step-1": "<mask token>\n\n\ndef readOnePolicy(path2):\n ethic_set = wn.synsets('ethic')\n standard_set = wn.synsets('standard')\n privacy_set = wn.synsets('privacy')\n education_set = wn.synsets('education')\n investment_set =... | [
1,
2,
3,
4,
5
] |
import matplotlib.pyplot as plt
Ci_MSB = [32,16,8,4,2,1]
Ci_LSB = [16,8,4,2,1]
CB = 1
CP_B = 0
CP_LSB = (32-1)*(CB+CP_B-1)+10
print(CP_LSB)
CP_MSB = 0
Csum_LSB = sum(Ci_LSB)+CP_LSB
Csum_MSB = sum(Ci_MSB)+CP_MSB
Cx = Csum_LSB*Csum_MSB+(CB+CP_B)*Csum_LSB+(CB+CP_B)*Csum_MSB
Wi_MSB = [Ci_MSB[i]*(CB+CP_B+Csum_LSB)/Cx for i... | normal | {
"blob_id": "b5ac3695a224d531f5baa53a07d3c894d44e8c4c",
"index": 395,
"step-1": "<mask token>\n\n\ndef AtoD(vin):\n code = [(0) for i in range(12)]\n code[0] = 1 if vin > 0 else 0\n for i in range(6):\n vin = vin - Wi_MSB[i] * (code[i] - 0.5) * 2\n code[i + 1] = 1 if vin > 0 else 0\n fo... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.10 on 2018-02-26 13:14
from __future__ import unicode_literals
import datetime
from django.db import migrations, models
import django.db.models.deletion
from django.utils.timezone import utc
class Migration(migrations.Migration):
dependencies = [
('user... | normal | {
"blob_id": "c6170678b523a105312d8ce316853859657d3c94",
"index": 2235,
"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 = [('user_detail... | [
0,
1,
2,
3,
4
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5, stride=1)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 48, kernel_size=5, stride... | normal | {
"blob_id": "9140da0b6c04f39a987a177d56321c56c01586e8",
"index": 3739,
"step-1": "<mask token>\n\n\nclass Classifier(nn.Module):\n\n def __init__(self, args, prob=0.5):\n super(Classifier, self).__init__()\n self.fc1 = nn.Linear(48 * 4 * 4, 100)\n self.bn1_fc = nn.BatchNorm1d(100)\n ... | [
5,
7,
8,
10,
11
] |
n = eval(input("Entrez valeur: "))
res = 0
while n > 0:
res += n%10
n //= 10
print(res, n)
print(res)
| normal | {
"blob_id": "391ecb2f23cc0ce59bd9fac6f97bd4c1788444b9",
"index": 4416,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile n > 0:\n res += n % 10\n n //= 10\n print(res, n)\nprint(res)\n",
"step-3": "n = eval(input('Entrez valeur: '))\nres = 0\nwhile n > 0:\n res += n % 10\n n //= 10\n ... | [
0,
1,
2,
3
] |
class Job:
def __init__(self, id, duration, tickets):
self.id = id
self.duration = duration
self.tickets = tickets
def run(self, time_slice):
self.duration -= time_slice
def done(self):
return self.duration <= 0
| normal | {
"blob_id": "cf7bd8aa9c92d1c3acb9ccc1658d66fa0e7a142d",
"index": 3777,
"step-1": "class Job:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "class Job:\n <mask token>\n\n def run(self, time_slice):\n self.duration -= time_slice\n <mask token>\n",
"step-3": "class Job:\n ... | [
1,
2,
3,
4
] |
import sys
import numpy as np
import math
import matplotlib.pyplot as plt
import random
def load_files(training, testing):
tr_feat = np.genfromtxt(training, usecols=range(256), delimiter=",")
tr_feat /= 255.0
tr_feat = np.insert(tr_feat, 0, 0, axis=1)
tr_exp = np.genfromtxt(training, usecols=range(-1)... | normal | {
"blob_id": "4af05a13264c249be69071447101d684ff97063e",
"index": 6725,
"step-1": "<mask token>\n\n\ndef load_files(training, testing):\n tr_feat = np.genfromtxt(training, usecols=range(256), delimiter=',')\n tr_feat /= 255.0\n tr_feat = np.insert(tr_feat, 0, 0, axis=1)\n tr_exp = np.genfromtxt(traini... | [
4,
5,
6,
7,
8
] |
import torch,cv2,os,time
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# GPU kullanımı
device=torch.device(0)
class NET(nn.Module):
def __init__(self):
super(). __init__()
... | normal | {
"blob_id": "ad63beedc460b3d64a51d0b1f81f8e44cb559749",
"index": 1655,
"step-1": "<mask token>\n\n\nclass NET(nn.Module):\n <mask token>\n\n def uzunluk(self, x):\n x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n x = F.max_pool2d(F.re... | [
3,
4,
5,
6,
7
] |
# Generated by Django 3.2.6 on 2021-08-15 05:17
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('website', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='tasks',
name='cleanlinessLevel',
... | normal | {
"blob_id": "6f9f204cbd6817d5e40f57e71614ad03b64d9003",
"index": 3152,
"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 = [('website', '... | [
0,
1,
2,
3,
4
] |
import pandas
import pytest
from tns_watcher import get_tns
from utils import load_config, log, Mongo
""" load config and secrets """
config = load_config(config_file="config.yaml")["kowalski"]
class TestTNSWatcher:
"""
Test TNS monitoring
"""
@pytest.mark.xfail(raises=pandas.errors.ParserError)
... | normal | {
"blob_id": "e7ffa852d16e8e55b4e2b6ab2383561fe359a169",
"index": 1778,
"step-1": "<mask token>\n\n\nclass TestTNSWatcher:\n <mask token>\n\n @pytest.mark.xfail(raises=pandas.errors.ParserError)\n def test_tns_watcher(self):\n log('Connecting to DB')\n mongo = Mongo(host=config['database'][... | [
2,
3,
4,
5,
6
] |
from datetime import datetime, timezone, timedelta
import json
import urllib.request
from mysql_dbcon import Connection
from model import SlackChannel, SlackUser, SlackMessage
# TODO set timezone at config
jst = timezone(timedelta(hours=+9), 'JST')
def get_new_message_list(channel_id: int):
with Connection() a... | normal | {
"blob_id": "2b141f12bec2006e496bf58a3fcb0167c95ab3b6",
"index": 2530,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_new_message_list(channel_id: int):\n with Connection() as cn:\n token, channel = cn.s.query(SlackChannel.token, SlackChannel.channel\n ).filter(SlackChann... | [
0,
1,
2,
3,
4
] |
from flask import Blueprint
web = Blueprint('web', __name__)
from app.web import auth
from app.web import user
from app.web import book
| normal | {
"blob_id": "02182f0379e58b64bbe17cc5f433e8aae7814976",
"index": 196,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nweb = Blueprint('web', __name__)\n<mask token>\n",
"step-3": "from flask import Blueprint\nweb = Blueprint('web', __name__)\nfrom app.web import auth\nfrom app.web import user\nfrom app.... | [
0,
1,
2
] |
#!/usr/bin/env python
# encoding: utf-8
import tweepy #https://github.com/tweepy/tweepy
import csv
import scraperwiki
import json
#Twitter API credentials - these need adding
consumer_key = ""
consumer_secret = ""
access_key = ""
access_secret = ""
def get_all_tweets(screen_name):
#Twitter only allows access to a ... | normal | {
"blob_id": "02230b44568808757fe45fd18d28881d9bc3e410",
"index": 8074,
"step-1": "#!/usr/bin/env python\n# encoding: utf-8\n\nimport tweepy #https://github.com/tweepy/tweepy\nimport csv\nimport scraperwiki\nimport json\n\n#Twitter API credentials - these need adding\nconsumer_key = \"\"\nconsumer_secret = \"\"\n... | [
0
] |
#!/usr/bin/env python3
# This is a tool to export the WA framework answers to a XLSX file
#
# This code is only for use in Well-Architected labs
# *** NOT FOR PRODUCTION USE ***
#
# Licensed under the Apache 2.0 and MITnoAttr License.
#
# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Lice... | normal | {
"blob_id": "c5e003d625d7798eaf4ef5bca28f6311edccb316",
"index": 7235,
"step-1": "<mask token>\n\n\nclass DateTimeEncoder(json.JSONEncoder):\n\n def default(self, z):\n if isinstance(z, datetime.datetime):\n return str(z)\n else:\n return super().default(z)\n\n\n<mask token... | [
13,
15,
16,
18,
19
] |
#include os
#include math
output_file = 'output/mvnt'
def file_writeout(srvN, pos);
with open(output_file, 'a') as f:
f.write(srvN, ' to ', pos)
return 0
class leg(legN):
def __init__(legN):
srvHY = 'srv' + legN + 'HY'
srvHX = 'srv' + legN + 'HX'
srvEY = 'srv' + le... | normal | {
"blob_id": "901f87752026673c41a70655e987ecc2d5cb369f",
"index": 7273,
"step-1": "#include os\n#include math\n\noutput_file = 'output/mvnt'\n\ndef file_writeout(srvN, pos);\n with open(output_file, 'a') as f:\n f.write(srvN, ' to ', pos)\n return 0\n \nclass leg(legN):\n def __init__(legN)... | [
0
] |
import numpy as np
import cv2
from DataTypes import FishPosition
class FishSensor(object):
def __init__(self):
self.cap = cv2.VideoCapture(0)
self.cap.set(3, 280)
self.cap.set(4, 192)
#cv2.namedWindow("image")
#lower_b, lower_g, lower_r = 0, 0, 80
lower_b, lower_g, lower_r = ... | normal | {
"blob_id": "9cea27abebda10deefa9e05ddefa72c893b1eb18",
"index": 1676,
"step-1": "import numpy as np\nimport cv2\nfrom DataTypes import FishPosition\n\nclass FishSensor(object):\n def __init__(self):\n\t self.cap = cv2.VideoCapture(0)\n\t self.cap.set(3, 280)\n\t self.cap.set(4, 192)\n\n\t #cv2.na... | [
0
] |
"""
Deprecated entry point for a component that has been moved.
"""
# currently excluded from documentation - see docs/README.md
from ldclient.impl.integrations.files.file_data_source import _FileDataSource
from ldclient.interfaces import UpdateProcessor
class FileDataSource(UpdateProcessor):
@classmethod
def... | normal | {
"blob_id": "ee68ebe146f948f3497577f40741e59b7421e652",
"index": 8186,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass FileDataSource(UpdateProcessor):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass FileDataSource(UpdateProcessor):\n\n @classmethod\n def factory(cls, **kwargs):... | [
0,
1,
2,
3,
4
] |
import arcade
WINDOW_WIDTH = 740
WINDOW_HEIGHT = 740
dark_green = (170, 216, 81)
light_green = (162, 210, 73)
snake_color = (72, 118, 235)
def square(square_x, square_y, square_width, square_height, square_color):
""" Code that sets up the squares for generation """
arcade.draw_rectangle_filled(square_x, squ... | normal | {
"blob_id": "fbe091b1cf3ecc2f69d34e3b1c399314b38ebc4a",
"index": 5656,
"step-1": "<mask token>\n\n\ndef generate_grid():\n \"\"\" Code that generates the grid \"\"\"\n y_offset = -10\n for a in range(20):\n x_offset = 10\n for b in range(1):\n y_offset += 20\n for c in ra... | [
5,
7,
8,
9,
10
] |
from Store import Store
from MusicProduct import MusicProduct
class MusicStore(Store):
def make_product(self, name):
'''Overides from parent - return a new MusicProduct Object'''
| normal | {
"blob_id": "0a50b31155afce2558ec066267a9fd0c56964759",
"index": 5653,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MusicStore(Store):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass MusicStore(Store):\n\n def make_product(self, name):\n \"\"\"Overides from parent - retur... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Wed May 8 15:05:51 2019
@author: Brian Heckman and Kyle Oprisko
"""
import csv
"""this file opens a csv file created in the csv creator class. The main purpose of this class is to
normalize the data in the csv file, so that it can be read by the neural network.
"""
... | normal | {
"blob_id": "ecbca04a58c19469e63ee2310e2b2f6b86c41199",
"index": 1011,
"step-1": "<mask token>\n\n\nclass CSV_Normalize:\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 <mask t... | [
11,
12,
19,
21,
23
] |
#!/usr/bin/env python3
def twoNumberSum(array, targetSum):
# Write your code here.
# O(n^2) time | O(1) space
''' Double for loop, quadratic run time
No variables increase as the input size increases,
therefore constant space complexity.
'''
for i in range(len(array) - 1):
firstNu... | normal | {
"blob_id": "a406efcab62b2af67484da776f01fc4e6d20b697",
"index": 984,
"step-1": "#!/usr/bin/env python3\n\ndef twoNumberSum(array, targetSum):\n # Write your code here.\n\n # O(n^2) time | O(1) space\n ''' Double for loop, quadratic run time\n No variables increase as the input size increases,\n ... | [
0
] |
from typing import List
class LanguageDefinition:
"""Language definition containing general constants and methods."""
@staticmethod
def get_translated_file_name(filename: str):
"""
:returns: Translated file name.
"""
return filename
@staticmethod
def create_projec... | normal | {
"blob_id": "672add6aa05e21d3605c05a23ff86281ffc3b17c",
"index": 9827,
"step-1": "<mask token>\n\n\nclass LanguageDefinition:\n <mask token>\n <mask token>\n\n @staticmethod\n def create_project_files(project_path: str, added_file_paths: List[str]\n =None) ->str:\n \"\"\"\n Creat... | [
6,
7,
8,
9
] |
from django.core.urlresolvers import reverse
from django.http import HttpResponse, HttpResponseRedirect, HttpResponseNotFound
from django.shortcuts import render_to_response
from django.template import RequestContext
from whydjango.casestudies.forms import SubmitCaseStudyForm
def case_study_submission(request, tem... | normal | {
"blob_id": "fe3e104cf213b21c33a4b5c6e1a61315c4770eda",
"index": 6821,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef case_study_submission(request, template_name='casestudies/submit.html'):\n form = SubmitCaseStudyForm(request.POST or None)\n if form.is_valid():\n form.save()\n ... | [
0,
1,
2,
3
] |
import string
import pandas as pd
import nltk
from nltk import word_tokenize
from nltk.stem import SnowballStemmer
from nltk.tokenize import WordPunctTokenizer
import json
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
import pickle
import re
import nlpaug.augmenter.... | normal | {
"blob_id": "326b2dcbef339aeb196bef23debad75fa079b121",
"index": 6435,
"step-1": "<mask token>\n\n\nclass Processing:\n <mask token>\n\n @property\n def vocab_size(self):\n return self.__vocab_size\n\n def normalize(self, s):\n s = s.lower()\n replacements = ('á', 'a'), ('é', 'e'... | [
12,
16,
17,
18,
19
] |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 23 15:26:47 2015
@author: tomhope
"""
import cPickle as pickle
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import CountVectorizer
import re
def tokenize_speeches(text):
text = re.sub('[\[\]<>\'\+\=\/(.?\",&*!_#:;@$%|)0-9]'," ", text)
... | normal | {
"blob_id": "17548459b83fe4dea29f20dc5f91196b2b86ea60",
"index": 4655,
"step-1": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 23 15:26:47 2015\n\n@author: tomhope\n\"\"\"\nimport cPickle as pickle\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.feature_extraction.text import CountVectorizer\nimpor... | [
0
] |
import cv2
import numpy as np
import matplotlib.pyplot as plt
'''
def diff_of_gaussians(img):
grey_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
blur_img_grey = cv2.GaussianBlur(grey_img, (9,9), 0)
blur_img_colour = cv2.GaussianBlur(img, (9,9), 0)
#plt.fig... | normal | {
"blob_id": "c3a7a8a006f717057a7ad2920f19d82842b04a85",
"index": 9510,
"step-1": "<mask token>\n\n\ndef canny(img):\n grey_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n blurred_img = cv2.GaussianBlur(grey_img, (9, 9), 0)\n canny_filtered = cv2.Canny(blurred_img, 30, 150)\n return canny_filtered\n\n\n... | [
4,
5,
6,
7,
8
] |
class Port(object):
def __init__(self, mac):
self.mac = mac
| normal | {
"blob_id": "cd89c9eaea9d331288fd07f1968ef9dce89b4a4b",
"index": 7228,
"step-1": "<mask token>\n",
"step-2": "class Port(object):\n <mask token>\n",
"step-3": "class Port(object):\n\n def __init__(self, mac):\n self.mac = mac\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
... | [
0,
1,
2
] |
from . import cli
cli.run()
| normal | {
"blob_id": "235623c3f557dbc28fbff855a618e4d26932ca65",
"index": 7630,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncli.run()\n",
"step-3": "from . import cli\ncli.run()\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
# coding: utf-8
import pandas as pd
import os
import numpy as np
import json as json
import mysql.connector as sqlcnt
import datetime as dt
import requests
from mysql.connector.constants import SQLMode
import os
import glob
import re
import warnings
warnings.filterwarnings("ignore")
from pathlib import Path
# In[... | normal | {
"blob_id": "2060f57cfd910a308d60ad35ebbbf9ffd5678b9c",
"index": 3519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwarnings.filterwarnings('ignore')\n<mask token>\nos.chdir(lib_path)\n<mask token>\nprint(res.summary())\n<mask token>\nX0\n<mask token>\nb\n<mask token>\ncovid_actual.loc[:, 'Date':'human... | [
0,
1,
2,
3,
4
] |
import Adafruit_BBIO.GPIO as GPIO
from pydrs import SerialDRS
import time
import sys
sys.dont_write_bytecode = True
class SyncRecv:
def __init__(self):
self._comport = '/dev/ttyUSB0'
self._baudrate = '115200'
self._epwm_sync_pin = 'GPIO2_23' # Input in BBB perspective
... | normal | {
"blob_id": "c716f43dbe62f662c60653f09be946a27c3fff66",
"index": 8069,
"step-1": "<mask token>\n\n\nclass SyncRecv:\n\n def __init__(self):\n self._comport = '/dev/ttyUSB0'\n self._baudrate = '115200'\n self._epwm_sync_pin = 'GPIO2_23'\n self._sync_in_pin = 'GPIO2_25'\n self... | [
2,
4,
5,
6,
7
] |
from flask import render_template
from database import db
from api import app
from models import create_models
# Create a URL route in application for "/"
@app.route('/')
def home():
return render_template('home.html')
# If in stand alone mode, run the application
if __name__ == '__main__':
db.connect()
c... | normal | {
"blob_id": "5a0a8205977e59ff59a5d334a487cf96eee514d2",
"index": 7211,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\ndef home():\n return render_template('home.html')\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\n@app.route('/')\ndef home():\n return render_template('ho... | [
0,
1,
2,
3,
4
] |
from urllib.request import urlopen
from json import loads
with urlopen('http://api.nbp.pl/api/exchangerates/tables/A/') as site:
data = loads(site.read().decode('utf-8'))
rates = data[0]['rates']
exchange = input('Jaką wartość chcesz wymienić na złotówki? ')
value, code = exchange.split(' ')
val... | normal | {
"blob_id": "3f3d7cdf7732b2a1568cd97574e1443225667327",
"index": 9622,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith urlopen('http://api.nbp.pl/api/exchangerates/tables/A/') as site:\n data = loads(site.read().decode('utf-8'))\n rates = data[0]['rates']\n exchange = input('Jaką wartość chc... | [
0,
1,
2,
3
] |
#
# Copyright (C) 2020 RFI
#
# Author: James Parkhurst
#
# This code is distributed under the GPLv3 license, a copy of
# which is included in the root directory of this package.
#
import logging
import numpy
from maptools.util import read, write
# Get the logger
logger = logging.getLogger(__name__)
def array_rebin(... | normal | {
"blob_id": "18dc01f3e1672407800e53d80a85ffc8d5b86c17",
"index": 7497,
"step-1": "<mask token>\n\n\ndef rebin(*args, **kwargs):\n \"\"\"\n Rebin the map\n\n \"\"\"\n if len(args) > 0 and type(args[0]) == 'str' or 'input_filename' in kwargs:\n func = mapfile_rebin\n else:\n func = arr... | [
1,
3,
4,
5,
6
] |
def divisible_by(numbers, divisor):
res = []
for e in numbers:
if e % divisor == 0:
res.append(e)
return res
| normal | {
"blob_id": "d7ff5bf5d8f397500fcac30b73f469316c908f15",
"index": 5042,
"step-1": "<mask token>\n",
"step-2": "def divisible_by(numbers, divisor):\n res = []\n for e in numbers:\n if e % divisor == 0:\n res.append(e)\n return res\n",
"step-3": null,
"step-4": null,
"step-5": nul... | [
0,
1
] |
from sense_hat import SenseHat
import time
import random
#Set game_mode to True for single roll returning value
#False for demonstration purposes
class ElectronicDie:
def __init__(self, mode):
self.game_mode = mode
sense = SenseHat()
#Colours
O = (0,0,0)
B = (0, 0, 255)
#Settings
#... | normal | {
"blob_id": "edfad88c837ddd3bf7cceeb2f0b1b7a5356c1cf7",
"index": 8998,
"step-1": "<mask token>\n\n\nclass ElectronicDie:\n\n def __init__(self, mode):\n self.game_mode = mode\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n ... | [
4,
5,
6,
7,
8
] |
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