code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
"""Test the init file of Mailgun."""
import hashlib
import hmac
import pytest
from homeassistant import config_entries, data_entry_flow
from homeassistant.components import mailgun, webhook
from homeassistant.config import async_process_ha_core_config
from homeassistant.const import CONF_API_KEY, CONF_DOMAIN
from hom... | normal | {
"blob_id": "a55024f0e5edec22125ce53ef54ee364be185cb8",
"index": 1099,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.fixture\nasync def http_client(hass, hass_client_no_auth):\n \"\"\"Initialize a Home Assistant Server for testing this module.\"\"\"\n await async_setup_component(hass, ... | [
0,
1,
2,
3,
4
] |
import re
list = ["Protein XVZ [Human]","Protein ABC [Mouse]","go UDP[3] glucosamine N-acyltransferase [virus1]","Protein CDY [Chicken [type1]]","Protein BBC [type 2] [Bacteria] [cat] [mat]","gi p19-gag protein [2] [Human T-lymphotropic virus 2]"]
pattern = re.compile("\[(.*?)\]$")
for string in list:
match = re.se... | normal | {
"blob_id": "21c12aabfb21e84f3ea546842fb55c41d2129ff9",
"index": 6526,
"step-1": "import re\nlist = [\"Protein XVZ [Human]\",\"Protein ABC [Mouse]\",\"go UDP[3] glucosamine N-acyltransferase [virus1]\",\"Protein CDY [Chicken [type1]]\",\"Protein BBC [type 2] [Bacteria] [cat] [mat]\",\"gi p19-gag protein [2] [Hum... | [
0
] |
clear ;
clc;
%-----------------------读入图像-------------------------------------%
markbefore=imread('p203.bmp');
markbefore2=rgb2gray(markbefore);
mark=im2bw(markbefore2);
figure(1);
subplot(2,3,1);
imshow(mark),title('水印图像');
[rm,cm]=size(mark);
cover=imread('pic.bmp');
cover1=imresize(cover,[512... | normal | {
"blob_id": "56d3e59e3e077b1febb834668aba44ce8dba13ae",
"index": 635,
"step-1": "clear ;\nclc;\n \n%-----------------------读入图像-------------------------------------%\nmarkbefore=imread('p203.bmp');\nmarkbefore2=rgb2gray(markbefore);\nmark=im2bw(markbefore2); \nfigure(1); \nsubplot(2,3,1); \nimshow... | [
0
] |
#!/usr/bin/env python3
# given a set A and n other sets.
# find whether set A is a strict superset of each of the n sets
# print True if yes, otherwise False
A = set(map(int, input().split()))
b = []
for _ in range(int(input())):
b.append(A > set(map(int, input().split())))
print(all(b))
| normal | {
"blob_id": "a9eb2b3f26396918c792de3f126e51bde334b709",
"index": 7777,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(int(input())):\n b.append(A > set(map(int, input().split())))\nprint(all(b))\n",
"step-3": "A = set(map(int, input().split()))\nb = []\nfor _ in range(int(input())):\n... | [
0,
1,
2,
3
] |
"""
module : watcher.py
description : Script to automatically watch a directory (via watchdog) for tests and run them via py.test
"""
import sys
import os.path
import subprocess
import time
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
class SpecificationsEventHandler(Fil... | normal | {
"blob_id": "95ea8a21d3ac44c7760179bc4ebf67f0c16e6a19",
"index": 2421,
"step-1": "<mask token>\n\n\nclass SpecificationsEventHandler(FileSystemEventHandler):\n <mask token>\n\n def __init__(self):\n self.paused = False\n self.banner = (\n '==========================================... | [
3,
4,
5,
6,
7
] |
import random
from typing import List
from faker import Faker
from call_center.src.actors.agent import InsuranceAgent
from call_center.src.actors.consumer import Consumer
from call_center.src.common.person import (
AGE,
AVAILABLE,
INSURANCE_OPERATION,
PHONE_NUMBER,
INCOME,
CARS_COUNT,
KID... | normal | {
"blob_id": "db31a69c57f773a79e5eaa8b3443b0366fd74861",
"index": 8565,
"step-1": "<mask token>\n\n\nclass ActorsCreator(metaclass=SingletonMeta):\n <mask token>\n\n def __init__(self):\n self.consumers = ActorsCreator.create_consumers()\n self.agents = ActorsCreator.create_agents()\n\n def... | [
6,
7,
8,
9,
10
] |
from django.shortcuts import render, HttpResponseRedirect, HttpResponse
from django.views.generic import View
from django.contrib.auth import login
from django.contrib.auth.models import User
class RegisterView(View):
def get(self, request):
return render(request, 'users/register.html', locals())
def... | normal | {
"blob_id": "c9191df0fc04818b4df9c93a9479f75a60688aa9",
"index": 6372,
"step-1": "<mask token>\n\n\nclass RegisterView(View):\n <mask token>\n <mask token>\n\n\nclass HomeView(View):\n\n def get(self, request):\n return HttpResponse(f'Home Page | Logged in as - {request.user}')\n",
"step-2": "<... | [
3,
4,
5,
6,
7
] |
from django.urls import path
from photo.api.views import api_photo_detail_view, api_photos_view
urlpatterns = [path('<int:id>', api_photo_detail_view, name='user_detail'),
path('', api_photos_view, name='users')]
| normal | {
"blob_id": "ab4145ccc0b360dcca9b9aa6ebe919bdddac65a2",
"index": 3962,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('<int:id>', api_photo_detail_view, name='user_detail'),\n path('', api_photos_view, name='users')]\n",
"step-3": "from django.urls import path\nfrom photo.api.vie... | [
0,
1,
2
] |
from collections import defaultdict, Counter
import numpy as np
import sys
import re
def parseFile(file, frequency_tree):
readnumber = re.compile('[r]+\d+')
line_spliter = re.compile('\t+')
colon_spliter = re.compile(':')
forward_reads = 0
reverse_reads = 0
unmatched_reads = 0
read_position... | normal | {
"blob_id": "227b71cb6d4cde8f498ad19c1c5f95f7fc572752",
"index": 6995,
"step-1": "<mask token>\n\n\ndef getChromosome(str):\n if str == '*' or str[3:] == 'X':\n return -1\n try:\n return int(str[3:])\n except:\n return -1\n",
"step-2": "<mask token>\n\n\ndef parseFile(file, freque... | [
1,
2,
3,
4,
5
] |
#! /usr/bin/env python
import smtpsend
S = smtpsend.Smtpsent(SUBJECT='Test')
S.sendemail('''
this is a test!
''')
| normal | {
"blob_id": "7754974e79202b2df4ab9a7f69948483042a67cc",
"index": 855,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nS.sendemail(\"\"\"\nthis is a test!\n\"\"\")\n",
"step-3": "<mask token>\nS = smtpsend.Smtpsent(SUBJECT='Test')\nS.sendemail(\"\"\"\nthis is a test!\n\"\"\")\n",
"step-4": "import smtp... | [
0,
1,
2,
3,
4
] |
import json
import glob
import argparse
from model.NewModel import runModel
from collections import namedtuple
import csv
OutputFile = "./HealthSimOutputSheet.csv"
parser = argparse.ArgumentParser(description='Select policy file')
parser.add_argument('-p', type=str, default='default', help='name of a a policy file')
... | normal | {
"blob_id": "894ce07c6443208483be2d3ef1409f12f24d99f3",
"index": 2852,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('-p', type=str, default='default', help=\n 'name of a a policy file')\nparser.add_argument('-n', type=int, default=100000, help='number of patients')\n<mask token>\... | [
0,
1,
2,
3,
4
] |
# Generated by Django 2.1.2 on 2018-10-25 09:36
import django.contrib.auth.models
import django.contrib.auth.validators
from django.db import migrations, models
import django.utils.timezone
import uuid
class Migration(migrations.Migration):
dependencies = [
('grafit', '0002_article'),
]
operati... | normal | {
"blob_id": "8b0eed6d1f24b5dd30726ce08c97354a5d5ab69b",
"index": 7597,
"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 = [('grafit', '0... | [
0,
1,
2,
3,
4
] |
INPUT_MINBIAS = '/build/RAWReference/MinBias_RAW_320_STARTUP.root'
INPUT_TTBAR = '/build/RAWReference/TTbar_RAW_320_STARTUP.root'
puSTARTUP_TTBAR = '/build/RAWReference/TTbar_Tauola_PileUp_RAW_320_STARTUP.root'
relval = {
'step1': { 'step': 'GEN-HLT',
'timesize': (100, ['MinBias','TTbar']),
'igprof': (50... | normal | {
"blob_id": "78c9f92349ba834bc64dc84f884638c4316a9ea4",
"index": 352,
"step-1": "<mask token>\n",
"step-2": "INPUT_MINBIAS = '/build/RAWReference/MinBias_RAW_320_STARTUP.root'\nINPUT_TTBAR = '/build/RAWReference/TTbar_RAW_320_STARTUP.root'\npuSTARTUP_TTBAR = (\n '/build/RAWReference/TTbar_Tauola_PileUp_RAW_... | [
0,
1,
2
] |
import requests
from bs4 import BeautifulSoup
import time
print("Put some unfamiliar skills")
unfamilar_skills = input(">")
print(f"Filtering result for {unfamilar_skills}...\n")
def find_jobs():
html_text = requests.get('https://www.timesjobs.com/candidate/job-search.html?searchType=personalizedSearch&from=submit... | normal | {
"blob_id": "92b71c67130cd37b2143fbd9ad71fe9a18b3f7e8",
"index": 2622,
"step-1": "<mask token>\n\n\ndef find_jobs():\n html_text = requests.get(\n 'https://www.timesjobs.com/candidate/job-search.html?searchType=personalizedSearch&from=submit&txtKeywords=python&txtLocation='\n ).text\n soup = ... | [
1,
2,
3,
4,
5
] |
from django import forms
from acl.models import Alert
class CreateAlertForm(forms.ModelForm):
class Meta:
model = Alert
exclude = ['role', 'age_analysis', 'Date_Uploaded', 'alias_name',
'CAMT_Reveiewer', 'Date_Regularised', 'alert_message', 'Count2']
| normal | {
"blob_id": "bfcf6e241881c4f668f926e087ab0f7dcad61dee",
"index": 5260,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass CreateAlertForm(forms.ModelForm):\n\n\n class Meta:\n model = Alert\n exclude = ['role', 'age_analysis', 'Date_Uploaded', 'alias_name',\n 'CAMT_Revei... | [
0,
1,
2
] |
# These are instance types to make available to all AWS EC2 systems, except the .
# PostgreSQL server, until the auto tuning playbook can tune for systems that
# small.
AWSGlobalInstanceChoices = [
't2.nano', 't2.micro',
't3.nano', 't3.micro',
't3a.nano', 't3a.micro',
]
class SpecValidator:
... | normal | {
"blob_id": "4db93bdab2d73e7226dcad61827f5faea8513767",
"index": 9888,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass SpecValidator:\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass SpecValidator:\n\n def __init__(self, type=None, default=None, choices=[], min=Non... | [
0,
1,
2,
3,
4
] |
"""
챕터: day4
주제: 반복문(for문)
문제: 1에서 100까지 합을 구하여 출력하시오.
작성자: 한현수
작성일: 2018.9.20.
"""
result = 0
for i in range(101):
result += i
print(result) | normal | {
"blob_id": "d2754099adebdb4bd2b028fdf9015571ad773754",
"index": 9313,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(101):\n result += i\nprint(result)\n",
"step-3": "<mask token>\nresult = 0\nfor i in range(101):\n result += i\nprint(result)\n",
"step-4": "\"\"\"\n챕터: day4\n주제:... | [
0,
1,
2,
3
] |
from rest_framework import serializers
from django.contrib.auth import password_validation
from rest_framework.validators import UniqueValidator
from .models import CustomUser, Role, Permission, ActionEntity
from .utils import create_permission
class ActionEntitySerializer(serializers.ModelSerializer):
id = ser... | normal | {
"blob_id": "b10a50ce649650542d176a2f6fb8c35c500fbc38",
"index": 3644,
"step-1": "<mask token>\n\n\nclass UserCreateSerializer(serializers.ModelSerializer):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n model = CustomUser\n fields =... | [
7,
17,
18,
23,
26
] |
from django.urls import path
from .views.home import Home
from .views.signup import Signup
from .views.login import Login
urlpatterns = [
path('', Home.as_view(), name='home'),
path('signup', Signup.as_view(), name='signup'),
path('login', Login.as_view(), name='login'),
]
| normal | {
"blob_id": "979a387e29867818ffad7291511ff0be40dee118",
"index": 1938,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', Home.as_view(), name='home'), path('signup', Signup\n .as_view(), name='signup'), path('login', Login.as_view(), name='login')]\n",
"step-3": "from django.url... | [
0,
1,
2,
3
] |
import time
import json
import pygame
from pygame.locals import *
import urllib.request
from pygame.color import THECOLORS
pygame.init()
Brack=[0,0,0]
White=[255,255,255]
Green=[0,255,0]
Red=[255,0,0]
Gray=[169,169,169]
button_text=["开 始","开 始","开 始","开 始","开 始"]
line=['http://localhost:5050/mixer/000','http://localhos... | normal | {
"blob_id": "609071fc3af1b526fbd4555ced2376f56ae0f3c3",
"index": 2174,
"step-1": "<mask token>\n\n\ndef Process(num, x, y, button_text, color):\n text_fmt1 = text_1.render(text[num], 1, Brack)\n screen.blit(text_fmt1, (x - 127, y))\n pygame.draw.rect(screen, Brack, [x, y, 60, 25], 2)\n pygame.draw.re... | [
2,
3,
4,
5,
6
] |
import numpy as np
import matplotlib.pyplot as plt
def cos_Taylor2(x, n):
s = 0
a = 1
for i in range(0, n+1):
s = s+a
a = -a*x**2 / ((2*i+1)*(2*i+2))
return s, abs(a)
vcos = np.vectorize(cos_Taylor2)
def cos_two_terms(x):
s = 0
a = 1
s = s+a
a = -a*x**2 / ((2*0+1)*(2*... | normal | {
"blob_id": "fb0dcb641dfb379751264dc0b18007f5d058d379",
"index": 3520,
"step-1": "<mask token>\n\n\ndef cos_two_terms(x):\n s = 0\n a = 1\n s = s + a\n a = -a * x ** 2 / ((2 * 0 + 1) * (2 * 0 + 2))\n s = s + a\n a = -a * x ** 2 / ((2 * 1 + 1) * (2 * 1 + 2))\n s = s + a\n a = -a * x ** 2 /... | [
2,
4,
5,
6,
7
] |
class Node:
def __init__(self, data):
self.data = data
self.prev = None
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def insertAtHead(self, newNode, curNode):
newNode.next = curNode
if curNode is not None: curNode.prev = newNode
... | normal | {
"blob_id": "a3cbdecbbfc49e8ac045f4aabbea6b9f54ed3d5f",
"index": 4904,
"step-1": "<mask token>\n\n\nclass LinkedList:\n\n def __init__(self):\n self.head = None\n\n def insertAtHead(self, newNode, curNode):\n newNode.next = curNode\n if curNode is not None:\n curNode.prev = ... | [
5,
7,
8,
9,
11
] |
/Users/andreilyskov/anaconda/lib/python3.5/sre_compile.py | normal | {
"blob_id": "faf4f4d26236ac555594ef6913a0d43c3516f1f2",
"index": 2063,
"step-1": "/Users/andreilyskov/anaconda/lib/python3.5/sre_compile.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from two_layer_net import TwoLayerNet
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label = True)
train_loss_list = []
#hiper param
iters_num = 1000
train_size = x_train.shape[0]
batch_size =... | normal | {
"blob_id": "dbe3aa107de8e62822803d1740773a4b22f41edf",
"index": 971,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.pardir)\n<mask token>\nfor i in range(iters_num):\n print(i)\n batch_mask = np.random.choice(train_size, batch_size)\n x_batch = x_train[batch_mask]\n t_batc... | [
0,
1,
2,
3,
4
] |
import sys
sys.stdin = open('magnet.txt', 'r')
from collections import deque
def check(t, d, c):
if t == 1:
if m1[2] != m2[-2] and not c:
check(t + 1, d * (-1), 1)
if d == 1:
m1.appendleft(m1.pop())
else:
m1.append(m1.popleft())
elif t == 4:
... | normal | {
"blob_id": "7e3a5e1f19683b1716f3c988dcc1e65fee1cae13",
"index": 8956,
"step-1": "<mask token>\n\n\ndef check(t, d, c):\n if t == 1:\n if m1[2] != m2[-2] and not c:\n check(t + 1, d * -1, 1)\n if d == 1:\n m1.appendleft(m1.pop())\n else:\n m1.append(m1.pop... | [
1,
2,
3,
4,
5
] |
import boto3
import pprint
import yaml
#initialize empty dictionary to store values
new_dict = {}
count = 0
new_dict2 = {}
# dev = boto3.session.Session(profile_name='shipt')
mybatch = boto3.client('batch')
#load config properties
with open('config.yml') as f:
content = yaml.load(f)
# pprint.pprint(content) #to... | normal | {
"blob_id": "3ba9ff00b0d6a2006c714a9818c8b561d884e252",
"index": 2302,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('config.yml') as f:\n content = yaml.load(f)\n<mask token>\nfor k, v in response.items():\n if k == 'jobDefinitions':\n new_dict = v[0]['containerProperties']\nprin... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('levantamiento', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='FichaTecnica',
field... | normal | {
"blob_id": "1049a7d2cdc54c489af6246ec014deb63a98f96d",
"index": 3951,
"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 = [('levantamien... | [
0,
1,
2,
3,
4
] |
# Developed by Lorenzo Mambretti, Justin Wang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://github.com/jtwwang/hanabi/blob/master/LICENSE
#
# Unless required by applic... | normal | {
"blob_id": "bbd5eb1f80843efdd2709aa19a65bf325a88f473",
"index": 8856,
"step-1": "<mask token>\n\n\ndef model_crossover(weights1, weights2):\n new_weights = []\n assert len(weights1) == len(weights2)\n if random.uniform(0, 1) > 0.3:\n print('crossover')\n for layer in range(len(weights1)):... | [
3,
4,
5,
6,
7
] |
import sqlite3
import pandas as pd
#%matplotlib inline
import matplotlib.pyplot as plt
db_filename = 'readonly/dinofunworld.db'
conn = sqlite3.connect(db_filename)
c = conn.cursor()
c.execute("SELECT a.Name, count(c.visitorID) \
FROM attraction as a, checkin c \
WHERE \
a.AttractionID = c.attraction \
AND a.Category l... | normal | {
"blob_id": "c19c3f580d7555379bd7e077b0264a3784179e93",
"index": 696,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nc.execute(\n \"SELECT a.Name, count(c.visitorID) FROM attraction as a, checkin c WHERE a.AttractionID = c.attraction AND a.Category like 'Thrill Rides%' GROUP BY a.AttractionID \"\n ... | [
0,
1,
2,
3,
4
] |
import random
consonants = [
'b', 'c', 'd', 'f', 'g',
'h', 'j', 'k', 'l', 'm',
'n', 'p', 'q', 'r', 's',
't', 'v', 'w', 'x', 'y',
'z'
]
vowels = [
'a', 'e',' i', 'o', 'u'
]
def make_word(user_input):
word = ""
for letter in user_input:
letter = letter.lower()
if letter... | normal | {
"blob_id": "a4f4137b9310ebc68515b9cae841051eda1f0360",
"index": 3522,
"step-1": "<mask token>\n\n\ndef make_word(user_input):\n word = ''\n for letter in user_input:\n letter = letter.lower()\n if letter == 'c':\n word += random.choice(consonants)\n elif letter == 'v':\n ... | [
1,
2,
4,
5,
6
] |
class Solution:
# @param num, a list of integer
# @return an integer
def longestConsecutive(self, num):
sted = {}
n = len(num)
for item in num:
if item in sted:
continue
sted[item] = item
if item-1 in sted:
sted[item... | normal | {
"blob_id": "d7c4bee7245dab1cbb90ee68b8e99994ce7dd219",
"index": 3295,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def longestConsecutive(self, num):\n sted = {}\n n = len(num)\n for item in num:\n if item in s... | [
0,
1,
2,
3
] |
from classNinapro import Ninapro
import numpy as np
import tensorflow as tf
print(tf.__version__)
Debug = True # for tensor dimensionality checking
ninapro = Ninapro()
ninapro.splitImagesLabels()
# Train
print('ninapro.TrainImages shape: ', ninapro.TrainImages.shape) # m x 16 x 30
print('ninapro.TrainLabels shape... | normal | {
"blob_id": "30aa8405ccf64ce8a05204f3f9fa2ffab436ad3b",
"index": 1578,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(tf.__version__)\n<mask token>\nninapro.splitImagesLabels()\nprint('ninapro.TrainImages shape: ', ninapro.TrainImages.shape)\nprint('ninapro.TrainLabels shape: ', ninapro.TrainLabels... | [
0,
1,
2,
3,
4
] |
#connect4_JayNa.py
#Jay Na
#CS111 Spring 2018
#This file creates a version of the game Connect4, where the user plays against an AI
from graphics import *
import random
class ConnectWindow:
def __init__(self):
self.window = GraphWin("Connect Four", 690, 590)
self.window.setMouseHandler(self.handleClick)
self.... | normal | {
"blob_id": "abbad57e945d2195021948a0e0838c6bfd9c6a1e",
"index": 769,
"step-1": "<mask token>\n\n\nclass ConnectWindow:\n <mask token>\n\n def startScreen(self):\n \"\"\"This function creates the board and intializes the board count for each column\"\"\"\n self.background = Rectangle(Point(0,... | [
2,
8,
10,
11,
16
] |
import json
import requests
import config
class RequestAnnotation:
def schedule(self,
command: str,
**kwargs):
response = requests.post(url=f"http://localhost:{config.annotation_port}/{command}",
json=kwargs)
# not 'text' for annotating, but... | normal | {
"blob_id": "6782761bcbf53ea5076b6dfb7de66d0e68a9f45d",
"index": 3123,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass RequestAnnotation:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass RequestAnnotation:\n\n def schedule(self, command: str, **kwargs):\n response = requests.... | [
0,
1,
2,
3,
4
] |
#
# Wrappers for model evaluation
#
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from modules import Classifier
from typing import Generator, NamedTuple, Optional, Union
from utils import expand_generator
class Evaluator(object):
class Result(NamedTuple):
... | normal | {
"blob_id": "493dbf85069f2115896a5f5f5d593c8d95b85cff",
"index": 4594,
"step-1": "<mask token>\n\n\nclass ModelEvaluator(Evaluator):\n\n def __init__(self, dataset: Dataset, batch_size: int, num_workers: int,\n mixed_precision: bool=True):\n self.dataset = dataset\n self.mixed_precision =... | [
5,
6,
7,
8,
9
] |
import csv
import os
with open("sample.csv") as rf:
csv_reader=csv.DictReader(rf)
with open("sample1.csv","w") as wf:
csv_headers=['fname','lname','email']
if os.path.isfile('sample1.csv'):
q=input("File already exists. Do you want to overwrite?")
if q.lower()=='yes':... | normal | {
"blob_id": "43196258b61801799b8d6b7d23f5816d84cb5dff",
"index": 7294,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('sample.csv') as rf:\n csv_reader = csv.DictReader(rf)\n with open('sample1.csv', 'w') as wf:\n csv_headers = ['fname', 'lname', 'email']\n if os.path.isfile... | [
0,
1,
2,
3
] |
from datetime import datetime
from random import seed
from pandas import date_range, DataFrame
import matplotlib.pyplot as plt
from matplotlib import style
from numpy import asarray
import strategy_learner as sl
from util import get_data
style.use('ggplot')
seed(0)
def run_algo(sym, investment, start_date, end_date... | normal | {
"blob_id": "c0f9a1c39ff5d7cc99a16cf00cddcc14705937ba",
"index": 3917,
"step-1": "<mask token>\n\n\ndef run_algo(sym, investment, start_date, end_date, bench_sym):\n learner = sl.StrategyLearner(bench_sym=bench_sym, verbose=verbose)\n learner.add_evidence(symbol=sym, start_date=start_date, end_date=\n ... | [
1,
2,
3,
4,
5
] |
class Solution:
def evalRPN(self, tokens: List[str]) -> int:
def operation(op1,op2,op):
if op == "+":
return op1 + op2
if op == "-":
return op1 - op2
if op == "*":
return op1 * op2
if op == "/":
... | normal | {
"blob_id": "6b597f1570c022d17e4476e2ab8817e724a166a7",
"index": 1096,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def evalRPN(self, tokens: List[str]) ->int:\n\n def operation(op1, op2, op):\n if op == '+':\n ... | [
0,
1,
2,
3
] |
from django.db import models
class crontab(models.Model):
name = models.CharField(max_length=20)
class converter(models.Model):
name = models.CharField(max_length=20)
class MainTable(models.Model):
rank = models.IntegerField(null=True)
coinid = models.CharField(max_length=30,null=True)
symbol = ... | normal | {
"blob_id": "0054921928838d9aee63cf58f50a0a01ee12635d",
"index": 6049,
"step-1": "<mask token>\n\n\nclass Table(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Price(models.Model):\n price = models.FloatField(null=True)\n\n\nclass Marketdata(... | [
5,
6,
10,
12,
14
] |
#TODO: allow workers to pull this from cache
RABBITMQ_IP = '172.23.105.82'
OBJECT_CACHE_IP = "172.23.105.69"
OBJECT_CACHE_PORT = "11911"
SERIESLY_IP = ''
COUCHBASE_IP = '172.23.105.54'
COUCHBASE_PORT = '8091'
COUCHBASE_USER = "Administrator"
COUCHBASE_PWD = "password"
SSH_USER = "root"
SSH_PASSWORD = "password"
WORKER... | normal | {
"blob_id": "e70ebd9bb9cd7027772ec117cb91349afba7ab10",
"index": 6390,
"step-1": "<mask token>\n",
"step-2": "RABBITMQ_IP = '172.23.105.82'\nOBJECT_CACHE_IP = '172.23.105.69'\nOBJECT_CACHE_PORT = '11911'\nSERIESLY_IP = ''\nCOUCHBASE_IP = '172.23.105.54'\nCOUCHBASE_PORT = '8091'\nCOUCHBASE_USER = 'Administrator... | [
0,
1,
2
] |
#This is a file from CS50 Finance
from functools import wraps
from flask import redirect, render_template, session
from threading import Thread
from flask_mail import Message
from application import app, mail
ALLOWED_EXTENSIONS = {"png", "PNG", "jpg", "jpeg", "JPG", "JPEG"}
def login_required(f):
"""
Decorat... | normal | {
"blob_id": "1a4da621add157fa6d1f578370d64594b102eeb5",
"index": 4245,
"step-1": "<mask token>\n\n\ndef login_required(f):\n \"\"\"\n Decorate routes to require login.\n\n http://flask.pocoo.org/docs/1.0/patterns/viewdecorators/\n \"\"\"\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\... | [
3,
4,
5,
6,
7
] |
"""
Created on Feb 10, 2013
@author: jens
Deprecated module for crystallogrphy related geometry operations. And a lot
of other stuff that I put here.
"""
import numpy as np
atomtable = {'H': 1, 'He': 2, 'Li': 3, 'Be': 4, 'B': 5, 'C': 6, 'N': 7, 'O': 8,
'F': 9, 'Ne': 10, 'Na': 11, 'Mg': 12, 'Al': 13, '... | normal | {
"blob_id": "27e685750e5caa2f80c5a6399b07435ee9aa9fb9",
"index": 7936,
"step-1": "<mask token>\n\n\ndef xd_element(name):\n \"\"\"\n Return the element of an atom as defined in it's label.\n \"\"\"\n try:\n name = name[:2]\n except:\n pass\n try:\n covalence_radius[name]\n ... | [
23,
33,
40,
50,
51
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('auth', '0001_initial'),
('c4c_app', '0006_c4cjob_complete'),
]
operations = [
migrations.AlterModelOptions(
... | normal | {
"blob_id": "30986eb0a6cd82f837dd14fb383529a6a41def9a",
"index": 8338,
"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 = [('auth', '000... | [
0,
1,
2,
3,
4
] |
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.classify import NaiveBayesClassifier
from nltk.probability import FreqDist
import csv
f = open('trolls.csv', 'r')
file = csv.reader(f)
sentences=[]
remarks=[]
psObject = PorterStemmer(... | normal | {
"blob_id": "0dbdd7f7adffed850f126a2054c764b421c6ab84",
"index": 6799,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor kk in file:\n paragraph += kk[0]\nf.close()\n<mask token>\nprint('most commons below...')\nprint(most_common_words)\n<mask token>\nfor i, j in most_common_words:\n most_cm_1.app... | [
0,
1,
2,
3,
4
] |
from flask import Flask, request, render_template, redirect
import os
import smtplib
from email.message import EmailMessage
app = Flask(__name__)
EMAIL_ADDRESS = os.environ.get('EMAIL_USER')
EMAIL_PASSWORD = os.environ.get('EMAIL_PASS')
@app.route('/')
def index():
return render_template('index.html')
@app.rout... | normal | {
"blob_id": "27d9e6a868cfc18780ec9615e8dbc3b5ea2fd0c3",
"index": 1399,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n\n@app.route('/submit', methods=['POST'])\ndef submit():\n if request.method == 'POST':\n name = request.form['name']\n e... | [
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from machina.apps.forum_conversation.abstract_models import AbstractPost
from machina.apps.forum_conversation.abstract_models import AbstractTopic
from machina.core.db.models import model_factory
from django.dispatch import receiver
from django.db.models... | normal | {
"blob_id": "1e81e0f3cb2fb25fdef08a913aa1ff77d0c2a562",
"index": 9204,
"step-1": "<mask token>\n\n\nclass UserNotification(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n notification_content = models.CharField(max_length=100)\n notification_link = models.CharField(max_length... | [
10,
11,
12,
13,
14
] |
import numpy as np
import time
import uuid
from datetime import datetime
log_host = "agent1"
class State:
def __init__(self, path, iterations):
self.path = path
self.iterations = iterations
def run(self):
assert 0, "run not implemented"
class BruteForceAttackState(State):
def ... | normal | {
"blob_id": "cf3b4e2c76091f95d24e8a987a63ece46503d6e8",
"index": 3459,
"step-1": "<mask token>\n\n\nclass BruteForceAttackState(State):\n\n def run(self):\n os_val = np.random.choice(['Windows7', 'Windows10', 'Ubuntu16',\n 'MacOS10'])\n addr_val = np.random.choice(['127.0.0.6', '127.0... | [
4,
7,
8,
9,
10
] |
def formula(a,b):
if(b == 0):
print "You can not divide by zero"
else:
return (a+b)/b
print formula(4,4)
print formula(2,0)
| normal | {
"blob_id": "dffd575b9d5b763abdbce6f88586c183b71086c4",
"index": 7701,
"step-1": "def formula(a,b):\n if(b == 0):\n print \"You can not divide by zero\"\n else:\n return (a+b)/b \n\n\nprint formula(4,4)\nprint formula(2,0)\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": ... | [
0
] |
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
from numpy.testing import assert_array_almost_equal
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import decomposition
from sklearn import datasets
def transform(x):
if x == 'Kama':
return 0
elif x =... | normal | {
"blob_id": "ef04e808a2a0e6570b28ef06784322e0b2ca1f8f",
"index": 4774,
"step-1": "<mask token>\n\n\ndef transform(x):\n if x == 'Kama':\n return 0\n elif x == 'Rosa':\n return 1\n else:\n return 2\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef transform(x):\n if x == 'K... | [
1,
2,
3,
4,
5
] |
from django.test import TestCase, Client
from pdf_crawler.models import Document
from rest_framework.reverse import reverse
class TestCase(TestCase):
client = Client()
def setUp(self):
Document.objects.create(name='First').save()
def test_endpoints(self):
"""
test for endpoints
... | normal | {
"blob_id": "0d28ab54f08301d9788ca9a5e46d522e043e9507",
"index": 4474,
"step-1": "<mask token>\n\n\nclass TestCase(TestCase):\n <mask token>\n\n def setUp(self):\n Document.objects.create(name='First').save()\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestCase(TestCase):\n <mask t... | [
2,
3,
4,
5
] |
# Ejercicio 28 - Hoja VI (5) - Indicar la nota ponderada según el criterio dado
# (parte teórica 60%, práctica 40%) de cada uno de un número determinado de alumnos
numalumnos=int(input("Introduce el número total de alumnos:\n"))
print("Usa el punto '.' para los decimales")
for contador in range(1,numalumnos+1):
... | normal | {
"blob_id": "f2056ff46ce6e38c3b6ca553bbdec7f59d60b198",
"index": 1417,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\"Usa el punto '.' para los decimales\")\nfor contador in range(1, numalumnos + 1):\n print(f'\\nDatos del alumno número {contador} de {numalumnos}:')\n teorica = float(input(... | [
0,
1,
2,
3
] |
version https://git-lfs.github.com/spec/v1
oid sha256:7f0b7267333e6a4a73d3df0ee7f384f7b3cb6ffb14ed2dc8a5894b853bac8957
size 1323
| normal | {
"blob_id": "f1972baee8b399c9a52561c8f015f71cb9922bb0",
"index": 4875,
"step-1": "version https://git-lfs.github.com/spec/v1\noid sha256:7f0b7267333e6a4a73d3df0ee7f384f7b3cb6ffb14ed2dc8a5894b853bac8957\nsize 1323\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
... | [
0
] |
# Copyright 2021 Google LLC. 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 required by applicable law or a... | normal | {
"blob_id": "30df17d636c33d2824aad7d7ef6aae7db83615ec",
"index": 8058,
"step-1": "<mask token>\n\n\nclass LatestBlessedModelStrategy(resolver.ResolverStrategy):\n <mask token>\n\n def _resolve(self, input_dict: Dict[str, List[types.Artifact]],\n model_channel_key: str, model_blessing_channel_key: st... | [
3,
4,
5,
6,
7
] |
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sea
import sklearn
import glob
import pydub
from pydub import AudioSegment
import time
import librosa
import noisereduce as nr
from scipy.io import wavfile
import IPython
import sounddevice as sd
from pysndfx ... | normal | {
"blob_id": "14bf4befdce4270b4514b4e643964182f9c49ff4",
"index": 8434,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(IPython.display.Audio(data=my, rate=sr))\nsd.play(my, sr)\n<mask token>\n",
"step-3": "<mask token>\nmy, sr = librosa.load(\n 'C:\\\\Users\\\\pranj\\\\Downloads\\\\IEMOCAP_full... | [
0,
1,
2,
3,
4
] |
import logging
def log_func(handler):
if handler.get_status() < 400:
log_method = logging.info
elif handler.get_status() < 500:
log_method = logging.warning
else:
log_method = logging.error
request_time = 1000.0 * handler.request.request_time()
log_method("%d %s %s (%s) %s ... | normal | {
"blob_id": "e403be68894ba283d71a0b71bb0bfd0adfab8c41",
"index": 8684,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef log_func(handler):\n if handler.get_status() < 400:\n log_method = logging.info\n elif handler.get_status() < 500:\n log_method = logging.warning\n else:\n ... | [
0,
1,
2,
3,
4
] |
year = int(input('西暦＞'))
if year % 4 == 0 and year % 100 != 0:
print('閏年')
pass
elif year % 400 == 0:
print('閏年')
pass
else:
print('平年')
pass
| normal | {
"blob_id": "b381d1110e6a7570cd872d689a43aba2d2580a23",
"index": 8449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif year % 4 == 0 and year % 100 != 0:\n print('閏年')\n pass\nelif year % 400 == 0:\n print('閏年')\n pass\nelse:\n print('平年')\n pass\n",
"step-3": "year = int(input('西暦＞... | [
0,
1,
2
] |
import logging
from django.contrib.auth import get_user_model
from django.db import models
from rest_framework import serializers
from rest_framework.test import APITestCase
from ..autodocs.docs import ApiDocumentation
from .utils import Deferred
log = logging.getLogger(__name__)
def get_serializer(endpoint, met... | normal | {
"blob_id": "04822e735c9c27f0e0fcc9727bcc38d2da84dee6",
"index": 7831,
"step-1": "<mask token>\n\n\nclass AutoTestCase(APITestCase):\n <mask token>\n\n @classmethod\n def setUpClass(cls):\n \"\"\"\n Создание пользователя для всех тестов, который цепляется через `settings.AUTH_USER_PK`\n\n ... | [
6,
11,
12,
14,
16
] |
# -*- coding: utf-8 -*-
import unittest
import torch
from pythainlp.transliterate import romanize, transliterate, pronunciate, puan
from pythainlp.transliterate.ipa import trans_list, xsampa_list
from pythainlp.transliterate.thai2rom import ThaiTransliterator
from pythainlp.corpus import remove
_BASIC_TESTS = {
... | normal | {
"blob_id": "486cfc4bb4b46d78715b11cba44656e8ba077c9b",
"index": 2551,
"step-1": "<mask token>\n\n\nclass TestTransliteratePackage(unittest.TestCase):\n <mask token>\n\n def test_romanize_royin_basic(self):\n for word in _BASIC_TESTS:\n expect = _BASIC_TESTS[word]\n self.assert... | [
6,
8,
10,
11,
12
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2017/6/20 下午4:00
# @Author : Huang HUi
# @Site :
# @File : query_parse.py
# @Software: PyCharm
from mysqlConnection import mysqlConnection
import yaml
import copy
import time
import csv
import json
from collections import OrderedDict
import ast
#
# GIV... | normal | {
"blob_id": "b52807a15cef8f07f685f8761a470d4a24d9c3dc",
"index": 6603,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef query_parse(GIVEN_QUERY):\n try:\n countryIds_query = list(map(lambda x: x['country_id'], GIVEN_QUERY[\n 'countries']))\n except:\n countryIds_query... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
import urllib
class LicenseChecker( object ):
def __init__( self ):
self.url = 'http://logon.guidoaccardo.com.ar/'
self.count_offline = 15
def __countTimes( self ):
ff = open( 'times.ehead', 'r' )
bb = ff.read()
ff.close()
return int( bb )
... | normal | {
"blob_id": "c70aa1a373530ac73553753e62d3989f5bc79287",
"index": 687,
"step-1": "<mask token>\n\n\nclass LicenseChecker(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass LicenseChecker(object):\n <mask token>\n <mask token>\n\n def ... | [
1,
3,
5,
6,
7
] |
from dbmanager import DbManager
from message import Message
def list_returner(f):
def wrapper(*args, **kwargs):
result = f(*args, **kwargs)
if result:
return result
else:
return [dict()]
return wrapper
class Messenger:
def __init__(self, messages_count=2... | normal | {
"blob_id": "4d1ea6522a01603f0159a1f27da70b65c4f387cb",
"index": 7093,
"step-1": "<mask token>\n\n\nclass Messenger:\n <mask token>\n\n def add_message(self, message):\n self.message_manager.add(message)\n\n @list_returner\n def get_room_messages(self):\n messages = []\n i = 6\n ... | [
5,
7,
8,
9
] |
import unittest
from game_of_life.board import Board
from game_of_life.cell import Cell, ALIVE, DEAD
def create_test_board(size):
board = Board(size)
board[0, 0].state = ALIVE
board[0, 1].state = ALIVE
board[2, 1].state = ALIVE
return board
class BoardTests(unittest.TestCase):
def test_get_n... | normal | {
"blob_id": "f644ff322d1268092dbdcbfc1a3c76006424184b",
"index": 1482,
"step-1": "<mask token>\n\n\nclass BoardTests(unittest.TestCase):\n\n def test_get_neighbours(self):\n board = create_test_board(3)\n self.assertListEqual(board.get_neighbour_states(1, 0), [None, None,\n ALIVE, ALI... | [
10,
11,
14,
15,
16
] |
#Kivy + Box2d test
#Not working...
from Box2D import *
from random import random
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.properties import NumericProperty, ObjectProperty
from kivy.lang import Builder
from kivy.clock import Clock
Builder.load_string('''
<PongBall>:
canvas:
C... | normal | {
"blob_id": "fa8431ae96cd6c1133d56285d0168f43d9068bc5",
"index": 2099,
"step-1": "<mask token>\n\n\nclass PongBall(Widget):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, **kwargs):\n super(PongBall, self).__init__(**kwargs)\n sel... | [
8,
12,
13,
15,
16
] |
import sys
lines = sys.stdin.readlines()
t = int(lines[0])
for i in range(t):
c = i*10+1
n = int(lines[c]) - 1
first = [x.strip() for x in [
lines[c+1],
lines[c+2],
lines[c+3],
lines[c+4]]]
first = [s.split() for s in first]
m = int(lines[c+5]) - 1
second = [x.... | normal | {
"blob_id": "d6bc8afcdb7636085b01add860f808024fbe566d",
"index": 2428,
"step-1": "import sys\n\nlines = sys.stdin.readlines()\n\nt = int(lines[0])\n\nfor i in range(t):\n c = i*10+1\n n = int(lines[c]) - 1\n first = [x.strip() for x in [\n lines[c+1],\n lines[c+2],\n lines[c+3],\n ... | [
0
] |
from rest_framework import serializers
from . import models
class RaumSerializer(serializers.ModelSerializer):
class Meta:
model = models.Raum
fields = [
"Raumnummer",
"Anzahl_Sitzplaetze",
"Beamer",
"Whiteboard",
]
class ZeitraumSerialize... | normal | {
"blob_id": "451c353a949458f5f71783c4aba1888c40018bfa",
"index": 9400,
"step-1": "<mask token>\n\n\nclass RaumbelegungSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = models.Raumbelegung\n fields = ['Belegt', 'Belegungsgrund']\n",
"step-2": "<mask token>\n\n\nclass Zeitraum... | [
1,
2,
3,
4,
5
] |
import xl2dict
myxlobject= XlToDict()
myxlobject.convert_sheet_to_dict(file_path="Soul Breaks.xlsx", sheet="First Sheet",
filter_variables_dict={"User Type" : "Admin", "Environment" : "Dev"}) | normal | {
"blob_id": "8ec981bf8746e09d3865bc20dcfbf2fbd797c145",
"index": 7511,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmyxlobject.convert_sheet_to_dict(file_path='Soul Breaks.xlsx', sheet=\n 'First Sheet', filter_variables_dict={'User Type': 'Admin',\n 'Environment': 'Dev'})\n",
"step-3": "<mask t... | [
0,
1,
2,
3,
4
] |
# # -*- coding: utf-8 -*-
#
# """
# Py40 PyQt5 tutorial
#
# This example shows three labels on a window
# using absolute positioning.
#
# author: Jan Bodnar
# website: py40.com
# last edited: January 2015
# """
#
# import sys
# from PyQt5.QtWidgets import QWidget, QLabel, QApplication
#
#
# class Example(QWidget):
#
# ... | normal | {
"blob_id": "e05dac901228e6972c1cb48ce2def3d248b4c167",
"index": 3053,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef absent(lectureid, sectionid):\n connection = psycopg2.connect(database='profmate', user='python',\n password='python', host='34.74.217.167', port='5432')\n cursor = c... | [
0,
1,
2,
3,
4
] |
import glob
import csv
import math
import pandas
# this is used to train the model, try different model, generate the csv file of the result
import pandas
import pandas as pd
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn import datasets
from sklearn.prepro... | normal | {
"blob_id": "a92384a6abee9e231092ee0e4dbdb60bafcc9979",
"index": 8782,
"step-1": "<mask token>\n\n\ndef naiveBayes(X_train, y_train):\n model = GaussianNB()\n model = model.fit(X_train, y_train)\n return model\n\n\ndef knn(X_train, y_train):\n model = KNeighborsClassifier()\n model = model.fit(X_t... | [
13,
14,
16,
17,
21
] |
# coding: utf-8
import datetime
import json
import requests
import os
import re
import sys
from todoist.api import TodoistAPI
#SLACK_CHANNEL = os.environ['SLACK_CHANNEL']
#SLACK_POSTURL = os.environ['SLACK_POSTURL']
TDIAPI = TodoistAPI(os.environ['TODOISTAPITOKEN'], cache=False)
TDIAPI.sync()
name = os.environ['TODOI... | normal | {
"blob_id": "3c3d45f0844496b8d623286b36a4935a154f410a",
"index": 4133,
"step-1": "<mask token>\n\n\ndef lambda_handler(event, context):\n if event['function'] == 'tasklist':\n msg = tasklist(name)\n if event['function'] == 'activity':\n msg = activity(name)\n return\n\n\n<mask token>\n\n\n... | [
3,
5,
6,
7,
8
] |
import matplotlib.pyplot as plt
import numpy as np
import random
plt.ion()
def draw_board(grid_size, hole_pos,wall_pos):
board = np.ones((grid_size,grid_size))
board[wall_pos] = 10
board[hole_pos] = 0
return board
class Game():
"""
A class which implements the Gobble game. Initializes with a ... | normal | {
"blob_id": "a74f2050a057f579a8a8b77ac04ef09073cdb6cf",
"index": 6057,
"step-1": "<mask token>\n\n\nclass Game:\n <mask token>\n\n def __init__(self, grid_size):\n self.grid_size = grid_size\n self.start_game(grid_size)\n plt.title(\"Nate's Lame Game\")\n\n def start_game(self, grid... | [
8,
9,
10,
12,
13
] |
"""Primer3 input form.
For details on input params see:
https://primer3.org/manual.html#globalTags
"""
from django import forms
from django.core.exceptions import ValidationError
from .fasta import Fasta
class PrimerForm(forms.Form):
"""Collect user input to run primer prediction."""
fasta = forms.CharFie... | normal | {
"blob_id": "6291375738db7914d551f9a1c6d2897b7d236b87",
"index": 1742,
"step-1": "<mask token>\n\n\nclass PrimerForm(forms.Form):\n <mask token>\n fasta = forms.CharField(initial='')\n primer_min = forms.IntegerField(initial=18, max_value=35)\n primer_max = forms.IntegerField(initial=27, max_value=35... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
"""\
Simple g-code streaming script for grbl
"""
import serial
import time
import csv
import json
import RPi.GPIO as GPIO
from multiprocessing import Process, Queue
class motion():
def __init__(self):
# Open grbl serial port
#self.s = serial.Serial("/dev/ttyUSB0",baudrate=115... | normal | {
"blob_id": "ac2d4372f8913ea9ae1066833cca09985e521f99",
"index": 383,
"step-1": "#!/usr/bin/env python\n\"\"\"\\\nSimple g-code streaming script for grbl\n\"\"\"\n \nimport serial\nimport time\nimport csv\nimport json\nimport RPi.GPIO as GPIO\nfrom multiprocessing import Process, Queue\nclass motion():\n def ... | [
0
] |
import math
from Config import defaults as df
from Utils.controls import sigmoid_decay
def f1(phi, phi_o, d):
"""sinusoidally growing function between (phi_o-d) to phi_o"""
return 1 - sigmoid_decay(phi, phi_o, d)
def f2(phi, sigma):
"""normal distribution"""
return math.exp(-phi ** 2 / sigma ** 2)
... | normal | {
"blob_id": "19bb3cd0c7862f39a78479d9a9703ebef198fc73",
"index": 3677,
"step-1": "<mask token>\n\n\ndef f1(phi, phi_o, d):\n \"\"\"sinusoidally growing function between (phi_o-d) to phi_o\"\"\"\n return 1 - sigmoid_decay(phi, phi_o, d)\n\n\ndef f2(phi, sigma):\n \"\"\"normal distribution\"\"\"\n retu... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 20 09:54:08 2017
@author: chuang
"""
import os
import pickle
#from collections import Counter
#import user_replace
import jieba
import re
from multiprocessing import Pool
#%%
# parameters for processing the dataset
DATA_PATH = '../data/weibo_single... | normal | {
"blob_id": "5fd54de3b2f9c2e18a283d016fc16e0e622dc6a0",
"index": 8415,
"step-1": "<mask token>\n\n\ndef replace_tokens(text, replace_dict=None):\n pattern = re.compile('|'.join(DELETE))\n text = re.sub(pattern, '', text)\n return text\n\n\ndef read_txt(file_path, encoding):\n with open(os.path.join(D... | [
6,
7,
10,
11,
12
] |
#===============================================================================
# @author: Daniel V. Stankevich
# @organization: RMIT, School of Computer Science, 2012
#
#
# This package contains representations of the following models:
# 'Particle' - an atomic element
# 'Swarm' - a set of p... | normal | {
"blob_id": "5c06229f8e80a7225620f25941cc5276a9021e53",
"index": 5353,
"step-1": "<mask token>\n\n\nclass SwarmModel:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass NeighbourhoodModel:\n _particles = []\n _bestPosition = None\n _bestPositionFitness =... | [
10,
11,
12,
15,
16
] |
from utilities import SumOneToN, RSS, MSE, R2Score
import numpy as np
import scipy.stats as st
class RidgeLinearModel:
covariance_matrix = None # covariance matrix of the model coefficients
covariance_matrix_updated = False
beta = None # coefficients of the modelfunction
var_vector = None
var_vecto... | normal | {
"blob_id": "a5dcc66ece4e58995fe86c3a399c45975a596b1a",
"index": 5836,
"step-1": "<mask token>\n\n\nclass RidgeLinearModel:\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 <mas... | [
6,
10,
11,
12,
13
] |
import qrcode
def generate_qr(query):
img = qrcode.make(query)
| normal | {
"blob_id": "e97bcf31657317f33f4a138ede80bb9171337f52",
"index": 4730,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate_qr(query):\n img = qrcode.make(query)\n",
"step-3": "import qrcode\n\n\ndef generate_qr(query):\n img = qrcode.make(query)\n",
"step-4": null,
"step-5": null,... | [
0,
1,
2
] |
import platform, sys, os, ensurepip
ensurepip.bootstrap()
try:
import pip
except ImportError:
print("Error: Failed to install pip, make sure you are running this script as admin.")
sys.exit()
arch = platform.architecture()[0]
wheelUrl = "https://raw.githubusercontent.com/Starfox64/pygame-installer/master/wheels... | normal | {
"blob_id": "b44f75db652b3a40cd9475bfe44027724e845252",
"index": 1146,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nensurepip.bootstrap()\ntry:\n import pip\nexcept ImportError:\n print(\n 'Error: Failed to install pip, make sure you are running this script as admin.'\n )\n sys.e... | [
0,
1,
2,
3,
4
] |
import torch
import torch_scatter
import torchgraphs as tg
import textwrap
from . import autograd_tricks as lrp
def patch():
torch.add = lrp.add
torch.cat = lrp.cat
torch.index_select = lrp.index_select
tg.utils.repeat_tensor = lrp.repeat_tensor
torch_scatter.scatter_add = lrp.scatter_add
torc... | normal | {
"blob_id": "faafc7cfd900d3f6fd6df30af5580f71eecfb279",
"index": 8298,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef computational_graph(op):\n if op is None:\n return 'None'\n res = f'{op.__class__.__name__} at {hex(id(op))}:'\n if op.__class__.__name__ == 'AccumulateGrad':\n ... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.2 on 2016-02-07 23:42
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('events', '0005_auto_20160207_1529'),
]
operations = [
migrations.AddField(
... | normal | {
"blob_id": "ab3609c27fa002d79735c5d5c09ec7a52fedd040",
"index": 3484,
"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 = [('events', '0... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import numpy as np
class LabeledArray:
@staticmethod
def get_label_for_indexes_upto(input_data, input_label, input_index):
df_input_data = pd.DataFrame(input_data)
df_labels = pd.DataFrame(input_label)
df_data_labels = pd.concat([df_input_data, df_labels], axis=1)
... | normal | {
"blob_id": "0dea8675d8050a91c284a13bcbce6fd0943b604e",
"index": 5135,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LabeledArray:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass LabeledArray:\n\n @staticmethod\n def get_label_for_indexes_upto(input_data, input_label, input_in... | [
0,
1,
2,
3
] |
from abc import abstractmethod
from anoncreds.protocol.repo.public_repo import PublicRepo
from anoncreds.protocol.types import ClaimDefinition, PublicKey, SecretKey, ID, \
RevocationPublicKey, AccumulatorPublicKey, Accumulator, TailsType, \
RevocationSecretKey, AccumulatorSecretKey, \
TimestampType
from an... | normal | {
"blob_id": "890841c8892e89375bb022f0d469fefc27414a2b",
"index": 5823,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass IssuerWalletInMemory(IssuerWallet, WalletInMemory):\n\n def __init__(self, claimDefId, repo: PublicRepo):\n WalletInMemory.__init__(self, claimDefId, repo)\n se... | [
0,
2,
4,
5,
6
] |
"""
AlbumInfo-related frames for the Album view.
"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Iterator, Collection, Any
from ds_tools.caching.decorators import cached_property
from tk_gui.elements import Element, HorizontalSeparator, Multiline, Text, Input, Image, Spacer
fr... | normal | {
"blob_id": "384588e1a767081191228db2afa4a489f967a220",
"index": 3952,
"step-1": "\"\"\"\nAlbumInfo-related frames for the Album view.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import TYPE_CHECKING, Iterator, Collection, Any\n\nfrom ds_tools.caching.decorators import cached_pro... | [
0
] |
import urllib.request
username = ''
link = r'https://www.instagram.com/' + username
html = urllib.request.urlopen(link)
print(html.read()) | normal | {
"blob_id": "db93de33f537eeaf64ca8e2b2b79aba1f592305b",
"index": 5434,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(html.read())\n",
"step-3": "<mask token>\nusername = ''\nlink = 'https://www.instagram.com/' + username\nhtml = urllib.request.urlopen(link)\nprint(html.read())\n",
"step-4": "i... | [
0,
1,
2,
3,
4
] |
print('Welcome aboard, Oleksij!')
| normal | {
"blob_id": "2b1ec29d665aa93cd53644b62efcd1305b34e13e",
"index": 2636,
"step-1": "<mask token>\n",
"step-2": "print('Welcome aboard, Oleksij!')\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from app import app
from flask import request
@app.route('/')
@app.route('/index')
def index():
return 'Hello world'
@app.route('/api_post', methods=['POST'])
def postJsonHandler():
print(request.is_json)
content = request.get_json()
print(content)
return 'JSON posted'
| normal | {
"blob_id": "9d8c4bf9f9279d5e30d0e9742cdd31713e5f4b9e",
"index": 2104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n return 'Hello world'\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\... | [
0,
1,
2,
3
] |
# este script comprar diferente metodos de base2number
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
#from matplotlib import pyplot as plt
#from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib import pyplot
import math
import os
import sys
import ... | normal | {
"blob_id": "9696e5799d46adb5b92c0900e2064b927addfd93",
"index": 2506,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor sequence_file in sequences:\n f_in = open(current_dir + '/sample_genomes/' + sequence_file, 'r')\n f_out.write(f_in.read())\n f_in.close()\n data = []\n fa_file = curre... | [
0,
1,
2,
3,
4
] |
"""
Physical units and dimensions
"""
from sympy import *
from sympy.core.basic import Atom
from sympy.core.methods import ArithMeths, RelMeths
class Unit(Atom, RelMeths, ArithMeths):
is_positive = True # make (m**2)**Rational(1,2) --> m
is_commutative = True
def __init__(self, name, a... | normal | {
"blob_id": "c0e1c0c4545777a669fac19900239ab9baade242",
"index": 5993,
"step-1": "<mask token>\n\n\nclass Unit(Atom, RelMeths, ArithMeths):\n is_positive = True\n is_commutative = True\n\n def __init__(self, name, abbrev):\n self.name = name\n self.abbrev = abbrev\n\n def tostr(self, le... | [
5,
6,
7,
9,
10
] |
from random import randrange
import random
"""
both user and computer funcs:
"""
def check_ok(boat, taken_positions):
# input: boat, taken_positions
# this func checks if the boat outside the playground or the position of the boat is already in taken_position
# return: boat. boat will returned as [-1] or its... | normal | {
"blob_id": "95584dfdb232be7f507dc9d29ed2f1d95fa2b653",
"index": 9642,
"step-1": "<mask token>\n\n\ndef check_ok(boat, taken_positions):\n boat.sort()\n for i in range(len(boat)):\n if boat[i] in taken_positions:\n boat = [-1]\n break\n elif boat[i] > 99 or boat[i] < 0:\... | [
7,
10,
11,
12,
16
] |
import os
import string
filenames = os.listdir('data/SENTIMENT_test')
filenames.sort()
outfile = open('sentiment_test.txt', 'w')
remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)
for filename in filenames:
infile = open('data/SENTIMENT_test/' + filename, errors='ignore')
infiletext... | normal | {
"blob_id": "6434e427c9015544985a38104cffeaa10866b9ea",
"index": 4585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfilenames.sort()\n<mask token>\nfor filename in filenames:\n infile = open('data/SENTIMENT_test/' + filename, errors='ignore')\n infiletext = infile.read()\n infiletext = infilet... | [
0,
1,
2,
3
] |
from bs4 import BeautifulSoup
import re
class Rules:
def __init__(self):
self.ruleCollection = {
"1" : self.rule1,
"2" : self.rule2,
"3" : self.rule3,
"4" : self.rule4,
"5" : self.rule5,
"6" : self.rule6,
"7" : self.rule7,
"8" : self.r... | normal | {
"blob_id": "7747cbb1a1ed191b616b0d1bcfd51cdea05067f5",
"index": 5954,
"step-1": "<mask token>\n\n\nclass Rules:\n\n def __init__(self):\n self.ruleCollection = {'1': self.rule1, '2': self.rule2, '3': self.\n rule3, '4': self.rule4, '5': self.rule5, '6': self.rule6, '7':\n self.ru... | [
8,
11,
13,
14,
17
] |
print('-' * 60)
print(
'Welcome to CLUB425, the most lit club in downtown ACTvF. Before you can enter, I need you yo answer some question...'
)
print()
age = input('What is your age today? ')
age = int(age)
if age >= 21:
print('Cool, come on in.')
else:
print(
'Your gonna need to back up. This c... | normal | {
"blob_id": "19ffac718008c7c9279fb8cbc7608597d2d3e708",
"index": 3937,
"step-1": "<mask token>\n",
"step-2": "print('-' * 60)\nprint(\n 'Welcome to CLUB425, the most lit club in downtown ACTvF. Before you can enter, I need you yo answer some question...'\n )\nprint()\n<mask token>\nif age >= 21:\n pri... | [
0,
1,
2
] |
import random
def Fun_hiraganas():
hiraganas = ['a', 'i', 'u', 'e', 'o', 'ka', 'ki', 'ku', 'ke', 'ko', 'sa', 'shi', 'su', 'se',
'so', 'ta', 'chi', 'tsu', 'te', 'to', 'na', 'ni', 'nu', 'ne', 'no', 'ha', 'hi', 'fu', 'he', 'ho']
print("escriba el hiragana", hiraganas[random.randint(0, len(hiraganas)-1)])
print("Hell... | normal | {
"blob_id": "1fe7d5db1b47ba082301d07d010c6796fbd7edb7",
"index": 6859,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Fun_hiraganas():\n hiraganas = ['a', 'i', 'u', 'e', 'o', 'ka', 'ki', 'ku', 'ke', 'ko',\n 'sa', 'shi', 'su', 'se', 'so', 'ta', 'chi', 'tsu', 'te', 'to', 'na',\n 'n... | [
0,
2,
3,
4,
5
] |
from django.contrib.auth.decorators import login_required
from django.shortcuts import render
from orders.models import Setting
def search(request):
return render(request, 'ui/search.html')
def search_printed(request):
print_url = ''
setting = Setting.objects.filter(name='printer').first()
if settin... | normal | {
"blob_id": "f16d43d9dfb3e9b9589fa92eb82aaa4c73fe48cd",
"index": 1264,
"step-1": "<mask token>\n\n\ndef search(request):\n return render(request, 'ui/search.html')\n\n\ndef search_printed(request):\n print_url = ''\n setting = Setting.objects.filter(name='printer').first()\n if setting != None:\n ... | [
2,
3,
4,
5
] |
#Eyal Reis - 203249354
from view import View
def main():
"""
primary game method
"""
view = View()
view.root.mainloop()
if __name__ == "__main__":
main()
| normal | {
"blob_id": "640eae824e43e394bf0624dd4cf7dcec78f43604",
"index": 4947,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n \"\"\"\n primary game method\n \"\"\"\n view = View()\n view.root.mainloop()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef main():\n \"\"\"\... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 4 12:14:16 2020
@author: mdevasish
"""
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression,Lasso,Ridge
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.model_selection import train_test_split
import job... | normal | {
"blob_id": "f07b95a3b18aecf6cadaa8398c9158a7cd10aeeb",
"index": 7101,
"step-1": "<mask token>\n\n\nclass model_construction:\n <mask token>\n\n def implement_model(self, filename):\n \"\"\"\n Method inside the model_construction class, used for implementing the model\n and return feat... | [
2,
3,
4,
5,
7
] |
# Generated by Django 3.2.2 on 2021-05-11 09:49
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('meeting', '0004_auto_20210511_0947'),
]
operations = [
migrations.AlterField(
model_name='event',
name='end',
... | normal | {
"blob_id": "1c1cd0eeea4dbf446aa4582f42ef1f3b5a4e8875",
"index": 7452,
"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 = [('meeting', '... | [
0,
1,
2,
3,
4
] |
# Pass Function
def hello_func():
pass
hello_func()
print(hello_func())
def hello_func():
hello_func()
print(hello_func)
# Function allows to reuse ,without repeat
def hello_func():
print('hello function!')
hello_func()
| normal | {
"blob_id": "94a0b341aac3683712578b31e98a0a5a6a643b57",
"index": 7646,
"step-1": "def hello_func():\n pass\n\n\n<mask token>\n",
"step-2": "def hello_func():\n pass\n\n\n<mask token>\n\n\ndef hello_func():\n print('hello function!')\n hello_func()\n",
"step-3": "def hello_func():\n pass\n\n\n<... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
"""
@Author: xiezizhe
@Date: 5/7/2020 下午8:52
"""
from typing import List
class KMP:
def partial(self, pattern):
""" Calculate partial match table: String -> [Int]"""
ret = [0]
for i in range(1, len(pattern)):
j = ret[i - 1]
while j > 0 and... | normal | {
"blob_id": "57de9a46dfbf33b117c2dfbb534a5020e019d520",
"index": 8513,
"step-1": "<mask token>\n\n\nclass Trie:\n\n def __init__(self):\n self.dicts = dict()\n\n def add(self, word):\n node = self.dicts\n for w in word:\n if w not in node:\n node[w] = dict()\n... | [
5,
7,
8,
10,
12
] |
from django.conf.urls import url
from .views.show import show_article, show_articles, export_db
urlpatterns = [
url(r'^$', show_articles, name='index'),
url(r'^article/$', show_article, name='article'),
url(r'^export/$', export_db, name='article'),
]
| normal | {
"blob_id": "9fdc7c1eb68a92451d41313861164a915b85fcee",
"index": 8988,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^$', show_articles, name='index'), url('^article/$',\n show_article, name='article'), url('^export/$', export_db, name='article')]\n",
"step-3": "from django.conf... | [
0,
1,
2,
3
] |
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