code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
# link https://deeplizard.com/learn/video/QK_PP_2KgGE
import gym
import numpy as np
import random
import time
from IPython.display import clear_output
# setup the env
env = gym.make("FrozenLake8x8-v0", is_slippery=False)
observation = env.reset()
# setup the q-table
action_space_size = env.action_space.n
state_space_... | normal | {
"blob_id": "b791afec1c9fb214d1f3b4ec0ec67f905d96aabf",
"index": 3249,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor episode in range(num_episodes):\n state = env.reset()\n done = False\n rewards_current_episode = 0\n for step in range(steps_per_episodes):\n exploration_rate_thres... | [
0,
1,
2,
3,
4
] |
from wtforms import StringField, PasswordField
from wtforms.validators import DataRequired
from flask_wtf import FlaskForm
# ...
class LoginForm(FlaskForm):
"""登录表单类"""
username = StringField('用户名', validators=[DataRequired()])
password = PasswordField('密码', validators=[DataRequired()]) | normal | {
"blob_id": "6ad2014191215dac97ad6fc6a026512c3d1866dc",
"index": 8244,
"step-1": "<mask token>\n\n\nclass LoginForm(FlaskForm):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass LoginForm(FlaskForm):\n <mask token>\n username = StringField('用户名', validators=[Dat... | [
1,
2,
3,
4,
5
] |
class Solution(object):
def gcdOfStrings(self, str1, str2):
if str1 == str2:
return str1
elif not str1 or not str2:
return ''
elif str1.startswith(str2):
return self.gcdOfStrings(str1[len(str2):], str2)
elif str2.startswith(str1):
retu... | normal | {
"blob_id": "ab632c3c8a7f295a890de19af82fde87c6d600bc",
"index": 1674,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def gcdOfStrings(self, str1, str2):\n if str1 == str2:\n return str1\n elif not str1 o... | [
0,
1,
2
] |
import splunk.admin as admin
import splunk.entity as en
class ConfigApp(admin.MConfigHandler):
def setup(self):
if self.requestedAction == admin.ACTION_EDIT:
for myarg in ['api_key']:
self.supportedArgs.addOptArg(myarg)
def handleList(self, confInfo):
confDict = self.readConf("appsetup")
if None != c... | normal | {
"blob_id": "8d6c58e9ef4e14a089a7eb33a92214d081ed7692",
"index": 8462,
"step-1": "<mask token>\n\n\nclass ConfigApp(admin.MConfigHandler):\n <mask token>\n\n def handleList(self, confInfo):\n confDict = self.readConf('appsetup')\n if None != confDict:\n for stanza, settings in conf... | [
3,
4,
5,
6,
7
] |
import psycopg2
host = "datavis.cauuh8vzeelb.us-east-1.rds.amazonaws.com"
database = "top5"
user = "teamwonder"
password = "visproject"
Gentrifying = [10002,10003,10009,10026,10027,10029,10030,10031,10032,10033,10034,10035,10037,10039,10040,10454,10455,10456,10457,10458,10459,10460,10474,11102,11103,11105,11106,11206... | normal | {
"blob_id": "0ebf5646ee9693b7d0c1de61436e05b3725b2c9f",
"index": 2560,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhost = 'datavis.cauuh8vzeelb.us-east-1.rds.amazonaws.com'\ndatabase = 'top5'\nuser = 'teamwonder'\npassword = 'visproject'\nGentrifying = [10002, 10003, 10009, 10026, 10027, 10029, 10030,... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: ascii -*-
"""
A script removing animations from SVG graphics.
"""
import sys, os, re
# etree fails utterly at producing nice-looking XML
from xml.dom import minidom
def process(inpt, outp):
def traverse(node):
for child in node.childNodes:
if child.nodeTy... | normal | {
"blob_id": "f819d1b1f2f6f3052247cda592007eac40aca37a",
"index": 7927,
"step-1": "<mask token>\n\n\ndef main():\n if len(sys.argv) != 3:\n sys.stderr.write('USAGE: %s input output\\n' % sys.argv[0])\n sys.stderr.flush()\n sys.exit(0)\n with open(sys.argv[1]) as inpt, open(sys.argv[2], ... | [
1,
2,
3,
4,
5
] |
from sqlitedict import SqliteDict
import sys
import socket
import urllib
import argparse
import zlib, pickle, sqlite3
import random
from datetime import datetime
import time
from urllib.parse import urlparse
import hashlib
import subprocess
import requests
from multiprocessing import Pool
def gz_encode(obj):
retur... | normal | {
"blob_id": "295d6a66335491b406f47212064da9fd5fca6eb6",
"index": 6812,
"step-1": "<mask token>\n\n\ndef gz_decode(obj):\n return pickle.loads(zlib.decompress(bytes(obj)))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef gz_encode(obj):\n return sqlite3.Binary(zlib.compress(pickle.dumps(obj, pickle.... | [
1,
2,
3,
4,
5
] |
a=10
b=20
c=400
d=100
e=500
f=30
z=a+b+c+d+e+f
print "The total sum is",z
print "variable d added"
print "Variable e added"
print "Variable f is equal to 30"
print "You are coming from test branch"
print "Your are very new in this branch"
| normal | {
"blob_id": "700d876dd45548b74b563ed86f8124fa666e1739",
"index": 2588,
"step-1": "a=10\nb=20\nc=400\nd=100\ne=500\nf=30\nz=a+b+c+d+e+f\nprint \"The total sum is\",z\nprint \"variable d added\"\nprint \"Variable e added\"\nprint \"Variable f is equal to 30\"\nprint \"You are coming from test branch\"\nprint \"You... | [
0
] |
# Generated by Django 2.0.1 on 2018-05-01 11:46
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('rover', '0002_auto_20180501_1431'),
]
operations = [
migrations.CreateModel(
name='RoverPage',
fields=[
... | normal | {
"blob_id": "fed94e0affa1fe6c705577a63fabee839aa9f05c",
"index": 5096,
"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 = [('rover', '00... | [
0,
1,
2,
3,
4
] |
print(1/2 * 2) # division ret
| normal | {
"blob_id": "2c1e51f2c392e77299463d95a2277b3d2ca7c299",
"index": 4336,
"step-1": "<mask token>\n",
"step-2": "print(1 / 2 * 2)\n",
"step-3": "print(1/2 * 2) # division ret\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
def test(x):
print x
| normal | {
"blob_id": "78e008b4a51cdbbb81dead7bc5945ee98ccad862",
"index": 8266,
"step-1": "def test(x):\n print x\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
"""
@version:
author:yunnaidan
@time: 2019/07/22
@file: download_mseed.py
@function:
"""
from obspy.clients.fdsn import Client
from obspy.core import UTCDateTime
import numpy as np
import obspy
import os
import re
import time
import glob
import shutil
import platform
import subprocess
import multiprocessing
def load_... | normal | {
"blob_id": "34db3c9998e1d7647dd954e82e18147504cc74fc",
"index": 6736,
"step-1": "<mask token>\n\n\ndef load_stations(filename):\n with open(filename, 'r') as f:\n sta_data = f.readlines()\n sta_list = []\n for l in range(1, len(sta_data)):\n sta_info = sta_data[l]\n net_name = re.s... | [
3,
5,
6,
7,
9
] |
from connection import Machine
from credentials import get_credentials
targets = ['45.32.13.245']
#targets = ['localhost']
input_file = 'cmd'
def main():
global targets
username, password = get_credentials('laozi')
remote_host = Machine(username, password)
for target in targets:
remote_host.co... | normal | {
"blob_id": "18bc8a8b1cbb544cfbe581e32ee5e509d67beafd",
"index": 1410,
"step-1": "<mask token>\n\n\ndef main():\n global targets\n username, password = get_credentials('laozi')\n remote_host = Machine(username, password)\n for target in targets:\n remote_host.connect(target)\n stdin, st... | [
1,
2,
3,
4,
5
] |
from __future__ import print_function
import ot
import torch
import numpy as np
from sklearn.neighbors import KernelDensity
from torch.utils.data import Dataset
import jacinle.io as io
import optimal_transport_modules.pytorch_utils as PTU
import optimal_transport_modules.generate_data as g_data
from optimal_transport_m... | normal | {
"blob_id": "0ee902d59d3d01b6ec8bb4cc8d5e8aa583644397",
"index": 1298,
"step-1": "<mask token>\n\n\ndef kde_Gaussian_fitting(miu, bandwidth):\n kde_analyzer = KernelDensity(kernel='gaussian', bandwidth=bandwidth).fit(\n miu)\n return kde_analyzer\n\n\n<mask token>\n\n\ndef second_moment_all_dist(bat... | [
12,
13,
17,
21,
22
] |
# -*- coding:utf-8 -*
import tushare as ts
import numpy as np
import pandas as pd
import datetime
import chardet
import urllib
import urllib2
import re
from bs4 import BeautifulSoup
import time
from pandas import Series,DataFrame
def get_relation(stock1,stock2):
hist_data = ts.get_hist_data(stock1,start='2018... | normal | {
"blob_id": "00f2aafe1a0c66d0414d189b9fa3bbc2da9fd727",
"index": 2066,
"step-1": "# -*- coding:utf-8 -*\nimport tushare as ts\nimport numpy as np\nimport pandas as pd\nimport datetime\nimport chardet\nimport urllib\nimport urllib2\nimport re\nfrom bs4 import BeautifulSoup\nimport time\nfrom pandas import Series... | [
0
] |
from django.urls import path
from . import views
urlpatterns = [
path('', views.home, name ='park-home'),
path('login/', views.login, name ='park-login'),
] | normal | {
"blob_id": "2fd490ca54f5d038997cec59a3e07c3f2c2d2538",
"index": 6757,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.home, name='park-home'), path('login/', views\n .login, name='park-login')]\n",
"step-3": "from django.urls import path\nfrom . import views\nurlpattern... | [
0,
1,
2,
3
] |
'''
Implement GreedyMotifSearch
http://rosalind.info/problems/ba2d/
Given: Integers k and t, followed by a collection of strings Dna.
Return: A collection of strings BestMotifs resulting from running GreedyMotifSearch(Dna, k, t). If at any step you find more than one Profile-most probable k-mer in a given string, use... | normal | {
"blob_id": "ed7fa6e6f30eb06400cb38128617967a597f6c04",
"index": 2450,
"step-1": "<mask token>\n\n\ndef greedy_motif_search(dnas, k, t):\n best_motifs = [dna[:k] for dna in dnas]\n best_score = score_motifs(best_motifs)\n for i in range(len(dnas[0]) - k + 1):\n print(i)\n motifs = [dnas[0]... | [
3,
5,
6,
7,
8
] |
from typing import List
h = 5
w = 4
horizontalCuts = [3]
verticalCuts = [3]
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int], verticalCuts: List[int]) -> int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
... | normal | {
"blob_id": "8fb559810fbf79f0849ed98e51d3f2ad1ccc4b8b",
"index": 8296,
"step-1": "<mask token>\n\n\nclass Solution:\n\n def maxArea(self, h: int, w: int, horizontalCuts: List[int],\n verticalCuts: List[int]) ->int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.appe... | [
2,
3,
4,
5,
6
] |
#coding: utf-8
import mmh3
from bitarray import bitarray
BIT_SIZE = 1 << 30
class BloomFilter:
def __init__(self):
# Initialize bloom filter, set size and all bits to 0
bit_array = bitarray(BIT_SIZE)
bit_array.setall(0)
self.bit_array = bit_array
def add(self, val):
... | normal | {
"blob_id": "5a103a4f72b9cd3ea3911aeefeeb2194c8ad7df0",
"index": 589,
"step-1": "<mask token>\n\n\nclass BloomFilter:\n\n def __init__(self):\n bit_array = bitarray(BIT_SIZE)\n bit_array.setall(0)\n self.bit_array = bit_array\n\n def add(self, val):\n point_list = self.get_posti... | [
4,
5,
6,
7,
9
] |
from Products.CMFPlone.utils import getFSVersionTuple
from bda.plone.ticketshop.interfaces import ITicketShopExtensionLayer
from plone.app.robotframework.testing import MOCK_MAILHOST_FIXTURE
from plone.app.testing import FunctionalTesting
from plone.app.testing import IntegrationTesting
from plone.app.testing import PL... | normal | {
"blob_id": "5d7080f2778133d1938853512ca038edcf7c0dc4",
"index": 1002,
"step-1": "<mask token>\n\n\nclass TicketshopATLayer(PloneSandboxLayer):\n defaultBases = PLONE_FIXTURE,\n\n def setUpZope(self, app, configurationContext):\n import Products.ATContentTypes\n self.loadZCML(package=Products... | [
4,
7,
10,
11,
14
] |
import numpy as np
def get_mask(mask):
r = mask[:, :, 0]
g = mask[:, :, 1]
return r // (r.max() or 1) * -1 + g // (g.max() or 1)
def calculate_brightness(image):
weights = np.array([0.299, 0.587, 0.114])
brightness_matrix = (image*weights).sum(axis=2)
return brightness_matrix
def calculate... | normal | {
"blob_id": "7130a382784955780a3f258c81ce05c61915af56",
"index": 5000,
"step-1": "<mask token>\n\n\ndef get_mask(mask):\n r = mask[:, :, 0]\n g = mask[:, :, 1]\n return r // (r.max() or 1) * -1 + g // (g.max() or 1)\n\n\n<mask token>\n\n\ndef extend(image, mask):\n brightness = calculate_brightness(i... | [
3,
6,
7,
9,
10
] |
# -*- coding: utf-8 -*- {{{
# vim: set fenc=utf-8 ft=python sw=4 ts=4 sts=4 et:
# Copyright (c) 2017, Battelle Memorial Institute
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistri... | normal | {
"blob_id": "6fdfcbcfdf2b680a1fbdb74f77fd5d1a9f7eac0b",
"index": 6105,
"step-1": "<mask token>\n\n\nclass VolttronWebRPC(object):\n\n def __init__(self, url, username='admin', password='admin'):\n \"\"\"\n :param url: Jsonrpc endpoint for posting data.\n :param username:\n :param p... | [
11,
12,
14,
15,
18
] |
#Sorting for a number list
#ascending and descending
ls=[1,34,23,56,34,67,87,54,62,31,66]
ls.sort(reverse=True)
print(ls)
ls.sort()
print(ls)
#Sorting a letter's list with different scenarios
ls_l=["aaa","ertdf","ieurtff","fnjr","resdjx","jfh","r","fd"]
#1-sort according to string length from small length to bigger
ls... | normal | {
"blob_id": "0e0e51904f05b41b4769b730c836568b8bb63869",
"index": 9564,
"step-1": "<mask token>\n\n\ndef secondItem(ls):\n return ls[2]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef FirstLetter(string):\n return string[0]\n\n\n<mask token>\n\n\ndef secondItem(ls):\n return ls[2]\n\n\n<mask tok... | [
1,
2,
3,
4,
5
] |
from collections import defaultdict
from typing import Union, Iterable, Sized
import numpy as np
from cached_property import cached_property
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
class Source... | normal | {
"blob_id": "e5d7cc65041d65f915d4882b4fdad5bebf79a067",
"index": 204,
"step-1": "<mask token>\n\n\nclass TextDataset(BaseDataset):\n\n def __init__(self, source_sentences: Union[Iterable, Sized],\n target_sentences: Union[Iterable, Sized], shuffle: bool=True,\n word_frequency_threshold: int=2):\... | [
12,
19,
20,
22,
27
] |
""" Implements BCFW for DIFFRAC objectives. """
import numpy as np
import os
from tqdm import tqdm
from numpy.linalg import norm as matrix_norm
import time
def get_feat_block(feats, block_idx, memory_mode, bias_value=-1.0):
"""Get feature for a given block."""
if memory_mode == 'RAM':
feat = feats[bl... | normal | {
"blob_id": "af02cd0778e19df7b11145c4863776a1afd1cca6",
"index": 1484,
"step-1": "\"\"\" Implements BCFW for DIFFRAC objectives. \"\"\"\n\nimport numpy as np\nimport os\nfrom tqdm import tqdm\nfrom numpy.linalg import norm as matrix_norm\nimport time\n\n\ndef get_feat_block(feats, block_idx, memory_mode, bias_va... | [
0
] |
"Base class for tests."
import argparse
import http.client
import json
import os
import re
import sys
import unittest
import jsonschema
import requests
SCHEMA_LINK_RX = re.compile(r'<([^>])+>; rel="([^"]+)')
JSON_MIMETYPE = 'application/json'
DEFAULT_SETTINGS = {
'ROOT_URL': 'http://127.0.0.1:5002/api',
'U... | normal | {
"blob_id": "c455de70a79f70f5f0e21391511f5035f1b4feb9",
"index": 646,
"step-1": "<mask token>\n\n\nclass Base(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n self.schemas = {}\n self.session = requests.Session()\n self.session.headers.update({'x-apikey': SETTINGS['APIKEY']})\... | [
8,
12,
13,
14,
16
] |
#!/usr/bin/python
import time
from daemon import runner
import graphitesend
from pywatts import get_data
class App():
def __init__(self):
self.stdin_path = '/dev/null'
self.stdout_path = '/dev/tty'
self.stderr_path = '/dev/tty'
self.pidfile_path = '/tmp/currentcost_daemon.pid'
self.pidfile_timeout = 5
... | normal | {
"blob_id": "1aa49bc9a3ea12dffff907d17bd40b4425f28e13",
"index": 9829,
"step-1": "#!/usr/bin/python\nimport time\nfrom daemon import runner\nimport graphitesend\nfrom pywatts import get_data\n\nclass App():\n\tdef __init__(self):\n\t\tself.stdin_path = '/dev/null'\n\t\tself.stdout_path = '/dev/tty'\n\t\tself.std... | [
0
] |
import sys
import json
with open(__file__.replace('.py', '.txt')) as f:
problem = f.read()
data = {
'problem': problem,
'example': """COM)B
B)C
C)D
D)E
E)F
B)G
G)H
D)I
E)J
J)K
K)L""" # should give 42
}
def solve_problem(input):
parents = {}
for i, line in enumerate(input.split('\n')):
... | normal | {
"blob_id": "e57680c9bd09866e68ade0cfea7ce83cd6d50f58",
"index": 1596,
"step-1": "<mask token>\n\n\ndef solve_problem(input):\n parents = {}\n for i, line in enumerate(input.split('\\n')):\n about, object = line.split(')')\n parents[object] = about\n orbit_counts = {'COM': 0}\n for obje... | [
3,
4,
5,
6,
7
] |
def Hello_worlder(x):
a = []
for i in range(x):
a.append('Hello world')
for i in a:
print(i)
Hello_worlder(10)
| normal | {
"blob_id": "4f116f3eec9198a56a047ab42ed8e018ebb794bb",
"index": 3528,
"step-1": "<mask token>\n",
"step-2": "def Hello_worlder(x):\n a = []\n for i in range(x):\n a.append('Hello world')\n for i in a:\n print(i)\n\n\n<mask token>\n",
"step-3": "def Hello_worlder(x):\n a = []\n f... | [
0,
1,
2
] |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imread
X = np.array([[51, 55], [14, 19], [0, 4]])
print(X)
A = np.array([[1, 2], [3, 4]])
B = np.array([10, 20])
print(A * B)
print(X[0])
print(X[0][1])
for row in X:
print(row)
newX = X.flatten()
print(newX)
print(X > 15)
# 데이터 ... | normal | {
"blob_id": "ba702a9c5d9d31e48b047c106d77cf1707031d70",
"index": 1795,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(X)\n<mask token>\nprint(A * B)\nprint(X[0])\nprint(X[0][1])\nfor row in X:\n print(row)\n<mask token>\nprint(newX)\nprint(X > 15)\n<mask token>\nplt.plot(x, y)\nplt.show()\n<mask... | [
0,
1,
2,
3,
4
] |
# Generated by Django 2.1.4 on 2019-04-17 03:56
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('historiasClinicas', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='actualizacion',
name='valor... | normal | {
"blob_id": "4aefabf064cdef963f9c62bd5c93892207c301d3",
"index": 3076,
"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 = [('historiasCl... | [
0,
1,
2,
3,
4
] |
from pycat.base.color import Color
from pycat.sprite import Sprite
from pycat.window import Window
from pyglet.gl.glext_arb import GL_FONT_HEIGHT_NV
from random import randint
window=Window()
class Chick(Sprite):
def on_create(self):
self.image = 'chick-a.png'
self.goto_random_position()
... | normal | {
"blob_id": "cc7942c406e9bcb5af43f131fdf0a6441f81c16a",
"index": 4260,
"step-1": "<mask token>\n\n\nclass Chick(Sprite):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Chick(Sprite):\n\n def on_create(self):\n self.image = 'chick-a.png'\n self.goto_random_position()... | [
1,
3,
4,
5,
6
] |
# uncompyle6 version 3.2.4
# Python bytecode 2.7 (62211)
# Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]
# Embedded file name: filecmp
import os, stat
from itertools import ifilter, ifilterfalse, imap, izip
__all__ = [
'cmp', 'dircmp', 'cmpfiles']
_cache = {}
BU... | normal | {
"blob_id": "38f6700b283bdc68a0271cb3ec397ce72aa2de3c",
"index": 6589,
"step-1": "# uncompyle6 version 3.2.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]\n# Embedded file name: filecmp\nimport os, stat\nfrom itertools imp... | [
0
] |
import datetime
import logging
import os
import requests
from bs4 import BeautifulSoup
import telebot
from azure.storage.blob import BlobClient
import hashlib
import azure.functions as func
def hash_string(input_string: str) -> str:
return hashlib.sha256(input_string.encode("utf-8")).hexdigest()
... | normal | {
"blob_id": "670a23aa910a6709735281b7e64e5254a19277c6",
"index": 7924,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef hash_string(input_string: str) ->str:\n return hashlib.sha256(input_string.encode('utf-8')).hexdigest()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef hash_string(inp... | [
0,
1,
2,
3,
4
] |
"""
All requests will be sent to backend as:
{
name: <class name>,
data: {
<all instance variables>
}
}
"""
class NewDriver:
def __init__(self, uri, authToken):
self.uri = uri
self.authorizationToken = authToken
class DriverClose:
def __init__(self... | normal | {
"blob_id": "dfcb095b26a21ba0c8ccc2a2c664bcfab29b8351",
"index": 8214,
"step-1": "<mask token>\n\n\nclass SessionRun:\n\n def __init__(self, sessionId, cypher, params):\n self.sessionId = sessionId\n self.cypher = cypher\n self.params = params\n\n\nclass SessionReadTransaction:\n\n def... | [
14,
16,
17,
19,
23
] |
from adb_local_installer.connection import ADBConnection
with ADBConnection("a95x01", domain="dohmens.local") as conn:
print(conn.conn) | normal | {
"blob_id": "6f583fde0eeab84984629b795e428300503a49c9",
"index": 9852,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith ADBConnection('a95x01', domain='dohmens.local') as conn:\n print(conn.conn)\n",
"step-3": "from adb_local_installer.connection import ADBConnection\nwith ADBConnection('a95x01',... | [
0,
1,
2,
3
] |
# Generated by Django 2.1.5 on 2019-01-21 22:51
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
ope... | normal | {
"blob_id": "a6cb7a134fb8480d344743bcb7bc8766146d256f",
"index": 8238,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
import torch
from torch import nn
import torch.nn.functional as F
class JointModel(nn.Module):
def __init__(self, d_v, d_e, d_t, encoder_layers, generator_layers,encoder_shortcut, generator_shortcut, generator_transform,
num_word, emb_size, word_rnn_size, word_rnn_num_layer, word_rnn_dropout, word... | normal | {
"blob_id": "4f3e297b6925f8d65aacaa59bb837e746747c33f",
"index": 2608,
"step-1": "<mask token>\n\n\nclass JointModel(nn.Module):\n\n def __init__(self, d_v, d_e, d_t, encoder_layers, generator_layers,\n encoder_shortcut, generator_shortcut, generator_transform, num_word,\n emb_size, word_rnn_siz... | [
5,
6,
8,
11,
12
] |
#!/usr/bin/env python3
import sys
import re
from collections import namedtuple
def isnum(name):
return name.startswith('-') or name.isdigit()
class WireValues:
def __init__(self):
self.wires = {}
def __getitem__(self, name):
return int(name) if isnum(name) else self.wires[name]
def _... | normal | {
"blob_id": "a5eb1f559972519dbe0f3702e03af77e61fbfb4e",
"index": 7985,
"step-1": "<mask token>\n\n\nclass WireValues:\n\n def __init__(self):\n self.wires = {}\n\n def __getitem__(self, name):\n return int(name) if isnum(name) else self.wires[name]\n\n def __setitem__(self, name, value):\n... | [
7,
14,
16,
18,
20
] |
# -*- coding: utf-8 -*-
import time
import datetime
def get_second_long(time_str=None):
if time_str is None:
return long(time.time())
time_array = time.strptime(time_str, "%Y-%m-%d %H:%M:%S")
return long(time.mktime(time_array))
def get_curtime_str():
return datetime.datetime.now()
def ge... | normal | {
"blob_id": "e735529eddd3a46ea335e593e5937558b50b142d",
"index": 2276,
"step-1": "<mask token>\n\n\ndef get_second_long(time_str=None):\n if time_str is None:\n return long(time.time())\n time_array = time.strptime(time_str, '%Y-%m-%d %H:%M:%S')\n return long(time.mktime(time_array))\n\n\n<mask t... | [
7,
9,
10,
11,
14
] |
from room import Room
from player import Player
from item import Item
# Declare all the rooms
items = {
'scimitar': Item('Scimitar', '+7 Attack'),
'mace': Item('Mace', '+13 Attack'),
'tower_shield': Item('Tower Shield', '+8 Block'),
'heraldic_shield': Item('Heraldic Shield', '+12 Block'),
'chainmail... | normal | {
"blob_id": "07a172c28057dc803efdbdc10a9e2e11df4e527b",
"index": 3134,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n if suppressRoomPrint:\n suppressRoomPrint = False\n else:\n print(player.location)\n print(\n f\"\"\"\n{player.location.name}\n {player.locatio... | [
0,
1,
2,
3,
4
] |
import pyttsx3
from pydub import AudioSegment
engine = pyttsx3.init() # object creation
""" RATE"""
#printing current voice rate
engine.setProperty('rate', 150) # setting up new voice rate
rate = engine.getProperty('rate') # getting details of current speaking rate
print (rate)
... | normal | {
"blob_id": "32f4f7ad61b99848c907e092c5ed7a839f0b352b",
"index": 6399,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nengine.setProperty('rate', 150)\n<mask token>\nprint(rate)\n<mask token>\nwhile i < l:\n engine.save_to_file(a[i], 'TTS/trump/{}.mp3'.format(str(i)))\n engine.runAndWait()\n if i... | [
0,
1,
2,
3,
4
] |
import torch
import torch.nn as nn
class ReconstructionLoss(nn.Module):
def __init__(self, config):
super(ReconstructionLoss, self).__init__()
self.velocity_dim = config.velocity_dim
def forward(self, pre_seq, gt_seq):
MSE_loss = nn.MSELoss()
rec_loss = MSE_loss(pre_seq[:, 1:-... | normal | {
"blob_id": "edc66bdc365f9c40ee33249bd2d02c0c5f28256a",
"index": 8386,
"step-1": "<mask token>\n\n\nclass VelocityLoss(nn.Module):\n\n def __init__(self, _mean, _std, config):\n super(VelocityLoss, self).__init__()\n self._mean = _mean\n self._std = _std\n self.device = config.devi... | [
14,
18,
19,
23,
24
] |
#!/usr/bin/env python
"""
Update the expected test outputs and inputs for rsmsummarize and rsmcompare tests.
This script assumes that you have already run `nose2 -s tests` and ran the entire
test suite. By doing so, the output has been generated under the given outputs
directory. And that is what will be used to gener... | normal | {
"blob_id": "7e20c61fa30ea93e69a2479e70449638eb52b7bb",
"index": 2964,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n parser = argparse.ArgumentParser(prog='update_test_files.py')\n parser.add_argument('--tests', dest='tests_dir', required=True, help=\n 'The path to the exi... | [
0,
1,
2,
3,
4
] |
import os
import shutil
import numpy as np
import unittest
from lsst.ts.wep.Utility import FilterType, runProgram
from lsst.ts.wep.WepController import WepController
from lsst.ts.wep.ctrlIntf.RawExpData import RawExpData
from lsst.ts.aoclcSim.Utility import getModulePath
from lsst.ts.aoclcSim.WepCmpt import WepCmpt
... | normal | {
"blob_id": "6e434ff213166768a6adadf99dc5d6d8611fa2ba",
"index": 2762,
"step-1": "<mask token>\n\n\nclass TestWepCmpt(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def testGetWepController(self):\n wepCntlr = self.wepCmpt.getWepController()\n sel... | [
9,
14,
16,
22,
23
] |
# coding=utf-8
"""
Given a binary tree, find its maximum depth.
The maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.
Example
Given a binary tree as follow:
1
/ \
2 3
/ \
4 5
The maximum depth is 3.
"""
"""
Definition of TreeNode:
"""
class ... | normal | {
"blob_id": "262d6722f4c158d0a41b22433792cdc35651d156",
"index": 9459,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n \"\"\"\n @param root: The root of binary tree.\n @return: An integer\n \"\"\"\n\n def maxDept... | [
1,
3,
4,
5,
6
] |
# ================================================== #
# MAIN WINDOW #
# ================================================== #
# Author: Brady Hammond #
# Created: 11/21/2017 #
# Last Edited: N/A ... | normal | {
"blob_id": "a555226b14223dca688d10b811eb36fb229360ce",
"index": 2457,
"step-1": "<mask token>\n\n\nclass UIMainWindow(object):\n <mask token>\n\n def retranslateUI(self):\n _translate = QtCore.QCoreApplication.translate\n self.main_window.setWindowTitle(_translate('main_window',\n ... | [
4,
6,
7,
8,
9
] |
class Solution(object):
def minimumTotal(self, triangle):
"""
:type triangle: List[List[int]]
:rtype: int
"""
t = triangle
if len(t) == 1:
return t[0][0]
ret = [0] * len(t)
ret[0] = t[0][0]
for i in range(1, len(t)):
fo... | normal | {
"blob_id": "84515ef6879b54b333f9afd48c6c4b7c43ff6957",
"index": 1068,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def minimumTotal(self, triangle):\n \"\"\"\n :type triangle: List[List[int]]\n :rtype: int... | [
0,
1,
2
] |
"""This file parses vbulletin forums"""
import re
import logging
from BeautifulSoup import BeautifulSoup as bs
import imaget
import pdb
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
date_marker = ["<!-- status icon and date -->", "<!-- / status icon and date -->"]
message_marker = ["<!-- messa... | normal | {
"blob_id": "0846f73482ad86158c3f4e37713d6d965e21d796",
"index": 2671,
"step-1": "\"\"\"This file parses vbulletin forums\"\"\"\n\nimport re\nimport logging\nfrom BeautifulSoup import BeautifulSoup as bs\nimport imaget\nimport pdb\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\ndate_... | [
0
] |
from datetime import date
atual = date.today().year
totmaior = 0
totmenor = 0
for pessoas in range(1, 8):
nasc = int(input(f'Qual sua data de nascimento? {pessoas}º: '))
idade = atual - nasc
if idade >= 21:
totmaior += 1
else:
totmenor += 1
print(f'Ao todo tivemos {totmaior} pessoas maio... | normal | {
"blob_id": "f6d7ce2d020d11086640a34aac656098ab0b0f33",
"index": 9495,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor pessoas in range(1, 8):\n nasc = int(input(f'Qual sua data de nascimento? {pessoas}º: '))\n idade = atual - nasc\n if idade >= 21:\n totmaior += 1\n else:\n ... | [
0,
1,
2,
3
] |
# -*- coding:utf-8 -*-
# Author: washing
# DateTime: 2022/5/18 10:28
# File: 0668.py
# Desc: CV
class Solution:
def findKthNumber(self, m: int, n: int, k: int) -> int:
return bisect_left(range(m * n), k, key=lambda x: x // n * n + sum(x // i for i in range(x // n + 1, m + 1)))
| normal | {
"blob_id": "ec9efeca7eef7b8ee25c1e089e675bdb1e53413b",
"index": 417,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def findKthNumber(self, m: int, n: int, k: int) ->int:\n return bisect_left(range(m * n), k, key=lambda x: x // n * n + s... | [
0,
1,
2,
3
] |
"""lendbooks URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-... | normal | {
"blob_id": "9e950f6fe895cfd497e94139397e8a0f19725dc0",
"index": 1902,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns += [url('^api-auth/', include('rest_framework.urls', namespace=\n 'rest_framework'))]\n",
"step-3": "<mask token>\nurlpatterns = [url('^admin/', admin.site.urls), url('^'... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
import sys
import setuptools
from distutils.core import setup
with open("README.md", "r") as fh:
long_description = fh.read()
def get_info():
init_file = 'PIKACHU/__init__.py'
with open(init_file, 'r') as f:
for line in f.readlines():
if "=" in line:
... | normal | {
"blob_id": "f14ff29a1a76c2916cb211c476a56aaa5061bf71",
"index": 8837,
"step-1": "<mask token>\n\n\ndef get_info():\n init_file = 'PIKACHU/__init__.py'\n with open(init_file, 'r') as f:\n for line in f.readlines():\n if '=' in line:\n exec(compile(line, '', 'exec'))\n re... | [
1,
2,
3,
4,
5
] |
import copy
import six
from eclcli.common import command
from eclcli.common import utils
from eclcli.storage.storageclient import exceptions
class ListVolumeType(command.Lister):
def get_parser(self, prog_name):
parser = super(ListVolumeType, self).get_parser(prog_name)
parser.add_argument(
... | normal | {
"blob_id": "c73bea686786a30f298500968cfd01e2d5125d75",
"index": 4013,
"step-1": "<mask token>\n\n\nclass ListVolumeType(command.Lister):\n <mask token>\n <mask token>\n\n\nclass ShowVolumeType(command.ShowOne):\n\n def get_parser(self, prog_name):\n parser = super(ShowVolumeType, self).get_parse... | [
4,
5,
6,
7,
8
] |
from flask import Flask, render_template
from config import Config
from flask_bootstrap import Bootstrap
from config import config_options
from flask_login import LoginManager
from flask_wtf.csrf import CSRFProtect
from flask_sqlalchemy import SQLAlchemy
login_manager = LoginManager()
login_manager.session_protection ... | normal | {
"blob_id": "2eecc852a6438db19e0ed55ba6cc6610d76c6ed0",
"index": 2207,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app(config_name):\n app = Flask(__name__)\n app.config.from_object(Config)\n app.config.from_object(config_options[config_name])\n app.config['SECRET_KEY'] = 'd... | [
0,
1,
2,
3,
4
] |
import sys,argparse
import os,glob
import numpy as np
import pandas as pd
import re,bisect
from scipy import stats
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.rcParams['font.size']=11
import seaborn as sns
sns.set(font_scale=1.1)
sns.set_style("whitegrid", {'axes.grid' : False})... | normal | {
"blob_id": "4ee47435bff1b0b4a7877c06fb13d13cf53b7fce",
"index": 3910,
"step-1": "<mask token>\n\n\ndef return_dci_df(DCI_dir, subdir, hm_mark, compr_type, suffix):\n dci_file = '{}/{}/{}_{}{}.csv'.format(DCI_dir, subdir, hm_mark,\n compr_type, suffix)\n if os.path.isfile(dci_file):\n dci_df ... | [
3,
4,
5,
6,
7
] |
import os
import sys
import logging.config
import sqlalchemy as sql
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Float, String, Text, Integer
import pandas as pd
import numpy as np
sys.path.append('./config')
import config
logging.basicC... | normal | {
"blob_id": "76f2312a01bf8475220a9fcc16209faddfccd2ae",
"index": 9754,
"step-1": "<mask token>\n\n\nclass BeanAttributes(Base):\n \"\"\" Defines the data model for the table `bean_attributes`. \"\"\"\n __tablename__ = 'bean_attributes'\n id = Column(Integer, primary_key=True)\n species = Column(Strin... | [
5,
6,
7,
8,
9
] |
from django.views.generic import TemplateView, FormView, CreateView, ListView
from .models import Order
from .form import OrderForm
class OrdersListView(ListView):
template_name = 'orders/index.html'
queryset = Order.objects.all()
context_object_name = 'order_list'
class OrderCreateView(CreateView):
... | normal | {
"blob_id": "afd184962e8e69843ca518e140d5fdde3d7c9ed2",
"index": 7456,
"step-1": "<mask token>\n\n\nclass OrderCreateView(CreateView):\n template_name = 'orders/form.html'\n form_class = OrderForm\n success_url = '/'\n",
"step-2": "<mask token>\n\n\nclass OrdersListView(ListView):\n <mask token>\n ... | [
2,
3,
4,
5
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import tornado.web
from sqlalchemy import desc
from sqlalchemy.orm import contains_eager
from main_app.models.post import Post
from main_app.models.thread import PostThread, User2Thread
from main_app.handlers.base_handler import BaseHandler
class API_Comments(BaseHan... | normal | {
"blob_id": "5186400c9b3463d6be19e73de665f8792d8d68c7",
"index": 6982,
"step-1": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport tornado.web\n\nfrom sqlalchemy import desc\nfrom sqlalchemy.orm import contains_eager\n\nfrom main_app.models.post import Post\nfrom main_app.models.thread import PostThread... | [
0
] |
## Author: Aleem Juma
import os
from app import app
import pandas as pd
# read in the quotes database
q = pd.read_csv(os.path.join('app','data','quotes_all.csv'), sep=';', skiprows=1, header=0)
# there are a few quote genres that don't occur in the model vocab
# replace them with appropriate words so the similarity ... | normal | {
"blob_id": "8f854f4f2c807f988945af4dc53dba93cfb31168",
"index": 9441,
"step-1": "<mask token>\n\n\ndef get_similarity(word1, word2):\n \"\"\"\n Returns a similarity score between two words\n \"\"\"\n tok1 = cache.get(word1, nlp(word1))\n tok2 = cache.get(word2, nlp(word2))\n return tok1.simila... | [
3,
5,
6,
7,
8
] |
def alt(h, dt):
t=0
while True:
t=t+1
a=(-6)*(t**4)+ h*(t**3)+2*(t**2)+t
if a<=0:
print('The balloon first touches ground at hour:')
print(t)
break
elif t==dt:
print('The balloon does not touch ground in the given tim... | normal | {
"blob_id": "592f29f08637e511bd7d49a3b58f69b700721d89",
"index": 8083,
"step-1": "<mask token>\n",
"step-2": "def alt(h, dt):\n t = 0\n while True:\n t = t + 1\n a = -6 * t ** 4 + h * t ** 3 + 2 * t ** 2 + t\n if a <= 0:\n print('The balloon first touches ground at hour:')... | [
0,
1,
2,
3
] |
import os
import sys
import json
from subprocess import Popen, PIPE, STDOUT
from twisted.internet.task import deferLater
from twisted.internet import reactor
from autobahn.twisted.websocket import WebSocketServerFactory, WebSocketServerProtocol, listenWS
from utils import rsync
# TODO: Add Twisted logger
# TODO: Cre... | normal | {
"blob_id": "30251b7c2ce30b7fa899a5885707c078788d0106",
"index": 1956,
"step-1": "import os\nimport sys\nimport json\nfrom subprocess import Popen, PIPE, STDOUT\n\nfrom twisted.internet.task import deferLater\nfrom twisted.internet import reactor\nfrom autobahn.twisted.websocket import WebSocketServerFactory, We... | [
0
] |
'''
runSPP.py - wrap spp peak caller
========================================
:Tags: Python
Purpose
-------
Runs the spp peak caller.
The workflow follows the tutorial at:
http://compbio.med.harvard.edu/Supplements/ChIP-seq/tutorial.html
Usage
-----
Documentation
-------------
Requirements:
* spp >= ?
* snow >... | normal | {
"blob_id": "e886b88a0b7e8c06772fe8a9554cab1bfe9e94a7",
"index": 7208,
"step-1": "<mask token>\n\n\ndef bamToBed(infile, outfile):\n \"\"\"convert bam to bed with bedtools.\"\"\"\n statement = 'bamToBed -i %(infile)s > %(outfile)s' % locals()\n E.debug(\"executing statement '%s'\" % statement)\n retc... | [
3,
4,
5,
6,
7
] |
import argparse
import requests
from ba_bypass_bruteforce import bruteforce, stop_brute, success_queue, dict_queue, success_username
from random import choice
from time import sleep
MAX_ROUND = 3 # 爆破的轮数
curr_round = 0 # 当前的轮数
sleep_time = 2 # 每一轮休眠的秒数
def login_limit_user():
"""
登录函数
"""
try:
... | normal | {
"blob_id": "94286fc36e06598b9faa65d9e5759f9518e436c6",
"index": 7979,
"step-1": "<mask token>\n\n\ndef login_limit_user():\n \"\"\"\n 登录函数\n \"\"\"\n try:\n login_info = dict_queue.get(block=False)\n except Exception as e:\n print('[Error] {0}'.format(repr(e)))\n return\n ... | [
4,
5,
6,
7,
8
] |
import os
import csv
import re
totWords = 0
wordLen = 0
totSentWithPunctuation = 0
sourceFile = os.path.join('Resources', 'paragraph_2.txt')
with open(sourceFile, 'r') as paragraph:
paragraph = paragraph.read().split("\n\n")
for sentence in paragraph:
# Remove punctuation from sentences
sentWithPunctua... | normal | {
"blob_id": "3cd7abf9659fe1db0ef3aa58df8dd7fd959e10a6",
"index": 386,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(sourceFile, 'r') as paragraph:\n paragraph = paragraph.read().split('\\n\\n')\nfor sentence in paragraph:\n sentWithPunctuation = sentence\n sentNoPunctuation = re.sub('... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
#from pandas import datetime
#from pandas.tseries.t
from sklearn.preprocessing import MinMaxScaler
#from statsmodels.tsa.seasonal import seasonal_decompose
from pandas import Series
data = pd.read_csv(
r'E:\... | normal | {
"blob_id": "19c0c3156488ce99316ce40f32e84e476b7afdac",
"index": 2754,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nafter_process.head(5)\nafter_process.to_csv(path_or_buf=\n 'E:\\\\Thesis Content\\\\ukdale CSV\\\\Without Noise\\\\Tvday.csv', sep=',',\n index_label='date')\n",
"step-3": "<mask ... | [
0,
1,
2,
3,
4
] |
import xml.etree.ElementTree as ET
from collections import OrderedDict
import json
import threading
class MyThread(threading.Thread):
def __init__(self, filenum):
threading.Thread.__init__(self)
self.filenum = filenum
print('Inicio del thread:', str(self.filenum))
def run(self):
... | normal | {
"blob_id": "9150eb53d309e75299775cd9524a688e8dc2ff76",
"index": 4210,
"step-1": "<mask token>\n\n\nclass MyThread(threading.Thread):\n\n def __init__(self, filenum):\n threading.Thread.__init__(self)\n self.filenum = filenum\n print('Inicio del thread:', str(self.filenum))\n <mask tok... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 5 14:23:28 2018
@author: emily
"""
import pipeline
import numpy as np
import matplotlib.pyplot as plt
import pstats
import cProfile
pr = cProfile.Profile()
pr.enable()
#def try_running():
max_it=200000
rnd_sd = 1
deps = np.concatenate((np.arange(0,10,0.2), np.aran... | normal | {
"blob_id": "cfe5d013c968afdbf1fc80e3c8c3233a3678450b",
"index": 9848,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npr.enable()\n<mask token>\nfor k in range(all_models[1,].size - 1):\n colstr = str(0.75 - k / 2 / all_models[1,].size)\n plt.plot(all_models[:, k], all_models[:, 0], '-', linewidth=... | [
0,
1,
2,
3,
4
] |
import json
import requests
from pyyoutube import Api
def get_data(YOUTUBE_API_KEY, videoId, maxResults, nextPageToken):
"""
Получение информации со страницы с видео по video id
"""
YOUTUBE_URI = 'https://www.googleapis.com/youtube/v3/commentThreads?key={KEY}&textFormat=plainText&' + \
... | normal | {
"blob_id": "4ed5ceb784fb1e3046ab9f10c4b556f2e94274db",
"index": 7054,
"step-1": "<mask token>\n\n\ndef get_data(YOUTUBE_API_KEY, videoId, maxResults, nextPageToken):\n \"\"\"\n Получение информации со страницы с видео по video id\n \"\"\"\n YOUTUBE_URI = (\n 'https://www.googleapis.com/youtub... | [
1,
2,
3,
4,
5
] |
from codecool_class import CodecoolClass
from mentor import Mentor
from student import Student
codecool_bp = CodecoolClass.create_local
| normal | {
"blob_id": "7e985f55271c8b588abe54a07d20b89b2a29ff0d",
"index": 8380,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncodecool_bp = CodecoolClass.create_local\n",
"step-3": "from codecool_class import CodecoolClass\nfrom mentor import Mentor\nfrom student import Student\ncodecool_bp = CodecoolClass.cre... | [
0,
1,
2
] |
import os
from CTFd.utils.encoding import hexencode
def generate_nonce():
return hexencode(os.urandom(32))
| normal | {
"blob_id": "4f91c57ad42759654a87328d5c92de8da14ca5ea",
"index": 2966,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate_nonce():\n return hexencode(os.urandom(32))\n",
"step-3": "import os\nfrom CTFd.utils.encoding import hexencode\n\n\ndef generate_nonce():\n return hexencode(os.u... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import keras
from keras.layers import Dense, Dropout, Input, Embedding, LSTM, Reshape, CuDNNLSTM
from keras.models import Model,Sequential
from keras.da... | normal | {
"blob_id": "bb335187dc61fae049ca4a9a55a93f856b3c7822",
"index": 2534,
"step-1": "# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport os\n#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\nimport keras\nfrom keras.layers import Dense, Dropout, ... | [
0
] |
def twoSensorAvg(input_data, duration=1):
times = {}
for i in input_data:
data = i.split(',')
time = int(int(data[1]) / (duration * 1000))
if time not in times:
times[time] = [0, 0]
times[time][0] += int(data[2])
times[time][1] += 1
ans = []
for i, v i... | normal | {
"blob_id": "836d712c811079f190eae9c2780131a844c9dddf",
"index": 3044,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test(input, output, duration):\n results = twoSensorAvg(input, duration)\n print(results)\n if len(results) != len(output):\n return False\n for i in range(len(... | [
0,
1,
2,
3
] |
'''
Created on Sep 23, 2016
@author: Andrew
'''
from pymongo import MongoClient
import re
client = MongoClient()
atMentions = re.compile(ur"@\w+", flags=re.I|re.U)
atMidnight = re.compile(u"@midnight", flags=re.I|re.U)
hashtag = re.compile(ur"#\w+", flags=re.I|re.U)
features = [("usf fwa forward most", ... | normal | {
"blob_id": "eb2bb06afb9aeb46ad02cbac145ccd817131074d",
"index": 1753,
"step-1": "'''\r\nCreated on Sep 23, 2016\r\n\r\n@author: Andrew\r\n'''\r\nfrom pymongo import MongoClient\r\nimport re\r\n\r\nclient = MongoClient()\r\n\r\natMentions = re.compile(ur\"@\\w+\", flags=re.I|re.U)\r\natMidnight = re.compile(u\"@... | [
0
] |
cardlist = []
card = []
for j in range(1,5):
for k in range(1,14):
if j == 1:
cardlist.append(["S", "{}".format(k)])
elif j == 2:
cardlist.append(["H", "{}".format(k)])
elif j == 3:
cardlist.append(["C", "{}".format(k)])
elif j == 4:
c... | normal | {
"blob_id": "937a101cf5c7e943fc62d18b77357eea151fdfaf",
"index": 7789,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor j in range(1, 5):\n for k in range(1, 14):\n if j == 1:\n cardlist.append(['S', '{}'.format(k)])\n elif j == 2:\n cardlist.append(['H', '{}'.for... | [
0,
1,
2,
3
] |
import argparse
def parse_args():
"""
Parse command-line arguments to train and evaluate a multimodal network for activity recognition on MM-Fit.
:return: Populated namespace.
"""
parser = argparse.ArgumentParser(description='baseline Mask R-CNN')
parser.add_argument('--dataset', required=True... | normal | {
"blob_id": "b6527a09f346ee1b7dd446a0ff21995a995481a8",
"index": 6640,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef parse_args():\n \"\"\"\n Parse command-line arguments to train and evaluate a multimodal network for activity recognition on MM-Fit.\n :return: Populated namespace.\n ... | [
0,
1,
2,
3
] |
#Checks if all declared prefixes are used in the RDF File
import glob
import logging
import sys
import Utility as utility
import re
# set log level
logging.basicConfig(level=logging.INFO)
root_path = "../"
rdf_file_extension = {".ttl":"turtle", ".nt":"nt", ".rdf":"application/rdf+xml"}
regex_prefix = {".ttl": r'@pr... | normal | {
"blob_id": "fe406f40b48bf4982e7a48737b6b30514ae1fa71",
"index": 7915,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlogging.basicConfig(level=logging.INFO)\n<mask token>\nfor extension in rdf_file_extension.keys():\n files_to_check = '**/*' + extension\n for filename in glob.iglob(root_path + fil... | [
0,
1,
2,
3,
4
] |
from django.db import models
from orders.constants import OrderStatus
from subscriptions.models import Subscription
class Order(models.Model):
subscription = models.OneToOneField(
Subscription,
on_delete=models.CASCADE,
related_name='order',
)
order_status = models.CharField(
... | normal | {
"blob_id": "78ddae64cc576ebaf7f2cfaa4553bddbabe474b7",
"index": 6918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Order(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Order(m... | [
0,
1,
2,
3,
4
] |
"""Produce a multi-panel figure of each output lead time in a forecast
"""
import matplotlib.pyplot as plt
import iris.plot as iplt
from irise import convert
from irise.plot.util import add_map
from myscripts import plotdir
from myscripts.models.um import case_studies
columns = 3
def main(forecast, name, levels, *a... | normal | {
"blob_id": "310e6e693cdce6ff71d06eac86214a21bef236d4",
"index": 7425,
"step-1": "<mask token>\n\n\ndef main(forecast, name, levels, *args, **kwargs):\n nt = len(forecast)\n rows = nt / columns + 1\n fig = plt.figure(figsize=(18, 10 * float(rows) / columns))\n for n, cubes in enumerate(forecast):\n ... | [
1,
2,
3,
4,
5
] |
# -*- coding:utf-8 -*-
from spider.driver.spider.base.spider import *
class LvmamaHotelSpider(Spider):
def get_comment_info2(self,shop_data):
params_list_comment1 = self.params_dict.get(ParamType.COMMENT_INFO_1)
comment_len = shop_data.get(FieldName.SHOP_COMMENT_NUM)
while(True):
... | normal | {
"blob_id": "931e73ffce6d24dbfb92501670245e20fc403a7a",
"index": 7969,
"step-1": "<mask token>\n\n\nclass LvmamaHotelSpider(Spider):\n\n def get_comment_info2(self, shop_data):\n params_list_comment1 = self.params_dict.get(ParamType.COMMENT_INFO_1)\n comment_len = shop_data.get(FieldName.SHOP_CO... | [
3,
4,
5,
6,
7
] |
/usr/local/python-3.6/lib/python3.6/abc.py | normal | {
"blob_id": "32d830f00a9d33b8f7f438c14b522ef186001bf3",
"index": 9392,
"step-1": "/usr/local/python-3.6/lib/python3.6/abc.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
import time
from selenium import webdriver
import os
from selenium.webdriver.common.by import By
with open("file.txt", "w") as file:
content = file.write("Tanyuhich")
try:
browser = webdriver.Chrome()
browser.get("http://suninjuly.github.io/file_input.html")
input1 = browser.find_element_by_name('... | normal | {
"blob_id": "03270285c6dc99d8dcb9804270421f36b573048c",
"index": 2863,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('file.txt', 'w') as file:\n content = file.write('Tanyuhich')\ntry:\n browser = webdriver.Chrome()\n browser.get('http://suninjuly.github.io/file_input.html')\n inpu... | [
0,
1,
2,
3
] |
import DB as db
import os
from Chart import Chart
import matplotlib.pyplot as plt
import numpy as np
table = db.get_researcher_copy()
chart_path = '../charts/discipline '
def get_discipline_with_more_female():
docs = table.aggregate([
{'$match':{'gender':{'$exists':1}}},
{'$unwind':'$labels'},
{'$group':{'_id'... | normal | {
"blob_id": "c585b1439217fff42945eeb9e02512d73f8ba19f",
"index": 5805,
"step-1": "<mask token>\n\n\ndef get_discipline_with_more_female():\n docs = table.aggregate([{'$match': {'gender': {'$exists': 1}}}, {\n '$unwind': '$labels'}, {'$group': {'_id': {'label': '$labels',\n 'gender': '$gender'}, ... | [
5,
6,
7,
8,
9
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import cv2
import imageio
import pandas as pd
import glob, os
import numpy as np
fileDir = os.getcwd()
# os.chdir("./train-jpg")
# there are 40480 training examples
# we will allocate 39000 for training
# and the remaining ... | normal | {
"blob_id": "a4deb67d277538e61c32381da0fe4886016dae33",
"index": 85,
"step-1": "<mask token>\n\n\nclass Net(nn.Module):\n\n def __init__(self, input_size, hidden_size, num_classes):\n super(Net, self).__init__()\n self.h1 = nn.Linear(input_size, hidden_size)\n self.h2 = nn.Linear(hidden_s... | [
3,
4,
5,
6,
7
] |
def solution(skill, skill_trees):
answer = 0
for tree in skill_trees:
able = True
for i in range(len(skill) - 1, 0, -1):
index = tree.find(skill[i])
if index != -1 and i > 0:
if tree[:index].find(skill[i - 1]) == -1:
able = False
... | normal | {
"blob_id": "a72d878d246a459038640bf9c1deff562994b345",
"index": 7338,
"step-1": "<mask token>\n",
"step-2": "def solution(skill, skill_trees):\n answer = 0\n for tree in skill_trees:\n able = True\n for i in range(len(skill) - 1, 0, -1):\n index = tree.find(skill[i])\n ... | [
0,
1,
2,
3
] |
from .. import db
class Account(db.Model):
id = db.Column(db.Integer, primary_key=True)
acc = db.Column(db.String(50), unique=True)#TODO 调整长度
pwd = db.Column(db.String(50))#TODO 调整长度
name = db.Column(db.String(20))
sex = db.Column(db.SmallInteger)
idno = db.Column(db.String(20))
phone = db... | normal | {
"blob_id": "b6824251b1165ca6c66049d40c79fccee6bc7d3a",
"index": 159,
"step-1": "<mask token>\n\n\nclass Consignor(db.Model):\n id = db.Column(db.Integer, db.ForeignKey('account.id'), primary_key=True)\n account = db.relationship('Account', uselist=False)\n indents = db.relationship('Indent', lazy='dyna... | [
8,
14,
15,
16,
18
] |
# coding=utf-8
while True:
a,b=input().split()
a=float(a)
b=float(b)
if b==0:
print("error")
else:
c=a/b+0.5
c=int(c)
print(c) | normal | {
"blob_id": "dab5e7ee1d14cba485cbaece1354ec8d686ca4ab",
"index": 9080,
"step-1": "<mask token>\n",
"step-2": "while True:\n a, b = input().split()\n a = float(a)\n b = float(b)\n if b == 0:\n print('error')\n else:\n c = a / b + 0.5\n c = int(c)\n print(c)\n",
"step... | [
0,
1,
2
] |
#!/usr/bin/env python
"""
Plot EEG data.
Usage:
plotting.py [options] [<file>]
Options:
-h --help Show this screen.
--version Show version.
--center Center the data before plotting
--sample-index=N Row index (indexed from one).
--transpose Transpose data.
--x... | normal | {
"blob_id": "5bd7160b6b2e283e221aeb0a6913e6d13511c1db",
"index": 7073,
"step-1": "<mask token>\n\n\nclass TopoPlot(object):\n <mask token>\n\n def __init__(self, data=None, axes=None):\n \"\"\"Setup defaults.\n\n Parameters\n ----------\n data : Pandas.Series or dict\n ... | [
19,
22,
23,
25,
30
] |
"""Tools for working with Scores."""
from typing import List, Optional
from citrine._serialization import properties
from citrine._serialization.polymorphic_serializable import PolymorphicSerializable
from citrine._serialization.serializable import Serializable
from citrine._session import Session
from citrine.informa... | normal | {
"blob_id": "a0086a9d27a091776378cd8bde31c59899fc07ac",
"index": 3122,
"step-1": "<mask token>\n\n\nclass LIScore(Serializable['LIScore'], Score):\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\n def __str__(sel... | [
12,
14,
16,
20,
21
] |
import tensorflow as tf
class PolicyFullyConnected:
def __init__(self, observation_space, action_space, batch_size, reuse):
height = observation_space[0]
width = observation_space[1]
self.observations = tf.placeholder(shape=(batch_size, height, width), dtype=tf.float32)
with tf.va... | normal | {
"blob_id": "ecf09f2c503452fefc427e8dbe151e7bc7ef677e",
"index": 6139,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PolicyFullyConnected:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass PolicyFullyConnected:\n\n def __init__(self, observation_space, action_space, batch_size, reu... | [
0,
1,
2,
3,
4
] |
"""
URL Configuration to test mounting created urls from registries
"""
from django.contrib import admin
from django.urls import include, path
from staticpages.loader import StaticpagesLoader
staticpages_loader = StaticpagesLoader()
urlpatterns = [
path("admin/", admin.site.urls),
# Add base pages urls usi... | normal | {
"blob_id": "333914f99face050376e4713ca118f2347e50018",
"index": 989,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns.append(path('sub/', include(\n 'sandbox.staticpages_testapp.sub_urls')))\n",
"step-3": "<mask token>\nstaticpages_loader = StaticpagesLoader()\nurlpatterns = [path('admin/... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python3
"""minimum time time to write operations of copy and paste"""
def minOperations(n):
"""
a method that calculates the fewest number of operations needed
to result in exactly n H characters in the file
"""
if n <= 1:
return 0
"""loop for n number of times"""
for i... | normal | {
"blob_id": "f14b9373e9bf1ad7fe2216dfefc1571f5380fb27",
"index": 6528,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef minOperations(n):\n \"\"\"\n a method that calculates the fewest number of operations needed\n to result in exactly n H characters in the file\n \"\"\"\n if n <= 1:... | [
0,
1,
2
] |
from django.shortcuts import render, get_object_or_404, redirect
from django.utils import timezone
from django.core.paginator import Paginator
from .models import post
from django.contrib.auth.decorators import login_required
from .forms import post_fo
from django.db.models import Q
def index(request):
posts_l... | normal | {
"blob_id": "2b88bec388f3872b63d6bfe200e973635bb75054",
"index": 5418,
"step-1": "<mask token>\n\n\ndef detail(request, post_id):\n po = get_object_or_404(post, pk=post_id)\n ratelist = [1, 2, 3, 4, 5]\n return render(request, 'detail.html', {'post': po, 'ratelist': ratelist})\n\n\n@login_required(login... | [
2,
4,
5,
6,
7
] |
import json
def get_json_data(page):
with open('geekshop/json_data.json', encoding='utf-8-sig') as file:
json_data = json.load(file)
return json_data[page]
def get_json_products_data(file_path):
with open(file_path, encoding='utf-8-sig') as file:
json_data = json.load(file)
return js... | normal | {
"blob_id": "08b53ba116b0c5875d39af4ce18296d547d5891d",
"index": 5692,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_json_products_data(file_path):\n with open(file_path, encoding='utf-8-sig') as file:\n json_data = json.load(file)\n return json_data\n",
"step-3": "<mask token... | [
0,
1,
2,
3,
4
] |
from __future__ import absolute_import, division, print_function
import numbers
import torch
from torch.distributions import constraints
from pyro.distributions.distribution import Distribution
from pyro.distributions.score_parts import ScoreParts
from pyro.distributions.util import broadcast_shape, sum_rightmost
... | normal | {
"blob_id": "0f0ea6f07f9a082042ed9aff7a95d372c32b5a13",
"index": 1897,
"step-1": "<mask token>\n\n\nclass ReshapedDistribution(TorchDistribution):\n <mask token>\n <mask token>\n\n def __init__(self, base_dist, sample_shape=torch.Size(),\n reinterpreted_batch_ndims=0):\n sample_shape = tor... | [
27,
35,
36,
39,
44
] |
# -*- coding: utf-8 -*-
"""
Noting is perfect, errors and timeouts may happen, and when such failures happen, the
consumer has to decide what to do with that. By default, the consumer would reject the
envelope (RabbitMQ message) when a failure happens. However, errors and timeouts
issues, unless there is a software bug... | normal | {
"blob_id": "848934680253ff2950db7723b1fe82b2ae799900",
"index": 801,
"step-1": "<mask token>\n\n\nclass LimitedRetriesPolicy(BaseRetryPolicy):\n <mask token>\n\n def __init__(self, consumer, retry_delays, retry_queue_suffix='retry',\n **kwargs):\n \"\"\"\n :param Consumer consumer: me... | [
9,
13,
15,
23,
27
] |
class Anagram(object):
def __init__(self, word):
self.word = word
self.canonical = self._canonicalize(word)
def _canonicalize(self, word):
return sorted(word.lower())
def _is_anagram(self, word):
return word != self.word and self._canonicalize(word) == self.canonical
... | normal | {
"blob_id": "44224985dbfa6234eff406149ce25e1d00b512e9",
"index": 620,
"step-1": "class Anagram(object):\n <mask token>\n <mask token>\n <mask token>\n\n def match(self, words):\n return filter(self._is_anagram, words)\n",
"step-2": "class Anagram(object):\n\n def __init__(self, word):\n ... | [
2,
3,
4,
5
] |
# 赛场统分
# 【问题】在编程竞赛中，有10个评委为参赛的选手打分，分数为0 ~ 100分。
# 选手最后得分为：去掉一个最高分和一个最低分后其余8个分数的平均值。请编写一个程序实现。
sc_lst = []
i = 1
while len(sc_lst) < 10:
try:
sc = int(input('请第%d位评委打分：' % i))
if sc > 0 and sc < 101:
sc_lst.append(sc)
i += 1
else:
print('超出范围，输入无效')
ex... | normal | {
"blob_id": "a17abd3947a946daf2c453c120f2e79d2ba60778",
"index": 901,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile len(sc_lst) < 10:\n try:\n sc = int(input('请第%d位评委打分：' % i))\n if sc > 0 and sc < 101:\n sc_lst.append(sc)\n i += 1\n else:\n ... | [
0,
1,
2,
3
] |
"""
7-4. Pizza Toppings: Write a loop that prompts the user to enter a series of
pizza toppings until they enter a 'quit' value. As they enter each topping,
print a message saying you’ll add that topping to their pizza.
"""
if __name__ == '__main__':
topping = None
while topping != "quit":
if topping:
... | normal | {
"blob_id": "4d07795543989fe481e1141756f988d276f82c02",
"index": 5348,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n topping = None\n while topping != 'quit':\n if topping:\n print(\"I'll add %s to your pizza!\" % topping)\n topping = input(\n ... | [
0,
1,
2
] |
# Generated by Django 2.2 on 2019-05-13 06:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('base_data_app', '0008_key_keyslider'),
]
operations = [
migrations.AddField(
model_name='key',
name='image',
... | normal | {
"blob_id": "ad53b100a1774f5429278379302b85f3a675adea",
"index": 8986,
"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 = [('base_data_a... | [
0,
1,
2,
3,
4
] |
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