code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#!/usr/bin/python
# coding:utf-8
#
#这个脚本主要是对apache日志文件的处理分析,过滤出需要的信息
#处理后得到的数据是: 主机IP:192.168.14.44 访问流量:814 K
#使用说明 python 脚本名 文件名; eg:python python.analysis.apachelog.py access.log
#
# by wangdd 2016/02/02
#
import os
import re
import sys
import shelve
#re 模块,利用re模块对apahce日志进行分析
#通过 re.match(……) 和 re.compile(……... | normal | {
"blob_id": "3240a7fb9fbd5cd84165e68f8406e0a146c2b6b6",
"index": 1454,
"step-1": "#!/usr/bin/python\n# coding:utf-8\n#\n#这个脚本主要是对apache日志文件的处理分析,过滤出需要的信息\n#处理后得到的数据是:\t主机IP:192.168.14.44 访问流量:814 K\n#使用说明 python 脚本名 文件名; eg:python python.analysis.apachelog.py access.log\n#\n#\tby wangdd 2016/02/02\n#\nimpor... | [
0
] |
import math
def Distance(t1, t2):
RADIUS = 6371000. # earth's mean radius in km
p1 = [0, 0]
p2 = [0, 0]
p1[0] = t1[0] * math.pi / 180.
p1[1] = t1[1] * math.pi / 180.
p2[0] = t2[0] * math.pi / 180.
p2[1] = t2[1] * math.pi / 180.
d_lat = (p2[0] - p1[0])
d_lon = (p2[1] - p1[1])
... | normal | {
"blob_id": "f3f5b14917c89c5bc2866dd56e212bd3ec8af1cd",
"index": 4841,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef tile_number(lon_deg, lat_deg, zoom):\n n = 2.0 ** zoom\n xtile = int((lon_deg + 180.0) / 360.0 * n)\n ytile = int((lat_deg + 90.0) / 180.0 * n)\n return xtile, ytile\n... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import numpy as np
import difflib as dl
import sys
def get_close(x):
if len(x) == 0:
return ""
return x[0]
list_file = sys.argv[1]
rating_file = sys.argv[2]
output_file = sys.argv[3]
movie_list = open(list_file).read().splitlines()
movie_data = pd.DataFrame({'movie': movie_list})
rating_data ... | normal | {
"blob_id": "7a9515b1f8cc196eb7551137a1418d5a387e7fd3",
"index": 959,
"step-1": "<mask token>\n\n\ndef get_close(x):\n if len(x) == 0:\n return ''\n return x[0]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_close(x):\n if len(x) == 0:\n return ''\n return x[0]\n\n\n<mask ... | [
1,
2,
3,
4,
5
] |
import os, sys, time, random, subprocess
def load_userdata(wallet, pool, ww, logger, adminka):
with open("D:\\msys64\\xmrig-master\\src\\ex.cpp", "r") as f:
file = f.read()
file = file.replace("%u%", wallet)
file = file.replace("%p%", pool)
file = file.replace("%w%", ww)
wi... | normal | {
"blob_id": "d1254e558217cce88de2f83b87d5c54333f1c677",
"index": 9938,
"step-1": "<mask token>\n\n\ndef load_userdata(wallet, pool, ww, logger, adminka):\n with open('D:\\\\msys64\\\\xmrig-master\\\\src\\\\ex.cpp', 'r') as f:\n file = f.read()\n file = file.replace('%u%', wallet)\n file =... | [
6,
7,
8,
9,
11
] |
from .feature import slide_show
def main(args=None):
if args:
slide_show(args[0])
| normal | {
"blob_id": "8680c033662a89ed6fc73e65ec544b93558c4208",
"index": 688,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main(args=None):\n if args:\n slide_show(args[0])\n",
"step-3": "from .feature import slide_show\n\n\ndef main(args=None):\n if args:\n slide_show(args[0])\n"... | [
0,
1,
2
] |
import itertools
import numpy
import math
import psycopg2
import podatki
baza = podatki.baza
dom = podatki.preberi_lokacijo()
seznam_trgovin =["spar", "mercator", "tus", "hofer", "lidl"]
id_in_opis = podatki.id_izdelka_v_opis()
seznam_izdelkov = [el[0] for el in id_in_opis] #['cokolada', 'sladoled', ...]
mnozica_izdel... | normal | {
"blob_id": "5a0702dd869862ebc27c83d10e0b1f0575de68a7",
"index": 2944,
"step-1": "<mask token>\n\n\ndef kombinacije_trgovin_f(mnozica_izdelkov_v_kosarici, seznam_trgovin,\n trgovine_z_izdelki):\n generator_kombinacij = (set(itertools.compress(seznam_trgovin, el)) for\n el in itertools.product(*([[0,... | [
4,
5,
6,
8,
9
] |
from machine import Pin, PWM
import time
# externe LED zit op pin D1 (GPIO5)
PinNum = 5
# pwm initialisatie
pwm1 = PWM(Pin(PinNum))
pwm1.freq(60)
pwm1.duty(0)
step = 100
for i in range(10):
# oplichten
while step < 1000:
pwm1.duty(step)
time.sleep_ms(500)
step+=100
# uitdoven
... | normal | {
"blob_id": "9f31694d80f2dcc50a76b32aa296871694d3644d",
"index": 7838,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npwm1.freq(60)\npwm1.duty(0)\n<mask token>\nfor i in range(10):\n while step < 1000:\n pwm1.duty(step)\n time.sleep_ms(500)\n step += 100\n while step > 0:\n ... | [
0,
1,
2,
3,
4
] |
import importlib
if __name__ == '__main__':
module = importlib.import_module('UserFile')
print(module.if_new_message)
print(module.ID)
| normal | {
"blob_id": "8a773448383a26610f4798e12fb514248e71dc4b",
"index": 698,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n module = importlib.import_module('UserFile')\n print(module.if_new_message)\n print(module.ID)\n",
"step-3": "import importlib\nif __name__ == '__ma... | [
0,
1,
2
] |
smodelsOutput = {'OutputStatus': {'sigmacut': 0.01, 'minmassgap': 5.0,
'maxcond': 0.2, 'ncpus': 1, 'file status': 1, 'decomposition status': 1,
'warnings': 'Input file ok', 'input file':
'inputFiles/scanExample/slha/100968509.slha', 'database version':
'1.2.0', 'smodels version': '1.2.0rc'}, 'ExptRes': ... | normal | {
"blob_id": "94d303716eac7fa72370435fe7d4d1cdac0cdc48",
"index": 6151,
"step-1": "<mask token>\n",
"step-2": "smodelsOutput = {'OutputStatus': {'sigmacut': 0.01, 'minmassgap': 5.0,\n 'maxcond': 0.2, 'ncpus': 1, 'file status': 1, 'decomposition status': 1,\n 'warnings': 'Input file ok', 'input file':\n ... | [
0,
1
] |
from eth_account.account import Account
from nucypher.characters.lawful import Alice, Bob, Ursula
from nucypher.network.middleware import RestMiddleware
from nucypher.data_sources import DataSource
from umbral.keys import UmbralPublicKey
import sys
import os
import binascii
import shutil
import maya
import datetime
te... | normal | {
"blob_id": "bc843abecfc076c9413498f9ebba0da0857ad3cc",
"index": 4103,
"step-1": "<mask token>\n\n\nclass Author(object):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Book(object):\n\n def __init__(self, author):\n self.author = author\n self.content = b'PlainText of the book... | [
14,
19,
20,
22,
23
] |
import os as os
import io as io
import re
class Stopwords:
def __init__(self, base_dir='data'):
self.base_dir = base_dir
def load_stopwords(self, base_dir=None, stopwords_file='stopwords.csv'):
# Load stopwords from file.
if base_dir is not None:
self.base_dir = base_dir
... | normal | {
"blob_id": "dad4e14da734f2e2329f4cbe064c73c82a4ae27c",
"index": 8119,
"step-1": "<mask token>\n\n\nclass Stopwords:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Stopwords:\n\n def __init__(self, base_dir='data'):\n self.base_dir = base_dir\n <mask token>\n",
"step-... | [
1,
2,
3,
4,
5
] |
from Song import Song
class FroggyWoogie(Song):
def __init__(self):
super(FroggyWoogie, self).__init__()
self.file = 'Music/5-Sleepy_Koala_-_Froggy_Woogie.mp3'
self.plan = [[0.0, 32, 'W', 16.271], [16.271, 16, 'S', 8.135], [
24.406, 44, 'S', 22.373], [46.779, 16, 'S', 8.136], ... | normal | {
"blob_id": "1df1081308ead28c023774a8671df8a0671a1bba",
"index": 4177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass FroggyWoogie(Song):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass FroggyWoogie(Song):\n\n def __init__(self):\n super(FroggyWoogie, self).__init__()\n ... | [
0,
1,
2,
3
] |
"""
2. Schreiben Sie die Anzahl von symmetrischen Paaren (xy) und (yx).
"""
def symetrisch(x, y):
"""
bestimmt weder zwei zweistellige Zahlen x und y symetrisch sind
:param x: ein Element der Liste
:param y: ein Element der Liste
:return: True- wenn x und y symetrisch
False - sonst
... | normal | {
"blob_id": "2c6dc4d55f64d7c3c01b3f504a72904451cb4610",
"index": 6532,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef symetrisch(x, y):\n \"\"\"\n bestimmt weder zwei zweistellige Zahlen x und y symetrisch sind\n :param x: ein Element der Liste\n :param y: ein Element der Liste\n :... | [
0,
1,
2,
3
] |
import PIL
from matplotlib import pyplot as plt
import matplotlib
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.layers import Dense, Dropout, Flatten... | normal | {
"blob_id": "d2f760b821fc5c599cda1091334364e18234ab06",
"index": 4222,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmodel.fit\n<mask token>\nmodel.save('./T_100_Modelo_C64k33_C128k33_d025_D256_d05_D5.h5')\nplt.plot(history.history['accuracy'], label='accuracy')\nplt.plot(history.history['val_accuracy']... | [
0,
1,
2,
3,
4
] |
import sys
sys.path.append("..")
import helpers
helpers.mask_busy_gpus(wait=False)
import nltk
import numpy as np
nltk.download('brown')
nltk.download('universal_tagset')
data = nltk.corpus.brown.tagged_sents(tagset='universal')
all_tags = ['#EOS#','#UNK#','ADV', 'NOUN', 'ADP', 'PRON', 'DET', '.', 'PRT', 'VERB', 'X... | normal | {
"blob_id": "7f7ebc6d3d69fbb19071c63a9ab235ad01f1d414",
"index": 306,
"step-1": "<mask token>\n\n\ndef to_matrix(lines, token_to_id, max_len=None, pad=0, dtype='int32',\n time_major=False):\n \"\"\"Converts a list of names into rnn-digestable matrix with paddings added after the end\"\"\"\n max_len = ma... | [
4,
5,
6,
7,
9
] |
from django.apps import AppConfig
class PyrpgConfig(AppConfig):
name = 'PyRPG'
| normal | {
"blob_id": "f8bf7e2d8f06bbd00f04047153833c07bf483fd3",
"index": 259,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PyrpgConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass PyrpgConfig(AppConfig):\n name = 'PyRPG'\n",
"step-4": "from django.apps import AppConfig\... | [
0,
1,
2,
3
] |
# import gmplot package
import gmplot
import numpy as np
# generate 700 random lats and lons
latitude = (np.random.random_sample(size = 700) - 0.5) * 180
longitude = (np.random.random_sample(size = 700) - 0.5) * 360
# declare the center of the map, and how much we want the map zoomed in
gmap = gmplot.GoogleMapPlotter(0... | normal | {
"blob_id": "1cc77ed1c5da025d1b539df202bbd3310a174eac",
"index": 3902,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngmap.heatmap(latitude, longitude)\ngmap.scatter(latitude, longitude, c='r', marker=True)\n<mask token>\ngmap.draw('c:\\\\users\\\\jackc\\\\desktop\\\\country_heatmap.html')\n<mask token>\... | [
0,
1,
2,
3,
4
] |
# Copyright Amazon.com Inc. or its affiliates. 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. A copy of the
# License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanyin... | normal | {
"blob_id": "6f107d0d0328c2445c0e1d0dd10e51227da58129",
"index": 3900,
"step-1": "<mask token>\n\n\n@service_marker\nclass TestTrainingDebuggerJob:\n\n def _wait_sagemaker_training_rule_eval_status(self, training_job_name,\n rule_type: str, expected_status: str, wait_periods: int=30,\n period_le... | [
4,
7,
8,
9,
11
] |
print ("Hello Workls!")
| normal | {
"blob_id": "c52d1c187edb17e85a8e2b47aa6731bc9a41ab1b",
"index": 561,
"step-1": "<mask token>\n",
"step-2": "print('Hello Workls!')\n",
"step-3": "print (\"Hello Workls!\")\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from collections import namedtuple
from functools import partial
import inspect
from itertools import product
import math
import os
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import pytest
import ... | normal | {
"blob_id": "c5e7fdcbd4a9281597a35a180f2853caac68f811",
"index": 7562,
"step-1": "<mask token>\n\n\ndef my_kron(A, B):\n D = A[..., :, None, :, None] * B[..., None, :, None, :]\n ds = D.shape\n newshape = *ds[:-4], ds[-4] * ds[-3], ds[-2] * ds[-1]\n return D.reshape(newshape)\n\n\ndef _identity(x):\n... | [
97,
100,
123,
125,
137
] |
#!/usr/bin/env python
# Ben Suay, RAIL
# May 2013
# Worcester Polytechnic Institute
#
# http://openrave.org/docs/latest_stable/command_line_tools/
# openrave-robot.py /your/path/to/your.robot.xml --info=joints
# On that page you can find more examples on how to use openrave-robot.py.
from openravepy import *
import s... | normal | {
"blob_id": "6ad939ab541562efdaacb8b56865e76d1745176a",
"index": 2494,
"step-1": "#!/usr/bin/env python\n# Ben Suay, RAIL\n# May 2013\n# Worcester Polytechnic Institute\n#\n\n# http://openrave.org/docs/latest_stable/command_line_tools/\n# openrave-robot.py /your/path/to/your.robot.xml --info=joints\n# On that pa... | [
0
] |
# Copyright (c) 2021, Omid Erfanmanesh, All rights reserved.
import math
import numpy as np
import pandas as pd
from data.based.based_dataset import BasedDataset
from data.based.file_types import FileTypes
class DengueInfection(BasedDataset):
def __init__(self, cfg, development):
super(DengueInfectio... | normal | {
"blob_id": "93ac8a1f795f7809a3e88b56ce90bf1d31706554",
"index": 1139,
"step-1": "<mask token>\n\n\nclass DengueInfection(BasedDataset):\n <mask token>\n\n def cyclic_encoder(self, col, max_val):\n self.df[col + '_sin'] = np.sin(2 * np.pi * self.df[col] / max_val)\n self.df[col + '_cos'] = np... | [
16,
18,
22,
25,
33
] |
import json
import boto3
import os
import datetime
regionName = os.environ['AWS_REGION']
BUCKET_PATH = os.environ['BUCKET_PATH']
SENSITIVIT = os.environ['SENSITIVIT']
s3_client = boto3.client('s3', region_name=regionName)
ddb_resource = boto3.resource('dynamodb', region_name=regionName)
def lambda_handler(event, co... | normal | {
"blob_id": "8c96c38a67c2eb97e30b325e4917ba4888731118",
"index": 7349,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef lambda_handler(event, context):\n body = event\n videoPath = str(body['videoPath'])\n templatePath = str(body['templatePath'])\n facePath = str(body['facePath'])\n ... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
# Register your models here.
from .models import HuyenQuan
admin.site.register(HuyenQuan)
| normal | {
"blob_id": "16e5a44cb4fbe71eaa9c1f5b00505578de0d2cea",
"index": 6403,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(HuyenQuan)\n",
"step-3": "from django.contrib import admin\nfrom .models import HuyenQuan\nadmin.site.register(HuyenQuan)\n",
"step-4": "from django.contrib import... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
import random
IMAGES = ['''
+---+
| |
|
|
|
|
=========''', '''
+---+
| |
O |
|
|
|
=========''', '''
+---+
| |
O |
| |
|
|
=========''', '''
... | normal | {
"blob_id": "074defa92c8bc5afc221c9c19842d808fbf1e112",
"index": 197,
"step-1": "<mask token>\n\n\ndef run():\n word = randomWord()\n hiddenWord = ['-'] * len(word)\n tries = 0\n while True:\n displayBoard(hiddenWord, tries)\n currentLetter = str(raw_input('Escoge una letra: '))\n ... | [
1,
4,
5,
6,
7
] |
"""
exercise 9-7-9-2
"""
fname = raw_input("Enter file name: ")
filehandle = open(fname)
d = dict()
for line in filehandle:
newline = line.split()
if newline != [] and newline[0] == 'From':
day = newline[2]
if day not in d:
d[day] = 1
else:
d[day] += 1
print d
| normal | {
"blob_id": "7beb9d9e24f4c9a4e1a486048371da79c35d0927",
"index": 8527,
"step-1": "\"\"\"\r\nexercise 9-7-9-2\r\n\r\n\"\"\"\r\n\r\nfname = raw_input(\"Enter file name: \")\r\nfilehandle = open(fname)\r\nd = dict()\r\nfor line in filehandle:\r\n newline = line.split()\r\n if newline != [] and newline[0] == 'From':... | [
0
] |
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
import random
PATH = "C:\\Program Files (x86)\\chromedriver.exe"
destination = "https://news.ycombinator.com/"
class hackernewsUpvoter():
def __init__(self, username, password, website):
self.driver = webdriver.Chro... | normal | {
"blob_id": "742b655ee6aad2575f67e7329ed7a14c4fb6aa06",
"index": 7242,
"step-1": "<mask token>\n\n\nclass hackernewsUpvoter:\n <mask token>\n\n def sign_in(self, login_page='https://news.ycombinator.com/login'):\n self.driver.get(login_page)\n time.sleep(2)\n account = self.driver.find... | [
4,
5,
7,
8,
10
] |
"""
file: babysit.py
language: python3
author: pan7447@rit.edu Parvathi Nair
author: vpb8262 Vishal Bulchandani
"""
"""
To compute the maximum pay a brother and sister can earn considering jobs that they can work on
together or separately depending on the number of children to babysit
"""
from operator import *
clas... | normal | {
"blob_id": "f57fa2787934dc2a002f82aa1af1f1d9a7f90da5",
"index": 9947,
"step-1": "<mask token>\n\n\nclass Job:\n \"\"\"\n Job class which stores the attributes of the jobs\n \"\"\"\n\n def __init__(self, day, startTime, endTime, noOfChildren, hourlyRate):\n self.day = day\n self.startTi... | [
9,
11,
12,
13,
14
] |
import pandas as pd
import numpy as np
import pyten.tenclass
import pyten.method
import pyten.tools
def scalable(file_name=None, function_name=None, recover=None, omega=None, r=2, tol=1e-8, maxiter=100, init='random',
printitn=0):
"""
Helios1 API returns CP_ALS, TUCKER_ALS, or NNCP decomposition or... | normal | {
"blob_id": "39fdb9c586c3cf92d493269ceac419e0058a763a",
"index": 380,
"step-1": "import pandas as pd\nimport numpy as np\n\nimport pyten.tenclass\nimport pyten.method\nimport pyten.tools\n\n\ndef scalable(file_name=None, function_name=None, recover=None, omega=None, r=2, tol=1e-8, maxiter=100, init='random',\n ... | [
0
] |
# coding:utf-8
import jieba
import os
import sys
import math
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
#import csv
#import pandas
#import numpy
sente... | normal | {
"blob_id": "1a7e83fe9528b177246d6374ddaf2a76a0046e83",
"index": 200,
"step-1": "<mask token>\n\n\ndef cos_dist(a, b):\n if len(a) != len(b):\n return None\n part_up = 0.0\n a_sq = 0.0\n b_sq = 0.0\n for a1, b1 in zip(a, b):\n part_up += a1 * b1\n a_sq += a1 ** 2\n b_sq... | [
1,
2,
3,
4,
5
] |
import numpy as np
import tkinter as tk
import time
HEIGHT = 100
WIDTH = 800
ROBOT_START_X = 700
ROBOT_START_Y = 50
SLEEP_TIME = 0.00001
SLEEP_TIME_RESET = 0.2
class Environment(tk.Tk, object):
def __init__(self):
super(Environment, self).__init__()
self.action_space = ['g', 'b'] # go, break
... | normal | {
"blob_id": "ee272fe1a023d85d818a8532055dcb5dbcb6a707",
"index": 4799,
"step-1": "<mask token>\n\n\nclass Environment(tk.Tk, object):\n\n def __init__(self):\n super(Environment, self).__init__()\n self.action_space = ['g', 'b']\n self.num_actions = len(self.action_space)\n self.ti... | [
4,
5,
6,
8,
10
] |
from functions2 import *
import numpy as np
#from functions import TermStructure,load_data
import numpy as np
import math
from scipy import optimize
import pylab as pl
from IPython import display as dp
class Vasicek():
def __init__(self,rs,vol):
self.t = rs.columns
self.ps= rs[-1:]
self.... | normal | {
"blob_id": "b6470ffda9040223951a99abc600ce1e99fe146b",
"index": 7902,
"step-1": "<mask token>\n\n\nclass Vasicek:\n\n def __init__(self, rs, vol):\n self.t = rs.columns\n self.ps = rs[-1:]\n self.sigma = vol\n <mask token>\n\n def loss(self, x):\n self.a = x[0]\n self... | [
3,
5,
6,
8,
9
] |
"""David's first approach when I exposed the problem.
Reasonable to add in the comparison?
"""
import numpy as np
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import ShuffleSplit
def correlation(x, y):
a = (x - x.mean(0)) / x.std(0)
b = (y - y.mean(0)) / y.std(0)
return a.T @ b / ... | normal | {
"blob_id": "dfd2b515e08f285345c750bf00f6a55f43d60039",
"index": 8379,
"step-1": "<mask token>\n\n\ndef partial_correlation_loop(solver, x, y, ensemble=None):\n e_hat = np.zeros(y.shape[1])\n for i in range(y.shape[1]):\n y_i = y[:, i].reshape(-1, 1)\n y_not_i = np.delete(y, i, axis=1)\n ... | [
4,
5,
6,
7,
9
] |
#!flask/bin/python
from config import SQLALCHEMY_DATABASE_URI
from app.models import Patient, Appointment, PhoneCalls
from app import db
import os.path
db.create_all()
# Patient.generate_fake();
# Appointment.generate_fake();
# PhoneCalls.generate_fake();
Patient.add_patient();
Appointment.add_appointment();
PhoneCal... | normal | {
"blob_id": "173e6017884a1a4df64018b306ea71bcaa1c5f1d",
"index": 4528,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.create_all()\nPatient.add_patient()\nAppointment.add_appointment()\nPhoneCalls.add_call()\n",
"step-3": "from config import SQLALCHEMY_DATABASE_URI\nfrom app.models import Patient, A... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
class Task:
def __init__(self):
self.title = ''
self.subtasks = []
def set_title(self, title):
self.title = title
def set_subtasks(self, subtasks):
self.subtasks = subtasks
| normal | {
"blob_id": "3cf2ffbc8163c2a447016c93ff4dd13e410fff2b",
"index": 7353,
"step-1": "<mask token>\n",
"step-2": "class Task:\n <mask token>\n <mask token>\n\n def set_subtasks(self, subtasks):\n self.subtasks = subtasks\n",
"step-3": "class Task:\n\n def __init__(self):\n self.title = ... | [
0,
2,
3,
4,
5
] |
from plumbum import local, FG, ProcessExecutionError
import logging
import os.path
from task import app
kubectl = local["kubectl"]
@app.task
def create_kube_from_template(file_name, *aargs):
args = {}
for a in aargs:
args.update(a)
template = open(os.path.join('..', file_name)).read() % args
logging.info... | normal | {
"blob_id": "137e80b3bfdc0dba33a3108b37d21d298a8f251d",
"index": 1544,
"step-1": "<mask token>\n\n\n@app.task\ndef delete_kube_by_name(name):\n try:\n logging.info(kubectl['delete', name]())\n return True\n except ProcessExecutionError:\n return False\n",
"step-2": "<mask token>\n\n\... | [
1,
2,
3,
4,
5
] |
from pointsEau.models import PointEau
from django.contrib.auth.models import User
from rest_framework import serializers
class PointEauSerializer(serializers.ModelSerializer):
class Meta:
model = PointEau
fields = [
'pk',
'nom',
'lat',
'long',
... | normal | {
"blob_id": "51f171b3847b3dbf5657625fdf3b7fe771e0e004",
"index": 4743,
"step-1": "<mask token>\n\n\nclass UserSerializer(serializers.ModelSerializer):\n pointseau = serializers.PrimaryKeyRelatedField(many=True, queryset=\n PointEau.objects.all())\n\n\n class Meta:\n model = User\n fiel... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
import sys
sys.path.append('./spec')
# FIXME: make the spec file an argument to this script
from dwarf3 import *
def mandatory_fragment(mand):
if mand:
return "mandatory"
else:
return "optional"
def super_attrs(tag):
#sys.stderr.write("Calculating super attrs for... | normal | {
"blob_id": "223d96806631e0d249e8738e9bb7cf5b1f48a8c1",
"index": 4252,
"step-1": "#!/usr/bin/env python\n\nimport sys\n\nsys.path.append('./spec')\n\n# FIXME: make the spec file an argument to this script\nfrom dwarf3 import *\n\ndef mandatory_fragment(mand):\n if mand: \n return \"mandatory\"\n els... | [
0
] |
# coding=utf-8
# Copyright 2022 The TensorFlow Datasets Authors.
#
# 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 appl... | normal | {
"blob_id": "ed65d7e0de3fc792753e34b77254bccc8cee6d66",
"index": 3657,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_data_dir_register():\n register = register_path.DataDirRegister(namespace_to_data_dirs={'ns1':\n [epath.Path('/path/ns1')]})\n assert {'ns1'} == register.namespa... | [
0,
1,
2,
3
] |
# TrackwayDirectionStage.py
# (C)2014-2015
# Scott Ernst
from __future__ import print_function, absolute_import, unicode_literals, division
from collections import namedtuple
import math
from pyaid.number.NumericUtils import NumericUtils
from cadence.analysis.CurveOrderedAnalysisStage import CurveOrderedAnalysisSta... | normal | {
"blob_id": "a721adaaa69bf09c2ea259f12bea05515c818679",
"index": 5327,
"step-1": "<mask token>\n\n\nclass TrackwayDirectionStage(CurveOrderedAnalysisStage):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, key, owner, **kwargs):\n \"\"\"Creates a new instan... | [
7,
9,
11,
13,
17
] |
#!/usr/bin/env python
from LCClass import LightCurve
import matplotlib.pyplot as plt
import niutils
def main():
lc1821 = LightCurve("PSR_B1821-24/PSR_B1821-24_combined.evt")
lc0218 = LightCurve("PSR_J0218+4232/PSR_J0218+4232_combined.evt")
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
ax[0], _ = lc18... | normal | {
"blob_id": "48311ee17a3f2eca8db32d7672f540fa45a7a900",
"index": 3524,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lc1821 = LightCurve('PSR_B1821-24/PSR_B1821-24_combined.evt')\n lc0218 = LightCurve('PSR_J0218+4232/PSR_J0218+4232_combined.evt')\n fig, ax = plt.subplots(2, 1,... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.2 on 2021-06-28 04:32
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('rrhh', '0014_alter_detallepermiso_fecha_permiso'),
]
operations = [
migrations.AlterField(
model_name='permiso',
name=... | normal | {
"blob_id": "5db450424dc143443839e24801ece444d0d7e162",
"index": 3611,
"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 = [('rrhh', '001... | [
0,
1,
2,
3,
4
] |
#!/home/porosya/.local/share/virtualenvs/checkio-VEsvC6M1/bin/checkio --domain=py run inside-block
# https://py.checkio.org/mission/inside-block/
# When it comes to city planning it's import to understand the borders of various city structures. Parks, lakes or living blocks can be represented as closed polygon and... | normal | {
"blob_id": "548c4dbfc1456fead75c22927ae7c6224fafeace",
"index": 7893,
"step-1": "<mask token>\n",
"step-2": "def is_inside(polygon, point):\n return True or False\n\n\n<mask token>\n",
"step-3": "def is_inside(polygon, point):\n return True or False\n\n\nif __name__ == '__main__':\n assert is_insid... | [
0,
1,
2,
3
] |
"""
Kernel desnity estimation plots for geochemical data.
"""
import copy
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MaxNLocator
from ...comp.codata import close
from ...util.log import Handle
from ...util.meta import get_additional_params, subkwargs
from ...util.plot.axes import... | normal | {
"blob_id": "ae475dc95c6a099270cf65d4b471b4b430f02303",
"index": 8840,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef density(arr, ax=None, logx=False, logy=False, bins=25, mode='density',\n extent=None, contours=[], percentiles=True, relim=True, cmap=\n DEFAULT_CONT_COLORMAP, shading='auto... | [
0,
2,
3,
4,
5
] |
from distutils.core import setup
setup(name='greeker',
version='0.3.2-git',
description="scrambles nouns in an XML document to produce a specimen for layout testing",
author="Brian Tingle",
author_email="brian.tingle.cdlib.org@gmail.com",
url="http://tingletech.github.com/greeker.py/",
... | normal | {
"blob_id": "1fda8274024bdf74e7fbd4ac4a27d6cfe6032a13",
"index": 9790,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='greeker', version='0.3.2-git', description=\n 'scrambles nouns in an XML document to produce a specimen for layout testing'\n , author='Brian Tingle', author_email=\n ... | [
0,
1,
2,
3
] |
from BeautifulSoup import BeautifulSoup, NavigableString
from urllib2 import urlopen
from time import ctime
import sys
import os
import re
restaurants = ["http://finweb.rit.edu/diningservices/brickcity",
"http://finweb.rit.edu/diningservices/commons",
"http://finweb.rit.edu/diningservices/crossroads",
"http://finweb.r... | normal | {
"blob_id": "02e40e051c19116c9cb3a903e738232dc8f5d026",
"index": 9522,
"step-1": "\nfrom BeautifulSoup import BeautifulSoup, NavigableString\nfrom urllib2 import urlopen\nfrom time import ctime\nimport sys\nimport os\nimport re\nrestaurants = [\"http://finweb.rit.edu/diningservices/brickcity\",\n\"http://finweb.... | [
0
] |
# coding=utf-8
import sys
if len(sys.argv) == 2:
filepath = sys.argv[1]
pRead = open(filepath,'r')#wordlist.txt
pWrite = open("..\\pro\\hmmsdef.mmf",'w')
time = 0
for line in pRead:
if line != '\n':
line = line[0: len(line) - 1] #去除最后的\n
if line == "sil ":
... | normal | {
"blob_id": "9bd6da909baeb859153e3833f0f43d8cbcb66200",
"index": 9324,
"step-1": "# coding=utf-8\nimport sys\nif len(sys.argv) == 2:\n filepath = sys.argv[1]\n pRead = open(filepath,'r')#wordlist.txt\n pWrite = open(\"..\\\\pro\\\\hmmsdef.mmf\",'w')\n time = 0\n for line in pRead:\n if line... | [
0
] |
import itertools
def odds(upper_limit):
return [i for i in range(1,upper_limit,2)]
def evens(upper_limit):
return [i for i in range(0,upper_limit,2)]
nested = [i**j for i in range(1,10) for j in range(1,4)]
vowels = ['a', 'e', 'i', 'o', 'u']
consonants = [chr(i) for i in range(97,123) if chr(i) not in vowe... | normal | {
"blob_id": "a2e4e4a0c49c319df2adb073b11107d3f520aa6e",
"index": 1883,
"step-1": "<mask token>\n\n\ndef evens(upper_limit):\n return [i for i in range(0, upper_limit, 2)]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef odds(upper_limit):\n return [i for i in range(1, upper_limit, 2)]\n\n\ndef even... | [
1,
3,
4,
5,
6
] |
N = int(input("ingrese el numero de datos a ingresar "))
SP = 0
SO = 0
CP = 0
for i in range(1,N+1,1):
NUM = int(input("ingrese un numero entero "))
if NUM > 0:
SP += NUM
CP += 1
else:
SO += NUM
PG = (SP+SO)/N
PP = SP/CP
print(f"hay { CP } numeros positivos, el promedio general es de... | normal | {
"blob_id": "efc0b8f1c4887810a9c85e34957d664b01c1e92e",
"index": 1453,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, N + 1, 1):\n NUM = int(input('ingrese un numero entero '))\n if NUM > 0:\n SP += NUM\n CP += 1\n else:\n SO += NUM\n<mask token>\nprint(\n ... | [
0,
1,
2,
3
] |
def test(name,message):
print("用户是：" , name)
print("欢迎消息是：",message)
my_list = ['孙悟空','欢迎来疯狂软件']
test(*my_list)
print('*****')
# ###########################
def foo(name,*nums):
print("name参数：",name)
print("nums参数:",nums)
my_tuple = (1,2,3)
foo('fkit',*my_tuple)
print('********')
foo(*my_tuple)
print(... | normal | {
"blob_id": "64fb006ea5ff0d101000dd4329b3d957a326ed1a",
"index": 2387,
"step-1": "def test(name, message):\n print('用户是：', name)\n print('欢迎消息是：', message)\n\n\n<mask token>\n",
"step-2": "def test(name, message):\n print('用户是：', name)\n print('欢迎消息是：', message)\n\n\n<mask token>\n\n\ndef foo(name,... | [
1,
3,
4,
5,
6
] |
from _math import Vector2, Vector3, Quaternion, Transform, Vector3Immutable, QuaternionImmutable, minimum_distance
from _math import mod_2pi
from math import pi as PI, sqrt, fmod, floor, atan2, acos, asin, ceil, pi, e
import operator
from sims4.repr_utils import standard_repr
import enum
import native.animation
import ... | normal | {
"blob_id": "a0310b1bab339064c36ff0fe92d275db7a6c5ba9",
"index": 8734,
"step-1": "<mask token>\n\n\ndef rad_to_deg(rad):\n return rad * 180 / PI\n\n\ndef angle_abs_difference(a1, a2):\n delta = sims4.math.mod_2pi(a1 - a2)\n if delta > sims4.math.PI:\n delta = sims4.math.TWO_PI - delta\n return... | [
52,
53,
55,
64,
75
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from flask import Flask, request, jsonify
from app import Node
from dbm2 import filemanager
fm = filemanager()
node = Node(fm)
app = Flask(__name__)
@app.route("/transactions/isfull",methods=['GET'])
def isFull():
return jsonify(node.isFull()), 200
@app.route("/tra... | normal | {
"blob_id": "45b46a08d8b304ac12baf34e0916b249b560418f",
"index": 7459,
"step-1": "<mask token>\n\n\n@app.route('/transactions/isfull', methods=['GET'])\ndef isFull():\n return jsonify(node.isFull()), 200\n\n\n@app.route('/transactions/new', methods=['POST'])\ndef newTransaction():\n transaction = request.g... | [
8,
11,
12,
13,
14
] |
import numpy as np
labels = np.load('DataVariationOther/w1_s500/targetTestNP.npy')
for lab in labels:
print(lab)
| normal | {
"blob_id": "a83988e936d9dee4838db61c8eb8ec108f5ecd3f",
"index": 4669,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor lab in labels:\n print(lab)\n",
"step-3": "<mask token>\nlabels = np.load('DataVariationOther/w1_s500/targetTestNP.npy')\nfor lab in labels:\n print(lab)\n",
"step-4": "impo... | [
0,
1,
2,
3
] |
__author__ = 'fshaw'
import gzip
import hashlib
import os
import uuid
import json
import jsonpickle
from chunked_upload.models import ChunkedUpload
from chunked_upload.views import ChunkedUploadView, ChunkedUploadCompleteView
from django.conf import settings
from django.core import serializers
from django.core.files.ba... | normal | {
"blob_id": "2b7415d86f9157ae55228efdd61c9a9e9920bc5c",
"index": 7716,
"step-1": "<mask token>\n\n\nclass CopoChunkedUploadCompleteView(ChunkedUploadCompleteView):\n do_md5_check = False\n\n def get_response_data(self, chunked_upload, request):\n \"\"\"\n Data for the response. Should return ... | [
12,
13,
14,
15,
18
] |
import os
import config
############################
# NMJ_RNAI LOF/GOF GENE LIST
def nmj_rnai_set_path():
return os.path.join(config.datadir, 'NMJ RNAi Search File.txt')
def nmj_rnai_gain_of_function_set_path():
return os.path.join(config.datadir, 'NMJ_RNAi_gain_of_function_flybase_ids.txt')
def get_n... | normal | {
"blob_id": "6a9d64b1ef5ae8e9d617c8b0534e96c9ce7ea629",
"index": 4951,
"step-1": "\nimport os\n\nimport config\n\n\n############################\n# NMJ_RNAI LOF/GOF GENE LIST\n\ndef nmj_rnai_set_path():\n return os.path.join(config.datadir, 'NMJ RNAi Search File.txt')\n\n\ndef nmj_rnai_gain_of_function_set_pa... | [
0
] |
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
class LogisticRegression:
'''LogisticRegression for binary classification
max_iter: the maximum iteration times for training
learning_rate: learing rate for gradiend decsend training
Input's shape shoul... | normal | {
"blob_id": "1dd62264aafe8ee745a3cfdfb994ac6a40c1af42",
"index": 1848,
"step-1": "<mask token>\n\n\nclass LogisticRegression:\n <mask token>\n\n def __init__(self, max_iter=2000, learning_rate=0.01):\n self.max_iter = max_iter\n self.learning_rate = learning_rate\n print('LogisticRegre... | [
7,
11,
13,
14,
15
] |
from sqlalchemy.orm import Session
from fastapi import APIRouter, Depends, File
from typing import List
from ..models.database import ApiSession
from ..schemas.images_schema import ImageReturn
from . import image_service
router = APIRouter()
@router.get("/", response_model=List[ImageReturn])
def get_all_images(db: ... | normal | {
"blob_id": "874ca60749dba9ca8c8ebee2eecb1b80da50f11f",
"index": 3782,
"step-1": "<mask token>\n\n\n@router.get('/', response_model=List[ImageReturn])\ndef get_all_images(db: Session=Depends(ApiSession)):\n return image_service.get_all_images(db)\n\n\n@router.get('/{image_id}', response_model=ImageReturn)\nde... | [
4,
5,
6,
7,
8
] |
"""
Make html galleries from media directories. Organize by dates, by subdirs or by
the content of a diary file. The diary file is a markdown file organized by
dates, each day described by a text and some medias (photos and movies).
The diary file can be exported to:
* an html file with the text and subset of medias a... | normal | {
"blob_id": "6018f35afc6646d0302ca32de649ffe7d544a765",
"index": 3377,
"step-1": "<mask token>\n\n\nclass Post:\n\n def __init__(self, date, text, medias):\n self.date = date\n self.text = text\n self.medias = medias\n self.dcim = []\n self.daterank = 0\n self.extra =... | [
79,
84,
88,
100,
110
] |
#! /usr/bin/python
import math
import sys
import os
import subprocess
#PTYPES = [ "eth_ip_udp_head_t", "ip_udp_head_t", "eth_32ip_udp_head_t", "eth_64ip_udp_head_t", "eth64_64ip64_64udp_head_t", "eth6464_64ip64_64udp_head_t" ]
#PTYPES = [ "eth_ip_udp_head_t", "eth_32ip_udp_head_t", "eth_64ip_udp_head_t", "eth64_64... | normal | {
"blob_id": "9101fc5b8ba04a1b72e0c79d5bf3e4118e1bad75",
"index": 5676,
"step-1": "#! /usr/bin/python\n\nimport math\nimport sys\nimport os\nimport subprocess\n\n\n#PTYPES = [ \"eth_ip_udp_head_t\", \"ip_udp_head_t\", \"eth_32ip_udp_head_t\", \"eth_64ip_udp_head_t\", \"eth64_64ip64_64udp_head_t\", \"eth6464_64ip... | [
0
] |
#!/usr/bin/env python3
import sys
class Parse:
data = []
def __parseLine(line):
"""Parse the given line"""
# extract name
name_len = line.index(" ")
name = line[:name_len]
line = line[name_len + 3:]
# array-ize 'electron' val
elec_pos = line.index("e... | normal | {
"blob_id": "cb77696a90716acdee83a1cf6162a8f42c524e11",
"index": 7612,
"step-1": "<mask token>\n\n\nclass Write:\n\n def __writeHeader(fd):\n \"\"\"Write html header\"\"\"\n print('<!DOCTYPE html>', '<html>', ' <head>',\n ' <title>Super Tableau 3000</title>',\n \" <meta c... | [
8,
11,
12,
13,
16
] |
#!/usr/bin/env python
def findSubset(s0, s, t):
mys0 = s0.copy()
mys = s.copy()
if t == 0 and mys0:
return mys0
elif t == 0: # and mys0 == set()
return True
else:
if len(mys) > 0:
p = mys.pop()
mys1 = mys0.copy()
mys1.add(p)
... | normal | {
"blob_id": "079610f2aaebec8c6e46ccf21a9d5728df1be8de",
"index": 4155,
"step-1": "<mask token>\n",
"step-2": "def findSubset(s0, s, t):\n mys0 = s0.copy()\n mys = s.copy()\n if t == 0 and mys0:\n return mys0\n elif t == 0:\n return True\n elif len(mys) > 0:\n p = mys.pop()\n... | [
0,
1,
2,
3
] |
import pymarc
from pymarc import JSONReader, Field, JSONWriter, XMLWriter
import psycopg2
import psycopg2.extras
import time
import logging
import json
#WRITTEN W/PYTHON 3.7.3
print("...starting export");
# constructing file and log name
timestr = time.strftime("%Y%m%d-%H%M%S")
logging.basicConfig(filename=timestr ... | normal | {
"blob_id": "d81e8478d60c9ee778e1aeb0dd7b05f675e4ecad",
"index": 2306,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('...starting export')\n<mask token>\nlogging.basicConfig(filename=timestr + '-export.log')\n<mask token>\nmatCursor.execute(select_all_mat)\n<mask token>\nfor m in materialTypes:\n ... | [
0,
1,
2,
3,
4
] |
# file /home/hep/ss4314/cmtuser/Gauss_v45r10p1/Gen/DecFiles/options/16303437.py generated: Wed, 25 Jan 2017 15:25:22
#
# Event Type: 16303437
#
# ASCII decay Descriptor: [Xi_b- -> (rho- -> pi- pi0) K- p+]cc
#
from Configurables import Generation
Generation().EventType = 16303437
Generation().SampleGenerationTool = "Si... | normal | {
"blob_id": "7cc9d445d712d485eaebd090d2485dac0c38b3fb",
"index": 5918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nGeneration().addTool(SignalRepeatedHadronization)\n<mask token>\nToolSvc().addTool(EvtGenDecay)\n<mask token>\n",
"step-3": "<mask token>\nGeneration().EventType = 16303437\nGeneration(... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 17/02/17 at 11:48 PM
@author: neil
Program description here
Version 0.0.1
"""
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import sys
# detect python version
# if python 3 do this:
if (sys.version_info > (3, 0)):
import tkint... | normal | {
"blob_id": "1576693264a334153c2752ab6b3b4b65daa7c37c",
"index": 8928,
"step-1": "<mask token>\n\n\nclass Add_Buttons(object):\n <mask token>\n\n def validate_inputs(self):\n try:\n self.button_labels = list(self.button_labels)\n for it in self.button_labels:\n i... | [
6,
8,
9,
10,
13
] |
#função: Definir se o número inserido é ímpar ou par
#autor: João Cândido
p = 0
i = 0
numero = int(input("Insira um número: "))
if numero % 2 == 0:
p = numero
print (p, "é um número par")
else:
i = numero
print (i, "é um número ímpar") | normal | {
"blob_id": "382bc321c5fd35682bc735ca4d6e293d09be64ec",
"index": 9990,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif numero % 2 == 0:\n p = numero\n print(p, 'é um número par')\nelse:\n i = numero\n print(i, 'é um número ímpar')\n",
"step-3": "p = 0\ni = 0\nnumero = int(input('Insira um... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
#coding=utf-8
"""
__init__.py
:license: BSD, see LICENSE for more details.
"""
import os
import logging
import sys
from logging.handlers import SMTPHandler, RotatingFileHandler
from flask import Flask, g, session, request, flash, redirect, jsonify, url_for
from flaskext.babel import Ba... | normal | {
"blob_id": "ef124e8c15ef347efd709a5e3fb104c7fd1bccde",
"index": 2753,
"step-1": "<mask token>\n\n\ndef on_identity_changed(app, identity):\n g.identity = identity\n session['identity'] = identity\n\n\ndef configure_signals(app):\n identity_changed.connect(on_identity_changed, app)\n\n\n<mask token>\n\n... | [
6,
8,
9,
10,
13
] |
import inspect
import json
import socket
import sys
import execnet
import logging
from remoto.process import check
class BaseConnection(object):
"""
Base class for Connection objects. Provides a generic interface to execnet
for setting up the connection
"""
executable = ''
remote_import_system... | normal | {
"blob_id": "ae38995d153deed2e6049b7b65fb5f28dfcef470",
"index": 1442,
"step-1": "<mask token>\n\n\nclass BaseConnection(object):\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, hostname, logger=None, sudo=False, threads=1, eager=\n True, detect_sudo=False, use_ssh=False, i... | [
19,
23,
27,
30,
31
] |
"""Tasks for managing Debug Information Files from Apple App Store Connect.
Users can instruct Sentry to download dSYM from App Store Connect and put them into Sentry's
debug files. These tasks enable this functionality.
"""
import logging
import pathlib
import tempfile
from typing import List, Mapping, Tuple
impor... | normal | {
"blob_id": "51bc2668a9f9f4425166f9e6da72b7a1c37baa01",
"index": 9628,
"step-1": "<mask token>\n\n\ndef inner_dsym_download(project_id: int, config_id: str) ->None:\n \"\"\"Downloads the dSYMs from App Store Connect and stores them in the Project's debug files.\"\"\"\n with sdk.configure_scope() as scope:\... | [
3,
5,
7,
9,
10
] |
from django.apps import AppConfig
class ClassromConfig(AppConfig):
name = 'classrom'
| normal | {
"blob_id": "a995305cb5589fa0cbb246ae3ca6337f4f2c3ca1",
"index": 8798,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ClassromConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ClassromConfig(AppConfig):\n name = 'classrom'\n",
"step-4": "from django.apps import ... | [
0,
1,
2,
3
] |
#exceptions.py
#-*- coding:utf-8 -*-
#exceptions
try:
print u'try。。。'
r = 10/0
print 'result:',r
except ZeroDivisionError,e:
print 'except:',e
finally:
print 'finally...'
print 'END'
try:
print u'try。。。'
r = 10/int('1')
print 'result:',r
except ValueError,e:
print 'ValueError:',e
... | normal | {
"blob_id": "1568cf544a4fe7aec082ef1d7506b8484d19f198",
"index": 3776,
"step-1": "#exceptions.py \n#-*- coding:utf-8 -*-\n\n#exceptions\ntry:\n print u'try。。。'\n r = 10/0\n print 'result:',r\nexcept ZeroDivisionError,e:\n print 'except:',e\nfinally:\n print 'finally...'\nprint 'END'\n\ntry:\n p... | [
0
] |
def get_value(li, row, column):
if row < 0 or column < 0:
return 0
try:
return li[row][column]
except IndexError:
return 0
n = int(input())
results = {}
for asdf in range(n):
table = []
title, rows, columns = input().split()
rows = int(rows)
columns =... | normal | {
"blob_id": "badbfdbdeb8b4fd40b1c44bf7dcff6457a0c8795",
"index": 7162,
"step-1": "<mask token>\n",
"step-2": "def get_value(li, row, column):\n if row < 0 or column < 0:\n return 0\n try:\n return li[row][column]\n except IndexError:\n return 0\n\n\n<mask token>\n",
"step-3": "d... | [
0,
1,
2,
3,
4
] |
# Identify a vowel
class MainInit(object):
def __init__(self):
self.vowel = str(input("Please type the character: \n"))
if len(self.vowel) > 1:
print("Invalid number of character")
else:
Vowel(self.vowel)
class Vowel(object):
def __init__(self, vo... | normal | {
"blob_id": "8d9f4bce998857bcc7bc2fda0b519f370bf957fe",
"index": 1497,
"step-1": "<mask token>\n\n\nclass Vowel(object):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Vowel(object):\n\n def __init__(self, vowels):\n self.vowels = vowels\n self.list = ['a', 'e', 'i'... | [
1,
2,
3,
5,
6
] |
from logging import getLogger
from time import sleep
from uuid import UUID
from zmq import Context, Poller, POLLIN, ZMQError, ETERM # pylint: disable-msg=E0611
from zhelpers import zpipe
from dcamp.service.configuration import Configuration
from dcamp.types.messages.control import SOS
from dcamp.types.specs import E... | normal | {
"blob_id": "fee757b91f8c2ca1c105d7e67636772a8b5eafd5",
"index": 8158,
"step-1": "<mask token>\n\n\n@runnable\nclass RoleMixin(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def _add_service(self, cls, *args, **kwargs):\n pipe, p... | [
4,
10,
11,
14,
15
] |
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import warnings
import pickle
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
impor... | normal | {
"blob_id": "1508697f93114d7f20182a3e9c1df5617904529a",
"index": 8725,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlr.fit(x_train, y_train)\n<mask token>\npickle.dump(lr, open('model.pkl', 'wb'))\n",
"step-3": "<mask token>\ndataset = pd.read_csv('heart.csv')\ndf = dataset.copy()\nX = df.drop(['targ... | [
0,
1,
2,
3,
4
] |
PROJECT_ID = "aaet-geoscience-dev"
# The tmp folder is for lasio I/O purposes
DATA_PATH = "/home/airflow/gcs/data/tmp"
# Credential JSON key for accessing other projects
# CREDENTIALS_JSON = "gs://aaet_zexuan/flow/keys/composer_las_merge.json"
CREDENTIALS_JSON = "keys/composer_las_merge.json"
# Bucket name fo... | normal | {
"blob_id": "0b2a036b806cca6e7f58008040b3a261a8bc844d",
"index": 4092,
"step-1": "<mask token>\n",
"step-2": "PROJECT_ID = 'aaet-geoscience-dev'\nDATA_PATH = '/home/airflow/gcs/data/tmp'\nCREDENTIALS_JSON = 'keys/composer_las_merge.json'\nBUCKET_LAS_MERGE = 'las_merged'\nBUCKET_LAS_SPLICE = 'us-central1-lithos... | [
0,
1,
2
] |
from django.contrib.auth import get_user_model
from django.db import models
from django.db.models.signals import post_save
from apps.common.constants import NOTIFICATION_TYPE_CHOICES, INFO
from apps.core.models import BaseModel
from apps.core.utils.helpers import get_upload_path
from apps.core.utils.push_notification ... | normal | {
"blob_id": "c2260278c8dfb353f55ee9ea3495049b08169447",
"index": 4115,
"step-1": "<mask token>\n\n\nclass City(BaseModel):\n name = models.CharField(max_length=255, db_index=True)\n\n def __str__(self):\n return self.name\n\n\nclass Article(BaseModel):\n created_by = models.ForeignKey(User, relat... | [
9,
10,
11,
12,
15
] |
# coding: gb18030
from setuptools import setup
setup(
name="qlquery",
version="1.0",
license="MIT",
packages=['qlquery'],
install_requires=[
'my-fake-useragent',
'requests',
'beautifulsoup4'
],
zip_safe=False
) | normal | {
"blob_id": "f11ede752df7d9aff672eee4e230b109fcbf987b",
"index": 8555,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='qlquery', version='1.0', license='MIT', packages=['qlquery'],\n install_requires=['my-fake-useragent', 'requests', 'beautifulsoup4'],\n zip_safe=False)\n",
"step-3": "... | [
0,
1,
2,
3
] |
# Autor : Kevin Oswaldo Palacios Jimenez
# Fecha de creacion: 16/09/19
# Se genera un bucle con for
# al no tener argumento print no genera ningun cambio
# mas que continuar a la siguiente linea
for i in range (1,11):
encabezado="Tabla del {}"
print(encabezado.format(i))
print()
# ... | normal | {
"blob_id": "86f365612e9f15e7658160ecab1d3d9970ca364e",
"index": 9699,
"step-1": "<mask token>\n",
"step-2": "for i in range(1, 11):\n encabezado = 'Tabla del {}'\n print(encabezado.format(i))\n print()\n for j in range(1, 11):\n salida = '{} x {} = {}'\n print(salida.format(i, j, i *... | [
0,
1,
2
] |
"""
"""
import json
import logging
import re
import asyncio
from typing import Optional
import discord
from discord.ext import commands
import utils
logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s")
log = logging.getLogger("YTEmbedFixer")
client = commands.Bot... | normal | {
"blob_id": "d73832d3f0adf22085a207ab223854e11fffa2e8",
"index": 6948,
"step-1": "<mask token>\n\n\ndef build_embed(_video_url: str, _video_image_url: Optional[str],\n _video_title: Optional[str], _author_name: Optional[str], _author_url:\n Optional[str]) ->discord.Embed:\n embed = discord.Embed(type='v... | [
1,
2,
3,
4,
5
] |
from random import randint, shuffle
class Generator:
opset = ['+', '-', '*', '/', '²', '√', 'sin', 'cos', 'tan']
@staticmethod
def generate(level):
"""
根据 level 生成指定等级的算术题
0：小学；1：初中；2：高中
"""
"""
生成操作数序列以及二元运算符序列
"""
length = randint(0 if lev... | normal | {
"blob_id": "6e3bb17696953256af6d8194128427acebf1daac",
"index": 524,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Generator:\n <mask token>\n\n @staticmethod\n def generate(level):\n \"\"\"\n 根据 level 生成指定等级的算术题\n 0：小学；1：初中；2：高中\n \"\"\"\n \"\"\"\n... | [
0,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""Module providing views for asset storage folder"""
from Products.Five.browser import BrowserView
from plone import api
from plone.app.contenttypes.interfaces import IImage
class AssetRepositoryView(BrowserView):
""" Folderish content page default view """
def contained_items(self, u... | normal | {
"blob_id": "70c20b38edb01552a8c7531b3e87a9302ffaf6c5",
"index": 5062,
"step-1": "<mask token>\n\n\nclass AssetRepositoryView(BrowserView):\n <mask token>\n\n def contained_items(self, uid):\n stack = api.content.get(UID=uid)\n return stack.restrictedTraverse('@@folderListing')()\n\n def i... | [
3,
4,
5,
6,
7
] |
import json
import time
from keySender import PressKey,ReleaseKey,dk
config = {
"Up": "W",
"Down": "S",
"Left": "A",
"Right": "D",
"Grab": "LBRACKET",
"Drop": "RBRACKET"
}
### Commands
# Move
def Move(direction,delay=.2):
PressKey(dk[config[direction]])
time.sleep(delay) # Replace with a better condition
Rele... | normal | {
"blob_id": "1e7789b154271eb8407a027c6ddf6c941cc69a41",
"index": 3070,
"step-1": "<mask token>\n\n\ndef Move(direction, delay=0.2):\n PressKey(dk[config[direction]])\n time.sleep(delay)\n ReleaseKey(dk[config[direction]])\n\n\ndef Action(direction, pull=None):\n delay = 0.6\n if pull:\n del... | [
2,
3,
4,
5,
6
] |
# collectd-vcenter - vcenter.py
#
# Author : Loic Lambiel @ exoscale
# Contributor : Josh VanderLinden
# Description : This is a collectd python module to gather stats from Vmware
# vcenter
import logging
import ssl
import time
from pysphere import VIServer
try:
import collectd
COLLECTD_ENABLED... | normal | {
"blob_id": "55f76ae1ffe0fb2d2ca2c7a20aab45ffb00cf178",
"index": 613,
"step-1": "<mask token>\n\n\nclass CollectdCollector(Collector):\n \"\"\"\n Handle dispatching statistics to collectd.\n\n \"\"\"\n NAME = 'vCenter'\n\n def __init__(self, *args, **kwargs):\n super(CollectdCollector, self... | [
11,
13,
19,
20,
24
] |
import numpy as np
import cv2 as cv
import random
import time
random.seed(0)
def displayImage(winName, img):
""" Helper function to display image
arguments:
winName -- Name of display window
img -- Source Image
"""
cv.imshow(winName, img)
cv.waitKey(0)
################################... | normal | {
"blob_id": "f7886f8d98ad0519f4635064f768f25dad101a3d",
"index": 2612,
"step-1": "<mask token>\n\n\ndef displayImage(winName, img):\n \"\"\" Helper function to display image\n arguments:\n winName -- Name of display window\n img -- Source Image\n \"\"\"\n cv.imshow(winName, img)\n cv.wai... | [
7,
8,
10,
12,
13
] |
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
#import matplotlib.pyplot as plt
import time
import os
import copy
import torch.nn.functional as F
from PIL import Image, ExifTag... | normal | {
"blob_id": "d807a363c08d117c848ffdc0a768c696ea7746bd",
"index": 1787,
"step-1": "<mask token>\n\n\ndef train_model_snapshot(model, criterion, lr, dataloaders, dataset_sizes,\n device, num_cycles, num_epochs_per_cycle):\n since = time.time()\n best_model_wts = copy.deepcopy(model.state_dict())\n best... | [
2,
3,
4,
5,
6
] |
"""
Написать программу, которая принимает строку
и выводит строку без пробелов и ее длину.
Для удаления пробелов реализовать доп функцию.
""" | normal | {
"blob_id": "1eab2ddda6fdd71db372e978caa6e7d24c7fe78e",
"index": 7724,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\n Написать программу, которая принимает строку\n и выводит строку без пробелов и ее длину.\n Для удаления пробелов реализовать доп функцию.\n\"\"\"",
"step-3": null,
"step-4": null,... | [
0,
1
] |
from os import path
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import Pipeline
from sta211.datasets import load_train_dataset, load_test_dataset, find_best_train_dataset
from sklearn.model_selection import GridSearchCV
from sta211.selection import get_naive_bayes, get_mlp, get_svm,... | normal | {
"blob_id": "c99878dbd5610c8a58f00912e111b1eef9d3893e",
"index": 7782,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngrid.fit(X, y)\n<mask token>\nprint('Result for {} configurations'.format(len(parameters)))\nfor p in parameters:\n print('{};{:.2f}%;{:.4f}%;±{:.4f}%'.format(', '.join(map(lambda k:\n... | [
0,
1,
2,
3,
4
] |
"""
You can perform the following operations on the string, :
Capitalize zero or more of 's lowercase letters.
Delete all of the remaining lowercase letters in .
Given two strings, and , determine if it's possible to make equal to as described. If so, print YES on a new line. Otherwise, print NO.
For example, give... | normal | {
"blob_id": "5fb998fa761b989c6dd423634824197bade4f8a5",
"index": 23,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef abbreviation(a, b):\n m, n = len(a), len(b)\n dp = [([False] * (m + 1)) for _ in range(n + 1)]\n dp[0][0] = True\n for i in range(n + 1):\n for j in range(1, m + ... | [
0,
1,
2,
3,
4
] |
import pandas as pd
df1 = pd.read_csv("../final/your_no.tsv", '\t')
df2 = pd.read_csv("../../Downloads/me.csv", '\t')
final = pd.concat([df1, df2])
final.to_csv('../../Downloads/final_con_final.tsv', sep='\t', index=False)
| normal | {
"blob_id": "cd5945631a9dd505bf67089bab8c5a37ad375129",
"index": 410,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfinal.to_csv('../../Downloads/final_con_final.tsv', sep='\\t', index=False)\n",
"step-3": "<mask token>\ndf1 = pd.read_csv('../final/your_no.tsv', '\\t')\ndf2 = pd.read_csv('../../Downlo... | [
0,
1,
2,
3,
4
] |
import time,pickle
from CNN_GPU.CNN_C_Wrapper import *
from pathlib import Path
FSIGMOIG = 0
FTANH = 2
FRELU = 4
REQUEST_INPUT = 0
REQUEST_GRAD_INPUT = 1
REQUEST_OUTPUT = 2
REQUEST_WEIGTH = 3
class CNN:
def __init__(self, inputSize, hitLearn=.1, momentum=.9, weigthDecay=.5, multip=1.0):
file = '%s/%s' %... | normal | {
"blob_id": "32db21ed7f57f29260d70513d8c34de53adf12d7",
"index": 5740,
"step-1": "<mask token>\n\n\nclass CNN:\n\n def __init__(self, inputSize, hitLearn=0.1, momentum=0.9, weigthDecay=\n 0.5, multip=1.0):\n file = '%s/%s' % (DIR_LIBRARY, 'gpu_function.cl')\n file = file.encode('utf-8')\n... | [
7,
12,
20,
21,
27
] |
a = 1
b = 2
print(a + b)
print("hello")
list = [1, 2, 3, 4, 5]
for i in list:
if i % 2 != 0:
print(i)
print("branch") | normal | {
"blob_id": "03b325094bd3e77f467e17ce54deb95bf2b5c727",
"index": 1724,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(a + b)\nprint('hello')\n<mask token>\nfor i in list:\n if i % 2 != 0:\n print(i)\nprint('branch')\n",
"step-3": "a = 1\nb = 2\nprint(a + b)\nprint('hello')\nlist = [1, 2... | [
0,
1,
2,
3
] |
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
__all__ = [
'mesh_add_vertex_to_face_edge'
]
def mesh_add_vertex_to_face_edge(mesh, key, fkey, v):
"""Add an existing vertex of the mesh to an existing face.
Parameters
----------
mesh :... | normal | {
"blob_id": "d9b6efce92e30267a9f992c4fea698fe14e0c3e4",
"index": 1398,
"step-1": "<mask token>\n\n\ndef mesh_add_vertex_to_face_edge(mesh, key, fkey, v):\n \"\"\"Add an existing vertex of the mesh to an existing face.\n\n Parameters\n ----------\n mesh : compas.datastructures.Mesh\n The mesh d... | [
1,
2,
3,
4,
5
] |
from enum import Enum
class ImageTaggingChoice(str, Enum):
Disabled = "disabled",
Basic = "basic",
Enhanced = "enhanced",
UnknownFutureValue = "unknownFutureValue",
| normal | {
"blob_id": "e3fe77867926d9d82963c8125048148de6998e2b",
"index": 4374,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ImageTaggingChoice(str, Enum):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ImageTaggingChoice(str, Enum):\n ... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import subprocess
import statsmodels.api as sm
import numpy as np
import math
'''
This function prcesses the gene file
Output is a one-row file for a gene
Each individual is in a column
Input file must have rowname
gene: gene ENSG ID of interest
start_col: column number which the gene exp value st... | normal | {
"blob_id": "2f64aac7032ac099870269659a84b8c7c38b2bf0",
"index": 8385,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef lm_res(snps, gene, cov):\n res = pd.DataFrame(np.zeros([snps.shape[0], 2], dtype=np.float32))\n res.index = snps.index\n res.columns = ['beta', 'pval']\n for i in rang... | [
0,
1,
2,
3,
4
] |
import cgi
import os
import math
import sys
from datetime import datetime
sys.path.append(os.path.join(os.path.dirname(__file__), 'pygooglechart-0.2.1'))
from google.appengine.ext import webapp
from google.appengine.ext.webapp.util import run_wsgi_app
from pygooglechart import PieChart3D
from LPData import Totals
fr... | normal | {
"blob_id": "d8c9e1098dde9d61341ebc3c55eada5592f4b71a",
"index": 2891,
"step-1": "<mask token>\n\n\ndef stacked_vertical():\n total = Totals.get_or_insert('total')\n if len(total.shirts) == 0:\n shirts = sorted(T_Shirts, key=lambda shirt: shirt[0])\n for shirt in shirts:\n total.sh... | [
4,
5,
6,
7,
8
] |
import pygame
from pygame import Rect, Color
from pymunk import Body, Poly
from config import WIDTH, HEIGHT
class Ground:
def __init__ (self, space):
# size
self.w = WIDTH - 20
self.h = 25
# position
self.x = 10
self.y = HEIGHT - self.h
# pygame... | normal | {
"blob_id": "32fc0db68c32c2e644f9c1c2318fbeff41a0543d",
"index": 5703,
"step-1": "<mask token>\n\n\nclass Ground:\n <mask token>\n <mask token>\n\n def draw(self, window):\n pygame.draw.rect(window, self.color, self.rect)\n return\n",
"step-2": "<mask token>\n\n\nclass Ground:\n\n def... | [
2,
3,
4,
5,
6
] |
from auction_type import AuctionType
from bid import Bid
class Auction(object):
def __init__(self, name, type, status, start_price, buy_now_price):
self.name = name
self.type = type
self.status = status
if AuctionType.BID == type:
self.start_price = start_price
... | normal | {
"blob_id": "9e05f883d80d7583c9f7e16b2fb5d3f67896388d",
"index": 5629,
"step-1": "<mask token>\n\n\nclass Auction(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Auction(object):\n\n def __init__(self, name, type, status, start_price, buy_now_price):\n self.name = ... | [
1,
2,
3,
4
] |
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def splitListToParts(self, root, k):
"""
:type root: ListNode
:type k: int
:rtype: List[ListNode]
"""
if not root:
return [None]*k
res... | normal | {
"blob_id": "6a609c91122f8b66f57279cff221ee76e7fadb8c",
"index": 7059,
"step-1": "# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n\tdef splitListToParts(self, root, k):\n\t\t\"\"\"\n\t\t... | [
0
] |
#!/usr/bin/env python
import rospy
import numpy as np
from sensor_msgs.msg import Image
import cv2, cv_bridge
from geometry_msgs.msg import Twist, Pose2D
from std_msgs.msg import String
import pytesseract as ocr
from PIL import Image as imagePil
import os
import time
from roseli.srv import CreateMap, CreateMapRequest
... | normal | {
"blob_id": "83ce5ee4d2a18caeb364b74c3739015fc0e1474c",
"index": 1344,
"step-1": "#!/usr/bin/env python\n\nimport rospy\nimport numpy as np\nfrom sensor_msgs.msg import Image\nimport cv2, cv_bridge\nfrom geometry_msgs.msg import Twist, Pose2D\nfrom std_msgs.msg import String\nimport pytesseract as ocr\nfrom PIL ... | [
0
] |
import datetime
from django.shortcuts import render
from lims.models import *
import os
import zipfile
def getpicture(word):
if word.split(".")[1] not in ["doc","docx"]:
return None
word_zip = word.split(".")[0] + ".zip"
path = ""
for i in word.split("/")[0:-1]:
path += i
... | normal | {
"blob_id": "e32c73abdcd384ee7c369182527cca6495f067b3",
"index": 1977,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef getData(request):\n index = request.GET.get('index')\n msg = '未查找到数据'\n if ExtExecute.objects.filter(query_code=index):\n ext = ExtExecute.objects.filter(query_cod... | [
0,
1,
2,
3,
4
] |
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