code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#!/usr/bin/python2.7
#
# Assignment2 Interface
#
import psycopg2
import os
import sys
import Assignment1 as a
# Donot close the connection inside this file i.e. do not perform openconnection.close()
#range__metadata = RangeRatingsMetadata
#roundR_metadata = RoundRobinRatingsMetadata
#rangetablepartition = rangeratings... | normal | {
"blob_id": "0c736bb5c88a8d7ee359e05fe12f0b77d83146c8",
"index": 3439,
"step-1": "#!/usr/bin/python2.7\n#\n# Assignment2 Interface\n#\n\nimport psycopg2\nimport os\nimport sys\nimport Assignment1 as a\n# Donot close the connection inside this file i.e. do not perform openconnection.close()\n#range__metadata = Ra... | [
0
] |
def get_partial_matched(n):
pi = [0] * len(n)
begin = 1
matched = 0
while begin + matched < len(n):
if n[begin + matched] == n[matched]:
matched += 1
pi[begin + matched - 1] = matched
else:
if matched == 0:
begin += 1
else:
... | normal | {
"blob_id": "16a77c45a58e31c575511146dfceeaef0a2bc3a7",
"index": 3640,
"step-1": "<mask token>\n\n\ndef get_common(h, n):\n pi = get_partial_matched(n)\n begin = 0\n matched = 0\n while begin + matched < len(h):\n if matched < len(n) and h[begin + matched] == n[matched]:\n matched +... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def read_contact():
contacts = Contact.select()
for contact in contacts:
print(contact)
print(contact.firstname + ' ' + contact.lastname + ' ' + contact.
phone + ' ' + contact.email + ' ' + contact.address)
def create_contact():
contact_firstname ... | flexible | {
"blob_id": "07544d1eb039da0081716aa489fc1a0a5a200145",
"index": 1072,
"step-1": "<mask token>\n\n\ndef read_contact():\n contacts = Contact.select()\n for contact in contacts:\n print(contact)\n print(contact.firstname + ' ' + contact.lastname + ' ' + contact.\n phone + ' ' + cont... | [
7,
9,
10,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
<|reserved_special_token_0|>
_sym_db.RegisterMessage(GetUserInterestRequest)
<|reserved_special_token_0|>
_sym_db.RegisterServiceDescriptor(_USERINTERESTSERVICE)
<|reserved_special_token_... | flexible | {
"blob_id": "654586443e96f84aae70b3ce3263b0458a27334b",
"index": 473,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n<mask token>\n_sym_db.RegisterMessage(GetUserInterestRequest)\n<mask token>\n_sym_db.RegisterServiceDescriptor(_USERINTERESTSERVICE)\n<mask toke... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def run() ->None:
os.environ['TZ'] = 'Europe/Brussels'
if sys.platform != 'win32':
from time import tzset
tzset()
print(datetime.now())
load_dotenv()
Log.setup()
token = os.getenv('DISCORD... | flexible | {
"blob_id": "a7123fa221555b15162dbab0d93a86965190b805",
"index": 4141,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run() ->None:\n os.environ['TZ'] = 'Europe/Brussels'\n if sys.platform != 'win32':\n from time import tzset\n tzset()\n print(datetime.now())\n load_dote... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
filenames.sort()
<|reserved_special_token_0|>
for filename in filenames:
infile = open('data/SENTIMENT_test/' + filename, errors='ignore')
infiletext = infile.read()
infiletext = infiletext.replace('\n', ' ')
infil... | flexible | {
"blob_id": "6434e427c9015544985a38104cffeaa10866b9ea",
"index": 4585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfilenames.sort()\n<mask token>\nfor filename in filenames:\n infile = open('data/SENTIMENT_test/' + filename, errors='ignore')\n infiletext = infile.read()\n infiletext = infilet... | [
0,
1,
2,
3
] |
from django import forms
from django.forms import widgets
from tsuru_dashboard import settings
import requests
class ChangePasswordForm(forms.Form):
old = forms.CharField(widget=forms.PasswordInput())
new = forms.CharField(widget=forms.PasswordInput())
confirm = forms.CharField(widget=forms.PasswordInput... | normal | {
"blob_id": "27fc11ae68531c7dbafdcf134f0eef019210e2de",
"index": 8347,
"step-1": "<mask token>\n\n\nclass PasswordRecoveryForm(forms.Form):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass TokenRequestForm(forms.Form):\n email = forms.EmailField()\n\n def send(self):\n url = '{0}/use... | [
14,
15,
18,
19,
20
] |
from setuptools import setup, find_packages
setup(name='qn',
version='0.2.2',
description='Handy functions I use everyday.',
url='https://github.com/frlender/qn',
author='Qiaonan Duan',
author_email='geonann@gmail.com',
license='MIT',
packages=find_packages(),
# install_... | normal | {
"blob_id": "3b307ae7f8b8b25c93eb2dc54b2603b1291b6232",
"index": 1789,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='qn', version='0.2.2', description=\n 'Handy functions I use everyday.', url='https://github.com/frlender/qn',\n author='Qiaonan Duan', author_email='geonann@gmail.com', ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TestCRMcreateCustomer(TestCRM):
<|reserved_special_token_0|>
def test_weiChat(self):
self.login()
self.createCustomer()
self.logout()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestCRMcreateCus... | flexible | {
"blob_id": "74bc530d53cd86c52c44ba8e98d4d8f502032340",
"index": 2423,
"step-1": "<mask token>\n\n\nclass TestCRMcreateCustomer(TestCRM):\n <mask token>\n\n def test_weiChat(self):\n self.login()\n self.createCustomer()\n self.logout()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\... | [
2,
3,
4,
5,
6
] |
import datetime
from ..core.indicator import Indicator, IndicatorState
from ..core.toolwindow import ToolWindow
class HaakePhoenix(ToolWindow):
required_devices = ['haakephoenix']
def __init__(self, *args, **wargs):
self.indicators = {}
super().__init__(*args, **wargs)
def init_gui(self... | normal | {
"blob_id": "25aa0766505b22588107d44e15c3596e9383d4e9",
"index": 486,
"step-1": "<mask token>\n\n\nclass HaakePhoenix(ToolWindow):\n <mask token>\n\n def __init__(self, *args, **wargs):\n self.indicators = {}\n super().__init__(*args, **wargs)\n\n def init_gui(self, *args, **kwargs):\n ... | [
8,
10,
13,
14,
15
] |
###
### Copyright 2009 The Chicago Independent Radio Project
### All Rights Reserved.
###
### Licensed under the Apache License, Version 2.0 (the "License");
### you may not use this file except in compliance with the License.
### You may obtain a copy of the License at
###
### http://www.apache.org/licenses/LICENS... | normal | {
"blob_id": "d077f32061b87a4bfd6a0ac226730957a4000804",
"index": 5859,
"step-1": "<mask token>\n\n\nclass UserNotAllowedError(Exception):\n \"\"\"Raised when the user is recognized but forbidden from entering.\"\"\"\n\n\nclass _Credentials(object):\n email = None\n security_token_is_stale = False\n\n\n<... | [
10,
11,
12,
13,
16
] |
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from matplotlib import pyplot as plt
def plot_feature_VS_Observed(feature, df, linecolor):
"""
This function plots the 1880-2004 time series plots for the selected feature and observed earth
:param
Input: df -- > The dataframe... | normal | {
"blob_id": "8348d353e6fdea77c9c994d541db1420ef57a797",
"index": 4399,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef plot_feature_VS_Observed(feature, df, linecolor):\n \"\"\"\n This function plots the 1880-2004 time series plots for the selected feature and observed earth\n :param\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(12):
if 'r' in input():
c += 1
print(c)
<|reserved_special_token_1|>
c = 0
for i in range(12):
if 'r' in input():
c += 1
print(c)
<|reserved_special_token_1|>
# ?????
c=0
for i in r... | flexible | {
"blob_id": "294b0dc7587ecd37887591da5a1afe96a4349f6b",
"index": 8711,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(12):\n if 'r' in input():\n c += 1\nprint(c)\n",
"step-3": "c = 0\nfor i in range(12):\n if 'r' in input():\n c += 1\nprint(c)\n",
"step-4": "# ????... | [
0,
1,
2,
3
] |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
data=pd.read_excel("data_SHA.xls")
fig,ax=plt.subplots()
ax.plot(data["Date"],data["HCHFI"],label="HCHFI")
ax.plot(data["Date"],data["SHA"]/2.67547,label="SSE Composite Index")
ax.plot(data["Date"],data["Hushen300 Index"]/3.20393,label="Hushen300 In... | normal | {
"blob_id": "91df15d6d89d070677704572d35218558317a6ec",
"index": 117,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nax.plot(data['Date'], data['HCHFI'], label='HCHFI')\nax.plot(data['Date'], data['SHA'] / 2.67547, label='SSE Composite Index')\nax.plot(data['Date'], data['Hushen300 Index'] / 3.20393, lab... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def isChar(c):
return c > 'a' and c < 'z' or c > 'A' and c < 'Z'
def isOperator(c):
return c in operators
def isDefun(line):
return '(' in line and ')' in line and sum([(i in line) for i in toDelete])
def isDefStruct(line):
return 'struct ' in line and len(line.split... | flexible | {
"blob_id": "082e3350c5827ff2ca909084f2d6a206ae21a7e6",
"index": 3240,
"step-1": "<mask token>\n\n\ndef isChar(c):\n return c > 'a' and c < 'z' or c > 'A' and c < 'Z'\n\n\ndef isOperator(c):\n return c in operators\n\n\ndef isDefun(line):\n return '(' in line and ')' in line and sum([(i in line) for i i... | [
10,
12,
15,
16,
17
] |
# Generated by Django 3.2.3 on 2021-05-23 19:41
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('main_app', '0002_notebook_smathphone'),
]
operations = [
migrations.RenameModel(
old_name='Smathphone',
new_name='Smartphone... | normal | {
"blob_id": "7e11a33d82926ed544640a0192e905d373f575da",
"index": 2766,
"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 = [('main_app', ... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
from apap.models import *
# Register your models here.
admin.site.register(Doggo)
admin.site.register(Profile) | normal | {
"blob_id": "22504b466cdeb380b976e23e2708e94131722e11",
"index": 8147,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Doggo)\nadmin.site.register(Profile)\n",
"step-3": "from django.contrib import admin\nfrom apap.models import *\nadmin.site.register(Doggo)\nadmin.site.register(Prof... | [
0,
1,
2,
3
] |
import os
import attr
import click
import guitarpro
import psutil
ALL = object()
@attr.s
class GPTools:
input_file = attr.ib()
output_file = attr.ib()
selected_track_numbers = attr.ib(default=None)
selected_measure_numbers = attr.ib(default=None)
selected_beat_numbers = attr.ib(default=None)
... | normal | {
"blob_id": "c6821cb8dd6f8d74ca20c03f87dae321eb869c32",
"index": 2454,
"step-1": "<mask token>\n\n\n@attr.s\nclass GPTools:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def parse(self):\n if self.input_file is None:\n self.in... | [
7,
8,
9,
11,
12
] |
from layout import UIDump
import Tkinter
from Tkinter import *
from ScriptGenerator import ScriptGen
class Divide_and_Conquer():
def __init__(self, XY):
self.XY = XY
self.user_val = 'None'
self.flag = 'green'
print self.XY
def bounds_Compare(self, bounds, filename):
""" Compares the bounds with Master... | normal | {
"blob_id": "7a65a5522db97a7a113a412883b640feede5bcee",
"index": 909,
"step-1": "from layout import UIDump\nimport Tkinter \nfrom Tkinter import *\nfrom ScriptGenerator import ScriptGen\n\nclass Divide_and_Conquer():\n\n\tdef __init__(self, XY):\n\t\tself.XY = XY\n\t\tself.user_val = 'None'\n\t\tself.flag = 'gre... | [
0
] |
<|reserved_special_token_0|>
class SensorDataFrame:
def __init__(self, data):
self.speed, self.steering, self.throttle, self.temp = data
self.timestamp = datetime.now()
def __str__(self):
return SENSOR_DATA_FORMAT.format(self.speed, self.steering, self.
throttle, self.tem... | flexible | {
"blob_id": "cf4170760fe6210d8b06f179484258f4ae3f8796",
"index": 7284,
"step-1": "<mask token>\n\n\nclass SensorDataFrame:\n\n def __init__(self, data):\n self.speed, self.steering, self.throttle, self.temp = data\n self.timestamp = datetime.now()\n\n def __str__(self):\n return SENSOR... | [
4,
6,
7,
8,
9
] |
#!/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
] |
N = int(input())
l = []
for n in range(N):
x = int(input())
l.append(x)
l.sort()
print(*l, sep='\n')
| normal | {
"blob_id": "a699b43c57c315967a6d1881d7012fee4a93607b",
"index": 6347,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor n in range(N):\n x = int(input())\n l.append(x)\nl.sort()\nprint(*l, sep='\\n')\n",
"step-3": "N = int(input())\nl = []\nfor n in range(N):\n x = int(input())\n l.append... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Markdown:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __processSingleLine(self, line):
if self.__isHeading(line):
self.__process('p')
self.__analyzing.append(re.sub('(#{1,6})', '', line).strip())
self.__proce... | flexible | {
"blob_id": "13e3337cf9e573b8906fe914a830a8e895af20ba",
"index": 3983,
"step-1": "<mask token>\n\n\nclass Markdown:\n <mask token>\n <mask token>\n\n def __processSingleLine(self, line):\n if self.__isHeading(line):\n self.__process('p')\n self.__analyzing.append(re.sub('(#{... | [
9,
10,
11,
12,
13
] |
# -*- coding: utf-8 -*-
# @Author: huerke
# @Date: 2016-09-03 10:55:54
# @Last Modified by: huerke
# @Last Modified time: 2016-09-03 15:54:50
from flask import render_template
from . import main
@main.app_errorhandler(404)
def page_not_found(e):
return render_template('404.html'), 404
@main.app_errorhandler... | normal | {
"blob_id": "021cbd1bd22f9ec48db2e52b2a98be169bbfdbbd",
"index": 5979,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@main.app_errorhandler(404)\ndef page_not_found(e):\n return render_template('404.html'), 404\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\n@main.app_errorhandler(404)\ndef ... | [
0,
1,
2,
3,
4
] |
# -*-coding:utf-8-*-
# Author: Scott Larter
import pygame
import pygame.draw
import numpy as np
from agent import *
from tools import *
SCREENSIZE = [1200, 400] # walls.csv
#SCREENSIZE = [1200, 650] # walls2.csv
RESOLUTION = 180
AGENTSNUM = 12
GROUPSNUM = 2
MAXGROUPSIZE = 6
MAXSUBGROUPSIZE = 3
BACKGROUNDCOLOR = [255... | normal | {
"blob_id": "00051a4087bfcf2e6826e9afa898830dc59aa5ab",
"index": 5451,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npygame.init()\n<mask token>\npygame.display.set_caption('Social Force Model - Crosswalk')\n<mask token>\nfor line in open(WALLSFILE, newline='', encoding='utf-8-sig'):\n coords = line.... | [
0,
1,
2,
3,
4
] |
from datetime import datetime
class Location:
def __init__(self, location_dict):
self.x = location_dict['x']
self.y = location_dict['y']
self.id = location_dict['id']
self.events = []
self.latest_average_value = 0
self.latest_event_count = 0
self.average_... | normal | {
"blob_id": "efbfe95acbe0b97e863c8788bca4a71633da36b3",
"index": 1906,
"step-1": "<mask token>\n\n\nclass Location:\n <mask token>\n <mask token>\n\n def update_overall_average_value(self):\n value_sum = 0\n for event in self.events:\n value_sum += event.value\n value_cou... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def generate_mutation(base):
"""
Taking into account the current base, base, return a mutation.
"""
if base in ['A', 'C', 'G', 'T']:
bases = ['A', 'C', 'G', 'T']
bases.remove(base)
return np.random.choice(bases)
else:
raise Exception('base i... | flexible | {
"blob_id": "d3f80deb72ca2bd91fc09b49ad644f54d339f962",
"index": 5819,
"step-1": "<mask token>\n\n\ndef generate_mutation(base):\n \"\"\"\n\tTaking into account the current base, base, return a mutation.\n\t\n\t\"\"\"\n if base in ['A', 'C', 'G', 'T']:\n bases = ['A', 'C', 'G', 'T']\n bases.r... | [
5,
6,
7,
9,
10
] |
# -*- coding:utf-8 -*-
__author__ = 'leandro'
from datetime import *
from PyQt4 import QtGui, QtCore
from baseDatos.ventas.venta import NotaCredito
from gui import CRUDWidget,MdiWidget
from ventanas import Ui_vtnDevolucionDeCliente, Ui_vtnReintegroCliente, Ui_vtnVentaContado
from baseDatos.obraSocial import ObraSoc... | normal | {
"blob_id": "59233cd45000cd6d6ad0876eb3812599392d7c05",
"index": 9357,
"step-1": "# -*- coding:utf-8 -*-\n__author__ = 'leandro'\n\n\nfrom datetime import *\n\nfrom PyQt4 import QtGui, QtCore\n\nfrom baseDatos.ventas.venta import NotaCredito\nfrom gui import CRUDWidget,MdiWidget\nfrom ventanas import Ui_vtnDevol... | [
0
] |
<|reserved_special_token_0|>
class Ui_aboutDialog(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Ui_aboutDialog(object):
def setupUi(self, aboutDialog):
aboutDialog.setObjectName('aboutDialog')
aboutD... | flexible | {
"blob_id": "25b3defc8410c72c7c6f25288af91bd0c826f2ed",
"index": 6051,
"step-1": "<mask token>\n\n\nclass Ui_aboutDialog(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_aboutDialog(object):\n\n def setupUi(self, aboutDialog):\n aboutDialog.setObjectName('aboutDi... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@periodic_task(run_every=crontab(minute='*/10'), name='scrape_espn_feed',
ignore_result=True)
def scrape_espn_feed():
"""
Saves latest image from Flickr
"""
thescores = doScoresScrape()
fixScores(thescore... | flexible | {
"blob_id": "a9a067ee3b176d2f2ca558b69ce2bc598bb31d22",
"index": 4501,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@periodic_task(run_every=crontab(minute='*/10'), name='scrape_espn_feed',\n ignore_result=True)\ndef scrape_espn_feed():\n \"\"\"\n Saves latest image from Flickr\n \"\"\"... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def solution(input):
k = 1
for v in sorted(input):
if v >= k:
k += 1
return k - 1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def solution(input):
k = 1
for v in sorted(input):
if v >= k:
... | flexible | {
"blob_id": "a89724be31b4ccc1a3d83305509d9624da364a0c",
"index": 6004,
"step-1": "<mask token>\n\n\ndef solution(input):\n k = 1\n for v in sorted(input):\n if v >= k:\n k += 1\n return k - 1\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef solution(input):\n k = 1\n for ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def create_app():
app = Flask(__name__)
app.config['SECRET_KEY'] = 'KARNISINGHSHEKHAWAT'
app.config['SQLALCHEMY_DATABASE_URL'] = f'sqlite:///{DBNAME}'
db.init_app(app)
from .views import views
from .auth ... | flexible | {
"blob_id": "c6fdb9c405427a3583a59065f77c75c4aa781405",
"index": 5417,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n app.config['SECRET_KEY'] = 'KARNISINGHSHEKHAWAT'\n app.config['SQLALCHEMY_DATABASE_URL'] = f'sqlite:///{DBNAME}'\n db.init_app(... | [
0,
1,
2,
3
] |
import json
import unittest
from music_focus.workflows.weibo_online import WeiboOnline
class Test(unittest.TestCase):
def setUp(self):
pass
def test(self):
workflow_input = {'result_type': 'posts'}
wf = WeiboOnline()
r = wf.run(workflow_input)
print(json.dumps(r, ensu... | normal | {
"blob_id": "7088f7233b67dcb855482a76d304aacc1a26abad",
"index": 3790,
"step-1": "<mask token>\n\n\nclass Test(unittest.TestCase):\n <mask token>\n\n def test(self):\n workflow_input = {'result_type': 'posts'}\n wf = WeiboOnline()\n r = wf.run(workflow_input)\n print(json.dumps(... | [
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class DailyCacheMiddleware(CacheMiddleware):
<|reserved_special_token_0|>
@property
def key_prefix(self):
return date.today().isoformat() + '/' + (self.__key_prefix or '')
@key_prefix.setter
def key_prefix(self, value):
self.__key_prefix = value
<|r... | flexible | {
"blob_id": "5b440484c5d7f066c54837c2812967a0ff360399",
"index": 9905,
"step-1": "<mask token>\n\n\nclass DailyCacheMiddleware(CacheMiddleware):\n <mask token>\n\n @property\n def key_prefix(self):\n return date.today().isoformat() + '/' + (self.__key_prefix or '')\n\n @key_prefix.setter\n ... | [
3,
4,
5,
6
] |
from django.db import models
from datetime import datetime
# Message model for testing purposes
class Message(models.Model):
type = models.CharField(max_length=10)
body = models.CharField(max_length=50)
def __str__(self):
return self.type + ":" + self.body
# Company model
class Co... | normal | {
"blob_id": "47f6c4b3c279a065b8f21dab2faa71271db8d6ab",
"index": 6680,
"step-1": "<mask token>\n\n\nclass Company(models.Model):\n <mask token>\n\n @classmethod\n def create(cls, name):\n company = cls(name=name)\n return company\n\n def __str__(self):\n return self.name\n\n\ncla... | [
7,
8,
11,
12,
13
] |
# -*- coding: utf-8 -*-
#
# This file is part of REANA.
# Copyright (C) 2017, 2018 CERN.
#
# REANA is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""Pytest configuration for REANA-Workflow-Controller."""
from __future__ import absolu... | normal | {
"blob_id": "502e92d3e5d059d73016702ce0b2591a123810d3",
"index": 6892,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.fixture(scope='module')\ndef base_app(tmp_shared_volume_path):\n \"\"\"Flask application fixture.\"\"\"\n config_mapping = {'SERVER_NAME': 'localhost:5000', 'SECRET_KEY'... | [
0,
1,
2,
3
] |
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib as mpl
from matplotlib import pyplot as plt
plt.style.use('seaborn-muted')
#from IPython import get_ipython
from IPython.display import HTML, Markdown
import air_cargo_problems as acp
problems = ['Air Cargo Problem 1',
'... | normal | {
"blob_id": "cd49230be3c418853aa2986ed727204e51a6b6ae",
"index": 3794,
"step-1": "<mask token>\n\n\ndef get_results_df(fname, problem):\n \"\"\"Process csv into dataframe.\n \"\"\"\n t = '\\t'\n val_cols = ['Actions', 'Expansions', 'GoalTests', 'NewNodes',\n 'PlanLength', 'ElapsedSeconds']\n ... | [
6,
12,
14,
16,
17
] |
# -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2011-Today Serpent Consulting Services Pvt.Ltd. (<http://www.serpentcs.com>).
# Copyright (C) 2004 OpenERP SA (<http://www.openerp.com>)
#
# Thi... | normal | {
"blob_id": "ac99c19294661657d383b036c9ab83e7b610cb7d",
"index": 6896,
"step-1": "<mask token>\n\n\nclass location_accommodation(models.AbstractModel):\n <mask token>\n <mask token>\n\n @api.multi\n def render_html(self, docids, data=None):\n report = self.env['report']._get_report_from_name(\... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for string in inputList:
hasDoubleDupes = False
hasTripleDupes = False
for char in string:
numRepeatsChar = string.count(char)
if numRepeatsChar == 2 and not hasDoubleDupes:
doubleDupes += 1... | flexible | {
"blob_id": "9620479e9ac27c1c7833c9a31b9cb18408b8d361",
"index": 4019,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor string in inputList:\n hasDoubleDupes = False\n hasTripleDupes = False\n for char in string:\n numRepeatsChar = string.count(char)\n if numRepeatsChar == 2 and ... | [
0,
1,
2,
3,
4
] |
def fibonacci(quantidade):
resultado = [1, 2]
# while True:
# substituir o while pelo for, em um range do 2° valor da lista, correr até
# o valor definido na função "Quantidade"
for _ in range(2, quantidade):
# desta forma ele irá realizar a função do 2° da lista até atingir
# o valor ... | normal | {
"blob_id": "83c7bb2e109f8affd9e2a12e8c5370b0f5a34048",
"index": 653,
"step-1": "<mask token>\n",
"step-2": "def fibonacci(quantidade):\n resultado = [1, 2]\n for _ in range(2, quantidade):\n resultado.append(sum(resultado[-2:]))\n return resultado\n\n\n<mask token>\n",
"step-3": "def fibonac... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class User_Game(CPU_Game):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class User_Game(CPU_Game):
def get_user_phrase(self):
correct_form = False
while not... | flexible | {
"blob_id": "d0dbf5a13b8e718ed426a254546ba13da12b2c3e",
"index": 4149,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass User_Game(CPU_Game):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass User_Game(CPU_Game):\n\n def get_user_phrase(self):\n correct_form = False\n whi... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@PublicAPI
class ParameterNoise(Exploration):
<|reserved_special_token_0|>
def __init__(self, action_space, *, framework: str, policy_config: dict,
model: ModelV2, initial_stddev: float=1.0, random_timesteps: int=
10000, sub_exploration: Optional[dict]=None, **kwa... | flexible | {
"blob_id": "b2b47b394eadebda5c51e89abd27832f9dbd4c8c",
"index": 4193,
"step-1": "<mask token>\n\n\n@PublicAPI\nclass ParameterNoise(Exploration):\n <mask token>\n\n def __init__(self, action_space, *, framework: str, policy_config: dict,\n model: ModelV2, initial_stddev: float=1.0, random_timesteps... | [
16,
17,
20,
21,
22
] |
<|reserved_special_token_0|>
def generate_str():
print(','.join(str(d) for d in data))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def generate_str():
print(','.join(str(d) for d in data))
def sample():
yield 'Is'
yield 'Chicago'
yield 'Not'
... | flexible | {
"blob_id": "4ce1e802831f09e503d18fd287cb35400986e3c8",
"index": 8095,
"step-1": "<mask token>\n\n\ndef generate_str():\n print(','.join(str(d) for d in data))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate_str():\n print(','.join(str(d) for d in data))\n\n\ndef sample():\n yield 'Is'... | [
1,
2,
4,
5,
6
] |
# Generated by Django 3.0.1 on 2020-01-11 19:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0004_auto_20191230_2037'),
]
operations = [
migrations.AddField(
model_name='user',
name='cir... | normal | {
"blob_id": "6aa762165dba891a3638d13862019dd342a7e05a",
"index": 7644,
"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 = [('users', '00... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class GoldpriceSpider(scrapy.Spider):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self):
self.browser = webdriver.PhantomJS()
self.price = None
def parse(self, response):
self.browser.get... | flexible | {
"blob_id": "e59404149c739a40316ca16ab767cbc48aa9b685",
"index": 3526,
"step-1": "<mask token>\n\n\nclass GoldpriceSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self):\n self.browser = webdriver.PhantomJS()\n self.price = None\n\n def parse(self... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
a = int(input('masukkan nilai = '))
if a > 60:
status = 'LULUS'
elif a <= 60:
status = 'TIDAK LULUS'
print(status)
ulang = input('apakah anda ingin mengulang? y/n = ')
<|reserved_s... | flexible | {
"blob_id": "759b440bf436afbfb081cf55eeb4a0f075ed3e6d",
"index": 9577,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n a = int(input('masukkan nilai = '))\n if a > 60:\n status = 'LULUS'\n elif a <= 60:\n status = 'TIDAK LULUS'\n print(status)\n ulang = input('ap... | [
0,
1,
2,
3
] |
def fun1(fun):
return "Hai!!!! "+fun
def message():
return "How are you"
res = fun1(message())
print(res)
| normal | {
"blob_id": "e9fff1fb0a79493d4d7f3417c7d554eb10a978a0",
"index": 6616,
"step-1": "<mask token>\n",
"step-2": "def fun1(fun):\n return 'Hai!!!! ' + fun\n\n\ndef message():\n return 'How are you'\n\n\n<mask token>\n",
"step-3": "def fun1(fun):\n return 'Hai!!!! ' + fun\n\n\ndef message():\n return ... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def byGuide(data, val=None, test=None):
val_guides = val
if val == None:
val_guides = ['GGGTGGGGGGAGTTTGCTCCTGG', 'GACCCCCTCCACCCCGCCTCCGG',
'GGCCTCCCCAAAGCCTGGCCAGG', 'GAACACAAAGCATAGACTGCGGG']
test_guides = test
if test == None:
test_guides = ... | flexible | {
"blob_id": "a0059563b2eed4ca185a8e0971e8e0c80f5fb8f8",
"index": 6668,
"step-1": "<mask token>\n\n\ndef byGuide(data, val=None, test=None):\n val_guides = val\n if val == None:\n val_guides = ['GGGTGGGGGGAGTTTGCTCCTGG', 'GACCCCCTCCACCCCGCCTCCGG',\n 'GGCCTCCCCAAAGCCTGGCCAGG', 'GAACACAAAGCA... | [
15,
16,
19,
21,
24
] |
# In the 20×20 grid below, four numbers along a diagonal line have been marked in red.
# The product of these numbers is 26 × 63 × 78 × 14 = 1788696.
# What is the greatest product of four adjacent numbers in the same direction
# (up, down, left, right, or diagonally) in the 20×20 grid?
import numpy as np
data = np.ge... | normal | {
"blob_id": "bacaaf5c91232d85f451c2c17a42cd2ec6966684",
"index": 1499,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(0, len(data[0, :]) - 3):\n for j in range(0, len(data[0, :]) - 3):\n product_hor = data[j, i] * data[j, i + 1] * data[j, i + 2] * data[j,\n i + 3]\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@application.route('/results', methods=['GET', 'POST'])
def get_results():
_logger_getting.warning('retrieving all student results')
data = Student.query.all()
_logger_getting.warning('the students results have been collected for {}'
.format(data))
return render_te... | flexible | {
"blob_id": "18f9e55b62b30ce8c9d4a57cd9c159543a738770",
"index": 4709,
"step-1": "<mask token>\n\n\n@application.route('/results', methods=['GET', 'POST'])\ndef get_results():\n _logger_getting.warning('retrieving all student results')\n data = Student.query.all()\n _logger_getting.warning('the students... | [
4,
6,
7,
8,
9
] |
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
] |
<|reserved_special_token_0|>
@dataclass
class Node:
age: int
num: int
label: str
alignment: []
def __init__(self, child1=None, child2=None):
self.child1 = child1
self.child2 = child2
<|reserved_special_token_0|>
def initializeClusters(t):
numNodes = len(t)
numLeaves = ... | flexible | {
"blob_id": "53cf2dfe3319c39ca6f1dc890eea578fae654b5b",
"index": 8847,
"step-1": "<mask token>\n\n\n@dataclass\nclass Node:\n age: int\n num: int\n label: str\n alignment: []\n\n def __init__(self, child1=None, child2=None):\n self.child1 = child1\n self.child2 = child2\n\n\n<mask to... | [
9,
12,
16,
17,
21
] |
class Animal:
def eat(self):
print('吃')
def bark(self):
print('喝')
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Dog(Animal):
def bark(self):
print('汪汪叫')
class XiaoTianQuan(Dog):
def bark(self):
print('像神一样的叫唤...')
Dog.bark(self)... | flexible | {
"blob_id": "d7aa85c2458ee12a8de0f75419945fbe2acdf95d",
"index": 3946,
"step-1": "class Animal:\n\n def eat(self):\n print('吃')\n\n def bark(self):\n print('喝')\n <mask token>\n <mask token>\n\n\nclass Dog(Animal):\n\n def bark(self):\n print('汪汪叫')\n\n\nclass XiaoTianQuan(Dog... | [
8,
9,
11,
12,
13
] |
# coding=utf-8
class Movie:
def __init__(self,movieid,moviename,score,poster):
self.movieid=movieid
self.moviename=moviename
self.score=score
self.poster=poster
for i in range(1,32):
print("<option value =\""+str(i)+"\">"+str(i)+"</option>") | normal | {
"blob_id": "856e62cf4cd443c7b3397e926f8fc4fece145f5b",
"index": 3447,
"step-1": "<mask token>\n",
"step-2": "class Movie:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class Movie:\n\n def __init__(self, movieid, moviename, score, poster):\n self.movieid = movieid\n self.moviename = mo... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
config.read('config.ini')
<|reserved_special_token_0|>
logging.getLogger('transformers.tokenization_utils').setLevel(logLevel +
oneLevelUp)
logging.getLogger('transformers.modeling_utils').setLevel(logLevel + oneLevelUp
)
... | flexible | {
"blob_id": "e4fb932c476ca0222a077a43499bf9164e1f27d0",
"index": 8896,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nconfig.read('config.ini')\n<mask token>\nlogging.getLogger('transformers.tokenization_utils').setLevel(logLevel +\n oneLevelUp)\nlogging.getLogger('transformers.modeling_utils').setLev... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# script :: creating a datamodel that fits mahout from ratings.dat
ratings_dat = open('../data/movielens-1m/users.dat', 'r')
ratings_csv = open('../data/movielens-1m/users.txt', 'w')
for line in ratings_dat:
arr = line.split('::')
new_line = '\t'.join(arr)
ratings_csv.write(new_line)
rati... | normal | {
"blob_id": "2dd59681a0dcb5d3f1143385100c09c7783babf4",
"index": 76,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in ratings_dat:\n arr = line.split('::')\n new_line = '\\t'.join(arr)\n ratings_csv.write(new_line)\nratings_dat.close()\nratings_csv.close()\n",
"step-3": "ratings_dat ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class AuthenticationCustom(admin.ModelAdmin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AuthenticationCustom(admin.ModelAdmin):
list_display = 'email', 'id'
... | flexible | {
"blob_id": "4957e62deec6192aabdf7144f02b28c7ce60ed4b",
"index": 4250,
"step-1": "<mask token>\n\n\nclass AuthenticationCustom(admin.ModelAdmin):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AuthenticationCustom(admin.ModelAdmin):\n list_display = 'email', 'id... | [
1,
2,
3,
4,
5
] |
def helloWorld():
print "We are in DEMO land!"
for i in range(10):
helloWorld()
print listBuilder()
def listBuilder():
b = []
for x in range(5):
b.append(10 * x)
return b
print "[done, for real]"
| normal | {
"blob_id": "57516a17c1f3ee208076852369999d74dbb2b3ba",
"index": 98,
"step-1": "def helloWorld():\n print \"We are in DEMO land!\"\n\nfor i in range(10):\n helloWorld()\nprint listBuilder()\n\ndef listBuilder():\n b = []\n for x in range(5):\n b.append(10 * x)\n return b\n\nprint \"[done, for real]\"\n",... | [
0
] |
<|reserved_special_token_0|>
def get_load(pkt):
ack = str(pkt[TCP].ack)
seq = str(pkt[TCP].seq)
src_ip_port = str(pkt[IP].src) + ':' + str(pkt[TCP].sport)
dst_ip_port = str(pkt[IP].dst) + ':' + str(pkt[TCP].dport)
load = pkt[Raw].load
pkt_frag_loads = frag_remover(ack, load)
pkt_frag_loads... | flexible | {
"blob_id": "3e0bc91b81d0f503b78c9ac685b05b7ecb754e28",
"index": 3460,
"step-1": "<mask token>\n\n\ndef get_load(pkt):\n ack = str(pkt[TCP].ack)\n seq = str(pkt[TCP].seq)\n src_ip_port = str(pkt[IP].src) + ':' + str(pkt[TCP].sport)\n dst_ip_port = str(pkt[IP].dst) + ':' + str(pkt[TCP].dport)\n loa... | [
3,
4,
5,
6,
7
] |
from django.contrib.auth import get_user_model
from django.test import TestCase
from .models import Order
from markets.models import Market
from tickers.models import Ticker
from trades.models import Trade
USER_MODEL = get_user_model()
class Matching:
@staticmethod
def get_bid_ask( market : Market):
... | normal | {
"blob_id": "866ee2c4fa52bf9bda4730c7a9d46bb4798adcd4",
"index": 1775,
"step-1": "<mask token>\n\n\nclass Matching:\n <mask token>\n <mask token>\n\n @staticmethod\n def process_order(self, order: Order):\n if order.status == Order.STATUS_WAITING_NEW:\n order.status = Order.STATUS_N... | [
7,
8,
9,
11,
12
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Author: André Pacheco
E-mail: pacheco.comp@gmail.com
This file implements the methods and functions to load the image as a PyTorch dataset
If you find any bug or have some suggestion, please, email me.
"""
from PIL import Image
from torch.utils import data
import t... | normal | {
"blob_id": "4e31c2a80bec77a1f5aafc8a91617fb4b2941788",
"index": 432,
"step-1": "<mask token>\n\n\nclass BuildDataset(data.Dataset):\n <mask token>\n\n def __init__(self, imgs_path, labels, extra_info=None, transform=None):\n \"\"\"\n The constructor gets the images path and their respectivel... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
inputDataSet.addSample((-1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1,
1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1), (
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
inputDataSet... | flexible | {
"blob_id": "a2569ccd509fa755f4cad026f483bcf891c6fb41",
"index": 8120,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ninputDataSet.addSample((-1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1,\n 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1, -1, -1, 1), (\n 1, 0, 0, 0, 0, 0, 0, 0, 0, 0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
win.show()
app.exec_()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app = qt.QApplication([])
win = mainWindow.RIXSMainWindow()
win.show()
app.exec_()
<|reserved_special_token_1|>
from PyMca5.PyMcaGui import PyM... | flexible | {
"blob_id": "34c8541e640596f51a5232cba06172df5814db14",
"index": 7734,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwin.show()\napp.exec_()\n",
"step-3": "<mask token>\napp = qt.QApplication([])\nwin = mainWindow.RIXSMainWindow()\nwin.show()\napp.exec_()\n",
"step-4": "from PyMca5.PyMcaGui import P... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import random
class Role:
"""
角色类
卧底
平民
"""
def __init__(self,key_word="",role_id = 0):
self.key_word = key_word
self.role_id = role_id #平民-0；卧底-1；
class User(Role):
"""
用户类
玩家
"""
def __init__(self,id,role_i... | normal | {
"blob_id": "3b5141a86948df6632612f6c9d7fc0089acc60aa",
"index": 5981,
"step-1": "<mask token>\n\n\nclass Role:\n <mask token>\n <mask token>\n\n\nclass User(Role):\n \"\"\"\n 用户类\n 玩家\n \"\"\"\n\n def __init__(self, id, role_id):\n self.id = id\n self.role_id = role_id\n",
"... | [
4,
5,
6,
7,
8
] |
class TrieNode:
def __init__(self):
self.children: Dict[str, TrieNode] = collections.defaultdict(TrieNode)
self.word: Optional[str] = None
class Solution:
def findWords(self, board: List[List[str]], words: List[str]) ->List[str]:
m = len(board)
n = len(board[0])
ans =... | normal | {
"blob_id": "f996dffcb9650663278ec1e31d9f88d50142f4ea",
"index": 4491,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def findWords(self, board: List[List[str]], words: List[str]) ->List[str]:\n m = len(board)\n n = len(board... | [
1,
2,
3,
4
] |
# coding: utf-8
import numpy as np
def sparse(n, k):
u"""
return k sparse vector,
the value of non-zero entries are
normal distributed N(0,1).
[args]
n: size of vector
k: number of nonzero entries
[return]
k-sparse vector
"""
z = np.zeros(n)
for i in np.r... | normal | {
"blob_id": "f0e4cd13571728d61566c4093586c91323629e0b",
"index": 7624,
"step-1": "# coding: utf-8\nimport numpy as np\n\n\n\ndef sparse(n, k):\n u\"\"\"\n return k sparse vector, \n the value of non-zero entries are \n normal distributed N(0,1).\n [args]\n n: size of vector\n k: numb... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class tenDParameters:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class tenDParameters:
def __init__(self, b: float, DM: float, pm_l: float, pm_b: float, vrad:
float, sb: float, spml: float, spmb: float, sdm: float, vc: float... | flexible | {
"blob_id": "82e7e22293551e061dcb295c52714c22df0ed0ce",
"index": 5678,
"step-1": "<mask token>\n",
"step-2": "class tenDParameters:\n <mask token>\n",
"step-3": "class tenDParameters:\n\n def __init__(self, b: float, DM: float, pm_l: float, pm_b: float, vrad:\n float, sb: float, spml: float, spm... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def limitZ(Z, limit=10):
for i in range(len(Z)):
for j in range(len(Z[i])):
if Z[i][j] > limit:
Z[i][j] = np.inf
if Z[i][j] < -limit:
Z[i][j] = -np.inf
def plotPontos3D(X, Y, Z):
fig = plt.figure()
ax = fig.add_... | flexible | {
"blob_id": "ff20b65f35614415ad786602c0fc2cabd08124fb",
"index": 4065,
"step-1": "<mask token>\n\n\ndef limitZ(Z, limit=10):\n for i in range(len(Z)):\n for j in range(len(Z[i])):\n if Z[i][j] > limit:\n Z[i][j] = np.inf\n if Z[i][j] < -limit:\n Z[i][... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def test_injection(fc):
from pykern import pkcompat, pkunit
from pykern.pkdebug import pkdc, pkdp, pkdlog
from pykern.pkunit import pkeq, pkok, pkre
import re
r = fc.get('myapp')
pkok(not re.search('googletag', pkcompat.from_bytes(r.data)),
'Unexpected inje... | flexible | {
"blob_id": "65b5db0bc6f23c342138060b7a006ff61e2dcf45",
"index": 3761,
"step-1": "<mask token>\n\n\ndef test_injection(fc):\n from pykern import pkcompat, pkunit\n from pykern.pkdebug import pkdc, pkdp, pkdlog\n from pykern.pkunit import pkeq, pkok, pkre\n import re\n r = fc.get('myapp')\n pkok... | [
1,
2,
3,
4,
5
] |
"""
This module contains the logic to resolve the head-tail orientation of a predicted video time series.
"""
import logging
import numpy as np
import numpy.ma as ma
from wormpose.pose.distance_metrics import angle_distance, skeleton_distance
from wormpose.pose.results_datatypes import (
BaseResults,
Shuffle... | normal | {
"blob_id": "b8fcd8e6dce8d210576bc4166dd258e5fd51278d",
"index": 517,
"step-1": "<mask token>\n\n\nclass _PartitionedResults(BaseResults):\n <mask token>\n\n def mask(self, indices):\n self.theta.mask[indices] = True\n self.skeletons.mask[indices] = True\n self.scores.mask[indices] = T... | [
10,
19,
20,
24,
26
] |
<|reserved_special_token_0|>
class xspecView(object):
<|reserved_special_token_0|>
def LoadSwiftPHAs(self, phaFiles):
"""
Load The Swift PHAs in time order
"""
for pha in phaFiles:
s = xs.Spectrum(pha)
s.ignore('**-15. 150.-**')
cnts = sum(... | flexible | {
"blob_id": "ba34bae7849ad97f939c1a7cb91461269cd58b64",
"index": 8994,
"step-1": "<mask token>\n\n\nclass xspecView(object):\n <mask token>\n\n def LoadSwiftPHAs(self, phaFiles):\n \"\"\"\n Load The Swift PHAs in time order\n\n \"\"\"\n for pha in phaFiles:\n s = xs.S... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('http://www.pythonchallenge.com/pc/def/ocr.html')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
INPUT_TEXT = string.ascii_lowercase
OUTPUT_TEXT = INPUT_TEXT[2:] + INPUT_TEXT[:2]
TRANSLATION_TABLE = str.maketra... | flexible | {
"blob_id": "3c03f71ef9de8825ecd7c89208c79f43c9fb7a56",
"index": 9594,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('http://www.pythonchallenge.com/pc/def/ocr.html')\n",
"step-3": "<mask token>\nINPUT_TEXT = string.ascii_lowercase\nOUTPUT_TEXT = INPUT_TEXT[2:] + INPUT_TEXT[:2]\nTRANSLATION_TABL... | [
0,
1,
2,
3,
4
] |
import sqlite3
import hashlib
users = []
class UserModel:
id = 0
def __init__(self, name, password, birth, sex, phone, email, id=0):
if(id == 0):
self.id = self.id + 1
else:
self.id = id
self.name = name
self.email = email
#處理密碼
s = has... | normal | {
"blob_id": "e675283f14a3d29fba878e7f6d9592130611c2be",
"index": 1469,
"step-1": "<mask token>\n\n\nclass UserModel:\n <mask token>\n\n def __init__(self, name, password, birth, sex, phone, email, id=0):\n if id == 0:\n self.id = self.id + 1\n else:\n self.id = id\n ... | [
6,
7,
8,
9,
12
] |
import subprocess
class BaseExecution:
def __init__(self, flag, parser):
self.flag = flag
self.parser = parser
def execute(self):
process = subprocess.Popen(f'df {self.flag}', shell=True, stdout=
subprocess.PIPE, stderr=subprocess.PIPE)
output, err = process.commu... | normal | {
"blob_id": "d8af43d24a2f2b99bc8b5098f251e017852d6d86",
"index": 1085,
"step-1": "<mask token>\n\n\nclass BaseExecution:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass BaseExecution:\n\n def __init__(self, flag, parser):\n self.flag = flag\n self.parser = parser\n ... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class PageDetector:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class PageDetector:
def __init__(self, driver):
self.selenium = SeleniumWrapper(driver)
<|reserved_special_token_0|>
<|re... | flexible | {
"blob_id": "603d7df0639def2b620cca2299077674e35a74b2",
"index": 5980,
"step-1": "<mask token>\n\n\nclass PageDetector:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass PageDetector:\n\n def __init__(self, driver):\n self.selenium = SeleniumWrapper(driver)\n <mask token>\... | [
1,
2,
3,
4,
5
] |
import sqlite3
forth = sqlite3.connect('databaserupin.db')
sql = "SELECT * from rupin;"
curforth = forth.cursor()
curforth.execute(sql)
result = curforth.fetchall()
for record in result:
print(record) | normal | {
"blob_id": "a7f082737bf476a4bc6a40c962764c05bed9ee14",
"index": 9247,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncurforth.execute(sql)\n<mask token>\nfor record in result:\n print(record)\n",
"step-3": "<mask token>\nforth = sqlite3.connect('databaserupin.db')\nsql = 'SELECT * from rupin;'\ncur... | [
0,
1,
2,
3,
4
] |
'''
-Medium-
*BFS*
You are given a 0-indexed integer array nums containing distinct numbers, an integer start, and an integer goal. There is an integer x that is initially set to start, and you want to perform operations on x such that it is converted to goal. You can perform the following operation repeatedly on the ... | normal | {
"blob_id": "50b2b9d1edc8eaa44050e2b3b2375e966f16e10c",
"index": 6997,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def minimumOperations(self, nums: List[int], start: int, goal: int) ->int:\n que = deque([(start... | [
1,
2,
3,
4,
5
] |
#-*- coding: utf-8 -*-
import re
import sys
import os
import pandas as pd
import jieba
import logging
import argparse
from sklearn.externals import joblib
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import f1_score,accuracy_score
from sklearn.feature_extraction.text im... | normal | {
"blob_id": "c879230efe12bde9042159da221a2b9b4c1d8349",
"index": 198,
"step-1": "<mask token>\n\n\ndef load_data_from_csv(file_name, header=0, encoding='utf-8'):\n data_df = pd.read_csv(file_name, header=header, encoding=encoding)\n return data_df\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef lo... | [
1,
2,
3,
4,
5
] |
from SpritesClass import Sprite
from JogadorClass import Jogador
from OpenGL.GL import *
from OpenGL.GLUT import *
from OpenGL.GLU import *
class Tela:
def __init__(self,j,t0):
self.telas = ["jogo","game over"] #telas existentes
self.estagio = "jogo"
self.j = j
#sprites
se... | normal | {
"blob_id": "d1f0baa1ff87ece50aaded5e60908269e81b6734",
"index": 1952,
"step-1": "<mask token>\n\n\nclass Tela:\n <mask token>\n <mask token>\n\n def setEstagio(self, temp):\n if temp in self.telas:\n self.estagio = temp\n else:\n print('Tela não existe, erro de digit... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in list(raw.keys()):
if len(i) > 8:
del raw[i]
print(raw)
print(len(list(raw.keys())))
np.save('shorten_raw_with_freq.npy', raw)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
raw = np.load('raw_wit... | flexible | {
"blob_id": "ffb17b370c892696b341f6d37a2cfe106a5670a5",
"index": 4265,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in list(raw.keys()):\n if len(i) > 8:\n del raw[i]\nprint(raw)\nprint(len(list(raw.keys())))\nnp.save('shorten_raw_with_freq.npy', raw)\n",
"step-3": "<mask token>\nraw ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
pub_socket.bind('tcp://*:%s' % port)
while True:
topic = 'test'
thisX = np.random.rand()
thisY = np.random.rand()
testDict = {'gaze': (thisX, thisY)}
pub_socket.send_string(topic, zmq.SNDMORE)
pub_socket.se... | flexible | {
"blob_id": "cb469b69bf974d39609f79c4f3be686d8106f971",
"index": 1431,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npub_socket.bind('tcp://*:%s' % port)\nwhile True:\n topic = 'test'\n thisX = np.random.rand()\n thisY = np.random.rand()\n testDict = {'gaze': (thisX, thisY)}\n pub_socket.... | [
0,
1,
2,
3,
4
] |
from datetime import datetime
class Guest:
def __init__(self, Name, FamilyName, Car, controlboard,
CarRotationManager, ID=0, linkedplatform=None,Start=0): # --Initializing Guest credentials/info---
self.Name = Name
self.FamilyName = FamilyName
self.Car = Car
... | normal | {
"blob_id": "3553fa72cb831f82a1030b9eadc9594eee1d1422",
"index": 2152,
"step-1": "<mask token>\n\n\nclass Guest:\n <mask token>\n\n def parked_and_linkedplatform_value(self):\n boolean, linkedplatform = (self.CarRotationManager.\n check_if_guest_parked(self))\n if boolean == True:\... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
"""
Calculate trigger efficiency error
"""
__author__ = "XIAO Suyu<xiaosuyu@ihep.ac.cn>"
__copyright__ = "Copyright (c) XIAO Suyu"
__created__ = "[2018-02-06 Tue 15:25]"
import math
n1 = 4212.0
n2 = 4237.0
N = 5000.0
eff = n1 / n2
err = math.sqrt(eff*(1-eff)/N)
print 'trig_eff = %.4f +- %f' ... | normal | {
"blob_id": "bac3f78b8eb9c4595bc9e8b85587819f92329729",
"index": 2295,
"step-1": "#!/usr/bin/env python\n\"\"\"\nCalculate trigger efficiency error\n\"\"\"\n\n__author__ = \"XIAO Suyu<xiaosuyu@ihep.ac.cn>\"\n__copyright__ = \"Copyright (c) XIAO Suyu\"\n__created__ = \"[2018-02-06 Tue 15:25]\"\n\nimport math\n\nn... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def user(request):
context = {'users': User.objects.all(), 'user_level': User.objects.get(
id=request.session['user_id'])}
return render(request, 'dashboard/user.html', context)
<|reserved_special_token_0|>
<... | flexible | {
"blob_id": "3d737d0ee9c3af1f8ebe4c6998ad30fa34f42856",
"index": 570,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef user(request):\n context = {'users': User.objects.all(), 'user_level': User.objects.get(\n id=request.session['user_id'])}\n return render(request, 'dashboard/user.htm... | [
0,
1,
2,
3,
4
] |
import yet
import pickle
sources = pickle.load(open("./db/source_list"))
addr_list = sources.keys()
'''
for i in range(len(addr_list)):
print addr_list[i],
try:
a = yet.tree(None, sources[addr_list[i]])
print ' Owner :',
for i in a.owner.keys():
print i+ '() ' + a.owner[... | normal | {
"blob_id": "1c55cfa03cd9210b7cf9e728732afe19930e9a41",
"index": 9786,
"step-1": "import yet\nimport pickle\n\nsources = pickle.load(open(\"./db/source_list\"))\naddr_list = sources.keys()\n\n'''\nfor i in range(len(addr_list)):\n print addr_list[i], \n try:\n a = yet.tree(None, sources[addr_list[i]... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def dist(counts):
n = abs(counts['n'] - counts['s'])
nw = abs(counts['nw'] - counts['se'])
ne = abs(counts['ne'] - counts['sw'])
return n + max(ne, nw)
<|reserved_special_token_0|>
<|reserved_special_token_1|... | flexible | {
"blob_id": "ac2e9145e3345e5448683d684b69d2356e3214ce",
"index": 9999,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef dist(counts):\n n = abs(counts['n'] - counts['s'])\n nw = abs(counts['nw'] - counts['se'])\n ne = abs(counts['ne'] - counts['sw'])\n return n + max(ne, nw)\n\n\n<mask ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "f3b466dc5b6149be82b096791ca8445faf169380",
"index": 5216,
"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 = [('orders', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.subplot(1, 2, 1)
for ls in mls:
plt.plot(*ls.xy)
plt.plot(*p.boundary.xy, '-.k')
plt.xlim([0, 5])
plt.ylim([0, 2])
plt.subplot(1, 2, 2)
for ls in results:
plt.plot(*ls.xy)
plt.xlim([0, 5])
plt.ylim([0, 2])
plt.show()
... | flexible | {
"blob_id": "9096ed4b68d2bef92df7db98589e744ddf3efad0",
"index": 350,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.subplot(1, 2, 1)\nfor ls in mls:\n plt.plot(*ls.xy)\nplt.plot(*p.boundary.xy, '-.k')\nplt.xlim([0, 5])\nplt.ylim([0, 2])\nplt.subplot(1, 2, 2)\nfor ls in results:\n plt.plot(*ls.... | [
0,
1,
2,
3,
4
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import numpy as np
def weight_init(layers):
for layer in layers:
if isinstance(layer, nn.BatchNorm1d):
layer.weight.data.fill_(1)
layer.bias.data.zero_()
elif isinstance(layer, nn.Lin... | normal | {
"blob_id": "2c2b075f9ea9e8d6559e44ad09d3e7767c48205e",
"index": 6772,
"step-1": "<mask token>\n\n\nclass LR(nn.Module):\n <mask token>\n <mask token>\n\n\nclass RNN(nn.Module):\n\n def __init__(self, feature_nums, hidden_dims, bi_lstm, out_dims=1):\n super(RNN, self).__init__()\n self.fea... | [
7,
9,
10,
11,
12
] |
num1 = input("첫 번째 실수 : ")
num2 = input("두 번째 실수 : ")
print(float(num1) + float(num2))
num1 = float(input("첫 번째 실수 : "))
num2 = float(input("두 번째 실수 : "))
print(num1 + num2)
| normal | {
"blob_id": "ee8bf681adcb07c4f79245c8f118131bbcabd2fa",
"index": 7920,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(float(num1) + float(num2))\n<mask token>\nprint(num1 + num2)\n",
"step-3": "num1 = input('첫 번째 실수 : ')\nnum2 = input('두 번째 실수 : ')\nprint(float(num1) + float(num2))\nnum1 = float(... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class FashionbertEvaluator(transformers.BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BertModel(config)
self.im_to_embedding = torch.nn.Linear(2048, 768)
self.im_to_embedding_norm = torch.nn.LayerNorm(config.hid... | flexible | {
"blob_id": "7a01bffa5d7f0d5ecff57c97478f2cf5e9a27538",
"index": 1210,
"step-1": "<mask token>\n\n\nclass FashionbertEvaluator(transformers.BertPreTrainedModel):\n\n def __init__(self, config):\n super().__init__(config)\n self.bert = BertModel(config)\n self.im_to_embedding = torch.nn.Li... | [
8,
10,
12,
13,
14
] |
###############################################################################
##
## Copyright (C) 2011-2014, NYU-Poly.
## Copyright (C) 2006-2011, University of Utah.
## All rights reserved.
## Contact: contact@vistrails.org
##
## This file is part of VisTrails.
##
## "Redistribution and use in source and binary for... | normal | {
"blob_id": "2a6b373c443a1bbafe644cb770bc163536dd5573",
"index": 3348,
"step-1": "<mask token>\n\n\ndef qInitResources():\n QtCore.qRegisterResourceData(1, qt_resource_struct, qt_resource_name,\n qt_resource_data)\n\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(1, qt_resource_struct, ... | [
2,
3,
4,
5,
6
] |
tn=int(input())
for ti in range(tn):
#ans = work()
rn,cn = [int(x) for x in input().split()]
evenRow='-'.join(['+']*(cn+1))
oddRow='.'.join(['|']*(cn+1))
artrn = rn*2+1
print(f'Case #{ti+1}:')
for ri in range(artrn):
defaultRow = evenRow if ri%2==0 else oddRow
if ri//2==0:
... | normal | {
"blob_id": "1972e3733918da654cd156a500432a35a239aed4",
"index": 1841,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor ti in range(tn):\n rn, cn = [int(x) for x in input().split()]\n evenRow = '-'.join(['+'] * (cn + 1))\n oddRow = '.'.join(['|'] * (cn + 1))\n artrn = rn * 2 + 1\n print(... | [
0,
1,
2,
3
] |
import requests
import datetime
from yahoo_finance import Share
def getYahooStock(ticker, date1, date2):
companyData = Share(ticker)
dataList = companyData.get_historical(date1, date2)
endData = dataList[0];
startData = dataList[len(dataList) - 1];
print ticker, float(startData['Open']), float(endD... | normal | {
"blob_id": "07854dc9e0a863834b8e671d29d5f407cdd1c13e",
"index": 9599,
"step-1": "import requests\nimport datetime\nfrom yahoo_finance import Share\n\ndef getYahooStock(ticker, date1, date2):\n companyData = Share(ticker)\n dataList = companyData.get_historical(date1, date2)\n endData = dataList[0];\n ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for char in word:
if count == 0:
print(char.upper(), end='')
count = 1
else:
print(char.lower(), end='')
count = 0
<|reserved_special_token_1|>
<|reserved_special_token_0|>
word = str(inp... | flexible | {
"blob_id": "bc837d95ef22bd376f8b095e7aeb1f7d15c0e22e",
"index": 941,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor char in word:\n if count == 0:\n print(char.upper(), end='')\n count = 1\n else:\n print(char.lower(), end='')\n count = 0\n",
"step-3": "<mask toke... | [
0,
1,
2,
3
] |
import pickle
if __name__ == '__main__':
with open('id_generator.bin', 'rb') as f:
print(pickle.load(f))
| normal | {
"blob_id": "080110e404cf5edfe53622a5942b53f9188ddd76",
"index": 1854,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n with open('id_generator.bin', 'rb') as f:\n print(pickle.load(f))\n",
"step-3": "import pickle\nif __name__ == '__main__':\n with open('id_gene... | [
0,
1,
2
] |
h = int(input())
a = int(input())
b = int(input())
c = (h - b + a - b - 1) // (a - b)
print(int(c))
| normal | {
"blob_id": "eea962d6c519bee802c346fcf8d0c7410e00c30b",
"index": 9587,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(int(c))\n",
"step-3": "h = int(input())\na = int(input())\nb = int(input())\nc = (h - b + a - b - 1) // (a - b)\nprint(int(c))\n",
"step-4": null,
"step-5": null,
"step-ids"... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class gramWishbone(Peripheral, Elaboratable):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class gramWishbone(Peripheral, Elaboratable):
def __init__(self, core, data_width=32, granularity=8):
... | flexible | {
"blob_id": "3775ba538d6fab13e35e2f0761a1cacbe087f339",
"index": 4723,
"step-1": "<mask token>\n\n\nclass gramWishbone(Peripheral, Elaboratable):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass gramWishbone(Peripheral, Elaboratable):\n\n def __init__(self, core, data_width=32, gra... | [
1,
2,
3,
4,
5
] |
from flask import Flask
from flask import request, redirect, render_template
from flask_bootstrap import Bootstrap
import urllib.request
import urllib.parse
import json
import uuid
import yaml
import hashlib
from Crypto import Random
from Crypto.Cipher import AES
import base64
app = Flask(__name__)
Bootstrap(app)
... | normal | {
"blob_id": "e55115a65ebee5d41dcd01a5cbabc328acf152da",
"index": 6079,
"step-1": "<mask token>\n\n\ndef encrypt(message, passphrase):\n passphrase = trans(passphrase)\n IV = Random.new().read(BLOCK_SIZE)\n aes = AES.new(passphrase, AES.MODE_CFB, IV)\n return base64.b32encode(IV + aes.encrypt(message)... | [
7,
9,
10,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
GERRIT_PORT = 29418
GERRIT_USERNAME = 'dci-ci-bot'
GERRIT_HOSTNAME = 'softwarefactory-project.io'
GERRIT_SSH_KEY_FILENAME = os.getenv('GERRIT_SSH_KEY_FILENAME',
'/home/dci/dci-ci-bot.id_rsa')
RHEL_AGENT_DIR = os.getenv('RHEL_A... | flexible | {
"blob_id": "8410ff0806766a09d346e930123a2696bebb4b60",
"index": 2821,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nGERRIT_PORT = 29418\nGERRIT_USERNAME = 'dci-ci-bot'\nGERRIT_HOSTNAME = 'softwarefactory-project.io'\nGERRIT_SSH_KEY_FILENAME = os.getenv('GERRIT_SSH_KEY_FILENAME',\n '/home/dci/dci-ci-... | [
0,
1,
2,
3
] |
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