code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import requests
from bs4 import BeautifulSoup
import codecs
url = "https://en.wikipedia.org/wiki/Pennsylvania_State_University"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
infoBox = soup.find("table", class_="infobox vcard")
webScrape = {"Univeristy": "The Pennsylvania State U... | normal | {
"blob_id": "f45ca4e75de7df542fbc65253bb9cc44a868522a",
"index": 6398,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor tr in infoBox.find_all('tr'):\n if len(tr.findChildren('th', recursive=False)) > 0 and len(tr.\n findChildren('td', recursive=False)) > 0:\n header = tr.findChildren(... | [
0,
1,
2,
3,
4
] |
j= float(input("juros"))
Q0= 1500
t= 36
Qf=Q0*(1+j)**t
print(round(Qf,2)) | normal | {
"blob_id": "700d6e0c7dab58ed0157265ff78021923c17e397",
"index": 5619,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(round(Qf, 2))\n",
"step-3": "j = float(input('juros'))\nQ0 = 1500\nt = 36\nQf = Q0 * (1 + j) ** t\nprint(round(Qf, 2))\n",
"step-4": "j= float(input(\"juros\"))\nQ0= 1500\nt= 36... | [
0,
1,
2,
3
] |
from zipline.api import (
# add_history,
history,
order_target_percent,
order,
record,
symbol,
get_datetime,
schedule_function,
)
from zipline.algorithm import TradingAlgorithm
from zipline.utils.factory import load_from_yahoo
import numpy as np
import pandas as pd
from datetime import datetime ... | normal | {
"blob_id": "2e8737a48bd04ef5c158afb23dc94476ea790e18",
"index": 2074,
"step-1": "from zipline.api import (\r\n\t# add_history,\r\n\thistory,\r\n\torder_target_percent,\r\n\torder,\r\n\trecord,\r\n\tsymbol,\r\n\tget_datetime,\r\n\tschedule_function,\r\n)\r\nfrom zipline.algorithm import TradingAlgorithm\r\nfrom ... | [
0
] |
# -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : gan_loss.py
# @Time : Created at 2019-07-11
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import torch
import torch.nn as nn
import config as cfg
class ... | normal | {
"blob_id": "9cea998d7d5cad3ddc00f667ca06151a938d48a1",
"index": 9424,
"step-1": "<mask token>\n\n\nclass GANLoss(nn.Module):\n <mask token>\n\n def __init__(self, loss_mode, which_net, which_D, target_real_label=1.0,\n target_fake_label=0.0, CUDA=False):\n \"\"\" Initialize the GAN's Discrim... | [
5,
6,
7,
8,
9
] |
import sys
import time
from abc import ABC, abstractmethod
from PySide6.QtGui import QPixmap
from PySide6.QtWidgets import QApplication
import inupdater.resource
from inupdater.splash import SplashScreen
class UserInterface(ABC):
"""Interface for GUI element"""
def __init__(self) -> None:
self.stat... | normal | {
"blob_id": "efeb069a7e2aab7262a557236c693752d2973523",
"index": 4169,
"step-1": "<mask token>\n\n\nclass UserInterface(ABC):\n <mask token>\n <mask token>\n\n @abstractmethod\n def show_message(self, msg: str):\n \"\"\"Show a message\"\"\"\n <mask token>\n\n @abstractmethod\n def clo... | [
14,
15,
16,
17,
19
] |
from mx.handlers import MainHandler
# handler for changing app language
class Locale(MainHandler):
"""
handles requests to change LOCALE or language for internationalization.
"""
def get(self):
locale = self.request.get('locale')
if not locale :
locale = LOCALE
locale... | normal | {
"blob_id": "bdcbb946dadf168149342c651ad03eaf4b748401",
"index": 6803,
"step-1": "<mask token>\n\n\nclass Locale(MainHandler):\n <mask token>\n <mask token>\n\n\nclass MainPage(MainHandler):\n\n def get(self):\n self.render('home.html')\n\n def post(self):\n pw = self.request.get('pw')\... | [
4,
5,
6,
7,
8
] |
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
] |
#!/usr/bin/env python
import os
from distutils.core import setup, Extension
import distutils.util
setup (name = 'pybanery',
version= '1.0',
description='Python interface for Kanbanery',
author = 'Pablo Lluch',
author_email = 'pablo.lluch@gmail.com',
py_modules = ['pybanery'],
... | normal | {
"blob_id": "60c862accbb9cda40ed4c45491f643f065e2868a",
"index": 6467,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='pybanery', version='1.0', description=\n 'Python interface for Kanbanery', author='Pablo Lluch', author_email=\n 'pablo.lluch@gmail.com', py_modules=['pybanery'], script... | [
0,
1,
2,
3
] |
import platform
import keyboard
import threading
import atexit
from threading import Timer
triggerCount = 0
triggerTimer = -1
result = None
def cleanup ():
print 'cleanup before exit'
clearTimer()
keyboard
triggerCount = 0
def clearTimer ():
global triggerTimer
global triggerCount
try:
... | normal | {
"blob_id": "9e8ed462e429d6c6c0fe232431ee1e98721863e9",
"index": 6148,
"step-1": "import platform\nimport keyboard\nimport threading\nimport atexit\nfrom threading import Timer\n\ntriggerCount = 0\ntriggerTimer = -1\n\nresult = None\n\ndef cleanup ():\n print 'cleanup before exit'\n clearTimer()\n keybo... | [
0
] |
"""inactivate fb posts
Revision ID: f37637c1bcf8
Revises: 43c7ecf8ed02
Create Date: 2017-06-22 12:01:59.623040
"""
from alembic import op
from pd.facebook.models import MediaType
# revision identifiers, used by Alembic.
revision = 'f37637c1bcf8'
down_revision = '43c7ecf8ed02'
branch_labels = None
depends_on = None
... | normal | {
"blob_id": "89ed30411c624e3d930db0bc0b5b716a10908727",
"index": 8259,
"step-1": "<mask token>\n\n\ndef upgrade():\n op.execute(' '.join([update.format('false'), where]))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef upgrade():\n op.execute(' '.join([update.format('false'), where]))\n\n\ndef dow... | [
1,
2,
3,
4,
5
] |
from django.apps import AppConfig
class WebApiAppConfig(AppConfig):
name = 'WebApiApp'
| normal | {
"blob_id": "cc97f70b9d41357f020ea9c59d8b149392a336cc",
"index": 9656,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n name = 'WebApiApp'\n",
"step-4": "from django.apps impo... | [
0,
1,
2,
3
] |
import streamlit as st
import tensorflow.keras
from PIL import Image, ImageOps
import numpy as np
st.set_option('deprecation.showfileUploaderEncoding', False)
np.set_printoptions(suppress=True)
model = tensorflow.keras.models.load_model('keras_model.h5')
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
st.ti... | normal | {
"blob_id": "746e0895f0fb971156e778cbff20317cc88441f1",
"index": 2059,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nst.set_option('deprecation.showfileUploaderEncoding', False)\nnp.set_printoptions(suppress=True)\n<mask token>\nst.title('Leaf Disease Detection Using Machine Learning')\n<mask token>\nif... | [
0,
1,
2,
3,
4
] |
import requests
import csv
from bs4 import BeautifulSoup
reservoirs = [["LVQ"], ["HTH"], ["APN"], ["KNT"], ["SHA"]]
for reservoir in reservoirs:
storageURL = "https://cdec.water.ca.gov/dynamicapp/QueryMonthly?s=" + reservoir[0]
storagePage = requests.get(storageURL)
storageSoup = BeautifulSoup(storagePage... | normal | {
"blob_id": "ebe7c245e3e14116a37020971e67ada054e0b434",
"index": 1171,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor reservoir in reservoirs:\n storageURL = ('https://cdec.water.ca.gov/dynamicapp/QueryMonthly?s=' +\n reservoir[0])\n storagePage = requests.get(storageURL)\n storageSou... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 14 22:09:56 2014
@author: duhan
"""
#arrayMapPath = r'/usr/local/lib/python2.7/dist-packages/ticketpitcher/data/3'
arrayMapPath = r'C:\Python27\Lib\site-packages\ticketpitcher\data'
#tempPath = r'/tmp/'
tempPath = 'd:\\temp\\'
| normal | {
"blob_id": "9627e8a468d3a75787c5a9e01856913fc8beb3c4",
"index": 1868,
"step-1": "<mask token>\n",
"step-2": "<mask token>\narrayMapPath = 'C:\\\\Python27\\\\Lib\\\\site-packages\\\\ticketpitcher\\\\data'\ntempPath = 'd:\\\\temp\\\\'\n",
"step-3": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 14 22:09... | [
0,
1,
2
] |
import numpy as np, argparse, sys, itertools, os, errno, warnings
from mpi4py import MPI
from enlib import enmap as en, powspec, utils
from enlib.degrees_of_freedom import DOF, Arg
from enlib.cg import CG
warnings.filterwarnings("ignore")
#from matplotlib.pylab import *
parser = argparse.ArgumentParser()
parser.add_ar... | normal | {
"blob_id": "6a601d1c7c3c162c0902d03e6c39f8d75d4bcaf0",
"index": 798,
"step-1": "import numpy as np, argparse, sys, itertools, os, errno, warnings\nfrom mpi4py import MPI\nfrom enlib import enmap as en, powspec, utils\nfrom enlib.degrees_of_freedom import DOF, Arg\nfrom enlib.cg import CG\nwarnings.filterwarning... | [
0
] |
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect
from flask import Flask, jsonify, render_template, redirect
from flask_pymongo import PyMongo
from config import mongo_password, mongo_username, sql_username, sql_pass... | normal | {
"blob_id": "15e1ce95398ff155fe594c3b39936d82d71ab9e2",
"index": 5015,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n pokemon_data = mongo.db.pokemon.find_one()\n return render_template('index.html', pokemon_data=pokemon_data)\n\n\n@app.route('/stats')\ndef stats():\n session = Session(eng... | [
3,
4,
5,
6,
7
] |
################################################################################
# Controller of the Darwin Squat-Stand task using numpy #
# Note: all joint data used in this file uses the dof indexing with #
# from the simulation environment, not the hardware. ... | normal | {
"blob_id": "97c5b75323bb143c87972b389e2f27e443c1e00c",
"index": 9945,
"step-1": "<mask token>\n\n\nclass NP_Net_MirrorSym:\n <mask token>\n\n def load_from_file(self, fname):\n params = joblib.load(fname)\n pol_scope = list(params.keys())[0][0:list(params.keys())[0].find('/')]\n obrms... | [
8,
12,
14,
16,
17
] |
import random
import matplotlib.pyplot as plt
import numpy as np
def dado(n):
i = 1
dos =0
tres =0
cuatro =0
cinco=0
seis =0
siete=0
ocho=0
nueve=0
diez=0
once=0
doce=0
cont = [0,0,0,0,0,0,0,0,0,0,0]
while i <= n:
r1 = random.randint(1,6)... | normal | {
"blob_id": "2d0d73c0ea20d6736c10d5201abcfa9d561ef216",
"index": 7474,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef dado(n):\n i = 1\n dos = 0\n tres = 0\n cuatro = 0\n cinco = 0\n seis = 0\n siete = 0\n ocho = 0\n nueve = 0\n diez = 0\n once = 0\n doce = 0\n... | [
0,
1,
2,
3,
4
] |
"""
Suffix Arrays - Optimized O(n log n) - prefix doubling
A suffix is a non-empty substring at the end of the string. A suffix array
contains all the sorted suffixes of a string
A suffix array provides a space efficient alternative to a suffix tree which
itself is a compressed version of a trie. Suffix array can do ... | normal | {
"blob_id": "5a2106f5255493d2f6c8cb9e06a2666c8c55ed38",
"index": 3852,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef prefix_doubling_suffix_array(n):\n n_len = len(n)\n if n_len == 0:\n return []\n if n_len == 1:\n return [0]\n suffixes = []\n for i in range(n_len):\... | [
0,
1,
2,
3,
4
] |
import os, json, locale, requests, dash, dash_table, copy, time, flask, base64
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
import pandas as pd
from os import listdir
import plotly.figure_factory as ff
from concurrent.futures import ThreadPoolExecutor, Process... | normal | {
"blob_id": "c5f41b69ac215bd661ee39bdc8c3119db9606ca8",
"index": 6020,
"step-1": "<mask token>\n\n\n@app.callback(Output('ganttpersoon', 'figure'), [Input(\n 'dropdownganttpersoon', 'value'), Input('dropdownganttpersoonstatus',\n 'value')])\ndef update_ganttpersoon(v1, v2):\n ganttdata = []\n for i, ... | [
2,
8,
10,
11,
12
] |
string1 = "Vegetable"
#string2 = "Fruit"
string2 = "vegetable"
print(string1 == string2)
print(string1 != string2)
if string1.lower() == string2.lower():
print("The strings are equal")
else:
print("The strings are not equal")
number1 = 25
number2 = 30
# ==
# !=
# >
# <
# >=
# <=
if number1 <... | normal | {
"blob_id": "fecaf41152e8c98784585abfdb3777fc0a4824f3",
"index": 1052,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(string1 == string2)\nprint(string1 != string2)\nif string1.lower() == string2.lower():\n print('The strings are equal')\nelse:\n print('The strings are not equal')\n<mask toke... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# encoding: utf-8
import multiprocessing
import time
import sys
def daemon():
p = multiprocessing.current_process()
print('Starting:', p.name, p.pid)
sys.stdout.flush()
time.sleep(2)
print('Exiting :', p.name, p.pid)
sys.stdout.flush()
def non_daemon():
p = multipr... | normal | {
"blob_id": "9bb6fd6fbe212bdc29e2d1ec37fa6ec6ca9a9469",
"index": 1060,
"step-1": "<mask token>\n\n\ndef daemon():\n p = multiprocessing.current_process()\n print('Starting:', p.name, p.pid)\n sys.stdout.flush()\n time.sleep(2)\n print('Exiting :', p.name, p.pid)\n sys.stdout.flush()\n\n\n<mask ... | [
2,
5,
6,
7,
8
] |
# Standard Library imports:
import argparse
import os
from pathlib import Path
from typing import Dict, List
# 3rd Party imports:
import keras.backend as K
from keras.layers import *
from keras.models import Model
import tensorflow as tf
from tensorflow.python.framework import graph_io, graph_util
from tensorflow.pyth... | normal | {
"blob_id": "a5f3af6fc890f61eecb35bd157fc51bb65b4c586",
"index": 3958,
"step-1": "<mask token>\n\n\ndef squeezenet_fire_module(input, input_channel_small=16,\n input_channel_large=64):\n channel_axis = 3\n input = Conv2D(input_channel_small, (1, 1), padding='valid')(input)\n input = Activation('relu'... | [
2,
4,
5,
6,
8
] |
import os
import os.path
import numpy as np
import pickle
import codecs
from konlpy.tag import Okt
from hyperparams import params
from gensim.models import FastText
#tokenizer
tokenizer = Okt()
def make_word_dictionary(word_dict_pkl_path=params['default_word_dict_pkl_path'], training_data_path = params['d... | normal | {
"blob_id": "430e971d2ae41bfd60e7416ecb2c26bb08e4df45",
"index": 6520,
"step-1": "<mask token>\n\n\ndef make_word_dictionary(word_dict_pkl_path=params[\n 'default_word_dict_pkl_path'], training_data_path=params[\n 'default_training_data_path']):\n word_dict = dict()\n if os.path.isfile(word_dict_pkl_... | [
1,
3,
5,
6,
7
] |
#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
n_points = 100
n_sims = 1000
def simulate_one_realisation():
return np.random.normal(1, 2, size=n_points)
def infer(sample):
return {'mean': np.mean(sample), 'std': np.std(sample)}
inference = [infer(simulate_one_realisation()) for _ ... | normal | {
"blob_id": "6e8ef901fc614ecbba25df01f84a43c429f25cf6",
"index": 4919,
"step-1": "<mask token>\n\n\ndef infer(sample):\n return {'mean': np.mean(sample), 'std': np.std(sample)}\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef simulate_one_realisation():\n return np.random.normal(1, 2, size=n_points... | [
1,
3,
4,
5,
6
] |
"""
help find Holly find dups in the PC's
Given a particular dir - report the dupset of each of the files so we can see
where the dups are
"""
import os, sys, re
from comms.dup_manager import DupManager
class DupFinder (DupManager):
base_archives_path = '/Volumes/archives/CommunicationsImageCollection/'
ba... | normal | {
"blob_id": "037a02ff2c0699acdd1fefbe60098c93cd99e777",
"index": 1987,
"step-1": "\"\"\"\nhelp find Holly find dups in the PC's\n\nGiven a particular dir - report the dupset of each of the files so we can see\nwhere the dups are\n\n\"\"\"\nimport os, sys, re\n\nfrom comms.dup_manager import DupManager\n\nclass D... | [
0
] |
#!/usr/bin/env python
'''
Created on 2011-08-27
@author: xion
Setup script for the seejoo project.
'''
import ast
import os
from setuptools import find_packages, setup
def read_tags(filename):
"""Reads values of "magic tags" defined in the given Python file.
:param filename: Python filename to read the tag... | normal | {
"blob_id": "9d1b795b561a26ae28e82833485ca6034438e78b",
"index": 8491,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef read_tags(filename):\n \"\"\"Reads values of \"magic tags\" defined in the given Python file.\n\n :param filename: Python filename to read the tags from\n :return: Dictio... | [
0,
2,
3,
4,
5
] |
"""Scans all files in this project for FIXME and TODO comments and writes them to todos.txt
has to be invoked while being in myLambda/ and not in e.g. myLambda/src"""
import sys
import os
import re
files = []
searchFiles = []
# get all subdirs and its files
for root, dirs, f in os.walk('./'):
files.append((root, f))
... | normal | {
"blob_id": "3bc6091d822fa197dcce3cd75fa9755dc9f93592",
"index": 7520,
"step-1": "\"\"\"Scans all files in this project for FIXME and TODO comments and writes them to todos.txt\nhas to be invoked while being in myLambda/ and not in e.g. myLambda/src\"\"\"\nimport sys\nimport os\nimport re\nfiles = []\nsearchFile... | [
0
] |
def f(p_arg, *s_args, **kw_args):
return (s_args[0] + kw_args['py'])+p_arg
r = f(3, 2, py = 1) ## value r => 6
| normal | {
"blob_id": "4a913cfdbddb2f6b5098395814f5fc1203192b9a",
"index": 4847,
"step-1": "<mask token>\n",
"step-2": "def f(p_arg, *s_args, **kw_args):\n return s_args[0] + kw_args['py'] + p_arg\n\n\n<mask token>\n",
"step-3": "def f(p_arg, *s_args, **kw_args):\n return s_args[0] + kw_args['py'] + p_arg\n\n\nr... | [
0,
1,
2,
3
] |
import re
def find_all_links(text):
result = []
iterator = re.finditer(r"https?\:\/\/(www)?\.?\w+\.\w+", text)
for match in iterator:
result.append(match.group())
return result | normal | {
"blob_id": "b8c7aa5ff7387eacb45d996fa47186d193b44782",
"index": 4823,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_all_links(text):\n result = []\n iterator = re.finditer('https?\\\\:\\\\/\\\\/(www)?\\\\.?\\\\w+\\\\.\\\\w+', text)\n for match in iterator:\n result.append(m... | [
0,
1,
2,
3
] |
from sense_hat import SenseHat
import time
sense = SenseHat()
b = (0, 0, 204) #Blue
w = (255, 255, 255) #White
e = (0, 0, 0) #Empty
y = (255, 255, 0) #Yellow
r = (255, 0, 0) #red
prenume = [
e, e, e, e, e, e, e, e,
e, e, e, e, e, e, e, e,
e, b, e, y, y, e, r, e,
b, e, b, y, e, y, r, e,
b, b, b, y,... | normal | {
"blob_id": "b9eeccbed63aa42afa09fe7ef782066f300255a1",
"index": 2173,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsense.set_pixels(prenume)\n",
"step-3": "<mask token>\nsense = SenseHat()\nb = 0, 0, 204\nw = 255, 255, 255\ne = 0, 0, 0\ny = 255, 255, 0\nr = 255, 0, 0\nprenume = [e, e, e, e, e, e, e,... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ==================================================
# @Author : Copyright@Ryuchen
# ==================================================
from .version import VERSION
__all__ = [
"VERSION"
]
| normal | {
"blob_id": "d815c6e233d81dfb144442a83e6006aa4e29bfce",
"index": 100,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['VERSION']\n",
"step-3": "from .version import VERSION\n__all__ = ['VERSION']\n",
"step-4": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# ==============================... | [
0,
1,
2,
3
] |
# coding=utf-8
# oscm_app/cart/models
# django imports
from django.core.urlresolvers import reverse
from django.db import models
from django.utils.translation import ugettext_lazy as _
# OSCM imports
from ...constants import CARTS, CART_STATUSES, DEFAULT_CART_STATUS
from ...utils import get_attr
from ..cart_manager i... | normal | {
"blob_id": "ae0ccbb9b0a2c61d9ee9615ba8d0c1a186a81c34",
"index": 3177,
"step-1": "<mask token>\n\n\nclass Cart(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n ... | [
6,
8,
9,
11,
12
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'Admin.ui'
#
# Created by: PyQt5 UI code generator 5.12
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
from qtpandas.views.DataTableView import DataTableWidget
from qtpandas.models.DataFra... | normal | {
"blob_id": "5e2a8e95af88a582b6e760a53dfd41f880d66963",
"index": 2670,
"step-1": "<mask token>\n\n\nclass Ui_Admin(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_Admin(object):\n\n def setupUi(self, Admin):\n Admin.setObjectName('Admin')\n Admin.resize(6... | [
1,
2,
3,
4,
5
] |
# dealing with the packet fragments and their reconsttruction
import logging
# shut up scapy
logging.getLogger("scapy.runtime").setLevel(logging.ERROR)
from scapy.all import *
conf.verb=0
from collections import OrderedDict
pkt_frag_loads = OrderedDict()
def get_load(pkt):
ack = str(pkt[TCP].ack)
seq = str... | normal | {
"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
] |
import random
import Manhattan_segmental_dist
# Greedy
# s: dictionary of points
# k: number of medoids
# returns
# k medoids from sample set s
def greedy(s, k):
# print("Hello Word!")
m_1 = random.choice(list(s.keys()))
medoids = {m_1: s[m_1]}
dimensions = list(range(len(s[m_1])))
s.pop(m_1... | normal | {
"blob_id": "9a02bd0bc14494db033c032003aa5baea111ea8c",
"index": 7185,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef greedy(s, k):\n m_1 = random.choice(list(s.keys()))\n medoids = {m_1: s[m_1]}\n dimensions = list(range(len(s[m_1])))\n s.pop(m_1)\n dist = {}\n for x in s:\n ... | [
0,
1,
2,
3
] |
from test.framework import TestCase
from test.mock import Mock
from package.util.svnutil import ReleaseXmlParser, Release
import time
class SvnUtilTests(TestCase):
def setUp(self):
r1 = Release()
r1.name = 'BETA1.1.0'
r1.type = 'BETA'
r1.version = '1.1.0'
r1.date = time.strp... | normal | {
"blob_id": "9c320db85ca1a9df6b91f6bb062e4d5c3d94ee91",
"index": 9516,
"step-1": "<mask token>\n\n\nclass SvnUtilTests(TestCase):\n\n def setUp(self):\n r1 = Release()\n r1.name = 'BETA1.1.0'\n r1.type = 'BETA'\n r1.version = '1.1.0'\n r1.date = time.strptime('2009-04-21 23:... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
class CacheDecorator:
def __init__(self):
self.cache = {}
self.func = None
def cachedFunc(self, *args):
if args not in self.cache:
print("Ergebnis berechnet")
self.cache[args] = self.func(*args)
else:... | normal | {
"blob_id": "b7f6207fe6c013a964258255445004c3f4e0adbb",
"index": 7217,
"step-1": "class CacheDecorator:\n <mask token>\n\n def cachedFunc(self, *args):\n if args not in self.cache:\n print('Ergebnis berechnet')\n self.cache[args] = self.func(*args)\n else:\n p... | [
3,
4,
5,
6,
7
] |
import pandas as pd
import os
import re
main_dir = r'C:\Users\Username\Desktop\Python\End-to-End-Data-Analysis\1. Get the Data\table'
file = 'CMBS Table.csv'
os.chdir(main_dir)
cmbs = pd.read_csv(file, encoding='ISO-8859-1')
# Delete extra Loan & Seller columns
loan_seller_cols = [val for val in cmbs.co... | normal | {
"blob_id": "eb890c68885cbab032ce9d6f3be3fd7013a2788b",
"index": 2140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.chdir(main_dir)\n<mask token>\nfor col in loan_seller_cols:\n cmbs.drop(columns=col, axis=1, inplace=True)\n<mask token>\nfor key, value in regex_dict.items():\n cmbs.columns = [... | [
0,
1,
2,
3,
4
] |
from django.conf.urls import patterns, include, url
from django.contrib.auth.decorators import login_required
from django.views.generic import TemplateView
from analyze import views
#from lecture import views
urlpatterns = patterns('',
url(r'^$', 'analyze.views.analyze', name='analyze'),
)
| normal | {
"blob_id": "035de226c2d2ee85cb7e319de35fb09b21bc523d",
"index": 9061,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = patterns('', url('^$', 'analyze.views.analyze', name='analyze'))\n",
"step-3": "from django.conf.urls import patterns, include, url\nfrom django.contrib.auth.decorators im... | [
0,
1,
2,
3
] |
from can.interfaces.ics_neovi.neovi_bus import NeoViBus
| normal | {
"blob_id": "6025b8d4015572ea1a760c1b4bc7200a1019c802",
"index": 5031,
"step-1": "<mask token>\n",
"step-2": "from can.interfaces.ics_neovi.neovi_bus import NeoViBus\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
import pygame
naytto = pygame.display.set_mode((740, 500))
pygame.display.set_caption("Piirtäminen")
x = 100
y = 300
def main():
while True:
tapahtuma = pygame.event.poll()
if tapahtuma.type == pygame.QUIT:
break
naytto.fill((0, 0, 0))
pygame.draw.line(naytto, (0, 0, ... | normal | {
"blob_id": "3fdb29797894737edae37ad7890e14cb9ce705e8",
"index": 5901,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npygame.display.set_caption('Piirtäminen')\n<mask token>\n\n\ndef main():\n while True:\n tapahtuma = pygame.event.poll()\n if tapahtuma.type == pygame.QUIT:\n ... | [
0,
2,
3,
4,
5
] |
__author__ = "Sarah Hazell Pickering (sarah.pickering@anu.edu.au)"
__date__ = "2018-11-15"
""" QC and Trimming with fastp
Trimming and QC with fastp.
Then subsampling of reads via seqtk.
Now starts with a sample/sample.file structure.
Number of reads to sample is can be supplied via pairs_to_sample
... | normal | {
"blob_id": "655e6531dc21dcdf8fa827184444cee483492b81",
"index": 7715,
"step-1": "__author__ = \"Sarah Hazell Pickering (sarah.pickering@anu.edu.au)\"\n__date__ = \"2018-11-15\"\n\n\"\"\" QC and Trimming with fastp\n\n Trimming and QC with fastp.\n Then subsampling of reads via seqtk.\n\n Now starts wit... | [
0
] |
quilogramas = float ( input ( "Insira o peso em Kg:" ))
libras = quilogramas / 0 , 45
print ( libras ) | normal | {
"blob_id": "9c35e64fd773c79dc20e6b388478e892bda85788",
"index": 1599,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(libras)\n",
"step-3": "quilogramas = float(input('Insira o peso em Kg:'))\nlibras = quilogramas / 0, 45\nprint(libras)\n",
"step-4": "quilogramas = float ( input ( \"Insira o ... | [
0,
1,
2,
3
] |
from neodroidagent.entry_points.agent_tests import sac_gym_test
if __name__ == "__main__":
sac_gym_test()
| normal | {
"blob_id": "e9890fcf9ad2a78b3400f6e4eeb75deac8edcd6a",
"index": 1609,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n sac_gym_test()\n",
"step-3": "from neodroidagent.entry_points.agent_tests import sac_gym_test\nif __name__ == '__main__':\n sac_gym_test()\n",
"step... | [
0,
1,
2,
3
] |
from count_freqs import *
from eval_gene_tagger import *
'''
Using gene.train gene.counts prediction file to evaluate the performance
Usage: python viterbi.py gene.counts gene.dev gene_dev.p1.out
'''
if __name__ == "__main__":
#if len(sys.argv)!=2: # Expect exactly one argument: the training data file
# ... | normal | {
"blob_id": "6dda23cc5d0083e72520b0664b6550ccb48e4b4f",
"index": 7288,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n counter1 = Hmm(3)\n input_counts = open('gene_NoClass.counts', 'r')\n dev_file = open('gene.dev', 'r+')\n output_file2 = open('gene_dev.NoClass.ou... | [
0,
1,
2,
3
] |
# Generated by Django 3.0.6 on 2020-07-06 17:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('s1app', '0004_auto_20200706_0753'),
]
operations = [
migrations.AlterField(
model_name='gall',
name='date',
... | normal | {
"blob_id": "a7d7408808f28343a51ff6522c5e14747c8c6e43",
"index": 9819,
"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 = [('s1app', '00... | [
0,
1,
2,
3,
4
] |
import requests
import sqlite3
url = 'http://dummy.restapiexample.com/api/v1/employees'
r = requests.get(url)
packages_json = r.json()
# Create the employee database if it does not exist
db = sqlite3.connect('employee.sqlite')
#create the table
db.execute("CREATE TABLE IF NOT EXISTS employee (id INTEGER P... | normal | {
"blob_id": "497203be99643e2bb0087977f292f4ed890f9ead",
"index": 7111,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.execute(\n 'CREATE TABLE IF NOT EXISTS employee (id INTEGER PRIMAR KEY, employee_name TEXT, employee_salary INTEGER, employee_age INTEGER, profile_image BLOB)'\n )\nfor employee ... | [
0,
1,
2,
3,
4
] |
import contextlib
import logging
import os
import pwd
import sys
from typing import Iterable
from sqlalchemy import Table, exists, null, select
from sqlalchemy.engine import Engine
from sqlalchemy.exc import DBAPIError
from sqlalchemy.pool import NullPool
from hades import constants
from hades.common import db
from h... | normal | {
"blob_id": "c9df53ac06b8bb106d73825d60fa885c06385e95",
"index": 8557,
"step-1": "<mask token>\n\n\ndef check_database(engine: Engine, user_name: pwd.struct_passwd, tables:\n Iterable[Table]):\n logger.info('Checking database access as user %s', user_name)\n try:\n conn = engine.connect()\n ex... | [
3,
4,
5,
6,
7
] |
import curses
from zeep import Client
from zeep import xsd
from zeep.plugins import HistoryPlugin
import time
from datetime import datetime
import os
LDB_TOKEN = 'NULLTOKEN'
WSDL = 'http://lite.realtime.nationalrail.co.uk/OpenLDBWS/wsdl.aspx?ver=2017-10-01'
if LDB_TOKEN == '':
raise Exception("Please configure y... | normal | {
"blob_id": "302634b93725ceb9333e236021cbb64e023ff798",
"index": 2135,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif LDB_TOKEN == '':\n raise Exception(\n 'Please configure your OpenLDBWS token in getDepartureBoardExample!')\n<mask token>\n\n\ndef main(stdscr):\n res = client.service.Get... | [
0,
2,
3,
4,
5
] |
#求11+12+13+。。。+m
m = int(input('请输入一个数：'))
S = m
for x in range(11,m):
S = S+x
print('sum =',S) | normal | {
"blob_id": "49ffa225d433ef2263159ba2145da5ba2a95d1f2",
"index": 4664,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor x in range(11, m):\n S = S + x\nprint('sum =', S)\n",
"step-3": "m = int(input('请输入一个数：'))\nS = m\nfor x in range(11, m):\n S = S + x\nprint('sum =', S)\n",
"step-4": "#求11+... | [
0,
1,
2,
3
] |
# Copyright (c) 2016, Xilinx, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of ... | normal | {
"blob_id": "15514d5636471b1a311641a40b6a00b81703cd2b",
"index": 6488,
"step-1": "<mask token>\n\n\nclass Grove_PIR(Pmod_IO):\n <mask token>\n\n def __init__(self, mb_info, gr_pin):\n \"\"\"Return a new instance of a PIR object. \n \n Parameters\n ----------\n mb_info : d... | [
3,
4,
5,
6,
7
] |
n = 5
a = '1'
if n == 1:
print(a)
else:
for i in range(2, n + 1):
if i == 2:
a = '11'
else:
count = 1
for j in range(len(a) - 1):
if j == len(a) - 2 :
if a[j] == a[j + 1]:
count += 1
... | normal | {
"blob_id": "26a778f16cc50d1a8791fb672fb8907464865f3f",
"index": 1349,
"step-1": "n = 5\na = '1'\nif n == 1:\n print(a)\nelse:\n for i in range(2, n + 1):\n if i == 2:\n a = '11'\n else:\n count = 1\n for j in range(len(a) - 1):\n if j == len(a)... | [
0
] |
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
import color_to_gray_operations
VIZ_PATH = '../output_data/visualizations/gray_intensities/'
def visualize_grayscale_intensities(img, out_path):
img_x, img_y = np.mgrid[0: img.shape[0], 0: img.shape... | normal | {
"blob_id": "21fec6d307b928a295f2ffbf267456f9cd9ea722",
"index": 9105,
"step-1": "<mask token>\n\n\ndef visualize_grayscale_intensities(img, out_path):\n img_x, img_y = np.mgrid[0:img.shape[0], 0:img.shape[1]]\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.plot_surface(img_x, img_y, img, r... | [
4,
6,
7,
8,
9
] |
# coding: UTF-8
from PIL import ImageFont,Image,ImageDraw
def min_element(table_d,ignoring_index = None):
min_i,min_j,min_e = 0,0,max(table_d.values())
for key in table_d.keys():
# ignore if i in key or j in key
if ignoring_index is not None:
i,j = key
if i in ignoring_index or j in ignoring_i... | normal | {
"blob_id": "aee009b37b99bf44e27c608470c43834a58e0cc7",
"index": 8490,
"step-1": "<mask token>\n\n\ndef to_dict(table):\n table_d = dict()\n for i in range(len(table)):\n for j in range(i):\n table_d[i, j] = table[i][j]\n table_d[j, i] = table[i][j]\n return table_d\n\n\ndef... | [
3,
4,
5,
6,
7
] |
from abc import abstractmethod
class BaseButton:
@abstractmethod
def render(self):
pass
@abstractmethod
def on_click(self):
pass
class WindowsButton(BaseButton):
def render(self):
print("Render window button")
def on_click(self):
print("On click")
class Ht... | normal | {
"blob_id": "003976d850e371e01e6d0a307d3cf366f962c53d",
"index": 4358,
"step-1": "<mask token>\n\n\nclass HtmlButton(BaseButton):\n\n def render(self):\n print('Render html button')\n <mask token>\n\n\nclass BaseDialog:\n\n @abstractmethod\n def create_button(self) ->BaseButton:\n pass\... | [
14,
15,
19,
23,
24
] |
#!/usr/bin/python
import sys, os, glob, numpy
wd = os.path.dirname(os.path.realpath(__file__))
sys.path.append(wd + '/python_speech_features')
from features import mfcc, logfbank
import scipy.io.wavfile as wav
DIR = '/home/quiggles/Desktop/513music/single-genre/classify-me/subset'
OUTDIR = wd + '/songdata/subset'
#... | normal | {
"blob_id": "cca1a491e2a48b4b0c7099a6c54e528158ef30bb",
"index": 5189,
"step-1": "<mask token>\n\n\ndef getMFCC(rate, sig):\n mfcc_feat = mfcc(sig, rate)\n return numpy.concatenate(getQuartileMeans(mfcc_feat))\n\n\ndef getLogFBank(rate, sig):\n logfbank_feat = logfbank(sig, rate)\n return numpy.conca... | [
3,
7,
8,
9,
10
] |
import numpy as np
import pandas as pd
import nltk
from collections import defaultdict
import os.path
stop_words = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours',
'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers',
'herself',... | normal | {
"blob_id": "0356b408624988100c10b20facecef14f1552203",
"index": 4537,
"step-1": "<mask token>\n\n\ndef build_statements_features(df, vectorizer, train=True, tokenizer=\n tokenizer_nltk):\n filtered_statements_dic = {}\n for index, row in df.iterrows():\n filtered_statement = []\n tokenize... | [
3,
5,
7,
9,
10
] |
# 다이얼
dial = ['ABC', 'DEF', 'GHI','JKL','MNO','PQRS','TUV','WXYZ']
cha = input()
num = 0
for i in range(len(cha)):
for j in dial:
if cha[i] in j:
num = num + dial.index(j) + 3
print(num) | normal | {
"blob_id": "774e607c693fa2d5199582302e466674f65b6449",
"index": 6213,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(len(cha)):\n for j in dial:\n if cha[i] in j:\n num = num + dial.index(j) + 3\nprint(num)\n",
"step-3": "dial = ['ABC', 'DEF', 'GHI', 'JKL', 'MNO', '... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
"""
Main training workflow
"""
from __future__ import division
import os
import time
import glob
import torch
import random
import signal
import argparse
from models.trainer import build_trainer
from models import data_loader, model_builder
from models.pytorch_pretrained_bert.modeling import... | normal | {
"blob_id": "3adb50a6375a73f786369dd22712a657b66f758e",
"index": 8432,
"step-1": "<mask token>\n\n\nclass Running(object):\n <mask token>\n\n def __init__(self, args, device_id):\n \"\"\"\n :param args: parser.parse_args()\n :param device_id: 0 or -1\n \"\"\"\n self.args ... | [
7,
16,
17,
18,
24
] |
class Solution(object):
def shortestPalindrome(self, s):
"""
:type s: str
:rtype: str
"""
left= 0
#for right in range(len(s)-1, -1, -1):
for right in reversed(range(len(s))):
if s[right] == s[left]:
left += 1
... | normal | {
"blob_id": "4d18c056845403adc9c4b5848fafa06d0fe4ff4c",
"index": 3165,
"step-1": "class Solution(object):\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Solution(object):\n\n def shortestPalindrome(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n left =... | [
1,
2,
3,
4,
5
] |
class Process:
def __init__(self, id, at, bt):
self.id = id
self.at = at
self.bt = bt
self.wt = 0
self.ct = 0
self.st = 0
self.tat = 0
def fill(self, st):
print('Current process:', self.id)
self.st = st
self.ct = self.st + self.bt
... | normal | {
"blob_id": "be58a2e0dcdbcb3a3df0da87be29ce7ebcee7fe9",
"index": 6185,
"step-1": "class Process:\n\n def __init__(self, id, at, bt):\n self.id = id\n self.at = at\n self.bt = bt\n self.wt = 0\n self.ct = 0\n self.st = 0\n self.tat = 0\n <mask token>\n <ma... | [
2,
4,
5,
6,
7
] |
from ROOT import *
import math
import os,sys,time,glob,fnmatch
import argparse
import ROOT
import sys
sys.path.append("utils")
from moments import *
from dirhandle import *
from plothandle import *
from AnalysisGeneratorMT import *
def doAnalysis( blabla):
return blabla.DoThreatdAnalysis()
if __name__ =... | normal | {
"blob_id": "7db31940aea27c10057e2ce1e02410994bd2039b",
"index": 3328,
"step-1": "from ROOT import *\nimport math\nimport os,sys,time,glob,fnmatch\nimport argparse\nimport ROOT\nimport sys\nsys.path.append(\"utils\")\nfrom moments import *\nfrom dirhandle import *\nfrom plothandle import *\nfrom AnalysisGene... | [
0
] |
import sys
from random import randint
if len(sys.argv) != 2:
print "Usage: generate.py <number of orders>"
sys.exit(1)
n = int(sys.argv[1])
for i in range(0, n):
action = 'A'
orderid = i + 1
side = 'S' if (randint(0,1) == 0) else 'B'
quantity = randint(1,100)
price = randint(100,200)
... | normal | {
"blob_id": "6267c999d3cec051c33cbcde225ff7acaa6bff74",
"index": 5383,
"step-1": "import sys\nfrom random import randint\n\nif len(sys.argv) != 2:\n print \"Usage: generate.py <number of orders>\"\n sys.exit(1)\n\nn = int(sys.argv[1])\n\nfor i in range(0, n):\n action = 'A'\n orderid = i + 1\n sid... | [
0
] |
# #!/usr/bin/python
# last edit abigailc@Actaeon on jan 27 2017
#pulling the taxonomy functions out of makespeciestree because I need to make them faster...
#insects is running for literally >20 hours.
names_file = "/Users/abigailc/Documents/Taxonomy_Stuff/taxdump/names.dmp"
nodes_file = "/Users/abigailc/Documents/... | normal | {
"blob_id": "5c1324207e24f2d723be33175101102bd97fe7a2",
"index": 4860,
"step-1": "<mask token>\n\n\ndef Ret_Sister_Same_Rank(string, nodes_file, names_file):\n print(string)\n interest_taxid = Str_To_Taxid(string, names_file)\n print(interest_taxid)\n up_taxid = Return_Parent(interest_taxid, nodes_fi... | [
2,
15,
18,
21,
27
] |
# -*- coding: utf-8 -*-
"""
Created on Wed May 16 10:17:32 2018
@author: pearseb
"""
#%% imporst
import os
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import cmocean.cm as cmo
import seaborn as sb
sb.set(style='ticks')
import mpl_toolkits.basemap as bm
import p... | normal | {
"blob_id": "635b02e03578d44f13530bd57ab1a99987d4909d",
"index": 5987,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsb.set(style='ticks')\n<mask token>\nos.chdir(\n 'C://Users/pearseb/Dropbox/PhD/My articles/nitrogen-carbon cycles/data_for_publication'\n )\n<mask token>\nlon_bnds[:, 0] += 360.0\n... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# coding: utf-8
# # PyCity School Analysis
# 1. Charter school types show better performace than District School types in all the scores.
# 2. Overall students are performing better in english between (80 to 84%), than math (76 to 84%)
# ### Note
# * Instructions have been included for each seg... | normal | {
"blob_id": "8488fdd216c30c3cb4b0060305af6708d890bc86",
"index": 8203,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nDistrict_Summary_df\n<mask token>\nschool_summary_df\n<mask token>\ntop_performing.head()\n<mask token>\ntop_performing.head()\n<mask token>\ngrade_math_score\n<mask token>\ngrade_reading... | [
0,
1,
2,
3,
4
] |
from Player import Player
class GameSequence:
'''
GameSequence summary: Keeps track of player turn sequence and Game end
Functionalities
-start game
-must start turns
-change turns
-end turns
-end game
'''
def __init__(self, ArrayofPlaye... | normal | {
"blob_id": "bdfd941be29a31d6c1bbedd270dadac844f49fc4",
"index": 1198,
"step-1": "<mask token>\n\n\nclass GameSequence:\n <mask token>\n <mask token>\n\n def changeMode(self, number):\n self.currentMode = self.modes[number]\n\n def startGame(self):\n self.currentTurn = 0\n \"\"\"... | [
6,
7,
8,
9,
11
] |
#!/usr/bin/env python3
''' towerdev - Ansible Tower Testing Framework
MIT License
Copyright © 2021 falcon78921
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including wit... | normal | {
"blob_id": "63e28e6a1ea5db1d1c41bbc755b9c33905e066bb",
"index": 9832,
"step-1": "<mask token>\n\n\ndef runTowerContainer(towerVersion, externalPort, osVersion, containerName,\n debug=False, **kwargs):\n \"\"\"Runs Tower container from pre-existing image\"\"\"\n allowedMemory = None\n if debug == Tru... | [
1,
3,
4,
5,
6
] |
import tkinter
import csv
import datetime
import time
root = tkinter.Tk()
root.title("Attendance")
root.geometry("+450+250")
ts = time.time()
date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
fileName = "Attendance/Attendance_"+date+".csv"
# open file
with open(fileName, newline="") as file:
reader ... | normal | {
"blob_id": "2343a9d3e253b5a0347b5890a5d7b9c3be777669",
"index": 5958,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nroot.title('Attendance')\nroot.geometry('+450+250')\n<mask token>\nwith open(fileName, newline='') as file:\n reader = csv.reader(file)\n r = 0\n for col in reader:\n c = ... | [
0,
1,
2,
3,
4
] |
group = {'A': 20, 'B': 15, 'C': 10}
def split_the_bill(x):
owed_dict = {}
sum = 0
people = 0
for key in x:
sum = sum + x[key]
people = people + 1
price_pp = sum / people
for key in x:
owed_value = x[key] - price_pp
owed_dict[key] = round(owed_value, 2)
retur... | normal | {
"blob_id": "69d7e7eb644a67ee921086005f0a55f39507f361",
"index": 2864,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef split_the_bill(x):\n owed_dict = {}\n sum = 0\n people = 0\n for key in x:\n sum = sum + x[key]\n people = people + 1\n price_pp = sum / people\n f... | [
0,
1,
2,
3
] |
import asyncio
import json
from functools import lru_cache
from pyrogram import Client
SETTINGS_FILE = "/src/settings.json"
CONN_FILE = "/src/conn.json"
def load_setting(setting: str):
with open(SETTINGS_FILE) as f:
return json.load(f)[setting]
@lru_cache()
def get_bot_name():
return load_setting(... | normal | {
"blob_id": "14e1af3d60efef842c72bf9b55143d0e14f3a7b8",
"index": 5897,
"step-1": "<mask token>\n\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_... | [
4,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
# !/usr/bin/python
import re
import sys
import xlwt
import os
'''
python logcat_time.py config_file logcat_file
'''
config_file = sys.argv[1]
logcat_file = sys.argv[2]
turns_time = 0
turn_compelete_flag = 0
def get_filePath_fileName_fileExt(filename):
(filepath, tempfilename) = os.path.s... | normal | {
"blob_id": "585c0f89605f1d791b449f42412174f06d0c5db5",
"index": 5163,
"step-1": "# -*- coding: utf-8 -*-\n# !/usr/bin/python\nimport re\nimport sys\nimport xlwt\nimport os\n\n'''\npython logcat_time.py config_file logcat_file\n'''\n\nconfig_file = sys.argv[1]\nlogcat_file = sys.argv[2]\n\nturns_time = 0\nturn_c... | [
0
] |
#!../virtual_env/bin/python
from migrate.versioning import api
from config import SQLALCHEMY_DATABASE_URI
from config import SQLALCHEMY_MIGRATE_REPO
from models.base import metadata
from sqlalchemy import create_engine
import os.path
engine = create_engine(SQLALCHEMY_DATABASE_URI)
metadata.create_all(engine)
if not... | normal | {
"blob_id": "9bbf0953d228c970764b8ba94675346820bc5d90",
"index": 3006,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmetadata.create_all(engine)\nif not os.path.exists(SQLALCHEMY_MIGRATE_REPO):\n api.create(SQLALCHEMY_MIGRATE_REPO, 'database repository')\n api.version_control(SQLALCHEMY_DATABASE_U... | [
0,
1,
2,
3,
4
] |
import random
def patternToNumber(pattern):
if len(pattern) == 0:
return 0
return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])
def symbolToNumber(symbol):
if symbol == "A":
return 0
if symbol == "C":
return 1
if symbol == "G":
return 2
if sy... | normal | {
"blob_id": "51848a64102f7fe8272fcf56a9792ed50c430538",
"index": 9115,
"step-1": "<mask token>\n\n\ndef patternToNumber(pattern):\n if len(pattern) == 0:\n return 0\n return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])\n\n\ndef symbolToNumber(symbol):\n if symbol == 'A':\n ... | [
8,
9,
11,
13,
15
] |
from flask.ext.wtf import Form
from wtforms import TextField
from wtforms.validators import Required
class VerifyHandphoneForm(Form):
handphone_hash = TextField('Enter verification code here', validators=[
Required()])
| normal | {
"blob_id": "cb0df06ee474576b3024678fa0f63ce400d773ea",
"index": 4096,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass VerifyHandphoneForm(Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass VerifyHandphoneForm(Form):\n handphone_hash = TextField('Enter verification code here', ... | [
0,
1,
2,
3
] |
def solve():
valid_passes = 0
with open('.\day4.txt') as fp:
for line in fp.read().strip().splitlines():
list_of_words = set()
add = 1
for word in line.split():
modified_word = ''.join(sorted(word))
if modified_word in list_of_words:
... | normal | {
"blob_id": "870d260b58c10e0379d66c3b44bc45594ff7d666",
"index": 4396,
"step-1": "<mask token>\n",
"step-2": "def solve():\n valid_passes = 0\n with open('.\\\\day4.txt') as fp:\n for line in fp.read().strip().splitlines():\n list_of_words = set()\n add = 1\n for w... | [
0,
1,
2,
3
] |
from SMP.motion_planner.node import PriorityNode
import numpy as np
from heapq import nsmallest
import sys
from SMP.motion_planner.plot_config import DefaultPlotConfig
from SMP.motion_planner.search_algorithms.best_first_search import GreedyBestFirstSearch
# imports for route planner:
class StudentMotionPlanner(Greedy... | normal | {
"blob_id": "6ecbe119c8a14776373d165dc05e81f91084893c",
"index": 4229,
"step-1": "<mask token>\n\n\nclass StudentMotionPlanner(GreedyBestFirstSearch):\n <mask token>\n\n def __init__(self, scenario, planningProblem, automata, plot_config=\n DefaultPlotConfig):\n super().__init__(scenario=scen... | [
9,
11,
12,
13,
17
] |
# This is a generated file, do not edit
from typing import List
import pydantic
from ..rmf_fleet_msgs.DockParameter import DockParameter
class Dock(pydantic.BaseModel):
fleet_name: str = "" # string
params: List[DockParameter] = [] # rmf_fleet_msgs/DockParameter
class Config:
orm_mode = True... | normal | {
"blob_id": "62d0818395a6093ebf2c410aaadeb8a0250707ab",
"index": 3865,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Dock(pydantic.BaseModel):\n fleet_name: str = ''\n params: List[DockParameter] = []\n\n\n class Config:\n orm_mode = True\n",
"step-3": "from typing import Lis... | [
0,
1,
2,
3
] |
import org.cogroo.gc.cmdline
import typing
class __module_protocol__(typing.Protocol):
# A module protocol which reflects the result of ``jp.JPackage("org.cogroo.gc")``.
cmdline: org.cogroo.gc.cmdline.__module_protocol__
| normal | {
"blob_id": "f615e7bbfa9179d0bfb321242cd8df4ae7b48993",
"index": 3181,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass __module_protocol__(typing.Protocol):\n cmdline: org.cogroo.gc.cmdline.__module_protocol__\n",
"step-3": "import org.cogroo.gc.cmdline\nimport typing\n\n\nclass __module_pr... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 19 09:31:20 2021
@author: dclabby
"""
import os
import cv2
import pickle
from utils import locateLetterRegions
# # Constants
# sourceFolder = '/home/dclabby/Documents/Springboard/HDAIML_SEP/Semester03/MachineLearning/Project/solving_captchas_code_e... | normal | {
"blob_id": "6109efeb3462ac2c5a94a68fbfa4f2f0617dd927",
"index": 1221,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef extractLetters(sourceFolder, trainRatio=0.8, destFolder=\n './data/separateLetters'):\n \"\"\" \n\n Parameters\n ----------\n sourceFolder : string\n DESC... | [
0,
1,
2,
3
] |
import json
import os
from flask import Flask, request, url_for
from flask_cors import CORS
from werkzeug.utils import secure_filename
from service.Binarizacion import Binarizacion
UPLOAD_FOLDER = './public/files'
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
CORS(app)
@app.route('/')
def hell... | normal | {
"blob_id": "b9c8689dbdf451e6a981f1abdae55771266fe231",
"index": 9129,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return 'Hello word'\n\n\n@app.route('/analyze', methods=['POST'])\ndef analyze():\n if request.method == 'POST':\n image_file = request.files['image']\n file_nam... | [
2,
3,
4,
5,
6
] |
# coding: utf-8
def init_list():
print("=== init_list ===")
l = list()
print(l)
l2 = []
print(l2)
l3 = list((1, 2))
print(l3)
l4 = [1, 2]
print(l4)
def insert_append_and_extend_list():
print("=== insert_append_and_extend_list ===")
l = ['e', 'h']
l.insert(-1, 'g')
... | normal | {
"blob_id": "1a710916461644a0676a3bd84926aeabb2aa3f71",
"index": 7127,
"step-1": "<mask token>\n\n\ndef get_len_count_index_list():\n print('=== get_len_count_index_list ===')\n l = ['a', 'b', 'c', 'd', 'e', 'e']\n print(l[0])\n print('len: {}'.format(len(l)))\n print('count d: {}'.format(l.count(... | [
10,
11,
12,
14,
15
] |
class FixtureBittrex:
PING = {"serverTime": 1582535502000}
MARKETS = [
{
"symbol": "ETH-BTC", "baseCurrencySymbol": "ETH", "quoteCurrencySymbol": "BTC",
"minTradeSize": "0.01314872", "precision": 8,
"status": "ONLINE", "createdAt": "2015-08-14T09:02:24.817Z"},
... | normal | {
"blob_id": "eba8e2bda786760898c10d3e75620144973d6236",
"index": 9555,
"step-1": "<mask token>\n",
"step-2": "class FixtureBittrex:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>... | [
0,
1,
2,
3
] |
if input is not None:
element = S(input)
if newChild is not None:
newChild = S(newChild)
element.replaceChild(existingChild, newChild)
| normal | {
"blob_id": "fdbb64159b72bf902efc3aa2eaa534e199dccf84",
"index": 8442,
"step-1": "<mask token>\n",
"step-2": "if input is not None:\n element = S(input)\nif newChild is not None:\n newChild = S(newChild)\nelement.replaceChild(existingChild, newChild)\n",
"step-3": null,
"step-4": null,
"step-5": nu... | [
0,
1
] |
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
] |
# Generated by Django 2.0.7 on 2018-08-14 21:31
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('orion_integration', '000... | normal | {
"blob_id": "5791c1efa82a1e02ca067e1db776e9d466a111e2",
"index": 1765,
"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 = [migrations.sw... | [
0,
1,
2,
3,
4
] |
from tkinter import *
from tkinter import messagebox
from tkinter import ttk
from PIL import Image, ImageTk
import time
import socket
import threading
root = Tk()
root.title("Tic-Tac-Toe")
root.geometry('600x600')
winner = False
def start_thread(target):
thread = threading.Thread(target=target)
... | normal | {
"blob_id": "cc924892afe179e55166ea9b237b2bfe8ea900df",
"index": 2120,
"step-1": "<mask token>\n\n\ndef start_thread(target):\n thread = threading.Thread(target=target)\n thread.daemon = True\n thread.start()\n\n\n<mask token>\n\n\ndef receive_data():\n while True:\n data = sock.recv(1024).dec... | [
5,
6,
7,
8,
9
] |
import os
import time
import argparse
import cPickle as pickle
from definitions import OieFeatures
from definitions.OieExample import OieExample
class FeatureLexicon:
"""
A wrapper around various dictionaries storing the mined data. It holds 5 dictionaries in total. Two of them store
mappings\n
- str ... | normal | {
"blob_id": "8102bdf4d29d2d3a1bdddbcfb6045b0660693996",
"index": 402,
"step-1": "import os\nimport time\nimport argparse\nimport cPickle as pickle\nfrom definitions import OieFeatures\nfrom definitions.OieExample import OieExample\n\n\nclass FeatureLexicon:\n \"\"\"\n A wrapper around various dictionaries ... | [
0
] |
# -*- coding: utf-8 -*-
# -------------------------------------------------------------------------------
# Name: sfp_googlesearch
# Purpose: Searches Google for content related to the domain in question.
#
# Author: Steve Micallef <steve@binarypool.com>
#
# Created: 07/05/2012
# Copyright... | normal | {
"blob_id": "3a6eaa238e78e7a818bcf6e18cc7881eadf94b07",
"index": 7863,
"step-1": "<mask token>\n\n\nclass sfp_googlesearch(SpiderFootPlugin):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def watchedEvents(self):\n return ['INTERNET_NAME']\n\n def prod... | [
4,
5,
7,
8,
9
] |
# Written by Jagannath Bilgi <jsbilgi@yahoo.com>
import sys
import json
import re
"""
Program accepts *.md document and converts to csv in required format
Program parse line by line and uses recursive method to traverse from leaf to root.
Single turn object (string, int etc) is used as point of return from recursio... | normal | {
"blob_id": "739921a6a09edbb81b442f4127215746c601a69a",
"index": 4990,
"step-1": "<mask token>\n\n\ndef obj_rec(obj, t, flag=0, acc=''):\n v_obj = type(obj)\n r = ''\n if type(obj) not in [dict, list, map]:\n ref_url = re.findall('\\\\((http.*?)\\\\)', obj)\n ref_title = re.findall('\\\\[[... | [
1,
2,
3,
4,
5
] |
from tkinter import*
me=Tk()
me.geometry("354x460")
me.title("CALCULATOR")
melabel = Label(me,text="CALCULATE HERE",bg='PINK',font=("ARIAL",25))
melabel.pack(side=TOP)
me.config(background='BROWN')
displayStr=StringVar()
op=""
def but(a):
global op
op=op+str(a)
displayStr.set(op)
def eq():
... | normal | {
"blob_id": "106cca8af164fa4ae946f77b40c76e03accf171c",
"index": 9645,
"step-1": "<mask token>\n\n\ndef but(a):\n global op\n op = op + str(a)\n displayStr.set(op)\n\n\ndef eq():\n global op\n result = str(eval(op))\n displayStr.set(result)\n op = ''\n\n\ndef clrbut():\n displayStr.set(''... | [
3,
4,
5,
6,
7
] |
import zipfile
zzz = zipfile.ZipFile('channel.zip','r')
filestr = '90052'
comment = []
for i in range(1000):
fname = filestr + ".txt"
for j in zzz.infolist():
if j.filename == fname :
print j.comment
comment.append(j.comment)
break
inzzz = zzz.open(fname).read()
print 'fname = ' + fname
... | normal | {
"blob_id": "34ad2e6fc7167766dac1ca962cab40511c89ad68",
"index": 7551,
"step-1": "import zipfile\n\nzzz = zipfile.ZipFile('channel.zip','r')\nfilestr = '90052'\ncomment = []\nfor i in range(1000):\n fname = filestr + \".txt\"\n for j in zzz.infolist():\n if j.filename == fname :\n print j.comment\n ... | [
0
] |
import numpy as np
from base_test import ArkoudaTest
from context import arkouda as ak
"""
Encapsulates unit tests for the pdarrayclass module that provide
summarized values via reduction methods
"""
class SummarizationTest(ArkoudaTest):
def setUp(self):
ArkoudaTest.setUp(self)
self.na = np.linsp... | normal | {
"blob_id": "88109909d0c80f25373f917426c3c3634bfc8114",
"index": 6267,
"step-1": "<mask token>\n\n\nclass SummarizationTest(ArkoudaTest):\n\n def setUp(self):\n ArkoudaTest.setUp(self)\n self.na = np.linspace(1, 10, 10)\n self.pda = ak.array(self.na)\n <mask token>\n\n def testMin(s... | [
6,
7,
8,
9,
11
] |
# helper functions to handle intcode
from collections import defaultdict
def read_code(string):
"""
string should be a comma-separated string.
"""
code = defaultdict(int)
for i, x in enumerate(string.split(',')):
code[i] = int(x)
return code
def to_ascii(line):
"""
Writes a ... | normal | {
"blob_id": "68c2fd1d8ca9e1dd9373ca9f641c2920c87b2392",
"index": 1346,
"step-1": "<mask token>\n\n\nclass IntCode:\n\n def __init__(self, code):\n self.code = code\n self.base = 0\n self.idx = 0\n self.terminated = False\n\n @staticmethod\n def load_code(code_string):\n ... | [
10,
11,
12,
13,
14
] |
class Account:
'''은행계좌를 표현하는 클래스'''
def __init__(self,name,account):
self.name = name
self._balance = amount
def __str__(self):
return '예금주 {}, 잔고 {}'.format(slef.name, self._balance)
def _info(self):
print('\t') | normal | {
"blob_id": "2dc4a4ae8e02e823073b1a9711dbd864a54bab43",
"index": 5072,
"step-1": "class Account:\n '''은행계좌를 표현하는 클래스'''\n \n\n def __init__(self,name,account):\n self.name = name\n self._balance = amount\n\n def __str__(self):\n return '예금주 {}, 잔고 {}'.format(slef.name, self._... | [
0
] |
from torch import Tensor
from torch.autograd import Variable
from torch.optim import Adam
from maac.utils.misc import hard_update, onehot_from_logits
from maac.utils.policies import DiscretePolicy
class AttentionAgent(object):
"""
General class for Attention agents (policy, target policy)
"""
def __i... | normal | {
"blob_id": "845d04312abc0e64a7810b52bbee333d2bdf3dfb",
"index": 7164,
"step-1": "<mask token>\n\n\nclass AttentionAgent(object):\n <mask token>\n\n def __init__(self, num_in_pol, num_out_pol, hidden_dim=64, lr=0.01,\n onehot_dim=0):\n \"\"\"\n Inputs:\n num_in_pol (int): nu... | [
4,
5,
6,
7
] |
#Coded by J. Prabhath
#14th April, 2020
#Released under GNU GPL
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
K = 96
Kp = 1
Td = 1.884
s1 = signal.lti([-1/Td],[0,-2,-4,-6], K)
s2 = signal.lti([],[0,-2,-4,-6], K)
w,mag1,phase1 = signal.bode(s1)
_,mag2,phase2 = signal.bode(s2)
plt.xlabel... | normal | {
"blob_id": "84e84d9f35702c2572ad5e7daa92a271674986dc",
"index": 3882,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.xlabel('Freq (in rad/s)')\nplt.ylabel('Phase (in deg)')\nplt.title('Phase plot')\nplt.semilogx(w, phase1, label='With Controller')\nplt.semilogx(w, phase2, label='Without Controller')... | [
0,
1,
2,
3,
4
] |
"""
Created on Wed Nov 6 13:03:42 2019
@author: antonio.blago
"""
#%% Connect to database
import sqlite3
conn = sqlite3.connect('Portfolio_dividens.db')
c = conn.cursor()
from sqlalchemy import create_engine #suport pd.dataframe to sql table
#import mysqlclient
engine = create_engine("sqlite:///Portf... | normal | {
"blob_id": "cee77a97503cca517d03ce7cce189974da282a03",
"index": 2500,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(inspect_tables) == 0:\n for k, t in enumerate(tickers):\n ticker_data = pd.DataFrame()\n try:\n ticker_data = wb.DataReader(t, data_source='yahoo', star... | [
0,
1,
2,
3,
4
] |
from guet.commands.strategies.strategy import CommandStrategy
class TooManyArgsStrategy(CommandStrategy):
def apply(self):
print('Too many arguments.')
| normal | {
"blob_id": "afd72ce2d9598f92937f3038eb0ef49b740b9977",
"index": 6846,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TooManyArgsStrategy(CommandStrategy):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TooManyArgsStrategy(CommandStrategy):\n\n def apply(self):\n print('To... | [
0,
1,
2,
3
] |
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