code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(5):
print(tej[i])
<|reserved_special_token_1|>
tej = 'votary'
for i in range(5):
print(tej[i])
<|reserved_special_token_1|>
tej="votary"
for i in range(5):
print(tej[i])
| flexible | {
"blob_id": "1f385fda1bdc0008ff91b935998c95c8ffcbd297",
"index": 2797,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(5):\n print(tej[i])\n",
"step-3": "tej = 'votary'\nfor i in range(5):\n print(tej[i])\n",
"step-4": "tej=\"votary\"\nfor i in range(5):\n\tprint(tej[i])\n",
"st... | [
0,
1,
2,
3
] |
# Library for Stalker project
#Libraries
import pandas as pd
import seaborn as sns
from IPython.display import Image, display
import matplotlib.pyplot as plt
# Google search
from googlesearch import search
# Tldextract to get domain of url
import tldextract as tld
# BeautifulSoup
from bs4 import BeautifulSoup as bs
f... | normal | {
"blob_id": "9c7ecd3c878d43633606439aa63f840176f20dee",
"index": 7941,
"step-1": "<mask token>\n\n\ndef find_webs(query):\n urls = []\n rrss = ['facebook', 'twitter', 'linkedin', 'instagram', 'youtube',\n 'pinterest', 'angel']\n sites = []\n red_social = False\n for s in search(query, tld='... | [
14,
15,
16,
20,
22
] |
<|reserved_special_token_0|>
class DownVoteHandler(MainHandler):
def get(self):
user = self.get_user()
if user:
post_id = self.request.get('post_id')
post = PostData.get_by_id(int(post_id))
voter_list = post.voter_list
if post.author == user:
... | flexible | {
"blob_id": "5711613df0bda10512466f147febcffacfe1607b",
"index": 7794,
"step-1": "<mask token>\n\n\nclass DownVoteHandler(MainHandler):\n\n def get(self):\n user = self.get_user()\n if user:\n post_id = self.request.get('post_id')\n post = PostData.get_by_id(int(post_id))\n... | [
2,
3,
4,
5,
6
] |
<|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": "e14b8d0f85042ceda955022bee08b3b3b4c2361d",
"index": 7367,
"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 = [('Asha', '000... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 18 18:53:02 2020
@author: vinhe
I followed below tutorial to push newly created csv to google sheets:
https://medium.com/craftsmenltd/from-csv-to-google-sheet-using-python-ef097cb014f9
"""
import gspread
from oauth2client.service_account import ServiceAcc... | normal | {
"blob_id": "ac2edcd6ea71ebdc5b1df5fd4211632b5d8e2704",
"index": 3019,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('C:/users/vinhe/code/projects/golf/golf_stats.csv', 'r') as file_obj:\n content = file_obj.read()\n client.import_csv(spreadsheet.id, data=content)\n",
"step-3": "<mask ... | [
0,
1,
2,
3,
4
] |
num=5
a=5
for row in range(num,0,-1):
for col in range(row,0,-1):
print(a,end="")
a-=1
print() | normal | {
"blob_id": "a567a2dc1dbb59979d849a5a772e4592910a9f27",
"index": 2783,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor row in range(num, 0, -1):\n for col in range(row, 0, -1):\n print(a, end='')\n a -= 1\n print()\n",
"step-3": "num = 5\na = 5\nfor row in range(num, 0, -1):\n for... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_token_lookups_with_full_data():
token_lookup = mango.SplTokenLookup.load(mango.SplTokenLookup.
DefaultDataFilepath)
assert token_lookup.find_by_symbol('BTC').mint == PublicKey(
'9n4nbM75f5Ui33ZbP... | flexible | {
"blob_id": "5e7a589af69a604021ed9558fcce721a8e254fee",
"index": 5269,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_token_lookups_with_full_data():\n token_lookup = mango.SplTokenLookup.load(mango.SplTokenLookup.\n DefaultDataFilepath)\n assert token_lookup.find_by_symbol('BTC... | [
0,
1,
2,
3,
4
] |
from igbot import InstaBot
from settings import username, pw
from sys import argv
def execute_script(InstaBot):
InstaBot.get_unfollowers()
#InstaBot.unfollow()
#InstaBot.follow()
#InstaBot.remove_followers()
def isheadless():
if len(argv) > 1:
if argv[1] == 'head':
return False
else:
raise ValueError("... | normal | {
"blob_id": "f379092cefe83a0a449789fbc09af490081b00a4",
"index": 3818,
"step-1": "<mask token>\n\n\ndef isheadless():\n if len(argv) > 1:\n if argv[1] == 'head':\n return False\n else:\n raise ValueError(\"optional arg must be : 'head'\")\n return True\n\n\n<mask token>\... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def parse_config_file_from_disk(path, confname='diskconf.json'):
json_path = str(path) + '/' + str(confname)
if not os.path.exists(json_path):
module_print('\tPath not exists: ' + str(json_path))
return None
try:
with open(json_path, 'r') as f:
... | flexible | {
"blob_id": "927470fe0087b17e5fe67a9b8b3cc13a40d8be1a",
"index": 7554,
"step-1": "<mask token>\n\n\ndef parse_config_file_from_disk(path, confname='diskconf.json'):\n json_path = str(path) + '/' + str(confname)\n if not os.path.exists(json_path):\n module_print('\\tPath not exists: ' + str(json_path... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
@pytest.mark.django_db
class TestImage(TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def setUp(self):
file1 = File(name='file1.jpg', file=BytesIO(b'abcdef'))
attachment1 = G(Attachment, original_filename=... | flexible | {
"blob_id": "e5bf57e7a171f7e42928b78d09dda7593a231cf9",
"index": 9841,
"step-1": "<mask token>\n\n\n@pytest.mark.django_db\nclass TestImage(TestCase):\n <mask token>\n <mask token>\n <mask token>\n\n def setUp(self):\n file1 = File(name='file1.jpg', file=BytesIO(b'abcdef'))\n attachment... | [
2,
3,
4,
5,
6
] |
import os,sys
import logging
from flask import Flask
from flask_bootstrap import Bootstrap
from flask_sqlalchemy import SQLAlchemy
def create_app():
app = Flask(__name__)
Bootstrap(app)
return app
logging.basicConfig(level=logging.DEBUG)
app = create_app()
app.config['WTF_CSRF_ENABLED'] = True
app.config[... | normal | {
"blob_id": "bd726c86bdecd0b63eb48d056932706d3ecf147d",
"index": 7665,
"step-1": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n Bootstrap(app)\n return app\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n Bootstrap(app)\n return ap... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
orange.eat()
apple.eat()
<|reserved_special_token_1|>
from foods.fruits import *
orange.eat()
apple.eat()
| flexible | {
"blob_id": "ad84a5bfcf82dff1f4a7e8f08f3c4243ad24de52",
"index": 7318,
"step-1": "<mask token>\n",
"step-2": "<mask token>\norange.eat()\napple.eat()\n",
"step-3": "from foods.fruits import *\norange.eat()\napple.eat()\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for j in range(1, 5):
for k in range(1, 14):
if j == 1:
cardlist.append(['S', '{}'.format(k)])
elif j == 2:
cardlist.append(['H', '{}'.format(k)])
elif j == 3:
cardli... | flexible | {
"blob_id": "937a101cf5c7e943fc62d18b77357eea151fdfaf",
"index": 7789,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor j in range(1, 5):\n for k in range(1, 14):\n if j == 1:\n cardlist.append(['S', '{}'.format(k)])\n elif j == 2:\n cardlist.append(['H', '{}'.for... | [
0,
1,
2,
3
] |
"""
table.py [-m] base1 base2 ... baseN
Combines output from base1.txt, base2.txt, etc., which are created by
the TestDriver (such as timcv.py) output, and displays tabulated
comparison statistics to stdout. Each input file is represented by
one column in the table.
Optional argument -m shows a final column with the m... | normal | {
"blob_id": "4e94e9e2b45d3786aa86be800be882cc3d5a80b5",
"index": 8328,
"step-1": "<mask token>\n\n\ndef suck(f):\n hamdevall = spamdevall = 0.0, 0.0\n cost = 0.0\n bestcost = 0.0\n fp = 0\n fn = 0\n un = 0\n fpp = 0.0\n fnp = 0.0\n unp = 0.0\n htest = 0\n stest = 0\n get = f.r... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class CalibraterBase:
def __init__(self, model_path: Union[str, Path], op_types_to_calibrate:
Optional[Sequence[str]]=None, augmented_model_path=
'augmented_model.onnx', symmetric=False, use_external_data_format=False
):
"""
:param model_path: ... | flexible | {
"blob_id": "a61132d2d504ed31d4e1e7889bde670853968559",
"index": 5739,
"step-1": "<mask token>\n\n\nclass CalibraterBase:\n\n def __init__(self, model_path: Union[str, Path], op_types_to_calibrate:\n Optional[Sequence[str]]=None, augmented_model_path=\n 'augmented_model.onnx', symmetric=False, u... | [
46,
56,
59,
60,
68
] |
from .parse_categories import extract_categories
from .parse_sections import extract_sections
from .utils import remove_xml_comments
def parse_page(page):
if 'redirect' in page.keys():
return
page_text = page['revision']['text']['#text']
page_text = remove_xml_comments(page_text)
title = page[... | normal | {
"blob_id": "0ad2e6d7e3fd61943fc1dfe6662110a6f48c1bd5",
"index": 5347,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef parse_page(page):\n if 'redirect' in page.keys():\n return\n page_text = page['revision']['text']['#text']\n page_text = remove_xml_comments(page_text)\n title ... | [
0,
1,
2
] |
from features.steps.web.test_home_page import *
from features.steps.mobile.test_home_page import *
from features.steps.web.test_login_page import *
| normal | {
"blob_id": "b09d0806dfc6f4badfd9f2ac9c3f6d17d3df8e8c",
"index": 3254,
"step-1": "<mask token>\n",
"step-2": "from features.steps.web.test_home_page import *\nfrom features.steps.mobile.test_home_page import *\nfrom features.steps.web.test_login_page import *\n",
"step-3": null,
"step-4": null,
"step-5":... | [
0,
1
] |
## This file is the celeryconfig for the Task Worker (scanworker).
from scanworker.commonconfig import *
import sys
sys.path.append('.')
BROKER_CONF = {
'uid' : '{{ mq_user }}',
'pass' : '{{ mq_password }}',
'host' : '{{ mq_host }}',
'port' : '5672',
'vhost' : '{{ mq_vhost }}',
}
BROKER_URL = 'amqp://... | normal | {
"blob_id": "1a569b88c350124968212cb910bef7b09b166152",
"index": 8990,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('.')\n<mask token>\n",
"step-3": "<mask token>\nsys.path.append('.')\nBROKER_CONF = {'uid': '{{ mq_user }}', 'pass': '{{ mq_password }}', 'host':\n '{{ mq_host }}', '... | [
0,
1,
2,
3,
4
] |
class Rational:
def __init__(self, numer, denom):
self.numer = numer
self.denom = denom
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __mul__(self, other):
return Rational(self.numer * other.numer, self.denom * other.denom)
<|reserved_special_token_0|>
<... | flexible | {
"blob_id": "8098b9c27689dd4168ef05c03d4ec00f67f8090e",
"index": 4771,
"step-1": "class Rational:\n\n def __init__(self, numer, denom):\n self.numer = numer\n self.denom = denom\n <mask token>\n <mask token>\n\n def __mul__(self, other):\n return Rational(self.numer * other.numer... | [
3,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class EPInfoLight(EP):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "e5abab3f718bbbd25dcfc49290383203d53248c3",
"index": 9464,
"step-1": "<mask token>\n\n\nclass EPInfoLight(EP):\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",
"ste... | [
1,
3,
4,
6,
8
] |
x = 5
y = x
print(id(x))
print(id(y))
print()
y = 3
print(id(x))
print(id(y))
print()
z = [1, 4, 3, 25]
w = z
print(z)
print(w)
print(id(z))
print(id(w))
print()
w[1] = 10
print(z)
print(w)
print(id(z))
print(id(w))
# So when you assign a mutable, you're actually assigning a reference to the mutable,
# and I... | normal | {
"blob_id": "956adc5961188458393b56564649ad0a3a787669",
"index": 7327,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(id(x))\nprint(id(y))\nprint()\n<mask token>\nprint(id(x))\nprint(id(y))\nprint()\n<mask token>\nprint(z)\nprint(w)\nprint(id(z))\nprint(id(w))\nprint()\n<mask token>\nprint(z)\nprin... | [
0,
1,
2,
3
] |
import numpy as np
import pickle
import preprocessor
import pandas as pd
import sys
from scipy import spatial
class Predict:
def __init__(self, text):
"""
taking the user input string
loading trained feature numpy array
loading the output for the numpy array
loading the ve... | normal | {
"blob_id": "26df6ddf3533a8648b59f0fa2b03f89c93af7491",
"index": 8154,
"step-1": "<mask token>\n\n\nclass Predict:\n\n def __init__(self, text):\n \"\"\"\n taking the user input string\n loading trained feature numpy array\n loading the output for the numpy array\n loading t... | [
3,
4,
5,
6
] |
from django.conf.urls import patterns, include, url
# Uncomment the next two lines to enable the admin:
from django.contrib import admin
admin.autodiscover()
from django.conf import settings
from django.conf.urls.static import static
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from dajaxice.co... | normal | {
"blob_id": "68a503b2a94304530e20d79baf9fb094024ba67e",
"index": 539,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.autodiscover()\n<mask token>\ndajaxice_autodiscover()\n<mask token>\nurlpatterns += staticfiles_urlpatterns()\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.2.8 on 2021-10-20 08:25
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('app', '0006_auto_20211020_0817'),
]
operations = [
migrations.AlterModelOptions(
name='currencies',
options={'verbose_name':... | normal | {
"blob_id": "a6cc0078fb37f9c63e119046193f521290c9fb21",
"index": 4634,
"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 = [('app', '0006... | [
0,
1,
2,
3,
4
] |
import urllib.request
from urllib.request import Request, urlopen
import json
from requests import get
from requests.exceptions import RequestException
from contextlib import closing
from bs4 import BeautifulSoup
"""
Web Scraper ======================================================================
"""
... | normal | {
"blob_id": "4c9a3983180cc75c39da41f7f9b595811ba0dc35",
"index": 8390,
"step-1": "<mask token>\n\n\ndef simple_get(url):\n \"\"\"\n Attempts to get the content at `url` by making an HTTP GET request.\n If the content-type of response is some kind of HTML/XML, return the\n text content, otherwise retu... | [
5,
6,
7,
8,
9
] |
# 1.闭包
# 2.装饰圈初识
# 3.标准版装饰器 | normal | {
"blob_id": "a1ebb00d7cda65cb528b2253e817d925214cdce3",
"index": 5847,
"step-1": "# 1.闭包\n# 2.装饰圈初识\n# 3.标准版装饰器",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
1
]
} | [
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
x, y, z = mc.player.getTilePos()
color = random.randrange(0, 9)
mc.setBlock(x, y, z - 1, 38, color)
time.sleep(0.01)
<|reserved_special_token_1|>
from mcpi.minecraft import Minecraft
import random, t... | flexible | {
"blob_id": "a2e00af84f743e949b53840ae6d5509e08935486",
"index": 7978,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n x, y, z = mc.player.getTilePos()\n color = random.randrange(0, 9)\n mc.setBlock(x, y, z - 1, 38, color)\n time.sleep(0.01)\n",
"step-3": "from mcpi.minecraft i... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(numbers + new_numbers)
print(numbers * 5)
<|reserved_special_token_1|>
numbers = [1, 1, 1, 1, 1]
new_numbers = [2, 2, 2, 3, 3]
print(numbers + new_numbers)
print(numbers * 5)
| flexible | {
"blob_id": "843df062702c9abf34cf14d911d927d786f1d912",
"index": 1573,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(numbers + new_numbers)\nprint(numbers * 5)\n",
"step-3": "numbers = [1, 1, 1, 1, 1]\nnew_numbers = [2, 2, 2, 3, 3]\nprint(numbers + new_numbers)\nprint(numbers * 5)\n",
"step-4"... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Binding:
def __init__(self, parent, binding):
self.parent = parent
self.binding = binding
<|reserved_special_token_0|>
def add(self, var_name, value):
self.binding[var_name] = value
<|reserved_special_token_0|>
class FunctionCall:
def... | flexible | {
"blob_id": "5fa91a5061a5e87a4a2b8fece0378299e87e5a48",
"index": 6694,
"step-1": "<mask token>\n\n\nclass Binding:\n\n def __init__(self, parent, binding):\n self.parent = parent\n self.binding = binding\n <mask token>\n\n def add(self, var_name, value):\n self.binding[var_name] = v... | [
42,
50,
56,
68,
73
] |
'''
Created on Nov 16, 2013
@author: mo
'''
import unittest
from Board import TicTacToe_Board
from ComputerPlayer import ComputerPlayer
from utils import debug_print as d_pr
from main import StartNewGame
class Test(unittest.TestCase):
def setUp(self):
self.the_board = TicTacToe_Board()
de... | normal | {
"blob_id": "1968923cd923e68dc5ff2148802f18e40a5e6c33",
"index": 939,
"step-1": "<mask token>\n\n\nclass Test(unittest.TestCase):\n <mask token>\n\n def tearDown(self):\n pass\n\n def test_these_should_win_for_x(self):\n self.assertEqual(TicTacToe_Board.IsWinningBoard_static([['x', 'x',\n ... | [
9,
12,
13,
14,
16
] |
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['savefig.dpi'] = 300 #图片像素
plt.rcParams['figure.dpi'] = 300 #分辨率
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
x_axis = [20,40,60,80,100]
rf = [184,174,166,159,157.5]
anns = [186,179,170,164,161]
adaboost = [187.5,1... | normal | {
"blob_id": "13342922022f0a0e8928c81c1c4716125af0b2c4",
"index": 418,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nax.set_xticks(x + width / 2)\nax.set_xticklabels(x_axis)\nplt.legend((p_rf[0], p_anns[0], p_adaboost[0]), ('RF', 'ANNs', 'AdaBoost'),\n loc='best', fontsize=20)\nplt.xticks(fontsize=18)... | [
0,
1,
2,
3,
4
] |
from typing import Dict, List
pilha = list()
print(pilha)
| normal | {
"blob_id": "f3f3bbb715f16dc84221f3349aa5f26e9a6dc7c8",
"index": 2726,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(pilha)\n",
"step-3": "<mask token>\npilha = list()\nprint(pilha)\n",
"step-4": "from typing import Dict, List\npilha = list()\nprint(pilha)\n",
"step-5": null,
"step-ids": [... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def getData():
power_file = './data/power_20210129_20210429_preprocess_1hour'
power_df = read_csv(power_file + '.csv', encoding='CP949', converters={
'date': int})
print(power_df.shape)
sensor_file = 'data/sensor_20210129_20210429_preprocess_1hour'
sensor_df = ... | flexible | {
"blob_id": "013189cd67cc44efd539c75ed235a0753d95f54e",
"index": 2165,
"step-1": "<mask token>\n\n\ndef getData():\n power_file = './data/power_20210129_20210429_preprocess_1hour'\n power_df = read_csv(power_file + '.csv', encoding='CP949', converters={\n 'date': int})\n print(power_df.shape)\n ... | [
1,
2,
3,
4,
5
] |
"""
Templating support library and renderer configuration.
"""
from restish import templating
class Templating(templating.Templating):
"""
Application-specific templating implementation.
Overriding "args" methods makes it trivial to push extra, application-wide
data to the templates without any assis... | normal | {
"blob_id": "18391df9a3e52400fe4fc54d6381b9ce21e25f0b",
"index": 2296,
"step-1": "<mask token>\n\n\nclass Templating(templating.Templating):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Templating(templating.Templating):\n \"\"\"\n Application-specific temp... | [
1,
3,
4,
5,
6
] |
class Day8MemoryManeuver:
def __init__(self, use_reference_count=False):
"""
Args:
use_reference_count (bool):
True: If an entry has child nodes, the meta data are referring to the results of
the child node
False: Sum all meta data up
... | normal | {
"blob_id": "84d096a51fa052ee210e975ab61c0cbbf05bc5ae",
"index": 8358,
"step-1": "class Day8MemoryManeuver:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "class Day8MemoryManeuver:\n <mask token>\n <mask token>\n\n def _solve(self, structure, pos):\n if pos >= len(structur... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def print_theta(theta, name='theta'):
theta_pd = pd.DataFrame(theta.reshape(1, -1), index=[name], columns=[
'mean', 'variance', 'max_range', 'min_range', 'anisotropy',
'head_west'])
print(theta_pd)
<|reserved_special_token_0|>
def visualize_one_m(m, vmin=-4, vm... | flexible | {
"blob_id": "09fb99a15c2727da2ef96028aca5513337449f62",
"index": 3772,
"step-1": "<mask token>\n\n\ndef print_theta(theta, name='theta'):\n theta_pd = pd.DataFrame(theta.reshape(1, -1), index=[name], columns=[\n 'mean', 'variance', 'max_range', 'min_range', 'anisotropy',\n 'head_west'])\n pri... | [
11,
13,
17,
19,
20
] |
class Tool:
<|reserved_special_token_0|>
def __repr__(self):
return f'Tool({self.name!r},{self.weight})'
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Tool:
def __init__(self, name, weight):
self.name = name
self.weight = weight
def __repr__(self):
... | flexible | {
"blob_id": "173b8e66ead62e3aa70805e42e06ea05257d5ee2",
"index": 2965,
"step-1": "class Tool:\n <mask token>\n\n def __repr__(self):\n return f'Tool({self.name!r},{self.weight})'\n\n\n<mask token>\n",
"step-2": "class Tool:\n\n def __init__(self, name, weight):\n self.name = name\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def open_dir(input_path, patterns):
"""
Opens the specified input path and returns any located excel file
:param patterns: the file extensions to glob over (eg xls, csv)
:param input_path: the starting path
:return: generator of all found files
"""
for ext in p... | flexible | {
"blob_id": "f831b77850dfe22232092f66705e36970828a75b",
"index": 4975,
"step-1": "<mask token>\n\n\ndef open_dir(input_path, patterns):\n \"\"\"\n Opens the specified input path and returns any located excel file\n :param patterns: the file extensions to glob over (eg xls, csv)\n :param input_path: t... | [
1,
2,
3,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(kwadraty, slownik, sep='\n')
<|reserved_special_token_1|>
lista = [x for x in range(11)]
kwadraty = [(i ** 2) for i in lista]
kwadraty = [(i, i ** 2, i ** 3) for i in range(-10, 11)]
zbior_wyr = {'aa', '1233', '111111'}
s... | flexible | {
"blob_id": "248b9b9d613f71e0130353f0792083b7d3f6ccd6",
"index": 7000,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(kwadraty, slownik, sep='\\n')\n",
"step-3": "lista = [x for x in range(11)]\nkwadraty = [(i ** 2) for i in lista]\nkwadraty = [(i, i ** 2, i ** 3) for i in range(-10, 11)]\nzbior_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Category(Enum):
ONES = 1
TWOS = 2
THREES = 3
FOURS = 4
FIVES = 5
SIXES = 6
YACHT = auto()
FULL_HOUSE = auto()
FOUR_OF_A_KIND = auto()
LITTLE_STRAIGHT = auto()
BIG_STRAIGHT = auto... | flexible | {
"blob_id": "40bc8122d98d407341a56251f9abfab019e0acd8",
"index": 625,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Category(Enum):\n ONES = 1\n TWOS = 2\n THREES = 3\n FOURS = 4\n FIVES = 5\n SIXES = 6\n YACHT = auto()\n FULL_HOUSE = auto()\n FOUR_OF_A_KIND = auto()... | [
0,
2,
3,
4
] |
__author__ = 'xcbtrader'
# -*- coding: utf-8 -*-
from bitcoin import *
def crear_addr_word(word):
priv = sha256(word)
pub = privtopub(priv)
addr = pubtoaddr(pub)
wif = encode_privkey(priv, 'wif')
return addr, priv, wif
word = input('Entra la palabra para crear direccion bitcoin:? ')
addr, priv, wif = crear_addr... | normal | {
"blob_id": "cc7a44754dc1371733420fd3a1e51ab6b5e7c4d8",
"index": 6898,
"step-1": "<mask token>\n\n\ndef crear_addr_word(word):\n priv = sha256(word)\n pub = privtopub(priv)\n addr = pubtoaddr(pub)\n wif = encode_privkey(priv, 'wif')\n return addr, priv, wif\n\n\n<mask token>\n",
"step-2": "<mask... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def test_line():
p1 = [0, 0, 0]
p2 = [1, 0, 0]
line = Line(p1, p2)
assert line.start == p1
assert line.end == p2
<|reserved_special_token_0|>
def test___getitem__():
p1 = [0, 0, 0]
p2 = [1, 0, 0]
line = Line(p1, p2)
assert line[0] == p1
assert l... | flexible | {
"blob_id": "03629e62b11e66eeb0e111fee551c75c8463cbb8",
"index": 1059,
"step-1": "<mask token>\n\n\ndef test_line():\n p1 = [0, 0, 0]\n p2 = [1, 0, 0]\n line = Line(p1, p2)\n assert line.start == p1\n assert line.end == p2\n\n\n<mask token>\n\n\ndef test___getitem__():\n p1 = [0, 0, 0]\n p2 ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def find_lt(a, x):
"""
Find rightmost value less than x in list a
Input: list a and value x
Output: rightmost value less than x in a
"""
i = bisect_left(a, x)
if i:
return a[i - 1]
raise ValueError
def find_ge(a, x):
"""
Find leftmost item... | flexible | {
"blob_id": "da751e96c225ebc2d30f3cce01ba2f64d0a29257",
"index": 3763,
"step-1": "<mask token>\n\n\ndef find_lt(a, x):\n \"\"\"\n Find rightmost value less than x in list a\n Input: list a and value x\n Output: rightmost value less than x in a\n \"\"\"\n i = bisect_left(a, x)\n if i:\n ... | [
7,
8,
9,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
if os.environ.get('DEBUG'):
import settings_local as settings
else:
import settings_prod as settings
except ImportError:
import settings
<|reserved_special_token_0|>
if redis_env:
redis = Redis... | flexible | {
"blob_id": "4c3a27bf1f7e617f4b85dc2b59efa184751b69ac",
"index": 3868,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n if os.environ.get('DEBUG'):\n import settings_local as settings\n else:\n import settings_prod as settings\nexcept ImportError:\n import settings\n<mask toke... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('your name is:' + x)
print(p)
<|reserved_special_token_1|>
x = str(input('please input your name:'))
y = int(input('please input your age:'))
p = int(2017 - y + 100)
print('your name is:' + x)
print(p)
<|reserved_specia... | flexible | {
"blob_id": "929f580e8e559f8309e19f72208bf4ff0d537668",
"index": 4935,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('your name is:' + x)\nprint(p)\n",
"step-3": "x = str(input('please input your name:'))\ny = int(input('please input your age:'))\np = int(2017 - y + 100)\nprint('your name is:' +... | [
0,
1,
2,
3
] |
date = input()
if date == ("DEC 25") or date == ("OCT 31"):
print("yup")
else:
print("nope") | normal | {
"blob_id": "bc5b368a710b8dfc4492b996c42c46638e1f538c",
"index": 9811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif date == 'DEC 25' or date == 'OCT 31':\n print('yup')\nelse:\n print('nope')\n",
"step-3": "date = input()\nif date == 'DEC 25' or date == 'OCT 31':\n print('yup')\nelse:\n ... | [
0,
1,
2,
3
] |
import math
def vol_shell(r1, r2):
a=abs((4/3)*math.pi*((r1**3)-(r2**3)))
return round(a,3)
print(vol_shell(3,3))
| normal | {
"blob_id": "cd234911c1f990b8029dfa792d132847bf39a6aa",
"index": 445,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef vol_shell(r1, r2):\n a = abs(4 / 3 * math.pi * (r1 ** 3 - r2 ** 3))\n return round(a, 3)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef vol_shell(r1, r2):\n a = ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class AppData:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_sp... | flexible | {
"blob_id": "7be54b2bd99680beed3e8e9cb14225756a71a4ea",
"index": 1135,
"step-1": "<mask token>\n\n\nclass AppData:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AppData:\n\n def __init__(se... | [
1,
5,
8,
9,
10
] |
import random
from common.ast import *
from mutate.mutate_ctrl import *
def _check_parent_type(node, nodes, types):
par = node
while(nodes[par] != None):
par = nodes[par]
if type(par) in types:
return True
return False
def mutate_operator(root, nodes, path):
candidates = [... | normal | {
"blob_id": "c0524301a79788aa34a039fc46799021fb45362c",
"index": 7141,
"step-1": "<mask token>\n\n\ndef mutate_operator(root, nodes, path):\n candidates = [node for node in nodes.keys() if type(node) in OP_TYPES.\n keys() and _check_parent_type(node, nodes, OP_PARENT_TYPES)]\n if len(candidates) == ... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 13 17:34:32 2019
@author: fanlizhou
Analyze codon usage of sequence from 'SP_gene_seq.txt' and 'LP_gene_seq.txt'
Plot heatmap of amino acid usage and codon usage
Plot codon usage in each gene for each amino acid. Genes were arranged so that
the ge... | normal | {
"blob_id": "ae7a2de8742e353818d4f5a28feb9bce04d787bb",
"index": 8382,
"step-1": "<mask token>\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\n 'Analyze codon usage of SP and LP\\n')\n parser.add_argument('sp_file', help='one input SP data file\\n')\n parser.add_argument('... | [
11,
13,
16,
20,
21
] |
<|reserved_special_token_0|>
class KnowValues(unittest.TestCase):
def test_ls_contributing(self):
""" To test the list of contributing centers """
sv = nao(gto=mol)
pb = prod_basis()
pb.sv = sv
pb.sv.ao_log.sp2rcut[0] = 10.0
pb.prod_log = sv.ao_log
pb.prod_... | flexible | {
"blob_id": "f82ddc34fde76ddfbbe75116526af45b83c1b102",
"index": 7895,
"step-1": "<mask token>\n\n\nclass KnowValues(unittest.TestCase):\n\n def test_ls_contributing(self):\n \"\"\" To test the list of contributing centers \"\"\"\n sv = nao(gto=mol)\n pb = prod_basis()\n pb.sv = sv... | [
2,
3,
4,
5,
6
] |
__author__ = 'mvoronin'
| normal | {
"blob_id": "e5a7b0cbc82b57578f6dcbf676e8f589c6e9ac1b",
"index": 5663,
"step-1": "<mask token>\n",
"step-2": "__author__ = 'mvoronin'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from pyzabbix import ZabbixMetric, ZabbixSender, ZabbixAPI
from datetime import datetime
from re import findall
# current_time = datetime.now().strftime("%H:%M:%S %d.%m.%Y")
class ZabbixItem():
def __init__(self, user, password, ext_group, ext_template, zabbix_host):
self.user = user
self.passwor... | normal | {
"blob_id": "14826b5b121ba2939519492c1e1d8700c32396d2",
"index": 8963,
"step-1": "<mask token>\n\n\nclass ZabbixItem:\n\n def __init__(self, user, password, ext_group, ext_template, zabbix_host):\n self.user = user\n self.password = password\n self.zabbix_host = zabbix_host\n self.... | [
6,
8,
9,
11,
12
] |
#!/usr/bin/env python3
"""
Python class to access Netonix® WISP Switch WebAPI
** NEITHER THIS CODE NOR THE AUTHOR IS ASSOCIATED WITH NETONIX® IN ANY WAY.**
This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software,... | normal | {
"blob_id": "743d261052e4532c1304647501719ad897224b4e",
"index": 8991,
"step-1": "<mask token>\n\n\nclass Netonix:\n <mask token>\n\n def _get(self, url, params=None, timeout=15, **kwargs):\n full_url = 'https://' + self.ip + self.url[url]\n return self.s.get(full_url, params=params, timeout=... | [
9,
11,
13,
14,
20
] |
import tensorflow as tf
import numpy as np
import math
import sys
import os
import numpy as np
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, '../utils'))
import tf_util
# from transform_nets import input_transform_net, feature_transform_net
import... | normal | {
"blob_id": "e4a0f26afe8c78e4abbd85834c96ed5ba84e1f0b",
"index": 3894,
"step-1": "<mask token>\n\n\nclass Network:\n\n def placeholder_inputs(self, batch_size, num_point):\n source_pointclouds_pl = tf.placeholder(tf.float32, shape=(\n batch_size, num_point, 3))\n return source_pointcl... | [
4,
6,
7,
8,
9
] |
"""
It's annoying that we have to do it here but for something like Ant, we're not going to be able to
specify it easily inside of the rbf_hyper_parameters file. Because, for something like Ant, we have
2 COM dimensions, and Bipedal we have 1.
So, we're going to do something similar to shaping_functions.
The way it'... | normal | {
"blob_id": "5529813e10e4a30a60c28242be9d1a8822fb58af",
"index": 9685,
"step-1": "<mask token>\n\n\ndef action_scaling(env, action_scaler):\n \"\"\"\n This is actually going to just be \"action scaling\". Because,\n it's all about the ratio, and the ratio doesn't change!\n \"\"\"\n try:\n s... | [
3,
4,
6,
7,
8
] |
import sys
import urllib
import urlparse
import xbmcgui
import xbmcplugin
import xbmcaddon
import shutil
from shutil import copyfile
base_url = sys.argv[0]
addon_handle = int(sys.argv[1])
args = urlparse.parse_qs(sys.argv[2][1:])
addon = xbmcaddon.Addon()
xbmcplugin.setContent(addon_handle, 'videos')
... | normal | {
"blob_id": "15bcfd8859322034ec76a8c861d2151153ab54af",
"index": 5120,
"step-1": "import sys\r\nimport urllib\r\nimport urlparse\r\nimport xbmcgui\r\nimport xbmcplugin\r\nimport xbmcaddon\r\nimport shutil\r\nfrom shutil import copyfile\r\n\r\nbase_url = sys.argv[0]\r\naddon_handle = int(sys.argv[1])\r\nargs = u... | [
0
] |
<|reserved_special_token_0|>
class Item:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, barcode):
self.barcode = barcode
self.marc = None
self.record = None
self.title = None
self.author = None
self.year = None
def _get_ma... | flexible | {
"blob_id": "abfff0901e5f825a473119c93f53cba206609428",
"index": 7482,
"step-1": "<mask token>\n\n\nclass Item:\n <mask token>\n <mask token>\n\n def __init__(self, barcode):\n self.barcode = barcode\n self.marc = None\n self.record = None\n self.title = None\n self.au... | [
6,
7,
8,
11,
12
] |
# Generated by Django 2.1.7 on 2019-03-23 17:14
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('currency_exchange', '0007_auto_20190323_1751'),
]
operations = [
migrations.AddField(
model_name='tasks',
name='hour... | normal | {
"blob_id": "1f63ce2c791f0b8763aeae15df4875769f6de848",
"index": 4942,
"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 = [('currency_ex... | [
0,
1,
2,
3,
4
] |
old_file = open("new.csv", "r")
new_file = open("new1,csv", "w")
for line in old_file.readlines():
cleaned_line =line.replace(',','.')
new_file.write(cleaned_line)
old_file.close
new_file.close | normal | {
"blob_id": "b3d26d01d45c073192d06c8e94c06f7eae267b14",
"index": 968,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in old_file.readlines():\n cleaned_line = line.replace(',', '.')\n new_file.write(cleaned_line)\nold_file.close\nnew_file.close\n",
"step-3": "old_file = open('new.csv', '... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def bubbleSort(arr):
k = len(arr)
for i in range(k):
for j in range(0, k - i - 1):
if arr[j] > arr[j + 1]:
arr[j], arr[j + 1] = arr[j + 1], arr[j]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def b... | flexible | {
"blob_id": "178f9dcd9cbea140abebd509b56979417b5d7503",
"index": 6785,
"step-1": "<mask token>\n",
"step-2": "def bubbleSort(arr):\n k = len(arr)\n for i in range(k):\n for j in range(0, k - i - 1):\n if arr[j] > arr[j + 1]:\n arr[j], arr[j + 1] = arr[j + 1], arr[j]\n\n\n... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class User(AbstractUser):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Profile(models.Model):
... | flexible | {
"blob_id": "360813a573f672e3ec380da4237a6e131dbcb7e6",
"index": 2345,
"step-1": "<mask token>\n\n\nclass User(AbstractUser):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Profile(models.Model):\n \"\"\"Profile model\"\... | [
5,
6,
8,
9,
10
] |
from rest_framework import serializers
from .models import Good, Favorite, Comment
class GoodSerializer(serializers.ModelSerializer):
class Meta:
model = Good
fields = ('user', 'article', 'created_at')
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favori... | normal | {
"blob_id": "fc8b9029955de6b11cbfe8e24107c687f49685c1",
"index": 9179,
"step-1": "<mask token>\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-2": "<mask token>\n\n\nclass Favor... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Post(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __str__(self):
return '{}'.format(self.title)
<|reserved_special_token_1|>
<|r... | flexible | {
"blob_id": "4fa9c00a07c8263a6a3afd460b84f21637a771ec",
"index": 3081,
"step-1": "<mask token>\n\n\nclass Post(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return '{}'.format(self.title)\n",
"step-2": "<mask token>\n... | [
2,
3,
4,
5,
6
] |
from django.contrib import admin
from .models import StoreId
# Register your models here.
class StoreIdAdmin(admin.ModelAdmin):
list_display = ('userid', 'aladin_id', 'yes24_id', 'ridibooks_id', 'start_date', 'end_date')
search_fields = ['userid', 'aladin_id', 'yes24_id', 'ridibooks_id']
admin.site.register(S... | normal | {
"blob_id": "6475fd59ba2414ea9a174297a8d94e5a2e0a7d8f",
"index": 3783,
"step-1": "<mask token>\n\n\nclass StoreIdAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass StoreIdAdmin(admin.ModelAdmin):\n list_display = ('userid', 'aladin_id', 'yes... | [
1,
2,
3,
4,
5
] |
import math
def sieve(limit):
ans = []
a = [1] * limit
a[0] = a[1] = 0
for i in range(2, limit):
if a[i] == 0:
continue
ans.append(i)
for j in range(i*i, limit, i):
a[j] = 0;
return ans
is_square = lambda x: int(math.sqrt(x) + 1e-9) ** 2 == x
N = 10... | normal | {
"blob_id": "ff6dc347637a81c9f6a541775646b4901d719790",
"index": 9478,
"step-1": "<mask token>\n\n\ndef sieve(limit):\n ans = []\n a = [1] * limit\n a[0] = a[1] = 0\n for i in range(2, limit):\n if a[i] == 0:\n continue\n ans.append(i)\n for j in range(i * i, limit, i)... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class FormulaTemplate:
def __init__(self, vi, w, k, h, m, timeout=3000000):
self.k = k
self.h = h
self.m = m
self.w = w
self.vi = vi
n = len(vi)
self.n = n
self.aeij = [[Int('ae' + str(i) + str(j)) for j in range(n)] for... | flexible | {
"blob_id": "81fce5314a7611de11648e412151112e29271871",
"index": 4626,
"step-1": "<mask token>\n\n\nclass FormulaTemplate:\n\n def __init__(self, vi, w, k, h, m, timeout=3000000):\n self.k = k\n self.h = h\n self.m = m\n self.w = w\n self.vi = vi\n n = len(vi)\n ... | [
11,
14,
15,
16,
22
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def solution(a, b):
answer = 0
for i in range(0, len(a)):
answer += a[i] * b[i]
print(answer)
return answer
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def solution(a, b):
answer = 0
for i in range(0, len(a))... | flexible | {
"blob_id": "5fd34c698c2060d5399ba43f6746527961aa574b",
"index": 9239,
"step-1": "<mask token>\n",
"step-2": "def solution(a, b):\n answer = 0\n for i in range(0, len(a)):\n answer += a[i] * b[i]\n print(answer)\n return answer\n\n\n<mask token>\n",
"step-3": "def solution(a, b):\n answ... | [
0,
1,
2,
3
] |
#coding=utf-8
# ycat 2017-10-20 create
# AGV的控制
import sys,os
import json
import setup
if __name__ == '__main__':
setup.setCurPath(__file__)
import utility
import enhance
import threading
import time
import log
import re
import lock
import json_codec
import driver.agv.hdcAgvApi as api
g_threads =[]
g_carts = No... | normal | {
"blob_id": "e2feb12b88babbbfa4cc8447c91e8a5b6c30f78b",
"index": 1466,
"step-1": "<mask token>\n\n\n@utility.init()\ndef init():\n if utility.is_test():\n return\n api.init()\n time.sleep(3)\n\n\ndef wait():\n global g_threads\n for t in g_threads:\n t.join()\n g_threads.clear()\n... | [
26,
29,
30,
34,
38
] |
<|reserved_special_token_0|>
class DeathsByEthnicity(PowerBiQuerier):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class DeathsByEthnicity(PowerBiQuerier):
<|reserved_special_token_0|>
def _parse_data(self, response_json: Dic... | flexible | {
"blob_id": "d975b74370acc72101f808e70bef64cee39a5ab8",
"index": 6204,
"step-1": "<mask token>\n\n\nclass DeathsByEthnicity(PowerBiQuerier):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass DeathsByEthnicity(PowerBiQuerier):\n <mask token>\n\n def _parse_data(self, response_json... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def sum_series(n, x=0, y=1):
"""sum_series returns the nth number of the Fibonacci, the Lucas sequence
or the Foo sequence where the first position is indexed at 0. Arguments x and y as integers
are optional... | flexible | {
"blob_id": "ca75e23d91eef8a5c5b78c0ea7c903b80640af25",
"index": 7957,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sum_series(n, x=0, y=1):\n \"\"\"sum_series returns the nth number of the Fibonacci, the Lucas sequence\n or the Foo sequence where the first position is indexed at 0. ... | [
0,
1,
2,
3,
4
] |
import csv
import os
import requests
from bs4 import BeautifulSoup
# open html file and parsing lxml
with open ('/Users/neeraj.joshi/Downloads/index.html') as html_file:
soup = BeautifulSoup(html_file, 'lxml')
#row = soup.find_all('tr')
#column = row.find_all('td')
#print(soup)
# create a file by any name and in o... | normal | {
"blob_id": "47be41bd5838b828acdc90c3ef5abdeec9da1e85",
"index": 1579,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('/Users/neeraj.joshi/Downloads/index.html') as html_file:\n soup = BeautifulSoup(html_file, 'lxml')\n<mask token>\nfor tree in soup.find_all('tr'):\n data = []\n for to... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
from collections import defaultdict
from cluster.common import Cluster
from cluster.tools import print_table
def check_status(args):
""" Print node details
:param args: Arguments from argparse
:type args: argparse.Namespace
"""
cluster = Cluster(jobs_qstat=True, nodes=True,... | normal | {
"blob_id": "381b59ab9fa85561932a9bfb9ab8cef635901a35",
"index": 7249,
"step-1": "<mask token>\n\n\ndef main():\n \"\"\" Execute main program\n \"\"\"\n import argparse\n parser = argparse.ArgumentParser(description='Check nodes status.')\n parser.add_argument('-o', '--show-job-owners', action='st... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/python
# coding=utf8
# author: Sun yang
import running
if __name__ == '__main__':
running.go() | normal | {
"blob_id": "12442e4debc7fbf102ab88b42464f4ca8eb91351",
"index": 8454,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n running.go()\n",
"step-3": "import running\nif __name__ == '__main__':\n running.go()\n",
"step-4": "#!/usr/bin/python\r\n# coding=utf8\r\n# author:... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class FlatbuffersConversionData(object):
"""Holds data needed to convert a set of json files to flatbuffer binaries.
Attributes:
schema: The path to the flatbuffer schema file.
input_files: A list of input files to convert.
output_path: The path to the output directory ... | flexible | {
"blob_id": "4989db28db0f823a54ff0942fbc40fc4640da38f",
"index": 3224,
"step-1": "<mask token>\n\n\nclass FlatbuffersConversionData(object):\n \"\"\"Holds data needed to convert a set of json files to flatbuffer binaries.\n\n Attributes:\n schema: The path to the flatbuffer schema file.\n input_files: ... | [
15,
18,
20,
22,
25
] |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics.pairwise import cosine_similarity
def get_df_4_model(user_id, n_recommendations = 20000):
'''this func... | normal | {
"blob_id": "5c8de06176d06c5a2cf78ac138a5cb35e168d617",
"index": 5122,
"step-1": "<mask token>\n\n\ndef get_df_4_model(user_id, n_recommendations=20000):\n \"\"\"this function generates the latent dataframes used for the prediction model\"\"\"\n print('Generating dataframe for recommendation model')\n r... | [
3,
4,
5,
6,
7
] |
#Una empresa les paga a sus empleados con base en las horas trabajadas en la semana.
#Realice un algoritmo para determinar el sueldo semanal de N trabajadores
#y, además, calcule cuánto pagó la empresa por los N empleados.
base = int(input("Dinero por hora trabajada: "))
emp = int(input("Dime el nº de empleados... | normal | {
"blob_id": "963e736fd4a942fb1c51e1e0a357ad6be48aed9a",
"index": 5985,
"step-1": "\r\n#Una empresa les paga a sus empleados con base en las horas trabajadas en la semana.\r\n#Realice un algoritmo para determinar el sueldo semanal de N trabajadores\r\n#y, además, calcule cuánto pagó la empresa por los N empleados... | [
0
] |
<|reserved_special_token_0|>
class StrComparison(MethodResource, Resource):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class StrComparison(MethodResource, Resource):
def get(self, domain):
domain_found = ''
similar = False
for row in df... | flexible | {
"blob_id": "6d974580ff546bda17caa1e61e2621b4bc705f3f",
"index": 2952,
"step-1": "<mask token>\n\n\nclass StrComparison(MethodResource, Resource):\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass StrComparison(MethodResource, Resource):\n\n def get(self, domain):\n domain_found = ''\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class NeuralNetwork:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def withSeed(self, seed):
self.seed = seed
return self
<|reserved_special_token_0|>
def withMinErrorPercen... | flexible | {
"blob_id": "0af45914c8c111a42b0b9684f5f0ee19ef5eeb70",
"index": 7548,
"step-1": "<mask token>\n\n\nclass NeuralNetwork:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def withSeed(self, seed):\n self.seed = seed\n return self\n <mask token>\n\n def withMinErro... | [
7,
12,
13,
14,
19
] |
<|reserved_special_token_0|>
def mapfn(k, v):
for w in v.split():
yield w, 1
def reducefn(k, vs):
result = 0
for v in vs:
result += v
return result
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def mapfn(k, v):
for w in v.split():
... | flexible | {
"blob_id": "09c6dd0f32b8d71dacdd8b10d995ea1575f91f6f",
"index": 2887,
"step-1": "<mask token>\n\n\ndef mapfn(k, v):\n for w in v.split():\n yield w, 1\n\n\ndef reducefn(k, vs):\n result = 0\n for v in vs:\n result += v\n return result\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n... | [
2,
3,
4,
5,
6
] |
import time
inputStr = """crruafyzloguvxwctqmphenbkd
srcjafyzlcguvrwctqmphenbkd
srijafyzlogbpxwctgmphenbkd
zrijafyzloguvxrctqmphendkd
srijabyzloguvowcqqmphenbkd
srijafyzsoguvxwctbmpienbkd
srirtfyzlognvxwctqmphenbkd
srijafyzloguvxwctgmphenbmq
senjafyzloguvxectqmphenbkd
srijafyeloguvxwwtqmphembkd
srijafyzlogurxtctqmpken... | normal | {
"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
] |
DEFAULT_SERVER_LISTEN_PORT = 2011
DEFAULT_CLIENT_LISTEN_PORT = 2012
import pickle
import socket
from player import Player
from averageddata import *
import zlib
import g
import pygame
from collections import defaultdict
from periodic import Periodic
import random
from projectile import Projectile
TICKTIME = 0.05
cla... | normal | {
"blob_id": "b7be9fd366d03068a5d6c3cee703d579b9866fd3",
"index": 7992,
"step-1": "DEFAULT_SERVER_LISTEN_PORT = 2011\nDEFAULT_CLIENT_LISTEN_PORT = 2012\n\nimport pickle\nimport socket\nfrom player import Player\nfrom averageddata import *\nimport zlib\nimport g\nimport pygame\nfrom collections import defaultdict\... | [
0
] |
from db import do_command, do_command_no_return, do_insert
def get_grocery(upc):
cmd = "SELECT name FROM grocery WHERE upc = ?"
rtVal = do_command(cmd, [upc])
length = len(rtVal)
if length > 0:
return {'success': bool(len(rtVal)), 'grocery': rtVal[0]}
return {'success': bool(len(rtVal))... | normal | {
"blob_id": "92b24fe82929ed4590e5350188673c2245136d03",
"index": 5554,
"step-1": "<mask token>\n\n\ndef get_grocery_id(upc):\n cmd = 'SELECT id FROM grocery WHERE upc = ?'\n rtVal = do_command(cmd, [upc])\n if len(rtVal) > 0:\n return rtVal[0]['id']\n else:\n return -1\n\n\n<mask token>... | [
3,
5,
6,
9,
10
] |
# Generated from /home/mridul/PycharmProjects/BTP_2k18-19/PlSql.g4 by ANTLR 4.7.2
from antlr4 import *
from io import StringIO
from typing.io import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\u020e")
buf.write("\u... | normal | {
"blob_id": "b6dbed95b321ac93c712c4735d601a00650b8dc4",
"index": 1552,
"step-1": "<mask token>\n\n\nclass PlSqlLexer(Lexer):\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 <ma... | [
1,
3,
4,
5,
6
] |
class Classifier(object):
"""
Trained classifier
"""
def __init__(self, classifier, scaler, orient, color_space,
pix_per_cell, cell_per_block, spatial_size, hist_bins):
"""
Initializes an instance.
Parameters
----------
classifier : Trained SciPy classif... | normal | {
"blob_id": "9188d58a6d9e832b8908b823d57249fcdd80ff51",
"index": 171,
"step-1": "<mask token>\n",
"step-2": "class Classifier(object):\n <mask token>\n <mask token>\n",
"step-3": "class Classifier(object):\n <mask token>\n\n def __init__(self, classifier, scaler, orient, color_space,\n pix... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@register.simple_tag
def random_quote():
"""Returns a random quote to be displayed on the community sandwich page"""
quotes = [
"""Growth is never by mere chance; it is the result of forces working together.
-Jam... | flexible | {
"blob_id": "6e73625adc10064cdb1b5f0546a4fc7320e9f5dc",
"index": 8366,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@register.simple_tag\ndef random_quote():\n \"\"\"Returns a random quote to be displayed on the community sandwich page\"\"\"\n quotes = [\n \"\"\"Growth is never by mere... | [
0,
1,
2,
3,
4
] |
n=int(input("n="))
x=int(input("x="))
natija=pow(n,x)+pow(6,x)
print(natija) | normal | {
"blob_id": "0d6490ae5f60ef21ad344e20179bd1b0f6aa761e",
"index": 6214,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(natija)\n",
"step-3": "n = int(input('n='))\nx = int(input('x='))\nnatija = pow(n, x) + pow(6, x)\nprint(natija)\n",
"step-4": "n=int(input(\"n=\"))\r\nx=int(input(\"x=\"))\r\nn... | [
0,
1,
2,
3
] |
# Copyright (c) 2008-2016 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Test the `interpolation` module."""
from __future__ import division
import logging
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_eq... | normal | {
"blob_id": "9e987e057ee5322765415b84e84ef3c4d2827742",
"index": 5466,
"step-1": "<mask token>\n\n\n@pytest.fixture()\ndef test_data():\n \"\"\"Return data used for tests in this file.\"\"\"\n x = np.array([8, 67, 79, 10, 52, 53, 98, 34, 15, 58], dtype=float)\n y = np.array([24, 87, 48, 94, 98, 66, 14, ... | [
7,
9,
10,
11,
13
] |
"""
Utilities for calculations based on antenna positions,
such as baseline and phase factor.
"""
import os
import numpy as np
import pickle
c = 299792458 # m / s
data_prefix = os.path.dirname(os.path.abspath(__file__)) + "/"
try:
ant_pos = dict(pickle.load(open(data_prefix + "ant_dict.pk", "rb")))
def base... | normal | {
"blob_id": "c455263b82c04fe2c5cc1e614f10a9962795f87e",
"index": 4349,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n ant_pos = dict(pickle.load(open(data_prefix + 'ant_dict.pk', 'rb')))\n\n def baselength(ant_ID1, ant_ID2):\n \"\"\"\n (Convenience function)\n Return the... | [
0,
1,
2,
3,
4
] |
from django.shortcuts import render, get_object_or_404
# Create your views here.
from django.http import HttpResponse
from .models import Post
from django.utils import timezone
def list_of_posts(request):
posts = (Post.objects
.filter(published_date__lte=timezone.now())
.order_b... | normal | {
"blob_id": "71a0900dc09b1ff55e4e5a4cc7cab617b9c73406",
"index": 4519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n return render(request, 'blog/post_detail.html', {'post': post})\n",
"step-3": "<mask token>\n\n\ndef li... | [
0,
1,
2,
3,
4
] |
import sys
import os
import traceback
from src.properties import *
from src.utils import *
from subprocess import call
from src.entity.cursor import Cursor
from curses import *
def main(screen, file_path):
setUpEnv()
text = readFileIfExist(file_path)
while 1:
try:
text = startEditing(s... | normal | {
"blob_id": "7a6d45ef87d93af9a15bd352b893164d3a36c399",
"index": 7545,
"step-1": "<mask token>\n\n\ndef main(screen, file_path):\n setUpEnv()\n text = readFileIfExist(file_path)\n while 1:\n try:\n text = startEditing(screen, text)\n printQuitOptions(screen)\n cha... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class ClientTaskStatus(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def start(self):
while True:
try:
self.get_task_status_info()
lines = StatusTask(self._taskstatus)
OutputManagement.ou... | flexible | {
"blob_id": "de0d0588106ab651a8d6141a44cd9e286b0ad3a5",
"index": 1299,
"step-1": "<mask token>\n\n\nclass ClientTaskStatus(object):\n <mask token>\n <mask token>\n\n def start(self):\n while True:\n try:\n self.get_task_status_info()\n lines = StatusTask(s... | [
2,
3,
4,
5,
6
] |
""""Pirata barba Negra ( màs de 2 pasos a las izquierda o a la derecha y se cae):
rampa para subir a su barco (5 pasos de ancho y 15 de largo")leer por teclado un valor entero.
a) si el entero es par 1 paso hacia adelante
b)si el entero es impar , pero el entero - 1 es divisible por 4, el pirata da un paso a la derec... | normal | {
"blob_id": "1829bd8e87c470a71fea97dd3a47c30477b6e6f1",
"index": 3109,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile numero_usuario < 0:\n print(\n 'Parece que el pirata se ha quedado dormido en la rampa intenta despertarlo ingresando otro nùmero '\n )\n numero_usuario = int(i... | [
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": "264896da4d92797b9f31e28c19a2e315efff815a",
"index": 138,
"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 = [('exchange', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
print('In Sample.py........')
ModBMW = Bmw.Bmw()
ModBMW.outModels()
ModAudi = Audi.Audi()
ModAudi.outModels()
ModNissan = Nissan.Nissan()
ModNissan.outModels()
<|reserved_sp... | flexible | {
"blob_id": "e15524d7ae87cbf0b10c54ee0bdc613ba589c1a9",
"index": 3812,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n print('In Sample.py........')\n ModBMW = Bmw.Bmw()\n ModBMW.outModels()\n ModAudi = Audi.Audi()\n ModAudi.outModels()\n ModNissan = Nissan.N... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
connection.execute(stmt)
func.update_annotations_db(Twitter_Sentiment_Analysis, connection,
'Export_csv5.csv')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
connection, Twitter_Sentiment_Analysis = func.Database... | flexible | {
"blob_id": "a558b42106b036719fe38ee6efd1c5b933290f52",
"index": 47,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nconnection.execute(stmt)\nfunc.update_annotations_db(Twitter_Sentiment_Analysis, connection,\n 'Export_csv5.csv')\n",
"step-3": "<mask token>\nconnection, Twitter_Sentiment_Analysis = ... | [
0,
1,
2,
3,
4
] |
'''
HTTP Test for channel details
'''
import sys
sys.path.append('..')
from json import load, dumps
import urllib.request
import urllib.parse
import pytest
PORT_NUMBER = '5204'
BASE_URL = 'http://127.0.0.1:' + PORT_NUMBER
#BASE_URL now is 'http://127.0.0.1:5321'
@pytest.fixture
def register_loginx2_create_invite():
... | normal | {
"blob_id": "c22b37bff74de7ea99f2009652dd00e57bb316b8",
"index": 4383,
"step-1": "<mask token>\n\n\n@pytest.fixture\ndef register_loginx2_create_invite():\n \"\"\"\n Registers, logs in 2 users, creates new channel\n \"\"\"\n req = urllib.request.Request(f'{BASE_URL}/workspace/reset', headers={\n ... | [
4,
5,
6,
7,
8
] |
class _ProtectedClass:
pass
class MyClass:
pass
class OtherClass(MyClass):
pass
def _protected_fun() ->MyClass:
return variable
<|reserved_special_token_0|>
def my_fun2() ->MyClass:
return variable
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class _ProtectedClass:
pa... | flexible | {
"blob_id": "b5949b40d731178bdbab776af8877921dcdfbf15",
"index": 3215,
"step-1": "class _ProtectedClass:\n pass\n\n\nclass MyClass:\n pass\n\n\nclass OtherClass(MyClass):\n pass\n\n\ndef _protected_fun() ->MyClass:\n return variable\n\n\n<mask token>\n\n\ndef my_fun2() ->MyClass:\n return variable... | [
5,
6,
7,
8,
9
] |
"""
* author - kajol
* date - 12/24/2020
* time - 1:24 PM
* package - com.bridgelabz.basicprograms
* Title - Print a table of the powers of 2 that are less than or equal to 2^N
"""
try:
number = int(input("Enter number: "))
#print power of 2 within given range
if number < 31:
for num... | normal | {
"blob_id": "b0f0bcfb5739d46de54cbe46614e82bf5a2d13fb",
"index": 9038,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n number = int(input('Enter number: '))\n if number < 31:\n for num in range(1, number + 1):\n print('2 ^', num, '=', 2 ** num)\n else:\n print('Ent... | [
0,
1,
2
] |
import pandas as pd
import numpy as np
import csv
#import nltk
#nltk.download('punkt')
from nltk.tokenize import sent_tokenize
csv_file=open("/home/debajit15/train+dev.csv")
pd.set_option('display.max_colwidth', None)
df=pd.read_csv(csv_file,sep=',');
df = df[pd.notnull(df['Aspects'])]
#print(df['Opinion_Words'].iloc[0... | normal | {
"blob_id": "c18c407476375fb1647fefaedb5d7ea0e0aabe3a",
"index": 929,
"step-1": "<mask token>\n\n\ndef train_validate_test_split(df, train_percent=0.8, validate_percent=0.2,\n seed=None):\n np.random.seed(seed)\n perm = np.random.permutation(df.index)\n m = len(df.index)\n train_end = int(train_pe... | [
2,
3,
4,
5,
6
] |
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