code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
from django.db import models
# Create your models here.
class UserInfo(models.Model):
uname = models.CharField('用户名', max_length=50, null=False)
upassword = models.CharField('密码', max_length=200, null=False)
email = models.CharField('邮箱', max_length=50, null=True)
phone = models.CharField('手机号', max_le... | normal | {
"blob_id": "dbec74ecf488ca98f3f441e252f79bc2bc0959c1",
"index": 4068,
"step-1": "<mask token>\n\n\nclass UserInfo(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\n\n class Meta:\n verbose_n... | [
4,
5,
6,
7,
8
] |
import unittest
from theoktany.serializers import serialize
class SerializerTest(unittest.TestCase):
class TestObject(object):
def __init__(self, **kwargs):
for name, value in kwargs.items():
self.__setattr__(name, value)
def test_serialize(self):
object_dict = ... | normal | {
"blob_id": "4e4d6a9ed07aa03c79dade05e01f226017b13de5",
"index": 9250,
"step-1": "<mask token>\n\n\nclass SerializerTest(unittest.TestCase):\n\n\n class TestObject(object):\n\n def __init__(self, **kwargs):\n for name, value in kwargs.items():\n self.__setattr__(name, value)\n... | [
3,
4,
5,
7
] |
import django.dispatch
property_viewed = django.dispatch.Signal(providing_args=["property","user", "request", "response"]) | normal | {
"blob_id": "00099cab0c816c76fc0fa94d7905175feb6919cf",
"index": 9795,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nproperty_viewed = django.dispatch.Signal(providing_args=['property', 'user',\n 'request', 'response'])\n",
"step-3": "import django.dispatch\nproperty_viewed = django.dispatch.Signal... | [
0,
1,
2,
3
] |
from helper import *
async def main(URL, buy_time):
browser, page = await get_window()
# 30s登陆时间
await page.goto('https://account.xiaomi.com/pass/serviceLogin?callback=http%3A%2F%2Forder.mi.com%2Flogin%2Fcallback%3Ffollowup%3Dhttps%253A%252F%252Fwww.mi.com%252F%26sign%3DNzY3MDk1YzczNmUwMGM4ODAxOWE0NjRiNTU... | normal | {
"blob_id": "1e87f625fb7bd9f9bf4233229332c909702954a5",
"index": 4334,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nasync def main(URL, buy_time):\n browser, page = await get_window()\n await page.goto(\n 'https://account.xiaomi.com/pass/serviceLogin?callback=http%3A%2F%2Forder.mi.com%... | [
0,
1,
2,
3
] |
from pkg.models.board import Board
class BaseAI:
_board: Board = None
def __init__(self, board=None):
if board is not None:
self.set_board(board)
def set_board(self, board):
self._board = board
def find_move(self, for_player):
pass
| normal | {
"blob_id": "b794a4cca3303ac7440e9aad7bc210df62648b51",
"index": 5476,
"step-1": "<mask token>\n\n\nclass BaseAI:\n _board: Board = None\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass BaseAI:\n _board: Board = None\n <mask token>\n <mask token>\n\n d... | [
1,
2,
3,
4,
5
] |
'''
This script will do auto-check in/out for ZMM100 fingerprint access control
device by ZKSoftware.
At my office, the manager uses an application to load data from the
fingerprint device. After he loads data, log in device's database is cleared.
So in my case, I write this script to automate checking in/out everyday... | normal | {
"blob_id": "3d1e6be71f92910cdc9eb2bf60ea7f8f1187f706",
"index": 3698,
"step-1": "<mask token>\n\n\ndef get_server_ip(device_ip):\n import socket\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.connect((device_ip, 80))\n return s.getsockname()[0]\n\n\ndef transfer_file(from_ip, to_ip, remo... | [
6,
12,
13,
14,
16
] |
#!/usr/bin/python3
def add_tuple(tuple_a=(), tuple_b=()):
if len(tuple_a) < 1:
a_x = 0
else:
a_x = tuple_a[0]
if len(tuple_a) < 2:
a_y = 0
else:
a_y = tuple_a[1]
if len(tuple_b) < 1:
b_x = 0
else:
b_x = tuple_b[0]
if len(tuple_b) < 2:
b... | normal | {
"blob_id": "1522ebb52504f7f27a526b597fe1e262bbcbfbb0",
"index": 4429,
"step-1": "<mask token>\n",
"step-2": "def add_tuple(tuple_a=(), tuple_b=()):\n if len(tuple_a) < 1:\n a_x = 0\n else:\n a_x = tuple_a[0]\n if len(tuple_a) < 2:\n a_y = 0\n else:\n a_y = tuple_a[1]\n ... | [
0,
1,
2
] |
# from cis_dna import
import cis_config as conf
import protocol_pb2 as proto
import uuid
import random
import math
import dna_decoding
import numpy as np
def move(cell):
pass
def is_alive(cell):
starvation_threshold = conf.ENERGY_THRESHOLD
if cell.energy_level < starvation_threshold:
return Fals... | normal | {
"blob_id": "de557c3c1455acc0a3facfca5729a010f3d123dc",
"index": 4208,
"step-1": "<mask token>\n\n\ndef is_alive(cell):\n starvation_threshold = conf.ENERGY_THRESHOLD\n if cell.energy_level < starvation_threshold:\n return False\n else:\n return True\n\n\n<mask token>\n\n\ndef dna_copy_or_... | [
2,
6,
7,
8,
9
] |
#!/usr/bin/env python
# coding=utf-8
import sys,os
dir = '/home/ellen/yjoqm/fdfs_client/pic'
def scp_file(filename):
cmd = 'scp ellen@61.147.182.142:/home/ellen/yjoqm/fdfs_client/pic/%s .' %filename
os.system(cmd)
def main(args):
args = sys.argv[1]
scp_file(args)
print 'done~~~~'
if __name_... | normal | {
"blob_id": "e2489f9d3041c45129fdd71da6652a6093c96d2d",
"index": 8487,
"step-1": "#!/usr/bin/env python\n# coding=utf-8\nimport sys,os\n\ndir = '/home/ellen/yjoqm/fdfs_client/pic'\n\ndef scp_file(filename):\n cmd = 'scp ellen@61.147.182.142:/home/ellen/yjoqm/fdfs_client/pic/%s .' %filename\n os.system(cmd)... | [
0
] |
import torch
import re
import sys
import os
import shutil
import filecmp
import numpy as np
from collections import defaultdict
from shutil import copyfile
sys.path.append('../')
class BoardParser:
def __init__(self):
self.file = open('../board_output', 'rb')
self.data = None
def update(se... | normal | {
"blob_id": "3668e8009dca4ea261bdfbd325331c338fdac5a9",
"index": 627,
"step-1": "<mask token>\n\n\nclass StatusParser:\n\n def __init__(self):\n self.board = np.memmap('../tmp/board', mode='r', dtype=np.int8,\n shape=(20, 10))\n self.combo = np.memmap('../tmp/combo', mode='r', dtype=n... | [
11,
12,
13,
15,
17
] |
"""main.py"""
import tkinter as tk
from tkinter import ttk
from ttkthemes import ThemedStyle
import wikipedia as wk
from newsapi import NewsApiClient as nac
import datetime
import random
class MainWindow:
"""Application controller object."""
def __init__(self):
self.p = None
self... | normal | {
"blob_id": "874fa927a1c0f1beeb31ca7b0de7fd2b16218ea4",
"index": 2756,
"step-1": "<mask token>\n\n\nclass MainWindow:\n <mask token>\n <mask token>\n\n def search_wikipedia(self):\n \"\"\"Safely browse wikipedia articles.\"\"\"\n self.summary.delete('1.0', tk.END)\n possibilities = ... | [
8,
9,
11,
12,
13
] |
import simplejson as json
json_list = [ "/content/squash-generation/squash/final/Custom.json",
"/content/squash-generation/squash/temp/Custom/final_qa_set.json",
"/content/squash-generation/squash/temp/Custom/generated_questions.json",
"/content/squash-generation/squash... | normal | {
"blob_id": "f37d016dc49820239eb42198ca922e8681a2e0a6",
"index": 6929,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in json_list:\n with open(i) as f:\n obj = json.load(f)\n f.close()\n outfile = open(i, 'w')\n outfile.write(json.dumps(obj, indent=4, sort_keys=True))\n o... | [
0,
1,
2,
3,
4
] |
n = input("Enter a number: ")
def fact(num):
factorial = 1
if int(num) >= 1:
for i in range (1,int(n)+1):
factorial = factorial * i
return factorial
print(fact(n)) | normal | {
"blob_id": "93b00b5c1bec38d2a4ac109f1533d3c0d9e99044",
"index": 5763,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef fact(num):\n factorial = 1\n if int(num) >= 1:\n for i in range(1, int(n) + 1):\n factorial = factorial * i\n return factorial\n\n\n<mask token>\n",
"... | [
0,
1,
2,
3,
4
] |
import subprocess
class Audio:
def __init__(self):
self.sox_process = None
def kill_sox(self, timeout=1):
if self.sox_process is not None:
self.sox_process.terminate()
try:
self.sox_process.wait(timeout=timeout)
except subprocess.TimeoutExpir... | normal | {
"blob_id": "d35d26cc50da9a3267edd2da706a4b6e653d22ac",
"index": 6555,
"step-1": "<mask token>\n\n\nclass Audio:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Audio:\n\n def __init__(self):... | [
1,
6,
8,
9,
10
] |
'''
Created on 4 Oct 2016
@author: MetalInvest
'''
def isHammerHangman(high, low, open, close):
body = abs(open - close)
leg = min(open, close) - low
return leg / body >= 2.0 and high/max(open, close) <= 1.08
def isEngulfing(df, bottom = True):
open_0 = df['open'][-1]
close_0 = d... | normal | {
"blob_id": "6e739c30b3e7c15bd90b74cfd5a1d6827e863a44",
"index": 4413,
"step-1": "<mask token>\n\n\ndef isHammerHangman(high, low, open, close):\n body = abs(open - close)\n leg = min(open, close) - low\n return leg / body >= 2.0 and high / max(open, close) <= 1.08\n\n\ndef isEngulfing(df, bottom=True):... | [
2,
3,
4,
5,
6
] |
from django.shortcuts import render, redirect
from django.utils.crypto import get_random_string
def index(request):
if not "word" in request.session:
request.session["word"] = 'Empty'
if not "count" in request.session:
request.session["count"] = 0
if request.method == "GET":
return... | normal | {
"blob_id": "2ec5e43860a1d248a2f5cd1abc26676342275425",
"index": 8589,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef reset(request):\n request.session['count'] = 0\n return redirect('/')\n",
"step-3": "<mask token>\n\n\ndef index(request):\n if not 'word' in request.session:\n ... | [
0,
1,
2,
3,
4
] |
import datetime
import shutil
from pathlib import Path
from jinja2 import Environment, FileSystemLoader
from dataclasses import dataclass
PATH_TO_TEMPLATES = Path('TEMPLATES/')
PATH_TO_RESOURCES = Path('RESOURCES/')
PATH_TO_OUTPUT = Path('../docs/')
URL_ROOT = "https://katys.cz/"
link_to_homepage = "/" # TODO: alwa... | normal | {
"blob_id": "5cc18af40befab444df44bf3da1f0175e5d18983",
"index": 8206,
"step-1": "<mask token>\n\n\n@dataclass()\nclass Page(object):\n title: str\n keywords: str\n description: str\n content_file: str\n url: str\n language: str\n last_mod: datetime.datetime\n phone: str = '+420 603 217 8... | [
3,
5,
6,
9,
10
] |
import requests
import os
from dotenv import load_dotenv
from datetime import datetime
load_dotenv(".env") # loads the environment file
USERNAME = os.getenv("USER")
TOKEN = os.getenv("TOKEN")
pixela_endpoint = "https://pixe.la/v1/users"
# MAKING AN ACCOUNT
user_params = {
"token": TOKEN,
... | normal | {
"blob_id": "ba34dfcad0cb9bac9c462bdf60e55dee6ba9d58d",
"index": 9255,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nload_dotenv('.env')\n<mask token>\nprint(response.text)\n<mask token>\n",
"step-3": "<mask token>\nload_dotenv('.env')\nUSERNAME = os.getenv('USER')\nTOKEN = os.getenv('TOKEN')\npixela_... | [
0,
1,
2,
3,
4
] |
'''
Given a string S and a string T,
find the minimum window in S which will contain all the characters in T in complexity O(n).
For example,
S = "ADOBECODEBANC"
T = "ABC"
Minimum window is "BANC".
Note:
If there is no such window in S that covers all characters in T, return the empty string "".
If there are multipl... | normal | {
"blob_id": "665a868ee71f247a621d82108e545257296e0427",
"index": 7048,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n def minWindow(self, source, target):\n s_char_count = defaul... | [
1,
3,
4,
5,
6
] |
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
] |
# This handle the url for routing
from django.urls import path
from . import views
# Defines views to pass dynamic data to listings page
urlpatterns = [
path('', views.index, name='listings'),
path('<int:listing_id>', views.listing, name='listing'),
path('search', views.search, name='search')
] | normal | {
"blob_id": "be894830bb0dde6bacaea6be823391e0445603c3",
"index": 1192,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.index, name='listings'), path(\n '<int:listing_id>', views.listing, name='listing'), path('search',\n views.search, name='search')]\n",
"step-3": "fr... | [
0,
1,
2,
3
] |
# Generated by Django 2.2 on 2021-01-31 14:11
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app', '0004_product_pr_number'),
]
operations = [
migrations.RemoveField(
model_name='payment',
name='PA_id',
... | normal | {
"blob_id": "388772386f25d6c2f9cc8778b7ce1b2ad0920851",
"index": 6986,
"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', '0004... | [
0,
1,
2,
3,
4
] |
import requests
import json
l = list()
with open ( "token.txt", "r") as f:
token = f.read()
# создаем заголовок, содержащий наш токен
headers = {"X-Xapp-Token" : token}
with open('dataset_24476_4.txt', 'r') as id:
for line in id:
address = "https://api.artsy.net/api/artists/" +... | normal | {
"blob_id": "e1ecc08f66e094841647f72b78bcd29ed8d32668",
"index": 5976,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('token.txt', 'r') as f:\n token = f.read()\n headers = {'X-Xapp-Token': token}\n with open('dataset_24476_4.txt', 'r') as id:\n for line in id:\n addr... | [
0,
1,
2,
3,
4
] |
#! /usr/bin/env python
import sys
import socket
def handle_connection(sock):
do_close = False
while 1:
try:
data = sock.recv(4096)
if not data: # closed! stop monitoring this socket.
do_close = True
break
print 'd... | normal | {
"blob_id": "fde4c10e2ed0ed38d683a220e2985c3f3f336601",
"index": 7258,
"step-1": "#! /usr/bin/env python\nimport sys\nimport socket\n\ndef handle_connection(sock):\n do_close = False\n \n while 1:\n try:\n data = sock.recv(4096)\n if not data: # closed! stop ... | [
0
] |
from data_structures.datacenter import Datacenter, urllib, json,
URL = "http://www.mocky.io/v2/5e539b332e00007c002dacbe"
def get_data(url, max_retries=5, delay_between_retries=1):
"""
Fetch the data from http://www.mocky.io/v2/5e539b332e00007c002dacbe
and return it as a JSON object.
Args:
... | normal | {
"blob_id": "e56a7912b9940b1cab6c19d0047f1f60f0083f66",
"index": 4911,
"step-1": "from data_structures.datacenter import Datacenter, urllib, json,\n\n\nURL = \"http://www.mocky.io/v2/5e539b332e00007c002dacbe\"\n\n\ndef get_data(url, max_retries=5, delay_between_retries=1):\n \"\"\"\n Fetch the data from ht... | [
0
] |
#!/g/kreshuk/lukoianov/miniconda3/envs/inferno/bin/python3
# BASIC IMPORTS
import argparse
import os
import subprocess
import sys
import numpy as np
# INTERNAL IMPORTS
from src.datasets import CentriollesDatasetOn, CentriollesDatasetBags, GENdataset
from src.utils import get_basic_transforms, log_info, get_resps_tran... | normal | {
"blob_id": "604c94e50b1fb9b5e451c4432113498410a4ac1f",
"index": 5262,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\n 'Run learning of simple CNN implementation')\n parser.add_argument('--model_name', type=str, defa... | [
0,
1,
2,
3
] |
from py.test import raises
from ..lazymap import LazyMap
def test_lazymap():
data = list(range(10))
lm = LazyMap(data, lambda x: 2 * x)
assert len(lm) == 10
assert lm[1] == 2
assert isinstance(lm[1:4], LazyMap)
assert lm.append == data.append
assert repr(lm) == '<LazyMap [0, 1, 2, 3, 4, 5,... | normal | {
"blob_id": "3e7d80fdd1adb570934e4b252bc25d5746b4c68e",
"index": 3912,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_lazymap():\n data = list(range(10))\n lm = LazyMap(data, lambda x: 2 * x)\n assert len(lm) == 10\n assert lm[1] == 2\n assert isinstance(lm[1:4], LazyMap)\n ... | [
0,
1,
2,
3
] |
# Stanley H.I. Lio
# hlio@hawaii.edu
# All Rights Reserved. 2018
import logging, time, sys
from serial import Serial
from . import aanderaa_3835
from . import aanderaa_4330f
from . import aanderaa_4531d
from . import aanderaa_4319a
logger = logging.getLogger(__name__)
# works with 3835 (DO), 4330F (DO), 4531D (DO),... | normal | {
"blob_id": "c52ad4040c14471319939605c400ff4d4ad982a7",
"index": 5213,
"step-1": "<mask token>\n\n\ndef aanderaa_read_universal(port, max_retry=3, parsers=[aanderaa_4531d.\n parse_4531d, aanderaa_4330f.parse_4330f, aanderaa_3835.parse_3835,\n aanderaa_4319a.parse_4319a]):\n logger.debug('aanderaa_read_u... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 5 10:04:05 2019
@author: cristina
"""
import numpy as np
from itertools import chain
from numpy import linalg as LA
diag = LA.eigh
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 13})
import time
pi = np.pi
exp = np.exp
t1 = tim... | normal | {
"blob_id": "f2ad95574b65b4d3e44b85c76f3a0150a3275cec",
"index": 2356,
"step-1": "<mask token>\n\n\ndef LDOS_up(omega, E, u, Damping):\n t = sum(u ** 2 / (omega - E + 1.0j * Damping))\n tt = -1 / pi * np.imag(t)\n return tt\n\n\ndef LDOS_down(omega, E, v, Damping):\n t = sum(v ** 2 / (omega + E + 1.0... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
from __future__ import division
import sys
import math
logs = sys.stderr
from collections import defaultdict
import time
from mytime import Mytime
import gflags as flags
FLAGS=flags.FLAGS
flags.DEFINE_string("weights", None, "weights file (feature instances and weights)", short_name="w")
flag... | normal | {
"blob_id": "e5fd0fc13a39444a934eea3bd24056073d28eff2",
"index": 9869,
"step-1": "#!/usr/bin/env python\n\nfrom __future__ import division\n\nimport sys\nimport math\nlogs = sys.stderr\nfrom collections import defaultdict\n\nimport time\nfrom mytime import Mytime\n\nimport gflags as flags\nFLAGS=flags.FLAGS\n\nf... | [
0
] |
from django.apps import AppConfig
class PrimaryuserConfig(AppConfig):
name = 'PrimaryUser'
| normal | {
"blob_id": "82c10076ba73723b696e3e33280296c2a24f20b9",
"index": 4187,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PrimaryuserConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass PrimaryuserConfig(AppConfig):\n name = 'PrimaryUser'\n",
"step-4": "from django.app... | [
0,
1,
2,
3
] |
from django.db import models
from datetime import datetime
# Create your models here.
class Notifications(models.Model):
username= models.CharField(max_length=20)
phone_number= models.BigIntegerField(default= 0)
email= models.EmailField()
firstname= models.CharField(max_length=20)
app_name= models... | normal | {
"blob_id": "51ed99a68486bd52499bbc28e68ff2312e02ea1f",
"index": 6604,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Notifications(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 ... | [
0,
1,
2,
3,
4
] |
# -*- coding:utf-8 -*-
import time
from abc import ABCMeta, abstractmethod
from xlreportform.worksheet import WorkSheet
__author__ = "Andy Yang"
class Bases(metaclass=ABCMeta):
def __init__(self):
pass
@abstractmethod
def set_style(self):
"""set workshet's style, indent,bor... | normal | {
"blob_id": "092c6d637fe85136b4184d05f0ac7db17a8efb3b",
"index": 6087,
"step-1": "<mask token>\n\n\nclass Bases(metaclass=ABCMeta):\n\n def __init__(self):\n pass\n\n @abstractmethod\n def set_style(self):\n \"\"\"set workshet's style, indent,border,font,and so on\"\"\"\n\n @abstractmet... | [
13,
14,
15,
16,
17
] |
# message 为定义的变量
message = 'Hello Python World '
print(message) | normal | {
"blob_id": "ee5e970f32b1d601f9dc3ab37a5028ce7ff8a32e",
"index": 1368,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(message)\n",
"step-3": "message = 'Hello Python World '\nprint(message)\n",
"step-4": "# message 为定义的变量\r\nmessage = 'Hello Python World '\r\nprint(message)",
"step-5": null... | [
0,
1,
2,
3
] |
import csv
from pprint import pprint as pp
with open('nodes_tags.csv', 'r') as f:
tags = csv.DictReader(f)
for row in tags:
if row['key'] == 'FIXME':
pp(row)
| normal | {
"blob_id": "d0981d279f7090d5309aa564252dba731a34a66b",
"index": 1424,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('nodes_tags.csv', 'r') as f:\n tags = csv.DictReader(f)\n for row in tags:\n if row['key'] == 'FIXME':\n pp(row)\n",
"step-3": "import csv\nfrom pprint... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import markdown
from django.db import models
from django.contrib.auth.models import User
from datetime import datetime
class MovieRankings(models.Model):
"""
各种电影排行榜.
"""
name = models.CharField(max_length=100)
def __unicode__(self)... | normal | {
"blob_id": "449ae193f8817d4ee2fe67eadf72d9c19b2c5e53",
"index": 1319,
"step-1": "<mask token>\n\n\nclass MovieRankings(models.Model):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Movie(models.Model):\n \"\"\"\n 电影的数据库表格\n \"\"\"\n movie_name = models.CharField(max_length=64, blan... | [
8,
10,
11,
12,
13
] |
# -*- coding: utf-8 -*-
__author__ = 'tqs'
from win32com.client import Dispatch
import win32com.client
import time
import os
import re
import win32api
'''
windows操作部分说明：
考试波及知识点：
1.删除文件及文件夹
2.复制文件及文件夹
3.移动文件及文件夹
4.文件及文件夹改名
5.文件属性
考试样例：
1、在“蕨类植物”文件夹中，新建一个子文件夹“薄囊蕨类”。
2、将文件“淡水藻.ddd”移动到“藻类植物”文件夹中。
3、设置“螺旋藻.aaa”文件属性为“只读”... | normal | {
"blob_id": "b453006b4d4c5f17bb58110fe8197d7796ca0c6c",
"index": 467,
"step-1": "<mask token>\n\n\nclass ExcelOperation:\n\n def __init__(self, filename=None):\n self.xlApp = win32com.client.Dispatch('Excel.Application')\n if filename:\n self.filename = filename\n self.xlBo... | [
42,
52,
64,
71,
87
] |
#!/usr/bin/python3
#coding:utf-8
"""
Author: Xie Song
Email: 18406508513@163.com
Copyright: Xie Song
License: MIT
"""
import torch
def get_sgd_optimizer(args, model):
opimizer = torch.optim.SGD(model.parameters(),lr=args.lr,weight_decay=1e-4)
return opimizer | normal | {
"blob_id": "5dca187cfe221f31189ca9a9309ece4b9144ac66",
"index": 2812,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_sgd_optimizer(args, model):\n opimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay\n =0.0001)\n return opimizer\n",
"step-3": "<mask token>\n... | [
0,
1,
2,
3
] |
from modeltranslation.translator import register, TranslationOptions
from .models import *
@register(PageTitleModel)
class TitleTranslationOptions(TranslationOptions):
fields = (
'name',
)
@register(NewsModel)
class ProjectTranslationOptions(TranslationOptions):
fields = (
'name',
... | normal | {
"blob_id": "9c29f04746de6847ad1bbdf08964d14e6c3766db",
"index": 8700,
"step-1": "<mask token>\n\n\n@register(NewsModel)\nclass ProjectTranslationOptions(TranslationOptions):\n fields = 'name', 'text'\n",
"step-2": "<mask token>\n\n\n@register(PageTitleModel)\nclass TitleTranslationOptions(TranslationOption... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 7 07:51:26 2017
@author: hcorrada
"""
from plagiarism_lib.article_db import ArticleDB
from plagiarism_lib.minhash import MinHash
from plagiarism_lib.lsh import LSH
import pandas as pd
import numpy as np
def _read_truthfile(filepath):
with op... | normal | {
"blob_id": "18b73a06c80272aff5c0e4b10473e95bd58466f3",
"index": 1197,
"step-1": "<mask token>\n\n\ndef _get_stats(candidate_pairs, truth_pairs):\n tp = len(candidate_pairs.intersection(truth_pairs))\n prec = 1.0 * tp / len(candidate_pairs)\n rec = 1.0 * tp / len(truth_pairs)\n print(' returned: %d,... | [
1,
2,
3,
4,
5
] |
Desafios:
1: Crie um script python que leia o nome de uma pessoa e mostre uma mensagem de boas-vindas de acordo com o valor digitado.
Script:
Desafio 01:
1: Crie um script python que leia o nome de uma pessoa
e mostre uma mensagem de boas-vindas de acordo com o valor digitado."""
nome=input('Qual é o seu nome?')
print... | normal | {
"blob_id": "80454a3935f0d42b5535440fc316af1b5598d8a1",
"index": 7090,
"step-1": "Desafios:\n1: Crie um script python que leia o nome de uma pessoa e mostre uma mensagem de boas-vindas de acordo com o valor digitado.\n\nScript:\nDesafio 01:\n1: Crie um script python que leia o nome de uma pessoa\ne mostre uma me... | [
0
] |
#!/usr/bin/env python
# script :: creating a datamodel that fits mahout from ratings.dat
ratings_dat = open('../data/movielens-1m/users.dat', 'r')
ratings_csv = open('../data/movielens-1m/users.txt', 'w')
for line in ratings_dat:
arr = line.split('::')
new_line = '\t'.join(arr)
ratings_csv.write(new_line)
rati... | normal | {
"blob_id": "2dd59681a0dcb5d3f1143385100c09c7783babf4",
"index": 76,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in ratings_dat:\n arr = line.split('::')\n new_line = '\\t'.join(arr)\n ratings_csv.write(new_line)\nratings_dat.close()\nratings_csv.close()\n",
"step-3": "ratings_dat ... | [
0,
1,
2,
3
] |
"""Support for Deebot Vaccums."""
import logging
from typing import Any, Mapping, Optional
import voluptuous as vol
from deebot_client.commands import (
Charge,
Clean,
FanSpeedLevel,
PlaySound,
SetFanSpeed,
SetRelocationState,
SetWaterInfo,
)
from deebot_client.commands.clean import CleanAc... | normal | {
"blob_id": "1ab690b0f9c34b1886320e1dfe8b54a5ec6cd4d1",
"index": 8712,
"step-1": "<mask token>\n\n\nclass DeebotVacuum(DeebotEntity, StateVacuumEntity):\n <mask token>\n\n def __init__(self, vacuum_bot: VacuumBot):\n \"\"\"Initialize the Deebot Vacuum.\"\"\"\n device_info = vacuum_bot.device_... | [
5,
6,
9,
10,
13
] |
# strspn(str1,str2)
str1 = '12345678'
str2 = '456'
# str1 and chars both in str1 and str2
print(str1 and str2)
str1 = 'cekjgdklab'
str2 = 'gka'
nPos = -1
for c in str1:
if c in str2:
nPos = str1.index(c)
break
print(nPos)
| normal | {
"blob_id": "5c30b0e952ddf2e05a7ad5f8d9bbd4f5e22f887d",
"index": 62,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(str1 and str2)\n<mask token>\nfor c in str1:\n if c in str2:\n nPos = str1.index(c)\n break\nprint(nPos)\n",
"step-3": "str1 = '12345678'\nstr2 = '456'\nprint(str1 ... | [
0,
1,
2,
3
] |
a= input("Enter number")
a= a.split()
b=[]
for x in a:
b.append(int(x))
print(b)
l=len(b)
c=0
s=0
for i in range(l):
s=len(b[:i])
for j in range(s):
if b[s]<b[j]:
c=b[s]
b.pop(s)
b.insert(b.index(b[j]),c)
print(b,b[:i],b[s])
| normal | {
"blob_id": "24de4f486d4e976850e94a003f8d9cbe3e518402",
"index": 33,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor x in a:\n b.append(int(x))\nprint(b)\n<mask token>\nfor i in range(l):\n s = len(b[:i])\n for j in range(s):\n if b[s] < b[j]:\n c = b[s]\n b.pop(s... | [
0,
1,
2,
3
] |
from PIL import Image, ImageDraw, ImageFont
import sys
### Create 1024,1024 pixel image with a white background.
img = Image.new("RGB", (1024, 1024), color = (255,255,255))
### Take text to be drawn on the image from the command terminal.
text = sys.argv[1]
### Chose favourite font and set size of the font.
fnt = Im... | normal | {
"blob_id": "053fa80c80d40cd28acb7d6a8bf1b2c30be9b36e",
"index": 7786,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nd.text(xy=(320, 420), text=text, font=fnt, fill=(0, 0, 0))\nimg.save(text + '.png')\n",
"step-3": "<mask token>\nimg = Image.new('RGB', (1024, 1024), color=(255, 255, 255))\ntext = sys.... | [
0,
1,
2,
3,
4
] |
import xlrd
from django.shortcuts import redirect
from django.contrib import messages
from django.utils.translation import ugettext_lazy as _
from django.core import validators
from utils.views import render_to
from accounts.models import Account
from .models import ExternalSubscriber
from .forms import ExternalSubsc... | normal | {
"blob_id": "2ec41e02c95a270455c096e85829b7220eeda0c7",
"index": 1317,
"step-1": "<mask token>\n\n\ndef validate_email(value, row_number):\n error_message = _(u'Invalid e-mail address on \"%d\" line.')\n return validators.EmailValidator(validators.email_re, unicode(\n error_message % row_number), 'i... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python
import math
def Main():
try:
radius = float(input("Please enter the radius: "))
area = math.pi * radius**2
print("Area =", area)
except:
print("You did not enter a number")
if __name__ == "__main__":
Main()
| normal | {
"blob_id": "33c4e0504425c5d22cefb9b4c798c3fd56a63771",
"index": 3641,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Main():\n try:\n radius = float(input('Please enter the radius: '))\n area = math.pi * radius ** 2\n print('Area =', area)\n except:\n print('You... | [
0,
1,
2,
3,
4
] |
""" Image Check / Compress Image"""
import re
import os
from PIL import Image
from common.constant import PATH
def check_image(file_type):
match = re.match("image/*", file_type)
return match
def compress_image(data):
with open(PATH.format(data['name']), 'wb+') as file:
file.write(data['binary'... | normal | {
"blob_id": "13fa650557a4a8827c9fb2e514bed178df19a32c",
"index": 1295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef check_image(file_type):\n match = re.match('image/*', file_type)\n return match\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef check_image(file_type):\n match =... | [
0,
1,
2,
3,
4
] |
# Obtener en otra lista unicamente números impares:
my_list = [1, 4, 5, 6, 9, 13, 19, 21]
# Vamos a hacer una list comprehension:
lista_impares = [num for num in my_list if num % 2 != 0]
print(my_list)
print(lista_impares)
print('')
# Vamos a usar filter:
lista_pares = list(filter(lambda x: x % 2 == 0 , my_list))
p... | normal | {
"blob_id": "e1913c80375e4871119182d0267e9f228818624f",
"index": 4309,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(my_list)\nprint(lista_impares)\nprint('')\n<mask token>\nprint(my_list)\nprint(lista_pares)\n",
"step-3": "my_list = [1, 4, 5, 6, 9, 13, 19, 21]\nlista_impares = [num for num in m... | [
0,
1,
2,
3
] |
import re
from .models import ValidatedStudent
from rest_framework.authtoken.models import Token
from django.contrib.auth.models import User
def get_token_from_request(request):
token_tuple = request.COOKIES.get('money_api_token')
matches = re.search(r'(<Token: (\S*)>)', token_tuple)
token = matches.group... | normal | {
"blob_id": "2187f38dc9b14ecc355e98fe15d36fdefd548f04",
"index": 1159,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_token_from_request(request):\n token_tuple = request.COOKIES.get('money_api_token')\n matches = re.search('(<Token: (\\\\S*)>)', token_tuple)\n token = matches.groups... | [
0,
1,
2,
3,
4
] |
"""Module for the bot"""
from copy import deepcopy
from time import sleep
import mcpi.minecraft as minecraft
from mcpi.vec3 import Vec3
import mcpi.block as block
from search import SearchProblem, astar, bfs
from singleton import singleton
_AIR = block.AIR.id
_WATER = block.WATER.id
_LAVA = block.LAVA.id
_BEDROCK =... | normal | {
"blob_id": "54f0ed5f705d5ada28721301f297b2b0058773ad",
"index": 2,
"step-1": "<mask token>\n\n\nclass _GenericBot:\n <mask token>\n\n def __init__(self, pos, inventory=None):\n \"\"\"Initialize with an empty inventory.\n\n inventory is a dictionary. If None, an empty one will be used.\"\"\"\... | [
52,
53,
58,
60,
79
] |
# -*- coding: utf-8 -*-
# @Time : 2022-03-09 21:51
# @Author : 袁肖瀚
# @FileName: WDCNN-DANN.py
# @Software: PyCharm
import torch
import numpy as np
import torch.nn as nn
import argparse
from model import WDCNN1
from torch.nn.init import xavier_uniform_
import torch.utils.data as Data
import matplotlib.py... | normal | {
"blob_id": "fd45657083942dee13f9939ce2a4b71ba3f67397",
"index": 3587,
"step-1": "<mask token>\n\n\ndef weight_init(m):\n class_name = m.__class__.__name__\n if class_name.find('Conv') != -1:\n xavier_uniform_(m.weight.data)\n if class_name.find('Linear') != -1:\n xavier_uniform_(m.weight.... | [
3,
5,
7,
9,
10
] |
# maze = [0, 3, 0, 1, -3]
with open('./day_5/input.txt') as f:
maze = f.readlines()
f.close
maze = [int(line.strip()) for line in maze]
# I think I will just expand on the original functions
# from now on rather than separating part one from two
def escape_maze(maze):
end = len(maze) - 1
step_counter = 0
... | normal | {
"blob_id": "a4dfac7e15064d92c806a4e3f972f06e4dca6b11",
"index": 5181,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef escape_maze(maze):\n end = len(maze) - 1\n step_counter = 0\n offset = 0\n while True:\n cur_index = offset\n offset = offset + maze[cur_index]\n ... | [
0,
1,
2,
3,
4
] |
import numpy as np
from scipy import stats
from scipy import interpolate
from math import factorial
from scipy import signal
"""
A continuous wavelet transform based peak finder. Tested exclusively on Raman spectra, however,
it should work for most datasets.
Parameters
----------
lowerBound: The lowest value of the... | normal | {
"blob_id": "8f5d9918260e2f50fb229a7067f820a186101b99",
"index": 1080,
"step-1": "<mask token>\n\n\nclass _spectra:\n\n def __init__(self, x, y):\n self.x = x\n self.y = y\n <mask token>\n\n def y(self):\n return intensities\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass... | [
3,
5,
6,
8,
11
] |
from math import ceil
n, k = map(int, input().split())
d = list(map(int, input().split()))
packs = [0]*k
for i in d:
packs[i%k] += 1
counter = packs[0]//2
if (k % 2) == 0:
counter += packs[k//2]//2
for i in range(1, ceil(k/2)):
counter += min(packs[i], packs[k-i])
print(counter*2)
| normal | {
"blob_id": "2226382c494af33957a44d9f1682f7deacf574a2",
"index": 2075,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in d:\n packs[i % k] += 1\n<mask token>\nif k % 2 == 0:\n counter += packs[k // 2] // 2\nfor i in range(1, ceil(k / 2)):\n counter += min(packs[i], packs[k - i])\nprint(cou... | [
0,
1,
2,
3,
4
] |
# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)
import numpy as np
from mne.io.pick import _picks_to_idx
def run_GLM(raw, design_matrix, noise_model='ar1', bins=100,
n_jobs=1, verbose=0):
"""
Run GLM on data using supplied design matrix.
This is a wrapper function fo... | normal | {
"blob_id": "8279c6d5f33d5580bef20e497e2948461a1de62c",
"index": 7951,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run_GLM(raw, design_matrix, noise_model='ar1', bins=100, n_jobs=1,\n verbose=0):\n \"\"\"\n Run GLM on data using supplied design matrix.\n\n This is a wrapper functio... | [
0,
1,
2,
3,
4
] |
import sys
import os
from django.conf import settings
BASE_DIR=os.path.dirname(__file__)
settings.configure(
DEBUG=True,
SECRET_KEY='ki==706e99f0ps9w5s*!kx%1^=5jq_k1c&4r@#e&ng9=xlm5_',
ROOT_URLCONF='sitebuilder.urls',
MIDDLEWARE_CLASSES=(),
INSTALLED_APPS=(
'django.contrib.staticfiles',
'django.contrib.webd... | normal | {
"blob_id": "d30e5e24dd06a4846fdde3c9fcac0a5dac55ad0d",
"index": 5916,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsettings.configure(DEBUG=True, SECRET_KEY=\n 'ki==706e99f0ps9w5s*!kx%1^=5jq_k1c&4r@#e&ng9=xlm5_', ROOT_URLCONF=\n 'sitebuilder.urls', MIDDLEWARE_CLASSES=(), INSTALLED_APPS=(\n 'd... | [
0,
1,
2,
3,
4
] |
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import datetime
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy... | normal | {
"blob_id": "7b047ba110732d1b0a749bcbbaa9b55306ca2071",
"index": 6434,
"step-1": "<mask token>\n\n\nclass ema(IStrategy):\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 <mask ... | [
2,
4,
5,
6,
7
] |
#from tinyTensor.Node import Node
import tinyTensor
import plotly.plotly as py
from graphviz import render
#from tinyTensor.Operation import Operation
def init():
global _default_graph
_default_graph = None
def postOrder(node):
nodes_postorder = []
def recurse(node):
if isinstan... | normal | {
"blob_id": "7bd2a29bff1e435cf813dd54109d7f4e17612425",
"index": 474,
"step-1": "<mask token>\n\n\nclass Graph:\n <mask token>\n\n def appendNode(self, node):\n if node.name in self.placeholderNames and node.isPlaceholder:\n raise Exception(\n 'Placeholder name \"{}\" is al... | [
5,
7,
8,
9,
10
] |
from rest_framework import serializers
from .models import *
class MovieSerializer(serializers.Serializer):
movie_name = serializers.ListField(child=serializers.CharField())
class FilmSerializer(serializers.ModelSerializer):
class Meta:
model = Movie
fields = '__all__'
| normal | {
"blob_id": "0509afdce0d28cc04f4452472881fe9c5e4fbcc4",
"index": 7825,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MovieSerializer(serializers.Serializer):\n <mask token>\n\n\nclass FilmSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Movie\n fields = ... | [
0,
2,
3,
4
] |
print("Praktikum Programa Komputer ")
print("Exercise 7.21")
print("")
print("===========================")
print("Nama : Ivanindra Rizky P")
print("NIM : I0320054")
print("")
print("===========================")
print("")
import random
a = [23, 45, 98, 36]
print('a = ', a)
print('random 1')
print('choice ... | normal | {
"blob_id": "6b731e329eec3947a17ef8ee8280f2ddf980c81c",
"index": 7154,
"step-1": "<mask token>\n",
"step-2": "print('Praktikum Programa Komputer ')\nprint('Exercise 7.21')\nprint('')\nprint('===========================')\nprint('Nama : Ivanindra Rizky P')\nprint('NIM : I0320054')\nprint('')\nprint('===========... | [
0,
1,
2,
3,
4
] |
import sys
from Node import Node
from PriorityQueue import PriorityQueue
def Print(text):
if text is None or len(text) == 0:
print('invalid text.')
print('--------------------------------------------------------------')
return
text_set = set()
for i in text:
t... | normal | {
"blob_id": "bcdd36b534fd3551de9cb40efc11581f4d95a002",
"index": 9717,
"step-1": "<mask token>\n\n\ndef Print(text):\n if text is None or len(text) == 0:\n print('invalid text.')\n print('--------------------------------------------------------------')\n return\n text_set = set()\n ... | [
5,
7,
8,
9,
10
] |
with open('rosalind_ba3d.txt','r') as f:
kmer_length = int(f.readline().strip())
seq = f.readline().strip()
dict = {}
for offset in range(len(seq)-kmer_length+1):
prefix = seq[offset:offset+kmer_length-1]
suffix = seq[offset+1:offset+kmer_length]
if prefix in dict:
dict[prefix].append(suffix)
else:
... | normal | {
"blob_id": "050f060bb9d3d46f8b87c9802356bd0da8f926f8",
"index": 6244,
"step-1": "<mask token>\n",
"step-2": "with open('rosalind_ba3d.txt', 'r') as f:\n kmer_length = int(f.readline().strip())\n seq = f.readline().strip()\n<mask token>\nfor offset in range(len(seq) - kmer_length + 1):\n prefix = seq[... | [
0,
1,
2,
3
] |
import data
import numpy as np
import matplotlib.pyplot as plt
import xgboost as xgb
import pandas as pd
import csv
from matplotlib2tikz import save as tikz_save
import trial_sets
def print_stats(trial_id, dl):
wrist_device, _, true_device = dl.load_oxygen(trial_id, iid=False)
print("Length of Dataframe: " ... | normal | {
"blob_id": "836e2fd6eca7453ab7a3da2ecb21705552b5f627",
"index": 5157,
"step-1": "<mask token>\n\n\ndef print_stats(trial_id, dl):\n wrist_device, _, true_device = dl.load_oxygen(trial_id, iid=False)\n print('Length of Dataframe: ' + str(data.get_df_length(wrist_device)))\n wrist_oxygen = wrist_device.v... | [
4,
6,
7,
8,
9
] |
from dagster import job, op
@op
def do_something():
return "foo"
@job
def do_it_all():
do_something()
| normal | {
"blob_id": "53cf6e97c3b71b1063d5b6bce5aa444933b69809",
"index": 3229,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@job\ndef do_it_all():\n do_something()\n",
"step-3": "<mask token>\n\n\n@op\ndef do_something():\n return 'foo'\n\n\n@job\ndef do_it_all():\n do_something()\n",
"step-4"... | [
0,
1,
2,
3,
4
] |
import os
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.data as td
import torchvision as tv
import pandas as pd
from PIL import Image
from matplotlib import pyplot as plt
from utils import imshow, NNRegressor
class DnCNN(NNRegressor):
def __init__(se... | normal | {
"blob_id": "9c60d82d42716abb036dc7297a2dca66f0508984",
"index": 7626,
"step-1": "<mask token>\n\n\nclass UDnCNN(NNRegressor):\n <mask token>\n <mask token>\n\n\nclass DUDnCNN(NNRegressor):\n\n def __init__(self, D, C=64):\n super(DUDnCNN, self).__init__()\n self.D = D\n k = [0]\n ... | [
4,
7,
8,
10,
11
] |
#!/usr/bin/python
from Tkinter import *
root = Tk()
root.title("Simple Graph")
root.resizable(0,0)
points = []
spline = 0
tag1 = "theline"
def point(event):
c.create_oval(event.x, event.y, event.x+1, event.y+1, fill="black", width="10.0")
points.append(event.x)
points.append(event.y)
print(event.x)
print(ev... | normal | {
"blob_id": "d88485e37d4df4cb0c8d79124d4c9c9ba18d124e",
"index": 9074,
"step-1": "#!/usr/bin/python\nfrom Tkinter import *\n\nroot = Tk()\n\nroot.title(\"Simple Graph\")\n\nroot.resizable(0,0)\n\npoints = []\n\nspline = 0\n\ntag1 = \"theline\"\n\ndef point(event):\n\tc.create_oval(event.x, event.y, event.x+1, ev... | [
0
] |
txt = './KF_neko.txt.mecab'
mapData = {}
listData = []
with open('./KF31.txt', 'w') as writeFile:
with open(txt, 'r') as readFile:
for text in readFile:
# print(text)
# \tで区切って先頭だけ見る
listData = text.split('\t')
# 表層形
surface = listData[0]
... | normal | {
"blob_id": "778ee9a0ea7f57535b4de88a38cd741f2d46e092",
"index": 6966,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('./KF31.txt', 'w') as writeFile:\n with open(txt, 'r') as readFile:\n for text in readFile:\n listData = text.split('\\t')\n surface = listData[0... | [
0,
1,
2,
3
] |
n, m = map(int, input().split())
li = list(map(int, input().split()))
max = 0
for i in range(0, n):
for j in range(i+1, n):
for k in range(j+1, n):
tmp = li[i] + li[j] + li[k]
if(tmp <= m and max < tmp):
max = tmp
print(max) | normal | {
"blob_id": "83d0a32ef2d365d17caa9d311c367ed5828559ac",
"index": 4153,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(0, n):\n for j in range(i + 1, n):\n for k in range(j + 1, n):\n tmp = li[i] + li[j] + li[k]\n if tmp <= m and max < tmp:\n m... | [
0,
1,
2,
3
] |
# coding=UTF-8
"""
View for managing accounts
"""
from django.contrib import messages
from django.http import Http404, HttpResponse
from django.shortcuts import redirect
from django import forms
from athena.core import render_to_response
from athena.users.models import User
from athena.users import must_be_admin
def... | normal | {
"blob_id": "a01ca49c3fa8ea76de2880c1b04bf15ccd341edd",
"index": 924,
"step-1": "<mask token>\n\n\ndef klist(**kwargs):\n kwargs.update({'teachers': [x for x in User.objects.filter(status=1) if\n not x.is_demo()], 'admins': User.objects.filter(status=2)})\n return kwargs\n\n\n<mask token>\n\n\n@must... | [
3,
6,
7,
8,
10
] |
#!/usr/bin/env python
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
# Workaround for segmentation fault for some versions when ndimage is imported after tensorflow.
import scipy.ndimage as nd
import argparse
import numpy as np
from pybh import tensorpack... | normal | {
"blob_id": "a283fd1e4098ea8bb3cc3580438c90e5932ba22f",
"index": 5852,
"step-1": "<mask token>\n\n\ndef dict_from_dataflow_generator(df):\n for sample in df.get_data():\n yield sample[0]\n\n\ndef split_lmdb_dataset(lmdb_input_path, lmdb_output_path1,\n lmdb_output_path2, split_ratio1, batch_size, sh... | [
5,
6,
7,
8,
9
] |
# -*- encoding: utf-8 -*-
"""
views: vistas sistema recomendador
@author Camilo Ramírez
@contact camilolinchis@gmail.com
camilortte@hotmail.com
@camilortte on Twitter
@copyright Copyright 2014-2015, RecomendadorUD
@license GPL
@date 2014-10... | normal | {
"blob_id": "c6cbd4d18363f00b73fac873ba45d6063bee7e64",
"index": 3074,
"step-1": "# -*- encoding: utf-8 -*-\n\"\"\"\n \n views: vistas sistema recomendador\n\n @author Camilo Ramírez\n @contact camilolinchis@gmail.com \n camilortte@hotmail.com\n @camilortte on Twi... | [
0
] |
# -coding: UTF-8 -*-
# @Time : 2020/06/24 20:01
# @Author: Liangping_Chen
# @E-mail: chenliangping_2018@foxmail.com
import requests
def http_request(url,data,token=None,method='post'):
header = {'X-Lemonban-Media-Type': 'lemonban.v2',
'Authorization':token}
#判断是get请求还是post请求
if method=='ge... | normal | {
"blob_id": "dd7c7fa6493a43988e1c8079797f6ff9b4d239dd",
"index": 4672,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef http_request(url, data, token=None, method='post'):\n header = {'X-Lemonban-Media-Type': 'lemonban.v2', 'Authorization': token}\n if method == 'get':\n result = reque... | [
0,
1,
2,
3,
4
] |
lista = [2, 3.2, 4, 52, 6.25]
s = sum(lista)
print(s)
| normal | {
"blob_id": "05aa8eac846154024d25d639da565135e41403c2",
"index": 9611,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(s)\n",
"step-3": "lista = [2, 3.2, 4, 52, 6.25]\ns = sum(lista)\nprint(s)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
'''
Statistics models module. This module contains the database models for the
Statistics class and the StatisticsCategory class.
@author Hubert Ngu
@author Jason Hou
'''
from django.db import models
class Statistics(models.Model):
'''
Statistics model class. This represents a single tuple in the
... | normal | {
"blob_id": "728f9402b3ce4b297be82b3ba1a17c4180ac7c0d",
"index": 8839,
"step-1": "<mask token>\n\n\nclass Statistics(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... | [
4,
5,
6,
7,
8
] |
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
def fully_connected(prev_layer, num_units, batch_norm, is_training=False):
layer = tf.layers.dense(prev_layer, num_units, use_bias=False, activation=None)... | normal | {
"blob_id": "17b3f51779bda5a48c4d77c35d6bbdd2aadb13cd",
"index": 1432,
"step-1": "<mask token>\n\n\ndef fully_connected(prev_layer, num_units, batch_norm, is_training=False):\n layer = tf.layers.dense(prev_layer, num_units, use_bias=False,\n activation=None)\n if batch_norm:\n layer = tf.laye... | [
1,
3,
4,
5,
6
] |
#!/usr/bin/env python
# coding:utf-8
"""
200. 岛屿数量
难度
中等
给定一个由 '1'（陆地）和 '0'（水）组成的的二维网格，计算岛屿的数量。一个岛被水包围，并且它是通过水平方向或垂直方向上相邻的陆地连接而成的。你可以假设网格的四个边均被水包围。
示例 1:
输入:
11110
11010
11000
00000
输出: 1
示例 2:
输入:
11000
11000
00100
00011
输出: 3
"""
# ===============================================================================... | normal | {
"blob_id": "b46f19708e9e2a1be2bbd001ca6341ee7468a60d",
"index": 7147,
"step-1": "<mask token>\n\n\nclass Solution(object):\n <mask token>\n directions = [(-1, 0), (0, 1), (1, 0), (0, -1)]\n\n def numIslands(self, grid):\n \"\"\"\n :type grid: List[List[str]]\n :rtype: int\n ... | [
3,
5,
7,
10,
12
] |
import os
import platform
import _winreg
def gid(x):
find=x
winreg = _winreg
REG_PATH1 = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall"
REG_PATH2 = r"SOFTWARE\WOW6432Node\Microsoft\Windows\CurrentVersion\Uninstall"
registry_key = winreg.OpenKey( winreg.HKEY_LOCAL_MACHINE, REG_PATH1, 0, win... | normal | {
"blob_id": "a444e215b64b3a2d7f736e38227b68c1a1b952a0",
"index": 7133,
"step-1": "import os\nimport platform\nimport _winreg\n\n\ndef gid(x):\n find=x\n winreg = _winreg\n REG_PATH1 = r\"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Uninstall\"\n REG_PATH2 = r\"SOFTWARE\\WOW6432Node\\Microsoft\\Windo... | [
0
] |
from mpi4py import MPI
import matplotlib
from tmm import coh_tmm
import pandas as pd
import os
from numpy import pi
from scipy.interpolate import interp1d
from joblib import Parallel, delayed
import numpy as np
import glob
import matplotlib.pyplot as plt
import pickle as pkl
import seaborn as sns
from scipy.optimize im... | normal | {
"blob_id": "f23bc0c277967d8e7a94a49c5a81ed5fb75d36cc",
"index": 9327,
"step-1": "<mask token>\n\n\nclass Memory:\n <mask token>\n <mask token>\n\n\n<mask token>\n\n\nclass DesignTracker:\n\n def __init__(self, epochs, **kwargs):\n \"\"\"\n This class tracks the best designs discovered.\n ... | [
11,
16,
20,
22,
26
] |
# import time module, Observer, FileSystemEventHandler
import os
import time
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
import pyAesCrypt
import logging
logging.basicConfig(filename="Decryptor.log",
level=logging.INFO, format="%(asctime)s:%(filename)s:... | normal | {
"blob_id": "6261d06ac7bdcb3ae25cd06338c4c41c3c5f5023",
"index": 7615,
"step-1": "<mask token>\n\n\nclass OnMyWatch:\n <mask token>\n <mask token>\n\n def run(self):\n event_handler = Handler()\n self.observer.schedule(event_handler, self.watchDirectory,\n recursive=True)\n ... | [
4,
7,
8,
10,
11
] |
# -*- coding: utf-8 -
#
# This file is part of gaffer. See the NOTICE for more information.
import os
from .base import Command
from ...httpclient import Server
class Load(Command):
"""\
Load a Procfile application to gafferd
======================================
This command allows you... | normal | {
"blob_id": "eb5256543d6095668d6eeaf6cfdc9f744d7c73c5",
"index": 2267,
"step-1": "<mask token>\n\n\nclass Load(Command):\n <mask token>\n <mask token>\n <mask token>\n\n def find_groupname(self, g, s):\n tries = 0\n while True:\n groups = s.groups()\n if g not in g... | [
2,
3,
5,
6,
7
] |
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this... | normal | {
"blob_id": "2e2de50a7d366ca1a98d29b33ed157a1e8445ada",
"index": 3523,
"step-1": "<mask token>\n\n\ndef git_short_hash():\n try:\n git_str = '+' + os.popen('git log -1 --format=\"%h\"').read().strip()\n except:\n git_str = ''\n else:\n if git_str == '+':\n git_str = ''\n ... | [
2,
3,
4,
5,
6
] |
import logging
from abc import ABC
from thraxisgamespatterns.application.handler_map_factory import TGHandlerMapFactory
from thraxisgamespatterns.eventhandling.event_distributor import TGEventDistributor
from thraxisgamespatterns.factories.logging_rule_engine_factory import TGLoggingRuleEngineFactory
class TGAbstract... | normal | {
"blob_id": "d499b4e189a0c3c6efa6a07871dbc6c2996a2dcb",
"index": 2245,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TGAbstractRegistry(ABC):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TGAbstractRegistry(ABC):\n\n def __init__(self):\n self.rule_engine = TGLoggingRule... | [
0,
1,
2,
3
] |
# Question link: https://www.hackerrank.com/challenges/30-scope/problem
# Code section:
def computeDifference(self):
# Add your code here
self.maximumDifference = -111111
for i in range(0,len(self.__elements)-1):
for j in range(i+1, len(self.__elements)):
diff = abs(self._... | normal | {
"blob_id": "eb90912d09fca52a43b28ec4c988e3658ddfc219",
"index": 605,
"step-1": "# Question link: https://www.hackerrank.com/challenges/30-scope/problem\n# Code section:\n\n def computeDifference(self):\n # Add your code here\n self.maximumDifference = -111111\n for i in range(0,len(self.__elem... | [
0
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import datetime
#takes in a sorted data frame holding the actuals, the predicted values (sorted descending) and the percentile of each obsevation
#and returns a new dataframe with all of the appropriate calculations
... | normal | {
"blob_id": "8e71ea23d04199e8fb54099c404c5a4e9af6c4b1",
"index": 9336,
"step-1": "<mask token>\n\n\ndef lift_calculations(df):\n df['sample_num'] = range(len(df))\n df['actual_sum'] = df['actual'].cumsum()\n df['per_sample_covered'] = (df['sample_num'] + 1) * 100 / len(df)\n df['per_pos_captured'] = ... | [
5,
7,
8,
10,
11
] |
import main
from pytest import approx
def test_duration():
ins = main.convert()
names = ins.multiconvert()
for name in names:
induration, outduration = ins.ffprobe(name[0], name[1])
assert induration == approx(outduration)
induration, outduration = ins.ffprobe(name[0], name[2])
... | normal | {
"blob_id": "92c247b827d2ca4dce9b631a2c09f2800aabe216",
"index": 6129,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_duration():\n ins = main.convert()\n names = ins.multiconvert()\n for name in names:\n induration, outduration = ins.ffprobe(name[0], name[1])\n assert... | [
0,
1,
2,
3,
4
] |
import veil_component
with veil_component.init_component(__name__):
from .material import list_category_materials
from .material import list_material_categories
from .material import list_issue_materials
from .material import list_issue_task_materials
from .material import get_material_image_url
... | normal | {
"blob_id": "acad268a228b544d60966a8767734cbf9c1237ac",
"index": 9979,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith veil_component.init_component(__name__):\n from .material import list_category_materials\n from .material import list_material_categories\n from .material import list_issue_... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# coding: utf-8
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tensorflow as tf
print(tf.__version__)
print(tf.keras.__version__)
print(tf.__path__)
import numpy as np
from tqdm import tqdm, tqdm_notebook
from utils import emphasis
import tensorflow.keras.backend as K
from tensorflow.... | normal | {
"blob_id": "08a0ab888886184f7447465508b6494b502821ea",
"index": 8903,
"step-1": "#!/usr/bin/env python\n# coding: utf-8\n\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nimport tensorflow as tf\nprint(tf.__version__)\nprint(tf.keras.__version__)\nprint(tf.__path__)\nimport numpy as np\n\nfrom tqdm import ... | [
0
] |
# #writing a file
# fout = open('Session14/output.txt', 'w')
# line1 = "How many roads must a man walk down\n"
# fout.write(line1)
# line2 = "Before you call him a man?\n"
# fout.write(line2)
# #when you are done writing, you should close the file.
# fout.close()
# #if you dont close the file, it gets closed for you wh... | normal | {
"blob_id": "de1262da699a18266ad8673597391f625783a44d",
"index": 5721,
"step-1": "<mask token>\n\n\ndef walk2(dirname):\n \"\"\"Prints the names of all files in \n dirname and its subdirectories.\n\n dirname: string name of directory\n \"\"\"\n for root, dirs, files in os.walk(dirname):\n f... | [
1,
2,
3,
4,
5
] |
# Adjust figure when using plt.gcf
ax = fig.gca()
ax.set_aspect('equal')
| normal | {
"blob_id": "24246427e2fde47bbc9d068605301f54c6ecbae5",
"index": 1797,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nax.set_aspect('equal')\n",
"step-3": "ax = fig.gca()\nax.set_aspect('equal')\n",
"step-4": "# Adjust figure when using plt.gcf\nax = fig.gca()\nax.set_aspect('equal')\n",
"step-5": ... | [
0,
1,
2,
3
] |
import logging
import os
import time
import urllib
from collections import namedtuple
from statistics import mean
from urllib.request import urlopen
import bs4
import regex as re
from tika import parser
from scipy.stats import ks_2samp
import config
from TFU.trueformathtml import TrueFormatUpmarkerHTML
from TFU.truefo... | normal | {
"blob_id": "4d2cb3e0bdd331a1de7f07eb0109f02c9cf832a8",
"index": 7441,
"step-1": "<mask token>\n\n\nclass PaperReader:\n <mask token>\n\n def __init__(self, _threshold=0.001, _length_limit=20000):\n with open(config.wordlist, 'r') as f:\n self.wordlist = [w for w in list(f.readlines()) if... | [
8,
10,
11,
12,
13
] |
# Example solution for HW 5
# %%
# Import the modules we will use
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %%
# ** MODIFY **
# Set the file name and path to where you have stored the data
filename = 'streamflow_week5.txt' #modified filename
filepath = os.path.join('../data', ... | normal | {
"blob_id": "5024db0538f0022b84c203882df9c35979ba978a",
"index": 4571,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(os.getcwd())\nprint(filepath)\n<mask token>\nprint(data.tail(14))\nprint(data.tail(14).describe())\nprint(data.tail(7).describe())\n<mask token>\nprint(data_2019['flow'].describe())... | [
0,
1,
2,
3,
4
] |
import random #importing the random library from python
answers = ["It is certain", "Without a doubt", "Yes, definitely",
"You may rely on it", "As I see it, yes", "Most likely",
"Outlook good", "Yes", "Signs point to yes", "Reply hazy, try again",
"Ask again later", "Better not tell yo... | normal | {
"blob_id": "b5e9af166f3b55e44d9273077e5acd05b1fd68fa",
"index": 2335,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile ans:\n ans = input('Ask the magic 8 ball a question. (Press enter to leave): \\n')\n print(random.choice(answers))\n",
"step-3": "<mask token>\nanswers = ['It is certain', '... | [
0,
1,
2,
3,
4
] |
# ----------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License
# ----------------------------------------------------------------------
"""Contains the Plugin object"""
import itertools
import os
import sys
... | normal | {
"blob_id": "d8befc4a79176aefcccd3dceddf04ca965601e5c",
"index": 2856,
"step-1": "<mask token>\n\n\n@Interface.staticderived\nclass Plugin(PluginBase):\n <mask token>\n <mask token>\n\n @staticmethod\n @Interface.override\n def Generate(open_file_func, global_custom_structs, global_custom_enums,\n... | [
8,
9,
10,
11,
15
] |
import csv
import sys
if len(sys.argv[1:]) == 5 :
(name_pos, start_pos, length_pos,
first_note_pos, second_note_pos) = [int(pos) for pos in sys.argv[1:]]
elif len(sys.argv[1:]) == 4 :
(name_pos, start_pos, length_pos,
first_note_pos) = [int(pos) for pos in sys.argv[1:]]
second_note_pos = None
e... | normal | {
"blob_id": "d7653a205fb8203fed4009846780c63dd1bcb505",
"index": 3603,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(sys.argv[1:]) == 5:\n name_pos, start_pos, length_pos, first_note_pos, second_note_pos = [int\n (pos) for pos in sys.argv[1:]]\nelif len(sys.argv[1:]) == 4:\n name_pos... | [
0,
1,
2,
3,
4
] |
from gerador_senha import gerar_senha
gerar_senha()
| normal | {
"blob_id": "e81da535408cc36655328b37ca99b4f775f3a78e",
"index": 8435,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngerar_senha()\n",
"step-3": "from gerador_senha import gerar_senha\ngerar_senha()\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
import logging
from .const import (
DOMAIN,
CONF_SCREENS
)
from typing import Any, Callable, Dict, Optional
from homeassistant.helpers.entity import Entity
from homeassistant.helpers.typing import (
ConfigType,
DiscoveryInfoType,
HomeAssistantType,
)
from homeassistant.core import callback
from home... | normal | {
"blob_id": "6f1b08a5ae1a07a30d89f3997461f4f97658f364",
"index": 4920,
"step-1": "<mask token>\n\n\nclass StateConverters:\n <mask token>\n <mask token>\n <mask token>\n\n\nclass DGUSScreen(Entity):\n\n def __init__(self, hass, screen):\n self._state = None\n self._hass = hass\n ... | [
7,
10,
11,
13,
14
] |
def IsPn(a):
temp = (24*a+1)**0.5+1
if temp % 6 == 0:
return True
else:
return False
def IsHn(a):
temp = (8*a+1)**0.5+1
if temp % 4 == 0:
return True
else:
return False
def CalTn(a):
return (a**2+a)/2
i = 286
while 1:
temp = CalTn(i)
if IsHn(temp) and IsPn(temp):
break
i += 1
print i,temp | normal | {
"blob_id": "7474e60feff61c4ef15680ecc09d910e6e1d6322",
"index": 4603,
"step-1": "def IsPn(a):\n\ttemp = (24*a+1)**0.5+1\n\tif temp % 6 == 0:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef IsHn(a):\n\ttemp = (8*a+1)**0.5+1\n\tif temp % 4 == 0:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef CalTn(a):\n\tr... | [
0
] |
"""
Given two strings, a and b, that may or may not be of the same length,
determine the minimum number of character deletions required to make
a and b anagrams. Any characters can be deleted from either of the strings.
"""
from collections import Counter
import math
import os
import random
import re
import sys
# Com... | normal | {
"blob_id": "3b15767988f1d958fc456f7966f425f93deb9017",
"index": 8302,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef makeAnagram(a, b):\n ct_a = Counter(a)\n ct_b = Counter(b)\n ct_a.subtract(ct_b)\n return sum(abs(i) for i in ct_a.values())\n\n\n<mask token>\n",
"step-3": "<mask t... | [
0,
1,
2,
3,
4
] |
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