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|>
@dp.message_handler(commands='upload', user_id=ADMINS, state='*')
async def upload_profile(command_msg: Message, state: FSMContext):
profile_msg = command_msg.reply_to_message
admin = command_msg.from_user
param = co... | flexible | {
"blob_id": "302accfd5001a27c7bbe6081856d43dbec704168",
"index": 339,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@dp.message_handler(commands='upload', user_id=ADMINS, state='*')\nasync def upload_profile(command_msg: Message, state: FSMContext):\n profile_msg = command_msg.reply_to_message\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class City(BaseModel):
name = models.CharField(max_length=255, db_index=True)
def __str__(self):
return self.name
class Article(BaseModel):
created_by = models.ForeignKey(User, related_name='articles', on_delete
=models.SET_NULL, null=True)
title = model... | flexible | {
"blob_id": "c2260278c8dfb353f55ee9ea3495049b08169447",
"index": 4115,
"step-1": "<mask token>\n\n\nclass City(BaseModel):\n name = models.CharField(max_length=255, db_index=True)\n\n def __str__(self):\n return self.name\n\n\nclass Article(BaseModel):\n created_by = models.ForeignKey(User, relat... | [
9,
10,
11,
12,
15
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def calc(x):
return str(math.log(abs(12 * math.sin(int(x)))))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def calc(x):
return str(math.log(abs(12 * math.sin(int(x)))))
... | flexible | {
"blob_id": "2a92c47231b75a441660fed80a9bce9a35695af5",
"index": 1222,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef calc(x):\n return str(math.log(abs(12 * math.sin(int(x)))))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef calc(x):\n return str(math.log(abs(12 * math.sin(int(x))... | [
0,
1,
2,
3,
4
] |
import os
import torch
from data_loader import FER
from torch.utils.data import DataLoader
from tqdm import tqdm
# from tensorboardX import SummaryWriter
import model as md
# train_writer = SummaryWriter(log_dir="log_last_last_last/train")
# valid_writer = SummaryWriter(log_dir="log_last_last_last/valid")... | normal | {
"blob_id": "c3aee5d822d48c9dc826f8f2f8d4a56e11513b9c",
"index": 2882,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmodel.to(device)\n<mask token>\n...\nfor epoch in range(epochs):\n running_loss = 0\n running_acc = 0\n train_loss = 0\n model.train()\n for image, label in tqdm(train_data... | [
0,
1,
2,
3,
4
] |
#HOW TO BUILD A SIMPLE CALCULATOR
#1.ADD
#2.SUBTRACT
#3.MULTIPLY
#4.DIVIDE
print("Select an operation to perform: ")
print("1.ADD")
print("2.SUBTRACT")
print("3.MULTIPLY")
print("4.DIVIDE")
print("5.SQUARE ROOT")
operation=input()
if operation=="1":
a=input("Enter first number: ")
b=input("Enter sec... | normal | {
"blob_id": "ea35180daecb8ca4b9bd351a949a4757b97322ec",
"index": 2819,
"step-1": "<mask token>\n",
"step-2": "print('Select an operation to perform: ')\nprint('1.ADD')\nprint('2.SUBTRACT')\nprint('3.MULTIPLY')\nprint('4.DIVIDE')\nprint('5.SQUARE ROOT')\n<mask token>\nif operation == '1':\n a = input('Enter ... | [
0,
1,
2,
3
] |
#----------- writing our for loop
""" number = [1,2,3,4,5]
friends = ['ahmet', 'mehmet','ayşe']
# for n in number:
# print(n)
# for n in friends:
# print(n)
def my_for_loop(my_iterable):
my_iterator = iter(my_iterable)
while True:
try:
print(next(my_iterator))
except StopI... | normal | {
"blob_id": "70325d0e5eb9dcd7a065f83eaf14647bc30bd7f3",
"index": 9053,
"step-1": "<mask token>\n",
"step-2": "\n#----------- writing our for loop\n\"\"\" number = [1,2,3,4,5]\nfriends = ['ahmet', 'mehmet','ayşe']\n\n# for n in number:\n# print(n)\n# for n in friends:\n# print(n)\n\ndef my_for_loop(my_i... | [
0,
1
] |
#This script reads through a Voyager import log and outputs duplicate bib IDs as well as the IDs of bibs, mfhds, and items created.
#import regular expressions and openpyxl
import re
import openpyxl
# prompt for file names
fname = input("Enter input file, including extension: ")
fout = input("Enter output file, witho... | normal | {
"blob_id": "fc06d8a26a99c16a4b38ad0b4bbb28a1dc522991",
"index": 6902,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith fh as f:\n lines = f.readlines()\n n_lines = len(lines)\n for i, line in enumerate(lines):\n line = line.rstrip()\n if line.startswith('\\tBibID & rank') and n... | [
0,
1,
2,
3,
4
] |
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
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def resolve_data(raw_data, derivatives_prefix):
derivatives = {}
if isinstance(raw_data, dict):
for k, v in raw_data.items():
if isinstance(v, dict):
derivatives.update(resolve_data(v, derivatives_prefix + k +
... | flexible | {
"blob_id": "31b109d992a1b64816f483e870b00c703643f514",
"index": 6577,
"step-1": "<mask token>\n",
"step-2": "def resolve_data(raw_data, derivatives_prefix):\n derivatives = {}\n if isinstance(raw_data, dict):\n for k, v in raw_data.items():\n if isinstance(v, dict):\n de... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
__import__('pkg_resources').require('Django==2.1.dev20180209010235')
<|reserved_special_token_0|>
exec(compile(open(__file__).read(), __file__, 'exec'))
<|reserved_special_token_1|>
__requires__ = 'Django==2.1.dev20180209010235... | flexible | {
"blob_id": "4bbf0a0fadc506ad3674912f1885525a94b5b1e9",
"index": 2807,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__import__('pkg_resources').require('Django==2.1.dev20180209010235')\n<mask token>\nexec(compile(open(__file__).read(), __file__, 'exec'))\n",
"step-3": "__requires__ = 'Django==2.1.dev... | [
0,
1,
2,
3
] |
'''
Given an infinite sorted array (or an array with unknown size), find if a given number ‘key’ is present in the array. Write a function to return the index of the ‘key’ if it is present in the array, otherwise return -1.
Since it is not possible to define an array with infinite (unknown) size, you will be provided ... | normal | {
"blob_id": "a9efa258c223460b2b79861acdde89161706ad9a",
"index": 8770,
"step-1": "<mask token>\n\n\nclass ArrayReader:\n\n def __init__(self, arr):\n self.arr = arr\n\n def get(self, index):\n if index > len(self.arr):\n return math.inf\n return self.arr[index]\n\n\n<mask to... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def squirrel_play(temp, is_summer):
if is_summer == True:
if 60 <= temp <= 100:
return True
else:
return False
if is_summer == False:
if 60 <= temp <= 90:
return True
else:
... | flexible | {
"blob_id": "48755cf48c6696259d0c319d382021f33751ac01",
"index": 9497,
"step-1": "<mask token>\n",
"step-2": "def squirrel_play(temp, is_summer):\n if is_summer == True:\n if 60 <= temp <= 100:\n return True\n else:\n return False\n if is_summer == False:\n if 6... | [
0,
1
] |
import datetime
from collections import defaultdict
from django.db.models import Prefetch
from urnik.models import Termin, Rezervacija, Ucilnica, DNEVI, MIN_URA, MAX_URA, Srecanje, Semester, RezervacijaQuerySet
class ProsteUcilniceTermin(Termin):
HUE_PRAZEN = 120 # zelena
HUE_POLN = 0 # rdeca
def __i... | normal | {
"blob_id": "3ce9c0aeb6b4e575fbb3fced52a86a1dcec44706",
"index": 4713,
"step-1": "<mask token>\n\n\nclass ProsteUcilnice(object):\n <mask token>\n\n def __init__(self, ucilnice):\n self.ucilnice = set(ucilnice)\n self.zasedenost_ucilnic = defaultdict(dict)\n self.rezerviranost_ucilnic ... | [
20,
22,
26,
27,
28
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def remove(string_input):
return string_input.replace(' ', '')
<|reserved_special_token_1|>
#Function to remove spaces in a string
def remove(string_input):
return string_input.replace(" ", "")
| flexible | {
"blob_id": "f327f408ae2759407ac9f01ad4feff5c6a0845f1",
"index": 9524,
"step-1": "<mask token>\n",
"step-2": "def remove(string_input):\n return string_input.replace(' ', '')\n",
"step-3": "#Function to remove spaces in a string\n\ndef remove(string_input):\n return string_input.replace(\" \", \"\")\n"... | [
0,
1,
2
] |
def test(name, message):
print('用户是：', name)
print('欢迎消息是：', message)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def test(name, message):
print('用户是：', name)
print('欢迎消息是：', message)
<|reserved_special_token_0|>
def foo(name, *nums):
print('name参数：', name)
print('nums参数:'... | flexible | {
"blob_id": "64fb006ea5ff0d101000dd4329b3d957a326ed1a",
"index": 2387,
"step-1": "def test(name, message):\n print('用户是：', name)\n print('欢迎消息是：', message)\n\n\n<mask token>\n",
"step-2": "def test(name, message):\n print('用户是：', name)\n print('欢迎消息是：', message)\n\n\n<mask token>\n\n\ndef foo(name,... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app_name = 'gymapp'
urlpatterns = [path('', ClientHomeView.as_view(), name='clienthome'), path(
'about/', ClientAboutView.as_view(), name='clientabout'), path(
'contact/', ClientContactCreateView.as_view(), name='clientcon... | flexible | {
"blob_id": "48a4331e4b26ea81f1c52ae76db1e92a57cb378c",
"index": 2654,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'gymapp'\nurlpatterns = [path('', ClientHomeView.as_view(), name='clienthome'), path(\n 'about/', ClientAboutView.as_view(), name='clientabout'), path(\n 'contact/', Clie... | [
0,
1,
2,
3
] |
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# ... | normal | {
"blob_id": "43e721ac45570e4f9ab9c1970abee3da6db40afa",
"index": 156,
"step-1": "<mask token>\n\n\n@six.add_metaclass(abc.ABCMeta)\nclass ParallelMigrationStrategy(base.BaseStrategy):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n ... | [
11,
13,
16,
18,
19
] |
<|reserved_special_token_0|>
class Hexapod:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def get_delta_L(self):
"""
Расчет геометрии положения точек A_i в каждый момент времени.
Отрисовка графиков изменения длин, скорости и ускорения для каждого привода по времени.
... | flexible | {
"blob_id": "9a672c17ee22a05e77491bc1449c1c1678414a8c",
"index": 3094,
"step-1": "<mask token>\n\n\nclass Hexapod:\n <mask token>\n <mask token>\n\n def get_delta_L(self):\n \"\"\"\n Расчет геометрии положения точек A_i в каждый момент времени.\n Отрисовка графиков изменения длин, с... | [
6,
9,
10,
11,
13
] |
<|reserved_special_token_0|>
def setup():
global pwm_A, pwm_B
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BOARD)
GPIO.setup(Motor_B_EN, GPIO.OUT)
GPIO.setup(Motor_B_Pin1, GPIO.OUT)
GPIO.setup(Motor_B_Pin2, GPIO.OUT)
pwm_B = GPIO.PWM(Motor_B_EN, 1000)
def motorStop():
GPIO.output(Motor_... | flexible | {
"blob_id": "7369d5a463b0f41c17d5648739d4730256e611f9",
"index": 9612,
"step-1": "<mask token>\n\n\ndef setup():\n global pwm_A, pwm_B\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(Motor_B_EN, GPIO.OUT)\n GPIO.setup(Motor_B_Pin1, GPIO.OUT)\n GPIO.setup(Motor_B_Pin2, GPIO.OUT... | [
4,
5,
6,
7,
8
] |
from django.apps import AppConfig
class Iapp1Config(AppConfig):
name = 'iapp1'
| normal | {
"blob_id": "c27ca6a8c38f2b96011e3a09da073ccc0e5a1467",
"index": 3386,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Iapp1Config(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Iapp1Config(AppConfig):\n name = 'iapp1'\n",
"step-4": "from django.apps import AppConfig... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(rsp.text)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
rsp = requests.get(
'https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=%s&secret=%s'
% ('wx27c0e6ef6a7f0716', '6e29e232... | flexible | {
"blob_id": "d86fe165e378e56650e3b76bf3d0f72e2a50a023",
"index": 5082,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(rsp.text)\n",
"step-3": "<mask token>\nrsp = requests.get(\n 'https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=%s&secret=%s'\n % ('wx27c0e6ef6a7f0... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class DAE(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def fast_training(self, sound):
self.core_size = 100
self.batch_size = 1000
self.Epoches = 50
self._main(sound, 100, 1000, 50)
def medium_training(self, sound):
... | flexible | {
"blob_id": "6f53702d9265a7fc57d2ec2e47dc35a0bc7a9f87",
"index": 9012,
"step-1": "<mask token>\n\n\nclass DAE(object):\n <mask token>\n <mask token>\n\n def fast_training(self, sound):\n self.core_size = 100\n self.batch_size = 1000\n self.Epoches = 50\n self._main(sound, 100... | [
4,
6,
9,
10,
12
] |
class ChartType:
Vanilla = "Vanilla"
Neopolitan = "Neopolitan"
| normal | {
"blob_id": "451a36eb205a269a05e3b3d89541278633d12aaa",
"index": 9781,
"step-1": "<mask token>\n",
"step-2": "class ChartType:\n <mask token>\n <mask token>\n",
"step-3": "class ChartType:\n Vanilla = 'Vanilla'\n Neopolitan = 'Neopolitan'\n",
"step-4": "\n\nclass ChartType:\n Vanilla = \"Vanil... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main(config, resume, infile, outfile, sigma, dur, half):
model = get_instance(module_arch, 'arch', config)
model.summary()
checkpoint = torch.load(resume)
state_dict = checkpoint['state_dict']
if config['... | flexible | {
"blob_id": "a2421a8673a524c32539555596711a71a8e00dbf",
"index": 439,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main(config, resume, infile, outfile, sigma, dur, half):\n model = get_instance(module_arch, 'arch', config)\n model.summary()\n checkpoint = torch.load(resume)\n state... | [
0,
1,
2,
3,
4
] |
import numpy as np
import os
import sys
file_path = sys.argv[1]
triplets = np.loadtxt(os.path.join(file_path, "kaggle_visible_evaluation_triplets.txt"),
delimiter="\t", dtype="str")
enum_users = np.ndenumerate(np.unique(triplets[:, 0]))
print(enum_users)
triplets[triplets[:, 0] == user... | normal | {
"blob_id": "f3d9e783491916e684cda659afa73ce5a6a5894a",
"index": 4063,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(enum_users)\n<mask token>\nprint(triplets)\n",
"step-3": "<mask token>\nfile_path = sys.argv[1]\ntriplets = np.loadtxt(os.path.join(file_path,\n 'kaggle_visible_evaluation_trip... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ConcurrentExecutor:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def run_io_func_sync(self, func, args=(), kwargs=None):
"""
:param func: callable
:param args: free params
:param kwargs: named... | flexible | {
"blob_id": "24b1afb18e1cfdc8d5a62f5ee0147b2d73bc10d8",
"index": 7492,
"step-1": "<mask token>\n\n\nclass ConcurrentExecutor:\n <mask token>\n <mask token>\n <mask token>\n\n def run_io_func_sync(self, func, args=(), kwargs=None):\n \"\"\"\n :param func: callable\n :param args: f... | [
3,
6,
8,
10,
11
] |
#! /usr/local/bin/python3
# -*- coding: utf-8 -*-
from requests_oauthlib import OAuth1Session
BASEURL = 'https://api.twitter.com/1.1/'
CK = '3rJOl1ODzm9yZy63FACdg'
CS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'
AT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'
AS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3E... | normal | {
"blob_id": "63bfaa6e191e6090060877e737f4b003bed559cf",
"index": 9140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_instance(rest_url, params):\n url = BASEURL + rest_url\n print(url)\n twitter = OAuth1Session(CK, CS, AT, AS)\n return twitter.get(url, params=params)\n",
"step-... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def error(msg):
__log_internal(ERROR_FILE, msg)
def info(msg):
__log_internal(LOG_FILE, msg)
def __log_internal(filename, msg):
now = datetime.datetime.now()
f = open(filename, 'a+')
f.write('{} : {}\n'.format(now.strftime('%Y-%m-%d %H:%M:%S'), msg))
f.close()
... | flexible | {
"blob_id": "0475c6cab353f0d23a4c4b7f78c1b47ecc5f8d3b",
"index": 4819,
"step-1": "<mask token>\n\n\ndef error(msg):\n __log_internal(ERROR_FILE, msg)\n\n\ndef info(msg):\n __log_internal(LOG_FILE, msg)\n\n\ndef __log_internal(filename, msg):\n now = datetime.datetime.now()\n f = open(filename, 'a+')\... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@api_view(['GET'])
def artifact_save_recommend(request, pageNo):
artifact_url = (
f'http://www.emuseum.go.kr/openapi/relic/list?serviceKey={service_key}&numOfRows=100&pageNo={pageNo}'
)
response = request... | flexible | {
"blob_id": "707e3e60d6d9a3db5b9bc733e912b34e2cec5974",
"index": 8585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@api_view(['GET'])\ndef artifact_save_recommend(request, pageNo):\n artifact_url = (\n f'http://www.emuseum.go.kr/openapi/relic/list?serviceKey={service_key}&numOfRows=100&p... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
@app.route('/')
def showMachineList():
return render_template('list.html')
@app.route('/insert_records', methods=['POST'])
def insert_records():
json_data = request.json['info']
nome = json_data['nome']
email = json_data['email']
telefone = json_data['telefone']
... | flexible | {
"blob_id": "05ca16303d0eb962249793164ac91795c45cc3c2",
"index": 9974,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef showMachineList():\n return render_template('list.html')\n\n\n@app.route('/insert_records', methods=['POST'])\ndef insert_records():\n json_data = request.json['info']\n nome = json_d... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def makeMnistModel():
mnist = tf.keras.datasets.mnist
(X_train, y_train), (_, _) = mnist.load_data()
X_train = X_train / 255.0
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape
=(28,... | flexible | {
"blob_id": "1555583cd3d8938cbaeeac2d1f74bb9c3858f26d",
"index": 4207,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef makeMnistModel():\n mnist = tf.keras.datasets.mnist\n (X_train, y_train), (_, _) = mnist.load_data()\n X_train = X_train / 255.0\n model = tf.keras.models.Sequential([... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
connection.open()
print('list of hbase tables {}'.format(connection.tables()))
<|reserved_special_token_0|>
for key, data in customers.scan():
keys.append(key)
data_list.append(data)
<|reserved_special_token_0|>
print('len... | flexible | {
"blob_id": "7b38c64174656d1c4ec2b0541e6ed8d6680af7d7",
"index": 9565,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nconnection.open()\nprint('list of hbase tables {}'.format(connection.tables()))\n<mask token>\nfor key, data in customers.scan():\n keys.append(key)\n data_list.append(data)\n<mask ... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.0.10 on 2020-12-19 15:07
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
("wagtailadmin", "0001_create_admin_access_permissions"),
]
operations = [
migrations.CreateModel(
name="Admi... | normal | {
"blob_id": "52a4213a1729e25f96faebc5fd4f299017446c5a",
"index": 6370,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ModelInterface(ProtoSerializable):
def reset(self):
raise NotImplementedError()
pass
def run(self):
raise NotImplementedError()
def stop(self):
raise NotImplementedError()
@property
def abstract_timestamp(self):
raise N... | flexible | {
"blob_id": "501b8a9307a1fd65a5f36029f4df59bbe11d881a",
"index": 6591,
"step-1": "<mask token>\n\n\nclass ModelInterface(ProtoSerializable):\n\n def reset(self):\n raise NotImplementedError()\n pass\n\n def run(self):\n raise NotImplementedError()\n\n def stop(self):\n raise ... | [
9,
10,
13,
14,
16
] |
# -*- coding: utf-8 -*-
import time
import datetime
def get_second_long(time_str=None):
if time_str is None:
return long(time.time())
time_array = time.strptime(time_str, "%Y-%m-%d %H:%M:%S")
return long(time.mktime(time_array))
def get_curtime_str():
return datetime.datetime.now()
def ge... | normal | {
"blob_id": "e735529eddd3a46ea335e593e5937558b50b142d",
"index": 2276,
"step-1": "<mask token>\n\n\ndef get_second_long(time_str=None):\n if time_str is None:\n return long(time.time())\n time_array = time.strptime(time_str, '%Y-%m-%d %H:%M:%S')\n return long(time.mktime(time_array))\n\n\n<mask t... | [
7,
9,
10,
11,
14
] |
containerized: "docker://quay.io/snakemake/containerize-testimage:1.0"
rule a:
output:
"test.out"
conda:
"env.yaml"
shell:
"bcftools 2> {output} || true"
| normal | {
"blob_id": "6e0d09bd0c9d1d272f727817cec65b81f83d02f5",
"index": 6742,
"step-1": "containerized: \"docker://quay.io/snakemake/containerize-testimage:1.0\"\n\nrule a:\n output:\n \"test.out\"\n conda:\n \"env.yaml\"\n shell:\n \"bcftools 2> {output} || true\"\n",
"step-2": null,
... | [
0
] |
import random
import Manhattan_segmental_dist
# Greedy
# s: dictionary of points
# k: number of medoids
# returns
# k medoids from sample set s
def greedy(s, k):
# print("Hello Word!")
m_1 = random.choice(list(s.keys()))
medoids = {m_1: s[m_1]}
dimensions = list(range(len(s[m_1])))
s.pop(m_1... | normal | {
"blob_id": "9a02bd0bc14494db033c032003aa5baea111ea8c",
"index": 7185,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef greedy(s, k):\n m_1 = random.choice(list(s.keys()))\n medoids = {m_1: s[m_1]}\n dimensions = list(range(len(s[m_1])))\n s.pop(m_1)\n dist = {}\n for x in s:\n ... | [
0,
1,
2,
3
] |
import datetime
import json
import logging
import requests
from lib.crits.exceptions import CRITsOperationalError
from lib.crits.vocabulary.indicators import IndicatorThreatTypes as itt
from lib.crits.vocabulary.indicators import IndicatorAttackTypes as iat
log = logging.getLogger()
class CRITsAPI():
def __init... | normal | {
"blob_id": "a505cc0e382554d65447a3fe3a56fac43c1964f2",
"index": 8133,
"step-1": "<mask token>\n\n\nclass CRITsAPI:\n <mask token>\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n ... | [
6,
7,
8,
9,
11
] |
#!/usr/bin/env python
#pylint: skip-file
"""
HostApi.py
Copyright 2016 Cisco Systems
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2... | normal | {
"blob_id": "4243c863827f1378c364171ca7d8fdabd42be22f",
"index": 3625,
"step-1": "<mask token>\n\n\nclass HostApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def getHostById(self, **kwargs):\n \"\"\"Retrieves host based on id\n\n Args:\n\n id, str: Host Id (requ... | [
2,
4,
5,
6,
7
] |
for t in range(int(input())):
st = list(input())
N, j = len(st), 1
for i in range(N // 2):
if st[i] == '*' or st[-i - 1] == '*':
break
elif st[i] != st[-i - 1]:
j = 0
break
print('#{} Exist'.format(t + 1)) if j else print('#{} Not exist'.format
... | normal | {
"blob_id": "21d499555b4bc4944996a57ae544a56aa317b00b",
"index": 4386,
"step-1": "<mask token>\n",
"step-2": "for t in range(int(input())):\n st = list(input())\n N, j = len(st), 1\n for i in range(N // 2):\n if st[i] == '*' or st[-i - 1] == '*':\n break\n elif st[i] != st[-i ... | [
0,
1
] |
<|reserved_special_token_0|>
def extract_title(page):
return page.find('header').find('h1').contents[0]
def extract_colours(page):
color_list = page.find('ul')
return list(dict.fromkeys(re.findall('#\\w+', str(color_list.contents))))
def get_colours_from_page(browser, baseurl, target_page):
respon... | flexible | {
"blob_id": "9fd33089a9dc919ef2fb2698059e60a24a0e05e6",
"index": 6118,
"step-1": "<mask token>\n\n\ndef extract_title(page):\n return page.find('header').find('h1').contents[0]\n\n\ndef extract_colours(page):\n color_list = page.find('ul')\n return list(dict.fromkeys(re.findall('#\\\\w+', str(color_list... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class PddMallGoodsSpider(scrapy.Spider):
<|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|>
def start_re... | flexible | {
"blob_id": "f33190df35a6b0b91c4dd2d6a58291451d06e29a",
"index": 3529,
"step-1": "<mask token>\n\n\nclass PddMallGoodsSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def start_requests(self):\n mall... | [
3,
4,
5,
9,
10
] |
import pandas as pd
dict_data = {'c0': [1, 2, 3], 'c1': [4, 5, 6], 'c2': [
7, 8, 9], 'c3': [10, 11, 12], 'c4': [13, 14, 15]}
df = pd.DataFrame(dict_data)
print(type(df))
print('\n')
print(df)
# <class 'pandas.core.frame.DataFrame'>
# c0 c1 c2 c3 c4
# 0 1 4 7 10 13
# 1 2 5 8 11 14
# 2 ... | normal | {
"blob_id": "22f4ae755e7ea43604db39452ca80f44f540708a",
"index": 9503,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(type(df))\nprint('\\n')\nprint(df)\n",
"step-3": "<mask token>\ndict_data = {'c0': [1, 2, 3], 'c1': [4, 5, 6], 'c2': [7, 8, 9], 'c3': [10, \n 11, 12], 'c4': [13, 14, 15]}\ndf =... | [
0,
1,
2,
3,
4
] |
"""Encoder module of Monodepth2
Code partially borrowed from
https://github.com/nianticlabs/monodepth2/blob/master/networks/resnet_encoder.py
"""
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import mxnet as mx
from mxnet.gluon import nn
from mxnet.context import cpu
fr... | normal | {
"blob_id": "62601eca767800f00b461ef46d72bddc5cf75de0",
"index": 1400,
"step-1": "<mask token>\n\n\nclass ResnetEncoder(nn.HybridBlock):\n <mask token>\n\n def __init__(self, backbone, pretrained, num_input_images=1, root=os.\n path.join(os.path.expanduser('~'), '.mxnet/models'), ctx=cpu(), **\n ... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
from math import factorial
F = [factorial(i) for i in range(10)]
#F[9] * 8 = 2903040 > this means no 8 digit numbers
#F[9] * 7 = 2540160 < this is the maximum that I could think of
total = 0
for i in xrange(10, 2540160):
if sum([F[int(d)] for d in str(i)]) == i:
total = total + i
p... | normal | {
"blob_id": "d2e8c95dc144aa83128cc815ad145982f64b1819",
"index": 3206,
"step-1": "#!/usr/bin/env python\n\nfrom math import factorial\n\nF = [factorial(i) for i in range(10)]\n#F[9] * 8 = 2903040 > this means no 8 digit numbers\n#F[9] * 7 = 2540160 < this is the maximum that I could think of\n\ntotal = 0\nfor i ... | [
0
] |
#coding=utf-8
'''
find words and count
By @liuxingpuu
'''
import re
fin= open("example","r")
fout = open("reuslt.txt","w")
str=fin.read()
reObj = re.compile("\b?([a-zA-Z]+)\b?")
words = reObj.findall(str)
word_dict={}
for word in words:
if(word_dict.has_key(word)):
word_dict[word.lower()]=max(word_dict[wor... | normal | {
"blob_id": "addab37cb23abead2d9f77a65336cd6026c52c68",
"index": 8559,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor word in words:\n if word_dict.has_key(word):\n word_dict[word.lower()] = max(word_dict[word.lower()], words.count(\n word.lower()) + words.count(word.upper()) + w... | [
0,
1,
2,
3,
4
] |
import httplib
import sys
http_server = "localhost:8000"
connection = httplib.HTTPConnection(http_server)
# Open test input.
test_file_path = "test_input"
test_f = open(test_file_path)
inputs = test_f.readlines()
inputs = [x.strip() for x in inputs]
test_f.close()
# Open expected input.
expected_file_path = "expect... | normal | {
"blob_id": "cd9b04a93d85ba0ee2a38b534386f9aec0ef6895",
"index": 5165,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntest_f.close()\n<mask token>\nexpected_f.close()\nassert len(inputs) == len(expecteds)\nfor i in range(len(inputs)):\n connection.request('GET', '<start>%s<end>' % inputs[i])\n resp... | [
0,
1,
2,
3,
4
] |
from __future__ import absolute_import
from builtins import str
from builtins import object
import unittest
import sys, os, re
import forcebalance
import abc
import numpy
from __init__ import ForceBalanceTestCase
class TestImplemented(ForceBalanceTestCase):
def test_implemented_targets_derived_from_target(self):
... | normal | {
"blob_id": "f91e1fdc31b2fe1aef15757576d847c617a86201",
"index": 1121,
"step-1": "<mask token>\n\n\nclass TestBromineObjective(ForceBalanceTestCase, ObjectiveTests):\n\n def setUp(self):\n self.options = forcebalance.parser.gen_opts_defaults.copy()\n self.options.update({'root': os.getcwd() + '/... | [
2,
11,
12,
15,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
version = 2, 5, 8
version_string = '.'.join(str(v) for v in version)
release_date = '2015.12.27'
<|reserved_special_token_1|>
version = (2, 5, 8)
version_string = ".".join(str(v) for v in version)
release_date = "2015.12.27"
| flexible | {
"blob_id": "28077af0759e062078f7b9d1f7bbbb93c62835cb",
"index": 5063,
"step-1": "<mask token>\n",
"step-2": "version = 2, 5, 8\nversion_string = '.'.join(str(v) for v in version)\nrelease_date = '2015.12.27'\n",
"step-3": "version = (2, 5, 8)\nversion_string = \".\".join(str(v) for v in version)\n\nrelease_... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def Sort(a):
i = 1
n = len(a)
while i < len(a):
j = i
print(i - 1, '\t', i)
while a[j - 1] > a[j] and j >= 0:
j -= 1
print('Key : ', a[i], ' inserting at: ', j, '\t in ', a... | flexible | {
"blob_id": "3f8b8b8cfbe712f09734d0fb7302073187d65a73",
"index": 982,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Sort(a):\n i = 1\n n = len(a)\n while i < len(a):\n j = i\n print(i - 1, '\\t', i)\n while a[j - 1] > a[j] and j >= 0:\n j -= 1\n pr... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def findFirst(arr, l, h, x):
if l > h:
return -1
mid = (l + h) // 2
if arr[mid] == x:
return mid
elif arr[mid] > x:
return findFirst(arr, l, mid - 1, x)
return findFirst(arr, mid + 1, h, x)
<|reserved_special_toke... | flexible | {
"blob_id": "b4783540224902b10088edbd038d6d664934a237",
"index": 4893,
"step-1": "<mask token>\n",
"step-2": "def findFirst(arr, l, h, x):\n if l > h:\n return -1\n mid = (l + h) // 2\n if arr[mid] == x:\n return mid\n elif arr[mid] > x:\n return findFirst(arr, l, mid - 1, x)\n... | [
0,
1,
2,
3
] |
import os
import pytest
from selenium.webdriver.remote.webdriver import WebDriver
from selenium.webdriver import Firefox
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.comm... | normal | {
"blob_id": "b6e28f29edd0c4659ab992b45861c4c31a57e7fd",
"index": 8920,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_gecko_driver():\n home_dir = os.getenv('HOME')\n return Firefox(executable_path=os.path.join(home_dir, 'bin', 'geckodriver')\n )\n\n\n@pytest.fixture\ndef driv... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class UserManager:
<|reserved_special_token_0|>
def validate_user(self, user_name, password):
user = self.find_user(user_name)
if not user:
raise ClientError()
self.validate_password(user, password)
return user
<|reserved_special_to... | flexible | {
"blob_id": "210199ed217db0d7a05e280f20e33496c0795f06",
"index": 9472,
"step-1": "<mask token>\n\n\nclass UserManager:\n <mask token>\n\n def validate_user(self, user_name, password):\n user = self.find_user(user_name)\n if not user:\n raise ClientError()\n self.validate_pas... | [
11,
17,
19,
21,
27
] |
from os.path import basename
from .FileInfo import FileInfo
class mrk_file(FileInfo):
"""
.mrk specific file container.
"""
def __init__(self, id_=None, file=None, parent=None):
super(mrk_file, self).__init__(id_, file, parent)
self._type = '.mrk'
#region class methods
def __get... | normal | {
"blob_id": "8e9aec7d3653137a05f94e4041d28f3423122751",
"index": 3990,
"step-1": "<mask token>\n\n\nclass mrk_file(FileInfo):\n <mask token>\n\n def __init__(self, id_=None, file=None, parent=None):\n super(mrk_file, self).__init__(id_, file, parent)\n self._type = '.mrk'\n <mask token>\n\... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
<|reserved_special_token_1|>
from .cli import cli
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
<|reserved_special_token_1|>
from .cli impor... | flexible | {
"blob_id": "069338b188f3cf16357b2502cbb3130b69918bd9",
"index": 286,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n exit(cli.main(prog_name='htmap'))\n",
"step-3": "from .cli import cli\nif __name__ == '__main__':\n exit(cli.main(prog_name='htmap'))\n",
"step-4": "... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def dfs(i):
if temp[i]:
return
temp[i] = True
if i in odd:
for j in graph[i]:
even.add(j)
dfs(j)
else:
for j in graph[i]:
odd.add(j)
dfs(j)
<|reserved_special_token_0|>
<|reserved_special_token_1|>... | flexible | {
"blob_id": "bab6b9a0178da119f753deb6c626dd5c41db2bdd",
"index": 2004,
"step-1": "<mask token>\n\n\ndef dfs(i):\n if temp[i]:\n return\n temp[i] = True\n if i in odd:\n for j in graph[i]:\n even.add(j)\n dfs(j)\n else:\n for j in graph[i]:\n odd.a... | [
1,
2,
3,
4,
5
] |
# vim: tabstop=4 expandtab autoindent shiftwidth=4 fileencoding=utf-8
from django.contrib.auth.decorators import login_required
from django.contrib.auth import models as auth_models
from django.contrib.auth import forms as auth_forms
from django.contrib.auth import authenticate, login
from django.core.urlresolvers i... | normal | {
"blob_id": "22da05d9bf6139a0306bfb2d1df96e9e2cf6a0c6",
"index": 475,
"step-1": "<mask token>\n\n\n@login_required\ndef get_verification_code(request):\n \"\"\"Maybe ajaxify this in the future\n \"\"\"\n if request.user.get_profile().is_verified:\n messages.info(request, 'Olet jo vahvistanut osoi... | [
1,
2,
3,
4,
5
] |
import tkinter as tk # Import tkinker for GUI creation
from PIL import Image, ImageTk # Allow images to be used as backgrounds
import socket # Importing sockets for low level implementation of networks
import select # Importing select to poll between the user input and received message
import sys # Getting inp... | normal | {
"blob_id": "5e17299e6a409e433e384935a815bab6ce178ff5",
"index": 3031,
"step-1": "<mask token>\n\n\ndef sigint_handler(signum, frame):\n print('\\n Disconnecting from server')\n sys.exit()\n\n\n<mask token>\n\n\ndef sendUsernameToServer(username_entry):\n username = username_entry.encode('utf-8')\n u... | [
4,
5,
6,
7,
9
] |
# Copyright (C) 2011 Ruckus Wireless, Inc. All rights reserved.
# Please make sure the following module docstring is accurate since it will be used in report generation.
"""
Description:
@author: Chris Wang
@contact: cwang@ruckuswireless.com
@since: Aug-09, 2010
Prerequisite (Assumptions about the sta... | normal | {
"blob_id": "25288a6dd0552d59f8c305bb8edbbbed5d464d5b",
"index": 9997,
"step-1": "# Copyright (C) 2011 Ruckus Wireless, Inc. All rights reserved.\n# Please make sure the following module docstring is accurate since it will be used in report generation.\n\n\"\"\"\n Description: \n @author: Chris Wang\n @con... | [
0
] |
<|reserved_special_token_0|>
class SideEnum(str, Enum):
BUY = 'B'
SELL = 'S'
class BaseClient:
def __init__(self, client: 'StakeClient'):
self._client = weakref.proxy(client)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if TYPE_CHECKING:
from stake.client import StakeClient
... | flexible | {
"blob_id": "f13ccbfb27788deca0d4f4b58a4e9e8c7e8e0306",
"index": 1644,
"step-1": "<mask token>\n\n\nclass SideEnum(str, Enum):\n BUY = 'B'\n SELL = 'S'\n\n\nclass BaseClient:\n\n def __init__(self, client: 'StakeClient'):\n self._client = weakref.proxy(client)\n",
"step-2": "<mask token>\nif TY... | [
4,
5,
6,
7,
8
] |
#
# @lc app=leetcode id=67 lang=python3
#
# [67] Add Binary
#
# https://leetcode.com/problems/add-binary/description/
#
# algorithms
# Easy (46.70%)
# Likes: 2566
# Dislikes: 331
# Total Accepted: 572.1K
# Total Submissions: 1.2M
# Testcase Example: '"11"\n"1"'
#
# Given two binary strings a and b, return their ... | normal | {
"blob_id": "227a56c970a74d515ab694d2c0924885e2209cfe",
"index": 7089,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def addBinary(self, a: str, b: str) ->str:\n if len(a) < len(b):\n a = '0' * (len(b) - len(a)) + a\n e... | [
0,
1,
2,
3
] |
import csv
import Feature_extraction as urlfeature
import trainer as tr
import warnings
warnings.filterwarnings("ignore")
def resultwriter(feature, output_dest):
flag = True
with open(output_dest, 'w') as f:
for item in feature:
w = csv.DictWriter(f, item[1].keys())
if flag:
... | normal | {
"blob_id": "9d190face528d1a237f4c92bfb94a399f61a5af2",
"index": 9317,
"step-1": "<mask token>\n\n\ndef resultwriter(feature, output_dest):\n flag = True\n with open(output_dest, 'w') as f:\n for item in feature:\n w = csv.DictWriter(f, item[1].keys())\n if flag:\n ... | [
4,
5,
6,
7,
8
] |
from trac.db import DatabaseManager
def do_upgrade(env, ver, cursor):
"""Change schema name from taskboard_schema to agiletools_version
"""
cursor.execute('UPDATE system SET name=%s WHERE name=%s',
("agiletools_version", "taskboard_schema"))
| normal | {
"blob_id": "56ed5bb22d77f4d8c061f97d832a60ed9a106549",
"index": 5231,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef do_upgrade(env, ver, cursor):\n \"\"\"Change schema name from taskboard_schema to agiletools_version\n \"\"\"\n cursor.execute('UPDATE system SET name=%s WHERE name=%s', ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def dir_create(path):
"""创造新的文件夹。
:param path: 文件夹路径
:return:
"""
if os.path.exists(path) and os.listdir(path) != []:
shutil.rmtree(path)
os.makedirs(path)
if not os.path.exists(path):
os.makedirs(path)
def read_dicom(path):
"""读取一个病例... | flexible | {
"blob_id": "4905b820f33619a80a9915d0603bc39e0d0368d9",
"index": 6175,
"step-1": "<mask token>\n\n\ndef dir_create(path):\n \"\"\"创造新的文件夹。\n\n :param path: 文件夹路径\n :return:\n \"\"\"\n if os.path.exists(path) and os.listdir(path) != []:\n shutil.rmtree(path)\n os.makedirs(path)\n i... | [
7,
8,
9,
10,
11
] |
import operator
def group_by_owners(files):
print(files, type(files))
for k, v in files.items():
# for v in k:
print(k, v)
# if k[v] == k[v]:
# print("same", v)
for f in files:
print(f[0])
for g in v:
print(g)
_files = sorted(files.items(... | normal | {
"blob_id": "4843239a41fe1ecff6c8c3a97aceef76a3785647",
"index": 7334,
"step-1": "<mask token>\n\n\ndef group_by_owners(files):\n print(files, type(files))\n for k, v in files.items():\n print(k, v)\n for f in files:\n print(f[0])\n for g in v:\n print(g)\n _files = so... | [
1,
2,
3,
4,
5
] |
"""
Urls for CAE_Web Audio_Visual app.
"""
from django.conf.urls import url
from . import views
app_name = 'cae_web_audio_visual'
urlpatterns = [
]
| normal | {
"blob_id": "5debc97e99bbd78b17e545896d718d4b0eac8519",
"index": 2430,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'cae_web_audio_visual'\nurlpatterns = []\n",
"step-3": "<mask token>\nfrom django.conf.urls import url\nfrom . import views\napp_name = 'cae_web_audio_visual'\nurlpatterns = ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
np.set_printoptions(suppress=True)
<|reserved_special_token_0|>
for image in path:
n1 = cv2.imread(image)
n2 = cv2.resize(n1, (244, 244))
images.append(n2)
print(image)
<|reserved_special_token_0|>
if prediction[0]... | flexible | {
"blob_id": "13b69ec61d6b2129f1974ce7cae91c84100b3b58",
"index": 449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.set_printoptions(suppress=True)\n<mask token>\nfor image in path:\n n1 = cv2.imread(image)\n n2 = cv2.resize(n1, (244, 244))\n images.append(n2)\n print(image)\n<mask token>... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with alive_bar(100) as bar:
for i in range(100):
sleep(0.03)
bar()
with alive_bar(200, bar='bubbles', spinner='notes2') as bar:
for i in range(200):
sleep(0.03)
... | flexible | {
"blob_id": "06f961c07695d1c312cb943afbfa64508a709c7e",
"index": 1076,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith alive_bar(100) as bar:\n for i in range(100):\n sleep(0.03)\n bar()\n with alive_bar(200, bar='bubbles', spinner='notes2') as bar:\n for i in range... | [
0,
1,
2,
3
] |
# Ejercicio 28 - Hoja VI (5) - Indicar la nota ponderada según el criterio dado
# (parte teórica 60%, práctica 40%) de cada uno de un número determinado de alumnos
numalumnos=int(input("Introduce el número total de alumnos:\n"))
print("Usa el punto '.' para los decimales")
for contador in range(1,numalumnos+1):
... | normal | {
"blob_id": "f2056ff46ce6e38c3b6ca553bbdec7f59d60b198",
"index": 1417,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\"Usa el punto '.' para los decimales\")\nfor contador in range(1, numalumnos + 1):\n print(f'\\nDatos del alumno número {contador} de {numalumnos}:')\n teorica = float(input(... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def uppercase_first_letter(string: str) ->str:
return string[0:1].upper() + string[1:]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def uppercase_first_letter(string: str) ->s... | flexible | {
"blob_id": "0555c577a8fb746cf2debb929d02b46cd3be4d7b",
"index": 1062,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef uppercase_first_letter(string: str) ->str:\n return string[0:1].upper() + string[1:]\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef uppercase_first_letter(string: str... | [
0,
1,
2,
3
] |
from quantopian.algorithm import order_optimal_portfolio
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import SimpleMovingAverage
from quantopian.pipeline.filters im... | normal | {
"blob_id": "c447d1fe38a4af43de39e05d46dacbe88249d427",
"index": 3654,
"step-1": "<mask token>\n\n\ndef compute_target_weights(context, data):\n \"\"\"\n Compute ordering weights.\n \"\"\"\n weights = {}\n if context.longs:\n long_weight = 0.5 / len(context.longs)\n if context.shorts:\n ... | [
1,
2,
4,
6,
7
] |
import re
IS_WITH_SINGLETON_REGEX = re.compile("(!=|==)\s*(True|False|None)")
def check_is_with_singleton(physical_line, line_number):
match_obj = IS_WITH_SINGLETON_REGEX.search(physical_line)
if match_obj is not None:
offset = match_obj.span()[0]
return (0, 12, (line_number, offset), "Use eq... | normal | {
"blob_id": "cf6d3a0fbf2a2daf8432622f780e138784ec505d",
"index": 8300,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef check_is_with_singleton(physical_line, line_number):\n match_obj = IS_WITH_SINGLETON_REGEX.search(physical_line)\n if match_obj is not None:\n offset = match_obj.span... | [
0,
1,
2,
3,
4
] |
from __future__ import print_function
from itertools import permutations
s, space, k = raw_input().partition(' ')
for t in sorted(list(permutations(s, int(k)))):
print(*t, sep='')
| normal | {
"blob_id": "37580939a0e58bdffb8cfad8252f339a7da4446e",
"index": 1130,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor t in sorted(list(permutations(s, int(k)))):\n print(*t, sep='')\n",
"step-3": "<mask token>\ns, space, k = raw_input().partition(' ')\nfor t in sorted(list(permutations(s, int(k)... | [
0,
1,
2,
3
] |
def get_ecgs_by_query(json_data, query):
ecgs_ids = []
for case_id in json_data.keys():
print(case_id)
if query.is_query_ok(json_data[case_id]):
ecgs_ids.append(case_id)
return ecgs_ids
def save_new_dataset_by_ids(old_json, ecg_ids_to_save, name_new_dataset):
"""
Saves... | normal | {
"blob_id": "445ae195edfe9fe9ee58c6c5a14ec787719d698c",
"index": 7454,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef save_new_dataset_by_ids(old_json, ecg_ids_to_save, name_new_dataset):\n \"\"\"\n Saves json only with selected (by id) patients.\n :param old_json: initail dataset dict\n... | [
0,
1,
2,
3
] |
# Lahman.py
# Convert to/from web native JSON and Python/RDB types.
import json
# Include Flask packages
from flask import Flask
from flask import request
import copy
import SimpleBO
# The main program that executes. This call creates an instance of a
# class and the constructor starts the runtime.
app = Flask(__na... | normal | {
"blob_id": "d03a8076b77851ae4df5cf657ff898eb132c49c3",
"index": 5616,
"step-1": "<mask token>\n\n\ndef parse_and_print_args():\n fields = None\n in_args = None\n if request.args is not None:\n in_args = dict(copy.copy(request.args))\n fields = copy.copy(in_args.get('fields', None))\n ... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while x <= 24:
if x % 5 == 0:
x = x + 1
continue
print(x)
x = x + 1
<|reserved_special_token_1|>
x = 1
while x <= 24:
if x % 5 == 0:
x = x + 1
continue
print(x)
x = x + 1
| flexible | {
"blob_id": "61cfc583cd87ac0528cb07f4e051392167414920",
"index": 1960,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile x <= 24:\n if x % 5 == 0:\n x = x + 1\n continue\n print(x)\n x = x + 1\n",
"step-3": "x = 1\nwhile x <= 24:\n if x % 5 == 0:\n x = x + 1\n ... | [
0,
1,
2
] |
"""A number can be broken into different contiguous sub-subsequence parts.
Suppose, a number 3245 can be broken into parts like 3 2 4 5 32 24 45 324 245.
And this number is a COLORFUL number, since product of every digit of a contiguous subsequence is different
"""
def colorful(A):
sA = str(A)
len_sA = len(s... | normal | {
"blob_id": "41013469e65e45f6c909d66c2a54eaf11dfd474c",
"index": 3077,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef colorful(A):\n sA = str(A)\n len_sA = len(sA)\n if len_sA == 1:\n return 1\n dig_list = []\n for i in range(len_sA):\n for j in range(i, len_sA):\n ... | [
0,
1,
2,
3
] |
run=[] #Creating a empty list
no_players=int(input("enter the number of the players in the team :"))
for i in range (no_players):
run_score=int(input("Enter the runs scored by the player "+str(i+1)+":"))
run.append(run_score)
#code for the average score of the team
def average(run):
print("________... | normal | {
"blob_id": "3d7ca468a1f7aa1602bff22167e9550ad515fa79",
"index": 4777,
"step-1": "<mask token>\n\n\ndef average(run):\n print('____________________________________')\n sum = 0\n for i in range(0, len(run)):\n sum += run[i]\n avg = sum / len(run)\n print('Average score of the team is :',... | [
4,
5,
6,
7,
8
] |
#Las listas son similares a las tuplas
# con la diferencia de que permiten modificar los datos una vez creados
miLista = ['cadena', 21, 2.8, 'nuevo dato', 25]
print (miLista)
miLista[2] = 3.8 #el tercer elemento ahora es 3.8
print(miLista)
miLista.append('NuevoDato')
print(miLista)
| normal | {
"blob_id": "27ec06d084bf819383801be0351c04e7d1fc1752",
"index": 5176,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(miLista)\n<mask token>\nprint(miLista)\nmiLista.append('NuevoDato')\nprint(miLista)\n",
"step-3": "miLista = ['cadena', 21, 2.8, 'nuevo dato', 25]\nprint(miLista)\nmiLista[2] = 3.... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_swap():
cds = np.load('h3o_data/ffinal_h3o.npy')
dws = np.load('h3o_data/ffinal_h3o_dw.npy')
cds = cds[:10]
a = symm.swap_two_atoms(cds, dws, atm_1=1, atm_2=2)
b = symm.swap_group(cds, dws, atm_list_... | flexible | {
"blob_id": "4ecd756b94b0cbab47a8072e9bccf26e2dd716d0",
"index": 7833,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_swap():\n cds = np.load('h3o_data/ffinal_h3o.npy')\n dws = np.load('h3o_data/ffinal_h3o_dw.npy')\n cds = cds[:10]\n a = symm.swap_two_atoms(cds, dws, atm_1=1, atm... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def getNativeBlockNum(n, k):
"""Get number of native blocks."""
return k * (n - k)
<|reserved_special_token_0|>
def getNodeIdList(n, k):
"""Find the node id for a segment of blocks."""
"""Return a list of node id for the blocks."""
nodeidList = []
segmentSize =... | flexible | {
"blob_id": "0ebd19079a16a6e3da34da2ecfda0d159b8580b2",
"index": 9527,
"step-1": "<mask token>\n\n\ndef getNativeBlockNum(n, k):\n \"\"\"Get number of native blocks.\"\"\"\n return k * (n - k)\n\n\n<mask token>\n\n\ndef getNodeIdList(n, k):\n \"\"\"Find the node id for a segment of blocks.\"\"\"\n \"... | [
10,
12,
13,
15,
16
] |
<|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": "fa045ccd4e54332f6c05bf64e3318e05b8123a10",
"index": 3317,
"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 = [('notificatio... | [
0,
1,
2,
3,
4
] |
"""
Auteur:Fayçal Chena
Date : 07 avril 2020
Consignes :
Écrire une fonction alea_dice(s) qui génère trois nombres (pseudo) aléatoires à l’aide
de la fonction randint du module random, représentant trois dés (à six faces avec
les valeurs de 1 à 6), et qui renvoie la valeur booléenne True si les dés forment un 421,
et l... | normal | {
"blob_id": "ad5a9e353d065eee477381aa6b1f233f975ea0ed",
"index": 3374,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef foo_6(x, y):\n return y, x\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef foo_6(x, y):\n return y, x\n\n\n<mask token>\nfoo_6(a, b)\nprint(a, b)\n",
"step-4": "<... | [
0,
1,
2,
3,
4
] |
from subprocess import check_output
import json
import datetime
date = datetime.datetime.now()
mo = date.month
day = date.day
year = date.year
str = '{0}-{1}-{2}'.format(mo, day, year)
instances = json.loads(check_output("aws lightsail get-instances", shell=True))
inst_names = []
inst_dict = {}
for instance in instan... | normal | {
"blob_id": "2023e0b749338488e63cbbb475b7a915bccccce0",
"index": 7531,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor instance in instances['instances']:\n inst_names.append(instance['name'])\n inst_dict[instance['name']] = []\nprint(inst_names)\n<mask token>\nfor snapshot in snapshots['instanc... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class GlibConan(ConanFile):
<|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|>
def build(self):
... | flexible | {
"blob_id": "e49c5c6475a1210a9657d7bbd0490c8d20863718",
"index": 2285,
"step-1": "<mask token>\n\n\nclass GlibConan(ConanFile):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def build(self):\n args = ['--disable-static'... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def executeUpgrade():
shell.executeCommand('pkg upgrade')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def executeUpgrade():
shell.executeCommand('pkg upgrade')
<|reserved_special_token_0|>
def executeFindByName(name):
shell... | flexible | {
"blob_id": "db55a603615c7d896569ada84f3110dd6c0ce45f",
"index": 1250,
"step-1": "<mask token>\n\n\ndef executeUpgrade():\n shell.executeCommand('pkg upgrade')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef executeUpgrade():\n shell.executeCommand('pkg upgrade')\n\n\n<mask token>\n\n\ndef execute... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(D.cumsum())
print(D.rolling(2).sum())
<|reserved_special_token_1|>
<|reserved_special_token_0|>
D = pd.Series(range(0, 20))
print(D.cumsum())
print(D.rolling(2).sum())
<|reserved_special_token_1|>
import pandas as pd
D... | flexible | {
"blob_id": "7639b80c9e6e1b2e1e55a47a862c433b64168cf6",
"index": 7475,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(D.cumsum())\nprint(D.rolling(2).sum())\n",
"step-3": "<mask token>\nD = pd.Series(range(0, 20))\nprint(D.cumsum())\nprint(D.rolling(2).sum())\n",
"step-4": "import pandas as pd\... | [
0,
1,
2,
3,
4
] |
class subset:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class subset:
def __init__(self, weight, itemSet, size, setNum):
self.weight = weight
self.itemSet = itemSet
self.size = size
self.setNum = setNum
def findCover(base, arr)... | flexible | {
"blob_id": "b865c37623f405f67592d1eabc620d11ff87827e",
"index": 3378,
"step-1": "class subset:\n <mask token>\n\n\n<mask token>\n",
"step-2": "class subset:\n\n def __init__(self, weight, itemSet, size, setNum):\n self.weight = weight\n self.itemSet = itemSet\n self.size = size\n ... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Detector(object):
<|reserved_special_token_0|>
def __init__(self, prototxt, caffemodel, gpu_id, dataset='coco', scale=
600, max_size=1000, transpose=(2, 0, 1), mean=[102.9801, 115.9465,
122.7717]):
if gpu_id < 0:
caffe.set_mode_cpu()
... | flexible | {
"blob_id": "de12c6d78c0144978ffc651829364de16930b173",
"index": 2078,
"step-1": "<mask token>\n\n\nclass Detector(object):\n <mask token>\n\n def __init__(self, prototxt, caffemodel, gpu_id, dataset='coco', scale=\n 600, max_size=1000, transpose=(2, 0, 1), mean=[102.9801, 115.9465, \n 122.77... | [
6,
7,
8,
9,
10
] |
"""
Code for Alexa skill to check PB tracking
"""
from __future__ import print_function
import traceback
import requests
import os
import json
# --------------- Helpers that build all of the responses ----------------------
def build_speechlet_response(title, output, reprompt_text, should_end_session):
return {... | normal | {
"blob_id": "a5ef2adbf85b5ab80c59697340f94bc57d60952e",
"index": 4463,
"step-1": "<mask token>\n\n\ndef build_response(session_attributes, speechlet_response):\n return {'version': '1.0', 'sessionAttributes': session_attributes,\n 'response': speechlet_response}\n\n\ndef get_welcome_response():\n \"... | [
9,
10,
12,
13,
14
] |
# -*- coding: utf-8 -*-
import base64
import logging
from decimal import Decimal
import requests
from django import forms
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from currencies.currencies import decimal_round
from payments.systems import base
from payments.systems.ban... | normal | {
"blob_id": "15c1db535beb115c45aeba433a946255f70fa86e",
"index": 7845,
"step-1": "<mask token>\n\n\nclass DepositForm(base.DepositForm):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @classmethod\n def is_automatic(cls, instance):\n r... | [
9,
10,
11,
13,
17
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# ==================================================
# @Author : Copyright@Ryuchen
# ==================================================
from .version import VERSION
__all__ = [
"VERSION"
]
| normal | {
"blob_id": "d815c6e233d81dfb144442a83e6006aa4e29bfce",
"index": 100,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['VERSION']\n",
"step-3": "from .version import VERSION\n__all__ = ['VERSION']\n",
"step-4": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# ==============================... | [
0,
1,
2,
3
] |
from fastapi import APIRouter, Depends, status, Response
from typing import List
import schemas, database
from sqlalchemy.orm import Session
import repository.blog as blog
from .oauth2 import get_current_user
router = APIRouter(
prefix="/blog",
tags=['Blog'])
@router.get('/', status_code=statu... | normal | {
"blob_id": "7fd5e83d28e919e7b94cea290c6b4db3378938b6",
"index": 4600,
"step-1": "<mask token>\n\n\n@router.get('/', status_code=status.HTTP_200_OK, response_model=List[\n schemas.ShowBlog])\ndef all_blog(db: Session=Depends(database.get_db), current_user: schemas.\n User=Depends(get_current_user)):\n r... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class EUDataCenter(DataCenter):
<|reserved_special_token_0|>
@classmethod
def PRODUCTION(cls):
"""
This method represents the Zoho CRM Production environment in EU domain
:return: An instance of Environments
"""
return DataCenter.Enviro... | flexible | {
"blob_id": "27c364ccf4a6703f74c95ebb386f8ced38b1eafd",
"index": 4960,
"step-1": "<mask token>\n\n\nclass EUDataCenter(DataCenter):\n <mask token>\n\n @classmethod\n def PRODUCTION(cls):\n \"\"\"\n This method represents the Zoho CRM Production environment in EU domain\n :return: An... | [
4,
5,
7,
8,
9
] |
import pygame
import time
from menus import MainMenu
from scenes import TestWorldGen
from scenes import TestAnimation
from scenes import TestLevel2
from scenes import MainGame
import random
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720),
... | normal | {
"blob_id": "91806afea92587476ac743346b88098b197a033c",
"index": 9706,
"step-1": "<mask token>\n\n\nclass GameManager:\n\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720), flags=pygame.\n FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n self.running = True\n... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def lambda_handler(event, context):
current_date = datetime.now(pytz.timezone('US/Central'))
yesterday_date = current_date - timedleta(days=1)
yesterday_date_string = yesterday_date.strftime('%Y-%m-%dT')
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('App... | flexible | {
"blob_id": "64d955d568a6bfec50aad36c9c4f1e36998e4d74",
"index": 7467,
"step-1": "<mask token>\n\n\ndef lambda_handler(event, context):\n current_date = datetime.now(pytz.timezone('US/Central'))\n yesterday_date = current_date - timedleta(days=1)\n yesterday_date_string = yesterday_date.strftime('%Y-%m-... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def split(string):
"""
Function takes input of a string and returns an array of strings
the original string should be comma separated with a space after
the comma in order for this function to be accurate.
"""
names = []
index = 0
last = 0
for letter in... | flexible | {
"blob_id": "f57490c8f4a5ba76824c3b41eb18905eb2213c23",
"index": 5107,
"step-1": "<mask token>\n\n\ndef split(string):\n \"\"\"\n Function takes input of a string and returns an array of strings\n the original string should be comma separated with a space after\n the comma in order for this function ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
admin.site.register(Blog)
<|reserved_special_token_1|>
from django.contrib import admin
from pages.blog.models import Blog
admin.site.register(Blog)
| flexible | {
"blob_id": "534aaf8371707089522af014a93f3ff6c4f913ff",
"index": 8510,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Blog)\n",
"step-3": "from django.contrib import admin\nfrom pages.blog.models import Blog\nadmin.site.register(Blog)\n",
"step-4": null,
"step-5": null,
"step-... | [
0,
1,
2
] |
# Midterm Review Class!
'''
This is a Multi line comment:
'''
# Break and Continue
# for i in range(10):
# if i == 5:
# continue
# print(i)
# Prints 0-4, 6-9
# # Structure
# Some MCQ
# Some T/F
# Some short answer
# # Lists
# Append
# remove
# del
# ... | normal | {
"blob_id": "3d3b77630d275f830daf9f6e0d50a77ef624521e",
"index": 7139,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(0, 100, 10):\n print(i + 10)\n",
"step-3": "# Midterm Review Class!\n\n'''\nThis is a Multi line comment:\n'''\n\n# Break and Continue\n # for i in range(10):\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def test_json_float():
assert_equals(json(1.234), '1.234')
def test_json_array():
data = [1, 2, 3]
assert_equals(json(data), '[1,2,3]')
def test_json_array02():
data = ['bla', 1, 1.2]
assert_equals(json(data), '["bla",1,1.2]')
def test_json_dict():
data = {'f... | flexible | {
"blob_id": "09ce2aeccfd1f3f4f130fd79001db47485cc95c2",
"index": 9891,
"step-1": "<mask token>\n\n\ndef test_json_float():\n assert_equals(json(1.234), '1.234')\n\n\ndef test_json_array():\n data = [1, 2, 3]\n assert_equals(json(data), '[1,2,3]')\n\n\ndef test_json_array02():\n data = ['bla', 1, 1.2]... | [
12,
13,
19,
22,
24
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.