code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
'''harvestPRR: analyze Public Record Requests from CSV data provided by NextRequest
Created 27 Aug 20
@author: rik@electronicArtifacts.com
'''
from collections import defaultdict
import csv
import datetime
import json
import random
import re
import requests
import sys
import time
import urllib
import re
PRRDateFm... | normal | {
"blob_id": "b3758e42b52bb50d806832c6a3a76ae0537266de",
"index": 8043,
"step-1": "<mask token>\n\n\ndef freqHist3(tbl):\n \"\"\"python3 version\n\tASSUME: values are frequencies, returns sorted list of (val,freq) items in descending freq order\n\t\"\"\"\n from functools import cmp_to_key\n\n def cmpd1(a... | [
10,
11,
13,
14,
16
] |
class Solution:
def sumSubarrayMins(self, A: List[int]) ->int:
stack = []
prev = [None] * len(A)
for i in range(len(A)):
while stack and A[stack[-1]] >= A[i]:
stack.pop()
prev[i] = stack[-1] if stack else -1
stack.append(i)
stack =... | normal | {
"blob_id": "97029ac9f05037bf9304dacf86c35f5534d887c4",
"index": 8303,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def sumSubarrayMins(self, A: List[int]) ->int:\n stack = []\n prev = [None] * len(A)\n for i in range(len(... | [
0,
1,
2
] |
# -*- 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
] |
import argparse
import requests
from ba_bypass_bruteforce import bruteforce, stop_brute, success_queue, dict_queue, success_username
from random import choice
from time import sleep
MAX_ROUND = 3 # 爆破的轮数
curr_round = 0 # 当前的轮数
sleep_time = 2 # 每一轮休眠的秒数
def login_limit_user():
"""
登录函数
"""
try:
... | normal | {
"blob_id": "94286fc36e06598b9faa65d9e5759f9518e436c6",
"index": 7979,
"step-1": "<mask token>\n\n\ndef login_limit_user():\n \"\"\"\n 登录函数\n \"\"\"\n try:\n login_info = dict_queue.get(block=False)\n except Exception as e:\n print('[Error] {0}'.format(repr(e)))\n return\n ... | [
4,
5,
6,
7,
8
] |
<|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": "b07d042c61e9e6647822989444e72db2e01c64d0",
"index": 5751,
"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 = [('devices_col... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@uvicore.service()
class Mail:
def __init__(self, *, mailer: str=None, mailer_options: Dict=None, to:
List=[], cc: List=[], bcc: List=[], from_name: str=None,
from_address: str=None, subject: str=None, html: str=None, text:
str=None, attachments: List=[]) ->No... | flexible | {
"blob_id": "c87ede0e3c6d4cc305450f68b4cf61fb63986760",
"index": 8676,
"step-1": "<mask token>\n\n\n@uvicore.service()\nclass Mail:\n\n def __init__(self, *, mailer: str=None, mailer_options: Dict=None, to:\n List=[], cc: List=[], bcc: List=[], from_name: str=None,\n from_address: str=None, subj... | [
6,
8,
9,
10,
15
] |
<|reserved_special_token_0|>
class Ui_MapGraphTab(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Ui_MapGraphTab(object):
def setupUi(self, MapGraphTab):
MapGraphTab.setObjectNam... | flexible | {
"blob_id": "03a13037a9a102397c8be4d9f0f4c5e150965808",
"index": 8666,
"step-1": "<mask token>\n\n\nclass Ui_MapGraphTab(object):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_MapGraphTab(object):\n\n def setupUi(self, MapGraphTab):\n MapGraphTab.setO... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print
<|reserved_special_token_1|>
a = 'Hello, World!'
print
| flexible | {
"blob_id": "b779cfc6d6456a370092bf1cfa5904c869b7466a",
"index": 9219,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint\n",
"step-3": "a = 'Hello, World!'\nprint\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
import json
import requests
class Bitcoin:
coindesk = 'https://api.coindesk.com/v1/bpi/currentprice.json'
def __init__(self):
pass
def get_current_price(self, url=coindesk):
self.resp = requests.get(url)
if self.resp.status_code == 200:
return json.loads(self.resp.con... | normal | {
"blob_id": "3bfe4021d5cf9bd24c0fb778b252bc04c6ac47ed",
"index": 1847,
"step-1": "<mask token>\n\n\nclass Bitcoin:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Bitcoin:\n <mask token>\n\n def __init__(self):\n pass\n <mask token>... | [
1,
2,
4,
5,
6
] |
import ipaddress
import subprocess
from subprocess import Popen, PIPE
import time
ip_net = ipaddress.ip_network('192.168.0.100/30')
for i in ip_net.hosts():
# print(i)
host_add = str(i)
toping = subprocess.Popen(['ping', '-n', '3',host_add],stdout=PIPE)
output = toping.communicate()[0]
... | normal | {
"blob_id": "414fb437783fcfb55f542f072aaf3a8bb02b441e",
"index": 8275,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in ip_net.hosts():\n host_add = str(i)\n toping = subprocess.Popen(['ping', '-n', '3', host_add], stdout=PIPE)\n output = toping.communicate()[0]\n hostalive = toping.re... | [
0,
1,
2,
3,
4
] |
class Solution(object):
def smallestGoodBase(self, n):
"""
:type n: str
:rtype: str
"""
# k is the base and the representation is
# m bits of 1
# We then have from math
# (k**m - 1) / (k-1) = n
# m = log_k (n * k - n + 1)
# m needs to b... | normal | {
"blob_id": "de287d1bc644fdfd0f47bd8667580786b74444d0",
"index": 8863,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n <mask token>\n",
"step-3": "class Solution(object):\n <mask token>\n\n def solve_equation(self, m, n):\n k_l, k_h = 2, n - 1\n while... | [
0,
1,
2,
3,
4
] |
import random
import cv2
img = cv2.imread('assets/logo.jpg', -1)
print(img.shape) #3 channels, bgr
#look at the 257. row and pixel 400 --> has bgr values: [41 98 243]
print(img[257][400])
'''
# manipulate the first 100 rows, all columns, and randomize the 3 pixel values
# (rows, colums, pixels) where pixels: b,g,... | normal | {
"blob_id": "35e66e5e154f5cd70f187a1cde33cef71102e1a6",
"index": 6829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(img.shape)\nprint(img[257][400])\n<mask token>\ncv2.imshow('Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nimg = cv2.imread('assets/logo.jpg', ... | [
0,
1,
2,
3,
4
] |
from pymongo import MongoClient
from modules.linkedinSearch import SearchClass
from config import Config
class LinkedinSearch:
def __init__(self):
self.client = MongoClient(Config.MONGO_URI)
db = self.client.linkedin_db
self.collection = db.search
self.dict = {}
self.obj ... | normal | {
"blob_id": "3e8860c22ff3092304df57aa7f5dbcb6ccda7dd8",
"index": 5249,
"step-1": "<mask token>\n\n\nclass LinkedinSearch:\n <mask token>\n <mask token>\n\n def db_fetch(self, query):\n self.collection.create_index([('name', 'text')])\n lst = []\n cursor = self.collection.find({'$tex... | [
2,
4,
5,
6,
7
] |
import torch
from torchvision import datasets, transforms
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from PIL import Image
import requests
from io import BytesIO
from net import Net
class predict_guitar():
def __init__(self):
"""Model is lo... | normal | {
"blob_id": "8743be809953f59bd14431e509042c4c51d9fab4",
"index": 4175,
"step-1": "<mask token>\n\n\nclass predict_guitar:\n <mask token>\n\n def softmax(self, vector):\n \"\"\"Softmax function for calculating probs\"\"\"\n e = np.exp(vector)\n return e / e.sum()\n <mask token>\n",
... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def gcd_naive(a, b):
x = 5
while x > 1:
if a % b != 0:
c = a % b
a = b
b = c
else:
x = 1
return b
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def gcd_naive(a, b):
... | flexible | {
"blob_id": "c70681f5ff8d49a243b7d26164aa5430739354f4",
"index": 6936,
"step-1": "<mask token>\n\n\ndef gcd_naive(a, b):\n x = 5\n while x > 1:\n if a % b != 0:\n c = a % b\n a = b\n b = c\n else:\n x = 1\n return b\n\n\n<mask token>\n",
"step-... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
@ray.remote
def run(run_config: dict, wrks: dict) ->dict:
try:
add_spk_role()
except:
print('run, spark: ignore')
os.chdir(microps_dir)
base_spk_config = spk.apps_config_map['sparkperfml']
base_spk_config = spk.patched_app_config(base_spk_config, {'app_... | flexible | {
"blob_id": "25595b5f86a41fee1dc43f199f3bcff73f6d256b",
"index": 9418,
"step-1": "<mask token>\n\n\n@ray.remote\ndef run(run_config: dict, wrks: dict) ->dict:\n try:\n add_spk_role()\n except:\n print('run, spark: ignore')\n os.chdir(microps_dir)\n base_spk_config = spk.apps_config_map[... | [
1,
2,
3,
4,
5
] |
import numpy as np
from base_test import ArkoudaTest
from context import arkouda as ak
"""
Encapsulates unit tests for the pdarrayclass module that provide
summarized values via reduction methods
"""
class SummarizationTest(ArkoudaTest):
def setUp(self):
ArkoudaTest.setUp(self)
self.na = np.linsp... | normal | {
"blob_id": "88109909d0c80f25373f917426c3c3634bfc8114",
"index": 6267,
"step-1": "<mask token>\n\n\nclass SummarizationTest(ArkoudaTest):\n\n def setUp(self):\n ArkoudaTest.setUp(self)\n self.na = np.linspace(1, 10, 10)\n self.pda = ak.array(self.na)\n <mask token>\n\n def testMin(s... | [
6,
7,
8,
9,
11
] |
from typing import List
class Solution:
def findSubsequences(self, nums: List[int]) ->List[List[int]]:
res: List[List[int]] = []
s = set()
def deep(pos: int, tmp: List[int]):
if pos == len(nums):
if len(tmp) < 2:
return
for ... | normal | {
"blob_id": "3edfc1098c775fa31456aa3cc938051b2dbb8697",
"index": 1664,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def findSubsequences(self, nums: List[int]) ->List[List[int]]:\n res: List[List[int]] = []\n s = set()\n\n def deep(pos: int, tmp: List[int... | [
0,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestTmdb(BaseTestCase):
<|reserved_special_token_0|>
def test_discover(self):
""" Testing the TMDB API discover endpoint """
response = Tmdb.discover()
self.assertTrue(int(response.status_code) == 200)
data = response.json()
self.asse... | flexible | {
"blob_id": "9e9403ea1c128e07803d080b337003055759c5ae",
"index": 4507,
"step-1": "<mask token>\n\n\nclass TestTmdb(BaseTestCase):\n <mask token>\n\n def test_discover(self):\n \"\"\" Testing the TMDB API discover endpoint \"\"\"\n response = Tmdb.discover()\n self.assertTrue(int(respon... | [
4,
5,
6,
7,
10
] |
<|reserved_special_token_0|>
class Person:
def __init__(self, name, surname, job, salary):
self.name = name
self.surname = surname
self.job = job
self.salary = salary
def create(name):
conn = db.connect(name + '.db')
c = conn.cursor()
c.execute(
"""CREATE TAB... | flexible | {
"blob_id": "7ff19ee35422395f78dca1e17a736df20a40ea98",
"index": 7569,
"step-1": "<mask token>\n\n\nclass Person:\n\n def __init__(self, name, surname, job, salary):\n self.name = name\n self.surname = surname\n self.job = job\n self.salary = salary\n\n\ndef create(name):\n conn... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
@driver_api.route('/<int:driver_id>', methods=['PUT'])
def update(driver_id):
req_data = request.get_json()
data, error = driver_schema.load(req_data, partial=True)
if error:
return custom_response({'Error': 'Driver not found.'}, 400)
driver = DriverModel.get_one_d... | flexible | {
"blob_id": "ee7820d50b5020a787fbaf012480e8c70bc0ee41",
"index": 1690,
"step-1": "<mask token>\n\n\n@driver_api.route('/<int:driver_id>', methods=['PUT'])\ndef update(driver_id):\n req_data = request.get_json()\n data, error = driver_schema.load(req_data, partial=True)\n if error:\n return custom... | [
2,
5,
7,
9,
10
] |
<|reserved_special_token_0|>
class DecoderBase(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self):
self._predictor = 'decoder'
self._label = None
pass
@abstractmethod
def set_label(self, label):
self._label = label
<|reserved... | flexible | {
"blob_id": "0d8a26ef4077b40e8255d5bb2ce9217b51118780",
"index": 7364,
"step-1": "<mask token>\n\n\nclass DecoderBase(object):\n <mask token>\n <mask token>\n\n def __init__(self):\n self._predictor = 'decoder'\n self._label = None\n pass\n\n @abstractmethod\n def set_label(se... | [
4,
5,
7,
8,
10
] |
<|reserved_special_token_0|>
class TestPluginFunimationNow(unittest.TestCase):
def test_arguments(self):
from streamlink_cli.main import setup_plugin_args
session = Streamlink()
parser = MagicMock()
group = parser.add_argument_group('Plugin Options').add_argument_group(
... | flexible | {
"blob_id": "266add60be2b6c2de5d53504cbabf754aa62d1b0",
"index": 9806,
"step-1": "<mask token>\n\n\nclass TestPluginFunimationNow(unittest.TestCase):\n\n def test_arguments(self):\n from streamlink_cli.main import setup_plugin_args\n session = Streamlink()\n parser = MagicMock()\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
firebase_admin.initialize_app(cred, {'databaseURL':
'https://mikro-b4844.firebaseio.com/'})
<|reserved_special_token_0|>
print(ref.get())
<|reserved_special_token_0|>
while True:
print(ref.get())
if ref.get() == 'Off' ... | flexible | {
"blob_id": "acff8618754658104ac36214901d346447a0134f",
"index": 811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfirebase_admin.initialize_app(cred, {'databaseURL':\n 'https://mikro-b4844.firebaseio.com/'})\n<mask token>\nprint(ref.get())\n<mask token>\nwhile True:\n print(ref.get())\n if re... | [
0,
1,
2,
3,
4
] |
from __future__ import annotations
import ibis
from ibis import _
def test_format_sql_query_result(con, snapshot):
t = con.table("airlines")
query = """
SELECT carrier, mean(arrdelay) AS avg_arrdelay
FROM airlines
GROUP BY 1
ORDER BY 2 DESC
"""
schema = ibis.schema({"... | normal | {
"blob_id": "97ff8dae060475b0efbc8d39e9fc251be8ac091b",
"index": 6264,
"step-1": "<mask token>\n\n\ndef test_memoize_insert_sort_key(con, snapshot):\n table = con.table('airlines')\n t = table['arrdelay', 'dest']\n expr = t.group_by('dest').mutate(dest_avg=t.arrdelay.mean(), dev=t.\n arrdelay - t... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
try:
from setuptools import setup
from setuptools import find_packages
has_setup_tools = true
except ImportError:
from distutils.core import setup
has_setup_tools = false
with open('README.md', 'r') as fh:
long_description = fh.read()
... | flexible | {
"blob_id": "5d988d159902e4a4cb17ee0ec61153de2dda4691",
"index": 9120,
"step-1": "<mask token>\n",
"step-2": "try:\n from setuptools import setup\n from setuptools import find_packages\n has_setup_tools = true\nexcept ImportError:\n from distutils.core import setup\n has_setup_tools = false\nwit... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def weib(x, nn, a):
return a / nn * (x / nn) ** (a - 1) * n.exp(-(x / nn) ** a)
<|reserved_special_token_0|>
print('distancias de KS para os modelos matematicos:', diffN, diffN2, diffU,
diffU2, diffW, diffP)
<|reserved... | flexible | {
"blob_id": "647258ee5f2f6f1cb8118bcf146b8959c65b70cd",
"index": 8045,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef weib(x, nn, a):\n return a / nn * (x / nn) ** (a - 1) * n.exp(-(x / nn) ** a)\n\n\n<mask token>\nprint('distancias de KS para os modelos matematicos:', diffN, diffN2, diffU,\n ... | [
0,
2,
3,
4,
5
] |
import speech_recognition as sr
import pyttsx3
import pywhatkit
import datetime
listner = sr.Recognizer()
engine = pyttsx3.init()
#change voices
voices = engine.getProperty('voices')
engine.setProperty('voice',voices[10].id)
rate = engine.getProperty('rate')
engine.setProperty('rate', 150)
#for machine to say
def t... | normal | {
"blob_id": "c4f437e6f5aaeccb6dd0948c3ed1f1d465bb29ce",
"index": 1200,
"step-1": "<mask token>\n\n\ndef talk(text):\n engine.say(text)\n engine.runAndWait()\n\n\ndef takeCommand():\n try:\n with sr.Microphone() as sc:\n print('Listening......')\n vc = listner.listen(sc)\n ... | [
3,
4,
5,
6,
7
] |
"""
@Description:
@Author : HCQ
@Contact_1: 1756260160@qq.com
@Project : pytorch
@File : call_test
@Time : 2022/5/24 下午10:19
@Last Modify Time @Version @Desciption
-------------------- -------- -----------
2022/5/24 下午10:19 1.0 None
"""
class Person():
def __cal... | normal | {
"blob_id": "7b1c7228c1fc9501ab857cba62a7e073691e75c9",
"index": 755,
"step-1": "<mask token>\n\n\nclass Person:\n\n def __call__(self, name):\n print('__call__' + ' Hello ' + name)\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Person:\n\n def __call__(self, name):\n ... | [
2,
3,
4,
5,
6
] |
from vmgCommanderBase import CommanderBase
from vmgInstallerApt import InstallerApt
from vmgInstallerYum import InstallerYum
from vmgConfigLinux import ConfigLinux
from runCommands import *
import shutil
import os
import time
from vmgLogging import *
from writeFormat import *
from vmgControlVmware import *
from vmgUtil... | normal | {
"blob_id": "22fe07a237f2c5f531d189c07596a22df191d038",
"index": 1140,
"step-1": "from vmgCommanderBase import CommanderBase\nfrom vmgInstallerApt import InstallerApt\nfrom vmgInstallerYum import InstallerYum\nfrom vmgConfigLinux import ConfigLinux\nfrom runCommands import *\nimport shutil\nimport os\nimport tim... | [
0
] |
# Copyright (c) 2020 Hai Nguyen
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
import tensorflow.keras.backend as K
def dice_coef(y_true, y_pred):
smooth = 1.
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_... | normal | {
"blob_id": "18b10a68b2707b7bfeccbd31c5d15686453b3406",
"index": 6253,
"step-1": "<mask token>\n\n\ndef false_pos(y_true, y_pred):\n smooth = 1\n y_pred_pos = K.round(K.clip(y_pred, 0, 1))\n y_pos = K.round(K.clip(y_true, 0, 1))\n y_neg = 1 - y_pos\n fp = K.sum(y_neg * y_pred_pos)\n fp_ratio = ... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('nodes_tags.csv', 'r') as f:
tags = csv.DictReader(f)
for row in tags:
if row['key'] == 'FIXME':
pp(row)
<|reserved_special_token_1|>
import csv
from pprint import pprint as pp
with open('n... | flexible | {
"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
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mkrandom.settings')
<|reserved_special_token_0|>
django.setup()
<|reserved_special_token_0|>
for char in char_names:
index = x - y + 1
name = char_names[x]
if 'Yoshi (' ... | flexible | {
"blob_id": "dbda5df7dff3f8acc320ffe7b9c7c279ebed2cc2",
"index": 7108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mkrandom.settings')\n<mask token>\ndjango.setup()\n<mask token>\nfor char in char_names:\n index = x - y + 1\n name = char_names[x]\... | [
0,
1,
2,
3,
4
] |
from rest_framework import serializers
from .models import SensorValue
class SensorValueSerializer(serializers.ModelSerializer):
timestamp = serializers.DateTimeField(required=False)
class Meta:
model = SensorValue
fields = ("id", "timestamp", "sensor_type", "value")
| normal | {
"blob_id": "39312ec60c9ef1c9c95cf4206b6d0bbdb0aedf94",
"index": 9042,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass SensorValueSerializer(serializers.ModelSerializer):\n <mask token>\n\n\n class Meta:\n model = SensorValue\n fields = 'id', 'timestamp', 'sensor_type', 'valu... | [
0,
1,
2,
3,
4
] |
# POST API for Red Alert project - NLP and Metalearning components
# Insikt Intelligence S.L. 2019
import pandas as pd
import pickle
from flask import Flask, render_template, request, jsonify
from utilities import load_data, detect_language
from preprocessing import preprocess, Tagger, remove_stopwords
import json
fro... | normal | {
"blob_id": "b51e0ee80a2488197470627821204d1f74cd62a1",
"index": 5437,
"step-1": "<mask token>\n\n\n@app.route('/probability', methods=['POST'])\ndef make_probability():\n try:\n data = request.get_json()\n except Exception as e:\n raise e\n if data == {}:\n return bad_request()\n ... | [
7,
8,
10,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def how_many_seconds(hrs_int):
secs_int = None
if hrs_int > 0 and hrs_int is not None:
secs_int = hrs_int * 60 * 60
return secs_int
else:
raise TypeError('Invalid input type')
<|reserved_spe... | flexible | {
"blob_id": "34c7e6b6bc687bc641b7e3b9c70fd0844af8e340",
"index": 8969,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef how_many_seconds(hrs_int):\n secs_int = None\n if hrs_int > 0 and hrs_int is not None:\n secs_int = hrs_int * 60 * 60\n return secs_int\n else:\n rai... | [
0,
1,
2
] |
# 引入基础的工作表
from openpyxl import Workbook
# 引入增强的修改功能
from openpyxl.styles import Font,Alignment,Border,Side,PatternFill,colors
# import openpyxl
def make_example():
# 设定文件目录
addr = './example.xlsx'
# 初始化文件，切换到活动的工作表
work_book = Workbook()
# 读取文件采用
# work_book = openpyxl.load_workbook... | normal | {
"blob_id": "d7524a455e62594e321b67f0a32a5c3a7437c1d6",
"index": 1093,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef make_example():\n addr = './example.xlsx'\n work_book = Workbook()\n work_sheet = work_book.active\n work_sheet['A1'] = 'Hello World!'\n select_cell = work_sheet.ce... | [
0,
1,
2,
3,
4
] |
def chessKnight(cell):
pivot = "abcdefgh"
count = 8
for i in range(len(pivot)):
if cell[0] == pivot[i]:
vertical_4 , vertical_2 = False , False
if int(cell[1]) == 8 or int(cell[1]) == 1:
vertical_4 = True
count -= 4
elif int(cell[1]... | normal | {
"blob_id": "c1335a8128ad4ba6ce6942e80f3c8b68a4210902",
"index": 6355,
"step-1": "<mask token>\n",
"step-2": "def chessKnight(cell):\n pivot = 'abcdefgh'\n count = 8\n for i in range(len(pivot)):\n if cell[0] == pivot[i]:\n vertical_4, vertical_2 = False, False\n if int(ce... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while t:
n = int(input())
a = list(map(int, input().split()))
a.sort(reverse=True)
s = 0
for i in range(n):
k = a[i] - i
if k >= 0:
s += k
print(s % 1000000007)
t -= 1
<|re... | flexible | {
"blob_id": "44bf409d627a6029ab4c4f1fff99f102b8d57279",
"index": 3954,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile t:\n n = int(input())\n a = list(map(int, input().split()))\n a.sort(reverse=True)\n s = 0\n for i in range(n):\n k = a[i] - i\n if k >= 0:\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [url('^$', views.index_view, name='accounts.index'), url(
'^login/$', views.login_view, name='accounts.login'), url('^logout/$',
views.logout_view, name='accounts.logout'), url('^registro/$', views.
regis... | flexible | {
"blob_id": "b4d09b6d8ad5f0584f74adc0fd8116265bb6649b",
"index": 4641,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^$', views.index_view, name='accounts.index'), url(\n '^login/$', views.login_view, name='accounts.login'), url('^logout/$',\n views.logout_view, name='accounts.... | [
0,
1,
2,
3
] |
import requests
import json
import hashlib
import os
def pull_from_solr(output_directory):
solr_url = 'http://54.191.81.42:8888/solr/collection1/select?q=*%3A*&wt=json&indent=true'
# TODO: ask about auth for this
req = requests.get(solr_url)
if req.status_code != 200:
raise
new_data = r... | normal | {
"blob_id": "47b40e4311f76cd620b7c6ed6b39216d866fa857",
"index": 8530,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef pull_from_solr(output_directory):\n solr_url = (\n 'http://54.191.81.42:8888/solr/collection1/select?q=*%3A*&wt=json&indent=true'\n )\n req = requests.get(solr... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
def combine(self, n: int, k: int) ->List[List[int]]:
if ... | flexible | {
"blob_id": "e4a2c605ef063eee46880515dfff05562916ab81",
"index": 9976,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def combine(self, n: int, k: int) ->List[List[int]]:\n if k == 0:\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Auth:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@app.route('/welcome/<username>/suffix/<message>')
def welcome(username, message):
return jsonify({'comment': f'Hello {username}, {message}!'})
... | flexible | {
"blob_id": "8fcc2a13fd5a803e2d755a567c78c8274bd88aad",
"index": 7283,
"step-1": "<mask token>\n\n\nclass Auth:\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/welcome/<username>/suffix/<message>')\ndef welcome(username, message):\n return jsonify({'comment': f'Hello {usern... | [
1,
3,
6,
9,
10
] |
import cv2
import pytesseract
import os
from PIL import Image
import numpy as np
from helper_functions import Helper
class ImageData:
# multipliers to get portion of image with interval value
__bottom_thresh = 0.9
__left_thresh = 0.35
__right_thresh = 0.65
# (words, offset) to contour interval value
__words_of... | normal | {
"blob_id": "d3be26d56b3597a5d9e3a870b735a30d90d1e501",
"index": 8165,
"step-1": "<mask token>\n\n\nclass ImageData:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, image):\n self.image = image\n self._contour_interval_dist = None\... | [
6,
10,
11,
12,
17
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def index(request):
return render(request, 'munchiesfastfood/home.html', {'drinks': [
'Pineapple Juice', 'Green Juice', 'Soft Drinks',
'Carlo Rosee Drinks'], 'dishes': ['Beef Steak',
'Tomato with Chic... | flexible | {
"blob_id": "e279ca43ce2c582c702f1c6a0c1acf37eb9bcefe",
"index": 5603,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef index(request):\n return render(request, 'munchiesfastfood/home.html', {'drinks': [\n 'Pineapple Juice', 'Green Juice', 'Soft Drinks',\n 'Carlo Rosee Drinks'], 'd... | [
0,
1,
2
] |
import sys
import numpy as np
import math
import matplotlib.pyplot as plt
import random
def load_files(training, testing):
tr_feat = np.genfromtxt(training, usecols=range(256), delimiter=",")
tr_feat /= 255.0
tr_feat = np.insert(tr_feat, 0, 0, axis=1)
tr_exp = np.genfromtxt(training, usecols=range(-1)... | normal | {
"blob_id": "4af05a13264c249be69071447101d684ff97063e",
"index": 6725,
"step-1": "<mask token>\n\n\ndef load_files(training, testing):\n tr_feat = np.genfromtxt(training, usecols=range(256), delimiter=',')\n tr_feat /= 255.0\n tr_feat = np.insert(tr_feat, 0, 0, axis=1)\n tr_exp = np.genfromtxt(traini... | [
4,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
"""
Description: This modules is used for testing. Testing is performed based on the list of commands given to perform in a website
Version : v1.5
History :
v1.0 - 08/01/2016 - Initial version
v1.1 - 08/05/2016 - Modified to accept List input.
... | normal | {
"blob_id": "9e77385933cf6e381f25bea9020f909d5dc6817d",
"index": 4744,
"step-1": "# -*- coding: utf-8 -*-\n\"\"\"\n Description: This modules is used for testing. Testing is performed based on the list of commands given to perform in a website\n Version : v1.5\n History :\n v1.0 - 0... | [
0
] |
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print("chars: ", chars)
#one-hot encoding
char_to_ix = { ch:i for i,ch in enumerate(chars) }
ix_to_char = { i:ch for i,ch in enumerate(chars) }
it... | normal | {
"blob_id": "d988cfebeec37df700f46bbb027a4980ba624d30",
"index": 6639,
"step-1": "<mask token>\n\n\ndef lossFun(inputs, targets, hprev):\n x, h, yprime = {}, {}, {}\n h[-1] = np.copy(hprev)\n loss = 0\n for t in range(len(inputs)):\n x[t] = np.zeros((vocab_size, 1))\n x[t][inputs[t]] = ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
lr.fit(x_train, y_train)
<|reserved_special_token_0|>
pickle.dump(lr, open('model.pkl', 'wb'))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
dataset = pd.read_csv('heart.csv')
df = dataset.copy()
X = df.drop(['targe... | flexible | {
"blob_id": "1508697f93114d7f20182a3e9c1df5617904529a",
"index": 8725,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlr.fit(x_train, y_train)\n<mask token>\npickle.dump(lr, open('model.pkl', 'wb'))\n",
"step-3": "<mask token>\ndataset = pd.read_csv('heart.csv')\ndf = dataset.copy()\nX = df.drop(['targ... | [
0,
1,
2,
3,
4
] |
'''
log.py
version 1.0 - 18.03.2020
Logging fuer mehrere Szenarien
'''
# Imports
import datetime
# Globale Variablen
ERROR_FILE = "error.log"
LOG_FILE = "application.log"
def error(msg):
__log_internal(ERROR_FILE, msg)
def info(msg):
__log_internal(LOG_FILE, msg)
def __log_internal(filenam... | normal | {
"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
] |
# -*- coding: utf-8 -*-
# Third party imports
import numpy as np
# Local application imports
from mosqito.sound_level_meter import noct_spectrum
from mosqito.sq_metrics.loudness.loudness_zwst._main_loudness import _main_loudness
from mosqito.sq_metrics.loudness.loudness_zwst._calc_slopes import _calc_slopes
from mosq... | normal | {
"blob_id": "75716aaaca63f8ca6d32c885021c1dc0f9a12dac",
"index": 793,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef loudness_zwst(signal, fs=None, field_type='free', is_sdt_output=False):\n \"\"\"Zwicker-loudness calculation for stationary signals\n\n Calculates the acoustic loudness accor... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python2
import os
import sys
import textwrap
COMMAND = (
'convert -size 1920x1080 canvas:"rgb(149, 1, 1)" '
'-font Dejavu-Sans-Bold -pointsize {0} -gravity center -stroke none '
'-fill white -annotate 0 "{1}" -size 1920x1080 "{2}.png"'
)
def makeimage(text, point_size=100, width=30):
t... | normal | {
"blob_id": "a486ec6b27a6b84e454a1bed096be9fe22d91612",
"index": 1561,
"step-1": "<mask token>\n\n\ndef makeimage(text, point_size=100, width=30):\n tw = textwrap.TextWrapper(width=width)\n text = '\\n'.join(a.replace('\\\\n', '\\n') for a in tw.wrap(text))\n filename = ''.join(c for c in text.replace('... | [
2,
3,
4,
5,
6
] |
from torch.utils.data import IterableDataset, DataLoader
from torch import nn
from torch.nn import functional as F
from triplet_training_generator import get_train_test_apikeys, training_generator
from pathlib import Path
from transformers import AutoModel
import torch
from tqdm import tqdm
import pandas as pd
MEMMAP_... | normal | {
"blob_id": "650f00dd9740d62546eb58724e6e5a74398b3e59",
"index": 2522,
"step-1": "<mask token>\n\n\nclass DataGenerator(IterableDataset):\n <mask token>\n <mask token>\n\n\nclass CrossEncoderModel(torch.nn.Module):\n\n def __init__(self):\n super(CrossEncoderModel, self).__init__()\n self.... | [
4,
7,
9,
10,
11
] |
""" OCR that converts images to text """
from pytesseract import image_to_string
from PIL import Image
print image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))
#print image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))
#pr... | normal | {
"blob_id": "91ac4a23573abcb0ab024830dbc1daebd91bd40d",
"index": 2355,
"step-1": "\"\"\" OCR that converts images to text \"\"\"\n\nfrom pytesseract import image_to_string\nfrom PIL import Image\n\nprint image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))\n\n#print ... | [
0
] |
#!/usr/bin/env python
# Title : STACK_BostonHousing.py
# Description : Stacking was the natural progression of our algorithms trial.
# In here, we'll use prediction from a number of models in order
# to improve accuracy as it add linearly independent data to our
# ... | normal | {
"blob_id": "21c581131cff8cf2f4aa407055184d56865a6335",
"index": 9783,
"step-1": "<mask token>\n\n\nclass Ensemble(object):\n \"\"\"Ensemble base_models on train data than fit/predict\n\n The object input is composed of 'n_splits', 'stacker' and list of\n 'base_models'.\n\n The __init__ method self-a... | [
4,
5,
6,
7,
8
] |
#!/usr/bin/python
#
# Copyright 2018-2020 Polyaxon, Inc.
#
# 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 ... | normal | {
"blob_id": "fd391d28d76b0c1b3cf6d0b5134390ab3f1267fb",
"index": 5152,
"step-1": "<mask token>\n\n\nclass CliConfigManager(BaseConfigManager):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @classmethod\n def _get_count(cls):\n config = cls.get_config_o... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(n)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
heat = Heatmodel()
n = heat.get_component_name()
print(n)
<|reserved_special_token_1|>
from pymt_heat import Heatmodel
heat = Heatmodel()
n = heat.get_compon... | flexible | {
"blob_id": "82801ce564f4f29e084e6f842d7868eb60f582cb",
"index": 6225,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(n)\n",
"step-3": "<mask token>\nheat = Heatmodel()\nn = heat.get_component_name()\nprint(n)\n",
"step-4": "from pymt_heat import Heatmodel\nheat = Heatmodel()\nn = heat.get_comp... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def näita_tabelit(ttt, tabel):
hetkeseis(tabel)
ttt.blit(tabel, (0, 0))
pygame.display.flip()
def hiire_positsioon_tabelis(Xkoordinaat, Ykoordinaat):
if Ykoordinaat < 100:
rida = 0
elif Ykoordinaat < 200:
rida = 1
else:
rida = 2
if Xko... | flexible | {
"blob_id": "a667c4cb0a30ee67fe982bb96ece6bb75f25f110",
"index": 7084,
"step-1": "<mask token>\n\n\ndef näita_tabelit(ttt, tabel):\n hetkeseis(tabel)\n ttt.blit(tabel, (0, 0))\n pygame.display.flip()\n\n\ndef hiire_positsioon_tabelis(Xkoordinaat, Ykoordinaat):\n if Ykoordinaat < 100:\n rida = ... | [
6,
7,
9,
10,
11
] |
#!/usr/bin/python
def check(n):
if n == 0 :
print "neither Positive nor Negative"
if n < 0 :
print "Negative"
if n > 0 :
print "Positive"
print "10 is ", check(10)
print "-5 is ", check(-5)
print "0 is ", check(0) | normal | {
"blob_id": "9c6bb885c05ee13a283b09861a5aa7c5e62677cb",
"index": 1008,
"step-1": "#!/usr/bin/python\ndef check(n):\n if n == 0 :\n print \"neither Positive nor Negative\"\n if n < 0 :\n print \"Negative\"\n if n > 0 :\n print \"Positive\"\n\n\n\nprint \"10 is \", check(10)\nprint \"... | [
0
] |
<|reserved_special_token_0|>
class Punkt(Figura):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Linia(Figura):
def __init__(self):
print('Tworze obiekt klasy Linia...')
def wyswietl(self):
print('Me... | flexible | {
"blob_id": "774bf2b49f6e546f16294edc17e9ac34fa8a9ba8",
"index": 2711,
"step-1": "<mask token>\n\n\nclass Punkt(Figura):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Linia(Figura):\n\n def __init__(self):\n print('Tworze obiekt klasy Linia...')\n\n def wyswietl(... | [
27,
30,
31,
34,
41
] |
def heapify(lst, index, heap_size):
largest = index
left_index = 2 * index + 1
right_index = 2 * index + 2
if left_index < heap_size and lst[left_index] > lst[largest]:
largest = left_index
if right_index < heap_size and lst[right_index] > lst[largest]:
largest = right_index
if l... | normal | {
"blob_id": "d8ea396ff8514cc10e02072ea478f0276584153d",
"index": 3274,
"step-1": "<mask token>\n",
"step-2": "def heapify(lst, index, heap_size):\n largest = index\n left_index = 2 * index + 1\n right_index = 2 * index + 2\n if left_index < heap_size and lst[left_index] > lst[largest]:\n lar... | [
0,
1,
2
] |
users = {1: "Tom", 2: "Bob", 3: "Bill"}
elements = {"Au": "Oltin", "Fe": "Temir", "H": "Vodorod", "O": "Kislorod"} | normal | {
"blob_id": "a24ab93983546f8ae0fab042c121ac52388e62e8",
"index": 2967,
"step-1": "<mask token>\n",
"step-2": "users = {(1): 'Tom', (2): 'Bob', (3): 'Bill'}\nelements = {'Au': 'Oltin', 'Fe': 'Temir', 'H': 'Vodorod', 'O': 'Kislorod'}\n",
"step-3": "users = {1: \"Tom\", 2: \"Bob\", 3: \"Bill\"}\n\nelements = {\... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class PrintTree(object):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class PrintTree(object):
def printTree(self, root):
if not root:
return
"""
定义next_last为下一层的最后一个，cur_last为当前层最后一个
temp用... | flexible | {
"blob_id": "4ddff57790ad191fc29fc092bcc714f0b6273100",
"index": 7755,
"step-1": "<mask token>\n\n\nclass PrintTree(object):\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass PrintTree(object):\n\n def printTree(self, root):\n if not root:\n return\n \"\"\"\n 定义next_la... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(selected_movies)
<|reserved_special_token_0|>
print(selected_movies2)
<|reserved_special_token_1|>
movies = ['Abraham Lincoln', 'Blue Steel', 'Behind Office Doors',
'Bowery at Midnight', 'Captain Kidd', 'Debbie Does D... | flexible | {
"blob_id": "8435a69ee9793435c7483df9bb15f01ef8051479",
"index": 3340,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(selected_movies)\n<mask token>\nprint(selected_movies2)\n",
"step-3": "movies = ['Abraham Lincoln', 'Blue Steel', 'Behind Office Doors',\n 'Bowery at Midnight', 'Captain Kidd',... | [
0,
1,
2,
3
] |
import webbrowser
import time
x=10
while x > 0:
print (x), time.sleep(1)
x=x-1
while x==0:
print ("MEOW")
webbrowser.open("https://www.youtube.com/watch?v=IuysY1BekOE")
| normal | {
"blob_id": "4d31357936ce53b2be5f9a952b99df58baffe7ea",
"index": 4937,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile x > 0:\n print(x), time.sleep(1)\n x = x - 1\nwhile x == 0:\n print('MEOW')\n webbrowser.open('https://www.youtube.com/watch?v=IuysY1BekOE')\n",
"step-3": "<mask token... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class DatabaseAdmin(admin.ModelAdmin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class AlarmAdmin(admin.ModelAdmin):
list_display... | flexible | {
"blob_id": "e1968e0d6146ce7656505eeed8e9f31daa4b558a",
"index": 5447,
"step-1": "<mask token>\n\n\nclass DatabaseAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n\n\nclass AlarmAdmin(admin.ModelAdmin):\n list_display = ['nam... | [
5,
7,
10,
11,
13
] |
from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.core.urlresolvers import reverse
import datetime
class Document(models.Model):
document = models.FileField(upload_to='documents/')
uploaded_at = models.DateTimeField(auto_now_add... | normal | {
"blob_id": "01b14da7d081a67bab6f9921bb1a6a4c3d5ac216",
"index": 3003,
"step-1": "<mask token>\n\n\nclass Assignment(models.Model):\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.name + '-' + self.technology\n\n\nclass Assestment(models.Model):\n name = mo... | [
7,
8,
11,
14,
15
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='Member',
fields=[
('id', models.AutoField(verbo... | normal | {
"blob_id": "4e383130b185c6147315517d166ffe66be1be40d",
"index": 4577,
"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 = []\n operat... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.remote_data
def test_from_sbdb():
""" test from_horizons method"""
data = Phys.from_sbdb('Ceres')
assert len(data.table) == 1
data = Phys.from_sbdb([(n + 1) for n in range(5)])
assert len(data.ta... | flexible | {
"blob_id": "0bfb089556bfa253bf139f03cd3079ced962d858",
"index": 1021,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.remote_data\ndef test_from_sbdb():\n \"\"\" test from_horizons method\"\"\"\n data = Phys.from_sbdb('Ceres')\n assert len(data.table) == 1\n data = Phys.from_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def nth_prime(n):
ans = 2
known = []
for _ in range(n):
while not all(ans % x != 0 for x in known):
ans += 1
known.append(ans)
return ans
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def nth_prime(... | flexible | {
"blob_id": "21fb9622add4d19b2914118e3afd3867b2368a50",
"index": 4913,
"step-1": "<mask token>\n",
"step-2": "def nth_prime(n):\n ans = 2\n known = []\n for _ in range(n):\n while not all(ans % x != 0 for x in known):\n ans += 1\n known.append(ans)\n return ans\n\n\n<mask t... | [
0,
1,
2,
3
] |
import Adafruit_BBIO.GPIO as GPIO
from pydrs import SerialDRS
import time
import sys
sys.dont_write_bytecode = True
class SyncRecv:
def __init__(self):
self._comport = '/dev/ttyUSB0'
self._baudrate = '115200'
self._epwm_sync_pin = 'GPIO2_23' # Input in BBB perspective
... | normal | {
"blob_id": "c716f43dbe62f662c60653f09be946a27c3fff66",
"index": 8069,
"step-1": "<mask token>\n\n\nclass SyncRecv:\n\n def __init__(self):\n self._comport = '/dev/ttyUSB0'\n self._baudrate = '115200'\n self._epwm_sync_pin = 'GPIO2_23'\n self._sync_in_pin = 'GPIO2_25'\n self... | [
2,
4,
5,
6,
7
] |
# encoding: utf-8
'''🤠 PDS Roundup: A step takes you further towards a complete roundup'''
from enum import Enum
from .util import commit, invoke
import logging, github3, tempfile, zipfile, os
_logger = logging.getLogger(__name__)
class Step(object):
'''An abstract step; executing steps comprises a roundup'''... | normal | {
"blob_id": "21e86e4719cda5c40f780aca6e56eb13c8c9b8e5",
"index": 988,
"step-1": "<mask token>\n\n\nclass StepName(Enum):\n <mask token>\n null = 'null'\n unitTest = 'unitTest'\n integrationTest = 'integrationTest'\n changeLog = 'changeLog'\n requirements = 'requirements'\n docs = 'docs'\n ... | [
15,
20,
21,
25,
27
] |
import numpy as np
import xgboost as xgb
from sklearn.grid_search import GridSearchCV #Performing grid search
import generateVector
from sklearn.model_selection import GroupKFold
from sklearn import preprocessing as pr
positiveFile="../dataset/full_data/positive.csv"
negativeFile="../dataset/full_data/negative.csv"
... | normal | {
"blob_id": "547844eca9eab097b814b0daa5da96d6a8ccee55",
"index": 5843,
"step-1": "import numpy as np\nimport xgboost as xgb\nfrom sklearn.grid_search import GridSearchCV #Performing grid search\nimport generateVector\nfrom sklearn.model_selection import GroupKFold\nfrom sklearn import preprocessing as pr\n\npo... | [
0
] |
def func():
print("这是无参数的打印")
func()
def func1(a):
print(f"这是有参数的打印:{a}")
func1("有参数a")
def func2(a, b):
return a + b
print(f"有返回值打印：{func2(3, 2)}")
def func3(a, b):
return
print(f"无返回值打印：{func3(3, 2)}")
| normal | {
"blob_id": "be892250c31198e801836dba24fa8218dd50e811",
"index": 1178,
"step-1": "<mask token>\n\n\ndef func3(a, b):\n return\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef func1(a):\n print(f'这是有参数的打印:{a}')\n\n\n<mask token>\n\n\ndef func2(a, b):\n return a + b\n\n\n<mask token>\n\n\ndef func... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def download_pdf(url, folder, name):
r = requests.get(url, allow_redirects=True)
file_path = join(folder, name + '.pdf')
open(file_path, 'wb').write(r.content)
return file_path
<|reserved_special_token_0|>
def pdf_2_images(url, dest_path):
new_file, filename = down... | flexible | {
"blob_id": "c6113088f45951bc4c787760b6ca0138265fb83f",
"index": 9966,
"step-1": "<mask token>\n\n\ndef download_pdf(url, folder, name):\n r = requests.get(url, allow_redirects=True)\n file_path = join(folder, name + '.pdf')\n open(file_path, 'wb').write(r.content)\n return file_path\n\n\n<mask token... | [
2,
3,
4,
5,
6
] |
n = int(input())
s = ""
for i in range(n):
l = list(map(lambda x:x*x,map(int, input().split())))
l.sort()
if l[0] + l[1] == l[2]:
s += "YES\n"
else:
s += "NO\n"
print(s,end="") | normal | {
"blob_id": "f8b473451a15e42319b60f44a527d715c0032614",
"index": 3411,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n l = list(map(lambda x: x * x, map(int, input().split())))\n l.sort()\n if l[0] + l[1] == l[2]:\n s += 'YES\\n'\n else:\n s += 'NO\\n'\nprint... | [
0,
1,
2,
3
] |
import sys
from sklearn.svm import SVC
from sklearn.model_selection import KFold,cross_validate,GridSearchCV
from data_prepr import data_preprocessing
import numpy as np
def main():
#if dataset is not provided on call terminate
if len(sys.argv)<2:
print("usage: python svm_parameter_tuning.py <input_file> ")
sys... | normal | {
"blob_id": "c5842b17b2587149cd13448593a6ed31b091ba77",
"index": 4971,
"step-1": "import sys\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import KFold,cross_validate,GridSearchCV\nfrom data_prepr import data_preprocessing\nimport numpy as np\n\n\ndef main():\n\t#if dataset is not provided on call t... | [
0
] |
#! /usr/bin/python3
print("content-type: text/html")
print()
import cgi
import subprocess as sp
import requests
import xmltodict
import json
db = cgi.FieldStorage()
ch=db.getvalue("ch")
url =("http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>" .format(ch))
u... | normal | {
"blob_id": "87a62f76027e0653f6966f76a42def2ce2a26ba3",
"index": 5893,
"step-1": "<mask token>\n",
"step-2": "print('content-type: text/html')\nprint()\n<mask token>\nprint(output)\n",
"step-3": "print('content-type: text/html')\nprint()\n<mask token>\ndb = cgi.FieldStorage()\nch = db.getvalue('ch')\nurl = (... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def line_endings(fname):
"""Return all line endings in the file.
"""
_endings = {line[-2:] for line in open(fname, 'rb').readlines()}
res = set()
for e in _endings:
if e.endswith(b'\r'):
res.add(b'\r')
elif e.endswith(b'\r\n'):
r... | flexible | {
"blob_id": "be279fe44b0d52c9d473e08d8b9c28d5b6386b45",
"index": 5184,
"step-1": "<mask token>\n\n\ndef line_endings(fname):\n \"\"\"Return all line endings in the file.\n \"\"\"\n _endings = {line[-2:] for line in open(fname, 'rb').readlines()}\n res = set()\n for e in _endings:\n if e.end... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python3
x = "Programming is like building a multilingual puzzle\n"
print (x)
| normal | {
"blob_id": "95c0ba757b7561ef6cc0ad312034e2695f8420c3",
"index": 3933,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(x)\n",
"step-3": "x = 'Programming is like building a multilingual puzzle\\n'\nprint(x)\n",
"step-4": "#!/usr/bin/env python3\n\nx = \"Programming is like building a multilingua... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ProxyScrapper:
def __init__(self):
self._proxies = []
def refresh(self):
session = requests.Session()
session.headers['User-Agent'] = UserAgent().random
print('Rotating proxy list'... | flexible | {
"blob_id": "647dde6e3288ded29336062b78baacc3a92908a7",
"index": 478,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ProxyScrapper:\n\n def __init__(self):\n self._proxies = []\n\n def refresh(self):\n session = requests.Session()\n session.headers['User-Agent'] = Use... | [
0,
4,
5,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('register/', RegisterUserAPIView.as_view()), path(
'get/token/', GetToken.as_view()), path('card/list/', ShowCardsAPIView.
as_view()), path('card/create/', CreateCardAPIView.as_view()), path(
'card/... | flexible | {
"blob_id": "aac334256c1e05ef33a54da19925911af6645a10",
"index": 9529,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('register/', RegisterUserAPIView.as_view()), path(\n 'get/token/', GetToken.as_view()), path('card/list/', ShowCardsAPIView.\n as_view()), path('card/create/', C... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
with open('input_trees.txt') as file:
map = file.readlines()
map = [line.strip() for line in map]
<|reserved_special_token_0|>
for slope in slopes:
treeCount = 0
row, column = 0, 0
while row + 1 < len(map):
row += slope[1]
... | flexible | {
"blob_id": "685fa78b9c3ec141ce1e9ab568e4ad8a0565d596",
"index": 4285,
"step-1": "<mask token>\n",
"step-2": "with open('input_trees.txt') as file:\n map = file.readlines()\n map = [line.strip() for line in map]\n<mask token>\nfor slope in slopes:\n treeCount = 0\n row, column = 0, 0\n while row... | [
0,
1,
2,
3
] |
from pprint import pprint
from collections import Counter
from copy import deepcopy
class Sudoku():
def __init__(self, grid):
'''
Initializes the grid
'''
self.grid = grid
self.sub_grid = self.create_sub_grid(self.grid)
def create_sub_grid(self, ... | normal | {
"blob_id": "4032503bba8a1dd273015d503f52b6ea2d932d1d",
"index": 3564,
"step-1": "<mask token>\n\n\nclass Sudoku:\n\n def __init__(self, grid):\n \"\"\"\n Initializes the grid\n \"\"\"\n self.grid = grid\n self.sub_grid = self.create_sub_grid(self.grid)\n\n def create... | [
10,
12,
13,
14,
15
] |
<|reserved_special_token_0|>
class MVBTest:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def doubleSpendTest(self):
"""
txOutputs is the genesis output.
txOutputs[0] was used twice in this test.
Both Tx1 and Tx2 make txOutputs[0] as input.
... | flexible | {
"blob_id": "8ad9efbbb2d9e2a5f73ebbb999da3ed93e4c1974",
"index": 9655,
"step-1": "<mask token>\n\n\nclass MVBTest:\n <mask token>\n <mask token>\n\n def doubleSpendTest(self):\n \"\"\"\n txOutputs is the genesis output.\n txOutputs[0] was used twice in this test.\n ... | [
11,
15,
17,
18,
19
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the
# Pystacho Project (https://github.com/aruderman/pystacho/).
# Copyright (c) 2021, Francisco Fernandez, Benjamin Marcologno, Andrés Ruderman
# License: MIT
# Full Text: https://github.com/aruderman/pystacho/blob/master/LICENSE
# ===... | normal | {
"blob_id": "d7e24730ce9f2835d55d3995abec2a7d00eb05ef",
"index": 9024,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(PATH / 'pystacho' / '__init__.py') as fp:\n for line in fp.readlines():\n if line.startswith('__version__ = '):\n VERSION = line.split('=', 1)[-1].replace('... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
np.random.seed(7)
<|reserved_special_token_0|>
tf.keras.backend.set_session(tf.Session(config=config))
np.set_printoptions(threshold=np.nan)
<|reserved_special_token_0|>
with open('gei.txt', 'rb') as fr:
x_train = pickle.load(... | flexible | {
"blob_id": "0681ab83843187701ac72018b6078f5141bf22e0",
"index": 3663,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.random.seed(7)\n<mask token>\ntf.keras.backend.set_session(tf.Session(config=config))\nnp.set_printoptions(threshold=np.nan)\n<mask token>\nwith open('gei.txt', 'rb') as fr:\n x_tra... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""microcms package, minimalistic flatpage enhancement.
THIS SOFTWARE IS UNDER BSD LICENSE.
Copyright (c) 2010-2012 Daniele Tricoli <eriol@mornie.org>
Read LICENSE for more informations.
"""
VERSION = (0, 2, 0)
| normal | {
"blob_id": "3e1c2d0c5bb30d093a99f10020af14db5436bf02",
"index": 5551,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nVERSION = 0, 2, 0\n",
"step-3": "# -*- coding: utf-8 -*-\n\"\"\"microcms package, minimalistic flatpage enhancement.\n\nTHIS SOFTWARE IS UNDER BSD LICENSE.\nCopyright (c) 2010-2012 Dani... | [
0,
1,
2
] |
from fbchat import Client
class IBehaviourBase(Client):
BreakFlag = False
def __init__(self,email,password, kwargs):
""""abstract class being parent of every user implemented behaviour;
it handles logging in and tasks on behaviour loader side"""
self.kwargs=kwargs
Client.__init_... | normal | {
"blob_id": "e67f27eec53901f27ba5a7ee7e2a20bbb1e8f7f9",
"index": 2237,
"step-1": "<mask token>\n\n\nclass IBehaviourBase(Client):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass IBehaviourBase(Client):\n <mask token>\n\n def __init__(self, email, password, kwa... | [
1,
3,
4,
5,
6
] |
# Software License Agreement (BSD License)
#
# Copyright (c) 2009-2011, Eucalyptus Systems, Inc.
# All rights reserved.
#
# Redistribution and use of this software in source and binary forms, with or
# without modification, are permitted provided that the following conditions
# are met:
#
# Redistributions of source ... | normal | {
"blob_id": "920cd41b18f5cfb45f46c44ed707cebe682d4dd9",
"index": 820,
"step-1": "# Software License Agreement (BSD License)\n#\n# Copyright (c) 2009-2011, Eucalyptus Systems, Inc.\n# All rights reserved.\n#\n# Redistribution and use of this software in source and binary forms, with or\n# without modification, ar... | [
0
] |
<|reserved_special_token_0|>
@paddle.no_grad()
class Val_model_subpixel(object):
<|reserved_special_token_0|>
def loadModel(self):
from utils.loader import modelLoader
self.net = modelLoader(model=self.model, **self.params)
checkpoint = paddle.load(self.weights_path)
self.net.... | flexible | {
"blob_id": "fc89fdf17f887ea398be5b36d4d6f0444d64b3e0",
"index": 8026,
"step-1": "<mask token>\n\n\n@paddle.no_grad()\nclass Val_model_subpixel(object):\n <mask token>\n\n def loadModel(self):\n from utils.loader import modelLoader\n self.net = modelLoader(model=self.model, **self.params)\n ... | [
3,
5,
6,
7,
8
] |
# lesson 4 Mateush Vilen
my_information = {
'name': 'Vilen',
'last_name': 'Mateush',
'how_old': 31,
'born_town': 'Khmelniysky'
}
dict_test = {key: key**2 for key in range(7)}
print('dict_test: ', dict_test)
elem_dict = 0
elem_dict = input('input number of elements:')
user_input_dict = {}
for key in ... | normal | {
"blob_id": "b000f293b50970233d5b71abc3e10e2ad57a3fc7",
"index": 1767,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('dict_test: ', dict_test)\n<mask token>\nfor key in range(0, int(elem_dict)):\n key = input('dict key: ')\n user_input_dict[key] = input('dict value:')\nprint(user_input_dict)... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
states.add('.'.join(str(n) for n in mem))
<|reserved_special_token_0|>
while True:
i = mem.index(max(mem))
x = mem[i]
mem[i] = 0
while x > 0:
i += 1
mem[i % size] += 1
x -= 1
steps += 1
... | flexible | {
"blob_id": "0e7d4b73cedf961677e6b9ea5303cdb3a5afa788",
"index": 3521,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nstates.add('.'.join(str(n) for n in mem))\n<mask token>\nwhile True:\n i = mem.index(max(mem))\n x = mem[i]\n mem[i] = 0\n while x > 0:\n i += 1\n mem[i % size] ... | [
0,
1,
2,
3,
4
] |
"""Google Scraper
Usage:
web_scraper.py <search> <pages> <processes>
web_scraper.py (-h | --help)
Arguments:
<search> String to be Searched
<pages> Number of pages
<processes> Number of parallel processes
Options:
-h, --help Show this screen.
"""
import re
from functools impo... | normal | {
"blob_id": "68dcac07bbdb4dde983939be98ece127d963c254",
"index": 3610,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_urls(search_string, start):\n temp = []\n url = 'http://www.google.com/search'\n payload = {'q': search_string, 'start': start}\n my_headers = {'User-agent': 'Mozi... | [
0,
2,
3,
4,
5
] |
from cudasim.ParsedModel import ParsedModel
import re
import copy
class Writer:
def __init__(self):
pass
# replace the species and parameters recursively
@staticmethod
def rep(string, find, replace):
ex = find + "[^0-9]"
while re.search(ex, string) is not None:
res... | normal | {
"blob_id": "acd0b9019ef413699b47ecb2b66a0980cf3aa81f",
"index": 9792,
"step-1": "<mask token>\n\n\nclass Writer:\n <mask token>\n\n @staticmethod\n def rep(string, find, replace):\n ex = find + '[^0-9]'\n while re.search(ex, string) is not None:\n res = re.search(ex, string)\n ... | [
2,
3,
4,
5,
6
] |
import datetime
now = datetime.datetime.now()
# Printing value of now.
print ("Time now : ", now)
| normal | {
"blob_id": "0110d26e17a5402c22f519d0aeb2aacca3279d00",
"index": 7792,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Time now : ', now)\n",
"step-3": "<mask token>\nnow = datetime.datetime.now()\nprint('Time now : ', now)\n",
"step-4": "import datetime\nnow = datetime.datetime.now()\nprint('T... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
s = '''Вбс лче ,мтс ооепта т.сбзек о ып гоэятмв,те гоктеивеысокячел–аонкы оах ннлнисьрнксе ьрм отаб тёьдр ннласааосд це аЧиу нвыанзи еслкмиетл,леево ннлтпо еик:ыаырялньб пнм би на це азоватоша Вепьлаяокеолвоытрх еытодрпьтае,кллгфм ытитослРянозит нсонунс.р лунттаё ооиВяе зн етвйеетелттв еСлл... | normal | {
"blob_id": "a8bed0b5a6a95d67b5602b395f1d0ea12cd53fb0",
"index": 9166,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef fence_decipher(m: str, key: int) ->str:\n chunklens = [(0) for _ in range(key)]\n nfence = 0\n dx = 1\n for i in m:\n chunklens[nfence] += 1\n nfence += ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('parent Folder is : ' + parentFolderPath)
<|reserved_special_token_0|>
print('output folder: ' + str(outputFolder))
print('output chunk folder: ' + str(outputChunkFolder))
print('mask output folder is: ' + str(outputMaskfold... | flexible | {
"blob_id": "dcfc6d76730ba3b33e64cc8f2c166f739bbde5ff",
"index": 3655,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('parent Folder is : ' + parentFolderPath)\n<mask token>\nprint('output folder: ' + str(outputFolder))\nprint('output chunk folder: ' + str(outputChunkFolder))\nprint('mask output fo... | [
0,
1,
2,
3,
4
] |
def warshall_floyd(N):
INF = 10 ** 20
path = [[INF for _ in range(N + 1)] for _ in range(N + 1)]
graph = get_graph()
for i in range(N + 1):
path[i][i] = 0
for g in graph:
x = g[0]
y = g[1]
l = g[2]
path[x][y] = path[y][x] = l
for start in range(N + 1):
... | flexible | {
"blob_id": "1e1f918ba24f5a5f13b9b01289ebfda65bae572d",
"index": 301,
"step-1": "def warshall_floyd(N):\n INF = 10 ** 20\n path = [[INF for _ in range(N + 1)] for _ in range(N + 1)]\n graph = get_graph()\n for i in range(N + 1):\n path[i][i] = 0\n for g in graph:\n x = g[0]\n ... | [
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def main():
try:
api = 'http://t.weather.itboy.net/api/weather/city/'
city_code = '101070201'
tqurl = api + city_code
response = requests.get(tqurl)
d = response.json()
print(d['status'])
if d['status'] == 200:
parent... | flexible | {
"blob_id": "4048d7bfc7922ef76d98d43e1ea266e732e0982e",
"index": 9111,
"step-1": "<mask token>\n\n\ndef main():\n try:\n api = 'http://t.weather.itboy.net/api/weather/city/'\n city_code = '101070201'\n tqurl = api + city_code\n response = requests.get(tqurl)\n d = response.j... | [
2,
3,
4,
5,
6
] |
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