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|>
while left_idx <= mid_idx and right_idx <= n - 1:
if n_list[left_idx] < n_list[right_idx]:
nn_list.append(n_list[left_idx])
left_idx += 1
elif n_list[left_idx] > n_list[right_idx]:
nn_list.append(n_... | flexible | {
"blob_id": "fb5508b1b5aa36c4921358d6ca7f96fc7d565241",
"index": 5104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile left_idx <= mid_idx and right_idx <= n - 1:\n if n_list[left_idx] < n_list[right_idx]:\n nn_list.append(n_list[left_idx])\n left_idx += 1\n elif n_list[left_idx]... | [
0,
1,
2,
3
] |
# bot.py
import os
import sqlite3
import json
import datetime
from dotenv import load_dotenv
import discord
from discord.ext import commands
from discord.ext.commands import Bot
from cogs.utils import helper as h
intents = discord.Intents.default()
intents.members = True
load_dotenv()
TOKEN = os.getenv('DISCORD_T... | normal | {
"blob_id": "849343561dd9bdcfc1da66c604e1bfa4aa10ddf3",
"index": 5359,
"step-1": "<mask token>\n\n\nclass LLKEventsBot(Bot):\n <mask token>\n\n async def on_ready(self):\n if not os.path.exists('db'):\n os.makedirs('db')\n if not os.path.exists('logs'):\n os.makedirs('lo... | [
1,
2,
4,
5,
6
] |
#coding=utf-8
import requests,sys
result_url=[]
def main():
counts=open(sys.argv[1]).readlines()
for line in open(sys.argv[1]):
line=line.strip("\n")
url=line
try:
#url="http://s6000.sgcc.com.cn/WebContent/s6000/main/index.jsp#no-back"
r=requests.get(u... | normal | {
"blob_id": "96a4659f03879e051af95b5aa9c1e1364015fb86",
"index": 8723,
"step-1": "<mask token>\n\n\ndef main():\n counts = open(sys.argv[1]).readlines()\n for line in open(sys.argv[1]):\n line = line.strip('\\n')\n url = line\n try:\n r = requests.get(url, verify=True, timeo... | [
1,
2,
3,
4,
5
] |
# ------------------------------------------------------------------------------------------------------
# Copyright (c) Leo Hanisch. All rights reserved.
# Licensed under the BSD 3-Clause License. See LICENSE.txt in the project root for license information.
# ---------------------------------------------------------... | normal | {
"blob_id": "917a291c7b62dee392d7411c3e039949d74d7af8",
"index": 1375,
"step-1": "<mask token>\n\n\nclass Nest:\n <mask token>\n <mask token>\n <mask token>\n\n def update_pos(self, new_position: Tuple[float, float]) ->None:\n \"\"\"\n If the new position's value is better than the old ... | [
2,
3,
4,
5,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def trainW2v(args):
clazz = [['Accidents', 'Arts', 'Attacks', 'Economy', 'Miscellaneous',
'Politics', 'Science', 'Sports', 'undefined'], ['Accidents', 'Arts',
'Attacks', 'Economy', 'Miscellaneous', 'Politics'... | flexible | {
"blob_id": "3bc9c6a66f749858ea5801202b0ac80755c1b347",
"index": 6493,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef trainW2v(args):\n clazz = [['Accidents', 'Arts', 'Attacks', 'Economy', 'Miscellaneous',\n 'Politics', 'Science', 'Sports', 'undefined'], ['Accidents', 'Arts',\n '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ProjectrolesConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ProjectrolesConfig(AppConfig):
name = 'projectroles'
<|reserved_special_token_1|>
... | flexible | {
"blob_id": "6a4585e0e2f5ebbd0f9a7fa203f76bb88ff9c2a0",
"index": 2920,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ProjectrolesConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ProjectrolesConfig(AppConfig):\n name = 'projectroles'\n",
"step-4": "from django.... | [
0,
1,
2,
3
] |
import requests
import json
import pyttsx
engine = pyttsx.init()
engine.say('Hello from Eliq.')
engine.runAndWait()
power_value = 0
power_value_int = 0
prompt=0
Eliq_just_NOW ={}
accesstoken = "xxxxxxxxxxxxxxxxxxxxxx"
#Say warning for power use over this limmit in Watts
level_warning = 2000
Eliq_request_string = (... | normal | {
"blob_id": "72abba6fa40441ab172bccb9065aaa0af5fefd64",
"index": 7209,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nengine.say('Hello from Eliq.')\nengine.runAndWait()\n<mask token>\nprint(power_str)\nif power_value_int > level_warning:\n engine.say(power_str)\n engine.say('Warning.')\n engine... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Nest:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def update_pos(self, new_position: Tuple[float, float]) ->None:
"""
If the new position's value is better than the old one, update the nests position and val... | flexible | {
"blob_id": "917a291c7b62dee392d7411c3e039949d74d7af8",
"index": 1375,
"step-1": "<mask token>\n\n\nclass Nest:\n <mask token>\n <mask token>\n <mask token>\n\n def update_pos(self, new_position: Tuple[float, float]) ->None:\n \"\"\"\n If the new position's value is better than the old ... | [
2,
3,
4,
5,
7
] |
from mathgraph3D.core.plot import *
from mathgraph3D.core.functions import *
| normal | {
"blob_id": "b58cc08f8f10220373fa78f5d7249bc883b447bf",
"index": 6991,
"step-1": "<mask token>\n",
"step-2": "from mathgraph3D.core.plot import *\nfrom mathgraph3D.core.functions import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from HurdleRace import hurdleRace
from ddt import ddt, data, unpack
import unittest
class test_AppendAndDelete3(unittest.TestCase):
def test_hurdleRace(self):
height = [1, 6, 3, 5, 2]
k = 4
sum_too_high = hurdleRace(k, height)
self.assertEqual(2, sum_too_high)
| normal | {
"blob_id": "ea86a2a9068c316d3efcbcb165a8ef3d3516ba1b",
"index": 4763,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass test_AppendAndDelete3(unittest.TestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass test_AppendAndDelete3(unittest.TestCase):\n\n def test_hurdleRace(self):\... | [
0,
1,
2,
3
] |
import cv2 as cv
from threading import Thread
class Reader(Thread):
def __init__(self, width, height, device=0):
super().__init__(daemon=True)
self._stream = cv.VideoCapture(device)
self._stream.set(cv.CAP_PROP_FRAME_WIDTH, width)
self._stream.set(cv.CAP_PROP_FRAME_HEIGHT, height)... | normal | {
"blob_id": "73bf31e43394c3f922b00b2cfcd5d88cc0e01094",
"index": 2339,
"step-1": "<mask token>\n\n\nclass Reader(Thread):\n <mask token>\n\n def __del__(self):\n self._frame = None\n self._stream.release()\n <mask token>\n\n def read(self):\n return self._frame\n",
"step-2": "<... | [
3,
4,
5,
6
] |
import json
import os
import ipdb
from tqdm import tqdm
import argparse
from os import listdir
from os.path import isfile, join
import pickle
import joblib
from collections import Counter
from shutil import copyfile
import networkx as nx
import spacy
import nltk
import numpy as np
nltk.download('stopwords')
nltk_stopw... | normal | {
"blob_id": "2da7892722afde5a6f87e3bd6d5763c895ac96c9",
"index": 284,
"step-1": "<mask token>\n\n\nclass Lang:\n\n def __init__(self):\n super(Lang, self).__init__()\n self.word2index = {}\n self.word2count = {}\n self.index2word = {}\n self.n_words = 0\n\n def index_word... | [
5,
8,
9,
11,
13
] |
o = input()
v = []
s = 0
for i in range(12):
col = []
for j in range(12):
col.append(float(input()))
v.append(col)
a = 1
for i in range(1, 12):
for j in range(a):
s += v[i][j]
a+=1
if o == 'S':
print("%.1f"%s)
if o == 'M':
print("%.1f"%(s/66))
| normal | {
"blob_id": "0df20722fba6223c9d4fc9f72bfb399b479db6ac",
"index": 7917,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(12):\n col = []\n for j in range(12):\n col.append(float(input()))\n v.append(col)\n<mask token>\nfor i in range(1, 12):\n for j in range(a):\n s ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Idea:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def cmd(self):
return 'intellij-idea-ultimate-edition %s' % self.folder
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Idea:
def __init__(self, folder):
self.folde... | flexible | {
"blob_id": "90fc6e37e3988a2014c66913db61749509db2d53",
"index": 1036,
"step-1": "<mask token>\n\n\nclass Idea:\n <mask token>\n <mask token>\n\n def cmd(self):\n return 'intellij-idea-ultimate-edition %s' % self.folder\n",
"step-2": "<mask token>\n\n\nclass Idea:\n\n def __init__(self, fold... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
files = ['arria2_ddr3.qip']
<|reserved_special_token_1|>
files = [
"arria2_ddr3.qip"
]
| flexible | {
"blob_id": "cad881dd29be16de8375b3ce6e4a437562a05097",
"index": 5426,
"step-1": "<mask token>\n",
"step-2": "files = ['arria2_ddr3.qip']\n",
"step-3": "files = [\n \"arria2_ddr3.qip\"\n ]\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
import json
import logging
logger = logging.getLogger(__name__)
from django.db.models import Q
from channels_api.bindings import ResourceBinding
from .models import LetterTransaction, UserLetter, TeamWord, Dictionary
from .serializers import LetterTransactionSerializer, UserLetterSerializer, TeamWordSerializer
cla... | normal | {
"blob_id": "c2e0f2eda6ef44a52ee4e192b8eb71bde0a69bff",
"index": 8954,
"step-1": "<mask token>\n\n\nclass TeamWordBinding(ResourceBinding):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @classmethod\n def group_names(self, instance, action):\n return [str(instance.user.... | [
13,
14,
15,
17,
18
] |
import http.server
import socketserver
from http.server import BaseHTTPRequestHandler, HTTPServer
import time
import json
import io
import urllib
import requests
from lib.Emby_ws import xnoppo_ws
from lib.Emby_http import *
from lib.Xnoppo import *
from lib.Xnoppo_TV import *
import lib.Xnoppo_AVR
import shutil
import ... | normal | {
"blob_id": "2ff85ac059f160fcc6b39b4298e8216cbad77ab3",
"index": 504,
"step-1": "<mask token>\n\n\ndef get_version():\n return '2.01'\n\n\n<mask token>\n\n\ndef restart():\n print('restart')\n try:\n emby_wsocket.stop()\n except:\n sys.exit()\n sys.exit()\n print('fin restart')\n\... | [
21,
24,
25,
27,
28
] |
<|reserved_special_token_0|>
class Running(object):
<|reserved_special_token_0|>
def __init__(self, args, device_id):
"""
:param args: parser.parse_args()
:param device_id: 0 or -1
"""
self.args = args
self.device_id = device_id
self.model_flags = ['hid... | flexible | {
"blob_id": "3adb50a6375a73f786369dd22712a657b66f758e",
"index": 8432,
"step-1": "<mask token>\n\n\nclass Running(object):\n <mask token>\n\n def __init__(self, args, device_id):\n \"\"\"\n :param args: parser.parse_args()\n :param device_id: 0 or -1\n \"\"\"\n self.args ... | [
7,
16,
17,
18,
24
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='Beach',
fields=[
('id', models.AutoField(verbos... | normal | {
"blob_id": "9555e5f75e3045afff6da9228764fca542caf539",
"index": 2448,
"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
] |
import pandas as pd
import numpy as np
import random
import copy
class Node(object):
'''
Defines a Node Class for storing characteristics and CPT of each node
'''
def __init__(self,name):
self.parents = []
self.children = []
self.name = name
self.cpt=[]
self... | normal | {
"blob_id": "eb4bc008b7e68f8a6e80e837fa970d77a5ed3547",
"index": 8218,
"step-1": "<mask token>\n\n\nclass Node(object):\n \"\"\"\n Defines a Node Class for storing characteristics and CPT of each node\n \"\"\"\n\n def __init__(self, name):\n self.parents = []\n self.children = []\n ... | [
12,
13,
15,
17,
20
] |
<|reserved_special_token_0|>
def run_final_test_days():
sqs = [5]
cams = [1]
permutations = [(True, True, True)]
permutations_names = ['all data perez']
for pidx, p in enumerate(permutations):
for s in sqs:
for c in cams:
data = DataFrameSequenceMulti(False, p[0... | flexible | {
"blob_id": "af903feda57e4ace0c7f909abbeb86bb9a7e4d8c",
"index": 1806,
"step-1": "<mask token>\n\n\ndef run_final_test_days():\n sqs = [5]\n cams = [1]\n permutations = [(True, True, True)]\n permutations_names = ['all data perez']\n for pidx, p in enumerate(permutations):\n for s in sqs:\n... | [
3,
5,
7,
8,
9
] |
import tensorflow as tf
from typing import Optional, Tuple, Union, Callable
_data_augmentation = tf.keras.Sequential(
[
tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
]
)
def _freeze_model(
model: tf.ker... | normal | {
"blob_id": "86d42716e05155f9e659b22c42635a8f5b8c4a60",
"index": 753,
"step-1": "<mask token>\n\n\ndef generate_model(base_model: tf.keras.Model, img_shape: Tuple[Optional[\n int], Optional[int], Optional[int]], freeze: Union[bool, int, float]=\n False, preprocess_input: Optional[Callable]=None, use_data_a... | [
1,
2,
3,
4,
5
] |
#day11
n = int(input("Enter a number: "))
c = 0
a,b = 0, 1
list = [a, b]
for i in range(2,n+1):
c = a+b
list.append(c)
a,b = b, c
print(n,"th fibonacci number is ",list[n])
| normal | {
"blob_id": "255cdbce1f9f7709165b1a29362026ad92ba4712",
"index": 2303,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(2, n + 1):\n c = a + b\n list.append(c)\n a, b = b, c\nprint(n, 'th fibonacci number is ', list[n])\n",
"step-3": "n = int(input('Enter a number: '))\nc = 0\na, ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(accuracy_score(true_labels, guesses))
print(recall_score(true_labels, guesses))
print(precision_score(true_labels, guesses))
print(f1_score(true_labels, guesses))
<|reserved_special_token_0|>
print(confusion_matrix(true_labe... | flexible | {
"blob_id": "faa53db9dd581b6508fb9e4042ec86ebaf850e60",
"index": 5320,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(accuracy_score(true_labels, guesses))\nprint(recall_score(true_labels, guesses))\nprint(precision_score(true_labels, guesses))\nprint(f1_score(true_labels, guesses))\n<mask token>\n... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def yolo():
root = 'Z:\\'
name = '23367640.png'
execution_path = os.getcwd()
yolo_path = 'Z:\\yolo.h5'
localdir = False
detector = ObjectDetection()
detector.setModelTypeAsYOLOv3()
if localdir:
detector.setModelPath(os.path.join(execution_path, yolo... | flexible | {
"blob_id": "c80ae9d2eb07fd716a80a5e2d7b5237925fda02c",
"index": 5861,
"step-1": "<mask token>\n\n\ndef yolo():\n root = 'Z:\\\\'\n name = '23367640.png'\n execution_path = os.getcwd()\n yolo_path = 'Z:\\\\yolo.h5'\n localdir = False\n detector = ObjectDetection()\n detector.setModelTypeAsYO... | [
2,
3,
4,
5,
6
] |
import abc
import numpy as np
import ray
from tqdm.autonotebook import tqdm
from src.algorithm.info_theory.it_estimator import (CachingEstimator,
MPCachingEstimator)
from src.algorithm.utils import differ, independent_roll, union
class FeatureSelector(metaclass=ab... | normal | {
"blob_id": "983473129bfd56138a615e0f5bdb1353e9c6d8af",
"index": 6441,
"step-1": "<mask token>\n\n\nclass FeatureSelector(metaclass=abc.ABCMeta):\n <mask token>\n\n def _setup(self):\n self.n_features = self.trajectories[0].shape[1] - 1\n self.id_reward = self.n_features\n self.set_rew... | [
16,
18,
20,
22,
23
] |
# apport hook for oem-config; adds log file
import os.path
def add_info(report):
if os.path.exists('/var/log/oem-config.log'):
report['OemConfigLog'] = ('/var/log/oem-config.log',)
| normal | {
"blob_id": "74b1cdcb1aaf6cde7e8ce3eeb73cd82689719b00",
"index": 6404,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef add_info(report):\n if os.path.exists('/var/log/oem-config.log'):\n report['OemConfigLog'] = '/var/log/oem-config.log',\n",
"step-3": "import os.path\n\n\ndef add_info... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class API:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved... | flexible | {
"blob_id": "da66b254afb3a8fcd3783a38d8624caa917e58c3",
"index": 652,
"step-1": "<mask token>\n\n\nclass API:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n ... | [
12,
15,
20,
23,
26
] |
# pylint: disable=missing-docstring,function-redefined
import uuid
from behave import given, then, when
import requests
from features.steps import utils
from testsuite.oauth import authorize
from testsuite import fhir
ERROR_AUTHORIZATION_FAILED = 'Authorization failed.'
ERROR_BAD_CONFORMANCE = 'Could not parse conf... | normal | {
"blob_id": "ef0c9f740f1ca0906aeb7a5c5e5d35baca189310",
"index": 6128,
"step-1": "<mask token>\n\n\n@given('I am logged in')\ndef step_impl(context):\n assert context.oauth is not None, ERROR_AUTHORIZATION_FAILED\n assert context.oauth.access_token is not None, ERROR_AUTHORIZATION_FAILED\n\n\n@given('I am ... | [
9,
10,
12,
15,
17
] |
def has23(nums):
this = nums[0] == 2 or nums[0] == 3
that = nums[1] == 2 or nums[1] == 3
return this or that
| normal | {
"blob_id": "174c4c1ed7f2197e012644999cf23f5e82f4b7c3",
"index": 3148,
"step-1": "<mask token>\n",
"step-2": "def has23(nums):\n this = nums[0] == 2 or nums[0] == 3\n that = nums[1] == 2 or nums[1] == 3\n return this or that\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
... | [
0,
1
] |
from collections import Counter
import pandas as pd
import string
from collections import namedtuple, defaultdict
import csv
import sys
import torch
import numpy as np
from sklearn.preprocessing import LabelEncoder
from scipy.sparse import coo_matrix
from tqdm import tqdm
device = torch.device('cuda' if torch.cuda.is_... | normal | {
"blob_id": "613b060ee50b49417342cfa70b36f77d112dcc58",
"index": 2951,
"step-1": "<mask token>\n\n\ndef get_data():\n df = pd.read_csv('./data/filteredCorpus.csv')\n df_filt = df[df['outcome'] == True]\n df_filt = df_filt[df_filt['role'] == 'speaker']\n df_filt = df_filt[df_filt['source'] == 'human']... | [
4,
5,
6,
7,
8
] |
import io
import xlsxwriter
import zipfile
from django.conf import settings
from django.http import Http404, HttpResponse
from django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin
from django.contrib.auth.decorators import login_required
from django.contrib import messages
from django.views.generic... | normal | {
"blob_id": "a9ebd323d4b91c7e6a7e7179329ae80e22774927",
"index": 4843,
"step-1": "<mask token>\n\n\nclass PeriodoUpdateView(LoginRequiredMixin, UpdateView):\n <mask token>\n <mask token>\n <mask token>\n\n def get_form_kwargs(self, *args, **kwargs):\n kwargs = super(PeriodoUpdateView, self).ge... | [
67,
76,
95,
101,
108
] |
from django.contrib import admin
from django.urls import path, include
from .views import hindex,galeria,mision_vision,direccion,registro,login,logout_vista,registro_insumo,admin_insumos
urlpatterns = [
path('',hindex,name='HINDEX'),
path('galeria/',galeria,name='GALE'),
path('mision/',mision_vision,name=... | normal | {
"blob_id": "dff5a46c6f1eb715fe5e1eec87e42ceb295b0eae",
"index": 4650,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', hindex, name='HINDEX'), path('galeria/', galeria,\n name='GALE'), path('mision/', mision_vision, name='MISION'), path(\n 'direccion/', direccion, name='UBICA... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
"""
Plot EEG data.
Usage:
plotting.py [options] [<file>]
Options:
-h --help Show this screen.
--version Show version.
--center Center the data before plotting
--sample-index=N Row index (indexed from one).
--transpose Transpose data.
--x... | normal | {
"blob_id": "5bd7160b6b2e283e221aeb0a6913e6d13511c1db",
"index": 7073,
"step-1": "<mask token>\n\n\nclass TopoPlot(object):\n <mask token>\n\n def __init__(self, data=None, axes=None):\n \"\"\"Setup defaults.\n\n Parameters\n ----------\n data : Pandas.Series or dict\n ... | [
19,
22,
23,
25,
30
] |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | normal | {
"blob_id": "f4b704a1416bfd6524340a68a20981957abf4340",
"index": 9850,
"step-1": "<mask token>\n\n\nclass KibbleESWrapper(object):\n <mask token>\n\n def __init__(self, ES):\n self.ES = ES\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def scroll(sel... | [
17,
19,
23,
25,
28
] |
<|reserved_special_token_0|>
@app.route('/')
def hello():
return 'Flask setup'
def sheets_row_writer(data_list):
print('sheets method invoked')
credentials = ServiceAccountCredentials.from_json_keyfile_name(
'mechnepal-test-54c4387178d9.json', scope)
client = gspread.authorize(credentials)
... | flexible | {
"blob_id": "267cb37f2ccad5b02a809d9b85327eacd9a49515",
"index": 1061,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return 'Flask setup'\n\n\ndef sheets_row_writer(data_list):\n print('sheets method invoked')\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\n 'mec... | [
5,
6,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def solution(record):
answer = []
db = {}
chatting = []
for log in record:
log_list = log.split()
if log_list[0] == 'Enter':
db[log_list[1]] = log_list[2]
chatting.append([True, log_list[1]])
eli... | flexible | {
"blob_id": "3ffe16494eb45896563a2952f3bcf80fc19b2750",
"index": 1226,
"step-1": "<mask token>\n",
"step-2": "def solution(record):\n answer = []\n db = {}\n chatting = []\n for log in record:\n log_list = log.split()\n if log_list[0] == 'Enter':\n db[log_list[1]] = log_lis... | [
0,
1,
2,
3
] |
# 튜플(tuple) - 리스트와 구조가 비슷함
#변경, 삭제 할 수 없다.
t = ('코스모스', '민들레', '국화')
print(t)
print(t[:2])
print(t[1:])
#del t[0] - 삭제 안됨
#t[2] ="매화" - 수정 안됨
t2 = (1, 2, 3)
t3 = (4,) # 1개 추가하기 (쉼표를 붙임)
print(t2)
print(t3)
print(t2 + t3) # 요소 더하기
| normal | {
"blob_id": "45fcafdd30f890ddf5eaa090152fde2e2da4dbef",
"index": 732,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(t)\nprint(t[:2])\nprint(t[1:])\n<mask token>\nprint(t2)\nprint(t3)\nprint(t2 + t3)\n",
"step-3": "t = '코스모스', '민들레', '국화'\nprint(t)\nprint(t[:2])\nprint(t[1:])\nt2 = 1, 2, 3\nt3 = ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
from lemonpie import lemonpie
from flask_debugtoolbar import DebugToolbarExtension
def main():
lemonpie.debug = True
lemonpie.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False
toolbar = DebugToolbarExtension(lemonpie)
lemonpie.run('0.0.0.0')
if __name__ == '__main__':
main()
| normal | {
"blob_id": "328c483bf59c6b84090e6bef8814e829398c5a56",
"index": 6954,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lemonpie.debug = True\n lemonpie.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False\n toolbar = DebugToolbarExtension(lemonpie)\n lemonpie.run('0.0.0.0')\n\n\n<m... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def dict_from_dataflow_generator(df):
for sample in df.get_data():
yield sample[0]
def split_lmdb_dataset(lmdb_input_path, lmdb_output_path1,
lmdb_output_path2, split_ratio1, batch_size, shuffle,
serialization_name, compression, compression_arg, max_num_samples=None)... | flexible | {
"blob_id": "a283fd1e4098ea8bb3cc3580438c90e5932ba22f",
"index": 5852,
"step-1": "<mask token>\n\n\ndef dict_from_dataflow_generator(df):\n for sample in df.get_data():\n yield sample[0]\n\n\ndef split_lmdb_dataset(lmdb_input_path, lmdb_output_path1,\n lmdb_output_path2, split_ratio1, batch_size, sh... | [
5,
6,
7,
8,
9
] |
from queue import Queue
class Node():
def __init__(self, value, left=None, right=None):
self.value = value
self.left = left
self.right = right
def array_to_tree_dfs(array):
n = len(array)
if n>0:
root = Node(array[0])
def dfs(node, index):
# if index >= n:
... | normal | {
"blob_id": "a52762fb13c04ced07a41a752578c4173d1eac42",
"index": 8350,
"step-1": "<mask token>\n\n\nclass Node:\n\n def __init__(self, value, left=None, right=None):\n self.value = value\n self.left = left\n self.right = right\n\n\n<mask token>\n\n\ndef tree_to_array_bfs(root):\n q = Q... | [
4,
5,
6,
7,
8
] |
# Copyright 2008 Google 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 law or agreed to in writing,... | normal | {
"blob_id": "57d1fb805fce2ba75ea2962598e809ba35fd7eb6",
"index": 3490,
"step-1": "<mask token>\n\n\ndef _find_warnings(filename, lines, ast_list, static_is_optional):\n\n def print_warning(node, name):\n print(\"{}:{}: static data '{}'\".format(filename, lines.\n get_line_number(node.start),... | [
2,
3,
4,
5,
6
] |
import json
from flask import current_app, request, jsonify, make_response
from flask_cors import cross_origin
from alerta.auth.utils import is_authorized, create_token, get_customer
from alerta.utils.api import absolute_url, deepmerge
from . import auth
try:
import saml2
import saml2.entity
import saml... | normal | {
"blob_id": "b233d212f3a6c453786dc54b2d43578e1faae417",
"index": 7292,
"step-1": "<mask token>\n\n\ndef spConfig():\n return saml2.config.Config()\n\n\ndef saml_client():\n saml2_config_default = {'entityid': absolute_url(), 'service': {'sp': {\n 'endpoints': {'assertion_consumer_service': [(absolut... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class PurchaseDetail(models.Model):
PRODUCT_CHOICES = ('WOOD', 'Wood'), ('GLASS', 'Glass'), ('PLASTIC',
'Plastic'), ('LEATHER', 'Leather'), ('FABRIC', 'Fabric'), ('STEEL',
'Steel')
purchase = models.ForeignKey(Purchase, on_delete=models.CASCADE)
product_name = ... | flexible | {
"blob_id": "bb3c42c9f87a463b9f18601c9e3897b6d21351d5",
"index": 7356,
"step-1": "<mask token>\n\n\nclass PurchaseDetail(models.Model):\n PRODUCT_CHOICES = ('WOOD', 'Wood'), ('GLASS', 'Glass'), ('PLASTIC',\n 'Plastic'), ('LEATHER', 'Leather'), ('FABRIC', 'Fabric'), ('STEEL',\n 'Steel')\n purc... | [
4,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class MonitorLocation(Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Meta:
app_lab... | flexible | {
"blob_id": "1a4132358fa9bd4cd74970286ec8bb212b1857cd",
"index": 5247,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MonitorLocation(Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n app_label = 'sentry'\n db_... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .line_detection_research import score_pixel_v3p2
| flexible | {
"blob_id": "305554fc86ddc116677b6d95db7d94d9f2213c41",
"index": 5088,
"step-1": "<mask token>\n",
"step-2": "from .line_detection_research import score_pixel_v3p2\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
def book(request):
Book.objects.create(title=request.POST['b_title'], desc=request.POST[
'b_desc'])
return redirect('/')
def author(request):
context = {'the_auths': Author.objects.all()}
return render(request, 'author.html', context)
def auth(request):
Aut... | flexible | {
"blob_id": "02bec34b138d53235dc944adeae8ccb8d6b3d340",
"index": 4424,
"step-1": "<mask token>\n\n\ndef book(request):\n Book.objects.create(title=request.POST['b_title'], desc=request.POST[\n 'b_desc'])\n return redirect('/')\n\n\ndef author(request):\n context = {'the_auths': Author.objects.all... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class ChangeEmail(forms.Form):
<|reserved_special_token_0|>
class ChangePassword(forms.Form):
oldPassword = forms.CharField(required=True, min_length=8, max_length=
80, widget=forms.PasswordInput(attrs={'name': 'oldPassword'}))
password1 = forms.CharField(required=Tr... | flexible | {
"blob_id": "503726cd2d70286189f4b8e02acaa3d5f6e29e12",
"index": 8538,
"step-1": "<mask token>\n\n\nclass ChangeEmail(forms.Form):\n <mask token>\n\n\nclass ChangePassword(forms.Form):\n oldPassword = forms.CharField(required=True, min_length=8, max_length=\n 80, widget=forms.PasswordInput(attrs={'n... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_parse_header():
print()
for case_id, case in CASES:
ss = SchemaSheet.from_dictreader(case)
tc = ss.table_config
info_cc = tc.columns[INFO]
assert info_cc.name == INFO
asse... | flexible | {
"blob_id": "25dc0395da1f1ac2ccd990151c3e5b250802b402",
"index": 2749,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_parse_header():\n print()\n for case_id, case in CASES:\n ss = SchemaSheet.from_dictreader(case)\n tc = ss.table_config\n info_cc = tc.columns[INFO... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import folium
ctx = '../data/'
json = ctx + 'us-states.json'
csv = ctx + 'US_Unemployment_Oct2012.csv'
data = pd.read_csv(csv)
m = folium.Map(location=[37, -102], zoom_start=5)
m.choropleth(geo_data=json, name='choropleth', data=data, columns=['State',
'Unemployment'], Key_on='feature.id', fill_... | normal | {
"blob_id": "382cb55a6b849f0240276d8f45746e995b16d714",
"index": 4455,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nm.choropleth(geo_data=json, name='choropleth', data=data, columns=['State',\n 'Unemployment'], Key_on='feature.id', fill_color='YlGn', fill_opacity=\n 0.7, line_opacity=0.2, legend_... | [
0,
1,
2,
3
] |
# cases where DictAchievement should unlock
# >> CASE
{'name': 'John Doe', 'age': 24}
# >> CASE
{
'name': 'John Doe',
'age': 24
}
# >> CASE
func({'name': 'John Doe', 'age': 24})
| normal | {
"blob_id": "874fa2a6afdd04f3f2232a86f56d220447160ede",
"index": 5167,
"step-1": "<mask token>\n",
"step-2": "{'name': 'John Doe', 'age': 24}\n{'name': 'John Doe', 'age': 24}\nfunc({'name': 'John Doe', 'age': 24})\n",
"step-3": "# cases where DictAchievement should unlock\n\n# >> CASE\n{'name': 'John Doe', '... | [
0,
1,
2
] |
import json
import sys
from os import listdir
from os.path import isfile, join
import params
def encodeText(tweet_text):
tweet_text = tweet_text.replace('\n',' ')
return str(tweet_text)
def parse_file(file_in, file_out):
ptrFile_in = open(file_in, "r")
ptrFile_out = open(file_out, "w", encoding=... | normal | {
"blob_id": "e3afaabc1f7f64b9189fc88dd478ed75e81f35e1",
"index": 4564,
"step-1": "<mask token>\n\n\ndef parse_file(file_in, file_out):\n ptrFile_in = open(file_in, 'r')\n ptrFile_out = open(file_out, 'w', encoding='utf-8')\n cleanLines = []\n for line in ptrFile_in:\n cleanLine = {}\n l... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
result_dir = 'results'
data_dir = 'datasets'
cache_dir = f'{ROOT_PATH}/data/cache'
run_dir_ignore = ['results', 'datasets', 'cache']
use_treeconnect = False
treeconnect_threshold = 1024
vgg16 = 'vgg16_zhang_perceptual.pkl'
model =... | flexible | {
"blob_id": "cb904408486ad9ea8cc0c8ff2ec393e480309a57",
"index": 2403,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nresult_dir = 'results'\ndata_dir = 'datasets'\ncache_dir = f'{ROOT_PATH}/data/cache'\nrun_dir_ignore = ['results', 'datasets', 'cache']\nuse_treeconnect = False\ntreeconnect_threshold = 1... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('test 123123')
| flexible | {
"blob_id": "c6d8b9faa610e817c449eee94d73c61cb62fa272",
"index": 8878,
"step-1": "<mask token>\n",
"step-2": "print('test 123123')\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
def forc(X):
Xi = X['Xi']
Yi = X['Yi']
Zi = X['Zi']
SEi = X['SEi']
Pi = X['Pi']
Hc1 = X['Hc1']
Hc2 = X['Hc2']
Hb1 = X['Hb1']
Hb2 = X['Hb2']
style = {'description_width': 'initial'}
colorbar_widge = widgets.Checkbox(value=False, description=
... | flexible | {
"blob_id": "e5a4ae2ec0fab1ca8cdce229c69725ece2dcc476",
"index": 8272,
"step-1": "<mask token>\n\n\ndef forc(X):\n Xi = X['Xi']\n Yi = X['Yi']\n Zi = X['Zi']\n SEi = X['SEi']\n Pi = X['Pi']\n Hc1 = X['Hc1']\n Hc2 = X['Hc2']\n Hb1 = X['Hb1']\n Hb2 = X['Hb2']\n style = {'description_w... | [
6,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Vocabulary(db.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class... | flexible | {
"blob_id": "834469f9c6e065fb29dfe1fd3e421fbb752f5094",
"index": 7708,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Vocabulary(db.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Vocabulary(db.Model):\n _id = db.Column... | [
0,
1,
2,
3
] |
import numpy as np
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
img = cv2.imread('modi.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
#Write the for loop code h... | normal | {
"blob_id": "759ff4cc123e85bdc8c1457bb521cd35841956cd",
"index": 482,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv... | [
0,
1,
2,
3,
4
] |
import numpy.random as rnd
import numpy as np
B=100000
N1=50
N2=50
p1mle=0.3
p2mle=0.4
taumle=p2mle-p1mle
estimate=[]
for i in range(B):
p1=0.0
for j in range(N1):
if(rnd.uniform(0,1)<p1mle):
p1+=1
p1/=N1
p2=0.0
for j in range(N2):
if(rnd.uniform(0,1)<p2mle):
p2+=1
p2/=N2
estimate.append(p2-p... | normal | {
"blob_id": "0db0daf9bea254cffaec1280cd13b2d70368cd94",
"index": 289,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(B):\n p1 = 0.0\n for j in range(N1):\n if rnd.uniform(0, 1) < p1mle:\n p1 += 1\n p1 /= N1\n p2 = 0.0\n for j in range(N2):\n if rnd.u... | [
0,
1,
2,
3,
4
] |
"""This program displays a customizable list of items by priority value,
with priority 1 being the highest. Allows the user to add, edit,
mark complete, show completed (hidden), and remove items. Stores the list of
items in a .txt file located where this program's main.py file is. All
changes are automatically save... | normal | {
"blob_id": "168a12e6653a0526f29c163913def50147481154",
"index": 632,
"step-1": "<mask token>\n\n\nclass ListItem:\n \"\"\"A custom object that stores four pieces of data representing each\n entry in the todo list. Contains the text of the todo list entry,\n the priority of the entry, the group code (NY... | [
8,
11,
12,
18,
24
] |
<|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 = [m... | flexible | {
"blob_id": "fa09937ce64952795ae27cb91bf2c52dfb3ef4da",
"index": 4532,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [migrations.sw... | [
0,
1,
2,
3,
4
] |
from flask import Blueprint, request, make_response
from flask_expects_json import expects_json
from server.validation.schemas import guest_calendar_schema
from tools.for_db.work_with_booking_info import add_booking_info_and_get_uuid
from tools.for_db.work_with_links import get_link
from tools.build_response import bui... | normal | {
"blob_id": "75ef5dd2b82cf79819f18045559f9850c74bb55a",
"index": 5565,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@guest_calendar_post.route('/calendars/<link_id>/bookings/', methods=['POST'])\n@expects_json(guest_calendar_schema)\ndef booking(link_id):\n request_body = request.get_json()\n ... | [
0,
1,
2,
3
] |
import requests, vars
def Cardid(name):
query = {"key":vars.Key, "token":vars.Token, "cards":"visible"}
execute = requests.request("GET", vars.BoardGetUrl, params=query).json()
for row in execute['cards']:
if row['name'] == name:
cardID = 1
break
else:
ca... | normal | {
"blob_id": "68493acce71060799da8c6cb03f2ddffce64aa92",
"index": 8970,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Cardid(name):\n query = {'key': vars.Key, 'token': vars.Token, 'cards': 'visible'}\n execute = requests.request('GET', vars.BoardGetUrl, params=query).json()\n for row in... | [
0,
1,
2,
3
] |
import shell
def executeUpgrade():
shell.executeCommand('pkg upgrade')
def executeInstall(pkg_name):
shell.executeCommand('pkg install ' + pkg_name)
def executeRemove(pkg_name):
shell.executeCommand('pkg remove ' + pkg_name)
shell.executeCommand('pkg autoremove')
def executeFindByName(name):
... | normal | {
"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
] |
#!/usr/bin/env python
# coding: utf-8
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tensorflow as tf
print(tf.__version__)
print(tf.keras.__version__)
print(tf.__path__)
import numpy as np
from tqdm import tqdm, tqdm_notebook
from utils import emphasis
import tensorflow.keras.backend as K
from tensorflow.... | normal | {
"blob_id": "08a0ab888886184f7447465508b6494b502821ea",
"index": 8903,
"step-1": "#!/usr/bin/env python\n# coding: utf-8\n\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nimport tensorflow as tf\nprint(tf.__version__)\nprint(tf.keras.__version__)\nprint(tf.__path__)\nimport numpy as np\n\nfrom tqdm import ... | [
0
] |
from odoo import models, fields, api
class Aceptar_letras_wizard(models.TransientModel):
_name = 'aceptar_letras_wizard'
_description = "Aceptar letras"
def _get_letras(self):
if self.env.context and self.env.context.get('active_ids'):
return self.env.context.get('active_ids')
... | normal | {
"blob_id": "4ad3390f8f2c92f35acde507be7a7b713af997f2",
"index": 5092,
"step-1": "<mask token>\n\n\nclass Aceptar_letras_wizard(models.TransientModel):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @api.multi\n def aceptar_letras(self):\n active_ids = self.env.context.g... | [
2,
3,
4,
5,
6
] |
"""PriceTrail URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class... | normal | {
"blob_id": "06627821c09d02543974a3c90664e84e11c980ed",
"index": 7631,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^admin/', admin.site.urls), url('^logout/$', auth_views\n .logout, {'next_page': '/'}, name='logout'), url('^$', index_view, name\n ='index'), url('^login/$', lo... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class ReloadModelHandler(BaseHandler):
def __init__(self, application, request, **kwargs):
super(ReloadModelHandler, self).__init__(application, request, **kwargs
)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cla... | flexible | {
"blob_id": "a8ae59bb525c52ef852655f0ef1e32d96c8914d6",
"index": 1356,
"step-1": "<mask token>\n\n\nclass ReloadModelHandler(BaseHandler):\n\n def __init__(self, application, request, **kwargs):\n super(ReloadModelHandler, self).__init__(application, request, **kwargs\n )\n <mask token>\n... | [
2,
3,
4,
5,
6
] |
# CS 5010 Project
# Team Metro
# Test the data cleaning
import unittest
from cleaning_data import dfClean # import the dataframe we created after cleaning the data
class DataTypesTestCase(unittest.TestCase):
# we will test that each column has the correct data type
# note that there is a strange occurenc... | normal | {
"blob_id": "9d0727970c760a9a8123c5c07359ba5c538cea3c",
"index": 5926,
"step-1": "<mask token>\n\n\nclass DataTypesTestCase(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_is_rain_a_float(self):\n rain = dfClean.iloc[4908, 2]\n self.assertTrue(isinstance(rain, float))\n <... | [
18,
19,
29,
31,
33
] |
def test(name,message):
print("用户是：" , name)
print("欢迎消息是：",message)
my_list = ['孙悟空','欢迎来疯狂软件']
test(*my_list)
print('*****')
# ###########################
def foo(name,*nums):
print("name参数：",name)
print("nums参数:",nums)
my_tuple = (1,2,3)
foo('fkit',*my_tuple)
print('********')
foo(*my_tuple)
print(... | normal | {
"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|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
def containsDuplicate(self, nums) ->bool:
d = {}
for elem in nums:
if elem in d:
r... | flexible | {
"blob_id": "89256a38208be92f87115b110edc986cebc95306",
"index": 8440,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class Solution:\n\n def containsDuplicate(self, nums) ->bool:\n d = {}\n for elem in nums:\n if elem in ... | [
0,
1,
2,
3,
4
] |
#encoding=utf-8
import pytest
from frame_project.实战2.main_page import MainPage
class TestMian:
def test_mian(self):
MainPage().goto_marketpage().goto_search().search()
if __name__ == '__main__':
pytest.main(['test_case.py','-s','-v'])
| normal | {
"blob_id": "e1751cc6f76f56e62cd02d61db65f1c27a4ff1b9",
"index": 7351,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestMian:\n\n def test_mian(self):\n MainPage().goto_marketpage().goto_search().search()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass TestMian:\n\n de... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LabeledArray:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LabeledArray:
@staticmethod
def get_label_for_indexes_upto(input_data, input_label, input_index):... | flexible | {
"blob_id": "0dea8675d8050a91c284a13bcbce6fd0943b604e",
"index": 5135,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LabeledArray:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass LabeledArray:\n\n @staticmethod\n def get_label_for_indexes_upto(input_data, input_label, input_in... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class DongPhamTestCli(CLI):
<|reserved_special_token_0|>
def __init__(self, _mininet, _env):
self.env = _env
self.net = _mininet
self._testCLI = {}
CLI.__init__(self, _mininet)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "7636925982434b12307383ba7b01f931f7ea6e24",
"index": 5927,
"step-1": "<mask token>\n\n\nclass DongPhamTestCli(CLI):\n <mask token>\n\n def __init__(self, _mininet, _env):\n self.env = _env\n self.net = _mininet\n self._testCLI = {}\n CLI.__init__(self, _mininet)\n ... | [
10,
18,
21,
26,
27
] |
#!/usr/bin/env python
# USAGE: day_22_01.py
# Michael Chambers, 2017
class Grid:
def __init__(self, startFile):
# Load initial infected sites
# Origin is top-left of input file
self.infected = set()
posx = 0
with open(startFile, 'r') as fo:
for i, line in enumerate(fo):
line = line.rstrip()
posx... | normal | {
"blob_id": "f840624ec11679d576fbb80f8e753c59663a7ee2",
"index": 9168,
"step-1": "<mask token>\n\n\nclass ComplexGrid:\n\n def __init__(self, startFile):\n self.weakened = set()\n self.infected = set()\n self.flagged = set()\n posx = 0\n with open(startFile, 'r') as fo:\n ... | [
6,
7,
11,
13,
14
] |
from django.shortcuts import render
from rest_framework import status, viewsets , response
from . import models
from . import serializers
# Create your views here.
class TodoViewset(viewsets.ModelViewSet):
queryset = models.Todo.objects.all()
serializer_class = serializers.TodoSerializer
| normal | {
"blob_id": "1c668cf6f145b85a09b248fefda46e928de64e41",
"index": 5041,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TodoViewset(viewsets.ModelViewSet):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TodoViewset(viewsets.ModelViewSet):\n queryset = models.Todo.... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
from __future__ import division, unicode_literals
import unittest
from monty.inspect import *
class LittleCatA(object):
pass
class LittleCatB(LittleCatA):
pass
class LittleCatC(object):
pass
class LittleCatD(LittleCatB):
pass
class InspectTest(unittest.TestCase):
def test_fu... | normal | {
"blob_id": "89605ff723d2f78e85cae458d576494718b5d456",
"index": 1193,
"step-1": "<mask token>\n\n\nclass InspectTest(unittest.TestCase):\n\n def test_func(self):\n self.assertTrue(find_top_pyfile())\n self.assertTrue(caller_name())\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask toke... | [
2,
5,
6,
9,
10
] |
def towers_of_hanoi(n, src, dest, temp,res):
if n==1:
s = 'disk 1 from ',src,'->',dest
res.append(s)
return
towers_of_hanoi(n-1, src, temp, dest, res)
s = 'disk ',n, ' from ',src,'->',dest
res.append(s)
towers_of_hanoi(n-1, temp, dest, src, res)
return res
def steps... | normal | {
"blob_id": "f23bfef2daf8fda4249435821dbc2e0b1846e3d6",
"index": 9842,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef steps_in_tower_of_hanoi(no_of_disks):\n res = towers_of_hanoi(no_of_disks, 'A', 'C', 'B', [])\n return res\n\n\n<mask token>\n",
"step-3": "def towers_of_hanoi(n, src, des... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def test_board_can_be_instatiated_with_any_set_of_pieces():
board = Board(initial_pieces={'a2': Pawn('white'), 'a6': Pawn('black')})
assert board.pieces_quantity() == 2
def test_piece_cant_capture_an_ally():
board = Board(initial_pieces={'e5': Pawn('white'), 'f3': Knight('wh... | flexible | {
"blob_id": "5f471fb75b1c4f6fc7aa4cb4f99f9c1a1a9f0ea1",
"index": 8595,
"step-1": "<mask token>\n\n\ndef test_board_can_be_instatiated_with_any_set_of_pieces():\n board = Board(initial_pieces={'a2': Pawn('white'), 'a6': Pawn('black')})\n assert board.pieces_quantity() == 2\n\n\ndef test_piece_cant_capture_a... | [
3,
7,
9,
10,
11
] |
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
def lengthOfLongestSubstring(self, s):
"""
:type s: str
:rtype: in... | flexible | {
"blob_id": "b7c43f4242e38318c9e5423ea73e9d9d86759a53",
"index": 4663,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n def lengthOfLongestSubstring(self, s):\n \"\"\"\n :type s: str\n ... | [
1,
2,
3,
4,
5
] |
import random
def take_second(element):
return element[1]
import string
def get_random_name():
name = ""
for i in range(random.randint(5, 15)):
name += random.choice(string.ascii_letters)
return name
imenik = [(777, "zejneba"), (324, "fahro"), (23, "fatih"), (2334, "muamer"), (435, "keri... | normal | {
"blob_id": "21ef8103a5880a07d8c681b2367c2beef727260f",
"index": 6536,
"step-1": "<mask token>\n\n\ndef take_second(element):\n return element[1]\n\n\n<mask token>\n\n\ndef get_random_name():\n name = ''\n for i in range(random.randint(5, 15)):\n name += random.choice(string.ascii_letters)\n r... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python3
import argparse
import boutvecma
import easyvvuq as uq
import chaospy
import os
import numpy as np
import time
from dask.distributed import Client
from dask_jobqueue import SLURMCluster
import matplotlib.pyplot as plt
if __name__ == "__main__":
parser = argparse.ArgumentParser(description... | normal | {
"blob_id": "416f4c6bbd2f2b9562ab2d1477df4ebc45070d8d",
"index": 5060,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='EasyVVUQ applied to BOUT++')\n parser.add_argument('--batch', '-b', help=\n 'Run on a batch (SLURM)... | [
0,
1,
2,
3
] |
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
from sklearn.svm import SVR
# In[2]:
from sklearn.preprocessing import StandardScaler
# In[3]:
#import matplotlib.pyplot as plt
# %matplotlib inline
# In[90]:
aapl = pd.read_csv('return_fcast.csv')
# In[79]:
y = aapl['return']
# In[80]:
... | normal | {
"blob_id": "4a8d203872a1e86c54142dea6cd04c1cac6bcfb2",
"index": 5067,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nregressor.fit(X, y)\n<mask token>\nX_test.shape\n<mask token>\ny_pred\n<mask token>\ny_pred\nfor i in range(len(y_pred)):\n print(y_pred[i])\n<mask token>\nVIX.iloc[40:2476]\n<mask tok... | [
0,
1,
2,
3,
4
] |
from django.shortcuts import render, HttpResponse, redirect
from ..login.models import *
from ..dashboard.models import *
def display(request, id):
context = {'job': Job.objects.get(id=int(id))}
return render(request, 'handy_helper_exam/display.html', context)
| normal | {
"blob_id": "f1fdba1c07a29aa22ee8d0dcbd6f902aa2e8b4c2",
"index": 9342,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef display(request, id):\n context = {'job': Job.objects.get(id=int(id))}\n return render(request, 'handy_helper_exam/display.html', context)\n",
"step-3": "from django.short... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 3 16:04:19 2018
@author: khanhle
"""
# Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Activation
from keras.utils import np_utils
from keras.layers.convolutional import Convolut... | normal | {
"blob_id": "721f23d2b6109194b8bca54b1cd04263e30cdf24",
"index": 3964,
"step-1": "<mask token>\n\n\ndef cnn_model():\n model = Sequential()\n model.add(ZeroPadding2D((1, 1), input_shape=(1, 20, window_sizes)))\n model.add(Convolution2D(32, nb_kernels, nb_kernels))\n model.add(Activation('relu'))\n ... | [
1,
2,
3,
4,
5
] |
#!python3
import requests
import time
log_file = open("logfile.txt", "w")
def generateLog(ctime1, request_obj):
log_file.write(ctime1 + "\t")
log_file.write("Status code: " + str(request_obj.status_code))
log_file.write("\n")
def is_internet():
"""Internet function"""
print(time.... | normal | {
"blob_id": "f229f525c610d9925c9300ef22208f9926d6cb69",
"index": 9985,
"step-1": "<mask token>\n\n\ndef generateLog(ctime1, request_obj):\n log_file.write(ctime1 + '\\t')\n log_file.write('Status code: ' + str(request_obj.status_code))\n log_file.write('\\n')\n\n\ndef is_internet():\n \"\"\"Internet ... | [
2,
3,
4,
5,
6
] |
import argparse
from ray.tune.config_parser import make_parser
from ray.tune.result import DEFAULT_RESULTS_DIR
EXAMPLE_USAGE = """
Training example:
python ./train.py --run DQN --env CartPole-v0 --no-log-flatland-stats
Training with Config:
python ./train.py -f experiments/flatland_random_sparse_small/global... | normal | {
"blob_id": "79a8ff0000f3be79a62d693ed6bae7480673d970",
"index": 6075,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_parser(parser_creator=None):\n parser = make_parser(parser_creator=parser_creator, formatter_class=\n argparse.RawDescriptionHelpFormatter, description=\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Unit(Atom, RelMeths, ArithMeths):
is_positive = True
is_commutative = True
def __init__(self, name, abbrev):
self.name = name
self.abbrev = abbrev
def tostr(self, level=0):
return self.abbrev
def __eq__(self, other):
return isin... | flexible | {
"blob_id": "c0e1c0c4545777a669fac19900239ab9baade242",
"index": 5993,
"step-1": "<mask token>\n\n\nclass Unit(Atom, RelMeths, ArithMeths):\n is_positive = True\n is_commutative = True\n\n def __init__(self, name, abbrev):\n self.name = name\n self.abbrev = abbrev\n\n def tostr(self, le... | [
5,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for key in my_dict:
print('{} - {}'.format(key, my_dict[key]))
<|reserved_special_token_1|>
my_dict = {'one': '1', 'two': '2'}
for key in my_dict:
print('{} - {}'.format(key, my_dict[key]))
| flexible | {
"blob_id": "1d524312cbd3b735850046131f31c03fdfa90bbc",
"index": 483,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor key in my_dict:\n print('{} - {}'.format(key, my_dict[key]))\n",
"step-3": "my_dict = {'one': '1', 'two': '2'}\nfor key in my_dict:\n print('{} - {}'.format(key, my_dict[key]))... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def similarity_metric(embedding1: numpy.ndarray, embedding2: numpy.ndarray
) ->float:
return numpy.nan_to_num(1 - cosine(embedding1, embedding2), 0)
<|reserved_special_token_1|>
import numpy
from scipy.spatial.distanc... | flexible | {
"blob_id": "ec9f27b4313f72ae6eb7e8280d47de226aeb6bb1",
"index": 2270,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef similarity_metric(embedding1: numpy.ndarray, embedding2: numpy.ndarray\n ) ->float:\n return numpy.nan_to_num(1 - cosine(embedding1, embedding2), 0)\n",
"step-3": "import ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
@tf.function
def train_discrepancy_1(main_data, main_labels, target_data):
with tf.GradientTape(persistent=True) as tape:
shared_main = [shared[i](main_data, training=True) for i in range(
NUM_MODELS)]
main_logits_1 = [main_classifier_1[i](shared_main[i], t... | flexible | {
"blob_id": "465d5baae8d5be77fbf3d550d10667da420a8fbe",
"index": 8608,
"step-1": "<mask token>\n\n\n@tf.function\ndef train_discrepancy_1(main_data, main_labels, target_data):\n with tf.GradientTape(persistent=True) as tape:\n shared_main = [shared[i](main_data, training=True) for i in range(\n ... | [
1,
3,
4,
5,
7
] |
<|reserved_special_token_0|>
class RecipeAdmin(admin.ModelAdmin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Meta:
model = Recipe
def make_visible(self, request, queryset):
queryset.update(visible... | flexible | {
"blob_id": "65bb3743ca569c295d85016c82c4f6f043778d3f",
"index": 8848,
"step-1": "<mask token>\n\n\nclass RecipeAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n model = Recipe\n\n def make_visible(self, request, queryset):\n ... | [
3,
5,
6,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ShortenConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ShortenConfig(AppConfig):
default_auto_field = 'django.db... | flexible | {
"blob_id": "8c2920db7fc49d56aa8da6289cd22272ed3e3283",
"index": 4402,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ShortenConfig(AppConfig):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ShortenConfig(AppConfig):\n default_auto_field = 'django.db.models.BigA... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
@DatasetReader.register('bertclassification')
class ClassificationReader(DatasetReader):
<|reserved_special_token_0|>
@overrides
def _read(self, file_path: str) ->Iterable[Instance]:
file_path = cached_path(file_path)
with open(file_path, 'r') as data_file:
... | flexible | {
"blob_id": "21172985bf36302f6b0b2101e353d9fbcafb0673",
"index": 6653,
"step-1": "<mask token>\n\n\n@DatasetReader.register('bertclassification')\nclass ClassificationReader(DatasetReader):\n <mask token>\n\n @overrides\n def _read(self, file_path: str) ->Iterable[Instance]:\n file_path = cached_... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def main():
x = float(input('Coordenada x: '))
y = float(input('Coordenada y: '))
if 1 <= y <= 2 and -3 <= x <= 3:
print('dentro')
elif (4 <= y <= 5 or 6 <= x <= 7) and (-4 <= x <= -3 or -2 <= x <= -1 or
1 <= x <= 2 or 3 <= x <... | flexible | {
"blob_id": "06cb832c3adae95fcd1d1d2d0663641d3ac671ef",
"index": 9132,
"step-1": "<mask token>\n",
"step-2": "def main():\n x = float(input('Coordenada x: '))\n y = float(input('Coordenada y: '))\n if 1 <= y <= 2 and -3 <= x <= 3:\n print('dentro')\n elif (4 <= y <= 5 or 6 <= x <= 7) and (-4... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def mk_dir_recursive(dir_path):
if os.path.isdir(dir_path):
return
h, t = os.path.split(dir_path)
if not os.path.isdir(h):
mk_dir_recursive(h)
new_path = join_paths(h, t)
if not os.path.isdir(new_path):
os.mkdir(new_path)
<|reserved_special_to... | flexible | {
"blob_id": "9f4cd9ed8aea03f5908aef4a154d964f0810619b",
"index": 9820,
"step-1": "<mask token>\n\n\ndef mk_dir_recursive(dir_path):\n if os.path.isdir(dir_path):\n return\n h, t = os.path.split(dir_path)\n if not os.path.isdir(h):\n mk_dir_recursive(h)\n new_path = join_paths(h, t)\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print((lambda myself: lambda n: IF(IS_ZERO(n))(lambda _: ONE)(lambda _:
MULT(n)(myself(myself)(SUB1(n)))))(lambda myself: lambda n: IF(IS_ZERO(
n))(lambda _: ONE)(lambda _: MULT(n)(myself(myself)(SUB1(n)))))(6))
<|reserv... | flexible | {
"blob_id": "f8601ed7ba7c2b8d2dd8d5f74f7b5ae8e99dad78",
"index": 186,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint((lambda myself: lambda n: IF(IS_ZERO(n))(lambda _: ONE)(lambda _:\n MULT(n)(myself(myself)(SUB1(n)))))(lambda myself: lambda n: IF(IS_ZERO(\n n))(lambda _: ONE)(lambda _: MULT(... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class MyTestCase(TestCase):
def test_mark_done(self):
user = User.objects.create_user(email='user@…', username='user',
password='somepasswd')
todo = Todo(title='SomeTitle', description='SomeDescr... | flexible | {
"blob_id": "5c81ddbc8f5a162949a100dbef1c69551d9e267a",
"index": 37,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MyTestCase(TestCase):\n\n def test_mark_done(self):\n user = User.objects.create_user(email='user@…', username='user',\n password='somepasswd')\n todo ... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
@csrf_exempt
def TBGRApi(request, tbgrno=0):
if request.method == 'GET':
tbgrs = TBGR.objects.all()
tbgrs_serializer = TBGRSerializer(tbgrs, many=True)
return JsonResponse(tbgrs_serializer.data, safe=False)
elif request.method == 'POST':
tbgr_data =... | flexible | {
"blob_id": "e0c6fb414d87c0a6377538089226e37b044edc70",
"index": 8383,
"step-1": "<mask token>\n\n\n@csrf_exempt\ndef TBGRApi(request, tbgrno=0):\n if request.method == 'GET':\n tbgrs = TBGR.objects.all()\n tbgrs_serializer = TBGRSerializer(tbgrs, many=True)\n return JsonResponse(tbgrs_se... | [
3,
4,
5,
7,
8
] |
# O(logn) T O(1) S
def binarySearch(array, target):
if len(array) == 0:
return -1
else:
return binarySearchR(array, target, 0, len(array) - 1)
def binarySearchR(array, target, leftPointer, rightPointer):
if leftPointer > rightPointer:
return -1
else:
midPointer = (leftP... | normal | {
"blob_id": "57d6b9e7f48d32e5d10bfd6a340ea56281f5d82d",
"index": 1890,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef binarySearchR(array, target, leftPointer, rightPointer):\n if leftPointer > rightPointer:\n return -1\n else:\n midPointer = (leftPointer + rightPointer) // 2\... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
import scrapy
import re
class LeedsAcUkSpider(scrapy.Spider):
name = 'leeds_ac_uk'
allowed_domains = ['webprod3.leeds.ac.uk']
start_urls = ['http://webprod3.leeds.ac.uk/catalogue/dynmodules.asp?Y=201920&M=ANAT-3105']
def parse(self, response):
item = {}
item['Su... | normal | {
"blob_id": "fb4a95197882cc6fe72a5f3c2420a474d9cd97aa",
"index": 7751,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LeedsAcUkSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n\n def parse(self, response):\n item = {}\n item['Subject'] = response.cs... | [
0,
2,
3,
4,
5
] |
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