code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
__author__ = 'GazouillisTeam'
import numpy as np
import os
import sys
import time
from keras.callbacks import Callback
def save_architecture(model, path_out):
"""
Based on the keras utils 'model.summary()'
"""
# Redirect the print output the a textfile
orig_stdout = sys.stdout
# and store the... | normal | {
"blob_id": "f8635c815b375dc77e971d4ea0f86547215ab2f9",
"index": 7987,
"step-1": "<mask token>\n\n\nclass ModelSaver(Callback):\n \"\"\"\n Keras callback subclass which defines a saving procedure of the model being trained : after each epoch,\n the last model is saved under the name 'after_random.cnn'. ... | [
6,
7,
8,
10,
13
] |
__all__ = ["loading"]
from . import loading
| normal | {
"blob_id": "f633496f1a7cd562fd41d697a2e26831ceaef479",
"index": 8047,
"step-1": "<mask token>\n",
"step-2": "__all__ = ['loading']\n<mask token>\n",
"step-3": "__all__ = ['loading']\nfrom . import loading\n",
"step-4": "__all__ = [\"loading\"]\n\nfrom . import loading\n",
"step-5": null,
"step-ids": [... | [
0,
1,
2,
3
] |
import os
from pathlib import Path
import shutil
from ament_index_python.packages import get_package_share_directory, get_package_prefix
import launch
import launch_ros.actions
def generate_launch_description():
cart_sdf = os.path.join(get_package_share_directory('crs_support'),
'sdf', 'cart.sdf')
car... | normal | {
"blob_id": "cc74163d5dbcc2b2ca0fe5222692f6f5e45f73fe",
"index": 2377,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate_launch_description():\n cart_sdf = os.path.join(get_package_share_directory('crs_support'),\n 'sdf', 'cart.sdf')\n cart_spawner = launch_ros.actions.Node(nod... | [
0,
1,
2
] |
import datetime
class Schedule:
def __init__(self, start, end, name, other): # Constructor
self.start = self.str_convert(start) # Schedule start time (ex. 9:00)
self.end = self.str_convert(end) # Schedule end time (ex. 22:00)
... | normal | {
"blob_id": "f56978d5738c2f8cb4ed5ce4f11d3aae6a9689b1",
"index": 4604,
"step-1": "<mask token>\n\n\nclass Schedule:\n\n def __init__(self, start, end, name, other):\n self.start = self.str_convert(start)\n self.end = self.str_convert(end)\n self.name = name\n self.other = other\n ... | [
9,
10,
12,
13,
14
] |
#-*- coding: utf-8 -*-
def print99():
"""
打印99乘法口诀表
:return:
"""
for i in range(1,10):
for j in range(1, i+1):
print('%dX%d=%2s ' %(j,i,i*j))
print('\n')
print99()
| normal | {
"blob_id": "90f1fd45d58c7e6f275a33cd9c693ff584b2df47",
"index": 1396,
"step-1": "<mask token>\n",
"step-2": "def print99():\n \"\"\"\n 打印99乘法口诀表\n :return:\n \"\"\"\n for i in range(1, 10):\n for j in range(1, i + 1):\n print('%dX%d=%2s ' % (j, i, i * j))\n print('\\n'... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
"""
maskAOI.py
Dan Fitch 20150618
"""
from __future__ import print_function
import sys, os, glob, shutil, fnmatch, math, re, numpy, csv
from PIL import Image, ImageFile, ImageDraw, ImageColor, ImageOps, ImageStat
ImageFile.MAXBLOCK = 1048576
DEBUG = False
AOI_DIR='/study/reference/public/IAP... | normal | {
"blob_id": "833053a5a75636267feaad5ddaa21dce1de34038",
"index": 5319,
"step-1": "<mask token>\n\n\ndef RepresentsInt(s):\n try:\n int(s)\n return True\n except ValueError:\n return False\n\n\n<mask token>\n\n\ndef drawOneEllipse(aoi, img, draw):\n if DEBUG:\n print('Ellipse ... | [
6,
8,
12,
13,
17
] |
import sys
import os
sys.path.append(os.pardir)
from ch03.softmax import softmax
from ch04.cross_entropy_error_batch import cross_entropy_error
import numpy as np
class SoftmaxWithLossLayer:
"""
x -> [Softmax] -> y -> [CrossEntropyError with t] -> out
In the textbook, this class has `loss` field.
"""... | normal | {
"blob_id": "8ae64c65d6d5dc9f2a99aeceff31657deff06c15",
"index": 5236,
"step-1": "<mask token>\n\n\nclass SoftmaxWithLossLayer:\n <mask token>\n\n def __init__(self):\n self.y = None\n self.t = None\n\n def forward(self, x, t):\n \"\"\"\n x: input to softmax\n t: teach... | [
4,
5,
6,
7,
8
] |
# Counts number of dumbbell curls in the video
import cv2
import mediapipe as mp
import base
import math
import numpy as np
class PoseEstimator(base.PoseDetector):
def __init__(self, mode=False, upperBody = False, smooth=True, detectConf=.5, trackConf=.5,
outFile="output.mp4", outWidth=720, outHe... | normal | {
"blob_id": "4a886437727ed6b48206e12b686a59a1d2a1c489",
"index": 4948,
"step-1": "<mask token>\n\n\nclass PoseEstimator(base.PoseDetector):\n <mask token>\n\n def findAngle(self, img, p1, p2, p3, draw=True):\n x1, y1 = self.lms[p1][1:]\n x2, y2 = self.lms[p2][1:]\n x3, y3 = self.lms[p3... | [
3,
5,
6,
7,
8
] |
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn import tree
import pickle as pk
X = pk.load(file=open('../data/temp/train.pkl', 'rb'))
y = pk.load(file=open('../data/temp/label.pkl', 'rb'))
X_train, X_test, y_train, y_test = train_test_... | normal | {
"blob_id": "539726df0e631c7a8edabf50fd739ee0497e3e97",
"index": 5557,
"step-1": "<mask token>\n\n\ndef train_model(model_name):\n if model_name == 'LinearRegression':\n model = LinearRegression()\n model.fit(X_train, y_train)\n score = model.score(X_test, y_test)\n print(score)\n ... | [
1,
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3,
4,
5
] |
s=input("enter a string")
u=0
l=0
for i in s:
if i.isupper():
u+=1
elif i.islower():
l+=1
print(u,l,end="") | normal | {
"blob_id": "bbb23d606b081d2591699cb6b9336c8766eea5b2",
"index": 2436,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in s:\n if i.isupper():\n u += 1\n elif i.islower():\n l += 1\nprint(u, l, end='')\n",
"step-3": "s = input('enter a string')\nu = 0\nl = 0\nfor i in s:\n i... | [
0,
1,
2,
3
] |
import sys
import psyco
sys.stdin = open("/home/shiva/Learning/1.txt", "r")
sys.stdout = open("/home/shiva/Learning/2.txt", "w")
def compute(plus,minus,total,inp):
if plus == 1 and minus == 0:
print(total); return
elif (plus == 1 and minus == 1):
print("Impossible"); return
elif (abs(plus-minus) > total):
pl... | normal | {
"blob_id": "d29c8ec737b8e962d381c8fdd0999e7e01847836",
"index": 5274,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef compute(plus, minus, total, inp):\n if plus == 1 and minus == 0:\n print(total)\n return\n elif plus == 1 and minus == 1:\n print('Impossible')\n ... | [
0,
2,
3,
4,
5
] |
import sqlite3
class announcement:
def __init__(eps_df, revenue_df):
conn = sqlite3.connect("earnings.db", timeout=120)
cur = conn.cursor()
symbol_href = self.driver.find_element_by_class_name("lfkTWp")
symbol = symbol_href.text
eps_history_df = pd.read_sql(
'... | normal | {
"blob_id": "b7738c27e11e9566d90157717633312031cdffd6",
"index": 818,
"step-1": "<mask token>\n\n\nclass announcement:\n\n def __init__(eps_df, revenue_df):\n conn = sqlite3.connect('earnings.db', timeout=120)\n cur = conn.cursor()\n symbol_href = self.driver.find_element_by_class_name('l... | [
6,
7,
8,
9,
11
] |
#!/usr/bin/python3
def divisible_by_2(my_list=[]):
if my_list is None or len(my_list) == 0:
return None
new = []
for num in my_list:
if num % 2 == 0:
new.append(True)
else:
new.append(False)
return new
| normal | {
"blob_id": "17f91b612fad14200d2911e2cb14e740b239f9ff",
"index": 4894,
"step-1": "<mask token>\n",
"step-2": "def divisible_by_2(my_list=[]):\n if my_list is None or len(my_list) == 0:\n return None\n new = []\n for num in my_list:\n if num % 2 == 0:\n new.append(True)\n ... | [
0,
1,
2
] |
from ethereum.common import mk_transaction_sha, mk_receipt_sha
from ethereum.exceptions import InsufficientBalance, BlockGasLimitReached, \
InsufficientStartGas, InvalidNonce, UnsignedTransaction
from ethereum.messages import apply_transaction
from ethereum.slogging import get_logger
from ethereum.utils import enco... | normal | {
"blob_id": "e364ba45513167966fe50e31a01f552ccedec452",
"index": 6552,
"step-1": "<mask token>\n\n\ndef add_transactions(shard_state, collation, txqueue, shard_id,\n min_gasprice=0, mainchain_state=None):\n \"\"\"Add transactions to a collation\n (refer to ethereum.common.add_transactions)\n \"\"\"\n... | [
4,
5,
6,
7,
10
] |
import re
import sys
import zipfile
import pathlib
from typing import IO, Any
from collections.abc import Mapping
import numpy.typing as npt
import numpy as np
from numpy.lib._npyio_impl import BagObj
if sys.version_info >= (3, 11):
from typing import assert_type
else:
from typing_extensions import assert_typ... | normal | {
"blob_id": "e2f134f5ff00405396b8bbf4edc263b70ef5d972",
"index": 2435,
"step-1": "<mask token>\n\n\nclass BytesWriter:\n <mask token>\n\n\nclass BytesReader:\n\n def read(self, n: int=...) ->bytes:\n ...\n\n def seek(self, offset: int, whence: int=...) ->int:\n ...\n\n\n<mask token>\n",
... | [
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6,
7,
8
] |
from django.utils.text import slugify
from pyexpat import model
from django.db import models
# Create your models here.
from rest_framework_simplejwt.state import User
FREQUENCY = (
('daily', 'Diario'),
('weekly', 'Semanal'),
('monthly', 'Mensual')
)
class Tags(models.Model):
name = models.CharField(m... | normal | {
"blob_id": "71503282e58f60e0936a5236edc094f1da937422",
"index": 6565,
"step-1": "<mask token>\n\n\nclass Tags(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n ordering = '-created_at',\n\n\nclass Newsletter(m... | [
5,
7,
8,
10,
11
] |
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("same_host").setMaster("local")
sc = SparkContext(conf=conf)
julyFirstLogs = sc.textFile("/Users/iamsuman/src/iamsuman/myspark/mypyspark/data/nasa_19950701.tsv")
augFirstLogs = sc.textFile("/Users/iamsuman/src/iamsuman/myspark/mypyspark/data/na... | normal | {
"blob_id": "36fce3837e0341d94ff6099a06be8cf757a1cfa9",
"index": 3596,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncleanedHostIntersection.saveAsTextFile('out/nasa_logs_same_hosts.csv')\n",
"step-3": "<mask token>\nconf = SparkConf().setAppName('same_host').setMaster('local')\nsc = SparkContext(conf... | [
0,
1,
2,
3,
4
] |
# Copyright (c) 2021 Koichi Sakata
from pylib_sakata import init as init
# uncomment the follows when the file is executed in a Python console.
# init.close_all()
# init.clear_all()
import os
import shutil
import numpy as np
from control import matlab
from pylib_sakata import ctrl
from pylib_sakata import plot
prin... | normal | {
"blob_id": "ad1aa69f92f104ac8b82aca3c0a64ce3de48b36d",
"index": 3847,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Start simulation!')\n<mask token>\nif os.path.exists(figurefolderName):\n shutil.rmtree(figurefolderName)\nos.makedirs(figurefolderName)\n<mask token>\nprint('Common parameters ... | [
0,
1,
2,
3,
4
] |
import numpy as np
import tensorflow as tf
def mfcc(data):
pass
def cut_frames(data):
pass
| normal | {
"blob_id": "8411acf6b27425357d212f5e220314daa019e023",
"index": 9669,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef cut_frames(data):\n pass\n",
"step-3": "<mask token>\n\n\ndef mfcc(data):\n pass\n\n\ndef cut_frames(data):\n pass\n",
"step-4": "import numpy as np\nimport tensorflo... | [
0,
1,
2,
3
] |
import sys
import os
import traceback
from src.properties import *
from src.utils import *
from subprocess import call
from src.entity.cursor import Cursor
from curses import *
def main(screen, file_path):
setUpEnv()
text = readFileIfExist(file_path)
while 1:
try:
text = startEditing(s... | normal | {
"blob_id": "7a6d45ef87d93af9a15bd352b893164d3a36c399",
"index": 7545,
"step-1": "<mask token>\n\n\ndef main(screen, file_path):\n setUpEnv()\n text = readFileIfExist(file_path)\n while 1:\n try:\n text = startEditing(screen, text)\n printQuitOptions(screen)\n cha... | [
4,
5,
6,
7,
8
] |
import os
import struct
import sys
import wave
sys.path.insert(0, os.path.dirname(__file__))
C5 = 523
B4b = 466
G4 = 392
E5 = 659
F5 = 698
VOLUME = 12000
notes = [
[VOLUME, C5],
[VOLUME, C5],
[VOLUME, B4b],
[VOLUME, C5],
[0, C5],
[VOLUME, G4],
[0, C5],
[VOLUME, G4],
[VOLUME, C5],
... | normal | {
"blob_id": "4fb563985bd99599e88676e167ee84a95b018aba",
"index": 5414,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.insert(0, os.path.dirname(__file__))\n<mask token>\nfor volume, frequency in notes:\n samples = square_wave(int(44100 / frequency // 2))\n samples = gain(samples, volume)\n... | [
0,
1,
2,
3,
4
] |
import random
import numpy as np
class Board:
def __init__(self, nrows, ncols, random_seed=42):
self.nrows = nrows
self.ncols = ncols
self.random = random.Random()
self.random.seed(random_seed)
self.board = np.zeros((nrows, ncols))
self.score = 0
self.__add_new_numbers()
# Initialize with 1/8 of the ... | normal | {
"blob_id": "cab45a823e319bd504b3db68cf70bff315f44fc6",
"index": 7462,
"step-1": "<mask token>\n\n\nclass Board:\n\n def __init__(self, nrows, ncols, random_seed=42):\n self.nrows = nrows\n self.ncols = ncols\n self.random = random.Random()\n self.random.seed(random_seed)\n ... | [
10,
12,
13,
14,
16
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 31 05:48:57 2019
@author: emama
"""
import datetime as dt
t = dt.datetime.today()
print(t) | normal | {
"blob_id": "b1fbc8f3616b70e5d35898fd895c37e838c87dc9",
"index": 9293,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(t)\n",
"step-3": "<mask token>\nt = dt.datetime.today()\nprint(t)\n",
"step-4": "<mask token>\nimport datetime as dt\nt = dt.datetime.today()\nprint(t)\n",
"step-5": "# -*- co... | [
0,
1,
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3,
4
] |
from rest_framework import serializers, viewsets, routers
from lamp_control.models import Lamp
class LampSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Lamp
fields = '__all__'
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset ... | normal | {
"blob_id": "aff1d702e591efcfc0fc93150a3fbec532408137",
"index": 55,
"step-1": "<mask token>\n\n\nclass LampViewSet(viewsets.ModelViewSet):\n serializer_class = LampSerializer\n queryset = Lamp.objects.all()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass LampSerializer(serializers.HyperlinkedMo... | [
2,
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7
] |
from GRAFICA_BRESENHAMS import Bresenhams
def main():
x = int(input('INGRESA VALOR PARA X: \n'))
y = int(input('INGRESA VALOR PARA Y: \n'))
x1 = int(input('INGRESA VALOR PARA X1: \n'))
y1 = int(input('INGRESA VALOR PARA Y1: \n'))
Bresenhams(x, y, x1, y1)
if __name__ == '__main__':
main()
| normal | {
"blob_id": "e75bee4e014aa369131c3e200ce874a8840b5690",
"index": 3573,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n x = int(input('INGRESA VALOR PARA X: \\n'))\n y = int(input('INGRESA VALOR PARA Y: \\n'))\n x1 = int(input('INGRESA VALOR PARA X1: \\n'))\n y1 = int(input('I... | [
0,
1,
2,
3
] |
from jox_api import label_image,Mysql,Utils
from jox_config import api_base_url
import json
class Menu():
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self,type,openid):
try:
if type == 'mine':
self.sql = "SEL... | normal | {
"blob_id": "4fa9d16f979acf3edce05a209e1c6636e50fc315",
"index": 222,
"step-1": "<mask token>\n\n\nclass Menu:\n <mask token>\n\n def get_menu(self, type, openid):\n try:\n if type == 'mine':\n self.sql = (\n \"SELECT * FROM get_menu WHERE openid='%s' ord... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
gp = pd.read_csv('graph6.csv')
N=gp['Starting-node'].max()
M=gp['Ending-node'].max()
N=max(N,M)
gp=gp.sort_values(by='Cost')
gp=gp.reset_index()
gp=gp.reset_index()
gp['tree label']=gp['level_0']
index=gp['index'].max()
gp.drop('index',axis... | normal | {
"blob_id": "719f7b7b2d8df037583263588e93d884ab3820fe",
"index": 5963,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ngp.drop('index', axis=1, inplace=True)\ngp.drop('level_0', axis=1, inplace=True)\nfor n in range(index + 1):\n Count = []\n Visit = []\n Visit2 = []\n for i in range(11):\n ... | [
0,
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3,
4
] |
# models.py- Team
from django.db import models
class Team(models.Model):
teamName = models.TextField()
#Seasons associated
#Registrants unique
return
| normal | {
"blob_id": "331b5f0a34db4d12d713439db3d2818e8c922310",
"index": 4236,
"step-1": "<mask token>\n\n\nclass Team(models.Model):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Team(models.Model):\n teamName = models.TextField()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ncl... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
from django.conf.urls import patterns, include, url
from apps.virt.views import node, domain,device,cluster,home
urlpatterns = patterns('',
# Home
url(r'^$', home.HomeView.as_view(), name='home'),
# Cluster
url(r'^cluster/status/$', cluster.ClusterStatusView.as_view(), name... | normal | {
"blob_id": "484d104a8481a707a187d0bcb30898c3459a88be",
"index": 389,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = patterns('', url('^$', home.HomeView.as_view(), name='home'),\n url('^cluster/status/$', cluster.ClusterStatusView.as_view(), name=\n 'cluster_status'), url('^node/list... | [
0,
1,
2,
3
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv
file_open = open("C:/Users/DI_Lab/Desktop/20년도 Kisti 과제/HMM/HMM(Up,Down).csv", 'r', encoding='UTF8')
save_file = open("C:/Users/DI_Lab/Desktop/20년도 Kisti 과제/HMM/HMM사후확률.csv", 'w', encoding='UTF8',newline='')
write = csv.writer(save_file)... | normal | {
"blob_id": "55977a673bb36900e1d797cb9ec330ce6d9aa717",
"index": 8232,
"step-1": "<mask token>\n\n\ndef count(a, b):\n a = int(a)\n b = int(b)\n if a == 0 and b == 0:\n return 0\n elif a == 0 and b == 1:\n return 1\n elif a == 1 and b == 0:\n return 2\n elif a == 1 and b ==... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: WuTian
# @Date : 2018/5/3
# @Contact : jsj0804wt@126.com
# @Desc :使用广度优先搜索查找芒果商
from collections import deque
graph = {}
graph["you"] = ["alice", "bob", "claire"]
graph["bob"] = ["anuj", "peggy"]
graph["alice"] = ["peggy"]
graph["claire"] = ["thom", "jonny"]
g... | normal | {
"blob_id": "e881fcfce933d8f3bafcbaab039ddcf98827bf5e",
"index": 4244,
"step-1": "<mask token>\n\n\ndef is_mango_seller(name):\n return name[-1] == 'm'\n\n\ndef search_mango_seller(name):\n search_queue = deque()\n searched = []\n global graph\n search_queue += graph[name]\n while search_queue:... | [
2,
3,
4,
5,
6
] |
import mlcd,pygame,time,random
PLAYER_CHAR=">"
OBSTACLE_CHAR="|"
screenbuff=[[" "," "," "," "," "," "," "," "," "," "," "," "],
[" "," "," "," "," "," "," "," "," "," "," "," "]]
player={"position":0,"line":0,"score":000}
game={"speed":4.05,"level":2.5,"obstacle":0}
keys={"space":False,"quit":False,"nex... | normal | {
"blob_id": "aeaab602cbb9fa73992eb5259e8603ecb11ba333",
"index": 4863,
"step-1": "<mask token>\n\n\ndef keypress():\n global keys\n keys['space'] = keys['quit'] = keys['next'] = False\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n ... | [
1,
2,
3,
4,
5
] |
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
print(accuracy_score(true_labels, guesses))
print(recall_score(true_labels, guesses))
print(precision_score(true_labels, guesses))
print(f1_score(true_labels, guesses))
from sklearn.metrics import confusion_matrix
print(confusion_matrix... | normal | {
"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
] |
# SymBeam examples suit
# ==========================================================================================
# António Carneiro <amcc@fe.up.pt> 2020
# Features: 1. Numeric length
# 2. Pin
# 3. Two rollers
# 4. Numeric distributed... | normal | {
"blob_id": "bdbeebab70a6d69e7553807d48e3539b78b48add",
"index": 2946,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntest_beam.add_support(0, 'roller')\ntest_beam.add_support(2, 'roller')\ntest_beam.add_support(6, 'pin')\ntest_beam.add_support(4, 'hinge')\ntest_beam.add_distributed_load(0, 4, -5)\ntest_... | [
0,
1,
2,
3,
4
] |
# 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": "47cee0c659976a2b74e2bb07f6c4d622ceab7362",
"index": 3866,
"step-1": "<mask token>\n\n\nclass AirflowSecurityManager(SecurityManagerOverride, SecurityManager,\n LoggingMixin):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask tok... | [
33,
36,
40,
47,
49
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'FormHello.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, Qt... | normal | {
"blob_id": "fc20a2bf09d510892a4d144fbbd2cb2012c3ad98",
"index": 8579,
"step-1": "<mask token>\n\n\nclass Ui_FormHello(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_FormHello(object):\n\n def setupUi(self, FormHello):\n FormHello.setObjectName('FormHello')\n ... | [
1,
2,
3,
4,
5
] |
#%%
### 날짜 데이터 분리
# 연-월-일 날짜 데이터에서 일부 분리 추출
import pandas as pd
df = pd.read_csv('../../datasets/part5/stock-data.csv')
# 문자열인 날짜 데이터를 판다스 Timestamp로 변환
df['new_Date'] = pd.to_datetime(df['Date']) # df에 새로운 열로 추가
print(df.head())
print()
# dt 속성을 이용하여 new_Data 열의 연-월-일 정보를 년, 월, 일로 구분
df['Year'] = df['new_... | normal | {
"blob_id": "d89e1d653c6db322feb6edba93cbfc622bf47aa2",
"index": 2781,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(df.head())\nprint()\n<mask token>\nprint(df.head())\nprint('------------------')\n<mask token>\nprint(df.head())\nprint('------------------')\ndf.set_index('Date_m', inplace=True)\n... | [
0,
1,
2,
3,
4
] |
import discord, requests
from random import choice
TOKEN = 'TOKEN'
CONTACT_EMAIL = None #'Contact email for getting 10000 words/day instead of 1000'
translate_command = '$t'
id_start = '<@!'
client = discord.Client()
def unescape(text):
return text.replace(''', '\'').replace('<','<').replace(... | normal | {
"blob_id": "1ab69874a89311b22220dda541dfe03462a98a55",
"index": 2243,
"step-1": "<mask token>\n\n\ndef unescape(text):\n return text.replace(''', \"'\").replace('<', '<').replace('>', '>')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef unescape(text):\n return text.replace(''', \"'... | [
1,
2,
3,
4,
5
] |
from pylab import *
def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)
axes([0.025,0.025,0.95,0.95])
contourf(X, Y, f(X,Y), 8, alpha=.75, cmap=cm.hot)
C = contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
clabel(C, inline=1, fo... | normal | {
"blob_id": "e9c439eafac8fd689980ffcb562f3b5ee903dd56",
"index": 2604,
"step-1": "<mask token>\n\n\ndef f(x, y):\n return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(x, y):\n return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y **... | [
1,
2,
3,
4,
5
] |
class Solution:
# @param arrive : list of integers
# @param depart : list of integers
# @param K : integer
# @return a boolean
def hotel(self, arrive, depart, K):
self.count = 0
self.temp = 0
for i in range(len(arrive)):
for j in range(i, len(depart)):
... | normal | {
"blob_id": "de6a6c2dc7bea255e5674663616c962c1d1625e0",
"index": 4138,
"step-1": "class Solution:\n # @param arrive : list of integers\n # @param depart : list of integers\n # @param K : integer\n # @return a boolean\n def hotel(self, arrive, depart, K):\n self.count = 0\n self.temp ... | [
0
] |
import math
getal1 = 5
getal2 = 7
getal3 = 8
getal4 = -4
getal5 = 2
print(getal1 * getal2 + getal3)
print(getal1 * (getal2 + getal3))
print(getal2 + getal3 / getal1)
print((getal2 + getal3) / getal1)
print(getal2 + getal3 % getal1)
print(abs(getal4 * getal1))
print(pow(getal3, getal5))
print(round(getal5 / getal2, 2))
... | normal | {
"blob_id": "30d75aafd9612ac02557b947fc4e3c2f7322a7fd",
"index": 3555,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(getal1 * getal2 + getal3)\nprint(getal1 * (getal2 + getal3))\nprint(getal2 + getal3 / getal1)\nprint((getal2 + getal3) / getal1)\nprint(getal2 + getal3 % getal1)\nprint(abs(getal4 *... | [
0,
1,
2,
3
] |
# SaveIsawQvector
import sys
import os
if os.path.exists("/opt/Mantid/bin"):
sys.path.append("/opt/mantidnightly/bin")
#sys.path.append("/opt/Mantid/bin") # Linux cluster
#sys.path.append('/opt/mantidunstable/bin')
else:
sys.path.append("C:/MantidInstall/bin") # Windows PC
# import ma... | normal | {
"blob_id": "b72bf00d156862c7bddecb396da3752be964ee66",
"index": 5463,
"step-1": "# SaveIsawQvector\r\n\r\nimport sys\nimport os\n\r\nif os.path.exists(\"/opt/Mantid/bin\"):\n sys.path.append(\"/opt/mantidnightly/bin\")\n #sys.path.append(\"/opt/Mantid/bin\") # Linux cluster\n #sys.path.append('... | [
0
] |
"""
definition of a sensor
"""
import datetime
import pytz
class tlimit:
def __init__(self, name, text):
self.name = name
self.text = text
time_limit = [
tlimit("All", "All Data"),
tlimit("day", "Current day"),
tlimit("24hours", "Last 24 hours"),
tlimit("3days", "Three last days"... | normal | {
"blob_id": "cb9ea8791009a29a24a76bc2b161e7f8599fec1b",
"index": 5780,
"step-1": "<mask token>\n\n\nclass tlimit:\n\n def __init__(self, name, text):\n self.name = name\n self.text = text\n\n\n<mask token>\n\n\nclass SensorData:\n date = datetime.datetime(1970, 1, 1, 0, 0, 0)\n server_room... | [
12,
13,
14,
18,
20
] |
from collections import defaultdict
squares = dict()
for i in range(2000):
squares[i * i] = i
perims = defaultdict(int)
for a in range(1, 1001):
for b in range(a + 1, 1001):
if a * a + b * b not in squares:
continue
c = squares[a * a + b * b]
perims[a + b + c] += 1
for perim,... | normal | {
"blob_id": "a3299a2945a638c74c2d16bc28079ed692718fbd",
"index": 2703,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(2000):\n squares[i * i] = i\n<mask token>\nfor a in range(1, 1001):\n for b in range(a + 1, 1001):\n if a * a + b * b not in squares:\n continue\n ... | [
0,
1,
2,
3
] |
from PyQt5.QtWidgets import QPushButton,QWidget,QApplication,QGridLayout,QListWidget,QLineEdit,QVBoxLayout,QLabel
import pyqtgraph as pg
import sys
import numpy as np
from tools import DataModel,HoldPositions
from load_sina import LoadNet
import time
from get_day_histroy import history
import pandas as pd
from volume ... | normal | {
"blob_id": "8ddb7abb480ea8ee674c59719c0946f133ef0a4b",
"index": 1303,
"step-1": "<mask token>\n\n\nclass addItemThread(QThread):\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\nclass Example(QWidget):\n\n def... | [
11,
12,
13,
14,
17
] |
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: Nirvana
#
# Created: 07/06/2014
# Copyright: (c) Nirvana 2014
# Licence: <your licence>
#-------------------------------------------------------------------------------
import r... | normal | {
"blob_id": "eb246beb05249f5dfde019b773698ba3bb1b1118",
"index": 544,
"step-1": "<mask token>\n\n\nclass Coin(object):\n\n def __init__(self):\n self.sideup = 'Heads'\n\n def toss(self):\n if random.randint(0, 1) == 0:\n self.sideup = 'Heads'\n else:\n self.sideup... | [
4,
5,
6,
7,
8
] |
from os.path import basename
from .FileInfo import FileInfo
class mrk_file(FileInfo):
"""
.mrk specific file container.
"""
def __init__(self, id_=None, file=None, parent=None):
super(mrk_file, self).__init__(id_, file, parent)
self._type = '.mrk'
#region class methods
def __get... | normal | {
"blob_id": "8e9aec7d3653137a05f94e4041d28f3423122751",
"index": 3990,
"step-1": "<mask token>\n\n\nclass mrk_file(FileInfo):\n <mask token>\n\n def __init__(self, id_=None, file=None, parent=None):\n super(mrk_file, self).__init__(id_, file, parent)\n self._type = '.mrk'\n <mask token>\n\... | [
4,
5,
6,
7,
8
] |
from django.urls import path
from django.conf.urls.i18n import urlpatterns
from . import views
urlpatterns = [
path('signup/', views.signup, name='signup'),
path('home', views.home, name='home'),
path('collab/', views.collab, name='collab'),
] | normal | {
"blob_id": "351963bee76ecaa9fa5c8d659f6d7c6ca9b22531",
"index": 2182,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('signup/', views.signup, name='signup'), path('home',\n views.home, name='home'), path('collab/', views.collab, name='collab')]\n",
"step-3": "from django.urls im... | [
0,
1,
2,
3
] |
import numpy as np
'''
1. Create 0-D array, 1-D array, 2-D array, 3-D array with following value
0-D: [2]
1-D: [3, 4, 5, 6, 7]
2-D: [[8, 1, 3], [2, 3, 4], [6, 2, 5]]
3-D: [[[1, 2, 4], [3, 3, 2], [1, 9, 1]], [[6, 8, 7], [9, 1, 0], [8, 2, 3]], [[5, 4, 1], [5, 7, 2], [3, 5, 9]]]
print them
'''
D0 = np.... | normal | {
"blob_id": "a868ecb6ea6a5c7a186ddd8fa4fb76d96efeb21d",
"index": 4140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('D0')\nprint(D0)\nprint('D1')\nprint(D1)\nprint('D2')\nprint(D2)\nprint('D3')\nprint(D3)\n<mask token>\nprint('D2')\nprint(D2)\n<mask token>\nprint('D3')\nprint(D3)\n<mask token>\np... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
from .models import Account
# Register your models here.
class AuthenticationCustom(admin.ModelAdmin):
list_display = ("email", "id")
search_fields = ["email", "mobile"]
admin.site.register(Account, AuthenticationCustom) | normal | {
"blob_id": "4957e62deec6192aabdf7144f02b28c7ce60ed4b",
"index": 4250,
"step-1": "<mask token>\n\n\nclass AuthenticationCustom(admin.ModelAdmin):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AuthenticationCustom(admin.ModelAdmin):\n list_display = 'email', 'id... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# coding: utf-8
from unittest import TestCase
from optimoida.logging import (
SUCCESS, FAILURE, logger)
class LoggerTestCase(TestCase):
def test_flag_value(self):
self.assertEqual(SUCCESS, "\x1b[34mSUCCESS\x1b[0m")
self.assertEqual(FAILURE, "\x1b[31mFAILURE\x1b[0m")
... | normal | {
"blob_id": "ac8c8dc4bcccef7942dd48d54902e13e811f950c",
"index": 5059,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LoggerTestCase(TestCase):\n\n def test_flag_value(self):\n self.assertEqual(SUCCESS, '\\x1b[34mSUCCESS\\x1b[0m')\n self.assertEqual(FAILURE, '\\x1b[31mFAILURE\\... | [
0,
2,
3,
4,
5
] |
# coding=gbk
from numpy import *
import fp_growth
'''
#创建树的一个单节点
rootNode=fp_growth.treeNode('pyramid',9,None)
#为其增加一个子节点
rootNode.children['eye']=fp_growth.treeNode('eye',13,None)
rootNode.disp()
#导入事务数据库实例
simpData=fp_growth.loadSimpData()
#print("simpData:")
#print(simpData)
#对数据进行格式化处理
initSet=fp_growth.cre... | normal | {
"blob_id": "e8b0e6e5e68933703e2ac8c9b2b62d68c0c2f53d",
"index": 8295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfp_growth.minTree(myFPtree, myHeaderTab, 100000, set([]), myFreqList)\nprint(len(myFreqList))\n",
"step-3": "<mask token>\nparsedDat = [line.split() for line in open('kosarak.dat').read... | [
0,
1,
2,
3,
4
] |
import os
import time
import re
import json
from os.path import join, getsize
from aiohttp import web
from utils import helper
TBL_HEAD = '''
<table class="table table-striped table-hover table-sm">
<thead>
<tr>
<th scope="col">Directory</th>
<th scope="col">Size</th>
</tr>
</thead>
<tbody>... | normal | {
"blob_id": "7c9b51ae7cde9c3a00888dac6df710b93af6dd7f",
"index": 4836,
"step-1": "<mask token>\n\n\ndef stats_count_info(request):\n root_path = request.app['PATH-DB']\n cpt = 0\n d = dict()\n dirs_data = dict()\n for root, dirs, files in os.walk(root_path, topdown=False):\n cpt += len(file... | [
2,
3,
4,
5,
6
] |
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# ... | normal | {
"blob_id": "2ab303a2f36cdd64e2119856312dd5e38ee728d6",
"index": 9632,
"step-1": "<mask token>\n\n\nclass LoadBalancerTest(common.HeatTestCase):\n\n def setUp(self):\n super(LoadBalancerTest, self).setUp()\n self.lb_template = {'AWSTemplateFormatVersion': '2010-09-09',\n 'Description'... | [
62,
89,
97,
102,
126
] |
# ---------------------------------------------------------------------
# Iskratel.ESCOM.get_version
# ---------------------------------------------------------------------
# Copyright (C) 2007-2018 The NOC Project
# See LICENSE for details
# ---------------------------------------------------------------------
# Pyth... | normal | {
"blob_id": "40b3c403f99044eb61740d62eda15ddd08b0f739",
"index": 1980,
"step-1": "<mask token>\n\n\nclass Script(BaseScript):\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 <m... | [
1,
2,
3,
4,
5
] |
forbidden = ['Key.esc', 'Key.cmd', 'Key.cmd_r', 'Key.menu', 'Key.pause',
'Key.scroll_lock', 'Key.print_screen', 'Key.enter', 'Key.space',
'Key.backspace', 'Key.ctrl_l', 'Key.ctrl_r', 'Key.alt_l', 'Key.alt_gr',
'Key.caps_lock', 'Key.num_lock', 'Key.tab', 'Key.shift', 'Key.shift_r',
'Key.insert', 'Key.del... | normal | {
"blob_id": "995dc34ea32de4566e2804b6797d9b551b733ff3",
"index": 3406,
"step-1": "<mask token>\n",
"step-2": "forbidden = ['Key.esc', 'Key.cmd', 'Key.cmd_r', 'Key.menu', 'Key.pause',\n 'Key.scroll_lock', 'Key.print_screen', 'Key.enter', 'Key.space',\n 'Key.backspace', 'Key.ctrl_l', 'Key.ctrl_r', 'Key.alt... | [
0,
1
] |
msg = "eduardo foi a feira"
if 'feira' in msg:
print('Sim, foi a feira')
else:
print('não ele não foi a feira')
| normal | {
"blob_id": "2a83bc9157e2210da46e58c56fc0b7199856f4c0",
"index": 6287,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif 'feira' in msg:\n print('Sim, foi a feira')\nelse:\n print('não ele não foi a feira')\n",
"step-3": "msg = 'eduardo foi a feira'\nif 'feira' in msg:\n print('Sim, foi a feir... | [
0,
1,
2,
3
] |
import gzip
import pickle as pkl
import time
from datetime import datetime
import grpc
import numpy as np
from sklearn.utils import shuffle
import neural_nets_pb2 as nn_pb
import neural_nets_pb2_grpc as nn_pb_grpc
from mnist_loader import load_data
from activations import *
# pylint: disable=too-many-arguments
cl... | normal | {
"blob_id": "fa6f251f27b645fc6827285b5578fd9634c8bb30",
"index": 6361,
"step-1": "<mask token>\n\n\nclass Layer(nn_pb_grpc.LayerDataExchangeServicer):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def init_weights(self, load_weights=None):\n ... | [
24,
27,
28,
32,
35
] |
# -*- coding:utf-8 -*-
# Copyright 2015 NEC Corporation. #
# #
# Licensed under the Apache License, Version 2.0 (the "License"); #
# you may not use this file except in compliance with the License... | normal | {
"blob_id": "b220189d506737bf8cff9e600d1cfd4d7bc8435d",
"index": 1434,
"step-1": "# -*- coding:utf-8 -*-\n\n# Copyright 2015 NEC Corporation. #\n# #\n# Licensed under the Apache License, Version 2.0 ... | [
0
] |
# -*- coding:utf-8 -*-
#实现同义词词林的规格化
with open('C:\\Users\\lenovo\\Desktop\\哈工大社会计算与信息检索研究中心同义词词林扩展版.txt') as f:
with open('convert.txt','a') as w:
for line in f:
data = line[8:-1].split()
for item in data:
tmp = data.copy()
... | normal | {
"blob_id": "9109e649a90730df022df898a7760140275ad724",
"index": 4854,
"step-1": "<mask token>\n",
"step-2": "with open('C:\\\\Users\\\\lenovo\\\\Desktop\\\\哈工大社会计算与信息检索研究中心同义词词林扩展版.txt') as f:\n with open('convert.txt', 'a') as w:\n for line in f:\n data = line[8:-1].split()\n ... | [
0,
1,
2
] |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#allisnone 20200403
#https://github.com/urllib3/urllib3/issues/1434
#https://github.com/dopstar/requests-ntlm2
#https://github.com/requests/requests-ntlm
#base on python3
#if you request https website, you need to add ASWG CA to following file:
#/root/.pyenv/versio... | normal | {
"blob_id": "a7fae2da8abba6e05b4fc90dec8826194d189853",
"index": 2758,
"step-1": "<mask token>\n\n\ndef get_random_ip_or_user(start, end, prefix='172.16.90.', type='ip'):\n if type == 'ip' and max(start, end) > 255:\n end = 255\n i = random.randint(start, end)\n return prefix + str(i)\n\n\ndef ge... | [
6,
7,
8,
9,
11
] |
# the main program of this project
import log
import logging
import os
from ast_modifier import AstModifier
from analyzer import Analyzer
class Demo():
def __init__(self):
self.log = logging.getLogger(self.__class__.__name__)
def start(self, filename: str):
self.log.debug('analyse file: ' + fil... | normal | {
"blob_id": "e989f73011559080f96802dba4db30361d5626f9",
"index": 4002,
"step-1": "<mask token>\n\n\nclass Demo:\n\n def __init__(self):\n self.log = logging.getLogger(self.__class__.__name__)\n\n def start(self, filename: str):\n self.log.debug('analyse file: ' + filename)\n astmodif =... | [
3,
4,
5,
6,
7
] |
from LinkedList import LinkedList
class ListNode(object):
def __init__(self, x):
self.val = x
self.next = None
class Solution(object):
def addTwoNumbers(self, l1, l2):
"""
:type l1: ListNode
:type l2: ListNode
:rtype: ListNode
"""
h1 = l1
... | normal | {
"blob_id": "0f3ecd0a7189f57fdbda2360f6e39bd6101e2fdb",
"index": 7435,
"step-1": "<mask token>\n\n\nclass ListNode(object):\n\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNod... | [
4,
5,
6,
7
] |
# 2019/10/08 2019년10월8일
ss = input('날짜: 년/월/일 입력-> ')
sslist = ss.split('/')
print(sslist)
print('입력하신 날짜의 10년 후 -> ', end='')
year = int(sslist[0]) + 10
print(str(year) + "년", end='')
print(sslist[1] + "월", end='')
print(sslist[2] + "일")
| normal | {
"blob_id": "fb2ef5a90b6e2582450726905868dd1b78e36166",
"index": 5008,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(sslist)\nprint('입력하신 날짜의 10년 후 -> ', end='')\n<mask token>\nprint(str(year) + '년', end='')\nprint(sslist[1] + '월', end='')\nprint(sslist[2] + '일')\n",
"step-3": "ss = input('날짜: 년... | [
0,
1,
2,
3
] |
#!/C:\Program Files (x86)\Python35-32
#importar librarias necesarias
from urllib.request import urlopen
from bs4 import BeautifulSoup
| normal | {
"blob_id": "7a59c8c883a9aaa723175783e01aa62e23503fde",
"index": 376,
"step-1": "<mask token>\n",
"step-2": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n",
"step-3": "#!/C:\\Program Files (x86)\\Python35-32\n\n#importar librarias necesarias\nfrom urllib.request import urlopen\nfrom bs4... | [
0,
1,
2
] |
import ttk
import Tkinter as tk
from rwb.runner.log import RobotLogTree, RobotLogMessages
from rwb.lib import AbstractRwbGui
from rwb.widgets import Statusbar
from rwb.runner.listener import RemoteRobotListener
NAME = "monitor"
HELP_URL="https://github.com/boakley/robotframework-workbench/wiki/rwb.monitor-User-Guide"... | normal | {
"blob_id": "572d58eec652207e6ec5a5e1d4c2f4310f2a70f3",
"index": 1665,
"step-1": "import ttk\nimport Tkinter as tk\nfrom rwb.runner.log import RobotLogTree, RobotLogMessages\nfrom rwb.lib import AbstractRwbGui\nfrom rwb.widgets import Statusbar\n\nfrom rwb.runner.listener import RemoteRobotListener\n\nNAME = \"m... | [
0
] |
__author__ = 'Vicio'
from Conexion.conexion import Conexion
class ConexionList():
def __init__(self):
self.conexion = Conexion()
def selectClientes(self):
pass
def selectProveedores(self):
pass
| normal | {
"blob_id": "6b4af452778bdf13ac18e8d260cf1c9176ca95e0",
"index": 8414,
"step-1": "<mask token>\n\n\nclass ConexionList:\n <mask token>\n\n def selectClientes(self):\n pass\n\n def selectProveedores(self):\n pass\n",
"step-2": "<mask token>\n\n\nclass ConexionList:\n\n def __init__(sel... | [
3,
4,
5,
6,
7
] |
import matplotlib.pyplot as plt
from partisan_symmetry_noplot import partisan_symmetry
for k in range(1,100):
a=[]
for i in range(1,100):
a.append([])
for j in range(1,100):
a[i-1].append(partisan_symmetry([5*i/100,.20,5*j/100],1000,False))
plt.imshow(a)
plt.colorbar()
p... | normal | {
"blob_id": "cfa0937f1c49b52283c562d9ab1cb0542e71b990",
"index": 5970,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor k in range(1, 100):\n a = []\n for i in range(1, 100):\n a.append([])\n for j in range(1, 100):\n a[i - 1].append(partisan_symmetry([5 * i / 100, 0.2, 5... | [
0,
1,
2,
3
] |
from collections import defaultdict as dd
def grouping(w):
d = dd(list)
for k, v in ((len([y for y in x if y.isupper()]), x) for x in sorted(w,
key=str.casefold)):
d[k].append(v)
return dict(sorted(d.items()))
| normal | {
"blob_id": "545794cf4f0b2ab63b6a90951a78f8bdaca3c9e6",
"index": 390,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef grouping(w):\n d = dd(list)\n for k, v in ((len([y for y in x if y.isupper()]), x) for x in sorted(w,\n key=str.casefold)):\n d[k].append(v)\n return dict(so... | [
0,
1,
2
] |
#!/usr/bin/env python3
import sys, os
import random
import numpy as np
import matplotlib as mpl
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
mpl.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import sha... | normal | {
"blob_id": "b4454d92ab8380e0eded2f7aed737378e1710c72",
"index": 9413,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif os.environ.get('DISPLAY', '') == '':\n print('no display found. Using non-interactive Agg backend')\n mpl.use('Agg')\n<mask token>\nsys.path.append(path_to_utils)\n<mask token>\n... | [
0,
1,
2,
3,
4
] |
# terminal based game in Python
from random import randint
print('Terminal based number guessing game')
while True:
try:
numberOfGames = int(input('Please choose how many games you want to play ---> '))
except:
print('Only numbes accepted')
continue
if (numberOfGames > 0 and numberO... | normal | {
"blob_id": "20c081dc47f541a988bccef89b8e51f446c80f58",
"index": 5471,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Terminal based number guessing game')\nwhile True:\n try:\n numberOfGames = int(input(\n 'Please choose how many games you want to play ---> '))\n except:\n... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 12 16:38:22 2017
@author: secoder
"""
import io
import random
import nltk
from nltk.tokenize import RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from collections import Ordered... | normal | {
"blob_id": "4a8a733a965e25ad7ef53600fad6dd47343655b0",
"index": 8677,
"step-1": "<mask token>\n\n\nclass recommendationsys:\n\n def __init__(self, nyear):\n self.activityyear = 10\n self.debug = 0\n self.nremd = 3\n PROJECT_DIRECTORY = 'output/project/' + project_name\n sel... | [
21,
25,
35,
43,
47
] |
# Cookies Keys
class Cookies:
USER_TOKEN = "utoken"
# Session Keys
class Session:
USER_ROOT_ID = "x-root-id"
class APIStatisticsCollection:
API_ACTION = "x-stats-api-action"
DICT_PARAMS = "x-stats-param-dict"
DICT_RESPONSE = "x-stats-resp-dict"
SUCCESS = "x-stats-success"
... | normal | {
"blob_id": "d0e5a3a6db0e27ecf157294850a48a19750a5ac2",
"index": 1667,
"step-1": "<mask token>\n\n\nclass Session:\n <mask token>\n\n\n class APIStatisticsCollection:\n API_ACTION = 'x-stats-api-action'\n DICT_PARAMS = 'x-stats-param-dict'\n DICT_RESPONSE = 'x-stats-resp-dict'\n ... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/python
#encoding=utf8
import sys
import tushare as ts
def local_main():
if len(sys.argv) != 2:
print sys.argv[0], " [stock id]"
return
stock_id = sys.argv[1]
df = ts.get_hist_data(stock_id)
df.to_excel(stock_id + '_his.xlsx', sheet_name = stock_id)
if __name__ == '__main__... | normal | {
"blob_id": "81a53d08ab36e85dd49cf1f3d9c22c1f18605149",
"index": 6233,
"step-1": "#!/usr/bin/python\n#encoding=utf8\n\nimport sys\nimport tushare as ts\n\ndef local_main():\n if len(sys.argv) != 2:\n print sys.argv[0], \" [stock id]\"\n return\n\n stock_id = sys.argv[1]\n df = ts.get_hist_... | [
0
] |
# from magicbot import AutonomousStateMachine, timed_state, state
# from components.drivetrain import Drivetrain, DrivetrainState
# from components.intake import Intake
# from fieldMeasurements import FieldMeasurements
# class PushBotAuto(AutonomousStateMachine):
# # this auto is intended to push other robots of... | normal | {
"blob_id": "fdef3e94bbeb29c25bf14e17cd1d013cf848bedc",
"index": 9456,
"step-1": "# from magicbot import AutonomousStateMachine, timed_state, state\n\n# from components.drivetrain import Drivetrain, DrivetrainState\n# from components.intake import Intake\n\n# from fieldMeasurements import FieldMeasurements\n\n# ... | [
1
] |
import re
from collections import OrderedDict
OPENING_TAG = '<{}>'
CLOSING_TAG= '</{}>'
U_LIST = '<ul>{}</ul>'
LIST_ITEM = '<li>{}</li>'
STRONG = '<strong>{}</strong>'
ITALIC = '<em>{}</em>'
PARAGRAPH = '<p>{}</p>'
HEADERS = OrderedDict({'######': 'h6',
'#####': 'h5',
'###... | normal | {
"blob_id": "6b0b60ec571cf026d0f0cff3d9517362c16b459b",
"index": 6092,
"step-1": "<mask token>\n\n\ndef replace_bold_tags(l=''):\n line_with_bold = re.match('(.*)__(.*)__(.*)', l)\n if line_with_bold:\n return line_with_bold.group(1) + STRONG.format(line_with_bold.group(2)\n ) + line_with... | [
5,
6,
8,
9,
10
] |
from django.urls import path,include
from .import views
urlpatterns = [
path('',views.home,name='home'),
path('category/',include('api.category.urls')),
path('product/',include('api.product.urls')),
path('user/',include('api.user.urls')),
path('order/',include('api.order.urls')),
path('payment... | normal | {
"blob_id": "fe12f6d3408ab115c5c440c5b45a9014cfee6539",
"index": 564,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.home, name='home'), path('category/', include\n ('api.category.urls')), path('product/', include('api.product.urls')),\n path('user/', include('api.user... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# Created by: Khang Le
# Created on: Dec 2019
# This program uses lists and rotation
def rotation(list_of_number, ratating_time):
numbers = list_of_number[0]
numbers = [list_of_number[(i + ratating_time) % len(list_of_number)]
for i, x in enumerate(list_of_number)]
... | normal | {
"blob_id": "74de0da708c7eb792dea15afb23713d9d71af520",
"index": 5491,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lst = []\n user_input = int(input('Enter number of elements : '))\n rotating_time = int(input('Enter how many times you want to rotate: '))\n print('The numb... | [
0,
1,
2,
3,
4
] |
import sys
def main(stream=sys.stdin):
"""
Input, output, and parsing, etc. Yeah.
"""
num_cases = int(stream.readline().strip())
for i in xrange(num_cases):
rows, cols = map(int, stream.readline().strip().split())
board = []
for r in xrange(rows):
board = board +... | normal | {
"blob_id": "5bcfb0d4fd371a0882dd47814935700eed7885ec",
"index": 6925,
"step-1": "import sys\n\ndef main(stream=sys.stdin):\n \"\"\"\n Input, output, and parsing, etc. Yeah.\n \"\"\"\n num_cases = int(stream.readline().strip())\n for i in xrange(num_cases):\n rows, cols = map(int, stream.re... | [
0
] |
from telegram.ext import Dispatcher,CommandHandler,CallbackQueryHandler
import random
from telegram import InlineKeyboardMarkup, InlineKeyboardButton
gifGOODBOAT = 'https://media3.giphy.com/media/3oz8xRQiRlaS1XwnPW/giphy.gif'
gifBADBOAT = 'https://media1.giphy.com/media/l2Je3n9VXC8z3baTe/giphy.gif'
gifGOODMAN = 'https... | normal | {
"blob_id": "bc3e94c3fb8e563f62fcf0ca628d4aa73c668612",
"index": 7097,
"step-1": "<mask token>\n\n\ndef treasure(update, context):\n msg1 = \"\"\"\n 欢迎来到寻宝游戏！这是一场惊悚又危险追逐战，智慧，运气和勇气都是成功的关键！记得要避开海盗！读一读规则吧！\n 1. 系统会自动给你分配即将要发生的事，做好心理准备!\n 2. 好的外表不一定有好的结果...\n 3. 点击按钮开始寻宝!\n -----------------------\... | [
2,
3,
4,
5,
6
] |
import unittest
import math
from python.src.sort.insertion import Insertion
from python.src.sort.selection import Selection
from python.src.sort.shell import Shell
from python.test.util.utilities import Utilities
class ElementarySortTest(unittest.TestCase):
def setUp(self):
self.n = 1000
def test_in... | normal | {
"blob_id": "779ef8942bfb55bf017a8da9dfe34c03ac574a9a",
"index": 2591,
"step-1": "<mask token>\n\n\nclass ElementarySortTest(unittest.TestCase):\n <mask token>\n\n def test_insertion_sort(self):\n insertion = Insertion()\n actual = Utilities.generate_random_array(self.n)\n expected = l... | [
5,
6,
7,
8
] |
import unittest
from datetime import datetime
from models import *
class Test_PlaceModel(unittest.TestCase):
"""
Test the place model class
"""
def setUp(self):
self.model = Place()
self.model.save()
def test_var_initialization(self):
self.assertTrue(hasattr(self.model, "... | normal | {
"blob_id": "c7881c0d06600a43bdc01f5e464127c596db6713",
"index": 7993,
"step-1": "<mask token>\n\n\nclass Test_PlaceModel(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_var_initialization(self):\n self.assertTrue(hasattr(self.model, 'city_id'))\n self.assertTrue(hasattr(sel... | [
2,
4,
5,
6,
7
] |
import requests
import json
r = requests.get('http://pythonspot.com/')
jsondata = str(r.headers).replace("'", '"')
print(jsondata)
#headerObj = json.loads(jsondata)
#ERROR >> json.decoder.JSONDecodeError: Expecting ',' delimiter: line 1 column 556 (char 555)
#print(headerObj)["server"]
#print(headerObj)['content-leng... | normal | {
"blob_id": "7404dd324d54bb072e56985716bbae746b4dd219",
"index": 1395,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(jsondata)\n",
"step-3": "<mask token>\nr = requests.get('http://pythonspot.com/')\njsondata = str(r.headers).replace(\"'\", '\"')\nprint(jsondata)\n",
"step-4": "import requests... | [
0,
1,
2,
3,
4
] |
import numpy
import matplotlib.pyplot as plt
numpy.random.seed(2)
# create datasets
x = numpy.random.normal(3, 1, 100)
y = numpy.random.normal(150, 40, 100) / x
# displaying original dataset
plt.scatter(x, y)
plt.title("Original dataset")
plt.xlabel("Minutes")
plt.ylabel("Spent money")
plt.show()
# train dataset wi... | normal | {
"blob_id": "9fd985e9675514f6c8f3ac5b91962eb744e0e82c",
"index": 6514,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnumpy.random.seed(2)\n<mask token>\nplt.scatter(x, y)\nplt.title('Original dataset')\nplt.xlabel('Minutes')\nplt.ylabel('Spent money')\nplt.show()\n<mask token>\nplt.scatter(train_x, trai... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# view_rows.py - Fetch and display the rows from a MySQL database query
# import the MySQLdb and sys modules
# katja seltmann April 16, 2013 to run on arthropod data in scan symbiota database
import MySQLdb
import sys
#connection information from mysql
#test database
connect = MySQLdb.connect("", u... | normal | {
"blob_id": "4d066a189bf5151534e0227e67cdc2eed5cd387c",
"index": 6745,
"step-1": "#!/usr/bin/python\n# view_rows.py - Fetch and display the rows from a MySQL database query\n# import the MySQLdb and sys modules\n# katja seltmann April 16, 2013 to run on arthropod data in scan symbiota database\n\nimport MySQLdb\... | [
0
] |
# Generated by Django 3.2.7 on 2021-09-23 07:33
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('sms_consumer', '0006_auto_20210923_0733'),
]
operations = [
migrations.RemoveField(
model_name='smslogmodel',
name='hello',
... | normal | {
"blob_id": "fc9742ceb3c38a5f8c1ad1f030d76103ba0a7a81",
"index": 3857,
"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 = [('sms_consume... | [
0,
1,
2,
3,
4
] |
import os
from multiprocessing import Pool
import glob
import click
import logging
import pandas as pd
from src.resampling.resampling import Resampler
# Default paths
path_in = 'data/hecktor_nii/'
path_out = 'data/resampled/'
path_bb = 'data/bbox.csv'
@click.command()
@click.argument('input_folder', type=click.Pat... | normal | {
"blob_id": "3479276d4769518aa60dcd4e1bb41a8a1a7d6517",
"index": 315,
"step-1": "<mask token>\n\n\n@click.command()\n@click.argument('input_folder', type=click.Path(exists=True), default=path_in)\n@click.argument('output_folder', type=click.Path(), default=path_out)\n@click.argument('bounding_boxes_file', type=c... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
"""This script draws a boxplot of each atom contribution to the cavity."""
import sys
if sys.version < "2.7":
print >> sys.stderr, "ERROR: This script requires Python 2.7.x. "\
"Please install it and try again."
exit(1)
try:
import matplotlib.pyplot as pyp... | normal | {
"blob_id": "9fdcaf65f070b7081afd327442dd20e3284c71eb",
"index": 7905,
"step-1": "<mask token>\n\n\ndef parse_args():\n import argparse\n import os.path\n\n def isfile(path):\n Error = argparse.ArgumentTypeError\n if not os.path.exists(path):\n raise Error(\"No such file: '{0}'\... | [
6,
7,
8,
11,
12
] |
# coding=utf-8
class Movie:
def __init__(self,movieid,moviename,score,poster):
self.movieid=movieid
self.moviename=moviename
self.score=score
self.poster=poster
for i in range(1,32):
print("<option value =\""+str(i)+"\">"+str(i)+"</option>") | normal | {
"blob_id": "856e62cf4cd443c7b3397e926f8fc4fece145f5b",
"index": 3447,
"step-1": "<mask token>\n",
"step-2": "class Movie:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class Movie:\n\n def __init__(self, movieid, moviename, score, poster):\n self.movieid = movieid\n self.moviename = mo... | [
0,
1,
2,
3,
4
] |
import numpy as np
def layer_forward(x, w):
"""
input:
- inputs (x): (N, d_1, ..., d_k),
- weights (w): (D, M)
"""
# intermediate value (z)
z = None
output = []
cache = (x, w, z, output)
return output, cache
def layer_backward(d_output, cache):
""" Re... | normal | {
"blob_id": "c1fd6e940b3b15ae01a102b3c0aba9bd327c77b2",
"index": 8403,
"step-1": "<mask token>\n\n\ndef layer_forward(x, w):\n \"\"\"\n input:\n - inputs (x): (N, d_1, ..., d_k),\n - weights (w): (D, M)\n \"\"\"\n z = None\n output = []\n cache = x, w, z, output\n r... | [
4,
5,
6,
7,
8
] |
name = 'Ледяная скорбь'
description = 'Тот кто держит этот клинок, должен обладать бесконечной силой. Подобно тому, как он разрывает плоть, он разрывает души.'
price = 3000
fightable = True
def fight_use(user, reply, room):
return 200 | normal | {
"blob_id": "7254e74ff3f562613cc610e4816a2d92b6b1cd4c",
"index": 6074,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef fight_use(user, reply, room):\n return 200\n",
"step-3": "name = 'Ледяная скорбь'\ndescription = (\n 'Тот кто держит этот клинок, должен обладать бесконечной силой. Подобн... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
#coding:utf-8
'''
Created on 2016年8月29日
@author: lichen
'''
def custom_proc(request):
"""
自定义context_processors
"""
return {
"context_test":"test"
} | normal | {
"blob_id": "43ecb173e3d306284f2122410b5b74945572f683",
"index": 8104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef custom_proc(request):\n \"\"\"\n 自定义context_processors\n \"\"\"\n return {'context_test': 'test'}\n",
"step-3": "#!/usr/bin/env python\n#coding:utf-8\n\n'''\nCreated... | [
0,
1,
2
] |
"""
复习
面向对象：考虑问题从对象的角度出发.
抽象：从多个事物中，舍弃个别的/非本质的特征(不重要)，
抽出共性的本质(重要的)过程。
三大特征：
封装：将每个变化点单独分解到不同的类中。
例如：老张开车去东北
做法：定义人类，定义车类。
继承：重用现有类的功能和概念，并在此基础上进行扩展。
统一概念
例如：图形管理器，统计圆形/矩形.....面积。
... | normal | {
"blob_id": "2749a262bf8da99aa340e878c15a6dba01acc38c",
"index": 7025,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\n 复习\n 面向对象：考虑问题从对象的角度出发.\n 抽象：从多个事物中，舍弃个别的/非本质的特征(不重要)，\n 抽出共性的本质(重要的)过程。\n 三大特征：\n 封装：将每个变化点单独分解到不同的类中。\n 例如：老张开车去东北\n ... | [
0,
1
] |
import whoosh.index as index
from whoosh.fields import *
from whoosh.qparser import MultifieldParser
from whoosh import scoring
w = scoring.BM25F(B=0.75, content_B=1.0, K1=1.5)
fieldnames = ["bill_text", "bill_title", "year", "sponsor_name", "subject"]
boosts = {"bill_text": 1, "bill_title": 2.5, "year": 0, "sponsor_n... | normal | {
"blob_id": "6a400419c26c62471dfc6893cc2d1ff6d88e49f4",
"index": 7518,
"step-1": "import whoosh.index as index\nfrom whoosh.fields import *\nfrom whoosh.qparser import MultifieldParser\nfrom whoosh import scoring\n\nw = scoring.BM25F(B=0.75, content_B=1.0, K1=1.5)\nfieldnames = [\"bill_text\", \"bill_title\", \"... | [
0
] |
from tkinter import *
root = Tk()
root.title("Calculator")
e = Entry(root, width = 50, borderwidth = 5)
e.grid(row = 0, column = 0, columnspan = 4, padx = 10, pady = 20)
def button_click(number):
digit = e.get()
e.delete(0, END)
e.insert(0, str(digit) + str(number))
def button_add():
global first_... | normal | {
"blob_id": "59a75f78c7a146dcf55d43be90f71abce2bcf753",
"index": 4934,
"step-1": "<mask token>\n\n\ndef button_add():\n global first_num\n global math\n math = 'addition'\n first_num = e.get()\n e.delete(0, END)\n\n\n<mask token>\n\n\ndef button_sub():\n global first_num\n global math\n m... | [
4,
5,
6,
7,
11
] |
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import math
from tkinter import *
from tkinter.ttk import *
from facedetectandtrack import *
x_vals = []
root = Tk()
counter=0
#def graph():
plt.style.use('seaborn')
def animate(i):
data = pd.read_csv('data.csv... | normal | {
"blob_id": "239f055fd76a3ecb5f384c256ad850ea42739b8f",
"index": 9710,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.style.use('seaborn')\n\n\ndef animate(i):\n data = pd.read_csv('data.csv')\n global x_vals\n global counter\n x_vals.append(counter)\n try:\n x = data.iloc[x_val... | [
0,
2,
3,
4,
5
] |
g=int(input())
num=0
while(g>0):
num=num+g
g=g-1
print(num)
| normal | {
"blob_id": "8b18f098080c3f5773aa04dffaff0639fe7fa74f",
"index": 8886,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile g > 0:\n num = num + g\n g = g - 1\nprint(num)\n",
"step-3": "g = int(input())\nnum = 0\nwhile g > 0:\n num = num + g\n g = g - 1\nprint(num)\n",
"step-4": "g=int(in... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
import sys
def add_them(a, b):
return a + b
def main():
print add_them(10, 21)
if __name__ == '__main__':
sys.exit(main())
| normal | {
"blob_id": "aebf1d64923c5f325c9d429be092deaa06f20963",
"index": 6232,
"step-1": "#!/usr/bin/env python\n\nimport sys\n\ndef add_them(a, b):\n return a + b\n\ndef main():\n print add_them(10, 21)\n\nif __name__ == '__main__':\n sys.exit(main())\n",
"step-2": null,
"step-3": null,
"step-4": null,
... | [
0
] |
from rest_framework import viewsets
from recruitment.serializers.LocationSerializer import LocationSerializer
from recruitment.models.Location import Location
import django_filters
class LocationViewSet(viewsets.ModelViewSet):
queryset = Location.objects.all().filter(deleted=0)
serializer_class = LocationSer... | normal | {
"blob_id": "aef45cb8ea9fcaeffcca147da7637536bcc4b226",
"index": 6217,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LocationViewSet(viewsets.ModelViewSet):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass LocationViewSet(viewsets.ModelViewSet):\n ... | [
0,
1,
2,
3,
4
] |
import numpy as np
import pandas as pd
from scipy import sparse, io
import cPickle as pickle
import sys
sys.path.append('code')
import models
import split
from itertools import chain
def test_simple_instance(items, item_numbers, negative_items, user):
model = models.Word2VecRecommender(size=200, window=max(item_nu... | normal | {
"blob_id": "04dc4d46a645a23913e33606c500037d37418cd7",
"index": 8114,
"step-1": "import numpy as np\nimport pandas as pd\nfrom scipy import sparse, io\nimport cPickle as pickle\nimport sys\nsys.path.append('code')\nimport models\nimport split\nfrom itertools import chain\n\ndef test_simple_instance(items, item_... | [
0
] |
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