code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import typing
import torch.nn as nn
from .torch_utils import get_activation, BatchNorm1d
from dna.models.torch_modules.torch_utils import PyTorchRandomStateContext
class Submodule(nn.Module):
def __init__(self, layer_sizes: typing.List[int], activation_name: str,
use_batch_norm: bool, use_skip: bool=Fals... | normal | {
"blob_id": "950b2906853c37cdeaa8ed1076fff79dbe99b6f8",
"index": 8327,
"step-1": "<mask token>\n\n\nclass Submodule(nn.Module):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Submodule(nn.Module):\n\n def __init__(self, layer_sizes: typing.List[int], activation_name: str,\n ... | [
1,
2,
3,
4
] |
import os
import sys
import pandas as pd
import pickle as pkl
from src.utils import image as im
if __name__ == '__main__':
pickled = True
create_sets = True
normed = False
if len(sys.argv) > 2:
filename = sys.argv[1]
else:
filename = os.path.join(os.path.pardir, os.path.pardir, 'data... | normal | {
"blob_id": "18a17c7326a6ae96f74c843d1a902074b377a6d2",
"index": 2701,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n pickled = True\n create_sets = True\n normed = False\n if len(sys.argv) > 2:\n filename = sys.argv[1]\n else:\n filename = os.pat... | [
0,
1,
2
] |
from django.db import connection
from .models import Order
from .models import Package
from .models import DeliveryStatus
from .models import CalcParameters
class DataService:
def __init__(self):
pass
@staticmethod
def get_all_orders():
orders = Order.objects.order_by('-order_date')
... | normal | {
"blob_id": "2e66a31638eb4e619f14a29d5d3847482d207003",
"index": 3996,
"step-1": "<mask token>\n\n\nclass StaticDataDao(type):\n\n @property\n def delivery_statuses(cls):\n if getattr(cls, '_delivery_statuses', None) is None:\n cls._delivery_statuses = list(DeliveryStatus.objects.all())\n... | [
5,
6,
7,
8,
12
] |
import webbrowser
import time
total = 3
count = 0
while count < total:
webbrowser.open('https://www.youtube.com/watch?v=GoSBNNgf_Vc')
time.sleep(5 * 60 * 60)
count += 1
| normal | {
"blob_id": "e11a04cad967ae377449aab8b12bfde23e403335",
"index": 8391,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile count < total:\n webbrowser.open('https://www.youtube.com/watch?v=GoSBNNgf_Vc')\n time.sleep(5 * 60 * 60)\n count += 1\n",
"step-3": "<mask token>\ntotal = 3\ncount = 0\n... | [
0,
1,
2,
3
] |
import numpy
from math import cos, sin, radians, tan
class Window:
# construtor
def __init__(self, world, xyw_min=None, xyw_max=None):
self.world = world
# caso em q é None
if xyw_min is None or xyw_max is None:
self.xyw_min = (-100, -100)
self.xyw_max = (100, 10... | normal | {
"blob_id": "deb0cd745eae97a6dbabdfab37e1c6d75e5372f0",
"index": 8422,
"step-1": "<mask token>\n\n\nclass Window:\n\n def __init__(self, world, xyw_min=None, xyw_max=None):\n self.world = world\n if xyw_min is None or xyw_max is None:\n self.xyw_min = -100, -100\n self.xyw_... | [
15,
16,
18,
20,
22
] |
# 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 __future__ import division
from collections import deque
import os
import warnings
import numpy as np
import keras.backend as K
import keras.layers as layers
import keras.optimizers as optimizers
from rl.core import Agent
from rl.util import *
def mean_q(y_true, y_pred):
return K.mean(K.max(y_pred, axis=-1... | normal | {
"blob_id": "a2fe62b6bbb6b753ef6aec6f44758b8aceeeafe6",
"index": 9691,
"step-1": "<mask token>\n\n\nclass UBDDPGAgent(Agent):\n <mask token>\n <mask token>\n <mask token>\n\n def compile(self, optimizer, metrics=[]):\n metrics += [mean_q]\n if type(optimizer) in (list, tuple):\n ... | [
9,
12,
15,
17,
18
] |
#/usr/bin/env python3
"""Demonstrates how to do deterministic task generation using l2l"""
import random
def fixed_random(func):
"""Create the data"""
def _func(self, i):
state = random.getstate()
if self.deterministic or self.seed is not None:
random.seed(self.seed + i)
... | normal | {
"blob_id": "7ee5779625d53ff1e18f73b20ba5849666f89b55",
"index": 2111,
"step-1": "<mask token>\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n retur... | [
3,
4,
6,
7,
8
] |
import librosa
import librosa.display
import matplotlib.pyplot as plt
import os
import numpy as np
import time
import multiprocessing as mp
from tempfile import TemporaryFile
class DataSet():
def __init__(self,training_folder):
self.training_folder = training_folder
print("load Data")
def load... | normal | {
"blob_id": "ba09dbe3fbca51ece8a7d482324a2dec32e7dc8a",
"index": 5016,
"step-1": "<mask token>\n\n\nclass DataSet:\n\n def __init__(self, training_folder):\n self.training_folder = training_folder\n print('load Data')\n <mask token>\n\n def readFiles(self, queue, file_list, start, end):\n ... | [
3,
4,
5,
6,
7
] |
from django.contrib import admin, messages
from django.conf.urls import url
from django.shortcuts import render
from django.contrib.sites.models import Site
from django.http import HttpResponseRedirect, HttpResponse
from website_data.models import *
from website_data.forms import *
import logging
# Get an instance of ... | normal | {
"blob_id": "614d6484678890df2ae0f750a3cad51a2b9bd1c6",
"index": 2315,
"step-1": "<mask token>\n\n\nclass WebsitePreferencesInstanceInline(admin.TabularInline):\n model = WebsitePreferences\n\n\nclass SiteAdmin(admin.ModelAdmin):\n list_filter = 'domain', 'name'\n inlines = [CustomSiteInstanceInline, We... | [
4,
10,
12,
14,
15
] |
from typing import List
import glm
import pxng
import OpenGL.GL as gl
class VertexArrayObject:
def __init__(self, primitive):
self._primitive = primitive
self._buffers: List[pxng.BufferObject] = []
self._indices = pxng.BufferObject(data_type=self.index_data_type,
... | normal | {
"blob_id": "7530c2c85f83d1714840ba97c1ec702f063658c5",
"index": 379,
"step-1": "<mask token>\n\n\nclass VertexArrayObject:\n\n def __init__(self, primitive):\n self._primitive = primitive\n self._buffers: List[pxng.BufferObject] = []\n self._indices = pxng.BufferObject(data_type=self.ind... | [
9,
11,
12,
13,
17
] |
#!/usr/bin/env python3
#
# Display all tags in the specified file for neoview.
# Author: Andrew Pyatkov <mrbiggfoot@gmail.com>
# License: MIT
#
"""
Display all tags in the specified file for neoview.
Output: {file_name}\t{tag_address}\t{displayable_tag_info}
"""
import argparse
#import os
#import re
import subprocess
... | normal | {
"blob_id": "b220cacc2530ca62b5599a9c1894e979bcfd5109",
"index": 9633,
"step-1": "<mask token>\n\n\ndef displayable_info(tagname, comment):\n cs = comment.split('\\t', 1)\n return ('{}{:<' + str(max_tag_len) + '}{} {}|{}{}{}|{} {}{}{}').format(\n COLOR_TAGNAME, tagname, COLOR_RESET, COLOR_BAR, COLOR... | [
1,
2,
3,
4,
5
] |
from Graph import *
from PrioQueue import *
from GShortestPath import *
from GSpanTree import *
from User import *
infinity = float("inf")
# 这是根据关键字找地点的方法，已经形成了某个依据属性的表后，通过关键词匹配来解决问题
# 最终输出一个yield出的迭代器，将其list化后就可以向末端输出了
def find_by_word(lst, word):
# 这个是字符串匹配函数,word是客户输入，lst是循环的东西
# 最好排成优先队列
... | normal | {
"blob_id": "b5ec6e0fc4239a53a882b455a113eaac4db6cef5",
"index": 2331,
"step-1": "<mask token>\n\n\nclass web:\n\n def __init__(self, lnum=0, land_list=[], graph_money=GraphAL(),\n graph_time=GraphAL(), graph_line=GraphAL()):\n self.graph_money = graph_money\n self.graph_time = graph_time... | [
15,
21,
24,
29,
32
] |
# A program to display and find the sum of a list of numbers using for loop
list=[10,20,30,40,50]
sum=0;
for i in list:
print(i)
sum=sum+i
print('sum =',sum) | normal | {
"blob_id": "88e34ee5cd5af7d3b04321c4aa4fc815f926add1",
"index": 7110,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in list:\n print(i)\n sum = sum + i\nprint('sum =', sum)\n",
"step-3": "list = [10, 20, 30, 40, 50]\nsum = 0\nfor i in list:\n print(i)\n sum = sum + i\nprint('sum =',... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import unittest
from selenium import webdriver
from appium import webdriver
from time import sleep
import os
from PublicResour import Desired_Capabilities
"""
登录状态下检查“我的”界面的所有的功能模块
大部分执行用例时在“我的”界面
"""
#Return ads path relative to this file not cwd
PATH = lambda p: ... | normal | {
"blob_id": "883a50cf380b08c479c30edad3a2b61a6f3075cc",
"index": 4030,
"step-1": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport unittest\nfrom selenium import webdriver\nfrom appium import webdriver\nfrom time import sleep\nimport os\nfrom PublicResour import Desired_Capabilities\n\n\"\"\"\n 登录状态下检查“我... | [
0
] |
from tkinter import *
# Everything in tkinter is a widget
# We start with the Root Widget
root = Tk()
# Creating a Label Widget
myLabel1 = Label(root, text="Hello User!")
myLabel2 = Label(root, text="Welcome to medBOT")
# Put labels onto the screen
myLabel1.grid(row=0, column=0)
myLabel2.grid(row=1, column=0)
# Grid... | normal | {
"blob_id": "93fe16e5a97ec2652c4f6b8be844244d9776ea2e",
"index": 4921,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmyLabel1.grid(row=0, column=0)\nmyLabel2.grid(row=1, column=0)\nroot.mainloop()\n",
"step-3": "<mask token>\nroot = Tk()\nmyLabel1 = Label(root, text='Hello User!')\nmyLabel2 = Label(ro... | [
0,
1,
2,
3,
4
] |
A = input("입력해주세요.\n") #입력값을 in_AAA로 칭한다
#\n은 문법의 줄바꾸기
print(A.upper()+" World!") #in_AAA를 출력 + "World!")
#upper()는 앞의 값을 대문자화+"
| normal | {
"blob_id": "8a54a71b08d10c5da9ca440e8e4f61f908e00d54",
"index": 9496,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(A.upper() + ' World!')\n",
"step-3": "A = input('입력해주세요.\\n')\nprint(A.upper() + ' World!')\n",
"step-4": "A = input(\"입력해주세요.\\n\") #입력값을 in_AAA로 칭한다\r\n ... | [
0,
1,
2,
3
] |
from django.db import models
from django.contrib.auth.models import AbstractUser, BaseUserManager
class UserManager(BaseUserManager):
#Necesar pentru a scoate username de la required
def create_user(self, email, password, **kwargs):
user = self.model(email=email, **kwargs)
user.set_password(... | normal | {
"blob_id": "85b8ffe1bca879acd86251e4662b33648b713588",
"index": 7243,
"step-1": "<mask token>\n\n\nclass Utilizator(AbstractUser):\n \"\"\" Tabel info utilizator \n nume - extras automat din email ([nume]@gmail.com)\n email - se va loga cu emailul\n parola -... | [
4,
5,
6,
7,
9
] |
"""
==============================
Visualize Cylinder with Wrench
==============================
We apply a constant body-fixed wrench to a cylinder and integrate
acceleration to twist and exponential coordinates of transformation
to finally compute the new pose of the cylinder.
"""
import numpy as np
from pytransform... | normal | {
"blob_id": "2019a2a5588e57164ff4226ef3bcbbc506f2b315",
"index": 7432,
"step-1": "<mask token>\n\n\ndef animation_callback(step, cylinder, cylinder_frame, prev_cylinder2world,\n Stheta_dot, inertia_inv):\n if step == 0:\n prev_cylinder2world[:, :] = np.eye(4)\n Stheta_dot[:] = 0.0\n wrench... | [
1,
3,
4,
5,
6
] |
#PortableKanban 4.3.6578.38136 - Encrypted Password Retrieval
#Python3 -m pip install des
#or
#pip install des
import json
import base64
from des import * #python3 -m pip install des, pip install des
import sys
def decode(hash):
hash = base64.b64decode(hash.encode('utf-8'))
key = DesKey(b"7ly6UznJ")
r... | normal | {
"blob_id": "136215a3ba99f74160373181c458db9bec4bb6b7",
"index": 977,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef decode(hash):\n hash = base64.b64decode(hash.encode('utf-8'))\n key = DesKey(b'7ly6UznJ')\n return key.decrypt(hash, initial=b'XuVUm5fR', padding=True).decode('utf-8')\n\n... | [
0,
1,
2,
3,
4
] |
# Import packages
import pandas
import requests
import lxml
# Get page content
url = "https://archive.fantasysports.yahoo.com/nfl/2017/189499?lhst=sched#lhstsched"
html = requests.get(url).content
df_list = pandas.read_html(html)
# Pull relevant URLs
| normal | {
"blob_id": "d46035699bee1ad9a75ea251c2c3ab8817d6a740",
"index": 4343,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurl = (\n 'https://archive.fantasysports.yahoo.com/nfl/2017/189499?lhst=sched#lhstsched'\n )\nhtml = requests.get(url).content\ndf_list = pandas.read_html(html)\n",
"step-3": "imp... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
def testeum():
a = 10
print(id(a))
def testedois():
a = 10
print(id(a)) | normal | {
"blob_id": "a2e2528f560f6117d4ceeb9cd20d3f6f6b2a30a7",
"index": 213,
"step-1": "<mask token>\n",
"step-2": "def testeum():\n a = 10\n print(id(a))\n\n\n<mask token>\n",
"step-3": "def testeum():\n a = 10\n print(id(a))\n\n\ndef testedois():\n a = 10\n print(id(a))\n",
"step-4": "# -*- co... | [
0,
1,
2,
3
] |
from numpy import*
a=int(input('numero: '))
b='*'
c='o'
for i in range(a):
d=(b*(a-i))+(c*(a-(a-i)))+(c*(a-(a-i)))+(b*(a-i))
print(d)
| normal | {
"blob_id": "155b243ad7d93bcf2b74cd5b2bd3409ab7ec7473",
"index": 8488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(a):\n d = b * (a - i) + c * (a - (a - i)) + c * (a - (a - i)) + b * (a - i)\n print(d)\n",
"step-3": "<mask token>\na = int(input('numero: '))\nb = '*'\nc = 'o'\nfo... | [
0,
1,
2,
3,
4
] |
import pygame
import textwrap
import client.Button as Btn
from client.ClickableImage import ClickableImage as ClickImg
from client.CreateDisplay import CreateDisplay
import client.LiverpoolButtons as RuleSetsButtons_LP
import client.HandAndFootButtons as RuleSetsButtons_HF
import client.HandManagement as HandManagement... | normal | {
"blob_id": "1cdd315eec6792a8588dc2e6a221bc024be47078",
"index": 7885,
"step-1": "<mask token>\n\n\nclass HandView:\n <mask token>\n\n def __init__(self, controller, display, ruleset):\n self.controller = controller\n self.display = display\n self.ruleset = ruleset\n self.Meld_T... | [
7,
9,
11,
12,
13
] |
print('hello world123')
| normal | {
"blob_id": "004a02f7ff49cb1b63ebedfcfcb4937377859099",
"index": 1187,
"step-1": "<mask token>\n",
"step-2": "print('hello world123')\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# Generated by Django 3.2.3 on 2021-07-24 12:14
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('profiles', '0018_userprofile_membership_fee_pending'),
]
operations = [
migrations.RenameField(
model_name='userprofile',
ol... | normal | {
"blob_id": "464980a2f17aeedfa08548d6c4e247f8c047e2cb",
"index": 5743,
"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 = [('profiles', ... | [
0,
1,
2,
3,
4
] |
import logging
class ConsoleLogger:
handlers = [
(logging.StreamHandler,
dict(),
"[%(name)s]\t %(asctime)s [%(levelname)s] %(message)s ",
logging.DEBUG)
]
def set_level(self, level):
self.logger.setLevel(level)
def debug(self, message):
self.logger... | normal | {
"blob_id": "5299f2c66fd287be667ecbe11b8470263eafab5c",
"index": 702,
"step-1": "<mask token>\n\n\nclass ConsoleLogger:\n <mask token>\n\n def set_level(self, level):\n self.logger.setLevel(level)\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __... | [
3,
6,
7,
8,
11
] |
from os import environ
from flask import Flask
from flask_restful import Api
from flask_migrate import Migrate
from applications.db import db
from applications.gamma_api import add_module_gamma
app = Flask(__name__)
app.config["DEBUG"] = True
app.config['SQLALCHEMY_DATABASE_URI'] = environ.get('DATABASE')
app.config... | normal | {
"blob_id": "fbb081fd52b14336ab4537bb795105bcd6a03070",
"index": 3045,
"step-1": "<mask token>\n\n\n@app.before_first_request\ndef create_tables():\n pass\n\n\n<mask token>\n",
"step-2": "<mask token>\ndb.init_app(app)\n<mask token>\n\n\n@app.before_first_request\ndef create_tables():\n pass\n\n\nadd_mod... | [
1,
2,
3,
4,
5
] |
'''
we have source files with a certain format and each file has 200 columns and there is a process that takes the source
files and loads into hbase and moves it into sql data warehouse. We have to create automated test scripts that compares
with with is with hbase and sql data warehouse. load into hbase and query the ... | normal | {
"blob_id": "7b38c64174656d1c4ec2b0541e6ed8d6680af7d7",
"index": 9565,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nconnection.open()\nprint('list of hbase tables {}'.format(connection.tables()))\n<mask token>\nfor key, data in customers.scan():\n keys.append(key)\n data_list.append(data)\n<mask ... | [
0,
1,
2,
3,
4
] |
##Arithmatic Progression
a = int(input ('Enter first number: '))
d = int(input('Enter common difference: '))
n = int(input('Number of term: '))
tn = a
while tn <= a + (n - 1) * d:
print(tn, end=" ")
tn += d
| normal | {
"blob_id": "e748261d1e5fd7921a022afefe5a5bea1fbfc67c",
"index": 9095,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile tn <= a + (n - 1) * d:\n print(tn, end=' ')\n tn += d\n",
"step-3": "a = int(input('Enter first number: '))\nd = int(input('Enter common difference: '))\nn = int(input('Numb... | [
0,
1,
2,
3
] |
from pyspark.sql import SQLContext, Row
from pyspark import SparkContext, SparkConf
from pyspark.sql.functions import col
import collections
# Create a Spark Session (the config bit is only for windows)
#conf = SparkConf().setAppName("SQL App").setMaster("local")
sc = SparkContext()
sqlCtx = SQLContext(sc)
def mapp... | normal | {
"blob_id": "e4bc2e97b70e2dc91dc86457866ec6b3531ef803",
"index": 8772,
"step-1": "<mask token>\n\n\ndef mapper(line):\n fields = line.split(',')\n return Row(ID=int(fields[0]), name=fields[1].encode('utf-8'), age=int(\n fields[2]), numFriends=int(fields[3]))\n\n\n<mask token>\n",
"step-2": "<mask ... | [
1,
2,
3,
4,
5
] |
"""CPU functionality."""
import sys
HLT = 0b00000001
LDI = 0b10000010
PRN = 0b01000111
MUL = 0b10100010
PUSH = 0b01000101
POP = 0b01000110
CMP = 0b10100111
CALL = 0b01010000
RET = 0b00010001
ADD = 0b10100000
CMP = 0b10100111
JMP = 0b01010100
JEQ = 0b01010101
JNE = 0b01010110
AND = 0b10101000
NOT = 0b01101001
OR = 0b10... | normal | {
"blob_id": "58d144b2c6c307719cef0b5097945c8206135ccf",
"index": 6048,
"step-1": "<mask token>\n\n\nclass CPU:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def op_ldi(self, operand_a, operand_b):\n self.reg[operand_a] = operand_b\n\n def op_prn(self, ... | [
13,
22,
23,
27,
32
] |
# -*- coding: utf-8 -*-
'''
Задание 12.3
Создать функцию print_ip_table, которая отображает таблицу доступных и недоступных IP-адресов.
Функция ожидает как аргументы два списка:
* список доступных IP-адресов
* список недоступных IP-адресов
Результат работы функции - вывод на стандартный поток вывода таблицы вида:
... | normal | {
"blob_id": "dd7e8556405f07172ce2b1e9f486c2cd2f4bad58",
"index": 7613,
"step-1": "<mask token>\n\n\ndef ping_ip_addresses(ip_addresses):\n result1 = []\n result2 = []\n for ip_address in ip_addresses:\n reply = subprocess.run(['ping', '-c', '3', '-n', ip_address],\n stdout=subprocess.P... | [
2,
3,
4,
5,
6
] |
#
# PySNMP MIB module Nortel-MsCarrier-MscPassport-AtmEbrMIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Nortel-MsCarrier-MscPassport-AtmEbrMIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:19:41 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4... | normal | {
"blob_id": "202670314ad28685aaa296dce4b5094daab3f47a",
"index": 4889,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif mibBuilder.loadTexts:\n mscAtmIfVpcSrcEbrOvRowStatusTable.setStatus('mandatory')\n<mask token>\nif mibBuilder.loadTexts:\n mscAtmIfVpcSrcEbrOvRowStatusEntry.setStatus('mandatory'... | [
0,
1,
2,
3
] |
# Formatters example
#
# Requirements:
# Go to the ../hello_world directory and do: python prepare_data.py
#
# Instructions:
#
# Just run this file:
#
# python table.py
# Output:
# * standard input – text table
# * table.html
# * cross_table.html
#
from cubes import Workspace, create_forma... | normal | {
"blob_id": "55e743cb027d27cc6b668424c1584f27a8e8c51a",
"index": 5707,
"step-1": "# Formatters example\n#\n# Requirements:\n# Go to the ../hello_world directory and do: python prepare_data.py\n#\n# Instructions:\n#\n# Just run this file:\n#\n# python table.py\n# Output:\n# * standard inp... | [
0
] |
"""
Proyecto SA^3
Autor: Mario Lopez
Luis Aviles
Joaquin V
Fecha: Octubre del 2012
versión: 1
"""
#Manejo de temlates en el HTML
import jinja2
from jinja2 import Environment, PackageLoader
import os
import cgi
import datetime
import urllib
# for hashing
import hashlib... | normal | {
"blob_id": "51cb750082ce93b6d14fe3aa40711836d493129c",
"index": 3692,
"step-1": "\"\"\"\r\nProyecto SA^3\r\nAutor: \tMario Lopez\r\n Luis Aviles\r\n\t\tJoaquin V\r\nFecha: Octubre del 2012\r\nversión: 1\r\n\"\"\"\r\n\r\n#Manejo de temlates en el HTML\r\nimport jinja2 \r\nfrom jinja2 i... | [
0
] |
import boto3
from app.models import *
from app.config import *
from app.lib.log import save_races_to_db, save_laptimes_to_db
from app.utils.utils import get_sec
import pandas as pd
def import_csv_from_aws():
client = boto3.client(
's3',
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCE... | normal | {
"blob_id": "b573db8ea0845fb947636b8d82ed462904c6005d",
"index": 5519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef import_csv_from_aws():\n client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n client.download_file('ergas... | [
0,
1,
2,
3
] |
"""
Tests for parsers.py
@author Kevin Wilson <khwilson@gmail.com>
"""
import crisis.parsers as undertest
import datetime
import unittest
class TestParsers(unittest.TestCase):
def test_parse_date(self):
date = '8/5/2013 16:14'
self.assertEqual(datetime.datetime(2013, 8, 5, 16, 14),
undertest.parse_date(da... | normal | {
"blob_id": "253d37f29e33f61d7e1a5ec2f9a1d6307a2ae108",
"index": 6921,
"step-1": "<mask token>\n\n\nclass TestParsers(unittest.TestCase):\n <mask token>\n\n def test_part_date_short(self):\n date = '8/5/13 16:14'\n self.assertEqual(datetime.datetime(2013, 8, 5, 16, 14), undertest.\n ... | [
3,
4,
5,
6,
7
] |
l = input().split("+")
l.sort()
print('+'.join(l))
| normal | {
"blob_id": "30d891c18f3635b7419fa0d0539b2665ad60b22c",
"index": 4748,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nl.sort()\nprint('+'.join(l))\n",
"step-3": "l = input().split('+')\nl.sort()\nprint('+'.join(l))\n",
"step-4": "l = input().split(\"+\")\r\r\nl.sort()\r\r\nprint('+'.join(l))\r\r\n",
... | [
0,
1,
2,
3
] |
from django import forms
from basic_app_new.models import *
class UpdateFood(forms.ModelForm):
class Meta:
model = Old_Food_Diary
fields = ['mfg_code', 'food_name', 'description', 'food_type',
'calories', 'fats', 'protein', 'carbohydrates', 'link_of_image',
'link_of_recip... | normal | {
"blob_id": "3a1b0b9891fec7b3d722f77cd2f3f6efa878a7a0",
"index": 4255,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass UpdatePurchaseFood(forms.ModelForm):\n\n\n class Meta:\n model = purchase_cards\n fields = ['food_name', 'description', 'ss_code', 'calorie', 'fat',\n ... | [
0,
1,
2,
3
] |
# Generated by Django 3.2 on 2021-05-03 17:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('orders', '0005_alter_orderitem_price'),
]
operations = [
migrations.AddField(
model_name='order',
name='being_delivere... | normal | {
"blob_id": "f3b466dc5b6149be82b096791ca8445faf169380",
"index": 5216,
"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 = [('orders', '0... | [
0,
1,
2,
3,
4
] |
import os
import json
import pytest
from datetime import datetime
from collections import OrderedDict
import numpy as np
import pandas as pd
from sqlalchemy.exc import ProgrammingError
import pawprint
def test_create_table_with_default_options(pawprint_default_tracker_db):
"""Ensure the table is correctly crea... | normal | {
"blob_id": "89a75ae980b7b48d33d0e8aa53ec92296dbfbc8e",
"index": 2843,
"step-1": "<mask token>\n\n\ndef test_create_table_with_default_options(pawprint_default_tracker_db):\n \"\"\"Ensure the table is correctly created with the default schema.\"\"\"\n tracker = pawprint_default_tracker_db\n assert track... | [
8,
15,
18,
20,
21
] |
# Make an array of dictionaries. Each dictionary should have keys:
#
# lat: the latitude
# lon: the longitude
# name: the waypoint name
#
# Make up three entries of various values.
waypoints = [
{ 'lat': 106.72888 },
{ 'lon': 0.69622 },
{ 'name': 'Kepulauan Riau' }
]
# Write a loop that prints out all the... | normal | {
"blob_id": "5eee3953193e0fc9f44b81059ce66997c22bc8f1",
"index": 6960,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor dict in waypoints:\n print(dict)\n",
"step-3": "waypoints = [{'lat': 106.72888}, {'lon': 0.69622}, {'name': 'Kepulauan Riau'}]\nfor dict in waypoints:\n print(dict)\n",
"ste... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 22 20:21:16 2018
@author: Yijie
"""
#Q4:
#(1)
yours = ['Yale','MIT','Berkeley']
mine = ['Harvard','CAU','Stanford']
ours1 = mine + yours
ours2=[]
ours2.append(mine)
ours2.append(yours)
print(ours1)
print(ours2)
# Difference:the print out results indicate that the list 'o... | normal | {
"blob_id": "bf65d4a4e066e3e06b888d4b9ed49e10e66b4e78",
"index": 8145,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nours2.append(mine)\nours2.append(yours)\nprint(ours1)\nprint(ours2)\n<mask token>\nprint(ours1)\nprint(ours2)\n",
"step-3": "<mask token>\nyours = ['Yale', 'MIT', 'Berkeley']\nmine = ['... | [
0,
1,
2,
3
] |
#!/usr/bin/evn python
#-*-coding:utf8 -*-
import os, sys, json
class settings(object):
filename = ''
config = {}
def __init__(self):
self.DEBUG = os.environ.get('RdsMonitor_DEBUG', 0)
def get_settings(self):
"""Parses the settings from redis-live.conf.
"""
# TODO: Consider YAML. Human writable, mac... | normal | {
"blob_id": "2c960685eaa14861c1c5b3ddb38b366a3e0e8e86",
"index": 1339,
"step-1": "#!/usr/bin/evn python\n#-*-coding:utf8 -*-\n\n\nimport os, sys, json\n\nclass settings(object):\n\tfilename = ''\n\tconfig = {}\n\t\n\tdef __init__(self):\n\t\tself.DEBUG = os.environ.get('RdsMonitor_DEBUG', 0)\n\t\t\n\tdef get_set... | [
0
] |
import torch
from torchelie.data_learning import *
def test_pixel_image():
pi = PixelImage((1, 3, 128, 128), 0.01)
pi()
start = torch.randn(3, 128, 128)
pi = PixelImage((1, 3, 128, 128), init_img=start)
assert start.allclose(pi() + 0.5, atol=1e-7)
def test_spectral_image():
pi = SpectralIm... | normal | {
"blob_id": "73cacc1317c8624b45c017144bc7449bc99bd045",
"index": 9542,
"step-1": "<mask token>\n\n\ndef test_pixel_image():\n pi = PixelImage((1, 3, 128, 128), 0.01)\n pi()\n start = torch.randn(3, 128, 128)\n pi = PixelImage((1, 3, 128, 128), init_img=start)\n assert start.allclose(pi() + 0.5, at... | [
2,
3,
4,
5,
6
] |
input = open('in.txt')
output = open('out.py', 'w+')
def opstr(op):
if op == 'RSHIFT': return '>>'
if op == 'LSHIFT': return '<<'
if op == 'OR': return '|'
if op == 'AND': return '&'
if op == 'NOT': return '~'
raise RuntimeError('Unknown {0}'.format(op))
def funstr(fun):
return '{0}_fn'.f... | normal | {
"blob_id": "e68588dff0e54fa03dbb1c629c39d8312a0df26d",
"index": 7230,
"step-1": "<mask token>\n\n\ndef opstr(op):\n if op == 'RSHIFT':\n return '>>'\n if op == 'LSHIFT':\n return '<<'\n if op == 'OR':\n return '|'\n if op == 'AND':\n return '&'\n if op == 'NOT':\n ... | [
3,
4,
5,
6,
7
] |
# created by Angus Clark 9/2/17 updated 27/2/17
# ToDo impliment traceroute function into this
# Perhaps get rid of unnecessary itemediate temp file
import socket
import os
import json
import my_traceroute
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = '130.56.253.43'
#print host
port = 5201 # Change ... | normal | {
"blob_id": "792f62c72f1667f651567314b062d862abbc9aa5",
"index": 6692,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ns.bind((host, port))\ns.listen(5)\n<mask token>\nwhile True:\n c, addr = s.accept()\n f = open('temp.json', 'wb')\n l = c.recv(1024)\n while l:\n f.write(l)\n l ... | [
0,
1,
2,
3,
4
] |
#/usr/bin/env python
#v0.2
import random, time
mapHeight = 30
mapWidth = 30
fillPercent = 45
def generateNoise():
#generate a grid of cells with height = mapHeight and width = mapWidth with each cell either "walls" (true) or "floors" (false)
#border is guaranteed to be walls and all other spaces have a fi... | normal | {
"blob_id": "7feac838f17ef1e4338190c0e8c284ed99369693",
"index": 1628,
"step-1": "<mask token>\n\n\ndef generateNoise():\n caveMap = []\n column = 1\n row = 1\n while column <= mapWidth:\n while row <= mapHeight:\n if (column == 1 or column == mapWidth or row == 1 or row ==\n ... | [
5,
6,
8,
9,
11
] |
class Solution(object):
def removeNthFromEnd(self, head, n):
dummy = ListNode(-1)
dummy.next = head
first, second = dummy, dummy
for i in range(n):
first = first.next
while first.next:
first = first.next
second = second.next
second... | normal | {
"blob_id": "7e71c97070285b051b23448c755e3d41b2909dda",
"index": 3884,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def removeNthFromEnd(self, head, n):\n dummy = ListNode(-1)\n dummy.next = head\n first, s... | [
0,
1,
2
] |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import sys
import json
import math
from klpmln import MVPP
dprogram = '''
img(i1). img(i2).
addition(A,B,N) :- digit(A,1,N... | normal | {
"blob_id": "70b08b9e8c1510a9be48a4bc1de39c6c85b36eed",
"index": 2426,
"step-1": "<mask token>\n\n\nclass Net(nn.Module):\n\n def __init__(self):\n super(Net, self).__init__()\n self.encoder = nn.Sequential(nn.Conv2d(1, 6, 5), nn.MaxPool2d(2, 2),\n nn.ReLU(True), nn.Conv2d(6, 16, 5), ... | [
5,
6,
7,
8,
10
] |
class State:
def __init__(self, id):
self.id = id
def NotinClosed(problem, node): #restituisce 1 se lo stato non è stato già visitato (al netto di controlli sulla depth) è quindi bisogna aggiungerlo
NotVisited = 1
for tuple in problem.closed:
if node.state.id == tuple[0].id and node.depth... | normal | {
"blob_id": "200deda300e39b07e0e558277a340b7ad01c7dee",
"index": 2216,
"step-1": "<mask token>\n",
"step-2": "class State:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class State:\n\n def __init__(self, id):\n self.id = id\n\n\n<mask token>\n",
"step-4": "class State:\n\n def __init__(s... | [
0,
1,
2,
3,
4
] |
import thinkbayes2 as thinkbayes
from thinkbayes2 import Pmf
import thinkplot
class Dice2(Pmf):
def __init__(self, sides):
Pmf.__init__(self)
for x in range(1, sides + 1):
self.Set(x, 1)
self.Normalize()
if __name__ == "__main__":
d6 = Dice2(6)
dices = [d6] * 6
th... | normal | {
"blob_id": "236dd70dec8d53062d6c38c370cb8f11dc5ef9d0",
"index": 556,
"step-1": "<mask token>\n\n\nclass Dice2(Pmf):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Dice2(Pmf):\n\n def __init__(self, sides):\n Pmf.__init__(self)\n for x in range(1, sides + 1):\n ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
###############################################################################
# \file
#
# $Id:$
#
# Copyright (C) Brno University of Technology
#
# This file is part of software developed by Robo@FIT group.
#
# Author: Tomas Lokaj
# Supervised by: Michal Spanel (spanel@fit.vutbr.cz)
# Date: 12/... | normal | {
"blob_id": "3bf1b4cfce55820605653d9dc57bab839f2dea55",
"index": 5864,
"step-1": "#!/usr/bin/env python\n###############################################################################\n# \\file\n#\n# $Id:$\n#\n# Copyright (C) Brno University of Technology\n#\n# This file is part of software developed by Robo@FI... | [
0
] |
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def swapPairs(self, head: ListNode) -> ListNode:
dummy_head=ListNode(0)
dummy_head.next=head
pre=dummy_head
cur=head
... | normal | {
"blob_id": "4afc2ceed860c20af071e1d9ccaca17973cb9a8e",
"index": 7553,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def swapPairs(self, head: ListNode) ->ListNode:\n dummy_head = ListNode(0)\n dummy_head.next = head\n pre ... | [
0,
1,
2,
3
] |
subworkflow data:
workdir:
"../../data/SlideSeq/Puck_180819_10"
include: "../Snakefile"
| normal | {
"blob_id": "9847a9cd360649819f51abfe584fb51a81306f68",
"index": 4224,
"step-1": "subworkflow data:\n workdir:\n \"../../data/SlideSeq/Puck_180819_10\"\n\ninclude: \"../Snakefile\"\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
# #----------------------------------------#
# 3.4
#
# Question:
# Write a program which can map() to make a list whose elements are square of elements in [1,2,3,4,5,6,7,8,9,10].
#
| normal | {
"blob_id": "8c71bc5d53bf5c4cb20784659eddf8a97efb86ef",
"index": 8336,
"step-1": "#\t#----------------------------------------#\n#\t3.4\n#\t\n#\tQuestion:\n#\tWrite a program which can map() to make a list whose elements are square of elements in [1,2,3,4,5,6,7,8,9,10].\n#\t\n",
"step-2": null,
"step-3": nul... | [
1
] |
"""
Like Places but possibly script based and temporary.
Like a whisper command where is keeps tracks of participants.
""" | normal | {
"blob_id": "378c07c512425cb6ac6c998eaaa86892b02a37b8",
"index": 6905,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\nLike Places but possibly script based and temporary.\nLike a whisper command where is keeps tracks of participants.\n\"\"\"",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids":... | [
0,
1
] |
def play():
print("playing tank games...")
print("runing tank now!!!") | normal | {
"blob_id": "8c7fe90972feec19e280d3bccd39391af666608a",
"index": 9410,
"step-1": "<mask token>\n",
"step-2": "def play():\n print('playing tank games...')\n\n\n<mask token>\n",
"step-3": "def play():\n print('playing tank games...')\n\n\nprint('runing tank now!!!')\n",
"step-4": "def play():\n pri... | [
0,
1,
2,
3
] |
from practice.demo4 import paixu
if __name__ == '__main__':
n=int(input("请输入最大的数字范围："))
paixu(n) | normal | {
"blob_id": "a777c6d76ef2ae15544a91bcfba0dbeabce0470a",
"index": 5377,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n n = int(input('请输入最大的数字范围：'))\n paixu(n)\n",
"step-3": "from practice.demo4 import paixu\nif __name__ == '__main__':\n n = int(input('请输入最大的数字范围：')... | [
0,
1,
2,
3
] |
#!/usr/local/bin/python3.3
'''
http://projecteuler.net/problem=127()
abc-hits
Problem 127
The radical of n, rad(n), is the product of distinct prime factors of n. For example, 504 = 23 × 32 × 7, so rad(504) = 2 × 3 × 7 = 42.
We shall define the triplet of positive integers (a, b, c) to be an abc-hit if:
GCD(a, b) = ... | normal | {
"blob_id": "646f6a0afc3dc129250c26270dda4355b8cea080",
"index": 1003,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef problem127():\n GOAL = 120000\n rad = {}\n for primes in genFactors(GOAL):\n rad[product(primes)] = set(primes), product(set(primes))\n\n def relprime(s, t):\n ... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html
import codecs
import time
import json
import os
class OitYitikuscrapyDataPipeline(object):
def open_spider(self, spider):
... | normal | {
"blob_id": "315996a783d7b95fd87374a8fe2602a572de071e",
"index": 3495,
"step-1": "<mask token>\n\n\nclass OitYitikuscrapyDataPipeline(object):\n\n def open_spider(self, spider):\n path = 'D:\\\\xiti10001\\\\data\\\\{}\\\\'.format(time.strftime('%Y%m%d',\n time.localtime()))\n isExists... | [
2,
3,
4,
5,
6
] |
from __future__ import print_function
import math
import db
from db import writer
from enum import Enum
from Definitions.Graph import Task
class Constraint(Enum):
deadline = 1
budget = 2
none = 3
def f_range(x, y, jump):
while x < y:
yield x
x += jump
clas... | normal | {
"blob_id": "567076af26b8c93c68647103aeddf43aeb24db13",
"index": 2054,
"step-1": "<mask token>\n\n\nclass Resources(object):\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 @property\n def ... | [
22,
28,
32,
36,
40
] |
import numpy as np
import matplotlib.pyplot as plt
def sample_1(N):
numeros=np.array([-10, -5, 3, 9])
return np.random.choice(numeros, N, p=[0.1, 0.4, 0.2, 0.3])#devuelve distro aleatoria con las probabilidades indicadas
def sample_2(N):
return np.random.exponential(0.5,N)#devuelve numeros aleatorios con distro ex... | normal | {
"blob_id": "d2d04686b3d7f8d01ca195750ca625baa06ed098",
"index": 2835,
"step-1": "<mask token>\n\n\ndef sample_1(N):\n numeros = np.array([-10, -5, 3, 9])\n return np.random.choice(numeros, N, p=[0.1, 0.4, 0.2, 0.3])\n\n\ndef sample_2(N):\n return np.random.exponential(0.5, N)\n\n\ndef get_mean(sampling... | [
3,
4,
5,
6,
7
] |
"""
OBJECTIVE: Given a list, sort it from low to high using the QUICK SORT algorithm
Quicksort first divides a large array into two smaller sub-arrays: the low elements and the high elements.
Quicksort can then recursively sort the sub-arrays.
The steps are:
1. Pick an element, called a pivot, from the array.
2. Par... | normal | {
"blob_id": "04099c46c029af37a08b3861809da13b3cc3153b",
"index": 997,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef quick_sort(array: list) ->list:\n return []\n",
"step-3": "\"\"\"\nOBJECTIVE: Given a list, sort it from low to high using the QUICK SORT algorithm\n\nQuicksort first divides ... | [
0,
1,
2
] |
from django.urls import path
from django.views.decorators.csrf import csrf_exempt
from .views import TestView, index, setup_fraud_detection, verify_testing_works
urlpatterns = [
path('test/<str:name>/', index, name='index'),
path('ml/setup/', setup_fraud_detection, name='fraud_detection_setup'),
path('ml/... | normal | {
"blob_id": "263347d1d445643f9c84e36a8cbb5304581ebaf6",
"index": 3888,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('test/<str:name>/', index, name='index'), path(\n 'ml/setup/', setup_fraud_detection, name='fraud_detection_setup'), path\n ('ml/verify/', verify_testing_works, ... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/ads/googleads_v1/proto/services/user_interest_service.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobu... | normal | {
"blob_id": "654586443e96f84aae70b3ce3263b0458a27334b",
"index": 473,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n<mask token>\n_sym_db.RegisterMessage(GetUserInterestRequest)\n<mask token>\n_sym_db.RegisterServiceDescriptor(_USERINTERESTSERVICE)\n<mask toke... | [
0,
1,
2,
3,
4
] |
from modeller import *
from modeller.automodel import *
# This part was within the script loop_modelling_2
# Here is is in a separate file for loop_modelling_3 so the script can be run in parallel
class MyLoop(dopehr_loopmodel):
def select_atoms(self):
# Here only the second loop atoms are allowed to move s... | normal | {
"blob_id": "d058c3df8513e07e4ff7035aa5c5885819e43687",
"index": 7295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MyLoop(dopehr_loopmodel):\n <mask token>\n\n def select_loop_atoms(self):\n return selection(self.residue_range('218:', '231:'))\n",
"step-3": "<mask token>\n\n\n... | [
0,
2,
3,
4,
5
] |
import sys
class Bus:
def __init__(self):
self.seats=0
self.dict_seats={}
self.num_passenger = 0
def conctructor(self,seats):
self.seats=seats
for i in range(1,self.seats+1):
self.dict_seats.update({i:"Free"})
return self.dict_seats
def getOn(sel... | normal | {
"blob_id": "1396509f65d194eeaefa3841e152b7078abf0032",
"index": 5549,
"step-1": "<mask token>\n\n\nclass Bus:\n <mask token>\n <mask token>\n <mask token>\n\n def getOn_2(self, *names):\n str_names = str(names)\n str_names.strip('')\n list_names = str_names.split(' ')\n f... | [
3,
6,
7,
8,
9
] |
'''
Copyright 2014-2015 Reubenur Rahman
All Rights Reserved
@author: reuben.13@gmail.com
'''
import XenAPI
inputs = {'xenserver_master_ip': '15.22.18.17',
'xenserver_password': 'reuben',
'xenserver_user': 'root',
'vm_name': 'SLES11SP2x64',
'target_host': 'xenserver-2'
... | normal | {
"blob_id": "c173c4673fd716a8b88faf751639d52e9ea4ffab",
"index": 4482,
"step-1": "'''\nCopyright 2014-2015 Reubenur Rahman\nAll Rights Reserved\n@author: reuben.13@gmail.com\n'''\n\nimport XenAPI\n\ninputs = {'xenserver_master_ip': '15.22.18.17',\n 'xenserver_password': 'reuben',\n 'xenserver_u... | [
0
] |
# -*- coding: utf-8 -*-
# @Time : 2019/3/21 20:12
# @Author : for
# @File : test01.py
# @Software: PyCharm
import socket
s=socket.socket()
host=socket.gethostname()
port=3456
s.connect((host,port))
cmd=input(">>>")
s.sendall(cmd.encode())
data=s.recv(1024)
print(data.decode())
s.close()
| normal | {
"blob_id": "596814032218c3db746f67e54e4f1863753aea06",
"index": 6299,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ns.connect((host, port))\n<mask token>\ns.sendall(cmd.encode())\n<mask token>\nprint(data.decode())\ns.close()\n",
"step-3": "<mask token>\ns = socket.socket()\nhost = socket.gethostname... | [
0,
1,
2,
3,
4
] |
from django.shortcuts import render, redirect
from .models import Courses
from django.views.generic import CreateView, ListView, UpdateView, DeleteView
from .forms import CourceCreateForm
from django.urls import reverse_lazy
from django.urls import reverse
class CourceListView(ListView):
model = Courses
templ... | normal | {
"blob_id": "3340277df91f1421dab8d204eddce65b4604432b",
"index": 369,
"step-1": "<mask token>\n\n\nclass CourceCreateView(CreateView):\n template_name = 'cources/create_cource.html'\n form_class = CourceCreateForm\n success_url = reverse_lazy('cources:cource_list')\n\n\nclass CourceUpdateView(UpdateView... | [
4,
6,
7,
8
] |
import numpy as np
import dxchange
import ptychotomo
if __name__ == "__main__":
# read object
u = dxchange.read_tiff('data/init_object.tiff')
u = u+1j*u/2
nz, n, _ = u.shape
# parameters
center = n/2
ntheta = 384
ne = 3*n//2
ngpus = 1
pnz = nz//2
theta = np.linspace(0... | normal | {
"blob_id": "4ed6f4db4c9c3319d6289ba402f81bbd8accf915",
"index": 9782,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n u = dxchange.read_tiff('data/init_object.tiff')\n u = u + 1.0j * u / 2\n nz, n, _ = u.shape\n center = n / 2\n ntheta = 384\n ne = 3 * n // ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import json
import requests
BASE_URL = 'http://www.omdbapi.com/'
def rating_msg(rating):
if rating > 80:
return 'You should watch this movie right now!\n'
elif rating < 50:
return 'Avoid this movie at all cost!\n'
else:
re... | normal | {
"blob_id": "7f33effa86fc3a80fce0e5e1ecf97ab4ca80402d",
"index": 1833,
"step-1": "<mask token>\n\n\ndef rating_msg(rating):\n if rating > 80:\n return 'You should watch this movie right now!\\n'\n elif rating < 50:\n return 'Avoid this movie at all cost!\\n'\n else:\n return ''\n\n\... | [
1,
2,
3,
4,
5
] |
from django.contrib import admin
from .models import TutorialsReview, TutorialsReviewComment
admin.site.register(TutorialsReview)
admin.site.register(TutorialsReviewComment)
| normal | {
"blob_id": "fea0619263b081f60ed0a4e178ef777a8d5dc988",
"index": 6500,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(TutorialsReview)\nadmin.site.register(TutorialsReviewComment)\n",
"step-3": "from django.contrib import admin\nfrom .models import TutorialsReview, TutorialsReviewCo... | [
0,
1,
2
] |
import onmt
import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.cuda
from torch.autograd import Variable
class CopyGenerator(nn.Module):
"""
Generator module that additionally considers copying
words directly from the source.
"""
def __init__(self, opt, src_dict, tgt_d... | normal | {
"blob_id": "704b3c57ca080862bed7a4caa65d1c8d5a32fa0b",
"index": 168,
"step-1": "<mask token>\n\n\nclass CopyGenerator(nn.Module):\n <mask token>\n\n def __init__(self, opt, src_dict, tgt_dict):\n super(CopyGenerator, self).__init__()\n self.linear = nn.Linear(opt.rnn_size, tgt_dict.size())\n... | [
4,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import pytest
from unittest import TestCase
from pydsf.exceptions import DSFServiceError
from pydsf.service.response import parse_response
from pydsf.service.translations import translate_input_fields, translate_output_fields
class MockMessage(object):... | normal | {
"blob_id": "bbff797fab4ac7dc7e6adb81c0eeda561f8ee147",
"index": 9603,
"step-1": "<mask token>\n\n\nclass MockResponseError(object):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass MockResponseParsed(object):\n HOV = list()\n\n def __init__(self):\n self.HOV.append(('FODT', '010107'... | [
17,
24,
26,
29,
30
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 17 17:24:39 2020
@author: code
"""
import sys
import keras
import cv2
import numpy
import matplotlib
import skimage
print('Python: {}'.format(sys.version))
print('Numpy: {}'.format(numpy.__version__))
print('Keras: {}'.format(keras.__version__))
p... | normal | {
"blob_id": "e086bebaa166abeea066fe49076f1b007858951f",
"index": 7052,
"step-1": "<mask token>\n\n\ndef compare_images(target, ref):\n scores = []\n scores.append(psnr(target, ref))\n scores.append(mse(target, ref))\n scores.append(ssim(target, ref, multichannel=True))\n return scores\n\n\ndef pre... | [
3,
7,
9,
10,
12
] |
"""
Day 2
"""
with open('input.txt', 'r') as f:
lines = f.read()
lines = lines.split('\n')[:-1]
lines = [l.split(' ') for l in lines]
valid = 0
new_valid = 0
for cur_pw in lines:
letter = cur_pw[1].strip(':')
amount = cur_pw[2].count(letter)
rule = cur_pw[0].split('-')
rule = [int(r) for r in ru... | normal | {
"blob_id": "46a3c3777d90976c7d39772d2e94430506d3acd7",
"index": 8025,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('input.txt', 'r') as f:\n lines = f.read()\n<mask token>\nfor cur_pw in lines:\n letter = cur_pw[1].strip(':')\n amount = cur_pw[2].count(letter)\n rule = cur_pw[0].... | [
0,
1,
2,
3
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, models
from torchvision.utils import make_grid
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
from PIL import Image
from... | normal | {
"blob_id": "7821b07a49db9f3f46bedc30f2271160e281806f",
"index": 4814,
"step-1": "<mask token>\n\n\nclass ConvolutionalNetwork(nn.Module):\n\n def __init__(self):\n super().__init__()\n self.conv1 = nn.Conv2d(3, 6, 3, 1)\n self.conv2 = nn.Conv2d(6, 16, 3, 1)\n self.fc1 = nn.Linear(... | [
3,
4,
5,
6,
7
] |
import os
import sys
import re
import traceback
import logging
import queue
import threading
from logging.handlers import TimedRotatingFileHandler
from pathlib import Path
import click
import inotify.adapters
from inotify.constants import (IN_ATTRIB, IN_DELETE, IN_MOVED_FROM,
IN_MOVED_TO,... | normal | {
"blob_id": "3a96ede91069df0c71905415e598dbbd9d3056fd",
"index": 9730,
"step-1": "<mask token>\n\n\ndef configure_log(log_file, verbose=False):\n filename = log_file\n if log_file == 'STDOUT':\n handler = logging.StreamHandler(sys.stdout)\n elif log_file == 'STDERR':\n handler = logging.St... | [
7,
11,
12,
14,
16
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# you can use print for debugging purposes, e.g.
# print "this is a debug message"
def solution(A):
N = len (A)
#min_avg = min( (A[0] + A[1]) / 2, (A[0] + A[1] + A[2]) / 3)
min_avg = (A[0] + A[1]) / 2.0
min_idx = 0
now_avg = 0.0
for i in xra... | normal | {
"blob_id": "caa92eb5582135f60a6034cb83d364501361d00e",
"index": 7726,
"step-1": "<mask token>\n",
"step-2": "def solution(A):\n N = len(A)\n min_avg = (A[0] + A[1]) / 2.0\n min_idx = 0\n now_avg = 0.0\n for i in xrange(1, N - 1):\n now_avg = (A[i] + A[i + 1]) / 2.0\n if now_avg < ... | [
0,
1,
2
] |
# Copyright 2018 dhtech
#
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file
import lib
import urlparse
import yaml
MANIFEST_PATH = '/etc/manifest'
HTTP_BASIC_AUTH = None
def blackbox(name, backend, targets, params,
target='target', path='/probe', label... | normal | {
"blob_id": "f489058c922d405754ad32a737f67bc03c08772b",
"index": 701,
"step-1": "<mask token>\n\n\ndef blackbox(name, backend, targets, params, target='target', path='/probe',\n labels=None):\n labels = {} if labels is None else labels\n banned_oses = ['debian']\n filtered_targets = [x for x in targe... | [
3,
4,
5,
6,
7
] |
print('test 123123')
| normal | {
"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
] |
import os, random, string
from django.conf import settings
from django.template.loader import render_to_string
from django.core.mail import send_mail
def generate_temp_password():
length = 7
chars = string.ascii_letters + string.digits
rnd = random.SystemRandom()
return ''.join(rnd.choice(chars) for... | normal | {
"blob_id": "822fc2941099cb9d7791580678cfb2a89a987175",
"index": 4685,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef send_confirmation_email(user):\n try:\n confirmation_key = user.confirmation_key\n except:\n confirmation_key = user.add_unconfirmed_email(user.email)\n msg... | [
0,
1,
2,
3,
4
] |
ulang = 'y'
while True :
a = int(input ("masukkan nilai = "))
if a > 60 :
status = "LULUS"
elif a <= 60 :
status = "TIDAK LULUS"
print(status)
ulang = input("apakah anda ingin mengulang? y/n = ") | normal | {
"blob_id": "759b440bf436afbfb081cf55eeb4a0f075ed3e6d",
"index": 9577,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n a = int(input('masukkan nilai = '))\n if a > 60:\n status = 'LULUS'\n elif a <= 60:\n status = 'TIDAK LULUS'\n print(status)\n ulang = input('ap... | [
0,
1,
2,
3
] |
{'ivy': {'svm': ({'kernel': 'rbf', 'C': 10.0}, 0.034482758620689662, 0.035087719298245612), 'tuned_ensemble': ({'svm__C': 100000.0, 'rf__n_estimators': 101, 'cart__min_samples_leaf': 7, 'knn__n_neighbors': 2, 'rf__random_state': 1542, 'cart__max_depth': 33, 'cart__max_features': 0.35714285714285721, 'svm__kernel': 'sig... | normal | {
"blob_id": "fa02fb701b59728671a7e87147adaeb33422dcdb",
"index": 1600,
"step-1": "<mask token>\n",
"step-2": "{'ivy': {'svm': ({'kernel': 'rbf', 'C': 10.0}, 0.03448275862068966, \n 0.03508771929824561), 'tuned_ensemble': ({'svm__C': 100000.0,\n 'rf__n_estimators': 101, 'cart__min_samples_leaf': 7,\n '... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
import tensorflow as tf
from yolov3 import *
from predict import predict
from load import Weight_loader
class Yolo(Yolov3):
sess = tf.Session()
def __init__(self, input=None, weight_path=None, is_training=False):
self.is_training = is_training
try:
self... | normal | {
"blob_id": "f3d34379cc7fbfe211eeebec424112f3da0ab724",
"index": 7999,
"step-1": "<mask token>\n\n\nclass Yolo(Yolov3):\n <mask token>\n <mask token>\n <mask token>\n\n def freeze(self):\n graph_def = tf.graph_util.convert_variables_to_constants(sess=self.\n sess, input_graph_def=tf... | [
3,
4,
6,
7,
8
] |
def merge_the_tools(string, k):
if(len(string)%k != 0):
exit()
else:
L = []
for i in range(0, len(string), k):
L.append(''.join(list(dict.fromkeys(string[i:i+k]))))
print('\n'.join(L))
if __name__ == '__main__':
string, k = input(), int(input())
merge_the_to... | normal | {
"blob_id": "0004e90622f8b13ec7ce0c1f49e8c8df7ea07269",
"index": 7098,
"step-1": "<mask token>\n",
"step-2": "def merge_the_tools(string, k):\n if len(string) % k != 0:\n exit()\n else:\n L = []\n for i in range(0, len(string), k):\n L.append(''.join(list(dict.fromkeys(str... | [
0,
1,
2,
3
] |
# ---------------------MODULE 1 notes--------------------
# .
# .
# .
# .
# .
# .
# .
# .
# .
# .
# save as (file).py first if not it will not work
print("Hello")
# control s to save
| normal | {
"blob_id": "bb64da929ff2e1e04267518ec93a28bedb5a4de5",
"index": 7306,
"step-1": "<mask token>\n",
"step-2": "print('Hello')\n",
"step-3": "# ---------------------MODULE 1 notes--------------------\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n# .\r\n\r\n# save as (file).py first if not i... | [
0,
1,
2
] |
import pandas as pd
file = pd.read_csv("KDDTest+.csv")
with open("test_9feats.csv", "w") as f:
df = pd.DataFrame(file,
columns=[
"dst_host_srv_serror_rate", "dst_host_serror_rate",
"serror_rate", "srv_serror_rate", "count", "flag",
... | normal | {
"blob_id": "ce28330db66dcdfad63bdac698ce9d285964d288",
"index": 5124,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('test_9feats.csv', 'w') as f:\n df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',\n 'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',\n ... | [
0,
1,
2,
3,
4
] |
from django.db.models import Sum, Count
from django.db.models.functions import Coalesce
from django.utils.timezone import localtime
from .models import Quote, Vote
import pygal
from pygal.style import Style
style = Style(
background='transparent',
plot_background='transparent',
foreground='#3d3d3d',
foreground_s... | normal | {
"blob_id": "6f6f57ff317d7e3c6e6ae4d450c6fdf0e22eb4eb",
"index": 7256,
"step-1": "<mask token>\n\n\nclass QuotesByMonth:\n <mask token>\n <mask token>\n <mask token>\n\n\nclass QuotesByRating:\n\n def __init__(self):\n self.chart = pygal.Histogram(title='Quotes by Rating', margin=20,\n ... | [
9,
16,
20,
21,
23
] |
import collections
import re
from collections import Counter
import operator
import pickle
import math
import json
path='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'
#path=sys.argv[1]
badWordList = ['and','the','was','for']
RE=r'\b[^\W\d_]+\b'
# NEGATIVE TWEETS
c=collections.Counter()
NT="negativeTweets.txt/... | normal | {
"blob_id": "42e16def0fcf234f3d7c2709de36a321d8ddf29e",
"index": 7598,
"step-1": "import collections\nimport re\nfrom collections import Counter\nimport operator\nimport pickle\nimport math\nimport json\n\npath='C:/Users/rahul/Desktop/CSCI 544/HW 2/op_spam_train/'\n#path=sys.argv[1]\n\nbadWordList = ['and','the'... | [
0
] |
"""
Users model
"""
# Django
from django.conf import settings
from django.db import models
from django.contrib.auth.models import AbstractUser
from django.core.validators import RegexValidator
class User(AbstractUser):
"""User model"""
email = models.EmailField(
'email address',
... | normal | {
"blob_id": "360813a573f672e3ec380da4237a6e131dbcb7e6",
"index": 2345,
"step-1": "<mask token>\n\n\nclass User(AbstractUser):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Profile(models.Model):\n \"\"\"Profile model\"\... | [
5,
6,
8,
9,
10
] |
import pickle
import torch
data = pickle.load(open('dd0eb7901523d494d4aa324f474c782063e9e231.p', 'rb'))
torch.nn.functional.adaptive_avg_pool3d(**data)
| normal | {
"blob_id": "20d09a616133295a6162a7ab1d7970ccbaf6de95",
"index": 1331,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntorch.nn.functional.adaptive_avg_pool3d(**data)\n",
"step-3": "<mask token>\ndata = pickle.load(open('dd0eb7901523d494d4aa324f474c782063e9e231.p', 'rb'))\ntorch.nn.functional.adaptive_a... | [
0,
1,
2,
3
] |
""" OCR that converts images to text """
from pytesseract import image_to_string
from PIL import Image
print image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))
#print image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))
#pr... | normal | {
"blob_id": "91ac4a23573abcb0ab024830dbc1daebd91bd40d",
"index": 2355,
"step-1": "\"\"\" OCR that converts images to text \"\"\"\n\nfrom pytesseract import image_to_string\nfrom PIL import Image\n\nprint image_to_string(Image.open('/Users/williamliu/Desktop/Screen Shot 2014-09-27 at 11.45.34 PM.png'))\n\n#print ... | [
0
] |
# -*- coding: utf-8 -*-
class Config(object):
def __init__(self):
self.config_dict = {
"data_path": {
# "vocab_path": "../data/cnews/cnews.vocab.txt",
"vocab_path": "../data/rumor/cnews.vocab.txt",
# "trainingSet_path": "../data/cnews/cnews.trai... | normal | {
"blob_id": "9cb4e550a0d19b44ec8357882f353b04748b213b",
"index": 2589,
"step-1": "<mask token>\n",
"step-2": "class Config(object):\n <mask token>\n <mask token>\n",
"step-3": "class Config(object):\n <mask token>\n\n def get(self, section, name):\n return self.config_dict[section][name]\n... | [
0,
1,
2,
3,
4
] |
from tkinter import ttk
from chapter04a.validated_mixin import ValidatedMixin
class RequiredEntry(ValidatedMixin, ttk.Entry):
def _focusout_validate(self, event):
valid = True
if not self.get():
valid = False
self.error.set('A value is required')
return valid
| normal | {
"blob_id": "59047a113d76c64be48858258441fae5da505790",
"index": 5792,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass RequiredEntry(ValidatedMixin, ttk.Entry):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass RequiredEntry(ValidatedMixin, ttk.Entry):\n\n def _focusout_validate(self... | [
0,
1,
2,
3
] |
class RetModel(object):
def __init__(self, code = 0, message = "success", data = None):
self.code = code
self.msg = message
self.data = data
| normal | {
"blob_id": "ec395b93cecf8431fd0df1aa0151ebd32244c367",
"index": 4941,
"step-1": "<mask token>\n",
"step-2": "class RetModel(object):\n <mask token>\n",
"step-3": "class RetModel(object):\n\n def __init__(self, code=0, message='success', data=None):\n self.code = code\n self.msg = message... | [
0,
1,
2,
3
] |
"""
stanCode Breakout Project
Adapted from Eric Roberts's Breakout by
Sonja Johnson-Yu, Kylie Jue, Nick Bowman,
and Jerry Liao
YOUR DESCRIPTION HERE
"""
from campy.gui.events.timer import pause
from breakoutgraphics import BreakoutGraphics
FRAME_RATE = 1000 / 120 # 120 frames per second.
NUM_LIVES = 3
def main():
... | normal | {
"blob_id": "b218f5e401510f844006cb6079737b54aa86827b",
"index": 2194,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n graphics = BreakoutGraphics()\n lives = NUM_LIVES\n graphics.window.add(graphics.scoreboard, 0, graphics.window_height)\n while True:\n pause(FRAME_RA... | [
0,
2,
3,
4,
5
] |
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