code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
radius = int(input("enter the value for the radius of the cycle: "))
circumference = 2 * 3.14159 * radius
diameter = 2 * radius
area = 3.14159 * radius ** 2
print('circumference is ', circumference)
print('diameter is: ', diameter)
print('area is ', area)
| normal | {
"blob_id": "ab5412a3d22bd53a592c93bad4870b06fd9f0720",
"index": 4080,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('circumference is ', circumference)\nprint('diameter is: ', diameter)\nprint('area is ', area)\n",
"step-3": "radius = int(input('enter the value for the radius of the cycle: '))\... | [
0,
1,
2,
3
] |
import argparse
import os
import shutil
import time, math
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
im... | normal | {
"blob_id": "c9de51ee5a9955f36ecd9f5d92813821fb68fb3d",
"index": 4308,
"step-1": "<mask token>\n\n\nclass SpatialAttention(nn.Module):\n\n def __init__(self, kernel_size=7):\n super(SpatialAttention, self).__init__()\n self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2,\n ... | [
8,
10,
11,
13,
14
] |
from flask import Blueprint, render_template
from bashtube import cache
singlevideos = Blueprint('singlevideos', __name__, template_folder='templates')
@singlevideos.route('/')
def index():
return render_template('singlevideos/single.html')
| normal | {
"blob_id": "ee10bca1126b20378c4e9cea4d2dc7ed6a2044ab",
"index": 9187,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@singlevideos.route('/')\ndef index():\n return render_template('singlevideos/single.html')\n",
"step-3": "<mask token>\nsinglevideos = Blueprint('singlevideos', __name__, templa... | [
0,
1,
2,
3
] |
import numpy as np
import tensorflow as tf
class LocNet:
def __init__(self, scope, buttom_layer):
self.scope = scope
with tf.variable_scope(scope) as scope:
self.build_graph(buttom_layer)
self.gt_loc = tf.placeholder(dtype=tf.float32, shape=(None,4),name='gt_loc')
... | normal | {
"blob_id": "dd4dc1c4a0dc47711d1d0512ef3f6b7908735766",
"index": 3149,
"step-1": "<mask token>\n\n\nclass LocNet:\n\n def __init__(self, scope, buttom_layer):\n self.scope = scope\n with tf.variable_scope(scope) as scope:\n self.build_graph(buttom_layer)\n self.gt_loc = tf.... | [
2,
3,
4,
5,
6
] |
from __future__ import unicode_literals
import frappe, json
def execute():
for ps in frappe.get_all('Property Setter', filters={'property': '_idx'},
fields = ['doc_type', 'value']):
custom_fields = frappe.get_all('Custom Field',
filters = {'dt': ps.doc_type}, fields=['name', 'fieldname'])
if custom_fields:
... | normal | {
"blob_id": "6f951815d0edafb08e7734d0e95e6564ab1be1f7",
"index": 2375,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef execute():\n for ps in frappe.get_all('Property Setter', filters={'property': '_idx'\n }, fields=['doc_type', 'value']):\n custom_fields = frappe.get_all('Custom ... | [
0,
1,
2,
3
] |
class tenDParameters:
def __init__(self,
b: float,
DM: float,
pm_l: float,
pm_b: float,
vrad: float,
sb: float,
spml: float,
spmb: float,
sdm: float,
vc: float):
self.b = b
self.DM = DM
# this is actually pm_l * cos b, apparently
self.pm_l = pm... | normal | {
"blob_id": "82e7e22293551e061dcb295c52714c22df0ed0ce",
"index": 5678,
"step-1": "<mask token>\n",
"step-2": "class tenDParameters:\n <mask token>\n",
"step-3": "class tenDParameters:\n\n def __init__(self, b: float, DM: float, pm_l: float, pm_b: float, vrad:\n float, sb: float, spml: float, spm... | [
0,
1,
2,
3
] |
# 只放置可执行文件
#
# from ..src import package
# data_dict = package.pack()
# from ..src.plugins import * #解释一遍全放入内存
# from ..src import plugins #导入这个文件夹(包，模块，类库)，默认加载init文件到内存
#
#
# plugins.pack()
from ..src.script import run
if __name__ == '__main__':
run()
| normal | {
"blob_id": "4f870e0d86d9f9b8c620115a618ea32abc24c52d",
"index": 3008,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n run()\n",
"step-3": "from ..src.script import run\nif __name__ == '__main__':\n run()\n",
"step-4": "# 只放置可执行文件\n#\n# from ..src import package\n# d... | [
0,
1,
2,
3
] |
from django.shortcuts import render, redirect
from django.http import HttpResponse
from django.contrib.auth.decorators import login_required
from django.contrib.admin.views.decorators import staff_member_required
from lessons.models import Lesson, Question, Response
from usermanage.models import SchoolClass
import json... | normal | {
"blob_id": "ee417c5fff858d26ca60a78dffe4cff503a6f2b5",
"index": 6824,
"step-1": "<mask token>\n\n\n@login_required\ndef lessons_overview(request):\n if request.method == 'POST':\n if request.user.is_staff:\n school_class = SchoolClass.objects.get(id=request.POST['class_id'])\n sc... | [
7,
8,
9,
10,
11
] |
from django.urls import path
from . import views
app_name = 'orders'
urlpatterns = [
path('checkout' , views.order_checkout_view , name='orders-checkout') ,
]
| normal | {
"blob_id": "031f668fbf75b54ec874a59f53c60ceca53779cf",
"index": 8942,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'orders'\nurlpatterns = [path('checkout', views.order_checkout_view, name=\n 'orders-checkout')]\n",
"step-3": "from django.urls import path\nfrom . import views\napp_name... | [
0,
1,
2,
3
] |
"""
Tests for the Transformer RNNCell.
"""
import pytest
import numpy as np
import tensorflow as tf
from .transformer import positional_encoding, transformer_layer
from .cell import (LimitedTransformerCell, UnlimitedTransformerCell,
inject_at_timestep, sequence_masks)
def test_inject_at_timestep... | normal | {
"blob_id": "958f6e539f9f68892d77b6becc387581c6adfa16",
"index": 3366,
"step-1": "<mask token>\n\n\ndef test_inject_at_timestep():\n with tf.Graph().as_default():\n with tf.Session() as sess:\n in_seq = tf.constant(np.array([[[1, 2, 3, 4], [5, 6, 7, 8]], [[\n 9, 10, 11, 12], [... | [
3,
4,
5,
6,
7
] |
"""
WINRM Module to connect to windows host
"""
from winrm.protocol import Protocol
from lib import logger
class WINRM(object):
"""
WINRM Module to connect to windows host
"""
def __init__(self, host_ip, usr, pwd):
"""
- **parameters**, **types**, **return** and **return t... | normal | {
"blob_id": "96ac9088650490a7da00c7a20f634b76e673ca2d",
"index": 1174,
"step-1": "<mask token>\n\n\nclass WINRM(object):\n <mask token>\n <mask token>\n\n def connect(self):\n \"\"\"\n Method to connect to a Windows machine.\n \"\"\"\n try:\n self.host_win_ip =... | [
3,
4,
5,
6,
7
] |
class Solution(object):
def restoreIpAddresses(self, s):
"""
:type s: str
:rtype: List[str]
"""
def helper(sb, string, level):
if len(string) == 0:
if level == 4:
ans.append(sb[:-1])
return
if level ... | normal | {
"blob_id": "ec4348c61cd1c9130543bb20f9ca199399e1caff",
"index": 226,
"step-1": "class Solution(object):\n def restoreIpAddresses(self, s):\n \"\"\"\n :type s: str\n :rtype: List[str]\n \"\"\"\n\n def helper(sb, string, level):\n if len(string) == 0:\n ... | [
0
] |
#coding=utf-8
'''
Created on 04/09/2012
@author: Johnny
'''
from ckeditor.widgets import CKEditorWidget
from django.conf.urls import patterns, url
from django.shortcuts import render_to_response
from django.template import RequestContext
from django.templatetags.static import static
import views
from portfolio.models ... | normal | {
"blob_id": "caac9dfc7d52607c2af67ddc03a3a7bdae9911bb",
"index": 8204,
"step-1": "<mask token>\n\n\nclass ServicoForm(forms.ModelForm):\n <mask token>\n\n\n class Meta:\n model = Servico\n\n\nclass ServicosAdmin(CustomModelAdmin):\n list_display = 'imagem_icone', 'titulo', 'intro'\n list_displ... | [
13,
14,
15,
16,
19
] |
#!/usr/bin/env python
import sys,re
print('\n'.join(re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',sys.stdin.read())))
| normal | {
"blob_id": "4cefaa964251e77a05066af1f61f9fd2a4350d38",
"index": 7622,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('\\n'.join(re.findall(\n 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n , sys.stdin.read())))\n",
"step-3": "import sys, re\nprint(... | [
0,
1,
2,
3
] |
from django.db import models
class Building(models.Model):
Number = models.CharField(max_length=60)
Description = models.CharField(max_length=120)
OSMWAYID = models.DecimalField(decimal_places=0, max_digits=15) # the osm way id
Lat = models.CharField(max_length=20) #lat/lon of then center
Lon = m... | normal | {
"blob_id": "02ddf213cd3f455f8d8fbde8621fc4788124d5a9",
"index": 3714,
"step-1": "<mask token>\n\n\nclass Logger(models.Model):\n Facinet = models.ForeignKey('Facinet', null=False, blank=False,\n related_name='Loggers')\n loggerindex = models.IntegerField(unique=True, db_column='LoggerIndex')\n n... | [
4,
6,
7,
11,
12
] |
# The purpose of this module is essentially to subclass the basic SWIG generated
# pynewton classes and add a bit of functionality to them (mostly callback related
# stuff). This could be done in the SWIG interface file, but it's easier to do it
# here since it makes adding python-specific extensions to newton easier.
... | normal | {
"blob_id": "90d792fe18e589a0d74d36797b46c6ac1d7946be",
"index": 4303,
"step-1": "<mask token>\n\n\nclass ChamferCylinder(pynewton.ChamferCylinder):\n pass\n\n\nclass ConvexHull(pynewton.ConvexHull):\n pass\n\n\nclass ConvexHullModifier(pynewton.ConvexHullModifier):\n pass\n\n\nclass NullCollider(pynewt... | [
33,
52,
66,
68,
76
] |
data = {
'title': 'Dva leteca (gostimo na 2)',
'song': [
'x - - - - - x - - - - -',
'- x - - - x - - - x - -',
'- - x - x - - - x - x -',
'- - - x - - - x - - - x'
],
'bpm': 120,
'timeSignature': '4/4'
}
from prog import BellMusicCreator
exportFile = __file__.replac... | normal | {
"blob_id": "957fb1bd34d13b86334da47ac9446e30afd01678",
"index": 5477,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nBellMusicCreator().write(data, fp=exportFile)\n",
"step-3": "data = {'title': 'Dva leteca (gostimo na 2)', 'song': [\n 'x - - - - - x - - - - -', '- x - - - x - - - x - -',\n '- -... | [
0,
1,
2,
3,
4
] |
#!/bin/python3
# Implement a stack with push, pop, inc(e, k) operations
# inc (e,k) - Add k to each of bottom e elements
import sys
class Stack(object):
def __init__(self):
self.arr = []
def push(self, val):
self.arr.append(val)
def pop(self):
if len(self.arr):
return... | normal | {
"blob_id": "5ed439a2a7cfb9c941c40ea0c5eba2851a0f2855",
"index": 24,
"step-1": "<mask token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.arr = []\n\n def push(self, val):\n self.arr.append(val)\n\n def pop(self):\n if len(self.arr):\n return self.arr.pop()\n\... | [
5,
6,
7,
8,
10
] |
from bs4 import BeautifulSoup
import os, re, json
import pandas as pd
from urllib import request
from openpyxl import load_workbook
from bilibili.append_xlsx import append_df_to_excel
# 获取页面的所有的avid, title, url
def parse_html(content):
arr = []
# 使用beautifulsoup解析html文档
soup = BeautifulSoup(content)
... | normal | {
"blob_id": "a63718ba5f23d6f180bdafcb12b337465d6fa052",
"index": 4734,
"step-1": "<mask token>\n\n\ndef read_path(path):\n path_set = set()\n dir_path = os.listdir(path)\n for item in dir_path:\n child = os.path.join('%s/%s' % (path, item))\n path_set.add(child)\n return path_set\n\n\nd... | [
4,
5,
7,
8,
10
] |
"""
This class runs the RL Training
"""
from __future__ import division
import logging
import numpy as np
from data.data_provider import DataProvider
from episode.episode import Episode
from tracker import TrainingTracker
from tqdm import tqdm
class RLTrainer(object):
"""
Creates RL training object
"""
... | normal | {
"blob_id": "7c004cb0c9eefa5e88f5085fb3b2878db98d2b20",
"index": 3200,
"step-1": "<mask token>\n\n\nclass RLTrainer(object):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass RLTrainer(object):\n <mask token>\n\n def __init__(self, config_, grid_search=False):\n... | [
1,
3,
4,
5,
6
] |
"""
The :mod:`sklearn.experimental` module provides importable modules that enable
the use of experimental features or estimators.
The features and estimators that are experimental aren't subject to
deprecation cycles. Use them at your own risks!
"""
| normal | {
"blob_id": "d3952306679d5a4dc6765a7afa19ce671ff4c0b4",
"index": 8501,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\nThe :mod:`sklearn.experimental` module provides importable modules that enable\nthe use of experimental features or estimators.\n\nThe features and estimators that are experimental aren't subje... | [
0,
1
] |
import pickle
import saveClass as sc
import libcell as lb
import numpy as np
import struct
import os
# def save_Ldend(Ldends, bfname):
# # create a binary file
# bfname='Dend_length.bin'
# binfile = file(bfname, 'wb')
# # and write out two integers with the row and column dimension
# header = struc... | normal | {
"blob_id": "6eb8172e7e26ad6ec9cb0d30c5a0613ce79296e6",
"index": 8421,
"step-1": "<mask token>\n\n\ndef save_ave_replay(aveData, nIter, nStart, bfname):\n vd = np.zeros((nIter, 4, nStart))\n for i_trial in range(nIter):\n vv = aveData[i_trial]\n for i_dendrite in range(4):\n vvv = ... | [
1,
2,
3,
4,
5
] |
def findFirst(arr, l, h, x):
if l > h:
return -1
mid = (l + h) // 2
if arr[mid] == x:
return mid
elif arr[mid] > x:
return findFirst(arr, l, mid - 1, x)
return findFirst(arr, mid + 1, h, x)
def indexes(arr, x):
n = len(arr)
ind = findFirst(arr, 0, n - 1, x)
if i... | normal | {
"blob_id": "b4783540224902b10088edbd038d6d664934a237",
"index": 4893,
"step-1": "<mask token>\n",
"step-2": "def findFirst(arr, l, h, x):\n if l > h:\n return -1\n mid = (l + h) // 2\n if arr[mid] == x:\n return mid\n elif arr[mid] > x:\n return findFirst(arr, l, mid - 1, x)\n... | [
0,
1,
2,
3
] |
import os
_basedir = os.path.abspath(os.path.dirname(__file__))
DEBUG = True
SECRET_KEY = '06A52C5B30EC2960310B45E4E0FF21C5D6C86C47D91FE19FA5934EFF445276A0'
SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(_basedir, 'app.db')
SQLALCHEMY_ECHO = True
DATABASE_CONNECT_OPTIONS = {}
THREADS_PER_PAGE = 8
CSRF_ENABL... | normal | {
"blob_id": "6ee71cf61ae6a79ec0cd06f1ddc7dc614a76c7b9",
"index": 6547,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n_basedir = os.path.abspath(os.path.dirname(__file__))\nDEBUG = True\nSECRET_KEY = '06A52C5B30EC2960310B45E4E0FF21C5D6C86C47D91FE19FA5934EFF445276A0'\nSQLALCHEMY_DATABASE_URI = 'sqlite:///... | [
0,
1,
2,
3
] |
_registry = []
def registry(name):
_registry.append(name)
def registry_names():
return iter(_registry)
| normal | {
"blob_id": "51642dbb210600f9ca4e035fb884fbdda030fd04",
"index": 1491,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef registry_names():\n return iter(_registry)\n",
"step-3": "<mask token>\n\n\ndef registry(name):\n _registry.append(name)\n\n\ndef registry_names():\n return iter(_regis... | [
0,
1,
2,
3
] |
import uuid
from fastapi import APIRouter, Depends, HTTPException, Form, Body
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from sqlalchemy.orm import Session
# dependency
from configs.config_sqlalchemy import get_db
# schema
from schema import store_schema
# define the url the clie... | normal | {
"blob_id": "64bbf2e3b961a6e0b5d7e551278bb21990df2ed9",
"index": 5526,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@router.post('/account/register', summary='register to create a new store',\n response_model=store_schema.Store, status_code=201)\nasync def account_register(StoreName: str=Body(..... | [
0,
1,
2,
3,
4
] |
import torch
from training import PointNetTrain, PointAugmentTrain, Model
#from PointAugment.Augment.config import opts
from data_utils.dataloader import DataLoaderClass
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
import numpy as np
import yaml
def visualize_batch(pointclouds, pred_labels, labels,... | normal | {
"blob_id": "0ced42c8bfaad32fc2b397326150e6c7bc5cedab",
"index": 4991,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef visualize_batch(pointclouds, pred_labels, labels, categories):\n batch_size = len(pointclouds)\n fig = plt.figure(figsize=(8, batch_size / 2))\n ncols = 5\n nrows = ma... | [
0,
1,
2,
3,
4
] |
import os
import click
import csv
import sqlite3
from sqlite3.dbapi2 import Connection
import requests
import mimetypes
from urllib.parse import urljoin, urlparse
from lxml.html.soupparser import fromstring
from lxml import etree
from lxml.etree import tostring
from analysis import lmdict, tone_count_with_negation_chec... | normal | {
"blob_id": "88e4e6647d4720d1c99f3e3438100790903921b5",
"index": 9163,
"step-1": "<mask token>\n\n\n@click.command()\n@click.option('-s', '--batch-size', 'batch_size', default=50)\ndef analyze(batch_size):\n db = db_connect()\n db_ensure_init(db)\n cmd = db.execute('SELECT id, url FROM reports WHERE is_... | [
3,
6,
9,
10,
13
] |
#!/usr/bin/env python
# Copyright 2017 Google Inc. All Rights Reserved.
#
# 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 require... | normal | {
"blob_id": "fb9ae5b3cdeac0c254669e214779ad43a02bff6d",
"index": 4596,
"step-1": "<mask token>\n\n\ndef read_dataset(mode, args):\n\n def decode_example(protos, vocab_size):\n features = {'key': tf.FixedLenFeature(shape=[1], dtype=tf.int64),\n 'indices': tf.VarLenFeature(dtype=tf.int64), 'va... | [
3,
4,
5,
6,
7
] |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from rasa_core.actions.action import Action
from rasa_core.events import SlotSet
from rasa_core.dispatcher import Button, Element, Dispatcher
import json
import pickle
class ActionWeather(Action):
def na... | normal | {
"blob_id": "f87d08f3bb6faa237cce8379de3aaaa3270a4a34",
"index": 3854,
"step-1": "<mask token>\n\n\nclass ActionWeather(Action):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ActionWeather(Action):\n <mask token>\n\n def run(self, dispatcher, tracker, domain):\n loc = ... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2018-02-24 11:30
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.Create... | normal | {
"blob_id": "56157aaf3f98abc58572b45111becb91cb93f328",
"index": 2926,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
import matplotlib
from matplotlib.colors import to_hex
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import itertools as it
from pathlib import Path
import subprocess
from tqdm import tqdm
from koala import plotting as pl
from koala import phase_diagrams as pd
fr... | normal | {
"blob_id": "d429f03c0f0c241166d6c0a5a45dc1101bcaec16",
"index": 5878,
"step-1": "<mask token>\n\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.cha... | [
3,
4,
5,
6,
7
] |
class Restaurant():
"""A restaurant model."""
def __init__(self, restaurant_name, cuisine_type):
"""Initialize name and type."""
self.name = restaurant_name
self.type = cuisine_type
def describe_restaurant(self):
"""Prints restaurant information."""
print("The restaurant's name is " + self.name.title())... | normal | {
"blob_id": "4ecf976a7d655efb5af427083ec1943cae6fe56d",
"index": 3672,
"step-1": "class Restaurant:\n <mask token>\n <mask token>\n <mask token>\n\n def open_restaurant(self):\n \"\"\"Message indicating the restaurant is open.\"\"\"\n print('The restaurant is now open!')\n",
"step-2":... | [
2,
3,
4,
5,
6
] |
from parser import read_expression_line, read_expression_lines, read_assignment_line, read_import_line, Import
def test_expression():
lines = ['a % b']
expression, left = read_expression_lines(lines)
assert expression is not None and len(left) == 0, left
print "test_expression 0: {} {}".format(expressi... | normal | {
"blob_id": "657866affd653a99eb7d9a9a82b2f7d6503ec21a",
"index": 2468,
"step-1": "from parser import read_expression_line, read_expression_lines, read_assignment_line, read_import_line, Import\n\ndef test_expression():\n lines = ['a % b']\n expression, left = read_expression_lines(lines)\n assert expres... | [
0
] |
import reddit
import tts
import sys
import praw
import os
#TODO: CENSOR CURSE WORDS,tag images that have curse words in them. strip punctuation from comment replies mp3
#TODO: pay for ads :thinking: buy views?
#TODO: sort by top upvotes
#todo: remove the formatting stuff
#todo: redo ducking
#todo quick scri... | normal | {
"blob_id": "fd57e13269ca00ed5eb05e00bd7999c041141187",
"index": 4256,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'NOW PROCESSING POST ID: {POST_ID}')\n<mask token>\ntts.comment_to_mp3(post_title, './quota.txt', 'titles', 0, randomize=True)\n<mask token>\nfor comment in comments_from_post:\n ... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 11 11:11:32 2017
@author: lindseykitchell
"""
import pandas as pd
import numpy as np
from scipy.stats.stats import pearsonr
import matplotlib.pylab as plt
import glob
import os
pwd = os.getcwd()
df_dict = {}
subj_list = []
for file in glob.glob(pwd + "/*spectrum.json")... | normal | {
"blob_id": "f78f8f560b7eb70232658be762e2058535a68122",
"index": 9086,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor file in glob.glob(pwd + '/*spectrum.json'):\n subj_name = os.path.basename(file)[0:6]\n subj_list.append(subj_name)\n df_dict[os.path.basename(file)[0:6]] = pd.read_json(file... | [
0,
1,
2,
3,
4
] |
# PySNMP SMI module. Autogenerated from smidump -f python DOCS-IETF-QOS-MIB
# by libsmi2pysnmp-0.1.3 at Thu May 22 11:57:36 2014,
# Python version sys.version_info(major=2, minor=7, micro=2, releaselevel='final', serial=0)
# Imports
( Integer, ObjectIdentifier, OctetString, ) = mibBuilder.importSymbols("ASN1", "Integ... | normal | {
"blob_id": "b90678c8f7ad9b97e13e5603bdf1dc8cb3511ca5",
"index": 5432,
"step-1": "<mask token>\n\n\nclass DocsIetfQosRfMacIfDirection(Integer):\n subtypeSpec = Integer.subtypeSpec + SingleValueConstraint(2, 1)\n namedValues = NamedValues(('downstream', 1), ('upstream', 2))\n\n\nclass DocsIetfQosSchedulingT... | [
4,
5,
6,
7,
9
] |
# -*- coding: utf-8 -*-
#some xml helpers
from xml.dom.minidom import Document
class XMLReport:
def __init__(self, name):
self.doc = Document()
self.main_node = self.add(name, node=self.doc)
def add(self, name, node=None):
if node is None: node = self.main_node
elem = self.doc.createElement(name)
... | normal | {
"blob_id": "146487738006ce3efb5bd35c425835a1fd8e0145",
"index": 9490,
"step-1": "# -*- coding: utf-8 -*-\n#some xml helpers\nfrom xml.dom.minidom import Document\n\nclass XMLReport:\n def __init__(self, name):\n\tself.doc = Document()\n\tself.main_node = self.add(name, node=self.doc)\n \n def add(s... | [
0
] |
from django.shortcuts import render
from PIL import Image
from django.views.decorators import csrf
import numpy as np
import re
import sys
import os
from .utils import *
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import base64
sys.path.append(os.path.abspath("./models"))
O... | normal | {
"blob_id": "b84b3206e87176feee2c39fc0866ada994c9ac7a",
"index": 8655,
"step-1": "<mask token>\n\n\ndef convertImage(imgData):\n getI420FromBase64(imgData)\n\n\n<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.path.abspath('./models'))\n<mask token>\n\n\ndef getI420FromBase64(codec):\n base64... | [
1,
5,
6,
7,
8
] |
def calc_fib(n):
fib_lis = dict()
for i in range(n+1):
if (i <= 1):
fib_lis[i] = i
else:
fib_lis[i] = fib_lis[i-2] + fib_lis[i-1]
return fib_lis[n]
n = int(input())
print(calc_fib(n))
| normal | {
"blob_id": "426b711571d3b5c4f8c7b0bad3a613951902e60b",
"index": 4129,
"step-1": "<mask token>\n",
"step-2": "def calc_fib(n):\n fib_lis = dict()\n for i in range(n + 1):\n if i <= 1:\n fib_lis[i] = i\n else:\n fib_lis[i] = fib_lis[i - 2] + fib_lis[i - 1]\n return f... | [
0,
1,
2,
3,
4
] |
# Core Packages
import difflib
import tkinter as tk
from tkinter import *
from tkinter import ttk
from tkinter.scrolledtext import *
import tkinter.filedialog
import PyPDF2
from tkinter import filedialog
import torch
import json
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
# ... | normal | {
"blob_id": "e3dece36ba3e5b3df763e7119c485f6ed2155098",
"index": 795,
"step-1": "<mask token>\n\n\ndef get_summary():\n model = T5ForConditionalGeneration.from_pretrained('t5-small')\n tokenizer = T5Tokenizer.from_pretrained('t5-small')\n device = torch.device('cpu')\n text = str(url_display1.get('1.... | [
8,
9,
13,
14,
15
] |
"""
File: ex17_map_reduce.py
Author: TonyDeep
Date: 2020-07-21
"""
from functools import reduce
print('#1 map')
a_list = [2, 18, 9, 22, 17, 24, 8, 12, 27]
map_data = map(lambda x: x * 2 + 1, a_list)
new_list = list(map_data)
print(new_list)
print('\n#2 reduce')
b_list = [1, 2, 3, 4, 5]
reduce_data = reduce(lambda x,... | normal | {
"blob_id": "8e3b26826752b6b3482e8a29b9b58f5025c7ef58",
"index": 4758,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('#1 map')\n<mask token>\nprint(new_list)\nprint('\\n#2 reduce')\n<mask token>\nprint(reduce_data)\n",
"step-3": "<mask token>\nprint('#1 map')\na_list = [2, 18, 9, 22, 17, 24, 8, ... | [
0,
1,
2,
3,
4
] |
import sys
from PySide6.QtCore import *
from PySide6.QtWidgets import *
from PySide6.QtGui import *
from simple_drawing_window import *
class simple_drawing_window1( simple_drawing_window):
def __init__(self):
super().__init__()
def paintEvent(self, e):
p = QPainter()
p.begin(self)
"""
p.setPen(Q... | normal | {
"blob_id": "6fc43919f521234d0dc9e167bb72f014e9c0bf17",
"index": 2102,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass simple_drawing_window1(simple_drawing_window):\n <mask token>\n\n def paintEvent(self, e):\n p = QPainter()\n p.begin(self)\n \"\"\"\n\t\tp.setPen(QCo... | [
0,
2,
3,
4,
5
] |
# © MNELAB developers
#
# License: BSD (3-clause)
from .dependencies import have
from .syntax import PythonHighlighter
from .utils import count_locations, image_path, interface_style, natural_sort
| normal | {
"blob_id": "837534ebc953dae966154921709398ab2b2e0b33",
"index": 578,
"step-1": "<mask token>\n",
"step-2": "from .dependencies import have\nfrom .syntax import PythonHighlighter\nfrom .utils import count_locations, image_path, interface_style, natural_sort\n",
"step-3": "# © MNELAB developers\n#\n# License:... | [
0,
1,
2
] |
import os
basedir = os.path.abspath(os.path.dirname(__file__))
class FlaskConfig(object):
SECRET_KEY = os.environ.get('FLASK_SECRET_KEY') or 'TuLAsWbcoKr5YhDE'
BOOTSTRAP_SERVE_LOCAL = os.environ.get('FLASK_BOOTSTRAP_SERVE_LOCAL') or True
APPLICATION_ROOT = os.environ.get('FLASK_APPLICATION_ROOT') or ''
... | normal | {
"blob_id": "a0349abb3a56ff4bc1700dbf0fa5a1fc2e3453ce",
"index": 6469,
"step-1": "<mask token>\n\n\nclass FlaskConfig(object):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass FlaskConfig(object):\n SECRET_KEY = os.environ.get('FLASK_SECRET_KEY') or 'TuLAsWbcoKr5Y... | [
1,
2,
3,
4,
5
] |
import cv2
import pdb
import skvideo
import numpy as np
import pandas as pd
from tqdm import tqdm
from harp import fdops
from word2number import w2n
from harp.vid import VidReader
class RegPropData:
"""
Processes region proposal data.
"""
_df = None
props = None
"""Dictionary containing regio... | normal | {
"blob_id": "b10badc172be119be5b2ab8ccc32cc95a0ed1e7a",
"index": 2680,
"step-1": "<mask token>\n\n\nclass RegPropData:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, csv_path):\n \"\"\"\n Initialize a region proposal data instance.\n\n Param... | [
5,
7,
8,
9,
11
] |
""" Interfaces to Juju API ModelManager """
from conjureup import juju
@juju.requires_login
def list_models(user='user-admin'):
""" Lists Juju Models
Arguments:
user: Name of user to list models for.
Returns:
Dictionary of known Juju Models (default: user-admin)
"""
models = juju.CLIENT... | normal | {
"blob_id": "11045cffc6d47902be7236e1d684422317f2c5f9",
"index": 1444,
"step-1": "<mask token>\n\n\n@juju.requires_login\ndef list_models(user='user-admin'):\n \"\"\" Lists Juju Models\n\n Arguments:\n user: Name of user to list models for.\n\n Returns:\n Dictionary of known Juju Models (default: ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/2/18 22:27
# @Author : name
# @File : 01.requests第一血.py
import requests
if __name__ == "__main__":
# step1:指定url
url = r'https://www.sogou.com/'
# step2:发起请求
reponse = requests.get(url = url)
# setp3:获取响应数据 text返回的是字... | normal | {
"blob_id": "7ae6ed8797d6ee02effd04750e243c5a59840177",
"index": 8444,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n url = 'https://www.sogou.com/'\n reponse = requests.get(url=url)\n page_text = reponse.text\n print(page_text)\n with open('./sogou.html', 'w',... | [
0,
1,
2,
3
] |
from card import Card;
from deck import Deck;
import people;
import chip;
import sys;
import time;
def display_instructions() :
print('\nInstructions: The objective of this game is to obtain a hand of cards whose value is as close to 21 ');
print('as possible without going over. The numbered cards hav... | normal | {
"blob_id": "a7050ebd545c4169b481672aed140af610aea997",
"index": 4879,
"step-1": "<mask token>\n\n\ndef create_players(num):\n players_list = []\n for i in range(num):\n name = input(f'Player {i + 1}, what is your name? ')\n while name == '':\n name = input('Please enter your name:... | [
7,
19,
20,
21,
22
] |
from template.db import Database
from template.query import Query
import os
'''
READ ME!!
Before using this demo, be sure that the Tail_Const is set to a value high enough
to guaranteed that all updates are contained within the same block.
config.py -> TAIL_CONST = 4
This program is mean... | normal | {
"blob_id": "8f5b7711d913c7375d6816dd94731f1ce5ca1a62",
"index": 8289,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.open('ECS165')\nprint(db)\n<mask token>\nprint('Merge Start')\nq.table.merge(0)\nprint('Merge End')\ndb.close()\n",
"step-3": "<mask token>\ndb = Database()\ndb.open('ECS165')\nprint... | [
0,
1,
2,
3,
4
] |
class SmartChineseAnalyzer:
def __init__(self):
pass
def create_components(self, filename):
#tokenizer = SentenceTokenize(filename)
#result = WordTokenFilter(tokenizer)
#result = PorterStemFilter(result)
if self.stopwords:
result = StopFilter(result,... | normal | {
"blob_id": "e486e0ab91a8f5671435f5bbcf5340a62a970d3a",
"index": 8670,
"step-1": "<mask token>\n",
"step-2": "class SmartChineseAnalyzer:\n <mask token>\n <mask token>\n",
"step-3": "class SmartChineseAnalyzer:\n <mask token>\n\n def create_components(self, filename):\n if self.stopwords:\... | [
0,
1,
2,
3,
4
] |
"""
Add requests application (adding and managing add-requests)
"""
from flask import Blueprint
__author__ = 'Xomak'
add_requests = Blueprint('addrequests', __name__, template_folder='templates', )
from . import routes | normal | {
"blob_id": "d39965c3070ec25230b4d6977ff949b3db070ab6",
"index": 7399,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__author__ = 'Xomak'\nadd_requests = Blueprint('addrequests', __name__, template_folder='templates')\n<mask token>\n",
"step-3": "<mask token>\nfrom flask import Blueprint\n__author__ =... | [
0,
1,
2,
3
] |
from django.db import models
# Create your models here.
class Airlines(models.Model):
flight_number=models.CharField(max_length=8,unique=True)
airlines_id=models.CharField(max_length=10)
source=models.CharField(max_length=20)
destination=models.CharField(max_length=20)
departure=models.TimeField()
arrival=models... | normal | {
"blob_id": "e57b30a7a1cf987918abfb3cb7d612bdead2ddcd",
"index": 406,
"step-1": "<mask token>\n\n\nclass Bookings(models.Model):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Airlines(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask ... | [
1,
6,
7,
9,
10
] |
import os.path
class State:
def __init__(self):
self.states=[]
self.actions=[]
class Candidate:
def __init__(self,height,lines,holes,bump,fit):
self.heightWeight = height
self.linesWeight = lines
self.holesWeight = holes
self.bumpinessWeight = bump
... | normal | {
"blob_id": "94100d0253ee82513fe024b2826e6182f852db48",
"index": 2349,
"step-1": "import os.path\nclass State:\n\n\n def __init__(self):\n self.states=[]\n self.actions=[]\n\n\n\nclass Candidate:\n\n def __init__(self,height,lines,holes,bump,fit):\n\n self.heightWeight = height\n ... | [
0
] |
from tdm.lib.device import DddDevice, DeviceAction, DeviceWHQuery, Validity
class CallJohnDevice(DddDevice):
class MakeCall(DeviceAction):
def perform(self, select_contact, select_number):
contact = self.device.CONTACTS.get(select_contact)
number_type = self.device.CONTACT... | normal | {
"blob_id": "1dd235ecfe577b508d0777e8c70026114aeb154f",
"index": 6648,
"step-1": "from tdm.lib.device import DddDevice, DeviceAction, DeviceWHQuery, Validity\r\n\r\n\r\nclass CallJohnDevice(DddDevice):\r\n\r\n class MakeCall(DeviceAction):\r\n def perform(self, select_contact, select_number):\r\n ... | [
0
] |
#Author: Abeer Rafiq
#Modified: 11/23/2019 3:00pm
#Importing Packages
import socket, sys, time, json, sqlite3
import RPi.GPIO as GPIO
from datetime import datetime, date
#Creating a global server class
class GlobalServer:
#The constructor
def __init__(self, port, room_ip_addrs,
app_ip_addrs):... | normal | {
"blob_id": "7ce679d5b889493f278de6deca6ec6bdb7acd3f5",
"index": 910,
"step-1": "#Author: Abeer Rafiq\n#Modified: 11/23/2019 3:00pm\n\n#Importing Packages\nimport socket, sys, time, json, sqlite3\nimport RPi.GPIO as GPIO\nfrom datetime import datetime, date\n\n#Creating a global server class\nclass GlobalServer:... | [
0
] |
import SCons.Util
import xml.dom.minidom, re, os.path
################################################################################
# DocBook pseudobuilder
# TODO: Only generate the output formats that are known
################################################################################
def generate(env) :
... | normal | {
"blob_id": "cae49da8dd436fc51b472c4a88703d8bc6c79bda",
"index": 427,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef generate(env):\n\n def remove_doctype(target, source, env):\n f = open(str(target[0]))\n output = []\n for line in f.readlines():\n output.append... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
#pylint: skip-file
"""
HostApi.py
Copyright 2016 Cisco Systems
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2... | normal | {
"blob_id": "4243c863827f1378c364171ca7d8fdabd42be22f",
"index": 3625,
"step-1": "<mask token>\n\n\nclass HostApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def getHostById(self, **kwargs):\n \"\"\"Retrieves host based on id\n\n Args:\n\n id, str: Host Id (requ... | [
2,
4,
5,
6,
7
] |
x = 5
print(x , " "*3 , "5")
print("{:20d}".format(x))
| normal | {
"blob_id": "88542a18d98a215f58333f5dd2bf5c4b0d37f32f",
"index": 5539,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(x, ' ' * 3, '5')\nprint('{:20d}'.format(x))\n",
"step-3": "x = 5\nprint(x, ' ' * 3, '5')\nprint('{:20d}'.format(x))\n",
"step-4": "x = 5\nprint(x , \" \"*3 , \"5\")\nprint(\"{:2... | [
0,
1,
2,
3
] |
import sys
import time
def initialize(x: object) -> object:
# Create initialization data and take a lot of time
data = []
starttimeinmillis = int(round(time.time()))
c =0
file1 = sys.argv[x]
with open(file1) as datafile:
for line in datafile:
c+=1
if(c%100==0):
... | normal | {
"blob_id": "91f3aae4e74f371cadaf10385510bc1c80063f55",
"index": 7765,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef initialize(x: object) ->object:\n data = []\n starttimeinmillis = int(round(time.time()))\n c = 0\n file1 = sys.argv[x]\n with open(file1) as datafile:\n for... | [
0,
1,
2,
3
] |
import numpy as np
import matplotlib.pyplot as plt
##########################################
# line plot
#########################################
# x축 생략시 x축은 0, 1, 2, 3이 됨
"""
plt.plot([1, 4, 9, 16])
plt.show()
"""
# x축과 y축 지정
"""
plt.plot([10, 20, 30, 40], [1, 4, 9, 16])
plt.show()
"""
# 스타일지정
# 색깔, 마커, 선 순서로 ... | normal | {
"blob_id": "89ffb2da456d2edf15fde8adc01615a277c6caa1",
"index": 8522,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith plt.xkcd():\n plt.title('XKCD style plot!!!')\n plt.plot(X, C, label='cosine')\n t = 2 * np.pi / 3\n plt.scatter(t, np.cos(t), 50, color='blue')\n plt.annotate('0.5 He... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python3
__author__ = "yang.dd"
"""
example 090
"""
# list
# 新建list
testList = [10086, "中国移动", [1, 2, 3, 4]]
# 访问列表长度
print("list len: ", len(testList))
# 切片
print("切片（slice）:", testList[1:])
# 追加
print("追加一个元素")
testList.append("i'm new here!");
print("list len: ", len(testLi... | normal | {
"blob_id": "4f19eed272c12be137df92bfd3c72e978408c974",
"index": 3216,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('list len: ', len(testList))\nprint('切片（slice）:', testList[1:])\nprint('追加一个元素')\ntestList.append(\"i'm new here!\")\nprint('list len: ', len(testList))\nprint('last item :', testLi... | [
0,
1,
2,
3
] |
import os
from sqlalchemy import Column, ForeignKey, Integer, String, Float, Boolean, DateTime
from sqlalchemy import and_, or_
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy import create_engine, func
from sqlalchemy.orm import sessionmaker, scoped_sess... | normal | {
"blob_id": "6db0adf25a7cc38c8965c07cc80bde0d82c75d56",
"index": 3955,
"step-1": "<mask token>\n\n\nclass UsageData(Base):\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 dt... | [
9,
11,
12,
16,
21
] |
# SPDX-FileCopyrightText: 2013 The glucometerutils Authors
#
# SPDX-License-Identifier: Unlicense
| normal | {
"blob_id": "39ffb85fb10882041c2c9a81d796e7ff9df7d930",
"index": 8551,
"step-1": "# SPDX-FileCopyrightText: 2013 The glucometerutils Authors\n#\n# SPDX-License-Identifier: Unlicense\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
1
]
} | [
1
] |
import sys
def input(_type=str):
return _type(sys.stdin.readline().strip())
def main():
N, K, D = map(int, input().split())
rules = [tuple(map(int, input().split())) for _ in range(K)]
minv, maxv = min([r[0] for r in rules]), max([r[1] for r in rules])
while minv + 1 < maxv:
midv = (minv + maxv)//2
cnt, max_... | normal | {
"blob_id": "f0b98a3d6015d57a49e315ac984cac1cccf0b382",
"index": 6084,
"step-1": "<mask token>\n\n\ndef main():\n N, K, D = map(int, input().split())\n rules = [tuple(map(int, input().split())) for _ in range(K)]\n minv, maxv = min([r[0] for r in rules]), max([r[1] for r in rules])\n while minv + 1 <... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 24 20:59:36 2021
@author: Abeg
"""
#factorial using recursion
"""def factorial(n):
if n==0 or n==1:
return 1
elif n==2:
return n
else:
return n*factorial(n-1)
n=int(input("enter the no"))
print(factorial(n))"""
#fibonancci using recursi... | normal | {
"blob_id": "d1ee33ce6fb071aae800b0597a09e7039a209ec8",
"index": 2574,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef reverse(string):\n if len(string) == 0:\n return\n temp = string[0]\n reverse(string[1:])\n print(temp, end='')\n\n\n<mask token>\n",
"step-3": "<mask token>\... | [
0,
1,
2,
3,
4
] |
# coding: UTF-8
import os
import sys
if len(sys.argv) == 3:
fname = sys.argv[1]
out_dir = sys.argv[2]
else:
print "usage: vcf_spliter <input file> <output dir>"
exit()
count = 0
if not os.path.exists(out_dir):
os.makedirs(out_dir)
with open(fname, 'r') as f:
for l in f:
if l.strip()... | normal | {
"blob_id": "f410a77d4041514383110d9fd16f896178924d59",
"index": 8871,
"step-1": "# coding: UTF-8\n\nimport os \nimport sys\n\nif len(sys.argv) == 3:\n fname = sys.argv[1]\n out_dir = sys.argv[2]\nelse:\n print \"usage: vcf_spliter <input file> <output dir>\"\n exit()\n\ncount = 0\nif not os.path.exi... | [
0
] |
count=0
def merge(a, b):
global count
c = []
h = j = 0
while j < len(a) and h < len(b):
if a[j] <= b[h]:
c.append(a[j])
j += 1
else:
count+=(len(a[j:]))
c.append(b[h])
h += 1
if j == len(a):
for i in b[h:]:
... | normal | {
"blob_id": "cf3b66a635c6549553af738f263b035217e75a7a",
"index": 903,
"step-1": "<mask token>\n\n\ndef merge_sort(lists):\n if len(lists) <= 1:\n return lists\n middle = len(lists) // 2\n left = merge_sort(lists[:middle])\n right = merge_sort(lists[middle:])\n return merge(left, right)\n\n\... | [
1,
2,
3,
4,
5
] |
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from tensorflo... | normal | {
"blob_id": "11984027baf6d4c97b2976e4ac49a0e8ec62f893",
"index": 8709,
"step-1": "from math import sqrt\nfrom numpy import concatenate\nfrom matplotlib import pyplot\nfrom pandas import read_csv\nfrom pandas import DataFrame\nfrom pandas import concat\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn... | [
0
] |
from bs4 import BeautifulSoup
import urllib.request
import re
import math
url_header = "http://srh.bankofchina.com/search/whpj/search.jsp?erectDate=2016-01-25¬hing=2016-02-25&pjname=1314"
Webpage = urllib.request.urlopen(url_header).read()
Webpage=Webpage.decode('UTF-8')
# soup = BeautifulSoup(Webpage)
print (Webp... | normal | {
"blob_id": "62a86bd33755510f0d71f4920e63be1a3ce8c563",
"index": 6304,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(Webpage)\n<mask token>\nprint(a)\n<mask token>\nprint(total_page)\n",
"step-3": "<mask token>\nurl_header = (\n 'http://srh.bankofchina.com/search/whpj/search.jsp?erectDate=201... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
import pprint
import requests
import string
import subprocess
#Create three files
f_arptable = open( 'arptable', 'w+' )
f_maclist = open( 'maclist', 'w+' )
f_maclookup = open( 'maclookup', 'w+' )
#Give write permissions the three files
subprocess.call([ 'chmod','+w','maclist' ])
subprocess.call([ ... | normal | {
"blob_id": "d566104b00ffd5f08c564ed554e0d71279a93047",
"index": 6394,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsubprocess.call(['chmod', '+w', 'maclist'])\nsubprocess.call(['chmod', '+w', 'arptable'])\nsubprocess.call(['chmod', '+w', 'maclookup'])\nsubprocess.Popen(['arp', '-a'], stdout=f_arptable... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
#
# Script written by Legoktm, 2011
# Released into the Public Domain on November, 16, 2011
# This product comes with no warranty of any sort.
# Enjoy!
#
from commands import getoutput
def notify(string, program=False):
if not program:
command = 'growlnotify Python -m "%s"' %string
else:
command... | normal | {
"blob_id": "4318c99b3de9bb9c44eed57525c9ccbe82a17276",
"index": 5946,
"step-1": "#!/usr/bin/python\n#\n# Script written by Legoktm, 2011\n# Released into the Public Domain on November, 16, 2011\n# This product comes with no warranty of any sort.\n# Enjoy!\n#\nfrom commands import getoutput\ndef notify(string, p... | [
0
] |
import io
import os
from flask import Flask
from werkzeug.datastructures import FileStorage
import pytest
PNG_FILE = os.path.join(os.path.dirname(__file__), 'flask.png')
JPG_FILE = os.path.join(os.path.dirname(__file__), 'flask.jpg')
class TestConfig:
TESTING = True
MONGODB_DB = 'flask-fs-test'
MONGODB... | normal | {
"blob_id": "dfc412acc9b69f50396680db1b9f6feafe162996",
"index": 5571,
"step-1": "<mask token>\n\n\nclass TestConfig:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass TestFlask(Flask):\n\n def configure(self, *storages, **configs):\n import flask_file_system as fs\n ... | [
6,
10,
11,
13,
16
] |
class Figure:
area = 0
def __new__(cls, *args):
if cls is Figure:
return None
return object.__new__(cls)
def add_area(self, other):
if isinstance(other, Figure):
return self.area + other.area
else:
raise ValueError("Should pass Figure as... | normal | {
"blob_id": "ceab21e41adf171e99e6c3c8541c418d82db6168",
"index": 3272,
"step-1": "class Figure:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "class Figure:\n <mask token>\n\n def __new__(cls, *args):\n if cls is Figure:\n return None\n return object.__new__... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# caixinjun
import argparse
from sklearn import metrics
import datetime
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
import pickle
from sklearn import svm
import os
import warnings
warnings.filterwarnings('ignore')
def get_data(train_file):
targ... | normal | {
"blob_id": "199872ea459a9dba9975c6531034bdbc1e77f1db",
"index": 5875,
"step-1": "<mask token>\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\n<mask token>\n\n\ndef load_models(matrix_path, model_path):\n ... | [
3,
4,
5,
6,
8
] |
TheBeatles = ['John', 'Paul', 'George', 'Ringo']
Wings = ['Paul']
for Beatle in TheBeatles:
if Beatle in Wings:
continue
print Beatle
| normal | {
"blob_id": "9a54ff8e7e8d6d46860cb6173f03c52655b30f43",
"index": 6449,
"step-1": "TheBeatles = ['John', 'Paul', 'George', 'Ringo']\nWings = ['Paul']\n\nfor Beatle in TheBeatles:\n\t\tif Beatle in Wings:\n\t\t\t\tcontinue\n\t\tprint Beatle\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": nu... | [
0
] |
# Import smtplib for the actual sending function
import smtplib
# Import the email modules we'll need
from email.message import EmailMessage
# Open the plain text file whose name is in textfile for reading.
with open("testfile.txt") as fp:
# Create a text/plain message
msg = EmailMessage()
msg.set_content... | normal | {
"blob_id": "9feb24da78113310509664fa9efcf5f399be5335",
"index": 5914,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('testfile.txt') as fp:\n msg = EmailMessage()\n msg.set_content('test')\n<mask token>\ns.send_message(msg)\ns.quit()\n",
"step-3": "<mask token>\nwith open('testfile.txt... | [
0,
1,
2,
3,
4
] |
# The project is based on Tensorflow's Text Generation with RNN tutorial
# Copyright Petros Demetrakopoulos 2020
import tensorflow as tf
import numpy as np
import os
import time
# The project is based on Tensorflow's Text Generation with RNN tutorial
# Copyright Petros Demetrakopoulos 2020
import tensorflow as tf
impor... | normal | {
"blob_id": "5ff0c6bde8f3ffcb1f5988b0bbd1dfdd7fa2e818",
"index": 8800,
"step-1": "<mask token>\n\n\ndef yuh():\n corpus_path = '/tmp/data.txt'\n text = open(corpus_path, 'rb').read().decode(encoding='utf-8')\n text = preprocessText(text)\n corpus_words = corpusToList(text)\n map(str.strip, corpus_... | [
6,
7,
8,
9,
11
] |
from cryptography.exceptions import UnsupportedAlgorithm
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.serialization import load_ssh_public_key
from ingredients_http.schematics.types import ArrowType, KubeName
from schematics import Model
from schematics.exceptions import ... | normal | {
"blob_id": "a521220ac287a840b5c69e2d0f33daa588132083",
"index": 4983,
"step-1": "<mask token>\n\n\nclass RequestCreateKeypair(Model):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass ResponseKeypair(Model):\n name = KubeName(required=True, min_length=3)\n public_key = StringType(required=T... | [
4,
6,
10,
11,
12
] |
import unittest
from domain.Activity import Activity
from domain.NABException import NABException
from domain.Person import Person
from domain.ActivityValidator import ActivityValidator
from repository.PersonRepository import PersonRepository
from repository.PersonFileRepository import PersonFileRepository
from reposit... | normal | {
"blob_id": "130581ddb0394dcceabc316468385d4e21959b63",
"index": 8682,
"step-1": "<mask token>\n\n\nclass StatsControllerTestCase(unittest.TestCase):\n\n def setUp(self):\n pR = PersonRepository()\n aR = ActivityRepository()\n self.L = StatsController(pR, aR)\n self.p = Person(1, '... | [
4,
5,
6,
7,
9
] |
# -*- coding: utf-8 -*-
from matplotlib import pyplot as plt
from matplotlib import colors
import numpy as np
import sys
max_value = int(sys.argv[1])
file1 = open(sys.argv[2])
file2 = open(sys.argv[3])
file3 = open(sys.argv[4])
histogram = np.zeros(max_value, dtype=int).tolist()
highest_value = 0.0
sample_size = 0... | normal | {
"blob_id": "8356bc92a3a8b561d55bf5f2d9aeb0da89b730ca",
"index": 1387,
"step-1": "# -*- coding: utf-8 -*-\nfrom matplotlib import pyplot as plt\nfrom matplotlib import colors\nimport numpy as np\nimport sys\n\nmax_value = int(sys.argv[1])\n\nfile1 = open(sys.argv[2])\nfile2 = open(sys.argv[3])\nfile3 = open(sys.... | [
0
] |
salario = float(input('Qual o valor do seu Salario atual? R$ '))
novo = salario + (salario * 15 / 100)
print('Um funcioario que ganhava R$ {:.2f} com o aumento de 15% passa a ganhar R$ {:.2f}'.format(salario, novo)) | normal | {
"blob_id": "ffcd3c0086ff73eb722d867b335df23382615d20",
"index": 1657,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\n 'Um funcioario que ganhava R$ {:.2f} com o aumento de 15% passa a ganhar R$ {:.2f}'\n .format(salario, novo))\n",
"step-3": "salario = float(input('Qual o valor do seu Sa... | [
0,
1,
2,
3
] |
from allcode.controllers.image_classifiers.image_classifier import ImageClassifier
class ImageClassifierMockup(ImageClassifier):
def classify_images(self, images):
pass
def classify_image(self, image):
return {'final_class': 'dog',
'final_prob': .8}
| normal | {
"blob_id": "71fb9dc9f9ac8b1cdbc6af8a859dbc211512b4d1",
"index": 1675,
"step-1": "<mask token>\n\n\nclass ImageClassifierMockup(ImageClassifier):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ImageClassifierMockup(ImageClassifier):\n <mask token>\n\n def classify_image(self, ... | [
1,
2,
3,
4,
5
] |
def filter_lines(in_filename, in_filename2,out_filename):
"""Read records from in_filename and write records to out_filename if
the beginning of the line (taken up to the first comma at or after
position 11) is found in keys (which must be a set of byte strings).
"""
proper_convert = 0
missing_... | normal | {
"blob_id": "502e0f0c6376617dc094fcdd47bea9773d011864",
"index": 900,
"step-1": "<mask token>\n",
"step-2": "def filter_lines(in_filename, in_filename2, out_filename):\n \"\"\"Read records from in_filename and write records to out_filename if\n the beginning of the line (taken up to the first comma at or... | [
0,
1,
2,
3,
4
] |
import requests
import codecs
import urllib.request
import time
from bs4 import BeautifulSoup
from html.parser import HTMLParser
import re
import os
#input
Result_File="report.txt"
#deleting result file if exists
if os.path.exists(Result_File):
os.remove(Result_File)
#reading html file and parsing logic
f=codecs.o... | normal | {
"blob_id": "869bbc8da8cdb5de0bcaf5664b5482814daae53a",
"index": 6212,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif os.path.exists(Result_File):\n os.remove(Result_File)\n<mask token>\nwith open(Result_File, 'w') as r:\n r.write(\n 'OI_CE|Chng_in_OI_CE |Volume_CE|IV_CE|LTP_CE|NetChng_CE... | [
0,
1,
2,
3,
4
] |
import requests, csv, configuration
headers = {'Authorization': f'Bearer {configuration.CARRIERX_API_TOKEN}'}
url = f'{configuration.BASE_CARRIERX_API_URL}/core/v2/calls/call_drs'
date = configuration.DATE
i = 1
params = {'limit': '1', 'order': 'date_stop asc', 'filter':
f'date_stop ge {date}'}
r = requests.get(url... | normal | {
"blob_id": "8262d8b5bbb156eccae021c1c9333d3cd1a6260f",
"index": 9030,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(dr_items):\n with open('calls.csv', 'w', encoding='UTF8') as csv_file:\n csv_writer = csv.writer(csv_file)\n csv_header = ['dr_sid', 'date_start', 'number_src', 'n... | [
0,
1,
2,
3
] |
class Solution:
def countBits(self, num: int) -> List[int]:
total = []
for i in range(num + 1):
counter = bin(i).count('1')
# for j in bin(i):
# if j == '1':
# counter += 1
total.append(counter)
return total... | normal | {
"blob_id": "c6554ff18c23a61d3694e73b808f44c96f9a19c4",
"index": 2012,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def countBits(self, num: int) ->List[int]:\n total = []\n for i in range(num + 1):\n counter = bin(i).... | [
0,
1,
2,
3
] |
from torchvision import datasets, transforms
import torch
def load_data(data_folder, batch_size, train, num_workers=0, **kwargs):
transform = {
'train': transforms.Compose(
[transforms.Resize([256, 256]),
transforms.RandomCrop(224),
transforms.RandomHorizontalFli... | normal | {
"blob_id": "d99fd3dc63f6a40dde5a6230111b9f3598d3c5fd",
"index": 7830,
"step-1": "<mask token>\n\n\nclass _InfiniteSampler(torch.utils.data.Sampler):\n \"\"\"Wraps another Sampler to yield an infinite stream.\"\"\"\n\n def __init__(self, sampler):\n self.sampler = sampler\n\n def __iter__(self):\... | [
8,
9,
10,
11,
12
] |
from rest_framework.generics import GenericAPIView
from rest_framework.response import Response
from rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND
from ...models.brand import Brand
from ...models.product import type_currency_choices, type_condition_choices, User, Product
from ...models.product_c... | normal | {
"blob_id": "47e9b73fc7f6b3c8295e78d0cdb5aa51ca4c5f8d",
"index": 8140,
"step-1": "<mask token>\n\n\nclass UpdateProduct(GenericAPIView):\n <mask token>\n <mask token>\n <mask token>\n\n def get(self, request, *args, **kwargs):\n data = self.get_queryset()\n extract_sp = self.extract_fil... | [
11,
13,
16,
17,
19
] |
"""Functions for parsing various strings to RGB tuples."""
import json
import re
from pathlib import Path
import importlib.resources as resources
from pilutils.basic import hex_to_rgb
__all__ = [
"parse_hex6",
"parse_hex3",
"parse_rgbfunc_int",
"parse_rgbfunc_float",
"parse_rgbfunc_percent",
"... | normal | {
"blob_id": "978f3979aee1c4361483fd61b54352e7fff8d3b3",
"index": 697,
"step-1": "<mask token>\n\n\ndef parse_hex3(hex3):\n \"\"\"Example: #a3d\"\"\"\n if (m := re.match('^#?([0-9A-Fa-f]{3})$', hex3.strip())):\n h3 = m.group(1)\n return tuple(int(c * 2, 16) for c in h3)\n raise ValueError(f... | [
7,
10,
12,
13,
14
] |
import redis
r = redis.StrictRedis()
r.set("counter", 40)
print(r.get("counter"))
print(r.incr("counter"))
print(r.incr("counter"))
print(r.get("counter"))
| normal | {
"blob_id": "b38c9357030b2eac8298743cfb4d6c4d58c99ed4",
"index": 7463,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nr.set('counter', 40)\nprint(r.get('counter'))\nprint(r.incr('counter'))\nprint(r.incr('counter'))\nprint(r.get('counter'))\n",
"step-3": "<mask token>\nr = redis.StrictRedis()\nr.set('c... | [
0,
1,
2,
3,
4
] |
from setuptools import setup
setup(
name="CoreMLModules",
version="0.1.0",
url="https://github.com/AfricasVoices/CoreMLModules",
packages=["core_ml_modules"],
setup_requires=["pytest-runner"],
install_requires=["numpy", "scikit-learn", "nltk"],
tests_require=["pytest<=3.6.4"]
)
| normal | {
"blob_id": "24cd3a1a05a1cfa638b8264fd89b36ee63b29f89",
"index": 1625,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='CoreMLModules', version='0.1.0', url=\n 'https://github.com/AfricasVoices/CoreMLModules', packages=[\n 'core_ml_modules'], setup_requires=['pytest-runner'], install_requ... | [
0,
1,
2,
3
] |
CARD_SIZE = (70, 90)
SPACING = 3 | normal | {
"blob_id": "b8ebbef7403a71d6165a5462bc08e2634b4cebc5",
"index": 4287,
"step-1": "<mask token>\n",
"step-2": "CARD_SIZE = 70, 90\nSPACING = 3\n",
"step-3": "CARD_SIZE = (70, 90)\nSPACING = 3",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
# 1.- Crear una grafica que muestre la desviacion tipica de los datos cada dia para todos los pacientes
# 2.- Crear una grafica que muestre a la vez la inflamacion maxima, media y minima para cada dia
import numpy as np
data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')
import matplotlib.pyplot as pl... | normal | {
"blob_id": "52064b518ad067c9906e7de8542d9a399076a0b5",
"index": 4214,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.plot(data.std(axis=0))\nplt.show()\nplt.plot(data.max(axis=0))\nplt.plot(data.mean(axis=0))\nplt.plot(data.min(axis=0))\n",
"step-3": "<mask token>\ndata = np.loadtxt(fname='inflamm... | [
0,
1,
2,
3,
4
] |
../../2.0.2/mpl_examples/axes_grid/simple_axesgrid2.py | normal | {
"blob_id": "73d1129418711c35046a99c1972a413357079836",
"index": 3022,
"step-1": "../../2.0.2/mpl_examples/axes_grid/simple_axesgrid2.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
"""API - Files endpoints."""
import os
import click
import cloudsmith_api
import requests
from requests_toolbelt import MultipartEncoder, MultipartEncoderMonitor
from .. import ratelimits
from ..rest import create_requests_session
from ..utils import calculate_file_md5
from .exceptions import ApiException, catch_rai... | normal | {
"blob_id": "ee03263d92372899ec1feaf3a8ea48677b053676",
"index": 6281,
"step-1": "<mask token>\n\n\ndef get_files_api():\n \"\"\"Get the files API client.\"\"\"\n return get_api_client(cloudsmith_api.FilesApi)\n\n\ndef validate_request_file_upload(owner, repo, filepath, md5_checksum=None):\n \"\"\"Valid... | [
2,
3,
4,
5,
6
] |
import torch
import torchvision
from torch import nn
def get_resnet18(pre_imgnet=False, num_classes=64):
model = torchvision.models.resnet18(pretrained=pre_imgnet)
model.fc = nn.Linear(512, 64)
return model
| normal | {
"blob_id": "8e05b2723d8c50354e785b4bc7c5de8860aa706d",
"index": 5355,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_resnet18(pre_imgnet=False, num_classes=64):\n model = torchvision.models.resnet18(pretrained=pre_imgnet)\n model.fc = nn.Linear(512, 64)\n return model\n",
"step-3"... | [
0,
1,
2
] |
def add(a, b):
print "ADDING %d + %d" % (a, b)
return a + b
def subtract(a, b):
print "SUBTRACTING %d - %d" %(a, b)
return a - b
def multipy(a, b):
print "MULTIPLYING %d * %d" % (a, b)
return a * b
def divide(a, b):
print "DIVIDING %d / %d" % (a, b)
return a / b
print "Let's do some... | normal | {
"blob_id": "b4b80e40d12486881e37dd7ddeeef9c76417ebd9",
"index": 5906,
"step-1": "def add(a, b):\n print \"ADDING %d + %d\" % (a, b)\n return a + b\n\ndef subtract(a, b):\n print \"SUBTRACTING %d - %d\" %(a, b)\n return a - b\n\ndef multipy(a, b):\n print \"MULTIPLYING %d * %d\" % (a, b)\n retu... | [
0
] |
'''Mock classes that imitate idlelib modules or classes.
Attributes and methods will be added as needed for tests.
'''
from idlelib.idle_test.mock_tk import Text
class Editor:
'''Minimally imitate EditorWindow.EditorWindow class.
'''
def __init__(self, flist=None, filename=None, key=None, root=None):
... | normal | {
"blob_id": "3b7c30718838a164eaf3aa12cd7b6a68930346f8",
"index": 8604,
"step-1": "<mask token>\n\n\nclass UndoDelegator:\n <mask token>\n\n def undo_block_start(*args):\n pass\n\n def undo_block_stop(*args):\n pass\n",
"step-2": "<mask token>\n\n\nclass Editor:\n <mask token>\n\n d... | [
3,
7,
8,
9,
10
] |
import numpy as np
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
img = cv2.imread('modi.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
#Write the for loop code h... | normal | {
"blob_id": "759ff4cc123e85bdc8c1457bb521cd35841956cd",
"index": 482,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('img', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv... | [
0,
1,
2,
3,
4
] |
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