index int64 0 10k | blob_id stringlengths 40 40 | step-1 stringlengths 13 984k | step-2 stringlengths 6 1.23M ⌀ | step-3 stringlengths 15 1.34M ⌀ | step-4 stringlengths 30 1.34M ⌀ | step-5 stringlengths 64 1.2M ⌀ | step-ids listlengths 1 5 |
|---|---|---|---|---|---|---|---|
0 | aff1a9263e183610f403a4d6a7f27b45eacb7ff2 | <mask token>
| <mask token>
print(name * 1000)
| name = 'valentina '
print(name * 1000)
| null | null | [
0,
1,
2
] |
1 | eabf06481509962652812af67ad59da5cfe30fae | <mask token>
| <mask token>
__all__ = ('__title__', '__summary__', '__version__', '__author__',
'__license__', '__copyright__')
__title__ = 'mupub'
__summary__ = 'Musical score publishing utility for the Mutopia Project'
<mask token>
__version__ = '1.0.8'
__author__ = 'Glen Larsen, Chris Sawer'
__author_email__ = 'glenl.glx@gmail... | <mask token>
__all__ = ('__title__', '__summary__', '__version__', '__author__',
'__license__', '__copyright__')
__title__ = 'mupub'
__summary__ = 'Musical score publishing utility for the Mutopia Project'
<mask token>
__version__ = '1.0.8'
__author__ = 'Glen Larsen, Chris Sawer'
__author_email__ = 'glenl.glx@gmail... | """ mupub module.
"""
__all__ = (
'__title__', '__summary__', '__version__',
'__author__', '__license__', '__copyright__',
)
__title__ = 'mupub'
__summary__ = 'Musical score publishing utility for the Mutopia Project'
"""Versioning:
This utility follows a MAJOR . MINOR . EDIT format. Upon a major
release, t... | null | [
0,
1,
2,
3
] |
2 | 54f0ed5f705d5ada28721301f297b2b0058773ad | <mask token>
class _GenericBot:
<mask token>
def __init__(self, pos, inventory=None):
"""Initialize with an empty inventory.
inventory is a dictionary. If None, an empty one will be used."""
if inventory is None:
self._inventory = {}
else:
self._invent... | <mask token>
class _GenericBot:
<mask token>
def __init__(self, pos, inventory=None):
"""Initialize with an empty inventory.
inventory is a dictionary. If None, an empty one will be used."""
if inventory is None:
self._inventory = {}
else:
self._invent... | <mask token>
class _GenericBot:
<mask token>
def __init__(self, pos, inventory=None):
"""Initialize with an empty inventory.
inventory is a dictionary. If None, an empty one will be used."""
if inventory is None:
self._inventory = {}
else:
self._invent... | <mask token>
class _GenericBot:
<mask token>
def __init__(self, pos, inventory=None):
"""Initialize with an empty inventory.
inventory is a dictionary. If None, an empty one will be used."""
if inventory is None:
self._inventory = {}
else:
self._invent... | """Module for the bot"""
from copy import deepcopy
from time import sleep
import mcpi.minecraft as minecraft
from mcpi.vec3 import Vec3
import mcpi.block as block
from search import SearchProblem, astar, bfs
from singleton import singleton
_AIR = block.AIR.id
_WATER = block.WATER.id
_LAVA = block.LAVA.id
_BEDROCK =... | [
52,
53,
58,
60,
79
] |
3 | 45969b346d6d5cbdef2f5d2f74270cf12024072d | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('search', '0003_auto_20230209_1441')]
operations = [migrations.CreateModel(name='SearchSettings', fields=[(
'id', models.AutoField(auto_created=True, primary_key=True,
serialize=False, verbose_name='ID'))], options={'permissi... | from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('search', '0003_auto_20230209_1441')]
operations = [migrations.CreateModel(name='SearchSettings', fields=[(
'id', models.AutoField(auto_created=True, primary_key=True,
serialize=False, verbose_name... | # Generated by Django 4.1.9 on 2023-06-29 16:11
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("search", "0003_auto_20230209_1441"),
]
operations = [
migrations.CreateModel(
name="SearchSettings",
fields=[
... | [
0,
1,
2,
3,
4
] |
4 | 3fbf1768a2fe78df591c49490dfce5fb374e7fc2 | from functools import wraps
import os
def restoring_chdir(fn):
#XXX:dc: This would be better off in a neutral module
@wraps(fn)
def decorator(*args, **kw):
try:
path = os.getcwd()
return fn(*args, **kw)
finally:
os.chdir(path)
return decorator
clas... | null | null | null | null | [
0
] |
5 | 67b967b688aeac1270eee836e0f6e6b3555b933e | <mask token>
| <mask token>
if u_avg < u_bat_min:
print('proper shut down of the machine due to low battery')
else:
print('tout va bien dormez braves gens')
| <mask token>
pidcmes = Pidcmes()
u_bat_min = 3.7
n_moy = 20
stop_run = False
u_avg = pidcmes.get_tension(n_moy)
if u_avg < u_bat_min:
print('proper shut down of the machine due to low battery')
else:
print('tout va bien dormez braves gens')
| <mask token>
import time
import datetime as dt
from subprocess import call
from pidcmes_lib import Pidcmes
pidcmes = Pidcmes()
u_bat_min = 3.7
n_moy = 20
stop_run = False
u_avg = pidcmes.get_tension(n_moy)
if u_avg < u_bat_min:
print('proper shut down of the machine due to low battery')
else:
print('tout va bie... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This program is run at regular intervals to check the battery charge status of the uninterruptible power supply.
In our case, it is a LiPo battery with a nominal voltage of 3.7 volts. By setting the voltage for the
Raspberry PI shutdown procedure at 3.7 V,we ensure th... | [
0,
1,
2,
3,
4
] |
6 | c59707ba07c1659d94684c54cdd7bb2658cba935 | <mask token>
class H2OShuffleSplit(H2OBaseShuffleSplit):
<mask token>
def _iter_indices(self, frame, y=None):
"""Iterate the indices.
Parameters
----------
frame : H2OFrame
The frame to split
y : string, optional (default=None)
The column to ... | <mask token>
class H2OBaseShuffleSplit(six.with_metaclass(ABCMeta)):
"""Base class for H2OShuffleSplit and H2OStratifiedShuffleSplit. This
is used for ``h2o_train_test_split`` in strategic train/test splits of
H2OFrames. Implementing subclasses should override ``_iter_indices``.
Parameters
------... | <mask token>
def check_cv(cv=3):
"""Checks the ``cv`` parameter to determine
whether it's a valid int or H2OBaseCrossValidator.
Parameters
----------
cv : int or H2OBaseCrossValidator, optional (default=3)
The number of folds or the H2OBaseCrossValidator
instance.
Returns
... | <mask token>
def _build_repr(self):
cls = self.__class__
init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
init_signature = signature(init)
if init is object.__init__:
args = []
else:
args = sorted([p.name for p in init_signature.parameters.values() if
p... | from __future__ import division, print_function, absolute_import
import numbers
import warnings
from abc import ABCMeta, abstractmethod
import numpy as np
from .base import check_frame
from skutil.base import overrides
from sklearn.externals import six
from sklearn.base import _pprint
from sklearn.utils.fixes import si... | [
21,
29,
40,
43,
47
] |
7 | 41cfd558824b6561114a48a694b1e6e6a7cb8c05 | <mask token>
| <mask token>
def app(page):
if not login_status():
title_container = st.empty()
remail_input_container = st.empty()
rpw_input_container = st.empty()
rregister_button_container = st.empty()
email = remail_input_container.text_input('Email ')
password = rpw_input_cont... | import streamlit as st
from streamlit.components.v1 import components
from streamlit.report_thread import get_report_ctx
from util.session import *
from multipage import MultiPage
from pages import register
def app(page):
if not login_status():
title_container = st.empty()
remail_input_container =... | import streamlit as st
from streamlit.components.v1 import components
from streamlit.report_thread import get_report_ctx
from util.session import *
from multipage import MultiPage
from pages import register
def app(page):
if not login_status():
title_container = st.empty()
remail_input_container = ... | null | [
0,
1,
2,
3
] |
8 | f2bb44600f011a205c71985ad94c18f7e058634f | <mask token>
def from_url(url: str) ->Image.Image:
api_response = requests.get(url).content
response_bytes = BytesIO(api_response)
return Image.open(response_bytes)
def from_file(path: str) ->Union[Image.Image, None]:
if os.path.exists(path):
return Image.open(path)
else:
return ... | <mask token>
def get_img_from_file_or_url(img_format: str='JPEG') ->Callable[[str, str],
Image.Image]:
def _apply(filepath: str, url: str) ->Image.Image:
img = from_file(filepath)
if img is None:
img = from_url(url)
img.save(filepath, img_format)
return img.con... | <mask token>
def get_img_from_file_or_url(img_format: str='JPEG') ->Callable[[str, str],
Image.Image]:
def _apply(filepath: str, url: str) ->Image.Image:
img = from_file(filepath)
if img is None:
img = from_url(url)
img.save(filepath, img_format)
return img.con... | import os
import requests
from PIL import Image
from io import BytesIO
import csv
from typing import Iterable, List, Tuple, Dict, Callable, Union, Collection
def get_img_from_file_or_url(img_format: str='JPEG') ->Callable[[str, str],
Image.Image]:
def _apply(filepath: str, url: str) ->Image.Image:
im... | import os
import requests
from PIL import Image
from io import BytesIO
import csv
from typing import Iterable, List, Tuple, Dict, Callable, Union, Collection
# pull the image from the api endpoint and save it if we don't have it, else load it from disk
def get_img_from_file_or_url(img_format: str = 'JPEG') -> Callabl... | [
2,
3,
4,
5,
6
] |
9 | 302605d8bb45b1529742bf9441d476f0276085b9 | <mask token>
class MyMainWindow(QMainWindow):
<mask token>
<mask token>
def initConnect(self):
self.dataFileChooseButton.clicked.connect(self.chooseData)
self.dataFileChooseButtonT.clicked.connect(self.chooseData)
self.dataLossSimulateSettingButton.clicked.connect(self.
... | <mask token>
class MyMainWindow(QMainWindow):
<mask token>
def initUI(self):
self.statusBar().showMessage('Ready')
dataModule = QVBoxLayout()
self.dataFileChooseButton = QPushButton('选择数据')
self.dataFileChooseButton.setFont(QFont('微软雅黑', 16))
self.dataLossSimulateSetti... | <mask token>
class MyMainWindow(QMainWindow):
<mask token>
def initUI(self):
self.statusBar().showMessage('Ready')
dataModule = QVBoxLayout()
self.dataFileChooseButton = QPushButton('选择数据')
self.dataFileChooseButton.setFont(QFont('微软雅黑', 16))
self.dataLossSimulateSetti... | <mask token>
class MyMainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.windowLength = 1250
self.windowHigh = 900
self.fname = dict()
self.fname['New'] = None
self.fname['Tra'] = None
self.dataLossRate = dict()
self.dataSetLossValu... | import sys
from PyQt5.QtWidgets import (QMainWindow, QWidget, QHBoxLayout, QVBoxLayout, QFrame,
QSplitter, QStyleFactory, QApplication, QPushButton, QTextEdit, QLabel, QFileDialog, QMessageBox)
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QFont, QColor
import myLoadData
from UIPack import setLossParameterDia... | [
9,
11,
12,
15,
18
] |
10 | 5d9c8e235385ff53c7510994826ff3a04e4a5888 | <mask token>
class Model:
def __init__(self, dim_word, dim_char, dropout, learning_rate,
hidden_size_char, hidden_size_word, num_layers):
"""
:param dim_word: 词的维度
:param dim_char: 字符维度
:param dropout: dropout
:param learning_rate: 学习率
:param hidden_size_ch... | <mask token>
class Model:
def __init__(self, dim_word, dim_char, dropout, learning_rate,
hidden_size_char, hidden_size_word, num_layers):
"""
:param dim_word: 词的维度
:param dim_char: 字符维度
:param dropout: dropout
:param learning_rate: 学习率
:param hidden_size_ch... | <mask token>
class Model:
def __init__(self, dim_word, dim_char, dropout, learning_rate,
hidden_size_char, hidden_size_word, num_layers):
"""
:param dim_word: 词的维度
:param dim_char: 字符维度
:param dropout: dropout
:param learning_rate: 学习率
:param hidden_size_ch... | <mask token>
class Model:
def __init__(self, dim_word, dim_char, dropout, learning_rate,
hidden_size_char, hidden_size_word, num_layers):
"""
:param dim_word: 词的维度
:param dim_char: 字符维度
:param dropout: dropout
:param learning_rate: 学习率
:param hidden_size_ch... | """
@file : 001-rnn+lstm+crf.py
@author: xiaolu
@time : 2019-09-06
"""
import re
import numpy as np
import tensorflow as tf
from sklearn.metrics import classification_report
class Model:
def __init__(self, dim_word, dim_char, dropout, learning_rate,
hidden_size_char, hidden_size_word, num_l... | [
5,
8,
10,
11,
13
] |
11 | 54e04d740ef46fca04cf4169d2e7c05083414bd8 | <mask token>
class Player:
<mask token>
<mask token>
<mask token>
class Bullet:
def __init__(self, color):
self.x = 0
self.y = 0
self.angle = 0
self.color = color
def draw(self):
pygame.draw.rect(scr, self.color, pygame.Rect(self.x, self.y, 5, 5))
clas... | <mask token>
class Player:
def __init__(self):
self.x = 275
self.y = 275
self.image = pygame.image.load('player.jpg')
self.image1 = pygame.image.load('hearts.png')
self.lives = 5
def draw(self):
scr.blit(self.image, (self.x, self.y))
def rotate(self, x, y... | <mask token>
pygame.init()
scr = pygame.display.set_mode((700, 700))
enemies = []
hit = []
class Player:
def __init__(self):
self.x = 275
self.y = 275
self.image = pygame.image.load('player.jpg')
self.image1 = pygame.image.load('hearts.png')
self.lives = 5
def draw(se... | import random
import math
import time
import pygame
pygame.init()
scr = pygame.display.set_mode((700, 700))
enemies = []
hit = []
class Player:
def __init__(self):
self.x = 275
self.y = 275
self.image = pygame.image.load('player.jpg')
self.image1 = pygame.image.load('hearts.png')
... | import random
import math
import time
import pygame
pygame.init()
scr = pygame.display.set_mode((700,700))
enemies = []
#music = pygame.mixer.music.load('ENERGETIC CHIPTUNE Thermal - Evan King.mp3')
#pygame.mixer.music.play(-1)
hit = []
class Player:
def __init__(self):
self.x = 275
sel... | [
14,
17,
19,
20,
21
] |
12 | 0a7ffc027511d5fbec0076f6b25a6e3bc3dfdd9b | '''
Given a sorted array and a target value, return the index if the target is found.
If not, return the index where it would be if it were inserted in order.
You may assume no duplicates in the array.
Here are few examples.
[1,3,5,6], 5 -> 2
[1,3,5,6], 2 -> 1
[1,3,5,6], 7 -> 4
[1,3,5,6], 0 -> 0
'''
class Solution(o... | null | null | null | null | [
0
] |
13 | 2cbce618d1ec617d1c7dc0e9792b6a49361ec5a4 | <mask token>
| def mais_populoso(dic):
p = 0
sp = 0
for t, i in dic.items():
for m in dic[t].values():
p += m
if p > sp:
sp = p
x = t
return x
| null | null | null | [
0,
1
] |
14 | 2092ead8b8f268a22711b8af8052241c1ac00c15 | <mask token>
| <mask token>
print('%d시간에 %d%s 벌었습니다.' % (1, wage * 1, '달러'))
print('%d시간에 %d%s 벌었습니다.' % (5, wage * 5, '달러'))
print('%d시간에 %.1f%s 벌었습니다' % (1, 5710.8, '원'))
print('%d시간에 %.1f%s 벌었습니다' % (5, 28554.0, '원'))
| wage = 5
print('%d시간에 %d%s 벌었습니다.' % (1, wage * 1, '달러'))
print('%d시간에 %d%s 벌었습니다.' % (5, wage * 5, '달러'))
print('%d시간에 %.1f%s 벌었습니다' % (1, 5710.8, '원'))
print('%d시간에 %.1f%s 벌었습니다' % (5, 28554.0, '원'))
|
wage=5
print("%d시간에 %d%s 벌었습니다." %(1, wage*1, "달러"))
print("%d시간에 %d%s 벌었습니다." %(5, wage*5, "달러"))
print("%d시간에 %.1f%s 벌었습니다" %(1,5710.8,"원"))
print("%d시간에 %.1f%s 벌었습니다" %(5, 28554.0, "원"))
| null | [
0,
1,
2,
3
] |
15 | b5cbb73c152dd60e9063d5a19f6182e2264fec6d | #!/usr/bin/python
# coding=UTF-8
import sys
import subprocess
import os
def printReportTail(reportHtmlFile):
reportHtmlFile.write("""
</body>
</html>
""")
def printReportHead(reportHtmlFile):
reportHtmlFile.write("""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" ... | null | null | null | null | [
0
] |
16 | 805fc9a26650f85227d14da972311ffbd9dbd555 | <mask token>
| class Date:
<mask token>
| class Date:
def __init__(self, strDate):
strDate = strDate.split('.')
self.day = strDate[0]
self.month = strDate[1]
self.year = strDate[2]
| null | null | [
0,
1,
2
] |
17 | a7218971b831e2cfda9a035eddb350ecf1cdf938 | #!/usr/bin/python
# encoding: utf-8
#
# In case of reuse of this source code please do not remove this copyright.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Licen... | null | null | null | null | [
0
] |
18 | 038ccba05113fb7f2f589eaa7345df53cb59a5af | <mask token>
| <mask token>
def train(num_epochs=30):
lossfunction = nn.CrossEntropyLoss()
trainset = TrainDataSet()
model = BiAffineSrlModel(vocabs=trainset.vocabs)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_f = F... | <mask token>
config.add_option('-m', '--mode', dest='mode', default='train', type=
'string', help='[train|eval|pred]', action='store')
config.add_option('--seed', dest='seed', default=1, type='int', help=
'torch random seed', action='store')
def train(num_epochs=30):
lossfunction = nn.CrossEntropyLoss()
... | import sys
import torch
from torch import nn, autograd
import config
import time
import copy
import progressbar as pb
from dataset import TrainDataSet
from model import BiAffineSrlModel
from fscore import FScore
config.add_option('-m', '--mode', dest='mode', default='train', type=
'string', help='[train|eval|pred]'... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import torch
from torch import nn, autograd
import config
import time
import copy
import progressbar as pb
from dataset import TrainDataSet
from model import BiAffineSrlModel
from fscore import FScore
config.add_option('-m', '--mode', dest='mode', default='train... | [
0,
1,
2,
3,
4
] |
19 | b5180a2dbe1f12e1bbc92874c67ea99c9a84a9ed | <mask token>
| <mask token>
for card in cards:
try:
number = int(card)
if number % 2 == 0:
print(card, 'is an even card.')
except ValueError:
print(card, 'can not be divided')
| cards = ['2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K', 'A']
for card in cards:
try:
number = int(card)
if number % 2 == 0:
print(card, 'is an even card.')
except ValueError:
print(card, 'can not be divided')
|
# print all cards with even numbers.
cards = ["2", "3", "4", "5", "6", "7", "8", "9", "10", "J", "Q", "K", "A"]
for card in cards:
try:
number = int(card)
if number % 2 == 0: # modulo operator
print(card, "is an even card.")
except ValueError:
print (card, "can not be divi... | null | [
0,
1,
2,
3
] |
20 | a045423edd94d985dfc9660bcfe4a88c61bf4574 | #Script start
print"This is the two number subtraction python program."
a = 9
b = 2
c = a - b
print c
# Scrip close
| null | null | null | null | [
0
] |
21 | 13c9f0f58ec6da317c3802f594bb0db7c275dee9 | <mask token>
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
classIndex = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path, img), cv2.
IMREAD_... | <mask token>
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)
plt.imshow(img_array, cmap='gray')
plt.show()
print(img_array)
print(img_array.shape)
bre... | <mask token>
DATADIR = 'content/PetImages'
CATEGORIES = ['Cat', 'Dog']
img_array = []
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)
plt.imshow(img_array, cmap='gray')
plt.s... | <mask token>
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
import pickle
import random
import datetime
import tensorflow as tf
from tensorflow.python.keras.datasets import cifar10
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models imp... | '''
!pip install wget
from zipfile import ZipFile
import wget
print('Beginning file downlaod with wget module')
url = 'https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip'
wget.download(url, 'sample_data/')
print('2. Extract all files in ZIP to different dir... | [
1,
3,
4,
5,
6
] |
22 | 95c5971a102fb2ed84ab0de0471278d0167d8359 | <mask token>
| <mask token>
def matrix_divided(matrix, div):
"""Divides a Matrix
Args:
matrix: A list of lists of ints or floats
div: a non zero int or float
Exceptions:
TypeError: if the matrix and/or div is not as stated or the matrix elements
are not of the same size
ZeroDivisionError... | #!/usr/bin/python3
"""1. Divide a matrix """
def matrix_divided(matrix, div):
"""Divides a Matrix
Args:
matrix: A list of lists of ints or floats
div: a non zero int or float
Exceptions:
TypeError: if the matrix and/or div is not as stated or the matrix elements
are not of the... | null | null | [
0,
1,
2
] |
23 | 5fb998fa761b989c6dd423634824197bade4f8a5 | <mask token>
| <mask token>
def abbreviation(a, b):
m, n = len(a), len(b)
dp = [([False] * (m + 1)) for _ in range(n + 1)]
dp[0][0] = True
for i in range(n + 1):
for j in range(1, m + 1):
if a[j - 1] == b[i - 1]:
dp[i][j] = dp[i - 1][j - 1]
elif a[j - 1].upper() == b[i... | <mask token>
def abbreviation(a, b):
m, n = len(a), len(b)
dp = [([False] * (m + 1)) for _ in range(n + 1)]
dp[0][0] = True
for i in range(n + 1):
for j in range(1, m + 1):
if a[j - 1] == b[i - 1]:
dp[i][j] = dp[i - 1][j - 1]
elif a[j - 1].upper() == b[i... | <mask token>
import math
import os
import random
import re
import sys
def abbreviation(a, b):
m, n = len(a), len(b)
dp = [([False] * (m + 1)) for _ in range(n + 1)]
dp[0][0] = True
for i in range(n + 1):
for j in range(1, m + 1):
if a[j - 1] == b[i - 1]:
dp[i][j] = ... | """
You can perform the following operations on the string, :
Capitalize zero or more of 's lowercase letters.
Delete all of the remaining lowercase letters in .
Given two strings, and , determine if it's possible to make equal to as described. If so, print YES on a new line. Otherwise, print NO.
For example, give... | [
0,
1,
2,
3,
4
] |
24 | 5ed439a2a7cfb9c941c40ea0c5eba2851a0f2855 | <mask token>
class Stack(object):
def __init__(self):
self.arr = []
def push(self, val):
self.arr.append(val)
def pop(self):
if len(self.arr):
return self.arr.pop()
def inc(self, e, k):
count = min(len(self.arr), e)
for i in range(count):
... | <mask token>
class Stack(object):
def __init__(self):
self.arr = []
def push(self, val):
self.arr.append(val)
def pop(self):
if len(self.arr):
return self.arr.pop()
def inc(self, e, k):
count = min(len(self.arr), e)
for i in range(count):
... | <mask token>
class Stack(object):
def __init__(self):
self.arr = []
def push(self, val):
self.arr.append(val)
def pop(self):
if len(self.arr):
return self.arr.pop()
def inc(self, e, k):
count = min(len(self.arr), e)
for i in range(count):
... | <mask token>
class Stack(object):
def __init__(self):
self.arr = []
def push(self, val):
self.arr.append(val)
def pop(self):
if len(self.arr):
return self.arr.pop()
def inc(self, e, k):
count = min(len(self.arr), e)
for i in range(count):
... | #!/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... | [
5,
6,
7,
8,
10
] |
25 | 39f9341313e29a22ec5e05ce9371bf65e89c91bd | <mask token>
| <mask token>
for numbers in n_list:
n_dict = {}
for n in numbers:
if n in n_dict:
n_dict[n] += 1
else:
n_dict[n] = 1
mode = []
if len(n_dict) == 1 or len(n_dict) == len(numbers):
print(numbers, '= 없다')
else:
mode_count = max(n_dict.values())
... | <mask token>
n_list = [[12, 17, 19, 17, 23], [26, 37, 26, 37, 91], [28, 30, 32, 34, 144],
[10, 10, 10, 10, 10]]
for numbers in n_list:
n_dict = {}
for n in numbers:
if n in n_dict:
n_dict[n] += 1
else:
n_dict[n] = 1
mode = []
if len(n_dict) == 1 or len(n_dict)... | """
리스트에 있는 숫자들의 최빈값을 구하는 프로그램을 만들어라.
[12, 17, 19, 17, 23] = 17
[26, 37, 26, 37, 91] = 26, 37
[28, 30, 32, 34, 144] = 없다
최빈값 : 자료의 값 중에서 가장 많이 나타난 값
① 자료의 값이 모두 같거나 모두 다르면 최빈값은 없다.
② 자료의 값이 모두 다를 때, 도수가 가장 큰 값이 1개 이상 있으면 그 값은 모두 최빈값이다.
"""
n_list = [[12, 17, 19, 17, 23],
[26, 37, 26, 37, 91],
[28,... | null | [
0,
1,
2,
3
] |
26 | 312cc666c88fcd22882c49598db8c5e18bd3dae1 | <mask token>
| <mask token>
try:
from Cython.Build import cythonize
cython = True
except ImportError:
cython = False
if platform == 'darwin':
extra_compile_args = ['-O3', '-pthread', '-funroll-loops', '-std=c++0x',
'-stdlib=libc++', '-mmacosx-version-min=10.7']
else:
extra_compile_args = ['-O3', '-pthread'... | <mask token>
cython = True
try:
from Cython.Build import cythonize
cython = True
except ImportError:
cython = False
if platform == 'darwin':
extra_compile_args = ['-O3', '-pthread', '-funroll-loops', '-std=c++0x',
'-stdlib=libc++', '-mmacosx-version-min=10.7']
else:
extra_compile_args = ['-O... | from setuptools import setup, find_packages
from setuptools.extension import Extension
from sys import platform
cython = True
try:
from Cython.Build import cythonize
cython = True
except ImportError:
cython = False
if platform == 'darwin':
extra_compile_args = ['-O3', '-pthread', '-funroll-loops', '-std... | from setuptools import setup, find_packages
from setuptools.extension import Extension
from sys import platform
cython = True
try:
from Cython.Build import cythonize
cython = True
except ImportError:
cython = False
# Define the C++ extension
if platform == "darwin":
extra_compile_args = ['-O3', '-pthread',... | [
0,
1,
2,
3,
4
] |
27 | 2aec0581413d4fb0ffb4090231fde0fed974bf18 | <mask token>
| <mask token>
with open('./roc.txt', 'r') as fin:
with open('./roc_shuffle.txt', 'w') as fout:
tmp = []
for k, line in enumerate(fin):
i = k + 1
if i % 6 == 0:
idx = [0] + np.random.permutation(range(1, 5)).tolist()
for sen in np.take(tmp, idx).... | import numpy as np
import random
with open('./roc.txt', 'r') as fin:
with open('./roc_shuffle.txt', 'w') as fout:
tmp = []
for k, line in enumerate(fin):
i = k + 1
if i % 6 == 0:
idx = [0] + np.random.permutation(range(1, 5)).tolist()
for sen i... | import numpy as np
import random
with open("./roc.txt", "r") as fin:
with open("./roc_shuffle.txt", "w") as fout:
tmp = []
for k, line in enumerate(fin):
i = k + 1
if i % 6 == 0:
idx = [0] + np.random.permutation(range(1,5)).tolist()
for sen i... | null | [
0,
1,
2,
3
] |
28 | 4f13e2858d9cf469f14026808142886e5c3fcc85 | <mask token>
| class Solution:
<mask token>
<mask token>
| class Solution:
def merge(self, nums1, m, nums2, n):
"""
Do not return anything, modify nums1 in-place instead.
"""
if n == 0:
nums1 = nums1
if nums1[m - 1] <= nums2[0]:
for i in range(n):
nums1[m + i] = nums2[i]
elif nums1[0] ... | class Solution:
def merge(self, nums1, m, nums2, n):
"""
Do not return anything, modify nums1 in-place instead.
"""
if n == 0:
nums1 = nums1
if nums1[m - 1] <= nums2[0]:
for i in range(n):
nums1[m + i] = nums2[i]
elif nums1[0] ... | class Solution:
def merge(self, nums1, m, nums2, n):
"""
Do not return anything, modify nums1 in-place instead.
"""
if n == 0:
nums1 = nums1
if nums1[m-1] <= nums2[0]:
for i in range(n):
nums1[m+i] = nums2[i]
... | [
0,
1,
2,
3,
4
] |
29 | 57967f36a45bb3ea62708bbbb5b2f4ddb0f4bb16 | <mask token>
def _mako_get_namespace(context, name):
try:
return context.namespaces[__name__, name]
except KeyError:
_mako_generate_namespaces(context)
return context.namespaces[__name__, name]
<mask token>
def _mako_inherit(template, context):
_mako_generate_namespaces(context... | <mask token>
def _mako_get_namespace(context, name):
try:
return context.namespaces[__name__, name]
except KeyError:
_mako_generate_namespaces(context)
return context.namespaces[__name__, name]
def _mako_generate_namespaces(context):
pass
def _mako_inherit(template, context):
... | <mask token>
UNDEFINED = runtime.UNDEFINED
__M_dict_builtin = dict
__M_locals_builtin = locals
_magic_number = 10
_modified_time = 1428612037.145222
_enable_loop = True
_template_filename = (
'C:\\Users\\Cody\\Desktop\\Heritage\\chf\\templates/account.rentalcart.html'
)
_template_uri = '/account.rentalcart.html... | from mako import runtime, filters, cache
UNDEFINED = runtime.UNDEFINED
__M_dict_builtin = dict
__M_locals_builtin = locals
_magic_number = 10
_modified_time = 1428612037.145222
_enable_loop = True
_template_filename = (
'C:\\Users\\Cody\\Desktop\\Heritage\\chf\\templates/account.rentalcart.html'
)
_template_uri... | # -*- coding:ascii -*-
from mako import runtime, filters, cache
UNDEFINED = runtime.UNDEFINED
__M_dict_builtin = dict
__M_locals_builtin = locals
_magic_number = 10
_modified_time = 1428612037.145222
_enable_loop = True
_template_filename = 'C:\\Users\\Cody\\Desktop\\Heritage\\chf\\templates/account.rentalcart.html'
_t... | [
3,
5,
6,
7,
8
] |
30 | 5771f49ad5254588f1683a8d45aa81ce472bb562 |
def prime_sieve(n):
if n==2: return [2]
elif n<2: return []
s=range(3,n+1,2)
mroot = n ** 0.5
half=(n+1)/2-1
i=0
m=3
while m <= mroot:
if s[i]:
j=(m*m-3)/2
s[j]=0
while j<half:
s[j]=0
j+=m
i=i+1
m=2*i+3
return [2]+[x for x in s if x]
ps = prime_sieve(1000000)
def get_primes_upto(n):
... | null | null | null | null | [
0
] |
31 | 44d87f112ab60a202e4c8d64d7aec6f4f0d10578 | <mask token>
class IssueTitleFactory(factory.Factory):
"""
``issue`` must be provided
"""
FACTORY_FOR = models.IssueTitle
language = factory.SubFactory(LanguageFactory)
title = u'Bla'
class IssueFactory(factory.Factory):
FACTORY_FOR = models.Issue
total_documents = 16
number = fa... | <mask token>
class GroupFactory(factory.Factory):
<mask token>
<mask token>
class SubjectCategoryFactory(factory.Factory):
FACTORY_FOR = models.SubjectCategory
term = 'Acoustics'
class StudyAreaFactory(factory.Factory):
FACTORY_FOR = models.StudyArea
study_area = 'Health Sciences'
class ... | <mask token>
class UserFactory(factory.Factory):
<mask token>
@classmethod
def _setup_next_sequence(cls):
try:
return cls._associated_class.objects.values_list('id', flat=True
).order_by('-id')[0] + 1
except IndexError:
return 0
<mask token>
... | <mask token>
_HERE = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(_HERE, 'xml_samples', '0034-8910-rsp-48-2-0216.xml')
) as xml_file:
SAMPLE_XML = xml_file.read()
SAMPLE_TIFF_IMAGE = open(os.path.join(_HERE, 'image_test',
'sample_tif_image.tif'))
with open(os.path.join(_HERE, 'xml_sampl... | # coding: utf-8
import os
import factory
import datetime
from journalmanager import models
from django.contrib.auth.models import Group
from django.core.files.base import File
_HERE = os.path.dirname(os.path.abspath(__file__))
with open(os.path.join(_HERE, 'xml_samples', '0034-8910-rsp-48-2-0216.xml')) as xml_fil... | [
22,
39,
42,
45,
47
] |
32 | 81dfdf0479fc1f136fa5153840d8c7015f9db676 | <mask token>
| <mask token>
loops(loop, phoneNumber, message)
| <mask token>
phoneNumber = 'fill the number'
message = 'fill with ur message'
loop = 1
loops(loop, phoneNumber, message)
| from theMachine import loops
phoneNumber = 'fill the number'
message = 'fill with ur message'
loop = 1
loops(loop, phoneNumber, message)
| # required !!!
# pip install selenium
# pip install webdriver-manager
from theMachine import loops
# fill the number and message
# you can fill the number with array
phoneNumber = "fill the number"
message = "fill with ur message"
loop = 1 # this how many u want to loop
loops(loop, phoneNumber, message)... | [
0,
1,
2,
3,
4
] |
33 | 24de4f486d4e976850e94a003f8d9cbe3e518402 | <mask token>
| <mask token>
for x in a:
b.append(int(x))
print(b)
<mask token>
for i in range(l):
s = len(b[:i])
for j in range(s):
if b[s] < b[j]:
c = b[s]
b.pop(s)
b.insert(b.index(b[j]), c)
print(b, b[:i], b[s])
| a = input('Enter number')
a = a.split()
b = []
for x in a:
b.append(int(x))
print(b)
l = len(b)
c = 0
s = 0
for i in range(l):
s = len(b[:i])
for j in range(s):
if b[s] < b[j]:
c = b[s]
b.pop(s)
b.insert(b.index(b[j]), c)
print(b, b[:i], b[s])
| a= input("Enter number")
a= a.split()
b=[]
for x in a:
b.append(int(x))
print(b)
l=len(b)
c=0
s=0
for i in range(l):
s=len(b[:i])
for j in range(s):
if b[s]<b[j]:
c=b[s]
b.pop(s)
b.insert(b.index(b[j]),c)
print(b,b[:i],b[s])
| null | [
0,
1,
2,
3
] |
34 | 0ecd2a298203365b20b2369a99c3c1d7c0646f19 | # coding: utf-8
#ack program with the ackermann_function
""" ackermann_function """
def ack(m,n):
#n+1 if m = 0
if m is 0:
return n + 1
#A(m−1, 1) if m > 0 and n = 0
if m > 0 and n is 0:
return ack(m-1, 1)
#A(m−1, A(m, n−1)) if m > 0 and n > 0
if m > 0 and n > 0:
re... | null | null | null | null | [
0
] |
35 | a98be930058269a6adbc9a28d1c0ad5d9abba136 | <mask token>
def nums(phrase, morph=pymorphy2.MorphAnalyzer()):
""" согласование существительных с числительными, стоящими перед ними """
phrase = phrase.replace(' ', ' ').replace(',', ' ,')
numeral = ''
new_phrase = []
for word in phrase.split(' '):
if 'NUMB' in morph.parse(word)[0].tag:... | <mask token>
def play_wav(src):
wav = pyglet.media.load(sys.path[0] + '\\src\\wav\\' + src + '.wav')
wav.play()
time.sleep(wav.duration)
<mask token>
def nums(phrase, morph=pymorphy2.MorphAnalyzer()):
""" согласование существительных с числительными, стоящими перед ними """
phrase = phrase.rep... | <mask token>
def play_wav(src):
wav = pyglet.media.load(sys.path[0] + '\\src\\wav\\' + src + '.wav')
wav.play()
time.sleep(wav.duration)
def play_wav_inline(src):
wav = pyglet.media.load(sys.path[0] + '\\src\\wav\\' + src + '.wav')
wav.play()
<mask token>
def nums(phrase, morph=pymorphy2.Mor... | import sys
import time
import pymorphy2
import pyglet
import pyttsx3
import threading
import warnings
import pytils
warnings.filterwarnings('ignore')
<mask token>
rounds, breaths, hold = 4, 30, 13
def play_wav(src):
wav = pyglet.media.load(sys.path[0] + '\\src\\wav\\' + src + '.wav')
wav.play()
time.sleep... | import sys
import time
import pymorphy2
import pyglet
import pyttsx3
import threading
import warnings
import pytils
warnings.filterwarnings("ignore")
""" Количество раундов, вдохов в раунде, задержка дыхания на вдохе"""
rounds, breaths, hold = 4, 30, 13
def play_wav(src):
wav = pyglet.media.load(sys.path[0] + '... | [
10,
11,
13,
17,
18
] |
36 | 4f0933c58aa1d41faf4f949d9684c04f9e01b473 | <mask token>
| <mask token>
print(f'copying from {from_file} to {to_file}')
<mask token>
print(f'the input file is {len(indata)} bytes long')
print(f'does the output file exist? {exists(to_file)}')
print('return to continue, CTRL-C to abort')
input('?')
open(to_file, 'w').write(indata)
print('done!')
| <mask token>
from_file = input('form_file')
to_file = input('to_file')
print(f'copying from {from_file} to {to_file}')
indata = open(from_file).read()
print(f'the input file is {len(indata)} bytes long')
print(f'does the output file exist? {exists(to_file)}')
print('return to continue, CTRL-C to abort')
input('?')
open... | from os.path import exists
from_file = input('form_file')
to_file = input('to_file')
print(f'copying from {from_file} to {to_file}')
indata = open(from_file).read()
print(f'the input file is {len(indata)} bytes long')
print(f'does the output file exist? {exists(to_file)}')
print('return to continue, CTRL-C to abort')
i... | from os.path import exists
from_file = input('form_file')
to_file = input('to_file')
print(f"copying from {from_file} to {to_file}")
indata = open(from_file).read()#这种方式读取文件后无需close
print(f"the input file is {len(indata)} bytes long")
print(f"does the output file exist? {exists(to_file)}")
print("return to continue,... | [
0,
1,
2,
3,
4
] |
37 | 5c81ddbc8f5a162949a100dbef1c69551d9e267a | <mask token>
| <mask token>
class MyTestCase(TestCase):
def test_mark_done(self):
user = User.objects.create_user(email='user@…', username='user',
password='somepasswd')
todo = Todo(title='SomeTitle', description='SomeDescr', owner=user)
res = todo.mark_done(user)
self.assertTrue(res... | <mask token>
class MyTestCase(TestCase):
def test_mark_done(self):
user = User.objects.create_user(email='user@…', username='user',
password='somepasswd')
todo = Todo(title='SomeTitle', description='SomeDescr', owner=user)
res = todo.mark_done(user)
self.assertTrue(res... | from django.test import TestCase
from django.contrib.auth.models import User
from ..models import Todo
class MyTestCase(TestCase):
def test_mark_done(self):
user = User.objects.create_user(email='user@…', username='user',
password='somepasswd')
todo = Todo(title='SomeTitle', descripti... | # -*- coding: utf-8 -*-
from django.test import TestCase
from django.contrib.auth.models import User
from ..models import Todo
class MyTestCase(TestCase):
def test_mark_done(self):
user = User.objects.create_user(email='user@…', username='user', password='somepasswd')
todo = Todo(title='SomeTitl... | [
0,
2,
3,
4,
5
] |
38 | 509129052f97bb32b4ba0e71ecd7b1061d5f8da2 | <mask token>
| print(180 / 4)
| null | null | null | [
0,
1
] |
39 | 2c90c4e0b42a75d6d387b9b2d0118d8e991b5a08 | <mask token>
class BaseDBMgr:
def get_page(self, cls_: BaseMixin, filters: set, orders: Orders=list(),
field: tuple=(), page: int=1, per_page: int=10) ->dict:
"""获取分页数据
@param BaseMixin cls 数据库模型实体类
@param set filters 查询条件
@param str order 排序
@param tuple field 返回字... | <mask token>
class BaseDBMgr:
def get_page(self, cls_: BaseMixin, filters: set, orders: Orders=list(),
field: tuple=(), page: int=1, per_page: int=10) ->dict:
"""获取分页数据
@param BaseMixin cls 数据库模型实体类
@param set filters 查询条件
@param str order 排序
@param tuple field 返回字... | <mask token>
class BaseDBMgr:
def get_page(self, cls_: BaseMixin, filters: set, orders: Orders=list(),
field: tuple=(), page: int=1, per_page: int=10) ->dict:
"""获取分页数据
@param BaseMixin cls 数据库模型实体类
@param set filters 查询条件
@param str order 排序
@param tuple field 返回字... | <mask token>
Orders = List[Set(str, Union(str, int, decimal.Decimal))]
class BaseDBMgr:
def get_page(self, cls_: BaseMixin, filters: set, orders: Orders=list(),
field: tuple=(), page: int=1, per_page: int=10) ->dict:
"""获取分页数据
@param BaseMixin cls 数据库模型实体类
@param set filters 查询条件
... | import math
import decimal
from typing import Union, List, Set
from sqlalchemy import text
from .model import BaseMixin
from ..core.db import db
Orders = List[Set(str, Union(str, int, decimal.Decimal))]
class BaseDBMgr:
def get_page(self, cls_:BaseMixin, filters:set, orders:Orders=list(), field:tuple=(), pag... | [
3,
7,
8,
9,
11
] |
40 | cb2e800cc2802031847b170a462778e5c0b3c6f9 | <mask token>
class State(object):
def __init__(self, i, j, is_cliff=False, is_goal=False):
self.i = i
self.j = j
self.is_cliff = is_cliff
self.is_goal = is_goal
self.q_values = np.array([0.0, 0.0, 0.0, 0.0])
def __str__(self):
return '({}, {})'.format(self.i, ... | <mask token>
class State(object):
def __init__(self, i, j, is_cliff=False, is_goal=False):
self.i = i
self.j = j
self.is_cliff = is_cliff
self.is_goal = is_goal
self.q_values = np.array([0.0, 0.0, 0.0, 0.0])
def __str__(self):
return '({}, {})'.format(self.i, ... | <mask token>
N_ROWS = 6
N_COLUMNS = 10
class State(object):
def __init__(self, i, j, is_cliff=False, is_goal=False):
self.i = i
self.j = j
self.is_cliff = is_cliff
self.is_goal = is_goal
self.q_values = np.array([0.0, 0.0, 0.0, 0.0])
def __str__(self):
return ... | from math import *
from numpy import *
from random import *
import numpy as np
import matplotlib.pyplot as plt
from colorama import Fore, Back, Style
from gridworld import q_to_arrow
N_ROWS = 6
N_COLUMNS = 10
class State(object):
def __init__(self, i, j, is_cliff=False, is_goal=False):
self.i = i
... | from math import *
from numpy import *
from random import *
import numpy as np
import matplotlib.pyplot as plt
from colorama import Fore, Back, Style
from gridworld import q_to_arrow
N_ROWS = 6
N_COLUMNS = 10
class State(object):
def __init__(self, i, j, is_cliff=False, is_goal=False):
self.i = i
... | [
14,
16,
20,
21,
22
] |
41 | 52da8608e43b2d8dfe00f0956a1187fcf2e7b1ff | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('DHOPD', '0015_auto_20200515_0126')]
operations = [migrations.CreateModel(name='Patient_c', fields=[(
'patient_id', models.AutoField(max_length=200, primary_key=True,
serialize=False)), ('patient_fname', models.CharField(max_... | import datetime
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('DHOPD', '0015_auto_20200515_0126')]
operations = [migrations.CreateModel(name='Patient_c', fields=[(
'patient_id', models.AutoField(max_length=200, primary_key=True,
serialize=Fals... | # Generated by Django 2.2.6 on 2020-05-21 09:44
import datetime
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('DHOPD', '0015_auto_20200515_0126'),
]
operations = [
migrations.CreateModel(
name='Patient_c',
field... | [
0,
1,
2,
3,
4
] |
42 | 1084478226777b9259274e053984ac34d461198d | <mask token>
class TreePrinter:
@addToClass(Node)
def printTree(self, indent=0):
raise Exception('printTree not defined in class ' + self.__class__.
__name__)
@addToClass(Instruction)
def printTree(self, indent=0):
print_intended(self.type, indent)
<mask token>
@... | <mask token>
class TreePrinter:
@addToClass(Node)
def printTree(self, indent=0):
raise Exception('printTree not defined in class ' + self.__class__.
__name__)
@addToClass(Instruction)
def printTree(self, indent=0):
print_intended(self.type, indent)
@addToClass(Expres... | <mask token>
class TreePrinter:
@addToClass(Node)
def printTree(self, indent=0):
raise Exception('printTree not defined in class ' + self.__class__.
__name__)
@addToClass(Instruction)
def printTree(self, indent=0):
print_intended(self.type, indent)
@addToClass(Expres... | <mask token>
def addToClass(cls):
def decorator(func):
setattr(cls, func.__name__, func)
return func
return decorator
def print_intended(to_print, intend):
print(intend * '| ' + to_print)
class TreePrinter:
@addToClass(Node)
def printTree(self, indent=0):
raise Excep... | from .ast import *
# noinspection PyPep8Naming
def addToClass(cls):
def decorator(func):
setattr(cls, func.__name__, func)
return func
return decorator
def print_intended(to_print, intend):
print(intend * "| " + to_print)
# noinspection PyPep8Naming,PyUnresolvedReferences
class TreeP... | [
18,
21,
22,
24,
26
] |
43 | 999de0965efa3c1fe021142a105dcf28184cd5ba | <mask token>
| <mask token>
def parse(query):
print('parsing the query...')
query = dnf_converter.convert(query)
cp_clause_list = []
clause_list = []
for cp in query['$or']:
clauses = []
if '$and' in cp:
for clause in cp['$and']:
clauses.append(clause)
... | import dnf_converter
def parse(query):
print('parsing the query...')
query = dnf_converter.convert(query)
cp_clause_list = []
clause_list = []
for cp in query['$or']:
clauses = []
if '$and' in cp:
for clause in cp['$and']:
clauses.append(clause)
... | import dnf_converter
def parse(query):
print("parsing the query...")
query = dnf_converter.convert(query)
cp_clause_list = []
clause_list = []
for cp in query["$or"]:
clauses = []
if "$and" in cp:
for clause in cp["$and"]:
clauses.append(clause)
clause_list.append(clause)
else:
clause = cp
... | null | [
0,
1,
2,
3
] |
44 | cb08f64d1ad7e53f1041684d4ca4ef65036c138d | <mask token>
def is_element(el, tag):
return isinstance(el, Tag) and el.name == tag
class ElemIterator:
def __init__(self, els):
self.els = els
self.i = 0
def peek(self):
try:
return self.els[self.i]
except IndexError:
return None
def __next... | <mask token>
def is_element(el, tag):
return isinstance(el, Tag) and el.name == tag
class ElemIterator:
def __init__(self, els):
self.els = els
self.i = 0
def peek(self):
try:
return self.els[self.i]
except IndexError:
return None
def __next... | <mask token>
def is_element(el, tag):
return isinstance(el, Tag) and el.name == tag
class ElemIterator:
def __init__(self, els):
self.els = els
self.i = 0
def peek(self):
try:
return self.els[self.i]
except IndexError:
return None
def __next... | import json
import re
from bs4 import BeautifulSoup
from bs4.element import NavigableString, Tag
from common import dir_path
def is_element(el, tag):
return isinstance(el, Tag) and el.name == tag
class ElemIterator:
def __init__(self, els):
self.els = els
self.i = 0
def peek(self):
... | import json
import re
from bs4 import BeautifulSoup
from bs4.element import NavigableString, Tag
from common import dir_path
def is_element(el, tag):
return isinstance(el, Tag) and el.name == tag
class ElemIterator():
def __init__(self, els):
self.els = els
self.i = 0
def peek(self):
try:
... | [
10,
12,
14,
15,
16
] |
45 | 5082182af5a08970568dc1ab7a53ee5337260687 | <mask token>
| <mask token>
if __name__ == '__main__':
import matplotlib.pyplot as plt
import numpy as np
try:
from viscm import viscm
viscm(romaO_map)
except ImportError:
print('viscm not found, falling back on simple display')
plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto... | <mask token>
cm_data = [[0.45137, 0.22346, 0.34187], [0.45418, 0.22244, 0.3361], [
0.45696, 0.22158, 0.33043], [0.45975, 0.2209, 0.32483], [0.46251,
0.22035, 0.31935], [0.46527, 0.21994, 0.31394], [0.46803, 0.21968,
0.30862], [0.47078, 0.21958, 0.30337], [0.47352, 0.21962, 0.29822], [
0.47628, 0.21982... | from matplotlib.colors import LinearSegmentedColormap
cm_data = [[0.45137, 0.22346, 0.34187], [0.45418, 0.22244, 0.3361], [
0.45696, 0.22158, 0.33043], [0.45975, 0.2209, 0.32483], [0.46251,
0.22035, 0.31935], [0.46527, 0.21994, 0.31394], [0.46803, 0.21968,
0.30862], [0.47078, 0.21958, 0.30337], [0.47352, ... | #
# romaO
# www.fabiocrameri.ch/colourmaps
from matplotlib.colors import LinearSegmentedColormap
cm_data = [[0.45137, 0.22346, 0.34187],
[0.45418, 0.22244, 0.3361],
[0.45696, 0.22158, 0.33043],
[0.45975, 0.2209, 0.32483],
... | [
0,
1,
2,
3,
4
] |
46 | 3dd4b4d4241e588cf44230891f496bafb30c6153 | <mask token>
| <mask token>
print(df.head())
| <mask token>
n1 = 'ADS'
api_url = 'https://www.quandl.com/api/v3/datasets/WIKI/%s.csv' % n1
df = pd.read_csv(api_url)
df = df.head(100)
print(df.head())
| import requests
import json
import pandas as pd
n1 = 'ADS'
api_url = 'https://www.quandl.com/api/v3/datasets/WIKI/%s.csv' % n1
df = pd.read_csv(api_url)
df = df.head(100)
print(df.head())
|
import requests
import json
import pandas as pd
n1 = 'ADS'
api_url = 'https://www.quandl.com/api/v3/datasets/WIKI/%s.csv' % n1
df = pd.read_csv(api_url)
df = df.head(100)
print(df.head())
#print(list(data))
| [
0,
1,
2,
3,
4
] |
47 | a558b42106b036719fe38ee6efd1c5b933290f52 | <mask token>
| <mask token>
connection.execute(stmt)
func.update_annotations_db(Twitter_Sentiment_Analysis, connection,
'Export_csv5.csv')
| <mask token>
connection, Twitter_Sentiment_Analysis = func.Database_Acces(
'mysql://root@localhost/sentiment?charset=utf8mb4', 'utf8',
'Twitter_Sentiment_Analysis4')
stmt = "SET NAMES 'UTF8';"
connection.execute(stmt)
func.update_annotations_db(Twitter_Sentiment_Analysis, connection,
'Export_csv5.csv')
| from sqlalchemy import select, update
from sqlalchemy import Table, Column, String, Integer, Float, Boolean, Date, BigInteger
from sqlalchemy import create_engine, MetaData
import API_and_Database_function as func
import pandas as pd
import re
connection, Twitter_Sentiment_Analysis = func.Database_Acces(
'mysql://r... | #!/usr/local/bin/python
# -*- coding: utf-8 -*-
from sqlalchemy import select, update
from sqlalchemy import Table, Column, String, Integer, Float, Boolean, Date, BigInteger
from sqlalchemy import create_engine, MetaData
import API_and_Database_function as func
import pandas as pd
import re
connection, Twitter_Senti... | [
0,
1,
2,
3,
4
] |
48 | 10d35ba3c04d9cd09e152c575e74b0382ff60572 | <mask token>
class GcodeSender(object):
<mask token>
<mask token>
def __init__(self, **kwargs):
super(GcodeSender, self).__init__(**kwargs)
self._stop = threading.Event()
self.parsing_thread = None
self.command_queue = Queue()
self.line_number = 1
self.plot... | <mask token>
class GcodeSender(object):
<mask token>
<mask token>
def __init__(self, **kwargs):
super(GcodeSender, self).__init__(**kwargs)
self._stop = threading.Event()
self.parsing_thread = None
self.command_queue = Queue()
self.line_number = 1
self.plot... | <mask token>
class GcodeSender(object):
PEN_LIFT_PULSE = 1500
PEN_DROP_PULSE = 800
def __init__(self, **kwargs):
super(GcodeSender, self).__init__(**kwargs)
self._stop = threading.Event()
self.parsing_thread = None
self.command_queue = Queue()
self.line_number = 1
... | <mask token>
PORT = '/dev/ttys005'
SPEED = 4800.0
class GcodeSender(object):
PEN_LIFT_PULSE = 1500
PEN_DROP_PULSE = 800
def __init__(self, **kwargs):
super(GcodeSender, self).__init__(**kwargs)
self._stop = threading.Event()
self.parsing_thread = None
self.command_queue = ... | from pydispatch import dispatcher
import time
import serial
import threading
from queue import Queue
PORT='/dev/ttys005'
#PORT='/dev/tty.usbmodem1461'
SPEED=4800.0
class GcodeSender(object):
PEN_LIFT_PULSE = 1500
PEN_DROP_PULSE = 800
def __init__(self, **kwargs):
super(GcodeSender, self).__init_... | [
9,
14,
15,
16,
18
] |
49 | c105f06e302740e9b7be100df905852bb5610a2c | import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import struct
import wave
scale = 0.01
wav = wave.open('output.wav', 'r')
print 'channels %d'%wav.getnchannels()
print 'smpl width %d'%wav.getsampwidth()
print 'frame rate %f'%wav.getframerate()
nframes = wav.getnframes()
pri... | null | null | null | null | [
0
] |
50 | e1d0648825695584d3ea518db961a9178ea0c66a | <mask token>
def china_lunar():
today = str(date.today())
today_list = today.split('-')
lunar_day = lunar.getDayBySolar(int(datetime.datetime.now().year), int(
datetime.datetime.now().month), int(datetime.datetime.now().day))
if lunar_day.Lleap:
china_day = '农历:{0}月{1}'.format(ymc[luna... | <mask token>
def china_lunar():
today = str(date.today())
today_list = today.split('-')
lunar_day = lunar.getDayBySolar(int(datetime.datetime.now().year), int(
datetime.datetime.now().month), int(datetime.datetime.now().day))
if lunar_day.Lleap:
china_day = '农历:{0}月{1}'.format(ymc[luna... | <mask token>
ymc = [u'十一', u'十二', u'正', u'二', u'三', u'四', u'五', u'六', u'七', u'八', u'九', u'十'
]
rmc = [u'初一', u'初二', u'初三', u'初四', u'初五', u'初六', u'初七', u'初八', u'初九', u'初十',
u'十一', u'十二', u'十三', u'十四', u'十五', u'十六', u'十七', u'十八', u'十九', u'二十',
u'廿一', u'廿二', u'廿三', u'廿四', u'廿五', u'廿六', u'廿七', u'廿八', u'廿九', u'三... | import requests
import sxtwl
import datetime
from datetime import date
import lxml
from lxml import etree
ymc = [u'十一', u'十二', u'正', u'二', u'三', u'四', u'五', u'六', u'七', u'八', u'九', u'十'
]
rmc = [u'初一', u'初二', u'初三', u'初四', u'初五', u'初六', u'初七', u'初八', u'初九', u'初十',
u'十一', u'十二', u'十三', u'十四', u'十五', u'十六', u'十七'... | import requests
import sxtwl
import datetime
from datetime import date
import lxml
from lxml import etree
# 日历中文索引
ymc = [u"十一", u"十二", u"正", u"二", u"三", u"四", u"五", u"六", u"七", u"八", u"九", u"十"]
rmc = [u"初一", u"初二", u"初三", u"初四", u"初五", u"初六", u"初七", u"初八", u"初九", u"初十", \
u"十一", u"十二", u"十三", u"十四", u"十五", u"十... | [
4,
6,
7,
8,
9
] |
51 | 2c39660da8fe839c4634cd73ce069acc7b1b29b4 | <mask token>
| <mask token>
@measure_time_of_func
def fib(n):
sequence = [1, 1]
for i in range(2, n, 1):
sequence.append(sequence[i - 1] + sequence[i - 2])
return sequence
| <mask token>
def measure_time_of_func(func):
def wrapper_func(n):
start_time = time.time()
fib_seq = func(n)
end_time = time.time()
return fib_seq, end_time - start_time
return wrapper_func
@measure_time_of_func
def fib(n):
sequence = [1, 1]
for i in range(2, n, 1):
... | import time
def measure_time_of_func(func):
def wrapper_func(n):
start_time = time.time()
fib_seq = func(n)
end_time = time.time()
return fib_seq, end_time - start_time
return wrapper_func
@measure_time_of_func
def fib(n):
sequence = [1, 1]
for i in range(2, n, 1):
... | import time
# Decorator
def measure_time_of_func(func):
def wrapper_func(n):
start_time = time.time()
fib_seq = func(n)
end_time = time.time()
return (fib_seq, end_time - start_time)
return wrapper_func
# Returns a list with first n numbers of fibonacci sequence.
@measure_ti... | [
0,
1,
2,
3,
4
] |
52 | c87e6f8780bf8d9097f200c7f2f0faf55beb480c | <mask token>
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
<mask token>
| def transform_data(fn):
print(fn(10))
<mask token>
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
<mask token>
| def transform_data(fn):
print(fn(10))
<mask token>
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
<mask token>
def transform_data2(fn, *args):
for arg in args:
print('Result: {:^20.2f}'.format(fn(arg)))
<mask token>
| def transform_data(fn):
print(fn(10))
transform_data(lambda data: data / 5)
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
transform_data2(lambda data: data / 5, 10, 15, 22, 30)
def transform_data2(fn, *args):
for arg in args:
print('Result: {:^20.2f}'.format(fn(arg)... | # 1
def transform_data(fn):
print(fn(10))
# 2
transform_data(lambda data: data / 5)
# 3
def transform_data2(fn, *args):
for arg in args:
print(fn(arg))
transform_data2(lambda data: data / 5, 10, 15, 22, 30)
# 4
def transform_data2(fn, *args):
for arg in args:
print('Resu... | [
1,
2,
3,
4,
5
] |
53 | f4f08015b7638f4d6ea793350d5d19a3485978cd | <mask token>
def get_objectives(data):
"""Get a list of all first chromosomes' objective values."""
objectives = [math.log(population[0]['objective']) for population in data]
return objectives
def get_new_values(values):
"""Record any changes higher. Its size is the same as its argument's."""
ne... | <mask token>
def get_data():
"""Read output file to get data."""
try:
with open(CONS['OUTPUT_FILE'], 'r') as file:
data = json.load(file)[1]
return data
except FileNotFoundError:
print('Data file not found.')
exit()
def get_objectives(data):
"""Get a list ... | <mask token>
def get_data():
"""Read output file to get data."""
try:
with open(CONS['OUTPUT_FILE'], 'r') as file:
data = json.load(file)[1]
return data
except FileNotFoundError:
print('Data file not found.')
exit()
def get_objectives(data):
"""Get a list ... | <mask token>
import os
import json
import math
import matplotlib as maplot
import matplotlib.pyplot as pyplot
from datetime import datetime
from sub.inputprocess import CONSTANTS as CONS
def get_data():
"""Read output file to get data."""
try:
with open(CONS['OUTPUT_FILE'], 'r') as file:
d... | """Plot the output data.
"""
# Standard library
import os
import json
import math
import matplotlib as maplot
import matplotlib.pyplot as pyplot
from datetime import datetime
# User library
from sub.inputprocess import CONSTANTS as CONS
# **json.loads(json_data)
def get_data():
"""Read output file to get data."... | [
2,
4,
5,
6,
7
] |
54 | d2a153fffccd4b681eebce823e641e195197cde7 | <mask token>
class NamingConvention:
<mask token>
<mask token>
| <mask token>
class NamingConvention:
<mask token>
def __init__(self):
namingconventions = os.path.join(os.path.dirname(os.path.dirname(
__file__)), 'data', 'strings', 'namingconvention.json')
namingconventions = json.load(open(namingconventions))
for key, value in namingco... | <mask token>
class NamingConvention:
"""Imports naming conventions from the respective .json file and puts them
into class variables.
"""
def __init__(self):
namingconventions = os.path.join(os.path.dirname(os.path.dirname(
__file__)), 'data', 'strings', 'namingconvention.json')
... | <mask token>
import os
import json
class NamingConvention:
"""Imports naming conventions from the respective .json file and puts them
into class variables.
"""
def __init__(self):
namingconventions = os.path.join(os.path.dirname(os.path.dirname(
__file__)), 'data', 'strings', 'nam... | """
Created on 02.09.2013
@author: Paul Schweizer
@email: paulschweizer@gmx.net
@brief: Holds all the namingconventions for pandora's box
"""
import os
import json
class NamingConvention():
"""Imports naming conventions from the respective .json file and puts them
into class variables.
"""
def __init... | [
1,
2,
3,
4,
5
] |
55 | aff1d702e591efcfc0fc93150a3fbec532408137 | <mask token>
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset = Lamp.objects.all()
<mask token>
| <mask token>
class LampSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Lamp
fields = '__all__'
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset = Lamp.objects.all()
<mask token>
| <mask token>
class LampSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Lamp
fields = '__all__'
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset = Lamp.objects.all()
router = routers.DefaultRouter()
router.register('lamps', L... | from rest_framework import serializers, viewsets, routers
from lamp_control.models import Lamp
class LampSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Lamp
fields = '__all__'
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset ... | from rest_framework import serializers, viewsets, routers
from lamp_control.models import Lamp
class LampSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Lamp
fields = '__all__'
class LampViewSet(viewsets.ModelViewSet):
serializer_class = LampSerializer
queryset ... | [
2,
3,
5,
6,
7
] |
56 | c6502ea2b32ad90c76b6dfaf3ee3218d029eba15 | class NlpUtility:
<mask token>
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == 'NN':
nouns.push(word)
<mask token>
<mask token>
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos... | class NlpUtility:
<mask token>
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == 'NN':
nouns.push(word)
def get_verbs(self, tokens):
verbs = []
for word, pos in tokens:
if pos == 'VB':
nouns.pu... | class NlpUtility:
<mask token>
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == 'NN':
nouns.push(word)
def get_verbs(self, tokens):
verbs = []
for word, pos in tokens:
if pos == 'VB':
nouns.pu... | class NlpUtility:
"""
Utility methods to get particular parts of speech from a token set
"""
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == 'NN':
nouns.push(word)
def get_verbs(self, tokens):
verbs = []
for word, po... | class NlpUtility():
"""
Utility methods to get particular parts of speech from a token set
"""
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == "NN":
nouns.push(word)
def get_verbs(self, tokens):
verbs = []
for word, pos in tokens:
if pos == "VB":
nouns.push(word)
... | [
4,
5,
6,
7,
8
] |
57 | 675fbdfd519d00ab10bf613e8abb7338e484fe65 | <mask token>
| <mask token>
log.setLevel(logging.DEBUG)
<mask token>
stream_hander.setFormatter(formatter)
log.addHandler(stream_hander)
| <mask token>
formatter = logging.Formatter('%(asctime)s [%(levelname)s] : %(message)s')
log = logging.getLogger('othello')
log.setLevel(logging.DEBUG)
stream_hander = logging.StreamHandler()
stream_hander.setFormatter(formatter)
log.addHandler(stream_hander)
| import logging
formatter = logging.Formatter('%(asctime)s [%(levelname)s] : %(message)s')
log = logging.getLogger('othello')
log.setLevel(logging.DEBUG)
stream_hander = logging.StreamHandler()
stream_hander.setFormatter(formatter)
log.addHandler(stream_hander)
| import logging
formatter = logging.Formatter("%(asctime)s [%(levelname)s] : %(message)s")
log = logging.getLogger("othello")
log.setLevel(logging.DEBUG)
stream_hander = logging.StreamHandler()
stream_hander.setFormatter(formatter)
log.addHandler(stream_hander)
| [
0,
1,
2,
3,
4
] |
58 | d7b45e76f150107cd62be160e8938f17dad90623 | <mask token>
| <mask token>
with open('testfile_short1.csv', 'r') as original:
data = original.read()
for i in range(2):
with open('testfile_short3.csv', 'a') as modified:
modified.write(data)
| import pandas as pd
from sqlalchemy import create_engine
with open('testfile_short1.csv', 'r') as original:
data = original.read()
for i in range(2):
with open('testfile_short3.csv', 'a') as modified:
modified.write(data)
| import pandas as pd
from sqlalchemy import create_engine
# file = 'testfile.csv'
# print(pd.read_csv(file, nrows=5))
with open('testfile_short1.csv', 'r') as original: data = original.read()
for i in range(2):
with open('testfile_short3.csv', 'a') as modified: modified.write(data) | null | [
0,
1,
2,
3
] |
59 | 61454a3d6b5b17bff871ededc6ddfe8384043884 | <mask token>
class ItemEffect(AbstractItemEffect):
<mask token>
class BuffedByHealingWand(StatModifyingBuffEffect):
def __init__(self):
super().__init__(BUFF_TYPE, {HeroStat.HEALTH_REGEN: HEALTH_REGEN_BONUS}
)
<mask token>
| <mask token>
class ItemEffect(AbstractItemEffect):
def item_handle_event(self, event: Event, game_state: GameState):
if isinstance(event, PlayerDamagedEnemy):
game_state.player_state.gain_buff_effect(get_buff_effect(
BUFF_TYPE), BUFF_DURATION)
class BuffedByHealingWand(StatM... | <mask token>
BUFF_TYPE = BuffType.BUFFED_BY_HEALING_WAND
HEALTH_REGEN_BONUS = 1
BUFF_DURATION = Millis(5000)
class ItemEffect(AbstractItemEffect):
def item_handle_event(self, event: Event, game_state: GameState):
if isinstance(event, PlayerDamagedEnemy):
game_state.player_state.gain_buff_effe... | from pythongame.core.buff_effects import get_buff_effect, register_buff_effect, StatModifyingBuffEffect
from pythongame.core.common import ItemType, Sprite, BuffType, Millis, HeroStat
from pythongame.core.game_data import UiIconSprite, register_buff_text
from pythongame.core.game_state import Event, PlayerDamagedEnemy,... | from pythongame.core.buff_effects import get_buff_effect, register_buff_effect, StatModifyingBuffEffect
from pythongame.core.common import ItemType, Sprite, BuffType, Millis, HeroStat
from pythongame.core.game_data import UiIconSprite, register_buff_text
from pythongame.core.game_state import Event, PlayerDamagedEnemy,... | [
3,
4,
6,
7,
8
] |
60 | 4c60fd123f591bf2a88ca0affe14a3c3ec0d3cf6 | <mask token>
def range_func(measures):
scores = []
for entry in measures:
try:
curr = int(entry[1])
except:
curr = None
if curr is not None:
scores.append(curr)
if len(scores) < 1:
return 0
return max(scores) - min(scores)
<mask tok... | <mask token>
def range_func(measures):
scores = []
for entry in measures:
try:
curr = int(entry[1])
except:
curr = None
if curr is not None:
scores.append(curr)
if len(scores) < 1:
return 0
return max(scores) - min(scores)
<mask tok... | <mask token>
sc = SparkContext('local', 'weblog app')
effective_care = sc.textFile('file:///data/exercise1/effective_care').map(
lambda l: l.encode().split(',')).map(lambda x: (x[0], x[1:]))
procedure_care = effective_care.map(lambda p: (p[1][1], [p[0], p[1][2]]))
procedure_care_grouped = procedure_care.groupByKey(... | from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import *
sc = SparkContext('local', 'weblog app')
effective_care = sc.textFile('file:///data/exercise1/effective_care').map(
lambda l: l.encode().split(',')).map(lambda x: (x[0], x[1:]))
procedure_care = effective_care.map(la... | from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import *
sc = SparkContext("local", "weblog app")
effective_care = sc.textFile('file:///data/exercise1/effective_care').map(lambda l:l.encode().split(',')).map(lambda x: (x[0], x[1:]))
procedure_care = effective_care.map(lambda ... | [
1,
2,
3,
4,
5
] |
61 | 4264cba9a6c39219d21bd21d4b21009bacd1db38 | #!/usr/bin/python
import operator
import cgi, sys, LINK_HEADERS
import simplejson as json
from datetime import datetime
from dateutil import tz
from decimal import *
sys.path.insert(0, str(LINK_HEADERS.DAO_LINK))
from transaction_dao import Transaction_dao
from user_portfolio_dao import User_portfolio_dao
from user_st... | null | null | null | null | [
0
] |
62 | 5c30b0e952ddf2e05a7ad5f8d9bbd4f5e22f887d | <mask token>
| <mask token>
print(str1 and str2)
<mask token>
for c in str1:
if c in str2:
nPos = str1.index(c)
break
print(nPos)
| str1 = '12345678'
str2 = '456'
print(str1 and str2)
str1 = 'cekjgdklab'
str2 = 'gka'
nPos = -1
for c in str1:
if c in str2:
nPos = str1.index(c)
break
print(nPos)
| # strspn(str1,str2)
str1 = '12345678'
str2 = '456'
# str1 and chars both in str1 and str2
print(str1 and str2)
str1 = 'cekjgdklab'
str2 = 'gka'
nPos = -1
for c in str1:
if c in str2:
nPos = str1.index(c)
break
print(nPos)
| null | [
0,
1,
2,
3
] |
63 | a86b64ccd0dab4ab70ca9c2b7625fb34afec3794 | <mask token>
class SomeModelAdmin(SummernoteModelAdmin):
<mask token>
<mask token>
| <mask token>
class SomeModelAdmin(SummernoteModelAdmin):
summernote_fields = '__all__'
<mask token>
| <mask token>
class SomeModelAdmin(SummernoteModelAdmin):
summernote_fields = '__all__'
admin.site.register(ArticlePost, SummernoteModelAdmin)
| from django.contrib import admin
from django_summernote.admin import SummernoteModelAdmin
from .models import ArticlePost
class SomeModelAdmin(SummernoteModelAdmin):
summernote_fields = '__all__'
admin.site.register(ArticlePost, SummernoteModelAdmin)
| from django.contrib import admin
from django_summernote.admin import SummernoteModelAdmin
from .models import ArticlePost
# Register your models here.
class SomeModelAdmin(SummernoteModelAdmin): # instead of ModelAdmin
summernote_fields = '__all__'
admin.site.register(ArticlePost, SummernoteModelAdmin) | [
1,
2,
3,
4,
5
] |
64 | f17d33f1d035da42dc9a2b4c0c60beefc6a48dea | <mask token>
class TestExtractTrialData(unittest.TestCase):
def setUp(self):
self.main_path = Path(__file__).parent
self.training_lt5 = {'path': self.main_path / 'data' /
'session_training_lt5'}
self.biased_lt5 = {'path': self.main_path / 'data' /
'session_biased_l... | <mask token>
class TestExtractTrialData(unittest.TestCase):
def setUp(self):
self.main_path = Path(__file__).parent
self.training_lt5 = {'path': self.main_path / 'data' /
'session_training_lt5'}
self.biased_lt5 = {'path': self.main_path / 'data' /
'session_biased_l... | <mask token>
class TestExtractTrialData(unittest.TestCase):
def setUp(self):
self.main_path = Path(__file__).parent
self.training_lt5 = {'path': self.main_path / 'data' /
'session_training_lt5'}
self.biased_lt5 = {'path': self.main_path / 'data' /
'session_biased_l... | <mask token>
class TestExtractTrialData(unittest.TestCase):
def setUp(self):
self.main_path = Path(__file__).parent
self.training_lt5 = {'path': self.main_path / 'data' /
'session_training_lt5'}
self.biased_lt5 = {'path': self.main_path / 'data' /
'session_biased_l... | import functools
import shutil
import tempfile
import unittest
import unittest.mock
from pathlib import Path
import numpy as np
import pandas as pd
import one.alf.io as alfio
from ibllib.io.extractors import training_trials, biased_trials, camera
from ibllib.io import raw_data_loaders as raw
from ibllib.io.extractors... | [
27,
34,
37,
45,
49
] |
65 | 767c0e6d956701fcedddb153b6c47f404dec535a | <mask token>
class NetworkLookup:
def __init__(self):
self.loaded = 0
self.subnets = {}
self.vpcs = {}
def load(self):
if self.loaded:
return
client = boto3.client('ec2')
subnets_r = client.describe_subnets()
subnets_list = subnets_r['Subne... | <mask token>
class NetworkLookup:
def __init__(self):
self.loaded = 0
self.subnets = {}
self.vpcs = {}
def load(self):
if self.loaded:
return
client = boto3.client('ec2')
subnets_r = client.describe_subnets()
subnets_list = subnets_r['Subne... | <mask token>
class NetworkLookup:
def __init__(self):
self.loaded = 0
self.subnets = {}
self.vpcs = {}
def load(self):
if self.loaded:
return
client = boto3.client('ec2')
subnets_r = client.describe_subnets()
subnets_list = subnets_r['Subne... | import boto3
class NetworkLookup:
def __init__(self):
self.loaded = 0
self.subnets = {}
self.vpcs = {}
def load(self):
if self.loaded:
return
client = boto3.client('ec2')
subnets_r = client.describe_subnets()
subnets_list = subnets_r['Subne... | import boto3
class NetworkLookup:
def __init__(self):
self.loaded = 0
self.subnets = {}
self.vpcs = {}
def load(self):
if self.loaded:
return
client = boto3.client('ec2')
# load subnets
subnets_r = client.describe_subnets()
subnets_... | [
6,
7,
9,
10,
11
] |
66 | efcbe296ea72a94be967124a8ba8c84a524e2eb1 | <mask token>
| <mask token>
def filter_pos_rec(lst):
"""
@type lst: LinkedListRec
>>> lst = LinkedListRec([3, -10, 4, 0])
>>> pos = filter_pos_rec(lst)
>>> str(pos)
'3 -> 4'
"""
if lst.is_empty():
return lst
else:
pos_rec = LinkedListRec([])
if lst._first > 0:
... | __author__ = 'AChen'
<mask token>
def filter_pos_rec(lst):
"""
@type lst: LinkedListRec
>>> lst = LinkedListRec([3, -10, 4, 0])
>>> pos = filter_pos_rec(lst)
>>> str(pos)
'3 -> 4'
"""
if lst.is_empty():
return lst
else:
pos_rec = LinkedListRec([])
if lst._f... | __author__ = 'AChen'
from rec_linked_list import *
def filter_pos_rec(lst):
"""
@type lst: LinkedListRec
>>> lst = LinkedListRec([3, -10, 4, 0])
>>> pos = filter_pos_rec(lst)
>>> str(pos)
'3 -> 4'
"""
if lst.is_empty():
return lst
else:
pos_rec = LinkedListRec([])
... | null | [
0,
1,
2,
3
] |
67 | 4789546128263bd298f8f5827734f8402747b9ac | <mask token>
class OutgoingNetworkInputBuffer(InputBuffer):
<mask token>
<mask token>
class IncomingNetworkInputBuffer(InputBuffer):
def __init__(self, frame_limit=12):
super().__init__(left_action_name='', right_action_name='',
weak_punch_action_name='', frame_limit=frame_limit)
... | <mask token>
class InputBuffer:
<mask token>
class Value(Enum):
LEFT = 'l'
RIGHT = 'r'
UP = 'u'
DOWN = 'd'
WEAK_PUNCH = 'wp'
<mask token>
def __str__(self):
return f'{self._inputs}'
<mask token>
@property
def values(self) ->list:
... | <mask token>
class InputBuffer:
<mask token>
class Value(Enum):
LEFT = 'l'
RIGHT = 'r'
UP = 'u'
DOWN = 'd'
WEAK_PUNCH = 'wp'
def __init__(self, left_action_name: str, right_action_name: str,
weak_punch_action_name: str, frame_limit=12):
self._inpu... | <mask token>
class InputBuffer:
<mask token>
class Value(Enum):
LEFT = 'l'
RIGHT = 'r'
UP = 'u'
DOWN = 'd'
WEAK_PUNCH = 'wp'
def __init__(self, left_action_name: str, right_action_name: str,
weak_punch_action_name: str, frame_limit=12):
self._inpu... | from enum import Enum
from roll.input import Input
from roll.network import Server, Client
from assets.game_projects.fighter.src.game_properties import GameProperties
from assets.game_projects.fighter.src.network_message import NetworkMessage
class InputBuffer:
"""
Responsible for collecting game input from... | [
5,
12,
13,
15,
21
] |
68 | b693cc63e2ee4c994ef7b5e44faea99f15a021f6 | <mask token>
class QManeger(object):
<mask token>
<mask token>
def listening(self):
while True:
traces = self.q_trace.get(block=True)
for s, a, r in zip(traces[0], traces[1], traces[2]):
self._push_one(s, a, r)
if len(self.traces_s) > self.opt.b... | <mask token>
class QManeger(object):
def __init__(self, opt, q_trace, q_batch):
self.traces_s = []
self.traces_a = []
self.traces_r = []
self.lock = mp.Lock()
self.q_trace = q_trace
self.q_batch = q_batch
self.opt = opt
self.device = torch.device('c... | <mask token>
class QManeger(object):
def __init__(self, opt, q_trace, q_batch):
self.traces_s = []
self.traces_a = []
self.traces_r = []
self.lock = mp.Lock()
self.q_trace = q_trace
self.q_batch = q_batch
self.opt = opt
self.device = torch.device('c... | import torch
import torch.multiprocessing as mp
import random
class QManeger(object):
def __init__(self, opt, q_trace, q_batch):
self.traces_s = []
self.traces_a = []
self.traces_r = []
self.lock = mp.Lock()
self.q_trace = q_trace
self.q_batch = q_batch
sel... | import torch
import torch.multiprocessing as mp
import random
class QManeger(object):
def __init__(self, opt, q_trace, q_batch):
self.traces_s = []
self.traces_a = []
self.traces_r = []
self.lock = mp.Lock()
self.q_trace = q_trace
self.q_batch = q_batch
sel... | [
2,
4,
5,
6,
7
] |
69 | 3c0beb7be29953ca2d7b390627305f4541b56efa | <mask token>
def test_main_cnv():
main_cnv(tarfile)
<mask token>
| <mask token>
sys.path.append('../circos_report/cnv_anno2conf')
<mask token>
def test_main_cnv():
main_cnv(tarfile)
if __name__ == '__main__':
test_main_cnv()
| <mask token>
sys.path.append('../circos_report/cnv_anno2conf')
<mask token>
tarfile = {'yaml': 'data/test_app.yaml'}
def test_main_cnv():
main_cnv(tarfile)
if __name__ == '__main__':
test_main_cnv()
| import sys
sys.path.append('../circos_report/cnv_anno2conf')
from cnv_anno2conf import main_cnv
tarfile = {'yaml': 'data/test_app.yaml'}
def test_main_cnv():
main_cnv(tarfile)
if __name__ == '__main__':
test_main_cnv()
| import sys
sys.path.append("../circos_report/cnv_anno2conf")
from cnv_anno2conf import main_cnv
tarfile = {"yaml": "data/test_app.yaml"}
def test_main_cnv():
main_cnv(tarfile)
if __name__ == "__main__":
test_main_cnv()
| [
1,
2,
3,
4,
5
] |
70 | 8d0fcf0bf5effec9aa04e7cd56b4b7098c6713cb | <mask token>
| for i in range(-10, 0):
print(i, end=' ')
| for i in range(-10,0):
print(i,end=" ") | null | null | [
0,
1,
2
] |
71 | a14114f9bb677601e6d75a72b84ec128fc9bbe61 | <mask token>
| <mask token>
urlpatterns = [path('admin/', admin.site.urls), path('api/', include(
'api.urls')), path('api/adv/', include('adventure.urls'))]
| from django.contrib import admin
from django.urls import path, include, re_path
from django.conf.urls import include
from rest_framework.authtoken import views
urlpatterns = [path('admin/', admin.site.urls), path('api/', include(
'api.urls')), path('api/adv/', include('adventure.urls'))]
| from django.contrib import admin
from django.urls import path, include, re_path
from django.conf.urls import include
# from rest_framework import routers
from rest_framework.authtoken import views
# from adventure.api import PlayerViewSet, RoomViewSet
# from adventure.api import move
# router = routers.DefaultRoute... | null | [
0,
1,
2,
3
] |
72 | edb206a8cd5bc48e831142d5632fd7eb90abd209 | import tensorflow as tf
optimizer = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
_, l = sess.run([optimizer, loss], feed_dict={X:x, Y:y})
Session looks at all trainable variables that loss depends on and update them
tf.Variable(initializer=None, trainable=True, collections=None, validate_shape=True, caching... | null | null | null | null | [
0
] |
73 | 36991c3191ba48b1b9dbd843e279f8fe124f1339 | <mask token>
class Rouge(Character):
def special_attack1(self, opponent, hitdamage_callback, specatt_callback):
pass
def special_attack2(self, opponent, hitdamage_callback, specatt_callback):
pass
<mask token>
def regen_resource(self):
pass
def full_resource(self):
... | <mask token>
class Rouge(Character):
def special_attack1(self, opponent, hitdamage_callback, specatt_callback):
pass
def special_attack2(self, opponent, hitdamage_callback, specatt_callback):
pass
def heal(self, target):
pass
def regen_resource(self):
pass
def ... | __author__ = 'Jager'
<mask token>
class Rouge(Character):
def special_attack1(self, opponent, hitdamage_callback, specatt_callback):
pass
def special_attack2(self, opponent, hitdamage_callback, specatt_callback):
pass
def heal(self, target):
pass
def regen_resource(self):
... | __author__ = 'Jager'
from char import Character
class Rouge(Character):
def special_attack1(self, opponent, hitdamage_callback, specatt_callback):
pass
def special_attack2(self, opponent, hitdamage_callback, specatt_callback):
pass
def heal(self, target):
pass
def regen_res... | __author__ = 'Jager'
from char import Character
class Rouge (Character):
def special_attack1(self, opponent, hitdamage_callback, specatt_callback):
pass # hook method
def special_attack2(self, opponent, hitdamage_callback, specatt_callback):
pass # hook method
def heal(self, target... | [
5,
6,
7,
8,
9
] |
74 | 0de657ee173b606ad61d614a6168c00fcd571a70 | <mask token>
| <mask token>
def test_convert_nc_2010_to_na_2310():
ffi_in, ffi_out = 2010, 2310
infile = os.path.join(cached_outputs, f'{ffi_in}.nc')
outfile = os.path.join(test_outputs, f'{ffi_out}_from_nc_{ffi_in}.na')
x = nappy.nc_interface.nc_to_na.NCToNA(infile, requested_ffi=ffi_out)
x.writeNAFiles(outfile... | import os
from .common import cached_outputs, data_files, test_outputs
import nappy.nc_interface.na_to_nc
import nappy.nc_interface.nc_to_na
def test_convert_nc_2010_to_na_2310():
ffi_in, ffi_out = 2010, 2310
infile = os.path.join(cached_outputs, f'{ffi_in}.nc')
outfile = os.path.join(test_outputs, f'{ffi... | import os
from .common import cached_outputs, data_files, test_outputs
import nappy.nc_interface.na_to_nc
import nappy.nc_interface.nc_to_na
def test_convert_nc_2010_to_na_2310():
ffi_in, ffi_out = (2010, 2310)
infile = os.path.join(cached_outputs, f"{ffi_in}.nc")
outfile = os.path.join(test_outputs, f... | null | [
0,
1,
2,
3
] |
75 | 06638b361c1cbe92660d242969590dfa45b63a4d | <mask token>
| <mask token>
mathfont.save(f)
<mask token>
mathfont.save(f)
<mask token>
mathfont.save(f)
<mask token>
mathfont.save(f)
<mask token>
mathfont.save(f)
<mask token>
mathfont.save(f)
| <mask token>
v1 = 5 * mathfont.em
v2 = 1 * mathfont.em
f = mathfont.create('stack-bottomdisplaystyleshiftdown%d-axisheight%d' % (
v1, v2), 'Copyright (c) 2016 MathML Association')
f.math.AxisHeight = v2
f.math.StackBottomDisplayStyleShiftDown = v1
f.math.StackBottomShiftDown = 0
f.math.StackDisplayStyleGapMin = 0
f... | from utils import mathfont
import fontforge
v1 = 5 * mathfont.em
v2 = 1 * mathfont.em
f = mathfont.create('stack-bottomdisplaystyleshiftdown%d-axisheight%d' % (
v1, v2), 'Copyright (c) 2016 MathML Association')
f.math.AxisHeight = v2
f.math.StackBottomDisplayStyleShiftDown = v1
f.math.StackBottomShiftDown = 0
f.mat... | #!/usr/bin/env python3
from utils import mathfont
import fontforge
v1 = 5 * mathfont.em
v2 = 1 * mathfont.em
f = mathfont.create("stack-bottomdisplaystyleshiftdown%d-axisheight%d" % (v1, v2),
"Copyright (c) 2016 MathML Association")
f.math.AxisHeight = v2
f.math.StackBottomDisplayStyleShiftDown = ... | [
0,
1,
2,
3,
4
] |
76 | 2dd59681a0dcb5d3f1143385100c09c7783babf4 | <mask token>
| <mask token>
for line in ratings_dat:
arr = line.split('::')
new_line = '\t'.join(arr)
ratings_csv.write(new_line)
ratings_dat.close()
ratings_csv.close()
| ratings_dat = open('../data/movielens-1m/users.dat', 'r')
ratings_csv = open('../data/movielens-1m/users.txt', 'w')
for line in ratings_dat:
arr = line.split('::')
new_line = '\t'.join(arr)
ratings_csv.write(new_line)
ratings_dat.close()
ratings_csv.close()
| #!/usr/bin/env python
# script :: creating a datamodel that fits mahout from ratings.dat
ratings_dat = open('../data/movielens-1m/users.dat', 'r')
ratings_csv = open('../data/movielens-1m/users.txt', 'w')
for line in ratings_dat:
arr = line.split('::')
new_line = '\t'.join(arr)
ratings_csv.write(new_line)
rati... | null | [
0,
1,
2,
3
] |
77 | 5ce98ae241c0982eeb1027ffcff5b770f94ff1a3 | <mask token>
| <mask token>
with open('Civ VI Modding Companion - Events.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if i < 4:
i += 1
continue
eventName = row[3]
eventType = 'GameEvents' if len(row[10]) > 0 else 'Ev... | <mask token>
events = {}
eventTypes = set()
eventIndices = {}
i = 0
with open('Civ VI Modding Companion - Events.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if i < 4:
i += 1
continue
eventName = row[3]
... | import csv
import os
events = {}
eventTypes = set()
eventIndices = {}
i = 0
with open('Civ VI Modding Companion - Events.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if i < 4:
i += 1
continue
eventName = row[3... | import csv
import os
events = {}
eventTypes = set()
eventIndices = {}
i = 0
with open('Civ VI Modding Companion - Events.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if i < 4:
i += 1
continue
eventName = row[3]
eventType = "GameEvents" if... | [
0,
1,
2,
3,
4
] |
78 | 79c043fc862e77bea5adc3f1c6bb9a6272f19c75 | <mask token>
| <mask token>
name = socket.gethostname()
| import socket
name = socket.gethostname()
| #!/usr/bin/env python
import socket
name = socket.gethostname()
| null | [
0,
1,
2,
3
] |
79 | 22c498d84f40455d89ed32ccf3bf8778cb159579 | <mask token>
| <mask token>
if __name__ == '__main__':
bestPrecision = [0, 0, 0, 0, 0, 0]
bestPrecisionFile = ['', '', '', '', '', '']
bestRecall = [0, 0, 0, 0, 0, 0]
bestRecallFile = ['', '', '', '', '', '']
bestSupport = [0, 0, 0, 0, 0, 0]
bestSupportFile = ['', '', '', '', '', '']
bestF1_Score = [0, 0, ... | import os
import pandas as pd
from tabulate import tabulate
if __name__ == '__main__':
bestPrecision = [0, 0, 0, 0, 0, 0]
bestPrecisionFile = ['', '', '', '', '', '']
bestRecall = [0, 0, 0, 0, 0, 0]
bestRecallFile = ['', '', '', '', '', '']
bestSupport = [0, 0, 0, 0, 0, 0]
bestSupportFile = ['',... | import os
import pandas as pd
from tabulate import tabulate
if __name__ == '__main__':
bestPrecision = [0,0,0,0,0,0]
bestPrecisionFile = ['','','','','','']
bestRecall = [0,0,0,0,0,0]
bestRecallFile = ['','','','','','']
bestSupport = [0,0,0,0,0,0]
bestSupportFile = ['','','','','','']
bes... | null | [
0,
1,
2,
3
] |
80 | 5b8c95354f8b27eff8226ace52ab9e97f98ae217 | <mask token>
class my_image_csv_dataset(Dataset):
def __init__(self, data_dir, data, transforms_=None, obj=False,
minorities=None, diffs=None, bal_tfms=None):
self.data_dir = data_dir
self.data = data
self.transforms_ = transforms_
self.tfms = None
self.obj = obj
... | <mask token>
class my_image_csv_dataset(Dataset):
def __init__(self, data_dir, data, transforms_=None, obj=False,
minorities=None, diffs=None, bal_tfms=None):
self.data_dir = data_dir
self.data = data
self.transforms_ = transforms_
self.tfms = None
self.obj = obj
... | <mask token>
class my_image_csv_dataset(Dataset):
def __init__(self, data_dir, data, transforms_=None, obj=False,
minorities=None, diffs=None, bal_tfms=None):
self.data_dir = data_dir
self.data = data
self.transforms_ = transforms_
self.tfms = None
self.obj = obj
... | <mask token>
class my_image_csv_dataset(Dataset):
def __init__(self, data_dir, data, transforms_=None, obj=False,
minorities=None, diffs=None, bal_tfms=None):
self.data_dir = data_dir
self.data = data
self.transforms_ = transforms_
self.tfms = None
self.obj = obj
... | from dai_imports import*
from obj_utils import*
import utils
class my_image_csv_dataset(Dataset):
def __init__(self, data_dir, data, transforms_ = None, obj = False,
minorities = None, diffs = None, bal_tfms = None):
self.data_dir = data_dir
self.data = data
... | [
15,
16,
19,
25,
29
] |
81 | 64c32b3ada7fff51a7c4b07872b7688e100897d8 | class Node(object):
<mask token>
class tree(object):
def __init__(self):
self.root = None
def insert(self, root, value):
if self.root == None:
self.root = Node(value)
elif value < root.data:
if root.left is None:
root.left = Node(value)
... | class Node(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
self.parent = None
class tree(object):
def __init__(self):
self.root = None
def insert(self, root, value):
if self.root == None:
self.root = Node... | class Node(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
self.parent = None
class tree(object):
def __init__(self):
self.root = None
def insert(self, root, value):
if self.root == None:
self.root = Node... | class Node(object):
def __init__(self, data):
self.data = data
self.left = None
self.right = None
self.parent = None
class tree(object):
def __init__(self):
self.root = None
def insert(self, root, value):
if self.root == None:
self.root = Node... | class Node(object):
def __init__(self,data):
self.data = data
self.left = None
self.right = None
self.parent = None
class tree(object):
def __init__(self):
self.root = None
def insert(self,root,value):
if self.root == None:
self.root = No... | [
7,
8,
9,
10,
11
] |
82 | 88ec9484e934ce27b13734ca26f79df71b7677e6 | <mask token>
| <mask token>
if len(sys.argv) < 2:
print('Syntax : python %s <port>') % str(sys.argv[0])
else:
print('-' * 55)
print('HTB WEB-CHALLENGE coded by ZyperX [Freelance]')
print('-' * 55)
r = requests.session()
port = str(sys.argv[1])
url = 'http://docker.hackthebox.eu:'
url = url + port
u... | <mask token>
if len(sys.argv) < 2:
print('Syntax : python %s <port>') % str(sys.argv[0])
else:
print('-' * 55)
print('HTB WEB-CHALLENGE coded by ZyperX [Freelance]')
print('-' * 55)
r = requests.session()
port = str(sys.argv[1])
url = 'http://docker.hackthebox.eu:'
url = url + port
u... | import requests
from bs4 import BeautifulSoup
import sys
import re
if len(sys.argv) < 2:
print('Syntax : python %s <port>') % str(sys.argv[0])
else:
print('-' * 55)
print('HTB WEB-CHALLENGE coded by ZyperX [Freelance]')
print('-' * 55)
r = requests.session()
port = str(sys.argv[1])
url = 'ht... | import requests
from bs4 import BeautifulSoup
import sys
import re
if len(sys.argv)<2:
print("Syntax : python %s <port>")%(str(sys.argv[0]))
else:
print('-'*55)
print("HTB WEB-CHALLENGE coded by ZyperX [Freelance]")
print('-'*55)
r=requests.session()
port=str(sys.argv[1])
url="http://docker.hackthebox.eu:"
url=... | [
0,
1,
2,
3,
4
] |
83 | cd2e03666a890d6e9ea0fcb45fe28510d684916d | <mask token>
def squeezed(client_name):
return client_name.replace('Индивидуальный предприниматель', 'ИП')
def get_kkm_filled_fn(max_fill=80):
LOGIN_URL = 'https://pk.platformaofd.ru/auth/login'
API_URL = 'https://pk.platformaofd.ru/api/monitoring'
session = requests.Session()
print('-= подключе... | <mask token>
def squeezed(client_name):
return client_name.replace('Индивидуальный предприниматель', 'ИП')
def get_kkm_filled_fn(max_fill=80):
LOGIN_URL = 'https://pk.platformaofd.ru/auth/login'
API_URL = 'https://pk.platformaofd.ru/api/monitoring'
session = requests.Session()
print('-= подключе... | <mask token>
def squeezed(client_name):
return client_name.replace('Индивидуальный предприниматель', 'ИП')
def get_kkm_filled_fn(max_fill=80):
LOGIN_URL = 'https://pk.platformaofd.ru/auth/login'
API_URL = 'https://pk.platformaofd.ru/api/monitoring'
session = requests.Session()
print('-= подключе... | import requests
def squeezed(client_name):
return client_name.replace('Индивидуальный предприниматель', 'ИП')
def get_kkm_filled_fn(max_fill=80):
LOGIN_URL = 'https://pk.platformaofd.ru/auth/login'
API_URL = 'https://pk.platformaofd.ru/api/monitoring'
session = requests.Session()
print('-= подкл... | import requests
def squeezed (client_name):
return client_name.replace('Индивидуальный предприниматель', 'ИП')
def get_kkm_filled_fn(max_fill=80):
## возвращает список ККМ с заполнением ФН больше max_fill в %
LOGIN_URL = 'https://pk.platformaofd.ru/auth/login'
API_URL = 'https://pk.platformaofd.ru/api/mon... | [
2,
3,
4,
5,
6
] |
84 | 709f2425bc6e0b0b650fd6c657df6d85cfbd05fe | <mask token>
| <mask token>
def test_petite_vue(request):
return render(request, 'petite_vue_app/test-form.html')
| from django.shortcuts import render
def test_petite_vue(request):
return render(request, 'petite_vue_app/test-form.html')
| from django.shortcuts import render
# Create your views here.
def test_petite_vue(request):
return render(request, 'petite_vue_app/test-form.html')
| null | [
0,
1,
2,
3
] |
85 | a4deb67d277538e61c32381da0fe4886016dae33 | <mask token>
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(Net, self).__init__()
self.h1 = nn.Linear(input_size, hidden_size)
self.h2 = nn.Linear(hidden_size, hidden_size_1)
self.h3 = nn.Linear(hidden_size_1, hidden_size_2)
self.h4 =... | <mask token>
for file in glob.glob('*.jpg'):
images.append(file)
<mask token>
for i in range(train_num + test_num):
tags = labels.iloc[i]['tags']
if i < train_num:
train_images.append(imageio.imread(images[i], as_gray=True).flatten())
train_labels.append(int('cloudy' not in tags and 'haze' n... | <mask token>
fileDir = os.getcwd()
input_size = 65536
hidden_size = 20
hidden_size_1 = 15
hidden_size_2 = 10
hidden_size_3 = 5
num_classes = 1
learning_rate = 0.001
num_epochs = 5
train_num = 1000
test_num = 148
images = []
for file in glob.glob('*.jpg'):
images.append(file)
images = sorted(images, key=lambda filen... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import cv2
import imageio
import pandas as pd
import glob, os
import numpy as np
fileDir = os.getcwd()
input_size = 65536
hidden_size = 20
hidden_size_1 = 15
hidden_size_2 = 10
hidden_size_3 = 5
num_classes = 1
learning_rate ... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import cv2
import imageio
import pandas as pd
import glob, os
import numpy as np
fileDir = os.getcwd()
# os.chdir("./train-jpg")
# there are 40480 training examples
# we will allocate 39000 for training
# and the remaining ... | [
3,
4,
5,
6,
7
] |
86 | 914f477518918619e0e42184bd03c2a7ed16bb01 | <mask token>
class Relation_type(models.Model):
<mask token>
<mask token>
def __str__(self):
return str(self.name)
class Relation(models.Model):
id_relation = models.AutoField(primary_key=True)
id_person1 = models.ForeignKey(Person, on_delete=models.PROTECT,
related_name='who1')... | <mask token>
class Person(models.Model):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __str__(self):
return str(self.nickname) + ' ' + self.last_name + '' + self.first_name
class Contact_type... | <mask token>
class Location(models.Model):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
class Person(models.Model):
id_person = models.AutoField(primary_key=True)
nickname = models.Ch... | <mask token>
class Location(models.Model):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __str__(self):
return str(self.name) + ' - ' + str(self.country) + ': ' + str(self
.city)
c... | from django.db import models
class Location(models.Model):
id_location = models.AutoField(primary_key=True)
city = models.CharField(max_length=100, null=True)
street_name = models.CharField(max_length=100, null=True)
street_number = models.IntegerField(null=True)
zip = models.IntegerField(null=Tru... | [
9,
18,
20,
21,
24
] |
87 | cdbf9427d48f0a5c53b6efe0de7dfea65a8afd83 | <mask token>
def request_id():
global req_c, pid
if req_c is None:
req_c = random.randint(1000 * 1000, 1000 * 1000 * 1000)
if pid is None:
pid = str(os.getpid())
req_id = req_c = req_c + 1
req_id = hex(req_id)[2:].zfill(8)[-8:]
return pid + '-' + req_id
| <mask token>
def string_id(length=8):
""" Generate Random ID.
Random ID contains ascii letters and digitis.
Args:
length (int): Character length of id.
Returns:
Random id string.
"""
return ''.join(random.choice(string.ascii_letters + string.digits) for
_ in range(le... | <mask token>
random_generator = random.SystemRandom()
def string_id(length=8):
""" Generate Random ID.
Random ID contains ascii letters and digitis.
Args:
length (int): Character length of id.
Returns:
Random id string.
"""
return ''.join(random.choice(string.ascii_letters +... | import os
import random
import string
random_generator = random.SystemRandom()
def string_id(length=8):
""" Generate Random ID.
Random ID contains ascii letters and digitis.
Args:
length (int): Character length of id.
Returns:
Random id string.
"""
return ''.join(random.choi... | # -*- coding: utf-8 -*-
# Copyright (c) 2018-2020 Christiaan Frans Rademan <chris@fwiw.co.za>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the ... | [
1,
2,
3,
4,
5
] |
88 | c4624425f57211e583b5fbaec3943539ce6fea6f | <mask token>
| <mask token>
class BlogPostForm(forms.ModelForm):
class Meta:
model = BlogPost
fields = '__all__'
| from django import forms
from .models import BlogPost
class BlogPostForm(forms.ModelForm):
class Meta:
model = BlogPost
fields = '__all__'
| null | null | [
0,
1,
2
] |
89 | a42f36fca2f65d0c5c9b65055af1814d8b4b3d42 | <mask token>
| <mask token>
BUILTINS_MODULE_NAME = 'builtins'
<mask token>
| #!/usr/bin/env python3
# --------------------( LICENSE )--------------------
# Copyright (c) 2014-2023 Beartype authors.
# See "LICENSE" for further details.
'''
Project-wide **standard Python module globals** (i.e., global constants
describing modules and packages bundled with CPython's sta... | null | null | [
0,
1,
2
] |
90 | c23125018a77508dad6fd2cb86ec6d556fbd1019 | <mask token>
| <mask token>
os.system('psfex -dd > config.psfex')
if ic.use_backsub:
prefix = 'b'
else:
prefix = ''
<mask token>
f.write('\n')
f.write('#############################' + '\n')
f.write('##### Scripts for PSFEx #####' + '\n')
f.write('#############################' + '\n')
f.write('\n')
for i in np.arange(len(ic.... | <mask token>
start_time = time.time()
<mask token>
os.system('psfex -dd > config.psfex')
if ic.use_backsub:
prefix = 'b'
else:
prefix = ''
f = open('psfex_all.sh', 'w')
f.write('\n')
f.write('#############################' + '\n')
f.write('##### Scripts for PSFEx #####' + '\n')
f.write('########################... | <mask token>
import time
start_time = time.time()
import numpy as np
import glob, os
from astropy.io import fits
import init_cfg as ic
os.system('psfex -dd > config.psfex')
if ic.use_backsub:
prefix = 'b'
else:
prefix = ''
f = open('psfex_all.sh', 'w')
f.write('\n')
f.write('#############################' + '\n... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 21 11:40:26 2020
@author: jlee
"""
import time
start_time = time.time()
import numpy as np
import glob, os
from astropy.io import fits
import init_cfg as ic
# ----- Making scripts for PSFEx ----- #
os.system("psfex -dd > config.psfex")
if ic.... | [
0,
1,
2,
3,
4
] |
91 | 81688d51696156905736b5de7a4929387fd385ab | <mask token>
def train(cfg, epoch, data_loader, model):
data_time = AverageMeter('Data', ':6.3f')
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(len(data_loader) - 1, [batch_time, data_time,
losses], prefix=f'Epoch: [{epoch}]\t')
m... | <mask token>
def train(cfg, epoch, data_loader, model):
data_time = AverageMeter('Data', ':6.3f')
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(len(data_loader) - 1, [batch_time, data_time,
losses], prefix=f'Epoch: [{epoch}]\t')
m... | <mask token>
def train(cfg, epoch, data_loader, model):
data_time = AverageMeter('Data', ':6.3f')
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
progress = ProgressMeter(len(data_loader) - 1, [batch_time, data_time,
losses], prefix=f'Epoch: [{epoch}]\t')
m... | import argparse
import datetime
import importlib
import pprint
import time
import random
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from utils import get_git_state, time_print, AverageMeter, ProgressMeter, save_checkpoint
def train(cfg, epoch, data_loader, model):
data_time ... | import argparse
import datetime
import importlib
import pprint
import time
import random
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from utils import get_git_state, time_print, AverageMeter, ProgressMeter, save_checkpoint
def train(cfg, epoch, data_loader, model):
data_tim... | [
2,
3,
4,
5,
6
] |
92 | d90942f22cbbd9cfc3a431b7857cd909a7690966 | <mask token>
| OK = 200
CREATED = 201
NOT_MODIFIED = 304
UNAUTHORIZED = 401
FORBIDDEN = 403
BAD_REQUEST = 400
NOT_FOUND = 404
CONFLICT = 409
UNPROCESSABLE = 422
INTERNAL_SERVER_ERROR = 500
NOT_IMPLEMENTED = 501
SERVICE_UNAVAILABLE = 503
ADMIN = 'admin'
ELITE = 'elite'
NOOB = 'noob'
WITHDRAW = 'withdraw'
FUND = 'fund'
| null | null | null | [
0,
1
] |
93 | 54ec1961f4835f575e7129bd0b2fcdeb97be2f03 | <mask token>
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.execute('SELECT 1 FROM databases WHERE name = ?', (db_name,))
if cur.fetchone():
... | <mask token>
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.execute('SELECT 1 FROM databases WHERE name = ?', (db_name,))
if cur.fetchone():
... | <mask token>
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.execute('SELECT 1 FROM databases WHERE name = ?', (db_name,))
if cur.fetchone():
... | import configparser
import sqlite3
import time
import uuid
from duoquest.tsq import TableSketchQuery
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.exe... | import configparser
import sqlite3
import time
import uuid
from duoquest.tsq import TableSketchQuery
def input_db_name(conn):
while True:
db_name = input('Database name (default: concert_singer) > ')
if not db_name:
db_name = 'concert_singer'
cur = conn.cursor()
cur.ex... | [
6,
7,
8,
11,
12
] |
94 | 2fe20f28fc7bba6b8188f5068e2b3c8b87c15edc | <mask token>
def replaceNode(nfa, old, new):
if DEBUG:
print('R_Start(%s, %s) ---' % (old, new), nfa)
if old in nfa._deltas:
for input in nfa._deltas[old]:
nfa.addDelta(new, input, nfa._deltas[old][input])
del nfa._deltas[old]
if DEBUG:
print('R_SwitchedSource(%... | <mask token>
def copyDeltas(src):
out = dict()
for k in src:
out[k] = dict()
for k2 in src[k]:
out[k][k2] = copy(src[k][k2])
return out
def replaceNode(nfa, old, new):
if DEBUG:
print('R_Start(%s, %s) ---' % (old, new), nfa)
if old in nfa._deltas:
for ... | <mask token>
def copyDeltas(src):
out = dict()
for k in src:
out[k] = dict()
for k2 in src[k]:
out[k][k2] = copy(src[k][k2])
return out
def replaceNode(nfa, old, new):
if DEBUG:
print('R_Start(%s, %s) ---' % (old, new), nfa)
if old in nfa._deltas:
for ... | from util import AutomataError
from automata import NFA
from base import Node
from copy import copy, deepcopy
from os.path import commonprefix
DEBUG = False
LAMBDA = u'λ'
PHI = u'Ø'
def copyDeltas(src):
out = dict()
for k in src:
out[k] = dict()
for k2 in src[k]:
out[k][k2] = copy(... | from util import AutomataError
from automata import NFA
from base import Node
from copy import copy, deepcopy
from os.path import commonprefix
DEBUG = False
LAMBDA = u'\u03bb'
PHI = u'\u00d8'
def copyDeltas(src):
out = dict()
for k in src:
out[k] = dict()
for k2 in src[k]:
out[k]... | [
8,
9,
11,
13,
14
] |
95 | aa579025cacd11486a101b2dc51b5ba4997bf84a | <mask token>
| class UrlPath:
<mask token>
| class UrlPath:
@staticmethod
def combine(*args):
result = ''
for path in args:
result += path if path.endswith('/') else '{}/'.format(path)
return result
| class UrlPath:
@staticmethod
def combine(*args):
result = ''
for path in args:
result += path if path.endswith('/') else '{}/'.format(path)
#result = result[:-1]
return result | null | [
0,
1,
2,
3
] |
96 | a1304f290e0346e7aa2e22d9c2d3e7f735b1e8e7 |
# We don't need no stinking models but django likes this file to be there if you are an app
| null | null | null | null | [
1
] |
97 | 368e209f83cc0cade81791c8357e01e7e3f940c8 | <mask token>
| <mask token>
urllib3.disable_warnings()
<mask token>
print(key.decode('ascii'))
| <mask token>
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>') + 3:data.index(b'</b>')]
print(key.decode('ascii'))
| import requests
import urllib3
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>') + 3:data.index(b'</b>')]
print(key.decode('ascii'))
| #!/usr/bin/python3
import requests
import urllib3
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>')+3:data.index(b'</b>')]
print(key.decode('ascii'))
| [
0,
1,
2,
3,
4
] |
98 | 57516a17c1f3ee208076852369999d74dbb2b3ba | def helloWorld():
print "We are in DEMO land!"
for i in range(10):
helloWorld()
print listBuilder()
def listBuilder():
b = []
for x in range(5):
b.append(10 * x)
return b
print "[done, for real]"
| null | null | null | null | [
0
] |
99 | 174f744b641ee20272713fa2fe1991cb2c76830a | <mask token>
| <mask token>
class Brokerage(models.Model):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
... | <mask token>
class Brokerage(models.Model):
BrokerageName = models.CharField(max_length=500)
ReviewLink = models.CharField(max_length=1000)
ContactLink = models.CharField(max_length=1000)
TotalAgents = models.IntegerField()
Location = models.CharField(max_length=500)
Desks = models.IntegerFiel... | from django.db import models
class Brokerage(models.Model):
BrokerageName = models.CharField(max_length=500)
ReviewLink = models.CharField(max_length=1000)
ContactLink = models.CharField(max_length=1000)
TotalAgents = models.IntegerField()
Location = models.CharField(max_length=500)
Desks = mo... | from django.db import models
class Brokerage(models.Model):
BrokerageName = models.CharField(max_length=500)
#To-Do Fix additional settings for ImagesFields/FileFields
#BrokerageLogo = ImageField
ReviewLink = models.CharField(max_length=1000)
ContactLink = models.CharField(max_length=1000)
TotalAgents = models.I... | [
0,
1,
2,
3,
4
] |
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