index int64 0 10k | blob_id stringlengths 40 40 | step-1 stringlengths 0 305k | step-2 stringlengths 6 1.1M ⌀ | step-3 stringlengths 15 1.23M ⌀ | step-4 stringlengths 23 1.34M ⌀ | step-5 stringlengths 55 1.2M ⌀ | step-ids listlengths 1 5 |
|---|---|---|---|---|---|---|---|
200 | 1a7e83fe9528b177246d6374ddaf2a76a0046e83 | <mask token>
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
return None
... | <mask token>
reload(sys)
sys.setdefaultencoding('utf-8')
<mask token>
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq ... | <mask token>
reload(sys)
sys.setdefaultencoding('utf-8')
<mask token>
sentence1 = sys.argv[1]
sentence2 = sys.argv[2]
Divlist1 = jieba.lcut(sentence1, cut_all=True)
Divlist2 = jieba.lcut(sentence2, cut_all=True)
Sen = [' '.join(Divlist1), ' '.join(Divlist2)]
vectorizer = CountVectorizer()
transformer = TfidfTransformer... | import jieba
import os
import sys
import math
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
sentence1 = sys.argv[1]
sentence2 = sys.argv[2]
Divlist1 = jieba.lcut(... | # coding:utf-8
import jieba
import os
import sys
import math
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
#import csv
#import pandas
#import numpy
sente... | [
1,
2,
3,
4,
5
] |
201 | 7e7e96fb9377e4dc59a46a46951f5057ecae419a | <mask token>
| <mask token>
print(np.linalg.norm(mat))
| <mask token>
a = np.log(2) / 25
apdataX = np.random.random((5, 35))
quarter_way_arr = [False, False, False]
quarter_way_arr[0] = True
quarter_way_arr[1] = True
quarter_way_arr[2] = True
mat = np.eye(3)
print(np.linalg.norm(mat))
| import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from simulation_utils import box, simulation
from kinematics import pose3D
a = np.log(2) / 25
apdataX = np.random.random((5, 35))
quarter_way_arr... | # -*- coding: utf-8 -*-
import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from simulation_utils import box, simulation
from kinematics import pose3D
a = np.log(2)/25
apdataX = np.random.random(... | [
0,
1,
2,
3,
4
] |
202 | b6183daa943cc63fd2959e3e54fc1e6af5d761de | <mask token>
| <mask token>
print('valor de D: %.4f' % D)
print('valor de Rey: %.4f' % Rey)
print('valor de k: %.4f' % k)
| <mask token>
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 2e-06
D = (8 * f * L * Q * Q / (math.pi * math.pi * g * DeltaH)) ** 0.2
Re... | import math
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 2e-06
D = (8 * f * L * Q * Q / (math.pi * math.pi * g * DeltaH)) ** 0.2
Rey... | # -*- coding: utf-8 -*-
import math
#COMECE SEU CÓDIGO AQUI
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 0.000002
#PROCESSAMENTO
D =... | [
0,
1,
2,
3,
4
] |
203 | 1490fecd6e983c0e3093a45d77d6fb8afdb54718 | <mask token>
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching... | <mask token>
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching... | <mask token>
def parse_args():
"""
Usual pythonic way of parsing command line arguments
:return: all command line arguments read
"""
args = argparse.ArgumentParser('GODIT')
args.add_argument('-d', '--sketch_size', default=256, type=int, help=
'Sketch Vector Size')
args.add_argument... | import argparse
from sketching import Sketch
from anomaly_detection import AnomalyDetection
from graph_utils import GraphUtils
import glob
import numpy as np
import json
from collections import OrderedDict
from json import JSONDecoder
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics
from ... | # ******************************************************************************
# main.py
#
# Date Name Description
# ======== ========= ========================================================
# 6/5/19 Paudel Initial version,
# ***********************************************************************... | [
1,
2,
6,
9,
10
] |
204 | e5d7cc65041d65f915d4882b4fdad5bebf79a067 | <mask token>
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ->bool:
return hasattr(self.source, 'word_index') and hasattr(self.target,
... | <mask token>
class BaseDataset(SourceTargetMixin):
<mask token>
<mask token>
<mask token>
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ... | <mask token>
class SourceTargetMixin:
<mask token>
def __getitem__(self, item):
if item in ['source', 'target']:
return getattr(self, item)
raise TypeError(
'Subscription is available only with "source" and "target" keywords'
)
class BaseDataset(SourceTar... | from collections import defaultdict
from typing import Union, Iterable, Sized
import numpy as np
from cached_property import cached_property
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
class SourceT... | from collections import defaultdict
from typing import Union, Iterable, Sized
import numpy as np
from cached_property import cached_property
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
class Source... | [
18,
19,
24,
26,
27
] |
205 | e221553f866de8b3e175197a40982506bf8c1ef9 | <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
... | <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
... | <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
... | import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x... | import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x... | [
3,
4,
5,
6,
7
] |
206 | 39dda191ab2137b5f5538660f17e39b0a1358bf4 | <mask token>
| <mask token>
for i in range(nodes):
r, g, b = colorsys.hsv_to_rgb(float(i) / nodes, 1.0, 1.0)
R, G, B = int(255 * r), int(255 * g), int(255 * b)
color = [R, G, B]
print(color)
img[markers == i + 2] = list(color)
<mask token>
cv2.putText(img, text, (160, 20), font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
plt... | <mask token>
img = cv2.imread('coins.jpg')
b, g, r = cv2.split(img)
rgb_img = cv2.merge([r, g, b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
grayBlur = cv2.medianBlur(gray, 3)
ret, thresh = cv2.threshold(grayBlur, 200, 255, cv2.THRESH_BINARY_INV)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
opening ... | import numpy as np
import cv2
import colorsys
from matplotlib import pyplot as plt
img = cv2.imread('coins.jpg')
b, g, r = cv2.split(img)
rgb_img = cv2.merge([r, g, b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
grayBlur = cv2.medianBlur(gray, 3)
ret, thresh = cv2.threshold(grayBlur, 200, 255, cv2.THRESH_BINARY_INV)
... | import numpy as np
import cv2
import colorsys
from matplotlib import pyplot as plt
img = cv2.imread('coins.jpg')
b,g,r = cv2.split(img)
rgb_img = cv2.merge([r,g,b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Blurring image
grayBlur = cv2.medianBlur(gray, 3)
# Binary threshold
ret, thresh = cv2.threshold(grayBl... | [
0,
1,
2,
3,
4
] |
207 | 0e6e84a31b626639e2aa149fd1ef89f3ef251cd7 | <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
<mask token>
def set_dataset(self, dataset):
self.dataset = dataset
<mask token>
| <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
... | <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
... | from synda.tests.context.models import Context as Base
from synda.tests.tests.constants import DATASET_EXAMPLE
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self)... | # -*- coding: utf-8 -*-
##################################
# @program synda
# @description climate models data transfer program
# @copyright Copyright "(c)2009 Centre National de la Recherche Scientifique CNRS.
# All Rights Reserved"
# @license CeCILL (https://raw.g... | [
3,
4,
5,
6,
7
] |
208 | c0d71d970b2632dbf182a5ee8bad27d3e41578f6 | <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
<mask token>
| <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput(... | <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput(... | import sys
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput('i... | #!/Library/Frameworks/Python.framework/Versions/3.7/bin/python3
import sys
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if (line[0] == '+'):
sum += int(line[1:])
else:
sum -= int(line[1:... | [
1,
2,
3,
4,
5
] |
209 | f14d46bedd5f6e0081a982251ad45e95860ef310 | class HashTable:
<mask token>
<mask token>
<mask token>
<mask token>
| class HashTable:
<mask token>
def hash(self, chave):
return int(chave)
<mask token>
<mask token>
| class HashTable:
<mask token>
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
<mask token>
| class HashTable:
def __init__(self):
self.dados = []
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
<mask token>
| class HashTable:
def __init__(self):
self.dados = []
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
"""
backup = dados
dados = novo_array(t * 2)
for elemento in backup:
hash = hash(elemento.chave)
__put(... | [
1,
2,
3,
4,
5
] |
210 | 21a41356fcedb36223498db0fe783e4a9e8e1ba6 | <mask token>
| with open('Book1.txt', 'r') as file1:
with open('20k.txt', 'r') as file2:
same = set(file1).intersection(file2)
same.discard('\n')
with open('notin20kforBook1.txt', 'w') as file_out:
for line in same:
file_out.write(line)
with open('Book2.txt', 'r') as file3:
with open('20k.txt', 'r') as fil... | # help from https://stackoverflow.com/questions/19007383/compare-two-different-files-line-by-line-in-python
with open('Book1.txt', 'r') as file1:
with open('20k.txt', 'r') as file2:
same = set(file1).intersection(file2)
same.discard('\n')
with open('notin20kforBook1.txt', 'w') as file_out:
for line i... | null | null | [
0,
1,
2
] |
211 | de7b5e44c5c213e4ab70b0f8c0c402edaf4926e0 | <mask token>
| <mask token>
app_name = 'user'
urlpatterns = [url('^$', views.index, name='index'), url('login/', views.
login, name='login'), url('regist/', views.regist, name='regist'), url(
'^getuser\\w*/(?P<id>\\d*)', views.getUserById, name='getuser'), url(
'^sendmessage\\w*/(?P<user_telephone>\\d*)', views.sendMessag... | from django.conf.urls import url
from . import views
app_name = 'user'
urlpatterns = [url('^$', views.index, name='index'), url('login/', views.
login, name='login'), url('regist/', views.regist, name='regist'), url(
'^getuser\\w*/(?P<id>\\d*)', views.getUserById, name='getuser'), url(
'^sendmessage\\w*/(?P... |
from django.conf.urls import url
from .import views
app_name='user'
# user子路由
urlpatterns = [
# user首页
url(r'^$',views.index,name='index'),
# 用户登录
url('login/', views.login, name='login'),
# 用户注册
url('regist/', views.regist, name='regist'),
# 根据id判断用户是否存在
url(r'^getuser\w*/(?P<id>\... | null | [
0,
1,
2,
3
] |
212 | cc7f1f38efcd4d757c1d11e2bd53695fca44e15a | <mask token>
| <mask token>
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subclasses())
<mask token>
print(MeatPizza.is_a)
<mask token>
print(MeatPizza... | <mask token>
onto = get_ontology('http://test1.org/onto.owl')
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subclasses())
<mask token>
p... | <mask token>
from owlready2 import *
onto = get_ontology('http://test1.org/onto.owl')
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subc... | 'For learning OWL and owlready2'
'From "https://qiita.com/sci-koke/items/a650c09bf77331f5537f"'
'From "https://owlready2.readthedocs.io/en/latest/class.html"'
'* Owlready2 * Warning: optimized Cython parser module "owlready2_optimized" is not available, defaulting to slower Python implementation'
'↑ This wartning mean... | [
0,
1,
2,
3,
4
] |
213 | a2e2528f560f6117d4ceeb9cd20d3f6f6b2a30a7 | <mask token>
| def testeum():
a = 10
print(id(a))
<mask token>
| def testeum():
a = 10
print(id(a))
def testedois():
a = 10
print(id(a))
| # -*- coding: utf-8 -*-
def testeum():
a = 10
print(id(a))
def testedois():
a = 10
print(id(a)) | null | [
0,
1,
2,
3
] |
214 | e09af436f2fb37d16427aa0b1416d6f2d59ad6c4 | <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep =... | <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep =... | <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep =... | <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep =... | #!/usr/bin/env python3
import argparse
import os
import sys,shutil
from shutil import make_archive
import pathlib
from phpManager import execute,execute_outputfile
from datetime import date,datetime
import re
import pymysql
import tarfile
def append_log(log,message):
f = open(log, "a+")
today = datetime.now()... | [
6,
9,
12,
13,
15
] |
215 | 46adb1834f6013ca0f13a64f280182a805d76278 | <mask token>
| <mask token>
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=module, formatter_class=
... | <mask token>
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argp... | import sys
import argparse
import logging
from pathlib import Path
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must includ... | #!/usr/bin/python3
# encoding: utf-8
import sys
import argparse
import logging
from pathlib import Path
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: ... | [
0,
2,
4,
5,
6
] |
216 | c63e5a2178e82ec6e0e1e91a81145afb735bf7bf | <mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
... | <mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
... | __author__ = 'lei'
<mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
... | __author__ = 'lei'
import unittest
from ch3.node import TreeNode as t
import ch3.searchRange as sr
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
... | __author__ = 'lei'
import unittest
from ch3.node import TreeNode as t
import ch3.searchRange as sr
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b=t(1)
a.left = b
self.assertEqual(sr.searchRange(a,0,4), [1,2])
def test_2(self):
a = t(20)
b = ... | [
3,
4,
5,
6,
7
] |
217 | b77da75b01e96ff89f873f4c5764a62cf68cd576 | <mask token>
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = ('id', 'face_value', 'currency', 'country', 'year',
... | <mask token>
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = 'name', 'flag'
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.M... | <mask token>
__all__ = ('CatalogCoinListSerializer', 'CatalogCoinSerializer',
'SeriesListSerializer', 'CoinListSerializer', 'CoinSerializer',
'CountriesListSerializer')
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = 'name', 'flag'
class... | from rest_framework import serializers
from .models import *
__all__ = ('CatalogCoinListSerializer', 'CatalogCoinSerializer',
'SeriesListSerializer', 'CoinListSerializer', 'CoinSerializer',
'CountriesListSerializer')
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model =... | from rest_framework import serializers
from .models import *
__all__ = (
'CatalogCoinListSerializer', 'CatalogCoinSerializer', 'SeriesListSerializer', 'CoinListSerializer',
'CoinSerializer', 'CountriesListSerializer',
)
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
mode... | [
7,
8,
9,
10,
11
] |
218 | 1f0695f0e9745912d8ee3a87e6c9b1272e9ebbae | <mask token>
| <mask token>
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| <mask token>
filename = 'record_time.txt'
current_time = time.strftime('%a %H:%M:%S')
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| <mask token>
import time
filename = 'record_time.txt'
current_time = time.strftime('%a %H:%M:%S')
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| """
Writes day of the week and time to a file.
Script written for crontab tutorial.
Author: Jessica Yung 2016
"""
import time
filename = "record_time.txt"
# Records time in format Sun 10:00:00
current_time = time.strftime('%a %H:%M:%S')
# Append output to file. 'a' is append mode.
with open(filename, 'a') as hand... | [
0,
1,
2,
3,
4
] |
219 | 142a2ba3ec2f6b35f4339ed9fffe7357c1a85fa0 | <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
... | <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
... | <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
... | import requests
import time
import urllib
import argparse
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from fake_useragent import UserAgent
from multiprocessing import Pool
from lxml.html import fromstring
import os, sys
def search(url):
browser = we... | import requests
import time
import urllib
import argparse
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from fake_useragent import UserAgent
from multiprocessing import Pool
from lxml.html import fromstring
import os, sys
#text = 'chowchowbaby'
#url='https... | [
1,
2,
3,
4,
5
] |
220 | 2a19c2d6e51e9c123236c58f82de1a39e5db40f4 | import numpy as np
# Copyright 2011 University of Bonn
# Author: Hannes Schulz
def cnan(x):
""" check for not-a-number in parameter x """
if np.isnan(x).sum()>0:
import pdb
pdb.set_trace()
def get_curve_3D(eig, alpha=0.25,g23=0.5,g12=0.5): # renumerated according to sato et al: l3 is smallest... | null | null | null | null | [
0
] |
221 | 550f5ad4fef77d5795db0393ae0701f679143e72 | <mask token>
| <mask token>
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print('Error: ' + mockProgramInOutFilePath + ' i... | <mask token>
inputArgs = ' '.join(sys.argv[1:])
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print('Error:... | import sys
import os
inputArgs = ' '.join(sys.argv[1:])
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print... | #!/usr/bin/env python
# @HEADER
# ************************************************************************
#
# TriBITS: Tribal Build, Integrate, and Test System
# Copyright 2013 Sandia Corporation
#
# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
# the U.S. Govern... | [
0,
1,
2,
3,
4
] |
222 | 4fa9d16f979acf3edce05a209e1c6636e50fc315 | <mask token>
class Menu:
<mask token>
<mask token>
<mask token>
<mask token>
| <mask token>
class Menu:
<mask token>
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql = (
"SELECT * FROM get_menu WHERE openid='%s' order by watch DESC "
% openid)
self.resql = self.mysqlClass.sele... | <mask token>
class Menu:
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql = (
"SELECT * FROM get_menu WHERE openid='%s' order by watch D... | from jox_api import label_image, Mysql, Utils
from jox_config import api_base_url
import json
class Menu:
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql =... | from jox_api import label_image,Mysql,Utils
from jox_config import api_base_url
import json
class Menu():
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self,type,openid):
try:
if type == 'mine':
self.sql = "SEL... | [
1,
3,
5,
6,
7
] |
223 | e5e012e40a71dee9f4dbd9913590aef125b758df | <mask token>
class Visualiser:
<mask token>
<mask token>
<mask token>
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
c... | <mask token>
class Visualiser:
<mask token>
<mask token>
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []... | <mask token>
class Visualiser:
coordinate_map = 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
... | from classes.Board import Board
class Visualiser:
coordinate_map = 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for lo... | from classes.Board import Board
class Visualiser:
coordinate_map = ("a", "b", "c", "d", "e", "f", "g", "h")
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for ... | [
4,
5,
6,
7,
8
] |
224 | c4e4e54ac93c2acdbd3a1cd22b200341a6e45688 | import pyaudio
import numpy as np
from collections import OrderedDict
import utils
class MasterPlayer(object):
def __init__(self, volume=1., samplesPerSecond=44100):
self.p = pyaudio.PyAudio()
self.volume = volume
self.samplesPerSecond = samplesPerSecond
self.individual_callbacks =... | null | null | null | null | [
0
] |
225 | 27e9e63338d422b5fca6f7a67fa3d255602a3358 | <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
<mask token>
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * self.dim]
r1_local_plus = ... | <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4... | <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4... | from abstract_class_V import V
import torch
import torch.nn as nn
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
... | from abstract_class_V import V
import torch
import torch.nn as nn
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self,y,X,nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self... | [
3,
4,
5,
6,
7
] |
226 | ec4348c61cd1c9130543bb20f9ca199399e1caff | class Solution(object):
def restoreIpAddresses(self, s):
"""
:type s: str
:rtype: List[str]
"""
def helper(sb, string, level):
if len(string) == 0:
if level == 4:
ans.append(sb[:-1])
return
if level ... | null | null | null | null | [
0
] |
227 | d0a3f332e04627eb275168972bd92cd1ea9b9447 | <mask token>
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(... | <mask token>
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(... | <mask token>
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.en... | from board.ttt import TTT
from mctsai.mcts import MCTS
import unittest
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
s... | from board.ttt import TTT
from mctsai.mcts import MCTS
import unittest
# skip = [0, 1]
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print("Skipping ai self-play")
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(tt... | [
6,
7,
8,
9,
10
] |
228 | e95bda8be2294c295d89f1c035bc209128fa29c8 | def merge_the_tools(string, k):
# your code goes here
num_sub_strings = len(string)/k
#print num_sub_strings
for idx in range(num_sub_strings):
print "".join(set(list(string[idx * k : (idx + 1) * k])))
| null | null | null | null | [
0
] |
229 | f85a703b47d981397ed6048e941030a3fbee7b6d | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('talk', '0023_auto_20180207_1121')]
operations = [migrations.AddField(model_name='talkmedia', name=
'codelink', field=models.CharField(blank=True, max_length=255,
verbose_name='Source code'))]
| from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('talk', '0023_auto_20180207_1121')]
operations = [migrations.AddField(model_name='talkmedia', name=
'codelink', field=models.CharField(blank=True, max_length=255,
... | # -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2018-04-27 08:05
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('talk', '0023_auto_20180207_1121'),
]
operations = [
migrations.AddField(
... | [
0,
1,
2,
3,
4
] |
230 | 23375760c0943ca177b7009031d9d17a91165c5c | #!/usr/bin/env python
#--coding: utf8--
import time
if __name__ == '__main__':
date = time.strftime('%m-%d')
if date == '03-08':
print '女神节'
elif date == '02-14':
print '情人节'
else:
print '发红包'
print '这是一个测试题' | null | null | null | null | [
0
] |
231 | f8d815bcdc74452b66a1b3b33bf0fbe976e728c8 | <mask token>
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
img = Image.open(filepath).convert('L')
data = list(img.g... | <mask token>
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[i] for i in idx]
labels_shuffle = [labels[i] for i in idx]
return np.asarray(data_shu... | <mask token>
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[i] for i in idx]
labels_shuffle = [labels[i] for i in idx]
return np.asarray(data_shu... | import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import glob
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.s... | # This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy ... | [
1,
4,
5,
6,
7
] |
232 | ef85f94282bfd7c9491c4e28bab61aaab5c792a5 | <mask token>
class Ui_Tab(object):
<mask token>
<mask token>
| <mask token>
class Ui_Tab(object):
<mask token>
def retranslateUi(self, Tab):
_translate = QtCore.QCoreApplication.translate
Tab.setWindowTitle(_translate('Tab', 'Form'))
self.btn_enterPassword.setText(_translate('Tab', 'Enter Password'))
| <mask token>
class Ui_Tab(object):
def setupUi(self, Tab):
Tab.setObjectName('Tab')
Tab.resize(762, 523)
self.verticalLayout = QtWidgets.QVBoxLayout(Tab)
self.verticalLayout.setObjectName('verticalLayout')
self.hLayout = QtWidgets.QHBoxLayout()
self.hLayout.setObje... | from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Tab(object):
def setupUi(self, Tab):
Tab.setObjectName('Tab')
Tab.resize(762, 523)
self.verticalLayout = QtWidgets.QVBoxLayout(Tab)
self.verticalLayout.setObjectName('verticalLayout')
self.hLayout = QtWidgets.QHBoxLayout(... | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'src/ui_LibraryTab.ui'
#
# Created: Tue Jun 9 21:46:41 2015
# by: PyQt5 UI code generator 5.4
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Tab(object):
def setupUi(se... | [
1,
2,
3,
4,
5
] |
233 | 8c8bbbc682889c8d79c893f27def76ad70e8bf8d | <mask token>
| DATABASE_NAME = 'user_db'
| DATABASE_NAME = "user_db" | null | null | [
0,
1,
2
] |
234 | 9d904225afd4f4d0cf338ae16f031f8ab41639ad | <mask token>
class FragmentMakoChain(Fragment):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
... | <mask token>
class FragmentMakoChain(Fragment):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
... | <mask token>
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
Для того, чтобы цепочка отработала, шаблон должен наследоваться от шаблона
ifmo_xblock_base и определять блок block_body.
Порядок оборачивания не определён.
... | from mako.template import Template
from xblock.fragment import Fragment
from .lookup import TemplateLookup
from .utils import deep_update
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
Для того, чтобы цепочка отработала, шаблон ... | # -*- coding=utf-8 -*-
from mako.template import Template
from xblock.fragment import Fragment
from .lookup import TemplateLookup # xblock_ifmo.lookup
from .utils import deep_update
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
... | [
6,
7,
10,
11,
12
] |
235 | 8cc0393082448bb8f61068b5c96e89ef3aee77ed | <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
<mas... | <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
<mas... | <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
def... | <mask token>
def migrate_dn_pairs(settings):
grp_dn_pairs = get_group_dn_pairs()
if grp_dn_pairs is None:
logger.warning(
'get group dn pairs from db failed when migrate dn pairs.')
return
grp_dn_pairs.reverse()
for grp_dn_pair in grp_dn_pairs:
for config in setting... | #coding: utf-8
import logging
from threading import Thread
from ldap import SCOPE_BASE
from seafevents.ldap_syncer.ldap_conn import LdapConn
from seafevents.ldap_syncer.utils import bytes2str, add_group_uuid_pair
from seaserv import get_group_dn_pairs
logger = logging.getLogger(__name__)
def migrate_dn_pairs(set... | [
7,
8,
9,
10,
13
] |
236 | 21af630bf383ee1bdd0f644283f0ddadde71620a | #!/bin/usr/python2.7.x
import os, re, urllib2
def main():
ip = raw_input(" Target IP : ")
check(ip)
def check(ip):
try:
print "Loading Check File Uploader...."
print 58*"-"
page = 1
while page <= 21:
bing = "http://www.bing.com/search?q=ip%3A" + \
ip + "+upload&count=50&first=" + str(... | null | null | null | null | [
0
] |
237 | eb853e430b996a81dc2ef20c320979a3e04d956a | <mask token>
class Beautyleg7Spider(scrapy.Spider):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.cr... | <mask token>
class Beautyleg7Spider(scrapy.Spider):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.cr... | <mask token>
class Beautyleg7Spider(scrapy.Spider):
name = 'Beautyleg7Spider'
category_list = ['siwameitui', 'xingganmeinv', 'weimeixiezhen',
'ribenmeinv']
start_urls = [('http://www.beautyleg7.com/' + category) for category in
category_list]
const.REPEATED_THRESHOLD = 10
def __in... | import hashlib
import re
from datetime import datetime
import gevent
import requests
import scrapy
from gevent.pool import Pool
from lxml import etree
from scrapy.http import HtmlResponse
from sqlalchemy import create_engine, func
from sqlalchemy.orm import sessionmaker
from ..items import Album, AlbumImageRelationItem... | #!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import hashlib
import re
from datetime import datetime
import gevent
import requests
import scrapy
from gevent.pool import Pool
from lxml import etree
from scrapy.http import HtmlResponse
from sqlalchemy import create_engine, func
from sqlalchemy.orm import sessionmaker
... | [
7,
8,
11,
12,
13
] |
238 | bc4684d255a46427f708d8ce8bda2e12fb8c8ffe | <mask token>
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}... | <mask token>
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}... | <mask token>
API_KEY = '11111111111111111111111111111111'
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = respon... | from route4me import Route4Me
API_KEY = '11111111111111111111111111111111'
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
... | # -*- coding: utf-8 -*-
from route4me import Route4Me
API_KEY = "11111111111111111111111111111111"
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['err... | [
1,
2,
3,
4,
5
] |
239 | d015a1b27a3a9e7f5e6614da752137064000b905 | <mask token>
class my_model:
<mask token>
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"... | <mask token>
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(e... | <mask token>
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(e... | <mask token>
import tensorflow.keras as K
from GPyOpt.methods import BayesianOptimization
import pickle
import os
import numpy as np
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer... | #!/usr/bin/env python3
"""Transfer learning with xception"""
import tensorflow.keras as K
from GPyOpt.methods import BayesianOptimization
import pickle
import os
import numpy as np
class my_model():
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = ... | [
3,
4,
5,
6,
7
] |
240 | ef6f55bf27982f53441215da6822cfcdc80706a5 | <mask token>
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.html', context_dict)
| <mask token>
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.ht... | __author__ = 'Yun'
__project__ = 'DjangoBookTest2'
<mask token>
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.M... | __author__ = 'Yun'
__project__ = 'DjangoBookTest2'
from django.shortcuts import render_to_response
import datetime
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(r... | # -*- coding: utf-8 -*-
__author__ = 'Yun'
__project__ = 'DjangoBookTest2'
# from django.template import Template, Context
# from django.template.loader import get_template
# from django.http import HttpResponse
from django.shortcuts import render_to_response
import datetime
def current_datetime(request):
# now ... | [
1,
2,
3,
4,
5
] |
241 | e8226ab6be5c21335d843cba720e66646a2dee4e | import os
import requests
import sqlite3
from models import analytics, jcanalytics
def populate():
url = 'https://api.clicky.com/api/stats/4?site_id=100716069&sitekey=93c104e29de28bd9&type=visitors-list'
date = '&date=last-30-days'
limit = '&limit=all'
output = '&output=json'
total = url+date+limi... | null | null | null | null | [
0
] |
242 | e616d14827beaa08ab08219421cbf7990cf163fd | <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(se... | <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(se... | <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(se... | <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(se... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.InsureAdmitDTO import InsureAdmitDTO
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partn... | [
8,
12,
13,
14,
16
] |
243 | c4ca4b5c77c3c912b44a4853be30298ec845c4fd | <mask token>
| <mask token>
print(owog.find('e'))
print(owog.count('e'))
print(owog[2:10])
<mask token>
if a > b:
print('a too ih')
elif a == b:
print('tentsuu')
else:
print('b too ih')
<mask token>
for i in range(a, b + 1):
print(i)
| owog = 'Delger'
print(owog.find('e'))
print(owog.count('e'))
print(owog[2:10])
a = 21
b = 21
if a > b:
print('a too ih')
elif a == b:
print('tentsuu')
else:
print('b too ih')
a, b = input().split()
for i in range(a, b + 1):
print(i)
| #str
owog="Delger"
# len()- urt
# lower()- jijigruuleh
# upper()- tomruulah
# capitalize()- ehnii useg tomruulah
# replace()- temdegt solih
print(owog.find("e"))
print(owog.count("e"))
print(owog[2:10])
a=21
b=21
if a>b:
print("a too ih")
elif a==b:
print("tentsuu")
else:
print("b to... | null | [
0,
1,
2,
3
] |
244 | 050e2207ac7331444d39305869c4b25bcbc53907 | <mask token>
| <mask token>
print(
'Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}'
.format(scaler.scale_[8], scaler.min_[8]))
<mask token>
scaled_training_df.to_csv('sales_data_training_scaled.csv', index=False)
scaled_training_df.to_csv('sales_data_test_scaled.csv', index=False)
| <mask token>
training_data_df = pd.read_csv('sales_data_training.csv')
test_data_df = pd.read_csv('sales_data_test.csv')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_df)
print(
'Note: total_earnings values were scale... | import pandas as pd
from sklearn.preprocessing import MinMaxScaler
training_data_df = pd.read_csv('sales_data_training.csv')
test_data_df = pd.read_csv('sales_data_test.csv')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_... | import pandas as pd
from sklearn.preprocessing import MinMaxScaler
#loading data from CSV
training_data_df = pd.read_csv("sales_data_training.csv")
test_data_df = pd.read_csv("sales_data_test.csv")
#scaler
scaler = MinMaxScaler(feature_range=(0,1))
#scale both inputs and outputs
scaled_training = scaler.fit_transfor... | [
0,
1,
2,
3,
4
] |
245 | df64d769ffba8cddac34282a526122e3c941249d | #!/usr/bin/env python
import os
import tempfile
import shutil
import math
import sys
import subprocess
from irank.config import IrankOptionParser, IrankApp
from irank import db as irank_db
STATUS = 0
def main():
p = IrankOptionParser('%prog -d DEST playlist_name [playlist_name ...]')
p.add_option('-d', '--dest', he... | null | null | null | null | [
0
] |
246 | 21b295e28a7e4443ea116df1b22ff5074dca955a | <mask token>
| <mask token>
for i in range(1, n + 1):
print(i)
| n = int(input('nhap gia tri'))
for i in range(1, n + 1):
print(i)
| n =int(input("nhap gia tri"))
for i in range(1,n+1):
print(i) | null | [
0,
1,
2,
3
] |
247 | 93737e4c409d0efb1ae2263cb60d4b03d9aad0d8 | <mask token>
| <mask token>
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
<mask token>
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, file)):
name, ext = os.path.splitext(fi... | <mask token>
ap = argparse.ArgumentParser()
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, ... | import os
import shutil
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.pa... | import os
import shutil
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-D','--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.... | [
0,
1,
2,
3,
4
] |
248 | b5611c668a40e1735c92d6d00867885023ad713f | <mask token>
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c... | <mask token>
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c... | <mask token>
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = c... | target = []
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = co... | target=[]
with open('IntegerArray.txt','r') as f:
target=f.readlines()
for x in range(len(target)):
target[x]=int(target[x])
def f(A):
if len(A)==1:
return 0
else:
rightStart=len(A)//2
leftArray=A[0:rightStart]
righArray=A[rightStart:]
B,b=count_and_sort(leftArray)
C,c=count_and_sort(righA... | [
2,
3,
4,
5,
6
] |
249 | 09f032301fa9389f6b07687e0ee13844e0b4ddf3 | from artichoke import DefaultManager, Config
from artichoke.helpers import read, prompt
from fabric.api import env, task, run
import os
chars = ''.join(chr(c) if chr(c).isupper() or chr(c).islower() else '_' for c in range(256))
class MagicDefaultManager(DefaultManager):
def __init__(self, env):
self.en... | null | null | null | null | [
0
] |
250 | 68b967ecf18d576758cf05e889919944cfc34dcd | <mask token>
class Entity(Agent):
<mask token>
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
... | <mask token>
class Entity(Agent):
<mask token>
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
... | <mask token>
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position =... | <mask token>
from mesa import Agent
from random import shuffle
import numpy as np
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(self, unique_id, model):
super().__in... | """
generalised behaviour for actors and vacancies
"""
from mesa import Agent
from random import shuffle
import numpy as np
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(sel... | [
6,
7,
8,
9,
10
] |
251 | 46babde9c26a944c9d29121b6bbf89a32f242a81 | <mask token>
| <mask token>
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
... | <mask token>
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
... | import simple_draw as sd
import random
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, ... | import simple_draw as sd
import random
# sd.resolution = (1400, 900)
# Prepare data for the sun function
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(ra... | [
0,
1,
2,
3,
4
] |
252 | e736991f364ba9ff709348e4b1f612b1e9673281 | <mask token>
| <mask token>
app = Flask(__name__)
<mask token>
| from flask import *
app = Flask(__name__)
from app import views
from app import admin_views
from app import usr_reg
from app import cookie
from app import db_connect
| from flask import *
app=Flask(__name__)
from app import views
from app import admin_views
from app import usr_reg
from app import cookie
from app import db_connect | null | [
0,
1,
2,
3
] |
253 | 07215403750be53994ae36727b6f790202b88697 | # Inspiration: [Fake Album Covers](https://fakealbumcovers.com/)
from IPython.display import Image as IPythonImage
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import requests
from xml.etree import ElementTree as ET
def display_cover(top,bottom ):
name='album_art_raw.png'
alb... | null | null | null | null | [
0
] |
254 | 18d3f58048b7e5d792eb2494ecc62bb158ac7407 | <mask token>
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state... | <mask token>
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state... | <mask token>
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
d... | from flask import Flask
from flask import render_template
from flask import make_response
import json
from lib import powerswitch
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
... | from flask import Flask
from flask import render_template
from flask import make_response
import json
from lib import powerswitch
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
... | [
5,
6,
7,
8,
9
] |
255 | 869284fa531a93c1b9812ed90a560d0bb2f87e97 | <mask token>
| <mask token>
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| def ep(m, h, el, g=9.8):
E = m * h * g
if E < el:
print('le plus grand est : el')
else:
print('le plus grand est : E')
<mask token>
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| def ep(m, h, el, g=9.8):
E = m * h * g
if E < el:
print('le plus grand est : el')
else:
print('le plus grand est : E')
ep(3, 4, 5)
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| # fonction pour voir quel est le plus grand entre l'energie limite et l'enerve potentiel
def ep (m,h,el,g=9.8):
E=m*h*g
if E<el:
print ("le plus grand est : el")
else:
print ("le plus grand est : E")
ep(3,4,5)
#fontion fibonaci 0 1 1 2 3 5 8 13
def fibonaci(n):
for i in range(0,n,):
... | [
0,
1,
2,
3,
4
] |
256 | d126efa91b964a3a374d546bb860b39ae26dfa22 | <mask token>
class TestGetNumber(unittest.TestCase):
<mask token>
def test_fib(self):
self.assertEqual(Fib(5), 8)
<mask token>
| <mask token>
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
<mask token>
| <mask token>
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
if __name__ == '__main__':
unittest.main()
| <mask token>
import unittest
from bazel_tutorial.examples.py.lib import GetNumber
from bazel_tutorial.examples.py.fibonacci.fib import Fib
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
if __name_... | """A tiny example binary for the native Python rules of Bazel."""
import unittest
from bazel_tutorial.examples.py.lib import GetNumber
from bazel_tutorial.examples.py.fibonacci.fib import Fib
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
... | [
2,
3,
4,
5,
6
] |
257 | e582787a912f479830ed99575b2c6adb8088b4e5 | <mask token>
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_fa... | <mask token>
CORS(app)
<mask token>
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
... | <mask token>
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Orig... | from flask import Flask, request
from flask import jsonify
from preprocessing import QueryProcessor
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result... | from flask import Flask, request
from flask import jsonify
from preprocessing import QueryProcessor
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
resul... | [
2,
3,
4,
5,
6
] |
258 | cf0cf028d5f67e8deca8ebd3ad76d9c1e3563002 | #!/usr/bin/python2
import sys
import argparse
"""
This program generates an extract table having the following format:
<S1> <S2> <S3> ... <Sn> ||| <T1> <T2> <T3> ... <Tk> ||| 0-0
Each line is a mapping from a source sentence to target sentence
with special delimiter characters.
You can give the output of this s... | null | null | null | null | [
0
] |
259 | f8bf7e2d8f06bbd00f04047153833c07bf483fd3 | <mask token>
| <mask token>
class PyrpgConfig(AppConfig):
<mask token>
| <mask token>
class PyrpgConfig(AppConfig):
name = 'PyRPG'
| from django.apps import AppConfig
class PyrpgConfig(AppConfig):
name = 'PyRPG'
| null | [
0,
1,
2,
3
] |
260 | 48677d73f6489ce789884a9dff5d50c23f47d8b3 | <mask token>
class WindowFeatureExtractor(object):
<mask token>
<mask token>
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy ar... | <mask token>
class WindowFeatureExtractor(object):
<mask token>
def __init__(self, feature_extractors, min_feat_frequency, sparse=True,
feature_val=1):
"""
feature_extractors : list of fns
feature extraction fns
min_feat_frequency : in... | __author__ = 'simon.hughes'
<mask token>
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies them to a dataset of windows, and then vectorizes with
the sklearn DictVectorizer class
"""
def __init__(self, ... | __author__ = 'simon.hughes'
from sklearn.feature_extraction import DictVectorizer
from WindowFeatures import compute_middle_index
from collections import Counter
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies the... | __author__ = 'simon.hughes'
from sklearn.feature_extraction import DictVectorizer
from WindowFeatures import compute_middle_index
from collections import Counter
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies the... | [
5,
6,
8,
9,
10
] |
261 | 65aa85675393efa1a0d8e5bab4b1dbf388018c58 |
indelCost = 1
swapCost = 13
subCost = 12
noOp = 0
def alignStrings(x,y):
nx = len(x)
ny = len(y)
S = matrix(nx+1, ny+1) #??
for i in range (nx+1)
for j in range (ny+1)
if i == 0: #if the string is empty
S[i][j] = j #this will put all the letters from j in i
elif j == 0: #if the second string ... | null | null | null | null | [
0
] |
262 | 47c1ad4bd1ceffa38eef467ea8eb59dbd2fc2ebb | <mask token>
class PacketSender:
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked... | <mask token>
class PacketSender:
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def reset(self):
global seq_num
global sent_packets
global next_seq_num
global acked_packets
global acke... | <mask token>
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
seq_num = 0
next_seq_num = 0
sent_packets = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
was_reset = False
def reset(self):
global seq_num
... | from packet import Packet
from packetConstructor import PacketConstructor
import threading
import time
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
seq_num = 0
next_seq_num = 0
sent_packets = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock... | from packet import Packet
from packetConstructor import PacketConstructor
import threading
import time
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
# The next seq num for sent packets
seq_num = 0
# The next seq num for acks that we're waiting for
next_seq_num = 0
... | [
4,
6,
9,
10,
11
] |
263 | 24cdbbadc8ff1c7ad5d42eeb518cb6c2b34724a2 | <mask token>
class EfficientDoubleExchange(AnsatzElement):
<mask token>
<mask token>
<mask token>
<mask token>
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
... | <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescale... | <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescale... | <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescale... | from openfermion import QubitOperator, FermionOperator
from openfermion.transforms import jordan_wigner
from src.utils import QasmUtils, MatrixUtils
from src.ansatz_elements import AnsatzElement, DoubleExchange
import itertools
import numpy
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_... | [
5,
6,
7,
9,
11
] |
264 | 2843845848747c723d670cd3a5fcb7127153ac7e | <mask token>
| <mask token>
@app.route('/<sensor_id>', methods=['GET'])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
return {'sensor': sensors[sensor_pos]}
else:
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/<sensor_id... | <mask token>
def search_index_by_id(sensor_id: str):
for pos, sensor in enumerate(sensors):
if sensor.id == sensor_id:
return pos
return -1
@app.route('/<sensor_id>', methods=['GET'])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
... | <mask token>
app = Flask(__name__)
sensors = [ToggleSensor(id='s-01', description='lampadina'), ToggleSensor(
id='s-02', description='lampadina'), ToggleSensor(id='s-03',
description='allarme atomico'), ToggleSensor(id='s-04', description=
'porta aperta'), Sensor(id='temperature-01', description=
'senso... | from flask import Flask
from sim.toggle import ToggleSensor
from sim.sensor import Sensor
app = Flask(__name__)
sensors = [
ToggleSensor(id="s-01", description="lampadina"),
ToggleSensor(id="s-02", description="lampadina"),
ToggleSensor(id="s-03", description="allarme atomico"),
ToggleSensor(id="s-04... | [
0,
3,
4,
5,
7
] |
265 | f5bd41f4aaff616a332d80ec44c364ffc91c58f0 | <mask token>
| <mask token>
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv, skewed_slPri
| <mask token>
data = pd.read_csv('data/train.csv')
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv, skewed_slPri
| from scipy.stats import skew
import pandas as pd
import numpy as np
data = pd.read_csv('data/train.csv')
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])... | # %load q03_skewness_log/build.py
from scipy.stats import skew
import pandas as pd
import numpy as np
data = pd.read_csv('data/train.csv')
# Write code here:
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice... | [
0,
1,
2,
3,
4
] |
266 | d65f858c3ad06226b83d2627f6d38e03eae5b36c | <mask token>
| <mask token>
def line_evaluation(param_list, param_eval, file_name='line evaluation', **
kwargs):
"""
Evaluates a list of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list : array_like
List of values to test for parameter of interest.
... | <mask token>
def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16,
aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',
save_to_disk=True, save_each=1000, chunksize=1.0):
"""
Evaluates a grid of parameter pairs across repeated trials and aggregates the result.
... | <mask token>
import utils
import datetime
import itertools
import numpy as np
import recovery as rec
import sampling as smp
import graphs_signals as gs
import pathos.multiprocessing as mp
from tqdm import tqdm
def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16,
aggr_method=np.mean, save_d... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Phase transition module
"""
import utils
import datetime
import itertools
import numpy as np
import recovery as rec
import sampling as smp
import graphs_signals as gs
import pathos.multiprocessing as mp
from tqdm import tqdm
## MAIN FUNCTIONS ##
def grid_evalua... | [
0,
1,
2,
3,
4
] |
267 | f97a892e6e0aa258ad917c4a73a66e89b0dc3253 | <mask token>
| <mask token>
sys.path.extend(['detection', 'train'])
<mask token>
if test_mode in ['RNet', 'ONet']:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
if test_mode == 'ONet':
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
<mask token>
if config.input_mode == '1':
path... | <mask token>
sys.path.extend(['detection', 'train'])
<mask token>
test_mode = 'ONet'
thresh = [0.6, 0.7, 0.9]
min_face_size = 24
stride = 2
detectors = [None, None, None]
scale_factor = 0.79
model_path = ['model/PNet/', 'model/RNet/', 'model/ONet']
batch_size = config.batches
detectors[0] = FcnDetector(P_Net, model_pat... | import sys
sys.path.extend(['detection', 'train'])
from MtcnnDetector import MtcnnDetector
from detector import Detector
from fcn_detector import FcnDetector
from model_factory import P_Net, R_Net, O_Net
import config as config
from preprocess.utils import iou
import cv2
import os
from os.path import join, split
import... |
# coding: utf-8
# In[1]:
import sys
sys.path.extend(['detection', 'train'])
# from detection folder
from MtcnnDetector import MtcnnDetector
from detector import Detector
from fcn_detector import FcnDetector
# from train folder
from model_factory import P_Net, R_Net, O_Net
import config as config
from preprocess.uti... | [
0,
1,
2,
3,
4
] |
268 | f19d8aa2104240cc93a0146f1b14c635e7cd3a41 | #! /usr/bin/env python
import ldac
from numpy import *
import shearprofile as sp
import sys
import os, subprocess
import pylab
if len(sys.argv) != 6:
sys.stderr.write("wrong number of arguments!\n")
sys.exit(1)
catfile= sys.argv[1]
clusterz=float(sys.argv[2])
center= map(float,sys.argv[3].split(','))
pixsc... | null | null | null | null | [
0
] |
269 | 0e58834120c34b5152026bde6d089be19244e21a | import os
from MdApi import MdApi
class Adapter(MdApi):
def __init__(self):
super(Adapter, self).__init__()
def connect(self):
self.createFtdcMdApi(os.getcwd())
self.registerFront('tcp://180.168.146.187:10010')
def onFrontConnected(self):
print 'front succ... | null | null | null | null | [
0
] |
270 | df40b0628d6a180a98cd385145ee7c65ecb78256 | <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
<mask token>
class TFRecordProducer:
def remove_list(self, list1, list2):
i, j = 0, 0
tmp_list1 = []
tmp_list2 = []
while i < l... | <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker, target_phase=None,
mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_m... | <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker, target_phase=None,
mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_m... | import os
import tensorflow as tf
import torch
from tqdm import tqdm
from glob import glob
import numpy as np
from collections.abc import Iterable
from utils.hparams import HParam
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
... | import os
import tensorflow as tf
import torch
from tqdm import tqdm
from glob import glob
import numpy as np
from collections.abc import Iterable
from utils.hparams import HParam
#from utils.audio import Audio
#import librosa
#python encoder_inference.py --in_dir training_libri_mel/train/ --gpu_str 5
#python tfrecord... | [
5,
7,
8,
9,
10
] |
271 | ed7b29a4d7f3a48884434373418c3528f2f397ac | <mask token>
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp=0.2)
sys.stdout.write(next_char)
... | <mask token>
def main():
print('Loading model...')
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input('Enter a String: ').strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.s... | <mask token>
def main():
print('Loading model...')
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input('Enter a String: ').strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.s... | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['KERAS_BACKEND'] = 'tensorflow'
import numpy as np
import sys
from util import load_model
from keras.preprocessing.text import hashing_trick
from keras.preprocessing.sequence import pad_sequences
from southpark.southpark_generative import string_one_hot, cha... | #!/usr/bin/python3
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'
os.environ['KERAS_BACKEND'] = 'tensorflow'
import numpy as np
import sys
from util import load_model
from keras.preprocessing.text import hashing_trick
from keras.preprocessing.sequence import pad_sequences
from southpar... | [
2,
4,
5,
7,
8
] |
272 | b6d8a918659f733919fe3bb4be9037e36ad32386 | <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
<mask token>
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Graph:
def __init__(self, V: int, W: int):
self.... | <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Gr... | <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Gr... | import queue
import sys
import logging
from superai.common import InitLog
logger = logging.getLogger(__name__)
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw... | import queue
import sys
import logging
from superai.common import InitLog
logger = logging.getLogger(__name__)
# 2维到1维
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
# 1维到2维
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
# 10x10 cell idx 到 [x,y]
def idxToXY(idx, cel... | [
16,
18,
19,
23,
24
] |
273 | 7930bb813bd546747c7c65b661900939f5ba93f1 | <mask token>
| <mask token>
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == '>':
explosion_strength += int(user_input[n + 1])
if user_input[n + explosion_strength] != '>':
exploded_str = user_input[:n] + user_input[n +
explosion_strength + 1:]
... | user_input = input()
exploded_str = user_input
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == '>':
explosion_strength += int(user_input[n + 1])
if user_input[n + explosion_strength] != '>':
exploded_str = user_input[:n] + user_input[n +
ex... | user_input = input() #abv>1>1>2>2asdasd
exploded_str = user_input
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == ">":
explosion_strength += int(user_input[n+1])
if user_input[n+explosion_strength] != ">":
exploded_str = user_input[:n] + user_input[n+ex... | null | [
0,
1,
2,
3
] |
274 | c6cf085330f47ffb139c5acc91d91e9758f5396a | <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.clo... | <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.clo... | <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.clo... | from page_objects import PageObject, PageElement
class MainPage(PageObject):
level_menu_opened = False
level_menu_created = False
css_input = PageElement(css='input.input-strobe')
level_text_span = PageElement(css='span.level-text')
instruction_h2 = PageElement(css='h2.order')
enter_button = P... | from page_objects import PageObject, PageElement
class MainPage(PageObject):
level_menu_opened = False
level_menu_created = False
css_input = PageElement(css='input.input-strobe')
level_text_span = PageElement(css='span.level-text')
instruction_h2 = PageElement(css='h2.order')
enter_button = P... | [
4,
6,
7,
11,
12
] |
275 | c2ddf31bce4a5f3ae2b0d5455bbc9942f92bff40 | <mask token>
| <mask token>
with open(MODEL_LABELS_FILENAME, 'rb') as f:
lb = pickle.load(f)
<mask token>
for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER):
for name in tqdm(files, desc='Solving captchas'):
kernel = 5, 5
image = cv2.imread(os.path.join(root, name))
image = cv2.cvtColor(image, cv2.... | <mask token>
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
<mask token>
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct = 0
c5_correct = 0
total_correct = 0
incorrectly_segmented = 0
correct_guesses_dict = {}
MODEL_FILENAME = 'captcha_model.hdf5'
MODEL_LABELS_FILENAME = 'model_labels.dat'
CAPTCHA_IMAGE_FOLDER = 'tes... | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras.models import load_model
from utils import resize_to_fit, clear_chunks, stack_windows
from imutils import paths
import numpy as np
import imutils
import cv2 as cv2
import pickle
from tqdm import tqdm
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct = ... | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras.models import load_model
from utils import resize_to_fit, clear_chunks, stack_windows
from imutils import paths
import numpy as np
import imutils
import cv2 as cv2
import pickle
from tqdm import tqdm
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct ... | [
0,
1,
2,
3,
4
] |
276 | 75e6554ea3c327c87a2a65710a7f1d55e9933bb0 | <mask token>
def train():
args = get_args()
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
"""
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
... | <mask token>
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info('Snapshot f... | __author__ = 'BeiYu'
<mask token>
models = {'squeezenet': lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=512,
deep_features_size=256, backend='squeezenet', n_classes=3), 'densenet':
lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=
512, backend='densenet', n_classes=3), 'resnet18': lambda... | __author__ = 'BeiYu'
from utils.init_env import set_seed
from utils.options import *
import os
import logging
import torch
from torch import nn
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.autograd import Variable
from torch.utils.data import DataLoader
from modules.seg_dataset im... | # Author: BeiYu
# Github: https://github.com/beiyuouo
# Date : 2021/2/21 21:57
# Description:
__author__ = "BeiYu"
from utils.init_env import set_seed
from utils.options import *
import os
import logging
import torch
from torch import nn
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from ... | [
1,
3,
4,
5,
6
] |
277 | 58f3b8c5470c765c81f27d39d9c28751a8c2b719 | <mask token>
| <mask token>
print(f'Sua frase tem {n_a} letras a')
print(f'A letra A aparece pela primeira vez na {f_a}° posição')
print(f'A letra A apaerece pela ultima vez na {l_a}° posição')
| <mask token>
frase = str(input('Digite uma frase: ')).strip().lower()
n_a = frase.count('a')
f_a = frase.find('a') + 1
l_a = frase.rfind('a') - 1
print(f'Sua frase tem {n_a} letras a')
print(f'A letra A aparece pela primeira vez na {f_a}° posição')
print(f'A letra A apaerece pela ultima vez na {l_a}° posição')
| """Ex026 Faça um programa que leia uma frase pelo teclado e mostre:
Quantas vezes aparece a letra "A".
Em que posição ela aparece a primeira vez.
Em que posição ela aparece pela última vez."""
frase = str(input('Digite uma frase: ')).strip().lower()
n_a = frase.count('a')
f_a = frase.find('a')+1
l_a= frase.rfind('a')-1... | null | [
0,
1,
2,
3
] |
278 | d83f2d9bb25a46bc7344b420ce65bf729165e6b9 | <mask token>
| <mask token>
class FosAppConfig(AppConfig):
<mask token>
| <mask token>
class FosAppConfig(AppConfig):
name = 'fos_app'
| from django.apps import AppConfig
class FosAppConfig(AppConfig):
name = 'fos_app'
| from django.apps import AppConfig
class FosAppConfig(AppConfig):
name = 'fos_app'
| [
0,
1,
2,
3,
4
] |
279 | d2b05c5653ca6c6b7219f6c0393e81c9425b5977 | <mask token>
| <mask token>
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get('granule')
prefix = granule[0:-6]
print(prefix)
response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
print(resp... | <mask token>
s3 = boto3.client('s3')
bucket = os.getenv('SENTINEL_INPUT_BUCKET', None)
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get('granule')
prefix = granule[0:-6]
print(prefix)
... | <mask token>
from typing import Dict
import os
import re
import boto3
from botocore.errorfactory import ClientError
from datetime import date
s3 = boto3.client('s3')
bucket = os.getenv('SENTINEL_INPUT_BUCKET', None)
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, c... | """
HLS: Check if Twin Granule Exists
"""
from typing import Dict
import os
import re
import boto3
from botocore.errorfactory import ClientError
from datetime import date
s3 = boto3.client("s3")
bucket = os.getenv("SENTINEL_INPUT_BUCKET", None)
print(bucket)
if bucket is None:
raise Exception("No Input Bucket set"... | [
0,
2,
3,
4,
5
] |
280 | c73a199d1c1c1867f3d53ceebf614bc9b65c0d5e | <mask token>
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| <mask token>
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| <mask token>
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| from django.contrib import admin
from ticket.models import Ticket, UserTicket, AuxiliaryTicket
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.Model... | from django.contrib import admin
from ticket.models import Ticket, UserTicket, AuxiliaryTicket
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.Mode... | [
1,
2,
3,
4,
5
] |
281 | e564e0d05c3c0e60f356422722803df510d9dd0b | <mask token>
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
... | <mask token>
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
... | <mask token>
@jit(parallel=True)
def conventional_test(subject_array, typeII_error, typeI_error, repeat=1,
seq=True):
"""
A function gives the test results to a subject array given the probability of
type II error, the probability of Type I error, and the number of repeatition,
and setting of sequ... | <mask token>
@jit(parallel=True)
def conventional_test(subject_array, typeII_error, typeI_error, repeat=1,
seq=True):
"""
A function gives the test results to a subject array given the probability of
type II error, the probability of Type I error, and the number of repeatition,
and setting of sequ... | import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score, recall_score, f1_score
from scipy.optimize import fsolve
import numba
from numba import njit,jit
#
@jit(parallel = True)
def conventional_tes... | [
11,
12,
17,
18,
20
] |
282 | 9bc13c608c079cbf23ed04f29edd1fd836214cde | <mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
<mask token>
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentS... | <mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
... | <mask token>
class PostViewSet(viewsets.ModelViewSet):
<mask token>
<mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
i... | from rest_framework import viewsets, mixins
from .models import Comment, Post
from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer
class PostViewSet(viewsets.ModelViewSet):
serializer_class = PostSerializer
queryset = Post.objects.all()
class CommentViewSet(viewsets.GenericViewSet... | from rest_framework import viewsets, mixins
from .models import Comment, Post
from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer
class PostViewSet(viewsets.ModelViewSet):
serializer_class = PostSerializer
queryset = Post.objects.all()
class CommentViewSet(viewsets.GenericViewSet... | [
2,
3,
4,
6,
7
] |
283 | b11e2837d3ba9c14770b8039186a2175adc41ea1 | <mask token>
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has... | <mask token>
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has... | <mask token>
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using... | from .server import CanvasServer
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displayin... | from .server import CanvasServer
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None # type: ignore
def http_server(
file: str = None, host: str = "localhost", port: int = 5050
) -> CanvasServer:
"""Creates a new... | [
1,
2,
3,
4,
5
] |
284 | 2da7892722afde5a6f87e3bd6d5763c895ac96c9 | <mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_wo... | <mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_wo... | <mask token>
nltk.download('stopwords')
<mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
... | <mask token>
nltk.download('stopwords')
nltk_stopwords = nltk.corpus.stopwords.words('english')
data_path = '/home/joey.bose/dblp_papers_v11.txt'
save_path_base = '/home/joey.bose/aminer_data/'
load_path_rank_base = '/home/joey.bose/aminer_data_ranked/fos/'
save_path_graph_base = '/home/joey.bose/aminer_data_ranked/gra... | import json
import os
import ipdb
from tqdm import tqdm
import argparse
from os import listdir
from os.path import isfile, join
import pickle
import joblib
from collections import Counter
from shutil import copyfile
import networkx as nx
import spacy
import nltk
import numpy as np
nltk.download('stopwords')
nltk_stopw... | [
4,
8,
10,
11,
13
] |
285 | e38ae7f91deed1be00e60b7516210ea1feefe23e | <mask token>
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str... | <mask token>
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str... | <mask token>
logger = logging.getLogger(__name__)
DOCUMENTS = 1
PROGRESS_NOTES = 2
DOC_TYPE = {DOCUMENTS: {'file_type': 'Document', 'folder': 'Documents',
'class': DocumentIndex, 'log': 'd', 'dates': ['CREATED_TIMESTAMP',
'POST_DATE'], 'converters': {'FILENAME': strip, 'DISPLAY_DESC': strip,
'DOC_COMMENT': ... | import sys
import os
from configparser import ConfigParser
import logging
from mod_argparse import setup_cli
from checkers.IndexFile import DocumentIndex, ProgressNoteIndex
from checkers import source_files
from utilities import write_to_file, strip
logger = logging.getLogger(__name__)
DOCUMENTS = 1
PROGRESS_NOTES = 2
... | import sys
import os
from configparser import ConfigParser
import logging
from mod_argparse import setup_cli
from checkers.IndexFile import DocumentIndex, ProgressNoteIndex
from checkers import source_files
from utilities import write_to_file, strip # , write_to_db_isok
# import pandas as pd
logger = logging.getLogger... | [
4,
5,
6,
7,
8
] |
286 | 069338b188f3cf16357b2502cbb3130b69918bd9 | <mask token>
| <mask token>
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
| from .cli import cli
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
| from .cli import cli
if __name__ == "__main__":
exit(cli.main(prog_name="htmap"))
| null | [
0,
1,
2,
3
] |
287 | b52269237d66ea50c453395b9536f25f1310bf2e | #!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
import re
from blessings import Terminal
from validate_email import validate_email
import requests
import sys
_site_ = sys.argv[1]
_saida_ = sys.argv[2]
_file_ = open(_saida_, "w")
t = Terminal()
r = requests.get(_site_, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.... | null | null | null | null | [
0
] |
288 | 8c539dbbb762717393b9a71ddca8eb3872890854 | <mask token>
| <mask token>
def process_names():
"""
Opening, reading name file and building name array.
"""
with open(input_names_file, 'r') as data:
plaintext = data.read()
name_array = plaintext.split('\n')
final_name_list = []
for name in name_array:
if len(name.split(',')) == 2:
... | <mask token>
instruments_file = os.path.abspath('instruments.csv')
input_names_file = os.path.abspath('names.txt')
output_names_file = os.path.abspath('names.csv')
inst_name_file = os.path.abspath('name_instrument.csv')
reg_ex = '; |, |\\*|\n'
name_header = ['first_name', 'last_name']
def process_names():
"""
... | import re
import os
import pandas as pd
instruments_file = os.path.abspath('instruments.csv')
input_names_file = os.path.abspath('names.txt')
output_names_file = os.path.abspath('names.csv')
inst_name_file = os.path.abspath('name_instrument.csv')
reg_ex = '; |, |\\*|\n'
name_header = ['first_name', 'last_name']
def p... | import re
import os
import pandas as pd
instruments_file = os.path.abspath("instruments.csv")
input_names_file = os.path.abspath("names.txt")
output_names_file = os.path.abspath("names.csv")
inst_name_file = os.path.abspath("name_instrument.csv")
reg_ex = '; |, |\\*|\n'
name_header = ["first_name", "last_name"]
def ... | [
0,
1,
2,
3,
4
] |
289 | 0db0daf9bea254cffaec1280cd13b2d70368cd94 | <mask token>
| <mask token>
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform(0, 1) < p2mle:
p2 += 1
p2 /= N2
estimate.append(p2 - p1)
<mask token>
for t in allt:
cur = np.me... | <mask token>
B = 100000
N1 = 50
N2 = 50
p1mle = 0.3
p2mle = 0.4
taumle = p2mle - p1mle
estimate = []
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform(0, 1) < p2mle:
p2 +=... | import numpy.random as rnd
import numpy as np
B = 100000
N1 = 50
N2 = 50
p1mle = 0.3
p2mle = 0.4
taumle = p2mle - p1mle
estimate = []
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform... | import numpy.random as rnd
import numpy as np
B=100000
N1=50
N2=50
p1mle=0.3
p2mle=0.4
taumle=p2mle-p1mle
estimate=[]
for i in range(B):
p1=0.0
for j in range(N1):
if(rnd.uniform(0,1)<p1mle):
p1+=1
p1/=N1
p2=0.0
for j in range(N2):
if(rnd.uniform(0,1)<p2mle):
p2+=1
p2/=N2
estimate.append(p2-p... | [
0,
1,
2,
3,
4
] |
290 | a90b7e44cc54d4f96a13e5e6e2d15b632d3c4983 | <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774... | <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774... | <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774... | import random
import string
import steembase
import struct
import steem
from time import sleep
from time import time
from steem.transactionbuilder import TransactionBuilder
from steembase import operations
from steembase.transactions import SignedTransaction
from resultthread import MyThread
from charm.toolbox.pairingg... | import random
import string
import steembase
import struct
import steem
from time import sleep
from time import time
from steem.transactionbuilder import TransactionBuilder
from steembase import operations
from steembase.transactions import SignedTransaction
from resultthread import MyThread
from charm.toolbox.pairingg... | [
22,
26,
28,
30,
31
] |
291 | ee80169afd4741854eff8619822a857bbf757575 | <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self)... | <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self)... | <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self)... | <mask token>
import matplotlib.pyplot as plt
from numerical_functions import Timer
import numerical_functions.numba_funcs.indexing as indexing
import numpy as np
import unittest
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
... | '''
Created on 27 Mar 2015
@author: Jon
'''
import matplotlib.pyplot as plt
from numerical_functions import Timer
import numerical_functions.numba_funcs.indexing as indexing
import numpy as np
import unittest
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace( 0, 100 )
... | [
6,
7,
8,
11,
12
] |
292 | ce6dba2f682b091249f3bbf362bead4b95fee1f4 | <mask token>
| <mask token>
from .book import book
from . import style, to, read, dist
| """
.. currentmodule:: jotting
.. automodule:: jotting.book
:members:
.. automodule:: jotting.to
:members:
.. automodule:: jotting.read
:members:
.. automodule:: jotting.style
:members:
"""
from .book import book
from . import style, to, read, dist
| null | null | [
0,
1,
2
] |
293 | 99c839eddcbe985c81e709878d03c59e3be3c909 | #coding=utf-8
#########################################
# dbscan：
# 用法说明：读取文件
# 生成路径文件及簇文件，输出分类准确率
#########################################
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
from collections import defaultdict
import random
from math import *
import numpy
import datetime
... | null | null | null | null | [
0
] |
294 | bf8bbeb408cb75af314ef9f3907456036e731c0b | <mask token>
| def solution(S):
log_sep = ','
num_sep = '-'
time_sep = ':'
from collections import defaultdict
bill = defaultdict(int)
total = defaultdict(int)
calls = S.splitlines()
maximal = 0
free_number = 0
for call in calls:
hhmmss, number = call.split(log_sep)
hh, mm, ss =... | def solution(S):
# write your code in Python 3.6
# Definitions
log_sep = ','
num_sep = '-'
time_sep = ':'
# Initialization
from collections import defaultdict
# defaultdict initialize missing key to default value -> 0
bill = defaultdict(int)
total = defaultdict(int)
calls = S... | null | null | [
0,
1,
2
] |
295 | 35ae9c86594b50bbe4a67d2cc6b20efc6f6fdc64 | <mask token>
| <mask token>
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
<mask token>
django.setup()
<mask token>
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = lev1['code'] if lev1['code'] else ''
cat1.category_type = 1
cat1.save... | <mask token>
file_path = os.path.abspath(__file__)
dir_path = os.path.dirname(file_path)
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
<mask token>
django.setup()
<mask token>
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = l... | import os, sys
file_path = os.path.abspath(__file__)
dir_path = os.path.dirname(file_path)
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
import django
django.setup()
from goods.models import GoodsCategory
from db_tools.data.category_data import row_data
for lev1 in row_d... | #配置我们文件所在目录的搜寻环境
import os,sys
#第一步先拿到当前文件的路径
file_path = os.path.abspath(__file__)
#第二步 根据这个路径去拿到这个文件所在目录的路径
dir_path = os.path.dirname(file_path)
#第三步：讲这个目录的路径添加到我们的搜寻环境当中
sys.path.append(dir_path)
#第四步，动态设置我们的setting文件
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "gulishop.settings")
#第五步，让设置好的环境初始化生效
... | [
0,
1,
2,
3,
4
] |
296 | d34159536e860719094a36cfc30ffb5fcae72a9a | <mask token>
| <mask token>
print('Retriving', url)
<mask token>
print('Retrived', len(data), 'characters')
<mask token>
print(json.dumps(js, indent=4))
print('Place id', js['results'][0]['place_id'])
<mask token>
| <mask token>
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key': 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrived', len(data), 'characters')
js = json.loads(data)
print(json.dumps(js, inden... | import urllib.error, urllib.request, urllib.parse
import json
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key': 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrived', len(data), 'characters')... | #API End Points by Mitul
import urllib.error, urllib.request, urllib.parse
import json
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key' : 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrive... | [
0,
1,
2,
3,
4
] |
297 | c382b298cce8d7045d6ce8a84f90b3800dba7717 | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('products', '0003_auto_20200615_1225')]
operations = [migrations.AlterField(model_name='product', name=
'harmonizacao', field=models.TextField(null=True)), migrations.
AlterField(model_name='product', name='history', field=mo... | from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('products', '0003_auto_20200615_1225')]
operations = [migrations.AlterField(model_name='product', name=
'harmonizacao', field=models.TextField(null=True)), migrations.
AlterField(model_name='produc... | # Generated by Django 3.0.7 on 2020-06-15 15:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('products', '0003_auto_20200615_1225'),
]
operations = [
migrations.AlterField(
model_name='product',
name='harmoniza... | [
0,
1,
2,
3,
4
] |
298 | 3372d98ff91d90558a87293d4032820b1662d60b | <mask token>
| <mask token>
urlpatterns = patterns('', url('appmanagement', views.appmanagement, name=
'appmanagement'), url('^.*', views.index, name='index'))
| from django.conf.urls import patterns, url
from riskDashboard2 import views
urlpatterns = patterns('', url('appmanagement', views.appmanagement, name=
'appmanagement'), url('^.*', views.index, name='index'))
| from django.conf.urls import patterns, url
from riskDashboard2 import views
urlpatterns = patterns('',
#url(r'getdata', views.vulnData, name='getdata'),
url(r'appmanagement', views.appmanagement, name='appmanagement'),
url(r'^.*', views.index, name='index'),
)
| null | [
0,
1,
2,
3
] |
299 | 0465e33d65c2ce47ebffeec38db6908826bf4934 | <mask token>
| <mask token>
if people < cats:
print('Too many cats')
elif people > cats:
print('Not many cats')
else:
print('we cannnot decide')
| people = 20
cats = 30
dogs = 15
if people < cats:
print('Too many cats')
elif people > cats:
print('Not many cats')
else:
print('we cannnot decide')
| people = 20
cats = 30
dogs = 15
if people < cats:
print("Too many cats")
elif people > cats:
print("Not many cats")
else:
print("we cannnot decide") | null | [
0,
1,
2,
3
] |
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