Reset23/the-stack-v2-python · Datasets at Hugging Face

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/wake_up.spec

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leowf/wake_up

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refs/heads/master

2021-10-25T18:34:05.477721

2019-04-06T02:56:47

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# -*- mode: python -*- block_cipher = None a = Analysis(['wake_up.py'], pathex=['F:\\record'], binaries=[], datas=[], hiddenimports=[], hookspath=[], runtime_hooks=[], excludes=[], win_no_prefer_redirects=False, win_private_assemblies=False, cipher=block_cipher) pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher) exe = EXE(pyz, a.scripts, a.binaries, a.zipfiles, a.datas, name='wake_up', debug=False, strip=False, upx=True, console=False )

[ "noreply@github.com" ]

leowf.noreply@github.com

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/autourl/urls.py

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kns003/autourl

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refs/heads/master

2021-01-23T18:50:20.715694

2015-08-22T10:50:57

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2015-08-22T07:49:17

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"""autourl URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.8/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf.urls import include, url from django.contrib import admin urlpatterns = [ url(r'^admin/', include(admin.site.urls)), url(r'^ubuild/', include('ubuild.urls')), ]

[ "kns003@gmail.com" ]

kns003@gmail.com

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/simple-3layer-perceptron-predict.py

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aux-di/chainer-samples

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refs/heads/master

2020-04-03T09:52:34.226078

2016-08-12T13:47:02

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# -*- coding: utf-8 -*- import argparse import numpy as np import chainer from chainer import cuda, Function, gradient_check, report, training, utils, Variable from chainer import datasets, iterators, optimizers, serializers from chainer import Link, Chain, ChainList import chainer.functions as F import chainer.links as L from chainer.training import extensions import cv2 #from matplotlib import pylab as plt # Network definition class MLP(chainer.Chain): def __init__(self, n_in, n_units, n_out): super(MLP, self).__init__( l1=L.Linear(n_in, n_units), # first layer l2=L.Linear(n_units, n_units), # second layer l3=L.Linear(n_units, n_out), # output layer ) def __call__(self, x): h1 = F.relu(self.l1(x)) h2 = F.relu(self.l2(h1)) return self.l3(h2) def main(): parser = argparse.ArgumentParser(description='Chainer example: MNIST') parser.add_argument('--batchsize', '-b', type=int, default=1, help='Number of images in each mini batch') parser.add_argument('--epoch', '-e', type=int, default=100, help='Number of sweeps over the dataset to train') parser.add_argument('--gpu', '-g', type=int, default=-1, help='GPU ID (negative value indicates CPU)') parser.add_argument('--out', '-o', default='result', help='Directory to output the result') parser.add_argument('--resume', '-r', default='', help='Resume the training from snapshot') parser.add_argument('--unit', '-u', type=int, default=10, help='Number of units') args = parser.parse_args() # load a color image img1 = cv2.imread('images/zero.jpg', cv2.IMREAD_COLOR) img2 = cv2.imread('images/black.jpg', cv2.IMREAD_COLOR) img3 = cv2.imread('images/white.jpg', cv2.IMREAD_COLOR) # color -> grayscale imggray1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY) imggray2 = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY) imggray3 = cv2.cvtColor(img3, cv2.COLOR_RGB2GRAY) # image -> array gray = [] for y in range(len(imggray1)): for x in range(len(imggray1[y])): gray.append(imggray1[y][x]) imgdata1 = np.array(gray, dtype='f') imgdata1 = imgdata1.reshape(1, 1, 32, 32) imgdata1 = imgdata1 / 255.0 gray = [] for y in range(len(imggray2)): for x in range(len(imggray2[y])): gray.append(imggray2[y][x]) imgdata2 = np.array(gray, dtype='f') imgdata2 = imgdata2.reshape(1, 1, 32, 32) imgdata2 = imgdata2 / 255.0 gray = [] for y in range(len(imggray3)): for x in range(len(imggray3[y])): gray.append(imggray3[y][x]) imgdata3 = np.array(gray, dtype='f') imgdata3 = imgdata3.reshape(1, 1, 32, 32) imgdata3 = imgdata3 / 255.0 n_in = 32*32 print('GPU: {}'.format(args.gpu)) print('# unit: {}'.format(args.unit)) print('# Minibatch-size: {}'.format(args.batchsize)) print('# epoch: {}'.format(args.epoch)) print('') model = L.Classifier(MLP(n_in, args.unit, 3)) # Setup an optimizer optimizer = chainer.optimizers.Adam() optimizer.setup(model) # Load dataset x1 = imgdata1 x2 = imgdata2 x3 = imgdata3 y1 = np.array(0, dtype=np.int32) y2 = np.array(1, dtype=np.int32) y3 = np.array(2, dtype=np.int32) dd = [(x1, y1), (x2, y2), (x3, y3)] train, test = dd, dd train_iter = chainer.iterators.SerialIterator(train, args.batchsize) test_iter = chainer.iterators.SerialIterator(test, args.batchsize, repeat=False, shuffle=False) # Set up a trainer updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu) trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out) # Evaluate the model with the test dataset for each epoch trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu)) # Dump a computational graph from 'loss' variable at the first iteration # The "main" refers to the target link of the "main" optimizer. trainer.extend(extensions.dump_graph('main/loss')) # Take a snapshot at each epoch trainer.extend(extensions.snapshot()) # Write a log of evaluation statistics for each epoch trainer.extend(extensions.LogReport()) # Print selected entries of the log to stdout # Here "main" refers to the target link of the "main" optimizer again, and # "validation" refers to the default name of the Evaluator extension. # Entries other than 'epoch' are reported by the Classifier link, called by # either the updater or the evaluator. trainer.extend(extensions.PrintReport( ['epoch', 'main/loss', 'validation/main/loss', 'main/accuracy', 'validation/main/accuracy'])) # Print a progress bar to stdout trainer.extend(extensions.ProgressBar()) # Resume from a snapshot #chainer.serializers.load_npz(resume, trainer) # Run the training trainer.run() # Predictor xx = Variable(np.array([x1, ]), volatile=True) y = model.predictor(xx) print y.data print np.argmax(y.data) if __name__ == '__main__': main()

[ "aux_di@yahoo.co.jp" ]

aux_di@yahoo.co.jp

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8c488fd4b680919e777e34f8d61bcc03fb5948eb

/news.py

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permissive

atultherajput/iPyNews

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refs/heads/master

2020-03-08T08:29:09.199397

2018-04-04T08:47:52

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import requests def PyNews(option, c, q): apiKey = "9f6bb23dade24e2a9215192*********" #get api from https://newsapi.org top_headlines = "https://newsapi.org/v2/top-headlines?country="+c+"&apiKey="+apiKey everything = "https://newsapi.org/v2/everything?language=en&sortBy=popularity&q="+q+"&apiKey="+apiKey sources = "https://newsapi.org/v2/sources?apiKey="+apiKey results = [] if option == 1: news = requests.get(top_headlines).json() articles = news["articles"] for article in articles: results.append(article["title"]) elif option == 2: news = requests.get(everything).json() articles = news["articles"] for article in articles: results.append(article["title"]+":\n"+ article["description"]) elif option == 3: news = requests.get(sources).json() articles = news["sources"] for article in articles: results.append(article["name"]) for i in range(len(results)): print(str(i + 1)+'.', results[i]) def driver(option=1, c="in", q="india"): option = int(input("1. Top Headlines by country \n2. Everything by keyword \n3. News Sources \nSelect option: ")) if option == 1: c = input("Enter two digit country code: ") elif option == 2: q = input("Search Keyword: ") elif option == 3: pass else: print("Wrong Option!") driver() PyNews(option, c, q) if __name__ == '__main__': driver()

[ "atultherajput@gmail.com" ]

atultherajput@gmail.com

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/Ex9.5.py

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inwk6312fall2019/wordplay-Tarang97

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refs/heads/master

2020-07-30T01:04:07.531630

2019-09-21T21:57:47

2019-09-22T01:04:21

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#Write a function named uses_all that takes a word and a string of required letters, #and that returns True if the word uses all the required letters at least once. #How many words are there that use all the vowels aeiou? How about aeiouy? def uses_all(word, letters): for letter in letters: if letter not in word: return False return True fin = open('words.txt') count = 0 for line in fin: word = line.strip() if uses_all(word, 'aeiouy') == True: print(word) count += 1 print(count)

[ "tarangdube8@outlook.com" ]

tarangdube8@outlook.com

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/json-quiz/1.py

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hsayres/compjour-hw

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refs/heads/master

2021-01-18T15:12:33.080458

2015-06-10T01:20:31

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import requests import json data_url = "http://www.compjour.org/files/code/json-examples/analyticsgov-realtime.json" response = requests.get(data_url) text = response.text data = json.loads(text) print('A. ', data['name']) print('B. ', data['taken_at']) print('C. ', data['meta']['name']) print('D. ', data['data'][0]['active_visitors']) print('E. ', ', '.join(data.keys()))

[ "hsayres@stanford.edu" ]

hsayres@stanford.edu

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/A_C_TD_CNN_3_LSTM_GAMA_Navigation.py

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ZHONGJunjie86/Mixed_Input_PPO_CNN_LSTM_Car_Navigation

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refs/heads/master

2023-05-03T05:09:40.605705

2021-05-22T09:47:17

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from utils import cross_loss_curve, GAMA_connect,reset,send_to_GAMA from CV_input import generate_img import os from itertools import count import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.distributions import MultivariateNormal import numpy as np import pandas as pd import warnings import math warnings.filterwarnings("ignore") device = torch.device("cuda" if torch.cuda.is_available() else"cpu") save_curve_pic = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/result/Actor_Critic_3loss_curve.png' save_critic_loss = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/training_data/AC_critic_3loss.csv' save_reward = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/training_data/AC_3reward.csv' save_speed = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/training_data/AC_average_speed.csv' save_NPC_speed = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/training_data/NPC_speed.csv' state_size = 9 action_size = 1 Memory_size = 4 torch.set_default_tensor_type(torch.DoubleTensor) class Memory: def __init__(self): self.actions = [] self.states = [] self.states_next = [] self.states_img = [] self.states_img_next = [] self.logprobs = [] self.rewards = [] self.is_terminals = [] def clear_memory(self): del self.actions[:] del self.states[:] del self.states_next[:] del self.states_img[:] del self.states_img_next[:] del self.logprobs[:] del self.rewards[:] del self.is_terminals[:] class Actor(nn.Module): def __init__(self, state_size, action_size): super(Actor, self).__init__() self.conv1 = nn.Conv2d(3,16, kernel_size=3, stride=2, padding=0) # 237*237*N ->57*57*16 self.maxp1 = nn.MaxPool2d(3, stride = 2, padding=0) #79*79*16-> 39*39*32 self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=0) # 39*39*16-> 19*19*32 self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=0) # 19*19*32 -> 9*9*64 #self.maxp2 = nn.MaxPool2d(3, stride = 2, padding=0) # 19*19*64 -> 9*9*64 self.linear_CNN_1 = nn.Linear(5184, 256) self.linear_CNN_2 = nn.Linear(256*Memory_size,16*Memory_size) #(768,256) # self.state_size = state_size self.action_size = action_size self.linear0 = nn.Linear(self.state_size, 64) self.linear1 = nn.Linear(64, 128) self.linear2 = nn.Linear(128, 16)#128, 85) self.linear3 = nn.Linear(32*Memory_size,12)#(511,128) self.linear4 = nn.Linear(12,8) #(128,32) self.mu = nn.Linear(8,self.action_size) #32 self.sigma = nn.Linear(8,self.action_size) self.distribution = torch.distributions.Normal def forward(self, state,tensor_cv): # CV x = F.relu(self.maxp1(self.conv1(tensor_cv))) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) #x = F.relu(self.conv3(x)) x = x.view(x.size(0), -1) #展開 x = F.relu(self.linear_CNN_1(x)).reshape(1,256*Memory_size) x = F.relu(self.linear_CNN_2(x)).reshape(1,16*Memory_size) #.reshape(1,256) # num output_0 = F.relu(self.linear0(state)) output_1 = F.relu(self.linear1(output_0)) output_2 = F.relu(self.linear2(output_1)).reshape(1,16*Memory_size) #(1,255) # merge output_2 = torch.cat((x,output_2),1) output_3 = F.relu(self.linear3(output_2) ) # output_4 =F.relu(self.linear4(output_3)) #F.relu(self.linear4(output_3.view(-1,c))) # mu = torch.tanh(self.mu(output_4)) #有正有负 sigmoid 0-1 sigma = F.relu(self.sigma(output_4)) + 0.001 mu = torch.diag_embed(mu).to(device) sigma = torch.diag_embed(sigma).to(device) # change to 2D #dist = MultivariateNormal(mu,sigma) dist = self.distribution(mu, sigma)#MultivariateNormal(mu,sigma) #N(μ，σ^2) action = dist.sample() action_logprob = dist.log_prob(action) action = torch.clamp(action.detach(), -0.8, 0.6) #entropy = torch.sum(dist.entropy()) #entropy = dist.entropy().mean() #torch.sum(m_probs.entropy()) #entropy = 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(dist.scale) entropy = -torch.exp(action_logprob) * action_logprob return action,action_logprob,entropy class Critic(nn.Module): def __init__(self, state_size, action_size): super(Critic, self).__init__() self.conv1 = nn.Conv2d(3,16, kernel_size=3, stride=2, padding=0) # 237*237*N ->57*57*16 self.maxp1 = nn.MaxPool2d(3, stride = 2, padding=0) #79*79*16-> 39*39*32 self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=0) # 39*39*16-> 19*19*32 self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=0) # 19*19*32 -> 9*9*64 #self.maxp2 = nn.MaxPool2d(3, stride = 2, padding=0) # 19*19*64 -> 9*9*64 self.linear_CNN = nn.Linear(5184, 256) self.lstm_CNN = nn.LSTM(256,16, 1,batch_first=True) # self.state_size = state_size self.action_size = action_size self.linear0 = nn.Linear(self.state_size, 64) self.linear1 = nn.Linear(64, 128) self.linear2 = nn.Linear(128, 128) self.lstm3 = nn.LSTM(128,16,1, batch_first=True)#85 # self.LSTM_layer_3 = nn.LSTM(32*Memory_size,16,1, batch_first=True) #510,128 self.linear4 = nn.Linear(16,4) #128,32 self.linear5 = nn.Linear(4, action_size) #32 def forward(self, state, tensor_cv,h_state_cv_c=(torch.zeros(1,Memory_size,16).to(device), torch.zeros(1,Memory_size,16).to(device)),h_state_n_c=(torch.zeros(1,Memory_size,16).to(device), #1,3,85) torch.zeros(1,Memory_size,16).to(device)),h_state_3_c=(torch.zeros(1,1,16).to(device), torch.zeros(1,1,16).to(device))): #(1,1,128) # CV x = F.relu(self.maxp1(self.conv1(tensor_cv))) x = F.relu( self.conv2(x)) x = F.relu(self.conv3(x)).reshape(Memory_size,1,5184) #x = x.view(x.size(0), -1) #展開 x = F.relu(self.linear_CNN(x))#.reshape(1,768) x,h_state_cv_c = self.lstm_CNN(x ,h_state_cv_c) # #.unsqueeze(0) x = F.relu( x).reshape(1,16*Memory_size) #.reshape(1,255) torch.tanh # num output_0 = F.relu(self.linear0(state)) output_1 = F.relu(self.linear1(output_0)) output_2 = F.relu(self.linear2(output_1)) output_2,h_state_n_c = self.lstm3(output_2,h_state_n_c) # output_2 = F.relu(output_2) # output_2 = output_2.squeeze().reshape(1,16*Memory_size) # LSTM output_2 = torch.cat((x,output_2),1) output_2 = output_2.unsqueeze(0) output_3 , h_state_3_c= self.LSTM_layer_3(output_2,h_state_3_c ) # #,self.hidden_cell a,b,c = output_3.shape # output_4 = F.relu(self.linear4(output_3.view(-1,c))) value = torch.tanh(self.linear5(output_4)) return value ,(h_state_cv_c[0].data,h_state_cv_c[1].data),(h_state_n_c[0].data,h_state_n_c[1].data),(h_state_3_c[0].data,h_state_3_c[1].data) def main(): ################ load ################### actor_path = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_actor.pkl' critic_path = os.path.abspath(os.curdir)+'/PPO_Mixedinput_Navigation_Model/weight/AC_TD3_critic.pkl' if os.path.exists(actor_path): actor = Actor(state_size, action_size).to(device) actor.load_state_dict(torch.load(actor_path)) print('Actor Model loaded') else: actor = Actor(state_size, action_size).to(device) if os.path.exists(critic_path): critic = Critic(state_size, action_size).to(device) critic.load_state_dict(torch.load(critic_path)) print('Critic Model loaded') else: critic = Critic(state_size, action_size).to(device) critic_next = Critic(state_size, action_size).to(device) critic_next.load_state_dict(critic.state_dict()) print("Waiting for GAMA...") ################### initialization ######################## reset() episode = 4000 training_stage = 70 #100#80 Decay = training_stage*18 lr = 0.0001 sample_lr = [ 0.0001, 0.00009, 0.00008, 0.00007, 0.00006, 0.00005, 0.00004, 0.00003, 0.00002, 0.00001, 0.000009, 0.000008, 0.000007, 0.000006, 0.000005, 0.000004, 0.000003, 0.000002, 0.000001 ] #900 960 1020 1080 1140 if episode >= training_stage : #50 100 try: lr = sample_lr[int(episode // training_stage)] except(IndexError): lr = 0.000001* (0.9 ** ((episode- Decay// training_stage)) ) #100-1800#80-1440#65-1170#570 -- 30 optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999)) optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999)) test = "GAMA" state,reward,done,time_pass,over,_ = GAMA_connect(test) #connect print("done:",done,"timepass:",time_pass) log_probs = [] #log probability values = [] rewards = [] masks = [] total_loss = [] total_rewards = [] entropy = 0 loss = [] value = 0 log_prob = 0 gama = 0.9 average_speed = [] memory = Memory () ################## start ######################### while over!= 1: #普通の場合 if(done == 0 and time_pass != 0): #前回の報酬 average_speed.append(state[0]) reward = torch.tensor([reward], dtype=torch.float, device=device) rewards.append(reward) state = torch.DoubleTensor(state).reshape(1,state_size).to(device) state_img = generate_img() tensor_cv = torch.from_numpy(np.transpose(state_img, (2, 0, 1))).double().to(device)/255 if len(memory.states_next) ==0: #IndexError #for _ in range(3): memory.states_next = memory.states memory.states_next[Memory_size-1] = state memory.states_img_next = memory.states_img memory.states_img_next [Memory_size-1]= tensor_cv else: del memory.states_next[:1] del memory.states_img_next[:1] memory.states_next.append(state) memory.states_img_next.append(tensor_cv) state_next = torch.stack(memory.states_next).to(device).detach() tensor_cv_next = torch.stack(memory.states_img_next).to(device).detach() value_next,_,_,_ = critic_next(state_next,tensor_cv_next,h_state_cv_c,h_state_n_c,h_state_3_c ) #_next with torch.autograd.set_detect_anomaly(True): # TD:r(s) + gama*v(s+1) - v(s) advantage = reward.detach() + gama*value_next.detach() - value actor_loss = -(log_prob * advantage.detach() + 10*lr *entropy)# #critic_loss = (reward.detach() + gama*value_next.detach() - value).pow(2) critic_loss =torch.nn.SmoothL1Loss()(reward.detach() + gama*value_next.detach() , value) optimizerA.zero_grad() optimizerC.zero_grad() critic_loss.backward() actor_loss.backward() loss.append(critic_loss) optimizerA.step() optimizerC.step() critic_next.load_state_dict(critic.state_dict()) del memory.states[:1] del memory.states_img[:1] memory.states.append(state) memory.states_img.append(tensor_cv) state = torch.stack(memory.states).to(device).detach() ### tensor_cv = torch.stack(memory.states_img).to(device).detach() value,h_state_cv_c,h_state_n_c,h_state_3_c = critic(state,tensor_cv,h_state_cv_c,h_state_n_c,h_state_3_c) action,log_prob,entropy= actor(state,tensor_cv) # ,h_state_cv_a,h_state_n_a,h_state_3_a #,h_state_cv_a,h_state_n_a,h_state_3_a log_prob = log_prob.unsqueeze(0) #entropy += entropy send_to_GAMA([[1,float(action.cpu().numpy()*10)]]) #行 masks.append(torch.tensor([1-done], dtype=torch.float, device=device)) values.append(value) log_probs.append(log_prob) # 終わり elif done == 1: average_speed.append(state[0]) send_to_GAMA([[1,0]]) #先传后计算 print(state) rewards.append(reward) #contains the last reward = torch.tensor([reward], dtype=torch.float, device=device) rewards.append(reward) #contains the last total_reward = sum(rewards).cpu().detach().numpy() total_rewards.append(total_reward) #state = torch.FloatTensor(state).reshape(1,4).to(device) #last_value= critic(state) with torch.autograd.set_detect_anomaly(True): advantage = reward.detach() - value #+ last_value 最后一回的V(s+1) = 0 actor_loss = -( log_prob * advantage.detach() +10*lr * entropy) #+ lr * entropy #critic_loss = (reward.detach() - value).pow(2) #+ last_value critic_loss =torch.nn.SmoothL1Loss()(reward.detach() + gama*value_next.detach() , value) optimizerA.zero_grad() optimizerC.zero_grad() critic_loss.backward() actor_loss.backward() loss.append(critic_loss) optimizerA.step() optimizerC.step() critic_next.load_state_dict(critic.state_dict()) log_probs = [] values = [] rewards = [] masks = [] loss_sum = sum(loss).cpu().detach().numpy() total_loss.append(loss_sum) cross_loss_curve(loss_sum.squeeze(0),total_reward,save_curve_pic,save_critic_loss,save_reward,np.mean(average_speed)*10,save_speed,average_speed_NPC,save_NPC_speed) #total_loss,total_rewards loss = [] average_speed = [] memory.clear_memory() if episode > training_stage : #50 100 try: lr = sample_lr[int(episode // training_stage)] except(IndexError): lr = 0.000001* (0.9 ** ((episode-Decay) //training_stage)) optimizerA = optim.Adam(actor.parameters(), lr, betas=(0.95, 0.999)) optimizerC = optim.Adam(critic.parameters(), lr, betas=(0.95, 0.999)) torch.save(actor.state_dict(),actor_path) torch.save(critic.state_dict(),critic_path) print("----------------------------------Net_Trained---------------------------------------") print('--------------------------Iteration:',episode,'over--------------------------------') episode += 1 #最初の時 else: print('Iteration:',episode,"lr:",lr) state = np.reshape(state,(1,len(state))) #xxx state_img = generate_img() tensor_cv = torch.from_numpy(np.transpose(state_img, (2, 0, 1))).double().to(device)/255 state = torch.DoubleTensor(state).reshape(1,state_size).to(device) for _ in range(Memory_size): memory.states.append(state) memory.states_img.append(tensor_cv) state = torch.stack(memory.states).to(device).detach() ### tensor_cv = torch.stack(memory.states_img).to(device).detach() value,h_state_cv_c,h_state_n_c,h_state_3_c = critic(state,tensor_cv) #dist, # now is a tensoraction = dist.sample() action,log_prob,entropy = actor(state,tensor_cv) #, h_state_cv_a,h_state_n_a,h_state_3_a print("acceleration: ",action.cpu().numpy()) send_to_GAMA([[1,float(action.cpu().numpy()*10)]]) log_prob = log_prob.unsqueeze(0) #entropy += entropy state,reward,done,time_pass,over,average_speed_NPC = GAMA_connect(test) return None if __name__ == '__main__': main() """ class Actor(nn.Module): def __init__(self, state_size, action_size): super(Actor, self).__init__() self.conv1 = nn.Conv2d(3,8, kernel_size=8, stride=4, padding=0) # 500*500*3 -> 124*124*8 self.maxp1 = nn.MaxPool2d(4, stride = 2, padding=0) # 124*124*8 -> 61*61*8 self.conv2 = nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=0) # 61*61*8 -> 58*58*16 self.maxp2 = nn.MaxPool2d(2, stride=2, padding=0) # 58*58*16 -> 29*29*16 = 13456 self.linear_CNN_1 = nn.Linear(13456, 256) self.linear_CNN_2 = nn.Linear(768,48) #(768,256) # self.state_size = state_size self.action_size = action_size self.linear1 = nn.Linear(self.state_size, 128) self.linear2 = nn.Linear(128, 16)#128, 85) self.linear3 = nn.Linear(96,12)#(511,128) self.linear4 = nn.Linear(12,8) #(128,32) self.mu = nn.Linear(8,self.action_size) #32 self.sigma = nn.Linear(8,self.action_size) self.distribution = torch.distributions.Normal def forward(self, state,tensor_cv): # CV x = F.relu(self.maxp1( self.conv1(tensor_cv))) x = F.relu(self.maxp2( self.conv2(x))) x = x.view(x.size(0), -1) #展開 x = F.relu(self.linear_CNN_1(x)).reshape(1,768) x = F.relu(self.linear_CNN_2(x)).reshape(1,48) #.reshape(1,256) # num output_1 = F.relu(self.linear1(state)) output_2 = F.relu(self.linear2(output_1)).reshape(1,48) #(1,255) # merge output_2 = torch.cat((x,output_2),1) output_3 = F.relu(self.linear3(output_2) ) # output_4 =F.relu(self.linear4(output_3)) #F.relu(self.linear4(output_3.view(-1,c))) # mu = torch.tanh(self.mu(output_4)) #有正有负 sigmoid 0-1 sigma = F.relu(self.sigma(output_4)) + 0.001 mu = torch.diag_embed(mu).to(device) sigma = torch.diag_embed(sigma).to(device) # change to 2D #dist = MultivariateNormal(mu,sigma) dist = self.distribution(mu, sigma)#MultivariateNormal(mu,sigma) #N(μ，σ^2) action = dist.sample() action_logprob = dist.log_prob(action) action = torch.clamp(action.detach(), -0.8, 0.6) #entropy = torch.sum(dist.entropy()) #entropy = dist.entropy().mean() #torch.sum(m_probs.entropy()) #entropy = 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(dist.scale) entropy = -torch.exp(action_logprob) * action_logprob return action,action_logprob,entropy class Critic(nn.Module): def __init__(self, state_size, action_size): super(Critic, self).__init__() self.conv1 = nn.Conv2d(3,8, kernel_size=8, stride=4, padding=0) # 500*500*3 -> 124*124*8 self.maxp1 = nn.MaxPool2d(4, stride = 2, padding=0) # 124*124*8 -> 61*61*8 self.conv2 = nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=0) # 61*61*8 -> 58*58*16 self.maxp2 = nn.MaxPool2d(2, stride=2, padding=0) # 58*58*16 -> 29*29*16 = 13456 self.linear_CNN = nn.Linear(13456, 256) self.lstm_CNN = nn.LSTM(256,16, 1,batch_first=True) #768,255 # self.state_size = state_size self.action_size = action_size self.linear1 = nn.Linear(self.state_size, 128) self.linear2 = nn.Linear(128, 128) self.lstm3 = nn.LSTM(128,16,1, batch_first=True)#85 # self.LSTM_layer_3 = nn.LSTM(96,16,1, batch_first=True) #510,128 self.linear4 = nn.Linear(16,4) #128,32 self.linear5 = nn.Linear(4, action_size) #32 def forward(self, state, tensor_cv,h_state_cv_c=(torch.zeros(1,3,16).to(device), torch.zeros(1,3,16).to(device)),h_state_n_c=(torch.zeros(1,3,16).to(device), #1,3,85) torch.zeros(1,3,16).to(device)),h_state_3_c=(torch.zeros(1,1,16).to(device), torch.zeros(1,1,16).to(device))): #(1,1,128) # CV x = F.relu(self.maxp1( self.conv1(tensor_cv))) x = F.relu(self.maxp2( self.conv2(x))).reshape(3,1,13456) #x = x.view(x.size(0), -1) #展開 x = F.relu(self.linear_CNN(x))#.reshape(1,768) x,h_state_cv_c = self.lstm_CNN(x ,h_state_cv_c) # #.unsqueeze(0) x = F.relu( x).reshape(1,48) #.reshape(1,255) torch.tanh # num output_1 = F.relu(self.linear1(state)) output_2 = F.relu(self.linear2(output_1)) output_2,h_state_n_c = self.lstm3(output_2,h_state_n_c) # output_2 = F.relu(output_2) # output_2 = output_2.squeeze().reshape(1,48) # LSTM output_2 = torch.cat((x,output_2),1) output_2 = output_2.unsqueeze(0) output_3 , h_state_3_c= self.LSTM_layer_3(output_2,h_state_3_c ) # #,self.hidden_cell a,b,c = output_3.shape # output_4 = F.relu(self.linear4(output_3.view(-1,c))) value = torch.tanh(self.linear5(output_4)) return value ,(h_state_cv_c[0].data,h_state_cv_c[1].data),(h_state_n_c[0].data,h_state_n_c[1].data),(h_state_3_c[0].data,h_state_3_c[1].data) """ """ class Actor(nn.Module): def __init__(self, state_size, action_size): super(Actor, self).__init__() self.conv1 = nn.Conv2d(3,8, kernel_size=8, stride=4, padding=0) # 500*500*3 -> 124*124*8 self.maxp1 = nn.MaxPool2d(4, stride = 2, padding=0) # 124*124*8 -> 61*61*8 self.conv2 = nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=0) # 61*61*8 -> 58*58*16 self.maxp2 = nn.MaxPool2d(2, stride=2, padding=0) # 58*58*16 -> 29*29*16 = 13456 self.linear_CNN = nn.Linear(13456, 256) self.lstm_CNN = nn.LSTM(256, 85,1,batch_first=True) # self.state_size = state_size self.action_size = action_size self.linear1 = nn.Linear(self.state_size, 128) self.linear2 = nn.Linear(128, 128) self.lstm3 = nn.LSTM(128,85, 1,batch_first=True) self.LSTM_layer_3 = nn.LSTM(510,128,1, batch_first=True) #864 self.linear4 = nn.Linear(128,32) self.mu = nn.Linear(32,self.action_size) #256 linear2 self.sigma = nn.Linear(32,self.action_size) def forward(self, state,tensor_cv,h_state_cv_a=(torch.zeros(1,3,85).to(device), torch.zeros(1,3,85).to(device)),h_state_n_a=(torch.zeros(1,3,85).to(device), torch.zeros(1,3,85).to(device)),h_state_3_a=(torch.zeros(1,1,128).to(device), torch.zeros(1,1,128).to(device))): # CV x = F.relu(self.maxp1(self.conv1(tensor_cv))) x = F.relu(self.maxp2(self.conv2(x))).reshape(3,1,13456) #x = x.view(x.size(0), -1) #展開 x = F.relu(self.linear_CNN(x))#.reshape(1,768) x,h_state_cv_a = self.lstm_CNN(x) #,h_state_cv_a #.unsqueeze(0) x = F.relu( x).reshape(1,255) #torch.tanh # num output_1 = F.relu(self.linear1(state)) output_2 = F.relu(self.linear2(output_1)) output_2,h_state_n_a = self.lstm3(output_2) #,h_state_n_a output_2 = F.relu(output_2) # output_2 = output_2.squeeze().reshape(1,255) # LSTM output_2 = torch.cat((x,output_2),1) output_2 = output_2.unsqueeze(0) output_3 , h_state_3_a= self.LSTM_layer_3(output_2,h_state_3_a) #,self.hidden_cell a,b,c = output_3.shape # output_4 = F.relu(self.linear4(output_3.view(-1,c))) # mu = torch.tanh(self.mu(output_4)) #有正有负 sigmoid 0-1 sigma = F.relu(self.sigma(output_4)) + 0.001 mu = torch.diag_embed(mu).to(device) sigma = torch.diag_embed(sigma).to(device) # change to 2D dist = MultivariateNormal(mu,sigma) #N(μ，σ^2) entropy = dist.entropy().mean() action = dist.sample() action_logprob = dist.log_prob(action) action = torch.clamp(action.detach(), -0.8, 0.6) return action,action_logprob,entropy,(h_state_cv_a[0].data,h_state_cv_a[1].data),(h_state_n_a[0].data,h_state_n_a[1].data),(h_state_3_a[0].data,h_state_3_a[1].data) """

[ "is0541hx@ed.ritsumei.ac.jp" ]

is0541hx@ed.ritsumei.ac.jp

baf9440ec4050ee7b1f88215ed7d9fe4e3b4c53c

41ed3a71252f418f9331ee7a63023c6d9899b254

/classify_car_model.py

237a0a590493c774269b9c5d0f7211323082c24c

[]

no_license

may1114/triplet-loss

f2c83dcc095e92fc841022ab832f90fc3c15790f

0f2a8c339d3c74fa74928e55f879bc8ca11989ed

refs/heads/master

2022-04-21T23:51:49.767383

2020-04-16T13:11:57

256,210,796

1

0

null

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py

#!/usr/bin/env python # coding: utf-8 # In[ ]: from math import ceil from keras.applications.inception_v3 import InceptionV3 from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from keras.layers import Dense, GlobalAveragePooling2D from keras.models import Model from keras.optimizers import SGD from sklearn.utils import class_weight from utils import generator_batch, generator_batch_multitask FINE_TUNE = True USE_PROCESSING =False NEW_OPTIMIZER = True LEARNING_RATE = 0.001 NBR_EPOCHS = 100 BATCH_SIZE = 64 IMG_WIDTH = 299 IMG_HEIGHT = 299 monitor_index = 'acc' NBR_MODELS = 250 USE_CLASS_WEIGHTS = False RANDOM_SCALE = True MULTI_TASK = False NBR_COLOR = 7 train_path = './train_vehicleModel_list.txt' val_path = './val_vehicleModel_list.txt' # In[ ]: from keras.models import load_model if FINE_TUNE: #can load already fine_tune trained model if MULTI_TASK: model = load_model('./models/inception3_fine_tune_multi_task.h5') else: model = load_model('./models/inception3_fine_tune.h5') else: inception_model = InceptionV3(include_top = False, wegihts = None, input_tensor = None, input_shape(IMG_WIDTH, IMG_HEIGHT, 3), pooling='avg') x = inception_model.get_layer(index = -1).output x = Dense(1024, activation='relu')(x) model_out = Dense(NBR_MODELS, activation='softmax')(x) if MULTI_TASK: color_out = = Dense(NBR_COLOR, activation='softmax')(x) model = Model(inputs=inception_model.input, outputs = [model_out, color_out]) #after create model, create optimizer optimizer = SGD(lr = LEARNING_RATE, momentum = 0.9, nesterov = True) #compile model save_file = './models/inceptionv3_category_model.h5' if MULTI_TASK: save_file = './models/inceptionv3_model_color.h5' model.compile(loss=["categorical_crossentropy", "categorical_crossentropy"], #loss_weights = [0.6, 0.4], optimizer=optimizer, metrics=["accuracy"]) else: model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]) #create check point, early stop, change learning rate best_model = ModelCheckpoint('./models/inceptionv3_category_model.h5', monitor='val_'+monitor_index, verbose = 1, save_best_only = True) reduce_lr = ReduceLROnPlateau(monitor='val_'+monitor_index, factor = 0.5, patience = 3, verbose = 1, min_lr = 0.00001) early_stop = EarlyStop(monitor='val_'+monitor_index, patience=15, verbose = 1) train_data, train_label, val_data = get_train_val_data() steps_per_epoch = int(ceil(len(train_data) * 1. / BATCH_SIZE)) validation_steps = int(ceil(len(val_data) * 1. / BATCH_SIZE)) if USE_CLASS_WEIGHTS: # balance the wegiht class_weights = class_weight('balanced', np.unique(train_label), train_label) #train model if MULTI_TASK: model.fit_generator(generator_batch_multitask(train_data_lines, nbr_class_one = NBR_MODELS, nbr_class_two = NBR_COLORS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, random_scale = RANDOM_SCALE, shuffle = True, augment = True), steps_per_epoch = steps_per_epoch, epochs = NBR_EPOCHS, verbose = 1, validation_data = generator_batch_multitask(val_data_lines, nbr_class_one = NBR_MODELS, nbr_class_two = NBR_COLORS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, shuffle = False, augment = False), validation_steps = validation_steps, class_weight = class_weights, callbacks = [best_model, reduce_lr], max_queue_size = 80, workers = 8, use_multiprocessing=True) else: model.fit_generator(generator_batch(train_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, random_scale = RANDOM_SCALE, shuffle = True, augment = True), steps_per_epoch = steps_per_epoch, epochs = NBR_EPOCHS, verbose = 1, validation_data = generator_batch(val_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, shuffle = False, augment = False), validation_steps = validation_steps, class_weight = class_weights, callbacks = [best_model, reduce_lr, early_stop], max_queue_size = 80, workers = 8, use_multiprocessing=True) # In[ ]: def get_train_val_data(): train_data_lines = open(train_path).readlines() #only need those existed image files train_data_lines = [w for w in train_data_lines if os.path.exists(w.strip().split(' ')[0])] train_labels = [int(w.strip().split(' ')[-1]) for w in train_data_lines] val_data_lines = open(val_path).readlines() val_data_lines = [w for w in val_data_lines if os.path.exists(w.strip().split(' ')[0])] return train_data_lines, train_labels, val_data_lines # In[ ]: def get_fine_tune_model(train_data_lines, val_data_lines, steps_per_epoch, validation_steps, best_model_file): base_model = InceptionV3(include_top = False) x = base_model.output x = GlobalAveragePooling2D(x) x = Dense(1024, activation='relu')(x) pred = Dense(NBR_MODELS, activation='softmax')(x) model = Model(inputs = base_model.input, outputs = pred) #first, lock the base model layers weight #only train the top layers for layer in model.layers: layer.trainable = False best_model = ModelCheckpoint(best_model_file, monitor='val_acc', verbose = 1, save_best_only = True) reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=3, verbose=1, min_lr=0.00001) #compile model model.compile(optimizer='rmsprop', loss = 'categorical_crossentropy') model.fit_generator(generator_batch(train_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, random_scale = RANDOM_SCALE, shuffle = True, augment = True), steps_per_epoch = steps_per_epoch, epochs = NBR_EPOCHS, verbose = 1, validation_data = generator_batch(val_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, shuffle = False, augment = False), validation_steps = validation_steps, callbacks = [best_model, reduce_lr], max_queue_size = 80, workers = 8, use_multiprocessing=True) # after train the top weights, then make from the 250th layer of the base model can train for layer in base_model.layers[:249]: layer.trainable = False for layer in base_model.layers[249:]: layer.trainable = True model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy') model.fit_generator(generator_batch(train_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, random_scale = RANDOM_SCALE, shuffle = True, augment = True), steps_per_epoch = steps_per_epoch, epochs = NBR_EPOCHS, verbose = 1, validation_data = generator_batch(val_data_lines, NBR_MODELS = NBR_MODELS, batch_size = BATCH_SIZE, img_width = IMG_WIDTH, img_height = IMG_HEIGHT, shuffle = False, augment = False), validation_steps = validation_steps, callbacks = [best_model, reduce_lr], max_queue_size = 80, workers = 8, use_multiprocessing=True)

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/DGpxmRkADuZaWHJxZ_12.py

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[]

no_license

daniel-reich/turbo-robot

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""" Steve and Maurice have racing snails. They each have three, a slow `s`, medium `m` and fast `f` one. Although Steve's snails are all a bit stronger than Maurice's, Maurice has a trick up his sleeve. His plan is: 1. Round 1: `[s, f]` Sacrifice his slowest snail against Steve's fastest. 2. Round 2: `[m, s]` Use his middle snail against Steve's slowest. 3. Round 3: `[f, m]` Use his fastest snail against Steve's middle. Create a function that determines whether Maurice's plan will work by outputting `True` if Maurice wins 2/3 games. The function inputs: 1. List 1: `[s, m, f]` for Maurice. 2. List 2: `[s, m, f]` for Steve. ### Examples maurice_wins([3, 5, 10], [4, 7, 11]) ➞ True # Since the matches are (3, 11), (5, 4) and (10, 7), Maurice wins 2 out of 3. maurice_wins([6, 8, 9], [7, 12, 14]) ➞ False # Since the matches are (6, 14), (8, 7) and (9, 12), Steve wins 2 out of 3. maurice_wins([1, 8, 20], [2, 9, 100]) ➞ True ### Notes * Maurice wins if his competing snail's speed **strictly** exceeds Steve's snail's speed. * Steve will always play in this order: `[f, s, m]`. * The order you'll get the snails is always in ascending order. """ def maurice_wins(m_snails, s_snails): did_m_won = 0 if m_snails[0] > s_snails[2]: did_m_won += 1 else: pass if m_snails[1] > s_snails[0]: did_m_won += 1 else: pass if m_snails[2] > s_snails[1]: did_m_won += 1 else: pass if did_m_won == 2 or did_m_won == 3: return True else: return False

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

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/qiyou/migrations/0010_hot_route.py

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[]

no_license

ge-xiao/qiyou

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refs/heads/master

2022-11-25T13:03:46.580715

2020-07-24T05:04:51

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# Generated by Django 3.0.8 on 2020-07-19 12:27 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('qiyou', '0009_city_route'), ] operations = [ migrations.CreateModel( name='hot_route', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('route_name', models.CharField(max_length=100)), ('day_consume', models.CharField(max_length=20)), ('schedule', models.CharField(max_length=500)), ('scenery', models.CharField(max_length=500)), ], ), ]

[ "gx213213@126.com" ]

gx213213@126.com

db1b90dc116d1fcbb3ac1b117fdd082c92cd9c08

276db4291a895c8c310fd20e4b1e108d4a8b4112

/shop/api/products/schemas.py

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[]

no_license

DMS/shop.flask.zc

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b0c8cf616c4ebb9aaf5b6818fda3d527fec69199

refs/heads/master

2021-02-19T17:21:12.044994

2020-02-21T20:41:32

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from marshmallow import Schema, fields class TagSchema(Schema): tag_name = fields.Str() class CategorySchema(Schema): category_name = fields.Str() class ProductReviewSchema(Schema): id = fields.Integer() product_id = fields.Integer() rating = fields.Integer() review = fields.Str() class ProductSchema(Schema): id = fields.Integer() name = fields.Str() price = fields.Decimal() capacity = fields.Int() category = fields.Nested(CategorySchema) produced_at = fields.DateTime() reviews = fields.List(fields.Nested(ProductReviewSchema), required=False) tag_schema = TagSchema() category_schema = CategorySchema() product_schema = ProductSchema() products_schema = ProductSchema(many=True) product_review_schema = ProductReviewSchema() product_reviews_schema = ProductReviewSchema(many=True)

[ "huiyueguan111@gmail.com" ]

huiyueguan111@gmail.com

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/agents/a2c.py

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permissive

Aubret/gym-minigrid

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refs/heads/master

2021-06-06T20:23:48.382545

2021-06-04T12:31:37

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2019-02-08T14:58:54

2019-02-08T14:58:53

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import time import functools import tensorflow as tf import numpy as np from baselines import logger from baselines.common import set_global_seeds, explained_variance from baselines.common import tf_util from baselines.common.policies import build_policy from baselines.a2c.utils import Scheduler, find_trainable_variables from tensorflow import losses #from baselines.a2c.runner import Runner from runner import Runner class Model(object): """ We use this class to : __init__: - Creates the step_model - Creates the train_model train(): - Make the training part (feedforward and retropropagation of gradients) save/load(): - Save load the model """ def __init__(self, policy, env, nsteps, ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4, alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6), lrschedule='linear'): sess = tf_util.get_session() nenvs = env.num_envs nbatch = nenvs*nsteps with tf.variable_scope('a2c_model', reuse=tf.AUTO_REUSE): # step_model is used for sampling step_model = policy(nenvs, 1, sess) #just do one action step # train_model is used to train our network train_model = policy(nbatch, nsteps, sess) A = tf.placeholder(train_model.action.dtype, train_model.action.shape) ADV = tf.placeholder(tf.float32, [nbatch]) R = tf.placeholder(tf.float32, [nbatch]) LR = tf.placeholder(tf.float32, []) # Calculate the loss # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss # Policy loss neglogpac = train_model.pd.neglogp(A) # L = A(s,a) * -logpi(a|s) pg_loss = tf.reduce_mean(ADV * neglogpac) # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy. entropy = tf.reduce_mean(train_model.pd.entropy()) # Value loss vf_loss = losses.mean_squared_error(tf.squeeze(train_model.vf), R) loss = pg_loss - entropy*ent_coef + vf_loss * vf_coef # Update parameters using loss # 1. Get the model parameters params = find_trainable_variables("a2c_model") # 2. Calculate the gradients grads = tf.gradients(loss, params) if max_grad_norm is not None: # Clip the gradients (normalize) grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) grads = list(zip(grads, params)) # zip aggregate each gradient with parameters associated # For instance zip(ABCD, xyza) => Ax, By, Cz, Da # 3. Make op for one policy and value update step of A2C trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon) _train = trainer.apply_gradients(grads) lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule) def train(obs, states, rewards, masks, actions, values): # Here we calculate advantage A(s,a) = R + yV(s') - V(s) # rewards = R + yV(s') advs = rewards - values for step in range(len(obs)): cur_lr = lr.value() td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, LR:cur_lr} if states is not None: td_map[train_model.S] = states td_map[train_model.M] = masks policy_loss, value_loss, policy_entropy, _ = sess.run( [pg_loss, vf_loss, entropy, _train], td_map ) return policy_loss, value_loss, policy_entropy self.train = train self.train_model = train_model self.step_model = step_model self.step = step_model.step self.value = step_model.value self.initial_state = step_model.initial_state self.save = functools.partial(tf_util.save_variables, sess=sess) self.load = functools.partial(tf_util.load_variables, sess=sess) tf.global_variables_initializer().run(session=sess) class a2c: def __init__(self, network, env, seed=None, nsteps=4, total_timesteps=int(80e6), vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, lrschedule='linear', epsilon=1e-5, alpha=0.99, gamma=0.95, print_freq=100, load_path=None, **network_kwargs): set_global_seeds(seed) self.env= env self.nsteps=nsteps self.gamma = gamma self.print_freq = print_freq # Get the nb of env #self.env.num_envs=1 #probably parallelism self.policy = build_policy(env, network, **network_kwargs) #generate policy function, with step and evaluate method # Instantiate the model object (that creates step_model and train_model) self.model = Model(policy=self.policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule) #contain all necessery function if load_path is not None: self.model.load(load_path) # Instantiate the runner object self.runner = Runner(env, self.model, nsteps=self.nsteps, gamma=self.gamma) #Use to run n step in environment and give observations # Calculate the batch_size self.nbatch = self.env.num_envs*nsteps self.total_timesteps = total_timesteps # Start total timer self.tstart = time.time() self.freq_timer = self.tstart def step(self,obs): #self.episode_rewards = [0.0] #action = self.act(obs, update_eps=self.exploration.value(self.learning_steps))[0] #obs[None] actions, values, states, neglogp = self.model.step(obs)#,S=None,M=[False])#states is the initial state, run n steps in environment obs, rewards, dones, _ = self.env.step(actions)#look like S is the next state for evaluation #self.episode_rewards[-1] += rew # Last element #self.episode_rewards.append(0) #if done and len(self.episode_rewards) % self.print_freq == 0: # logger.record_tabular("steps", self.learning_steps) # logger.record_tabular("episodes", len(self.episode_rewards)) # logger.record_tabular("mean episode reward", round(np.mean(self.episode_rewards[-101:-1]), 1)) # logger.dump_tabular() return obs,actions,rewards,dones def learn(self,num_timesteps,timesteps=1): for update in range(timesteps): # Get mini batch of experiences obs, states, rewards, masks, actions, values =self.runner.run() # run n steps in environment policy_loss, value_loss, policy_entropy = self.model.train(obs, states, rewards, masks, actions, values) if num_timesteps % self.print_freq == 0: # Calculate the fps (frame per second) fps = int((num_timesteps * self.nbatch) / (time.time()-self.freq_timer)) # Calculates if value function is a good predicator of the returns (ev > 1) # or if it's just worse than predicting nothing (ev =< 0) print(values,rewards) ev = explained_variance(values, rewards) logger.record_tabular("nupdates", num_timesteps) logger.record_tabular("total_timesteps", num_timesteps*self.nbatch) logger.record_tabular("fps", fps) logger.record_tabular("policy_entropy", float(policy_entropy)) logger.record_tabular("value_loss", float(value_loss)) logger.record_tabular("explained_variance", float(ev)) logger.record_tabular("total_time", float(time.time()-self.tstart)) logger.dump_tabular() self.freq_timer = time.time() return self.model

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lalumiere3@hotmail.fr

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/t1.py

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permissive

Manthanc007/APS-2o2o

c39dbd0606325ab29363a86afc1da7d0dd2aef01

a84337c4e658a93b6c67515fa3ef59b09f2e5e94

refs/heads/master

2020-12-20T22:05:01.871426

2020-04-23T20:35:10

236,221,220

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UTF-8

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py

n=int(input()) l=list(map(int,input().split())) l.sort() alice=l[:len(l)//2] bob=l[len(l)//2:] bob.sort(reverse=True) if(n>2): alice=[i*10 for i in alice] final=sum(alice+bob) print(final,alice,bob) else: print(sum(l))

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/cms/plugins/text/migrations/0004_table_rename.py

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eduncan911/django_cms

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refs/heads/master

2021-01-20T03:17:50.894858

2017-04-26T20:24:08

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from south.db import db from django.db import models from cms.plugins.text.models import * class Migration: depends_on = ( ("cms", "0018_site_permissions"), ) def forwards(self, orm): db.rename_table("text_text", "cmsplugin_text") db.rename_table("text_publictext", "cmsplugin_textpublic") db.alter_column('cmsplugin_text', 'public_id', orm['text.text:public']) try: db.delete_foreign_key('cmsplugin_text' ,'public_id') except: pass db.drop_primary_key("cmsplugin_textpublic") db.rename_column("cmsplugin_textpublic", "publiccmsplugin_ptr_id", "cmspluginpublic_ptr_id") db.create_primary_key("cmsplugin_textpublic", ("cmspluginpublic_ptr_id",)) db.foreign_key_sql('cmsplugin_text' ,'public_id', 'cmsplugin_textpublic', 'cmspluginpublic_ptr_id') def backwards(self, orm): db.delete_foreign_key('cmsplugin_text' ,'public_id') db.drop_primary_key("cmsplugin_textpublic") db.rename_column("cmsplugin_textpublic", "cmspluginpublic_ptr_id", "publiccmsplugin_ptr_id") db.create_primary_key("cmsplugin_textpublic", ("publiccmsplugin_ptr_id",)) db.foreign_key_sql('cmsplugin_text' ,'public_id', 'cmsplugin_textpublic', "publiccmsplugin_ptr_id") db.rename_table("cmsplugin_text", "text_text") db.rename_table("cmsplugin_textpublic", "text_publictext") db.alter_column('cmsplugin_text', 'public_id', orm['text.text:public']) models = { 'cms.publiccmsplugin': { 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True', 'blank': 'True'}) }, 'sites.site': { 'Meta': {'db_table': "'django_site'"}, 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'cms.cmsplugin': { 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2009, 7, 2, 6, 35, 26, 823213)'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'inherited_public': ('django.db.models.fields.related.OneToOneField', [], {'blank': 'True', 'related_name': "'inherited_origin'", 'unique': 'True', 'null': 'True', 'to': "orm['cms.CMSPluginPublic']"}), 'language': ('django.db.models.fields.CharField', [], {'max_length': '5', 'db_index': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'page': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.Page']"}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.CMSPlugin']", 'null': 'True', 'blank': 'True'}), 'placeholder': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'plugin_type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'position': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, 'text.textpublic': { 'Meta': {'db_table': "'cmsplugin_textpublic'"}, 'body': ('django.db.models.fields.TextField', [], {}), 'cmspluginpublic_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.CMSPluginPublic']", 'unique': 'True', 'primary_key': 'True'}), 'mark_delete': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}) }, 'cms.page': { 'changed_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'created_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2009, 7, 2, 6, 35, 27, 318807)'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'in_navigation': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'db_index': 'True', 'blank': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'login_required': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'moderator_state': ('django.db.models.fields.SmallIntegerField', [], {'default': '1', 'blank': 'True'}), 'navigation_extenders': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '80', 'null': 'True', 'blank': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'children'", 'blank': 'True', 'null': 'True', 'to': "orm['cms.Page']"}), 'public': ('django.db.models.fields.related.OneToOneField', [], {'blank': 'True', 'related_name': "'origin'", 'unique': 'True', 'null': 'True', 'to': "orm['cms.PagePublic']"}), 'publication_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'publication_end_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'published': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'reverse_id': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '40', 'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['sites.Site']"}), 'soft_root': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True', 'blank': 'True'}), 'template': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, 'cms.cmspluginpublic': { 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2009, 7, 2, 6, 35, 28, 96687)'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language': ('django.db.models.fields.CharField', [], {'max_length': '5', 'db_index': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'mark_delete': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'page': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.PagePublic']"}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cms.CMSPluginPublic']", 'null': 'True', 'blank': 'True'}), 'placeholder': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'plugin_type': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}), 'position': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, 'text.text': { 'Meta': {'db_table': "'cmsplugin_text'"}, 'body': ('django.db.models.fields.TextField', [], {}), 'cmsplugin_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cms.CMSPlugin']", 'unique': 'True', 'primary_key': 'True'}), 'public': ('django.db.models.fields.related.OneToOneField', [], {'blank': 'True', 'related_name': "'origin'", 'unique': 'True', 'null': 'True', 'to': "orm['text.TextPublic']"}) }, 'cms.pagepublic': { 'changed_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'created_by': ('django.db.models.fields.CharField', [], {'max_length': '70'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2009, 7, 2, 6, 35, 27, 704298)'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'in_navigation': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'db_index': 'True', 'blank': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'login_required': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'mark_delete': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'moderator_state': ('django.db.models.fields.SmallIntegerField', [], {'default': '1', 'blank': 'True'}), 'navigation_extenders': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '80', 'null': 'True', 'blank': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'children'", 'blank': 'True', 'null': 'True', 'to': "orm['cms.PagePublic']"}), 'publication_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'publication_end_date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'published': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'reverse_id': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '40', 'null': 'True', 'blank': 'True'}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['sites.Site']"}), 'soft_root': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True', 'blank': 'True'}), 'template': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) } } complete_apps = ['text']

[ "eduncan911+github@gmail.com" ]

eduncan911+github@gmail.com

f012bf3372a3e6fe81130ba3ca388f4bc6b1b321

87247fc6010499ee572dead1ef5474ae952034c6

/venv/bin/python-config

d1c3fe7df82310ef6aab97caad6c21eb0f152af5

[]

no_license

fernandasj/vue-django

d4c6b0f198e1ce0e491657828f035c8307af761c

be0a92cfa1c6acd993b165f834bc9b9e28e2b558

refs/heads/master

2020-09-20T09:07:40.588250

2019-11-27T13:00:18

224,431,748

3

0

null

UTF-8

Python

false

2,361

#!/home/fernanda/IdeaProjects/vue-django/venv/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "fernanda.silva_sj@hotmail.com" ]

fernanda.silva_sj@hotmail.com

82f1a9eac4f13240266abc89375c0500b47a5771

e2ec890cc48d10087bac70223c307a5b788583e2

/test_precision.py

f95d30cf097e2a52495a8e42707cc7b149265f13

[]

no_license

abhi1kumar/CS6190_pml_project

489bc18bb2537be38f58fc61396111dede07cfb4

11b0bcbb6395216bc98f141cb0f3f5f2081f73f5

refs/heads/master

2022-03-26T12:46:01.576929

2019-12-14T05:27:41

null

0

null

UTF-8

Python

false

2,876

py

import argparse from hyperparameters import * from util import * from test.test_read_data_split import * from hyperparameters import * from EM_Algorithm import * parser = argparse.ArgumentParser(description='Read XML data from files') parser.add_argument('--dir_path' , type=str, default='data/Bibtex' , help='path of the directory containing data-file') parser.add_argument('--data_filename' , type=str, default='Bibtex_data.txt' , help='rel path of data_filename') parser.add_argument('--train_split_filename', type=str, default='bibtex_trSplit.txt' , help='rel path of train_split_filename') parser.add_argument('--test_split_filename' , type=str, default='bibtex_tstSplit.txt', help='rel path of test_split_filename') args = parser.parse_args() X, Y = get_data_labels_from_datafile(args.dir_path, args.data_filename) X, Y = torch.tensor(X).float().to(device), torch.tensor(Y).float().to(device) #normalise X X = normaliseX(X) #shuffle Y num_all_labels = Y.shape[1] shuffled_indices = np.array(random.sample(range(num_all_labels), num_all_labels)) print("shuffled_indices : ", shuffled_indices) Y = Y[:, shuffled_indices] train_indices_for_each_split = get_indices_for_different_splits(args.dir_path, args.train_split_filename) test_indices_for_each_split = get_indices_for_different_splits(args.dir_path, args.test_split_filename) num_splits = train_indices_for_each_split.shape[0] # Get some stats print("") print("Number of splits = {}".format(num_splits)) print("Train indices for each split =", train_indices_for_each_split.shape) print("Test indices for each split =", test_indices_for_each_split.shape) iteration = 25 for split_idx in range(num_splits): train_indices = train_indices_for_each_split[split_idx] test_indices = test_indices_for_each_split [split_idx] # Test data with seen and all labels test_X = X[test_indices] test_Y_seen = Y[test_indices, :num_seen_labels] test_Y = Y[test_indices] V = torch.load(save_folder+"/v_"+str(iteration)+".pt") W = torch.load(save_folder+"/w_"+str(iteration)+".pt") beta = torch.load(save_folder+"/beta_"+str(iteration)+".pt") psi = get_psi(beta, V, lambda_psi) print("Test Data X shape =", test_X.shape) print("Test Labels Y seen shape =", test_Y_seen.shape) prec_seen = precision_at_k(test_X, test_Y, W, beta, psi, V, topk, num_seen_labels, isSeen = True) prec_seen_unseen = precision_at_k(test_X, test_Y, W, beta, psi, V, topk, num_seen_labels, isSeenUnseen = True) print("Test_Precision_Seen : @1 : {:.4f} \t @3 : {:.4f} \t @5 : {:.4f} \n Test_Precision_Seen+Unseen : @1 : {:.4f} \t @3 : {:.4f} \t @5 : {:.4f}".format (prec_seen[0], prec_seen[1], prec_seen[2], prec_seen_unseen[0], prec_seen_unseen[1], prec_seen_unseen[2])) print("\n========================================================================\n") break

[ "aish322@gmail.com" ]

aish322@gmail.com

1ee934da947a05b39ed31df40719477ff5ab88a7

d8b798048bb56efc949b31771bf7eb4e98cb2a96

/1.5/Eligibility/Eligibility.py

b8e8e6a8046169fffc3d30580a6c1f81c4abd67e

[]

no_license

Varfor/Kattis

c144b535bc5b6c03c65c419d242e99f61731768a

c2c63e51a0e55e2e45d67ae37b1d178e707ad1cd

refs/heads/master

2020-03-24T06:42:30.023405

2020-01-29T08:59:38

142,538,602

0

2

null

UTF-8

Python

false

265

py

for i in range(int(input())): a, b, c, d = [x for x in input().split()] print(a, end = " ") if (int(b[:4])>=2010 or int(c[:4])>=1991): print('eligible') elif int(d)>=41: print('ineligible') else: print('coach petitions')

[ "38240861+Varfor@users.noreply.github.com" ]

38240861+Varfor@users.noreply.github.com

48b0be489716eeef9c8e8c096a4b1143b2c1b8ff

6a946e748aed8922feffecaca77b79b5d447c48f

/main.py

29d2f03fa800da7508cda85aeeac7e94a99484d8

[]

no_license

Zeroqube/Yandex_lyceum_project_api_bor

5171b594cd17c6c4e3c182804cc5a6dfe5925f69

25cd22c2a4b9c438469cb88dac54aceb5adf11e6

refs/heads/master

2023-04-12T01:03:21.812134

2021-04-26T19:00:48

360,931,559

0

null

UTF-8

Python

false

8,437

py

import asyncio import sqlite3 from discord.ext import commands import datetime as dt # в data храниться только токен для удобного использования и сохранения секретности токена from data import TOKEN SQL_FILE = 'bot_db' def normalize(a): res = [] for line in a: res.append('\t'.join(str(item) for item in line)) return res bot = commands.Bot(command_prefix="!") con = sqlite3.connect(SQL_FILE) cur = con.cursor() @bot.event async def on_ready(): print(f'{bot.user} has connected to Discord!') for guild in bot.guilds: print( f'{bot.user} подключились к чату:\n' f'{guild.name}(id: {guild.id})') @bot.command(name='set_task') async def set_task(ctx, name): if len(name) > 100: await ctx.send('Название задачи не должно превышать 100 символов!') return channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id, )).fetchone(): cur.execute('INSERT INTO channels VALUES (?)', (channel_id, )) if cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задача с именнем {name} уже есть!') return cur.execute('INSERT INTO todo(channel_id, task_name) VALUES (?, ?)', (channel_id, name, )) con.commit() await ctx.message.add_reaction('👍') print(channel_id, name) @bot.command(name='set_tasks') async def set_tasks(ctx, *names): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): cur.execute('INSERT INTO channels VALUES (?)', (channel_id,)) con.commit() for name in names: if len(name) > 100: await ctx.send('Название задачи не должно превышать 100 символов!') return if cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задача с именнем {name} уже есть!') return cur.execute('INSERT INTO todo(channel_id, task_name) VALUES (?, ?)', (channel_id, name, )) await ctx.message.add_reaction('👍') print(channel_id, name) con.commit() @bot.command(name='set_date') async def set_date(ctx, name, date): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задачи с именнем {name} ещё нет!') return day, month, year = map(int, date.split('.')) date = dt.date(year, month, day) cur.execute('''UPDATE todo SET date = ? WHERE channel_id = ? AND task_name = ?''', (date, channel_id, name)) con.commit() await ctx.message.add_reaction('👍') @bot.command(name='set_time') async def set_time(ctx, name, time): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задачи с именнем {name} ещё нет!') return hours, minutes = map(int, time.split(':')) print(hours, minutes) time = dt.time(hour=hours, minute=minutes) print(time) cur.execute('''UPDATE todo SET time = ? WHERE channel_id = ? AND task_name = ?''', (time, channel_id, name)) con.commit() await ctx.message.add_reaction('👍') @bot.command(name='set_description') async def set_description(ctx, name, *description): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задачи с именнем {name} ещё нет!') return description = ' '.join(description) cur.execute('''UPDATE todo SET description = ? WHERE channel_id = ? AND task_name = ?''', (description, channel_id, name)) con.commit() await ctx.message.add_reaction('👍') @bot.command(name='get_description') async def get_description(ctx, name): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задачи с именнем {name} ещё нет!') return res = cur.execute('SELECT date, time, description FROM todo WHERE channel_id = ?', (channel_id, )).fetchone() s = f'Вы описали задачу {name} {res[0]} {res[1]} так:\n' + str(res[2]) await ctx.send(s) @bot.command(name='get_tasks') async def get_tasks(ctx): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id, )).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name, date, time FROM todo WHERE channel_id = ?', (channel_id, )).fetchone(): await ctx.send('У вас нет сейчас задач') return res = cur.execute('SELECT task_name, date, time FROM todo WHERE channel_id = ?', (channel_id, )).fetchall() s = 'Ваши задачи:\n' + '\n'.join(normalize(res)) await ctx.send(s) @bot.command(name='get_with_date') async def get_with_date(ctx, date): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id, )).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name, date, time FROM todo WHERE channel_id = ?', (channel_id, )).fetchone(): await ctx.send('У вас нет сейчас задач') return if date == 'None': date = 'Nulle' else: day, month, year = map(int, date.split('.')) date = dt.date(year, month, day) res = cur.execute('SELECT task_name, date, time FROM todo WHERE channel_id = ? AND date = ?', (channel_id, date, )).fetchall() s = 'Ваши задачи:\n' + '\n'.join(normalize(res)) await ctx.send(s) @bot.command(name='delete') async def delete(ctx, name): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name FROM todo WHERE channel_id = ? AND task_name = ?', (channel_id, name)).fetchone(): await ctx.send(f'Задачи с именнем {name} ещё нет!') return cur.execute('''DELETE FROM todo WHERE channel_id = ? AND task_name = ?''', (channel_id, name, )) await ctx.send(f'Задача с именнем {name} успешно удалена') @bot.command(name='delete_all') async def delete_all(ctx): channel_id = ctx.author.id if not cur.execute('SELECT id FROM channels WHERE id = ?', (channel_id,)).fetchone(): await ctx.send('У вас ещё не было задач') return if not cur.execute('SELECT task_name, date, time FROM todo WHERE channel_id = ?', (channel_id, )).fetchone(): await ctx.send('У вас нет сейчас задач') return cur.execute('''DELETE FROM todo WHERE channel_id = ?''', (channel_id, )) await ctx.send('Все задачи успешно удалены') bot.run(TOKEN)

[ "maxbystrov2006@yandex.ru" ]

maxbystrov2006@yandex.ru

eb2e1b0baf857817dbf21814013801747d376aa2

b73e16120ea4e76991de735ee84d62c9a375f6af

/text.py

8dea38ef992717c27c69d1404b7fdeadfc36b690

[]

no_license

coke-killer/matploblibDemo

00e25aab9f300c04dd9a9b4854f0319bbb97f37d

45850510ffbe87911f141a670c63c5ef2550e3c6

refs/heads/master

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# __author__: "yudongyue" # date: 2021/3/24 import matplotlib.pyplot as plt fig = plt.figure() plt.axis([0, 10, 0, 10]) t = "This is a really long string that I'd rather have wrapped so that it" \ " doesn't go outside of the figure, but if it's long enough it will go" \ " off the top or bottom!" # plt.text(4, 1, t, ha='left', rotation=15, wrap=True) # plt.text(6, 5, t, ha='left', rotation=15, wrap=True) # plt.text(5, 5, t, ha='right', rotation=-15, wrap=True) plt.text(5, 10, t, fontsize=18, style='oblique', ha='center', va='top', wrap=True) # plt.text(3, 4, t, family='serif', style='italic', ha='right', wrap=True) # plt.text(-1, 0, t, ha='left', rotation=-15, wrap=True) plt.show()

[ "yudongyue@daqo.com" ]

yudongyue@daqo.com

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lmasciangioli/python

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refs/heads/master

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num = str(9119) str_num = str(num) first_digit = str_num[0] second_digit = str_num[1] third_digit = str_num[2] fourth_digit = str_num[3] first = int(first_digit) square_first = first**2 first_str = str(square_first) second = int(second_digit) square_second = second**2 second_str = str(square_second) third = int(third_digit) square_third = third**2 third_str = str(square_third) fourth = int(fourth_digit) square_fourth = fourth**2 fourth_str = str(square_fourth) numeros = first_str + second_str + third_str + fourth_str integer = int(numeros) print(integer)

[ "l.masciangioli@gmail.com" ]

l.masciangioli@gmail.com

c78cd1d4353ca1a0bf3d26428106e8899a0a7514

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hjw520wsr/BigdDataHomework

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# -*- coding: utf-8 -*- """ Created on Tue Jul 24 10:09:10 2018 @author: Nothing """ ls=open('学校和编号.txt',encoding='gbk').readlines() schoolls=[] numls=[] f=open('学校名称2.txt','a',encoding='utf-8') f1=open('学校招生人数1.txt','a',encoding='utf-8') for line in ls: schoolls.append(line.split(',')[0][1:]) numls.append(int(line.split(',')[1][0:-2])) f.write(str(schoolls)) f1.write(str(numls)) f.close() f1.close()

[ "noreply@github.com" ]

hjw520wsr.noreply@github.com

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/XYvyirQMkmPHGLaZi_3.py

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daniel-reich/ubiquitous-fiesta

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def boom_intensity(n): if n < 2 : return 'boom' boom = 'B' for i in range(n) : boom += 'o' boom += 'm' if n % 2 == 0: boom += '!' if n % 5 == 0: boom = boom.upper() return boom

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

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/advertisements/migrations/0009_auto_20210311_1001.py

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MonikaLu/Sellpoint

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# Generated by Django 3.1.6 on 2021-03-11 09:01 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('advertisements', '0008_advertisement_category'), ] operations = [ migrations.AlterField( model_name='advertisement', name='category', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.DO_NOTHING, related_name='advertisement', to='advertisements.category'), ), ]

[ "monika-luu@hotmail.com" ]

monika-luu@hotmail.com

0413e65cabbf039e6af3017c3f65c9cfa72d4387

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/ml3/envs/bullet_sim.py

11bf844481f3af0885e6a59611069154bde53c32

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permissive

rally12/LearningToLearn

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refs/heads/main

2023-06-29T21:42:07.423866

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# Copyright (c) Facebook, Inc. and its affiliates. import os import numpy as np import pybullet_utils.bullet_client as bc import pybullet_data import pybullet class BulletSimulation(object): def __init__(self, gui, controlled_joints, ee_idx, torque_limits,target_pos): if gui: self.sim = bc.BulletClient(connection_mode=pybullet.GUI) else: self.sim = bc.BulletClient(connection_mode=pybullet.DIRECT) self.sim.setAdditionalSearchPath(pybullet_data.getDataPath()) self.ee_idx = ee_idx self.cur_joint_pos = None self.cur_joint_vel = None self.curr_ee = None self.babbling_torque_limits = None self.logger = None self.controlled_joints = controlled_joints self.torque_limits = torque_limits self.n_dofs = len(controlled_joints) #pybullet.setAdditionalSearchPath(pybullet_data.getDataPath()) # TODO: the following should be extracted into some world model that is loaded independently of the robot #self.planeId = pybullet.loadURDF("plane.urdf",[0,0,0]) if target_pos is not None: self.cubeId = pybullet.loadURDF("sphere_small.urdf",target_pos) def disconnect(self): self.sim.disconnect() #pybullet.disconnect() def get_random_torques(self, time_horizon, bounds_low, bounds_high): trajectory= [] for t in range(time_horizon): torque_limits_babbling = bounds_high torques = np.random.uniform(-torque_limits_babbling, torque_limits_babbling) trajectory.append(np.array(torques)) return np.array(trajectory) def get_random_torques_uinit(self, time_horizon): trajectory= [] for t in range(time_horizon): torques = np.random.uniform(-0.3*self.torque_limits, 0.3*self.torque_limits) trajectory.append(np.array(torques)) return np.array(trajectory) def get_target_joint_configuration(self, target_position): self.reset() des_joint_state = self.sim.calculateInverseKinematics(self.robot_id, self.ee_idx, np.array(target_position), jointDamping = [0.1 for i in range(self.n_dofs)]) return np.asarray(des_joint_state) def reset(self, joint_pos=None, joint_vel=None): if joint_vel is None: joint_vel = list(np.zeros(self.n_dofs)) if joint_pos is None: joint_pos = list(np.zeros(self.n_dofs)) for i in range(self.n_dofs): self.sim.resetJointState(bodyUniqueId=self.robot_id, jointIndex=self.controlled_joints[i], targetValue=joint_pos[i], targetVelocity=joint_vel[i]) self.sim.stepSimulation() self.cur_joint_pos = self.get_current_joint_pos() self.cur_joint_vel = self.get_current_joint_vel() self.curr_ee = self.get_current_ee_state() return np.hstack([self.get_current_joint_pos(),self.get_current_joint_vel()]) def move_to_joint_positions(self, joint_pos, joint_vel=None): if joint_vel is None: joint_vel = [0]*len(joint_pos) for i in range(self.n_dofs): self.sim.resetJointState(bodyUniqueId=self.robot_id, jointIndex=self.controlled_joints[i], targetValue=joint_pos[i], targetVelocity=joint_vel[i]) self.sim.stepSimulation() self.cur_joint_pos = self.get_current_joint_pos() self.cur_joint_vel = self.get_current_joint_vel() self.curr_ee = self.get_current_ee_state() return np.hstack([self.cur_joint_pos,self.cur_joint_vel]) def get_MassM(self,angles): for link_idx in self.controlled_joints: self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) cur_joint_angles = list(angles) mass_m = self.sim.calculateMassMatrix(bodyUniqueId=self.robot_id, objPositions = cur_joint_angles) return np.array(mass_m) def get_F(self,angles,vel): for link_idx in self.controlled_joints: self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) cur_joint_angles = list(angles) cur_joint_vel = list(vel) torques = self.sim.calculateInverseDynamics(self.robot_id, cur_joint_angles, cur_joint_vel, [0]*self.action_dim) return np.asarray(torques) def joint_angles(self): return self.cur_joint_pos def joint_velocities(self): return self.cur_joint_vel def forwad_kin(self,state): return self.endeffector_pos() def endeffector_pos(self): return self.curr_ee def get_target_ee(self, state): for i in range(self.n_dofs): self.sim.resetJointState(bodyUniqueId=self.robot_id, jointIndex=self.controlled_joints[i], targetValue=state[i], targetVelocity=0.0) self.sim.stepSimulation() ls = self.sim.getLinkState(self.robot_id, self.ee_idx)[0] return ls def reset_then_step(self, des_joint_state, torque): # for link_idx in self.controlled_joints: # self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) for i in range(self.n_dofs): self.sim.resetJointState(bodyUniqueId=self.robot_id, jointIndex=self.controlled_joints[i], targetValue=des_joint_state[i], targetVelocity=des_joint_state[(i+self.n_dofs)]) return self.apply_joint_torque(torque)[0] def step_model(self,state,torque): return self.sim_step(state,torque) def sim_step(self,state,torque): for link_idx in self.controlled_joints: self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) if str(state.dtype).startswith('torch'): state = state.clone().detach().numpy() if str(torque.dtype).startswith('torch'): torque = torque.clone().detach().numpy() return self.reset_then_step(state,torque) def step(self,state,torque): for link_idx in self.controlled_joints: self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) if str(state.dtype).startswith('torch'): state = state.clone().detach().numpy() if str(torque.dtype).startswith('torch'): torque = torque.clone().detach().numpy() return self.reset_then_step(state,torque) def apply_joint_torque(self, torque): self.grav_comp = self.inverse_dynamics([0] * self.action_dim) torque = torque + self.grav_comp full_torque = torque.copy() #torque = torque.clip(-self.torque_limits, self.torque_limits) self.sim.setJointMotorControlArray(bodyIndex=self.robot_id, jointIndices=self.controlled_joints, controlMode=pybullet.TORQUE_CONTROL, forces=torque) self.sim.stepSimulation() cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_angles = [cur_joint_states[i][0] for i in range(self.n_dofs)] cur_joint_vel = [cur_joint_states[i][1] for i in range(self.n_dofs)] next_state = cur_joint_angles + cur_joint_vel ls = list(self.sim.getLinkState(self.robot_id, self.ee_idx)[0]) self.cur_joint_pos = self.get_current_joint_pos() self.cur_joint_vel = self.get_current_joint_vel() self.curr_ee = self.get_current_ee_state() return np.hstack([self.cur_joint_pos,self.cur_joint_vel]),self.curr_ee def get_current_ee_state(self): ee_state = self.sim.getLinkState(self.robot_id, self.ee_idx) return np.array(ee_state[0]) def get_current_joint_pos(self): cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_angles = [cur_joint_states[i][0] for i in range(self.n_dofs)] return np.array(cur_joint_angles) def get_current_joint_vel(self): cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_vel = [cur_joint_states[i][1] for i in range(self.n_dofs)] return np.array(cur_joint_vel) def get_current_joint_state(self): cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_angles = [cur_joint_states[i][0] for i in range(self.n_dofs)] cur_joint_vel = [cur_joint_states[i][1] for i in range(self.n_dofs)] return np.hstack([cur_joint_angles, cur_joint_vel]) def get_ee_jacobian(self): cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_angles = [cur_joint_states[i][0] for i in range(self.n_dofs)] cur_joint_vel = [cur_joint_states[i][1] for i in range(self.n_dofs)] bullet_jac_lin, bullet_jac_ang = self.sim.calculateJacobian( bodyUniqueId=self.robot_id, linkIndex=self.ee_idx, localPosition=[0, 0, 0], objPositions=cur_joint_angles, objVelocities=cur_joint_vel, objAccelerations=[0] * self.n_dofs, ) return np.asarray(bullet_jac_lin), np.asarray(bullet_jac_ang) def inverse_dynamics(self, des_acc): for link_idx in self.controlled_joints: self.sim.changeDynamics(self.robot_id, link_idx, linearDamping=0.0, angularDamping=0.0, jointDamping=0.0) cur_joint_states = self.sim.getJointStates(self.robot_id, self.controlled_joints) cur_joint_angles = [cur_joint_states[i][0] for i in range(self.n_dofs)] cur_joint_vel = [cur_joint_states[i][1] for i in range(self.n_dofs)] torques = self.sim.calculateInverseDynamics(self.robot_id, cur_joint_angles, cur_joint_vel, des_acc) return np.asarray(torques) def detect_collision(self): return False def return_grav_comp_torques(self): return 0.0 def get_pred_error(self,x,u): return np.zeros(len(u)) def sim_step_un(self,x,u): return np.zeros(len(u)),np.zeros(len(u)) def get_gravity_comp(self): return 0.0 class BulletSimulationFromURDF(BulletSimulation): def __init__(self, rel_urdf_path, gui, controlled_joints, ee_idx, torque_limits, target_pos): super(BulletSimulationFromURDF, self).__init__(gui, controlled_joints, ee_idx, torque_limits, target_pos) urdf_path = os.getcwd()+'/envs/'+rel_urdf_path print("loading urdf file: {}".format(urdf_path)) self.robot_id = self.sim.loadURDF(urdf_path, basePosition=[-0.5, 0, 0.0], useFixedBase=True) self.n_dofs = len(controlled_joints) pybullet.setAdditionalSearchPath(pybullet_data.getDataPath()) #self.planeId = pybullet.loadURDF("plane.urdf") self.sim.resetBasePositionAndOrientation(self.robot_id,[-0.5,0,0.0],[0,0,0,1]) self.sim.setGravity(0, 0, -9.81) dt = 1.0/240.0 self.dt = dt self.sim.setTimeStep(dt) self.sim.setRealTimeSimulation(0) self.sim.setJointMotorControlArray(self.robot_id, self.controlled_joints, pybullet.VELOCITY_CONTROL, forces=np.zeros(self.n_dofs)) class BulletSimulationFromMJCF(BulletSimulation): def __init__(self, rel_mjcf_path, gui, controlled_joints, ee_idx, torque_limits): super(BulletSimulationFromMJCF, self).__init__(gui, controlled_joints, ee_idx, torque_limits, None) print('hierhierhierhier') xml_path = os.getcwd()+'/envs/'+rel_mjcf_path if rel_mjcf_path[0] != os.sep: xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_mjcf_path) else: xml_path = rel_mjcf_path #xml_path = '/Users/sarah/Documents/GitHub/LearningToLearn/ml3/envs/mujoco_robots/reacher.xml' print("loading this mjcf file: {}".format(xml_path)) self.world_id, self.robot_id = self.sim.loadMJCF(xml_path) pybullet.setAdditionalSearchPath(pybullet_data.getDataPath()) print(pybullet_data.getDataPath()) self.planeId = pybullet.loadURDF("plane.urdf") #self.cubeId = pybullet.loadURDF("sphere_small.urdf", [0.02534078, -0.19863741, 0.01]) #0.02534078, -0.19863741 0.10534078, 0.1663741 self.n_dofs = len(controlled_joints) self.sim.setGravity(0, 0, -9.81) dt = 1.0/100.0 self.dt = dt self.sim.setTimeStep(dt) self.sim.setRealTimeSimulation(0) self.sim.setJointMotorControlArray(self.robot_id, self.controlled_joints, pybullet.VELOCITY_CONTROL, forces=np.zeros(self.n_dofs))

[ "sarahbechtle@googlemail.com" ]

sarahbechtle@googlemail.com

91766d1f77c32ec4c16e9725f4ffebca650eff16

f82757475ea13965581c2147ff57123b361c5d62

/gi-stubs/repository/GData/GDWhenClass.py

fa46cb618eef4de0a00aea3add814235951fb549

[]

no_license

ttys3/pygobject-stubs

9b15d1b473db06f47e5ffba5ad0a31d6d1becb57

d0e6e93399212aada4386d2ce80344eb9a31db48

refs/heads/master

2022-09-23T12:58:44.526554

2020-06-06T04:15:00

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8

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null

2020-06-05T15:57:54

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UTF-8

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# encoding: utf-8 # module gi.repository.GData # from /usr/lib64/girepository-1.0/GData-0.0.typelib # by generator 1.147 """ An object which wraps an introspection typelib. This wrapping creates a python module like representation of the typelib using gi repository as a foundation. Accessing attributes of the module will dynamically pull them in and create wrappers for the members. These members are then cached on this introspection module. """ # imports import gi as __gi import gi.overrides.GObject as __gi_overrides_GObject import gi.repository.Gio as __gi_repository_Gio import gobject as __gobject class GDWhenClass(__gi.Struct): """ :Constructors: :: GDWhenClass() """ def __delattr__(self, *args, **kwargs): # real signature unknown """ Implement delattr(self, name). """ pass def __dir__(self, *args, **kwargs): # real signature unknown """ Default dir() implementation. """ pass def __eq__(self, *args, **kwargs): # real signature unknown """ Return self==value. """ pass def __format__(self, *args, **kwargs): # real signature unknown """ Default object formatter. """ pass def __getattribute__(self, *args, **kwargs): # real signature unknown """ Return getattr(self, name). """ pass def __ge__(self, *args, **kwargs): # real signature unknown """ Return self>=value. """ pass def __gt__(self, *args, **kwargs): # real signature unknown """ Return self>value. """ pass def __hash__(self, *args, **kwargs): # real signature unknown """ Return hash(self). """ pass def __init_subclass__(self, *args, **kwargs): # real signature unknown """ This method is called when a class is subclassed. The default implementation does nothing. It may be overridden to extend subclasses. """ pass def __init__(self): # real signature unknown; restored from __doc__ pass def __le__(self, *args, **kwargs): # real signature unknown """ Return self<=value. """ pass def __lt__(self, *args, **kwargs): # real signature unknown """ Return self<value. """ pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __ne__(self, *args, **kwargs): # real signature unknown """ Return self!=value. """ pass def __reduce_ex__(self, *args, **kwargs): # real signature unknown """ Helper for pickle. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown """ Helper for pickle. """ pass def __repr__(self, *args, **kwargs): # real signature unknown """ Return repr(self). """ pass def __setattr__(self, *args, **kwargs): # real signature unknown """ Implement setattr(self, name, value). """ pass def __sizeof__(self, *args, **kwargs): # real signature unknown """ Size of object in memory, in bytes. """ pass def __str__(self, *args, **kwargs): # real signature unknown """ Return str(self). """ pass def __subclasshook__(self, *args, **kwargs): # real signature unknown """ Abstract classes can override this to customize issubclass(). This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached). """ pass def __weakref__(self, *args, **kwargs): # real signature unknown pass parent = property(lambda self: object(), lambda self, v: None, lambda self: None) # default _g_reserved0 = property(lambda self: object(), lambda self, v: None, lambda self: None) # default _g_reserved1 = property(lambda self: object(), lambda self, v: None, lambda self: None) # default __class__ = None # (!) real value is "<class 'gi.types.StructMeta'>" __dict__ = None # (!) real value is "mappingproxy({'__info__': StructInfo(GDWhenClass), '__module__': 'gi.repository.GData', '__gtype__': <GType void (4)>, '__dict__': <attribute '__dict__' of 'GDWhenClass' objects>, '__weakref__': <attribute '__weakref__' of 'GDWhenClass' objects>, '__doc__': None, 'parent': <property object at 0x7fd5e17433b0>, '_g_reserved0': <property object at 0x7fd5e17434a0>, '_g_reserved1': <property object at 0x7fd5e1743590>})" __gtype__ = None # (!) real value is '<GType void (4)>' __info__ = StructInfo(GDWhenClass)

[ "ttys3@outlook.com" ]

ttys3@outlook.com

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/src/solution/p122-efficient exponentiation/p122.py

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[]

no_license

glimmercn/Euler-Project

c34b4ac787c1926e23d289bd699282588b04ee87

d56249f274d0df74cb4465e25bd24ad1e36bb956

refs/heads/master

2021-01-25T07:39:57.640527

2014-03-31T22:13:34

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''' Created on 2013-3-27 @author: kan ''' import numpy as np import numpy as np l = [1] s = [l] idx = 0 reach200 = False expons = np.zeros(201) expons[0:2] = 1 dic = set() while not np.all(expons): for i in range(len(s[idx])): for j in range(i, len(s[idx])): if 201 > s[idx][i] + s[idx][j] > s[idx][-1] : new = s[idx][i] + s[idx][j] p1 = s[idx] + [new] if str(p1) not in dic: s.append(p1) dic.add(str(p1)) if expons[new] == 0: expons[new] = len(p1) - 1 idx += 1 print(np.sum(expons))

[ "huangkandiy@gmail.com" ]

huangkandiy@gmail.com

893778dab6a8e3cb9a2788d183809792c484e0ce

764a543c3c36b22928beceb0bdcc75426679d9b7

/articles/migrations/0001_initial.py

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[]

no_license

kashtanov1111/qwqw

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refs/heads/master

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# Generated by Django 3.1.13 on 2021-09-25 14:21 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Article', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=255)), ('body', models.TextField()), ('date', models.DateTimeField(auto_now_add=True)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "1kashtanov1111@gmail.com" ]

1kashtanov1111@gmail.com

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/globals.py

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[ "MIT" ]

permissive

SamirOmarov/galactic-collision

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refs/heads/master

2022-07-12T06:46:10.574344

2020-01-23T19:36:47

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2022-06-22T00:54:06

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from vpython import vector, color, sqrt, sphere, rate, scene from math import fsum from random import gauss import numpy as np # CONSTANTS # Universal gravitational constant G = 6.673e-11 scene.width = 1300 scene.height = 650 # Solar mass in kg (assume average stellar mass) SOLAR_MASS = 2.000e30 # Precalculated bounds to solar mass MIN_SOLAR_MASS = SOLAR_MASS * 0.5 MAX_SOLAR_MASS = SOLAR_MASS * 250 AVG_SOLAR_MASS = SOLAR_MASS * 3.0 # Scale distances for galactic scales DIST_SCALE = 1e20 # 1e20 # Galactic parameters MAX_ORBITAL_RADIUS = DIST_SCALE * 10 MIN_ORBITAL_RADIUS = DIST_SCALE * 0.15 MILKY_WAY_GALAXY_THICKNESS = DIST_SCALE * 0.9 ANDROMEDA_GALAXY_THICKNESS = DIST_SCALE * 0.2 # Milky Way contains about 300 billion stars NUM_STARS_MILKY_WAY = 700 # Andromeda Galaxy contains about 1 trillion (10^12) stars NUM_STARS_ANDROMEDA = 1400 # Graphical constants STAR_RADIUS = 0.025 dt = 1e17 # FUNCTIONS # Limit x between lower and upper def clamp(x, lower, upper): return max(min(x, upper), lower) # Return the force due to gravity on an object def gravity(mass1, mass2, radius): return G * mass1 * mass2 / radius**2 # Return the acceleration due to gravity on an object. def g_accel(mass, radius): # Limit minimum radius to avoid flinging out too many particles radius = max(radius, MIN_ORBITAL_RADIUS) return G * mass / radius / radius # Calculate acceleration on an object caused by galaxy def accel(obj, galaxy): r_galaxy = galaxy.pos - obj.pos # We have a = F / m = G * m_center / r ^2 return r_galaxy.norm() * g_accel(galaxy.mass, r_galaxy.mag)

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samir.omarov@gmail.com

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/env/lib/python3.6/site-packages/stripe/api_resources/transfer.py

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Ao99/e-commerce-django

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refs/heads/master

2022-12-14T01:56:19.266754

2021-09-09T18:18:47

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2022-12-08T03:22:13

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from __future__ import absolute_import, division, print_function from stripe import util from stripe.api_resources.abstract import CreateableAPIResource from stripe.api_resources.abstract import ListableAPIResource from stripe.api_resources.abstract import UpdateableAPIResource from stripe.api_resources.abstract import custom_method from stripe.api_resources.abstract import nested_resource_class_methods @custom_method("cancel", http_verb="post") @nested_resource_class_methods( "reversal", operations=["create", "retrieve", "update", "list"] ) class Transfer( CreateableAPIResource, ListableAPIResource, UpdateableAPIResource ): OBJECT_NAME = "transfer" def cancel(self, idempotency_key=None, **params): url = self.instance_url() + "/cancel" headers = util.populate_headers(idempotency_key) self.refresh_from(self.request("post", url, params, headers)) return self

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aodong99@gmail.com

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/app.py

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[]

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suneelisk/Word-Cloud

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refs/heads/main

2023-06-11T08:28:26.089204

2021-06-29T02:55:16

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import streamlit as st import pandas as pd import requests from bs4 import BeautifulSoup import regex as re from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt st.title('Simple Streamlit App') st.markdown(""" This app performs Word Cloud * **Python libraries:** streamlit, pandas, BeautifulSoup, wordcloud ...... * **Need to cantact: ** [SeaportAI.com](https://seaportai.com/contact-us/). """) st.set_option('deprecation.showPyplotGlobalUse', False) #file_bytes = st.file_uploader("Upload a file", type="csv") #if file_bytes is not None: st.sidebar.header("Select Link") links = ["https://seaportai.com/blog-predictive-maintenance/", "https://seaportai.com/2019/04/22/healthcare-analytics/", "https://seaportai.com/blog-rpameetsai/", "https://seaportai.com/covid-19/", "https://seaportai.com/industry4-0/"] URL = st.sidebar.selectbox('Link', links) st.sidebar.header("Select No.of words you want to display") words = st.sidebar.selectbox("No.of Words", range(10,1000,10)) if URL is not None: r = requests.get(URL) soup = BeautifulSoup(r.content, 'html.parser') table = soup.find('div', attrs = {'id':'main-content'}) text = table.text cleaned_text = re.sub('\t', "", text) cleaned_texts = re.split('\n', cleaned_text) cleaned_textss = "".join(cleaned_texts) #st.write(cleaned_textss) st.write("Word Cloud Plot") stopwords = set(STOPWORDS) wordcloud = WordCloud(background_color="white", max_words=words, stopwords=stopwords).generate(cleaned_textss) plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.show() st.pyplot()

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suneelisk.noreply@github.com

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/Function_temp.py

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[]

no_license

Yangzhichao95/qwertyuiop

b1370607a75df29206dc278f81c0d441dd1f277d

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refs/heads/master

2020-03-21T22:10:08.188261

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# -*- coding: utf-8 -*- """ Created on Sun Jul 1 10:02:52 2018 @author: 25008 This is a Temp file for the function match project contract and value """ import re def refine_output_contract(contract): # 修改合同最终输出格式 contract_return = [] for i in contract: contract_temp = re.sub('<', '<', i) contract_temp = re.sub('<', '<', contract_temp) contract_temp = re.sub('>', '>', contract_temp) contract_temp = re.sub('（', '(', contract_temp) contract_temp = re.sub('）', ')', contract_temp) contract_temp = re.sub('《|》|/', '', contract_temp) contract_return.append(contract_temp.capitalize()) return (contract_return) def refine_output_project(project): # 修改项目最终输出格式 project_return = [] for i in project: project_temp = re.sub('“|”|/', '', i) project_temp = re.sub('（', '(', project_temp) project_temp = re.sub('）', ')', project_temp) if re.search('编号', project_temp): project_temp = re.sub('$\w*编号.+$', '', project_temp) if len(project_temp) > 1 and re.search('\d', project_temp[len(project_temp) - 1]): project_temp = project_temp[:(len(project_temp) - 1)] project_return.append(project_temp.capitalize()) return(project_return) # 中文字典 chs_arabic_map = {u'〇' : 0, u'一' : 1, u'二' : 2, u'三' : 3, u'四' : 4, u'五' : 5, u'六' : 6, u'七' : 7, u'八' : 8, u'九' : 9, u'十' : 10, u'百' : 100, u'千' : 1000, u'万' : 10000, u'零' : 0, u'壹' : 1, u'贰' : 2, u'叁' : 3, u'肆' : 4, u'伍' : 5, u'陆' : 6, u'柒' : 7, u'捌' : 8, u'玖' : 9, u'拾' : 10, u'佰' : 100, u'仟' : 1000, u'萬' : 10000, u'亿' : 10**8, u'億' : 10**8, u'幺' : 1} def convert_chinese_digits_to_arabic(chinese_digits, chs_arabic_map): """ 将中文表述的数字转换成阿拉伯数字 :param chinese_digits: 待转换的中文表达数字 :return:转换结果 """ # 如果chinese_digits中有角分，先将元与角分分离 yuan_index = chinese_digits.index('元') jiao_fen = chinese_digits[(yuan_index+1):] jiao = 0 fen = 0 if len(jiao_fen): jiao_fen_reg = re.search('([零一二三四五六七八九〇壹贰叁肆伍陆柒捌玖]角)?([零一二三四五六七八九〇壹贰叁肆伍陆柒捌玖]分)?', jiao_fen) if jiao_fen_reg.group(1): jiao = chs_arabic_map[jiao_fen_reg.group(1)[0]]/10 if jiao_fen_reg.group(2): fen = chs_arabic_map[jiao_fen_reg.group(2)[0]]/100 jiaofen = jiao + fen result = 0 tmp = 0 hnd_mln = 0 for count in range(len(chinese_digits[:(yuan_index+1)])): curr_char = chinese_digits[count] curr_digit = chs_arabic_map.get(curr_char, None) if curr_digit is not None: if curr_digit == 10**8: result = result + tmp result = result * curr_digit hnd_mln = hnd_mln*10**8 + result result = 0 tmp = 0 elif curr_digit == 10**4: result = result + tmp result = result * curr_digit tmp = 0 elif curr_digit >=10: tmp = 1 if tmp ==0 else tmp result = result + curr_digit*tmp tmp = 0 elif curr_digit is not None: tmp = tmp*10 + curr_digit else: return (str(result + jiaofen) + '元') result = result + tmp result = result + hnd_mln return (str(result + jiaofen) + '元') def refine_money(str_money): # 修改金钱格式 if re.search('[零一二三四五六七八九〇壹贰叁肆伍陆柒捌玖拾佰仟萬億幺]', str_money): str_money = convert_chinese_digits_to_arabic(str_money, chs_arabic_map) money_raw = re.search('((\d*)(，|,)?)*(\d+)(\.?)(\d*)', str_money).group() money_raw = float(re.sub('，|,', '', money_raw)) unit = re.search('亿|惩|千万|百万|十万|万|千|百|十|元', str_money).group() if re.search('\.(\d*)', str(money_raw)): num_decimal = len(re.search('\.(\d*)', str(money_raw)).group(1)) else: num_decimal = 0 if unit == '亿' or unit == '惩': money = round(money_raw*100000000, max(num_decimal-8, 0)) elif unit == '千万': money = round(money_raw*10000000, max(num_decimal-7, 0)) elif unit == '百万': money = round(money_raw*1000000, max(num_decimal-6, 0)) elif unit == '十万': money = round(money_raw*100000, max(num_decimal-5, 0)) elif unit == '万': money = round(money_raw*10000, max(num_decimal-4, 0)) elif unit == '千': money = round(money_raw*1000, max(num_decimal-3, 0)) elif unit == '百': money = round(money_raw*100, max(num_decimal-2, 0)) elif unit == '十': money = round(money_raw*10, max(num_decimal-1, 0)) else: money = money_raw if money < 5000: return '' if float(money) == int(money): return (int(money)) else: return (float(money)) def refine_up_low(str_money): # 对于上下限不同的金额，提取出两个不同的金额 up_low = [x.group() for x in re.finditer('((\d*)(，|,)?)*(\d+)(\.?)(\d*)', str_money)] up_money = up_low[0] low_money = up_low[1] unit = re.search('亿|千万|百万|十万|万|千|百|十|元', str_money).group() return (refine_money(up_money + unit), refine_money(low_money + unit)) def refine_partya(partya): # 修改甲方的中间输出 partya = [re.sub('\-', '', x) for x in partya] #partya = [re.sub('（', '(', x) for x in partya] #partya = [re.sub('）', ')', x) for x in partya] return (partya) def refine_output_partya(partya): # 修改甲方的最终输出 partya = [re.sub('（', '(', x) for x in partya] partya = [re.sub('）', ')', x) for x in partya] return (partya) def refine_output_partyb(partyb): # 修改乙方的最终输出 partyb = [re.sub('（', '(', x) for x in partyb] partyb = [re.sub('）', ')', x) for x in partyb] partyb = [re.sub('$.*$', '', x) for x in partyb] partyb = [re.sub('\d|公司全资|全资', '', x) for x in partyb] # For some case partyb = [re.sub('\)|）', '', x) for x in partyb] return (partyb) def tiejian_key_value(soup): # 只匹配中国铁建 soupcontent = re.sub('<.+>|\n|\s', '', str(soup)) soupcontent = re.sub('<.+?>', '', soupcontent) content_split = re.split('一、本|二、本|三、本|四、本|五、本|六、本|七、本|八、本|九、本|十、本|\d、本|\d.本公司', soupcontent) if type(content_split) is list: content_split.pop(0) pat_partya = '公司收到(.+?)(发出的)?中标通知书' pat_partyb = '(所属子公司|下属子公司|所属|下属|子公司|、|及|和)([\w|（|）|$|$]+?)(公司)' pat_project = '中标(.+?)(，|。|；)' pat_money = '((\d*)(，|,)?)*(\d+)(\.?)(\d*) *(亿|惩|千万|百万|十万|万|千|百|十)?元' ob_partya = [] ob_partyb = [] ob_project = [] ob_contract = [] ob_money = [] ob_combo = [] for content in content_split: if re.search(pat_partya, content): ob_partya.append(re.search(pat_partya, content).group(1)) else: ob_partya.append('') ob_b = re.findall(pat_partyb, content) ob_b = [x[1] + x[2] for x in ob_b] ob_b = [x for x in ob_b if len(x) > 5] if len(ob_b) == 0: ob_partyb.append('中国铁建股份有限公司') else: ob_partyb.append(ob_b[0]) ob_combo.append('、'.join(ob_b[1:])) content = re.sub('中标通知书', '', content) if re.search(pat_project, content): ob_project.append(re.search(pat_project, content).group(1)) else: ob_project.append('') if re.search(pat_money, content): ob_money.append(refine_money(re.search(pat_money, content).group())) else: ob_money.append('') ob_contract.append('') ob_partya = refine_partya(ob_partya) return(zip(ob_partya, ob_partyb, ob_project, ob_contract, ob_money, ob_money, ob_combo)) def beiche_key_value(soup, fullname): # 只匹配中国北车、中国中车、中国南车 soupcontent = re.sub('<.+>|\n|\s', '', str(soup)) soupcontent = re.sub('<.+?>', '', soupcontent) content_split = re.split('\d、|\d.本公司', soupcontent) if type(content_split) is list: content_split.pop(0) pat_partya = '与(.+?)签订(了)?' pat_partyb = '(子公司|、|及|和)([\w|（|）|$|$]+?)(公司)' pat_contract = '签订(了)?([\w|\-|\.|，|,]+?)(合同)' pat_money = '((\d*)(，|,)?)*(\d+)(\.?)(\d*) *(亿|惩|千万|百万|十万|万|千|百|十)?元' ob_partya = [] ob_partyb = [] ob_project = [] ob_contract = [] ob_money = [] ob_combo = [] for content in content_split: if re.search(pat_partya, content): ob_partya.append(re.search(pat_partya, content).group(1)) else: ob_partya.append('') if re.search(pat_contract, content): contract_raw = re.search(pat_contract, content).group(2) + re.search(pat_contract, content).group(3) if re.search(pat_money, contract_raw): # 如果提取出的部分中有涉及金额 ob_money.append(refine_money(re.search(pat_money, contract_raw).group())) ob_contract.append(re.split('元(的?)', contract_raw)[2]) else: ob_contract.append(contract_raw) if re.search(pat_money, content): ob_money.append(refine_money(re.search(pat_money, content).group())) ob_b = re.findall(pat_partyb, content) ob_b = [x[1] + x[2] for x in ob_b] ob_b = [x for x in ob_b if len(x) > 5] if len(ob_b) == 0: ob_partyb.append(fullname) else: ob_partyb.append(ob_b[0]) ob_combo.append('、'.join(ob_b[1:])) # 乙对多个甲 if re.search('分别', ob_partya[len(ob_partya)-1]): partya = ob_partya.pop() partya = re.sub('分别', '', partya) partya = re.split('、|和|以及|及|', partya) _ = [ob_partya.append(x) for x in partya] ob_partyb.pop() _ = [ob_partyb.append(ob_b[0]) for x in partya] ob_combo.pop() _ = [ob_combo.append('') for x in partya] ob_contract.pop() contract_raw = re.search(pat_contract, content) contract = contract_raw.group(2) + contract_raw.group(3) if re.search(pat_money, contract): contract = re.split('元(的?)', contract)[2] contract = re.split('和|及|以及', contract) if len(contract) == len(partya): _ = [ob_contract.append(x) for x in contract] else: _ = [ob_contract.append(contract[0]) for x in partya] ob_money.pop() money = [x.group() for x in re.finditer(pat_money, content)] if len(money) == len(partya): _ = [ob_money.append(refine_money(x)) for x in money] else: _ = [ob_money.append(refine_money(money[0])) for x in partya] ob_partya = refine_partya(ob_partya) _ = [ob_project.append('') for x in ob_partya] return(zip(ob_partya, ob_partyb, ob_project, ob_contract, ob_money, ob_money, ob_combo)) def find_contract(soup): # 寻找合同 contract = '' pat_contract = '《.+?》' soupcontent = re.sub('<.+>|\n | ', '', str(soup)) soupcontent = re.sub('<.+?>', '', soupcontent) section1 = str(soup.find(id = 'SectionCode_1')) section1 = re.sub('<.+>|\n | ', '', section1) section1 = re.sub('<.+>', '', section1) # div = soup.findAll('div') # re.search('合同名称：([\n]*)([\w|（|）|$|$|\-|—|×|\+]+)', soupcontent) # 2 然后在section1里找是否有带有书名号的字段 if re.findall(pat_contract, section1): contract = re.findall(pat_contract, section1) if len(contract) > 0: contractcopy = contract.copy() for sub_contract in contractcopy: if re.search('议案|公告|公示|合同法|备忘录',sub_contract): contract.remove(sub_contract) continue if re.search('合同|协议|[a-zA-Z]', sub_contract) is None: contract.remove(sub_contract) if len(contract) > 0: count = dict() for i in contract: if i in count: count[i] = count[i] + 1 else: count[i] = 1 return(max(count, key = count.get)) # 3 最后在全文里找是否有带有书名号的字段 if re.findall(pat_contract, soupcontent[100:]): contract = re.findall(pat_contract, soupcontent[100:]) if len(contract) > 0: contractcopy = contract.copy() for sub_contract in contractcopy: if re.search('议案|公告|公示|合同法|备忘录',sub_contract): contract.remove(sub_contract) continue if re.search('合同|协议|[a-zA-Z]', sub_contract) is None: contract.remove(sub_contract) if len(contract) > 0: count = dict() for i in contract: if i in count: count[i] = count[i] + 1 else: count[i] = 1 return(max(count, key = count.get)) # 4 全文找关键词 if re.search('(签订了|签署了)([\w|（|）|$|$|\-|—|×|\+|/|《]+?)(合同|协议|合同书|协议书)', soupcontent): contract = re.search('(签订了|签署了)([\w|（|）|$|$|\-|—|×|\+|/|《]+?)(合同|协议|合同书|协议书)', soupcontent).group(2) + re.search('(签订了|签署了)([\w|（|）|$|$|\-|—|×|\+|/|《]+?)(合同|协议|合同书|协议书)', soupcontent).group(3) if len(contract) > 4: return(contract) else: return('') return('') def find_project(soup, contract): # 寻找项目名称 project = '' soupcontent = re.sub('\n | ', '', soup.get_text()) div = soup.findAll('div') strline = '' for line in div[1:]: # 1 在title里面找 if line.get('title') and ('、项目名称' in line.get('title') or '、工程名称' in line.get('title') or '、中标项目' in line.get('title') or '、采购项目名称' in line.get('title')): if re.search('：([、\w—\-~#·（）\[\]/\+：]+)', line.get('title')): project = re.search('：([、\w—\-~#·（）\[\]/\+：]+)', line.get('title')).group(1) return(project) else: strline = line.get_text() strline = re.sub('\n | ', '', strline) strline_split = re.split('\n', strline) strline_split = [x for x in strline_split if len(x) > 0] if len(strline_split) == 1 and re.search('，', strline_split[0]) is None: return (re.sub('。', '',strline_split[0])) for line in div[1:]: # 2 项目XX if line.get('title') and '项目' in line.get('title'): strline = re.sub('<.+>|\n | ', '', str(line)) strline = re.sub('<.+>', '', strline) if re.search('(项目名称|工程名称|中标名称|中标内容|中标项目)\w*：(\n)*([、\w—\-~#·（）\[\]/\+：]+)(，|。|；|\n)', strline): project = re.search('(项目名称|工程名称|中标名称|中标内容|中标项目)\w*：(\n)*([、\w—\-~#·（）\[\]/\+：]+)(，|。|；|\n)', strline).group(3) if re.search('中标', project) is None: return (project) if re.search('(项目名称|工程名称|中标名称|中标内容|中标项目)\w*：(\n)*([、\w—\-~#·（）\[\]/\+：]+)(，|。|；|\n)', soupcontent): project = re.search('(项目名称|工程名称|中标名称|中标内容|中标项目)\w*：(\n)*([、\w—\-~#·（）\[\]/\+：]+)(，|。|；|\n)', soupcontent).group(3) if len(project) > 7 and re.search('中标', project) is None: return (project) if re.search('为([、|\w|—|\-|~|#|·|$|$|（|）|\[|\]]+?)的?预?(中标单位|中标人|中标候选人)', soupcontent): project = re.search('为([、|\w|—|\-|~|#|·|$|$|（|）|\[|\]]+?)的?预?(中标单位|中标人|中标候选人)', soupcontent).group(1) if len(project) > 7 and re.search('中标|推荐', project) is None: return (project) if re.search('中标项目为([、|\w|—|\-|~|#|·|$|$|（|）|\[|\]]+?)(项目|工程|标段|采购.标|采购|活动|，|。)', soupcontent): content = re.search('中标项目为([、|\w|—|\-|~|#|·|$|$|（|）|\[|\]]+?)(项目|工程|标段|采购.标|采购|活动|，|。)', soupcontent) if re.search('，|。', content.group(2)) is None: project = content.group(1) + content.group(2) else: project = content.group(1) if len(project) > 7 and re.search('中标', project) is None: return (project) if re.search('“([、\w—\-~#·（）\[\]/\+：]+)”(.{2})', soupcontent): loc = [[i.start(), i.end()] for i in re.finditer('“([、\w—\-~#·（）\[\]/\+：]+)”(\w{2})', soupcontent)] content = [] for j in range(len(loc)): if j == 0: content.append(soupcontent[max(0, loc[j][0]-5) : loc[j][1]]) else: content.append(soupcontent[max(0, loc[j][0]-5, loc[j-1][1]) : loc[j][1]]) content = [re.search('“([、\w—\-~#·（）\[\]/\+：]+)(项目|标段|采购.标|采购|工程|”项目|”)', x).group() for x in content if re.search('以下简称|公示|公告|名单', x) is None and re.search('“([、\w—\-~#·（）\[\]/\+：]+)(项目|标段|采购.标|采购|工程|”项目|”)', x)] content = [x for x in content if re.search('项目|标段|采购.标|采购|工程', x)] if len(content) > 0 and len(content[0]) > 7: #只取第一个 return(content[0]) if re.search('中标([、\w—\-~#·（）\[\]/\+：]+)(项目|工程|标段|采购.标|采购|活动|）)', soupcontent): project_raw = re.findall('中标([、\w—\-~#·（）\[\]/\+：]+)(项目|工程|标段|采购.标|采购|活动|）)', soupcontent) project_raw = [x[0] + x[1] for x in project_raw] project_raw = [x for x in project_raw if len(x) > 7 and re.search('通知书', x) is None] if len(project_raw) > 0: len_project_raw = [len(x) for x in project_raw] return(project_raw[len_project_raw.index(max(len_project_raw))]) if re.search('在([、\w—\-~#·（）\[\]/\+：]+?)(项目|工程|标段|采购.标|采购)中', soupcontent): content = re.search('在([、\w—\-~#·（）\[\]/\+：]+?)(项目|工程|标段|采购.标|采购)中', soupcontent) project = content.group(1) + content.group(2) if len(project) > 7: return (project) return('') def find_money(soup): #寻找金钱 #pat_up_low_foreign = '((\d*)(，|,)?)*(\d+)(\.?)(\d*) *(—|\-|~)((\d*)(，|,)?)*(\d+)\.?(\d*)(亿|千万|百万|十万|万|千|百|十)?\w?元' pat_foreign = '((?![百千万佰仟萬亿億])[零一二三四五六七八九十百千万〇壹贰叁肆伍陆柒捌玖拾佰仟萬亿億幺]+(元|角|分))|((\d*)(，|,)?)*(\d+)(\.?)(\d*)(亿|千万|百万|十万|万|千|百|十)?\w?元' pat_foreign = '((?![百千万佰仟萬亿億])(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)+(元)(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)?(角)?(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)?(分)?)|((\d*)(，|,)?)*(\d+)(\.?)(\d*)(亿|千万|百万|十万|万|千|百|十)?\w?元' #pat = '([零一二三四五六七八九十百千万〇壹贰叁肆伍陆柒捌玖拾佰仟萬亿億幺]+?|((\d*)(，|,)?)*(\d+)(\.?)(\d*)(亿|千万|百万|十万|万|千|百|十)?)\w?元' #pat_up_low = '((\d*)(，|,)?)*(\d+)(\.?)(\d*)(—|\-|~)((\d*)(，|,)?)*(\d+)\.?(\d*) *(亿|千万|百万|十万|万|千|百|十)?元' #pat_money = '((?![百千万佰仟萬亿億])[零一二三四五六七八九十百千万〇壹贰叁肆伍陆柒捌玖拾佰仟萬亿億幺]+(元|角|分))|((\d*)(，|,)?)*(\d+)(\.?)(\d*)(亿|千万|百万|十万|万|千|百|十)?元' pat_money = '((?![百千万佰仟萬亿億])(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)+(元)(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)?(角)?(零|一|二|三|四|五|六|七|八|九|十|百|千|万|〇|壹|贰|叁|肆|伍|陆|柒|捌|玖|拾|佰|仟|萬|亿|億|幺)?(分)?)|((\d*)(，|,)?)*(\d+)(\.?)(\d*)(亿|千万|百万|十万|万|千|百|十)?元' soupcontent = re.sub('<.+>|\n | ', '', soup.get_text()) soupcontent = re.sub('=\d+', '', soupcontent) # For 250247.html soupcontent = re.sub('编码：\d+', '', soupcontent) section1 = str(soup.find(id = 'SectionCode_1')) section1 = re.sub('<.+>|\n | ', '', section1) section1 = re.sub('<.+?>', '', section1) section1 = re.sub('=\d+', '', section1) # For 250247.html section1 = re.sub('编码：\d+', '', section1) # 1 首先匹配合同金额上下限不一样 #if re.search(pat_up_low_foreign, soupcontent): # if re.search(pat_up_low, soupcontent): # up_low_raw = re.search(pat_up_low, soupcontent).group() # up_money, low_money = refine_up_low(up_low_raw) #else: # up_low_raw = re.search(pat_up_low_foreign, soupcontent).group() # up_money, low_money = refine_up_low(up_low_raw) #return (up_money, low_money) # 2 匹配上下限一样的金额 div = soup.findAll('div') strline = '' for line in div[1:]: # 1 在title里面找 if line.get('title') and re.search('((中标|合同)总?(金额|价))', line.get('title')): if re.search('：' + pat_money, line.get('title')): money = re.search('：' + pat_money, line.get('title')).group() return(refine_money(money), refine_money(money)) else: strline = line.get_text() strline = re.sub('\n | ', '', strline) strline_split = re.split('\n', strline) strline_split = [x for x in strline_split if len(x) > 0] if len(strline_split) == 1 and re.search(pat_foreign, strline): money = refine_money(re.search(pat_foreign, strline).group()) return(money, money) if [x.start() for x in re.finditer(pat_foreign, section1)]: loc = [[x.start(), x.end()] for x in re.finditer(pat_money, section1)] if len(loc) == 0: loc = [[x.start(), x.end()] for x in re.finditer(pat_foreign, section1)] if len(loc) > 1: money = [] for j in range(len(loc)): if j == 0: money.append(section1[max(0,loc[j][0]-20):loc[j][1]]) else: money.append(section1[max(0, loc[j][0]-20, loc[j-1][1]) : loc[j][1]]) moneycopy = money.copy() for i in range(len(moneycopy)): if re.search('资本|资产|收入|利润|合计|总共|收款|服务费', moneycopy[i]) and re.search('中标', moneycopy[i]) is None: money.remove(moneycopy[i]) continue if re.search('((中标|合同)总?(金额|价))', moneycopy[i]): if i == 0 or loc[i][0]-20 > loc[i-1][1]: raw_return = refine_money(re.search(pat_foreign, moneycopy[i][20:]).group()) else: raw_return = refine_money(re.search(pat_foreign, moneycopy[i][(loc[i][0]-loc[i-1][1]):]).group()) return (raw_return, raw_return) money = [re.search(pat_foreign, x).group() for x in money if re.search(pat_foreign, x)] if len(money) == 0: return('', '') else: money = [refine_money(x) for x in money if type(refine_money(x)) is not str] if len(money) == 0: return('', '') else: return (max(money), max(money)) else: money = section1[max(0,loc[0][0]-10):min(len(section1), loc[0][1])] if re.search('资本|资产|收入|利润|合计|总共|收款|服务费', money) and re.search('中标', money) is None: return('', '') else: money = refine_money(re.search(pat_foreign, money[10:]).group()) return (money, money) elif [x.start() for x in re.finditer(pat_foreign, soupcontent)]: loc = [[x.start(), x.end()] for x in re.finditer(pat_money, soupcontent)] if len(loc) == 0: loc = [[x.start(), x.end()] for x in re.finditer(pat_foreign, soupcontent)] if len(loc) > 1: money = [] for j in range(len(loc)): if j == 0: money.append(soupcontent[max(0,loc[j][0]-20):loc[j][1]]) else: money.append(soupcontent[max(0, loc[j][0]-20, loc[j-1][1]) : loc[j][1]]) moneycopy = money.copy() for i in range(len(moneycopy)): if re.search('资本|资产|收入|利润|合计|总共', moneycopy[i]) and re.search('中标', moneycopy[i]) is None: money.remove(moneycopy[i]) continue if re.search('((中标|合同)总?(金额|价))', moneycopy[i]): if i == 0 or loc[i][0]-20 > loc[i-1][1]: raw_return = refine_money(re.search(pat_foreign, moneycopy[i][20:]).group()) else: raw_return = refine_money(re.search(pat_foreign, moneycopy[i][(loc[i][0]-loc[i-1][1]):]).group()) return (raw_return, raw_return) money = [re.search(pat_foreign, x).group() for x in money if re.search(pat_foreign, x)] if len(money) == 0: return('', '') else: money = [refine_money(x) for x in money if type(refine_money(x)) is not str] if len(money) == 0: return('', '') else: return (max(money), max(money)) else: money = soupcontent[max(0,loc[0][0]-10):min(len(soupcontent), loc[0][1])] if re.search('资本|资产|收入|利润|合计|总共', money) and re.search('中标', money) is None: return('', '') else: money = refine_money(re.search(pat_foreign, money[10:]).group()) return (money, money) else: return('', '')

[ "y1995z02c07@gmail.com" ]

y1995z02c07@gmail.com

72ace06c05b70250bf6b0869b66f53f909901aa2

fc68fcf50fa6b62c346b6ee79718f59a9fdb7530

/Semantic-Segmentation/preprocess_semantic.py

c786576db67010b06c0e317251aeb0bea2165347

[]

no_license

Jeffrey-Ede/NN

2d5be7f2fc9c3fb7bc1a8fec5dc20184b0d9b57c

30370ea408f8f5e8f6c35448a847e1b171b372a4

refs/heads/master

2021-09-13T03:03:06.032555

2018-04-24T08:06:34

125,896,689

0

null

UTF-8

Python

false

1,889

py

import cv2 import numpy as np import os def sigmoidalise(name, size): def fancy_sigmoidaliser(img, scale=3): ''' Rescale intensities by projecting them with a signmoid based on their variance scale is the number of standard deviations to devide the image's deviations from the mean by ''' mean = np.mean(img) sigma = np.sqrt(np.var(img)) def sigmoidalise(x): res = (x-mean) / (scale*sigma) return np.exp(res) / (np.exp(res)+1.0) sigmoidaliser = np.vectorize(sigmoidalise, otypes=[np.float]) return sigmoidaliser(img.copy()) img = cv2.imread(name, cv2.IMREAD_UNCHANGED) #Crop image down to size, from the top left corner img = img[0:size, 0:size] return fancy_sigmoidaliser(img) #cv2.imshow("sdfsd", img) #cv2.waitKey(0) if __name__ == "__main__": dir = "Documents/semantic_stacks/lacy_carbon/raw1/" new_dir = "Documents/semantic_stacks/lacy_carbon/train/" def multiple_mags(raw_dir, proc_dir, sizes, save_size): '''Sigmoidalise the images at multiple magnification scales''' for size in sizes: #Apply variance-based sigmoidalisation to reduce the effect of outliers of the network sigmoidalisation = sigmoidalise(raw_dir, size) #Resize the image to the specified save size if size is not save_size: sigmoidalisation = cv2.resize(img, (save_size, save_size), interpolation=cv2.INTER_AREA if size < save_size else cv2.INTER_CUBIC) sigmoidalisation.save(proc_dir) #Sigmoidalise every file in the directory and save the sigmoidalisations sizes = [667, 512, 384, 256, 192, 128] save_size = 256 for f in os.listdir(dir): multiple_mags(dir+f, new_dir+f, sizes, save_size)

[ "JeffreyEde1@outlook.com" ]

JeffreyEde1@outlook.com

d4d5b2d5ccb2e68d72ec5e9eb7dfc674ae446598

96a34a048c783a75736bf0ec775df22142f9ee53

/packages/models-library/src/models_library/resource_tracker.py

412e587f451e696f7879886a351f752c4ab5b432

[ "MIT" ]

permissive

ITISFoundation/osparc-simcore

77e5b9f7eb549c907f6ba2abb14862154cc7bb66

f4c57ffc7b494ac06a2692cb5539d3acfd3d1d63

refs/heads/master

2023-08-31T17:39:48.466163

2023-08-31T15:03:56

118,596,920

39

29

MIT

2023-09-14T20:23:09

2018-01-23T10:48:05

Python

UTF-8

Python

false

447

py

from enum import auto from typing import TypeAlias from pydantic import PositiveInt from .utils.enums import StrAutoEnum ServiceRunId: TypeAlias = str PricingPlanId: TypeAlias = PositiveInt PricingDetailId: TypeAlias = PositiveInt class ResourceTrackerServiceType(StrAutoEnum): COMPUTATIONAL_SERVICE = auto() DYNAMIC_SERVICE = auto() class ServiceRunStatus(StrAutoEnum): RUNNING = auto() SUCCESS = auto() ERROR = auto()

[ "noreply@github.com" ]

ITISFoundation.noreply@github.com

f54cb6e71d41318e8e5d77aede25cf52f6813fd1

670033ae1c1792b84041f587180d52e12210e0db

/ch9/tooltip_test.py

c3bf33002d63af4879e52eb21751660d116d320f

[]

no_license

CheoR/STTWP

6adb0d79a6c81cdd810c5e943ef950a12f4f8b7f

c84ed364602840ed501831ef368e69850f07a57f

refs/heads/master

2021-01-18T00:13:58.570223

2015-06-24T17:42:07

null

0

null

UTF-8

Python

false

1,510

py

# -*- coding: utf-8 -*- from selenium import webdriver from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions from selenium.webdriver.support.ui import WebDriverWait import unittest class ToolTiptest(unittest.TestCase): """ToolTiptest Call mouse move event moves mouse cursor from current location to supplied element. """ URL = "http://jqueryui.com/tooltip/" def setUp(self): self.driver = webdriver.Firefox()# Chrome non-op self.driver.get(self.URL) self.driver.implicitly_wait(30) self.driver.maximize_window() def tearDown(self): self.driver.quit() def test_tool_tip(self): driver = self.driver frame_element = driver.find_element_by_class_name("demo-frame") driver.switch_to.frame(frame_element) age_field = driver.find_element_by_id("age") ActionChains(self.driver).move_to_element( age_field ).perform() tool_tip_element = WebDriverWait(self.driver, 10).until( expected_conditions.visibility_of_element_located( (By.CLASS_NAME, "ui-tooltip-content") ) ) # verify tooltip message self.assertEqual( "We ask for your age only for statistical purposes.", tool_tip_element.text ) if __name__ == '__main__': unittest.main(verbosity=2)

[ "cheo.roman+github@gmail.com" ]

cheo.roman+github@gmail.com

eaf0583389c889dcce3ea401465d2eb4fa6033e9

88c67aed0f059523f545053286c92ed78f82227c

/padmin.py

6739836756cb6a44addd980affe0de6484180993

[]

no_license

dravix/pyventa

bcc173342d3880fff4a77eb22f115447e6a2f744

2080925db7198ce9e799863c261671cef37b05d0

refs/heads/master

2021-01-01T18:38:11.089306

2018-07-17T22:59:59

7,473,803

0

null

UTF-8

Python

false

12,305

py

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys, os, datetime, urllib2, tarfile, base64, sqlite3 from time import time #aqui=os.getcwd() aqui="/usr/share/pyventa/" #sys.path.append(os.path.join("/usr/share/pyventa/admin")) home=os.path.join(os.path.expanduser('~'),"pyventa") if sys.platform == 'linux2': home=os.path.join(os.path.expanduser('~'),".pyventa") # Set process name. Only works on Linux >= 2.1.57. try: import ctypes libc = ctypes.CDLL('libc.so.6') libc.prctl(15, 'padmin\0') except: pass else: sys.stdout = open(os.path.join(os.path.expanduser('~'),"pad_salida.log"),"w") sys.stderr = open(os.path.join(os.path.expanduser('~'),"pad_error.log"),"w") #sys.path.append(os.path.join(aqui,'plugins')) #sys.path.append(os.path.join(aqui,'import')) #sys.path.append(os.path.join(aqui,"admin")) import datetime, tempfile #from PyQt4.QtGui import * #from PyQt4.QtCore import * #from PyQt4.QtCore import * from ui.ui_admin import Ui_admin import MySQLdb, ConfigParser #from db_conf import configurador from PyQt4.QtGui import QMessageBox, QDialog, QMainWindow, QInputDialog, QApplication,QTableWidgetItem from PyQt4.QtCore import SIGNAL, Qt,QTimer, QLocale from modulos.productos import Productos from modulos.impuestos import Impuestos from modulos.departamentos import Departamentos from modulos.familias import Familias from modulos.unidades import Unidades from modulos.control1 import Admin1 from modulos.compras import Compras from modulos.cajas import Cajas from modulos.clientes import Clientes from modulos.proveedores import Proveedores from modulos.usuarios import Usuarios from modulos.inventario import Inventario from modulos.faltantes import Faltantes from modulos.ofertas import Oferta from modulos.conexiones import Conexiones from ui.dlg_buscador import buscador from lib.utileria import MyListModel, MyTableModel from lib import libutil from lib.librerias.configurador import Configurador from lib.librerias.conexion import Conexion from lib.librerias.seguridad import Seguridad class Administrador(QMainWindow, Ui_admin): def __init__(self): QDialog.__init__(self) self.setupUi(self) self.curser=None self.cursor=None self.home=home self.cfg=Configurador(self) if self.cfg.stat: self.conexion=Conexion(self,self.cfg) if self.conexion.stat: self.cursor=self.conexion.cursor self.curser=self.conexion.curser self.conexion.db.text_factory = str self.iniciarEstilo() self.iniciarSesion() else: print "Error al iniciar configuraciones" sys.exit() #Inicio de funciones self.showMaximized() self.stack.setCurrentIndex(1) self.impuesto=Impuestos(self) self.departamento=Departamentos(self) self.familia=Familias(self) self.unidad=Unidades(self) self.productos=Productos(self) self.clientes=Clientes(self,True) self.proveedores=Proveedores(self,True) self.usuarios=Usuarios(self) self.ofertas=Oferta(self) self.cajas=Cajas(self,True) self.inv=Inventario(self) self.faltantes=Faltantes(self) self.compras=Compras(self) try: self.conexiones=Conexiones(self) except: self.tConexiones.setVisible() #Inicio de combos self.iniciarCombos() #self.familias=self.familia.getLista() self.menuHerramientas.addAction("Recargar estilo",self.iniciarEstilo) self.iniciarEstilo() def conexion(): self.conexion=Conexion(self,self.cfg) if self.conexion.stat: self.cursor=self.conexion.cursor self.curser=self.conexion.curser self.iniciarEstilo() self.iniciarSesion() def iniciarEstilo(self): try: kcss = open(os.path.join(os.path.dirname(os.path.realpath(__file__)),"perfil","estilos",self.cfg.get("pyventa","estilo")),"r") estilo=kcss.read() self.setStyleSheet(estilo) kcss.close() except: try: kcss = open(os.path.join(self.home,"estilos",self.cfg.get("pyventa","estilo")),"r") estilo=kcss.read() self.setStyleSheet(estilo) kcss.close() except: print "No se cargo el estilo "+self.cfg.get("pyventa","estilo") def iniciarSesion(self): dlg=Seguridad(self,nivel=5,logo=":/actions/images/padmin.simple.logo.png", nombre="Administrador de Pyventa") acceso=dlg.exec_() if acceso>-1: self.usuario=dlg.usuario self.cursor=dlg.cursor self.curser=dlg.curser else: sys.exit() def iniciarCombos(self): #self.cbpDepartamentos.clear() #self.cbpFamilias.clear() self.cbeFamilias.clear() self.cbeImpuestos.clear() self.cbeUnidades.clear() #combo=self.departamento.getModelo() #self.cbpDepartamentos.setModel(combo) #self.cbpDepartamentos.setModelColumn(1) #self.cbpFamilias.setModel(self.familia.getModelo()) self.cbeFamilias.setModel(self.familia.getModelo()) #self.cbpFamilias.setModelColumn(1) self.cbeFamilias.setModelColumn(1) self.cbeImpuestos.setModel(self.impuesto.getModelo()) self.cbeImpuestos.setModelColumn(1) self.cbeUnidades.setModel(self.unidad.getModelo()) self.cbeUnidades.setModelColumn(1) def goHome(self): self.stack.setCurrentIndex(0) #self.tbrProductos.hide() def tabular(self,tabla,sql,head): #Recibe una consulta de sql la ejecuta y tabula el resultado lista=[] try: self.cursor.execute(sql) result = self.cursor.fetchall() except MySQLdb.Error, e: print e else: tabla.setColumnCount(len(head)) tabla.setRowCount(len(result)) for i,data in enumerate(head): item = QTableWidgetItem(1) item.setText(str(data)) tabla.setHorizontalHeaderItem(i,item) for i,elem in enumerate(result): if len(elem)==len(head): lista.append(list(elem)) for j,data in enumerate(elem): item = QTableWidgetItem(1) item.setText(str(data)) tabla.setItem(i,j,item) result=None tabla.resizeColumnsToContents() return lista def entablar(self,tabla,lista,head): #Cumple la misma funcion que tabular, con la diferencia que esta recibe una lista de tuplas tabla.clear() tabla.setColumnCount(len(head)) tabla.setRowCount(len(lista)) for i,data in enumerate(head): item = QTableWidgetItem(1) item.setText(str(data)) tabla.setHorizontalHeaderItem(i,item) for i,elem in enumerate(lista): for j,data in enumerate(elem): item = QTableWidgetItem(1) item.setText(str(data)) tabla.setItem(i,j,item) tabla.resizeColumnsToContents() def tablar(self,tabla,lista,head): #Cumple la misma funcion que entablar, con la diferencia que esta recibe una lista de diccionarios tabla.clear() tabla.setColumnCount(len(head)) tabla.setRowCount(len(lista)) for i,data in enumerate(head): item = QTableWidgetItem(1) item.setText(str(data)) tabla.setHorizontalHeaderItem(i,item) for i,elem in enumerate(lista): for j,data in enumerate(elem): item = QTableWidgetItem(1) item.setText(str(elem[data])) tabla.setItem(i,j,item) tabla.resizeColumnsToContents() def copy(self,a): b=[] for i,fila in enumerate(a): tmp=[] for j,col in enumerate(fila): tmp.append(col) b.append(tmp) return b def setStatus(self,stat,msg='Estado de operaciones'): timer=QTimer() if stat==0: #Normal #self.pbStat.setStyleSheet("border:1.3px solid rgb(42, 129, 159); border-radius:12px; color:#fff;\nbackground-color: qlineargradient(spread:pad, x1:1, y1:1, x2:1, y2:0, stop:0 rgba(0, 102, 128, 255), stop:0.563636 rgba(0, 34, 43, 255), stop:0.568182 rgba(0, 65, 82, 255), stop:1 rgba(0, 67, 85, 255));") self.pbStat.setText("Sin operaciones") self.lblStatus.setText(msg) elif stat==1: #self.pbStat.setStyleSheet("border:1.3px solid rgb(84, 126, 28);border-radius:12px; color:#fff;background-color: qlineargradient(spread:pad, x1:1, y1:1, x2:1, y2:0, stop:0 rgba(111, 159, 42, 255), stop:0.495455 rgba(25, 114, 28, 255), stop:0.507692 rgba(87, 149, 42, 255), stop:1 rgba(110, 202, 58, 255));") self.pbStat.setText("Hecho") self.lblStatus.setText(msg) timer.start(3000) self.connect(timer, SIGNAL("timeout()"), lambda:self.setStatus(0)) elif stat==2: #ERROR self.pbStat.setStyleSheet("border:1.3px solid #700;border-radius:12px; color:#fff;\nbackground-color: qlineargradient(spread:pad, x1:1, y1:1, x2:0.995, y2:0.011, stop:0 rgba(86, 19, 19, 255), stop:1 rgba(176, 50, 50, 255));") self.pbStat.setText("Error!") self.lblStatus.setText(msg) timer.start(4000) self.connect(timer, SIGNAL("timeout()"), lambda:self.setStatus(0)) #def listarInventario(self): #sql="SELECT * FROM " #self.entabla(self.tblInventario,) def entabla(self,tabla,header,query,modelo=None): #tabla, header, query, modelo sql=str(query) try: self.cursor.execute(sql) lista=self.cursor.fetchall() except sqlite3.Error, e: print "Error al entablar" raise(e) except: print "Problema al ejecutar el query" else: if lista!=None: #print lista if modelo==None: modelo = MyTableModel(lista, header, self) tabla.setModel(modelo) else: modelo.setVector(lista) return modelo def listaTabla(self,tabla,header,lista,modelo=None): #tabla, header, lista, modelo if lista!=None: #print lista if modelo==None: modelo = MyTableModel(lista, header, self) tabla.setModel(modelo) else: modelo.setVector(lista) return modelo def enlista(self,lista,query,modelo=None): #lista, header, query, modelo sql=str(query) try: self.cursor.execute(sql) res=self.cursor.fetchall() except: print "Problema al ejecutar el query" else: if res!=None: res=list(res[0][1]) print res if modelo==None: modelo = MyListModel(res, self) lista.setModel(modelo) else: modelo.setVector(res) return modelo def aut(self,nivel): #recibe una clave (key) y un valor requerido (req) para ver si tiene el nivel necesario #if (self.stack.currentIndex()==modulo): Acceso =QInputDialog.getText(self, self.tr("Filtro de Acceso"),self.tr("Ingrese su clave de autorizacion."),QLineEdit.Password) key=str(Acceso[0]) self.cursor.execute('select id_usuario,nivel from usuarios where clave=MD5(\''+key+'\');') val=self.cursor.fetchone() if val!=None: if int(val[1])>=int(nivel): return val[0] else : return False def setUsuario(self,usuario): self.usuario=usuario def notify(self,tipo,event,detail='',time=5): dia=QMessageBox(self) timer=QTimer.singleShot(time*1000,dia.accept) # dia=QtGui.QDialog(self) dia.setText(event) dia.setInformativeText(detail) # dia.setDetailedText(detail) dia.setWindowModality(0) dia.setWindowOpacity(.8) dia.setStandardButtons(QMessageBox.NoButton) if tipo=='error': dia.setStyleSheet(".QMessageBox{background:rgba(250,30,10,255);color:#fff}QLabel{background:transparent;color:#fff}") dia.setIcon(QMessageBox.Critical) elif tipo=='info': dia.setStyleSheet(".QMessageBox{background:rgba(30,30,10,255);color:#fff}QLabel{background:transparent;color:#fff}") dia.setIcon(QMessageBox.Information) elif tipo=='advertencia': dia.setStyleSheet(".QMessageBox{background:rgba(255,200,0,255);color:#fff}QLabel{background:transparent;color:#fff}") dia.setIcon(QMessageBox.Warning) elif tipo=='exito': dia.setStyleSheet(".QMessageBox{background:rgba(0,128,0,255);color:#fff}QLabel{background:transparent;color:#fff}") dia.setIcon(QMessageBox.Information) dia.move(self.width()-dia.width(),104) # dia.addWidget(QLabel(event)) dia.setWindowFlags(dia.windowFlags()|Qt.FramelessWindowHint) dia.show() def closeEvent(self, event): self.conexion.commit() event.accept() print "Cerrando el administrador" #self.destory() from lib.dlg_ingreso import acceso if __name__=="__main__": app = QApplication(sys.argv) app.processEvents() QLocale.setDefault(QLocale(111,139)) #dlg=acceso() #key=-1 #key=dlg.exec_() #if key>0: aw = Administrador() #aw.setUsuario() aw.show() app.exec_()

[ "dravix@gmail.com" ]

dravix@gmail.com

6e3e3109f325e9b910cf211332c84e5546b3042c

4140a1eecd862356d7d41b171d8956a7ab96be7b

/nitro-python-1.0/nssrc/com/citrix/netscaler/nitro/resource/config/network/bridgegroup_nsip6_binding.py

2fb82fdd0791085d6769ccc6ee1dbb863a4cb702

[ "Apache-2.0", "Python-2.0", "LicenseRef-scancode-unknown-license-reference" ]

permissive

Citrix-TechSpecialist/NS-Init

1c4311fef80d47d80fb5bfe107df058f5ff93e20

bd1b695584a6acadec0140457782c7f4e97c266b

refs/heads/master

2020-12-02T10:00:13.245312

2017-07-09T09:30:51

96,673,273

1

0

null

UTF-8

Python

false

7,836

py

# # Copyright (c) 2008-2015 Citrix Systems, Inc. # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response from nssrc.com.citrix.netscaler.nitro.service.options import options from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util class bridgegroup_nsip6_binding(base_resource) : """ Binding class showing the nsip6 that can be bound to bridgegroup. """ def __init__(self) : self._ipaddress = "" self._td = 0 self._rnat = False self._id = 0 self._netmask = "" self.___count = 0 @property def id(self) : ur"""The integer that uniquely identifies the bridge group. Minimum value = 1 Maximum value = 1000. """ try : return self._id except Exception as e: raise e @id.setter def id(self, id) : ur"""The integer that uniquely identifies the bridge group. Minimum value = 1 Maximum value = 1000 """ try : self._id = id except Exception as e: raise e @property def td(self) : ur"""Integer value that uniquely identifies the traffic domain in which you want to configure the entity. If you do not specify an ID, the entity becomes part of the default traffic domain, which has an ID of 0. Minimum value = 0 Maximum value = 4094. """ try : return self._td except Exception as e: raise e @td.setter def td(self, td) : ur"""Integer value that uniquely identifies the traffic domain in which you want to configure the entity. If you do not specify an ID, the entity becomes part of the default traffic domain, which has an ID of 0. Minimum value = 0 Maximum value = 4094 """ try : self._td = td except Exception as e: raise e @property def netmask(self) : ur"""A subnet mask associated with the network address. """ try : return self._netmask except Exception as e: raise e @netmask.setter def netmask(self, netmask) : ur"""A subnet mask associated with the network address. """ try : self._netmask = netmask except Exception as e: raise e @property def ipaddress(self) : ur"""The IP address assigned to the bridge group. """ try : return self._ipaddress except Exception as e: raise e @ipaddress.setter def ipaddress(self, ipaddress) : ur"""The IP address assigned to the bridge group. """ try : self._ipaddress = ipaddress except Exception as e: raise e @property def rnat(self) : ur"""Temporary flag used for internal purpose. """ try : return self._rnat except Exception as e: raise e def _get_nitro_response(self, service, response) : ur""" converts nitro response into object and returns the object array in case of get request. """ try : result = service.payload_formatter.string_to_resource(bridgegroup_nsip6_binding_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.bridgegroup_nsip6_binding except Exception as e : raise e def _get_object_name(self) : ur""" Returns the value of object identifier argument """ try : if self.id is not None : return str(self.id) return None except Exception as e : raise e @classmethod def add(cls, client, resource) : try : if resource and type(resource) is not list : updateresource = bridgegroup_nsip6_binding() updateresource.id = resource.id updateresource.ipaddress = resource.ipaddress updateresource.netmask = resource.netmask updateresource.td = resource.td return updateresource.update_resource(client) else : if resource and len(resource) > 0 : updateresources = [bridgegroup_nsip6_binding() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i].id = resource[i].id updateresources[i].ipaddress = resource[i].ipaddress updateresources[i].netmask = resource[i].netmask updateresources[i].td = resource[i].td return cls.update_bulk_request(client, updateresources) except Exception as e : raise e @classmethod def delete(cls, client, resource) : try : if resource and type(resource) is not list : deleteresource = bridgegroup_nsip6_binding() deleteresource.id = resource.id deleteresource.ipaddress = resource.ipaddress deleteresource.netmask = resource.netmask deleteresource.td = resource.td return deleteresource.delete_resource(client) else : if resource and len(resource) > 0 : deleteresources = [bridgegroup_nsip6_binding() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].id = resource[i].id deleteresources[i].ipaddress = resource[i].ipaddress deleteresources[i].netmask = resource[i].netmask deleteresources[i].td = resource[i].td return cls.delete_bulk_request(client, deleteresources) except Exception as e : raise e @classmethod def get(cls, service, id) : ur""" Use this API to fetch bridgegroup_nsip6_binding resources. """ try : obj = bridgegroup_nsip6_binding() obj.id = id response = obj.get_resources(service) return response except Exception as e: raise e @classmethod def get_filtered(cls, service, id, filter_) : ur""" Use this API to fetch filtered set of bridgegroup_nsip6_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = bridgegroup_nsip6_binding() obj.id = id option_ = options() option_.filter = filter_ response = obj.getfiltered(service, option_) return response except Exception as e: raise e @classmethod def count(cls, service, id) : ur""" Use this API to count bridgegroup_nsip6_binding resources configued on NetScaler. """ try : obj = bridgegroup_nsip6_binding() obj.id = id option_ = options() option_.count = True response = obj.get_resources(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e @classmethod def count_filtered(cls, service, id, filter_) : ur""" Use this API to count the filtered set of bridgegroup_nsip6_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = bridgegroup_nsip6_binding() obj.id = id option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e class bridgegroup_nsip6_binding_response(base_response) : def __init__(self, length=1) : self.bridgegroup_nsip6_binding = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.bridgegroup_nsip6_binding = [bridgegroup_nsip6_binding() for _ in range(length)]

[ "Mayank@Mandelbrot.local" ]

Mayank@Mandelbrot.local

1ec730ee58eb570f7330d115e12bc4c37560f281

7722ef476351fa40675fc97b5a330ca7f94cb9db

/FuzzyDrink/wsgi.py

4b1bfadc9873174713ff39177378e0bb17d8b775

[]

no_license

drmarkthrasher/fuzzydrink

a73c94f01fc395e9e4fe9ce39ff19df75585ea38

09a297fdd679a5fc6b737ba11a9c30d767bb6cac

refs/heads/master

2020-04-09T01:10:15.756392

2018-12-06T04:21:14

159,894,301

0

null

UTF-8

Python

false

397

py

""" WSGI config for FuzzyDrink 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/2.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'FuzzyDrink.settings') application = get_wsgi_application()

[ "drmarkthrasher@gmail.com" ]

drmarkthrasher@gmail.com

f6aa0262cb69c272dd529ab640244a9ad4403ac9

a9d54de9e0b47f6e518e0ca8c66fd264531dfd9a

/back/app/views/__init__.py

05b33e32a9d976d39316d65c0ef9477a5b552804

[]

no_license

astex/fable

80d7bfecfff22fc2bc0ba090162716738762c8ed

96a4ae2aac35ec004b3ed8b481f357324c3c4088

refs/heads/master

2020-03-30T03:34:31.041247

2015-08-05T22:00:56

40,268,591

0

null

UTF-8

Python

false

441

py

from app.views import user, deployment, collection, run def register(app): user.UserView.register(app) user.SessionView.register(app) deployment.DeploymentView.register(app) collection.CollectionView.register(app) collection.StoryView.register(app) collection.TargetView.register(app) run.RunView.register(app) run.StepView.register(app) run.ResultView.register(app) run.ActionView.register(app)

[ "0astex@gmail.com" ]

0astex@gmail.com

27af94b12828d72d9f3e81820266d4f6f92c7ba8

1cf1698142740120ac344b79dc72683bd62c09bf

/projeto 1.py

e8c2134265fa9310a1e664ceb591505373d63162

[]

no_license

PedroMagaieski/projetos_python

bd66f4949dd0f51970102fd0f3bd85793b3ccf88

dbf37d5fe2f52d821956b045806d910a22be8d62

refs/heads/main

2023-02-17T03:25:53.678503

2021-01-13T20:13:13

null

0

null

UTF-8

Python

false

1,684

py

#!/usr/bin/env python # coding: utf-8 # In[13]: names = [] notes = [] idn = 'S' def get_avarage(notes): return sum(notes) / len(notes) def get_approved(notes): return [notes.index(note) for note in list(filter(lambda note: note >= 6, notes))] def get_failed(notes): return [notes.index(note) for note in list(filter(lambda note: note < 6, notes))] while idn == 'S' or idn == 's': names.append(input('Digite o nome do aluno: ')) notes.append(int(input('Digite a nota do aluno: '))) idn = input('Deseja digitar a noda de outro aluno? (\'S\' para sim): ') print('\n\n') for index in range(len(names)): print('* Aluno %s: %d' % (names[index], notes[index])) print('\nA média da turma é: ', get_avarage(notes)) print('\nAprovados') for index in get_approved(notes): print('- ', names[index]) print('\nReprovados') for index in get_failed(notes): print('- ', names[index]) # In[8]: alunos = [] notas = [] aprovados = [] reprovados = [] continuar = 'S' cont = 0 while continuar.upper()=='S': cont+=1 aluno = input("Digite o nome do aluno: ") alunos.append(aluno) nota = int(input("Digite a nota do aluno")) notas.append(nota) if(nota<6): reprovados.append(aluno) else: aprovados.append(aluno) continuar = input("Digite S se deseja digitar a nota de outro aluno: ") print("Todos os alunos cadastrados: ") for i in range(len(alunos)): print("%s: %s"%(alunos[i],notas[i])) print("\nA media da sala é: ",sum(notas)/cont) print("\naprovados: ") for i in aprovados: print(i) print("\nreprovados") for i in reprovados: print(i) # In[ ]:

[ "noreply@github.com" ]

PedroMagaieski.noreply@github.com

dbf346401a7da23e6c669222c0e8921531980339

3f286a92caf2ab828c64a4edfd773d3a5982378d

/checkers/pieces.py

936a29365ada7b37882c004e69b2a05470c5b591

[]

no_license

amitbiderman/Checkers

f0e3a0c6c0b068d0cc7b28ad570bfa7b8d1476ad

34ed866bef5f3b4626e066dd59c4a5a9f503d5f9

refs/heads/master

2023-02-01T00:24:12.019680

2020-12-07T12:43:27

297,669,488

1

0

null

UTF-8

Python

false

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py

from .constants import * import pygame class Piece: PADDING = 20 OUTLINE = 2 def __init__(self, row, col, color): self.row = row self.col = col self.color = color self.king = False self.x = 0 self.y = 0 self.calc_position() def calc_position(self): self.x = SQUARE_SIZE * self.col + SQUARE_SIZE // 2 self.y = SQUARE_SIZE * self.row + SQUARE_SIZE // 2 def make_king(self): self.king = True def move(self, row, col): self.row = row self.col = col self.calc_position() def draw(self, win): radius = SQUARE_SIZE // 2 - self.PADDING pygame.draw.circle(win, GREY, (self.x, self.y), radius + self.OUTLINE) pygame.draw.circle(win, self.color, (self.x, self.y), radius) if self.king: win.blit(CROWN, (self.x - CROWN.get_width() // 2, self.y - CROWN.get_height() // 2)) def __repr__(self): return str(self.color)

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/part2/part2.py

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"""Project, part 2""" import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint from n1 import network as net from p1 import flunet as fn import time def initialize(N0,L,Nt,pflag): """Generate network, and initial conditions If pflag is true, construct and return transport matrix """ #Generate network qmax,qnet,enet = net.generate(N0,L,Nt) #THERE ARE N = N0+NT NODES IN TOTAL N = N0+Nt #locate the first node with the highest degree for i in range(N0+Nt): if (qnet[i] == qmax): InfectedNode = i #The actual value of InfectedNode is i+1, this is just its index break #Setup the initial conditions InitialConditions = np.zeros((3*N,1)) #It is 3Nx1 InitialConditions[:N,0] = float(1) #first N are all 1, rest are 0 #apart from the infected node: InitialConditions[InfectedNode,0] = float(0.1) InitialConditions[InfectedNode+N,0] = 0.05 InitialConditions[InfectedNode+2*N,0] = 0.05 if pflag == True: #find the adjacency matrix, A A = net.adjacency_matrix(N0+Nt,enet) P = np.zeros((N,N)) scalar = np.dot(qnet,A).astype(float) #for i in range(N): # for j in range(N): # P[i,j] = float(qnet[i]*A[i,j])/float(np.dot(qnet,A[:,j])) for i in range(N): P[i,:] = (float(qnet[i])*A[i,:].astype(float))/scalar return InitialConditions[:,0], InfectedNode, P return InitialConditions[:,0], InfectedNode def solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,N0,L,Nt,P,verFlag): """Simulate network flu model at Ntime equispaced times between 0 and T (inclusive). Add additional input variables as needed clearly commenting what they are and how they are used additional input: P - the transport matrix assosciated with the generated network, used to calculate RHS N0 - initial number of nodes L - number of links added at each timestep Nt - total number of timesteps verFlag - tells you which version of RHS to use - "python", "fortran", "omp" """ #add input variables to RHS functions if needed def RHSnet(y,t,a,b0,b1,g,k,w,N,P): """RHS used by odeint to solve Flu model requires i, the index of the node being solved and N, the total number of nodes """ #y is really the initial condition, this needs to change #Split initial conditions into S,E,C #beta (b) definition b = b0 + (b1*(float(1)+np.cos(2.0*(np.pi)*float(t)))) #Here I have expanded the sum within dS/dt, dE/dt and dC/dt given, # and used the fact that the sum of Pij over i = 1 # #dS = k*(1-S[i])- S[i]*((b*C[i])+w)+(w*(np.dot(S[:,0],P[:,i]))) #dE = (b*C[i]*S[i]) - E[i]*(k+a+w)+(w*(np.dot(E[:,0],P[:,i]))) #dC = (a*E[i])-(C[i]*(g+k+w))+(w*(np.dot(C[:,0],P[:,i]))) # # dyprime = np.zeros((3*N,1)) S = np.zeros(N) E = np.zeros(N) C = np.zeros(N) S[:] = y[0:N] E[:] = y[N:2*N] C[:] = y[2*N:3*N] for i in range(N): #dyprime[i,0] = k*(1-y[i])- y[i]*((b*y[(2*N)+i])+w)+(w*(np.dot(y[0:N],P[i,:]))) dyprime[i,0] = k*(1-S[i]) - b*C[i]*S[i] + w*np.dot(P[i,:],S) - w*S[i] dyprime[i+N,0] = (b*y[(2*N)+i]*y[i]) - y[N+i]*(k+a+w)+(w*(np.dot(y[N:2*N],P[i,:]))) dyprime[i+(2*N),0] = (a*y[N+i])-(y[(2*N)+i]*(g+k+w))+(w*(np.dot(y[2*N:3*N],P[i,:]))) return dyprime[:,0] #def RHSnet(y,t,a,b0,b1,g,k,w,N,initialConditions,P): # #for i in range(N): # myInit = InitialConditions[i][0],InitialConditions[N+i][0],InitialConditions[2*N+i][0] # #for each node, extract its initial condition (could use if statement dependent on if infectedNode or not, but still O(1) # # Y = odeint(RHSnet,myInit,t,args=(a,b0,b1,g,k,w,N,i,InitialConditions)) #i is the node under consideration # # S_sol[:,i] = Y[:,0] # E_sol[:,i] = Y[:,1] # C_sol[:,i] = Y[:,2] #solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,N0,L,Nt): def RHSnetF(y,t,a,b0,b1,g,k,w,N,P): """RHS used by odeint to solve Flu model" Calculations carried out by fn.rhs """ #!rhs(n,y,t,a,b0,b1,g,k,w,dy,qnet,Anet) dy = fn.rhs(y,t,a,b0,b1,g,k,w,P,N) #for some reason, this takes N as the last value?! return dy def RHSnetFomp(y,t,a,b0,b1,g,k,w,N,P): """RHS used by odeint to solve Flu model Calculations carried out by fn.rhs_omp """ dy = fn.rhs_omp(y,t,a,b0,b1,g,k,w,P,2,N) return dy #there are N0+Nt nodes in total N = N0+Nt #Get the initial conditions #Initialise the time tprime = np.linspace(float(0),T,Ntime) #Add code here and to RHS functions above to simulate network flu model if (verFlag == 'python'): Y = odeint(RHSnet,y0,tprime,args=(a,b0,b1,g,k,w,N,P)) return tprime, Y[:,0:N],Y[:,N:2*N],Y[:,(2*N):3*N] elif (verFlag == 'fortran'): Y = odeint(RHSnetF,y0,tprime,args=(a,b0,b1,g,k,w,N,P)) return tprime, Y[:,0:N],Y[:,N:2*N],Y[:,(2*N):3*N] elif (verFlag == 'omp'): Y = odeint(RHSnetFomp,y0,tprime,args=(a,b0,b1,g,k,w,N,P)) return tprime, Y[:,0:N],Y[:,N:2*N],Y[:,(2*N):3*N] def analyze(N0,L,Nt,T,Ntime,a,b0,b1,g,k,threshold,warray,display=False): """analyze influence of omega on: 1. maximum contagious fraction across all nodes, Cmax 2. time to maximum, Tmax 3. maximum fraction of nodes with C > threshold, Nmax Input: N0,L,Nt: recursive network parameters T,Ntime,a,b0,b1,g,k: input for solveFluNet threshold: use to compute Nmax warray: contains values of omega """ Wlen = len(warray) Cmaxarray = np.zeros((Wlen,1)) Tmaxarray = np.zeros((Wlen,1)) Nmaxarray = np.zeros((Wlen,1)) InitialConditions, InfectedNode, P = initialize(N0,L,Nt,True) N = N0+Nt for i in range(Wlen): #FOR EVERY W, SOLVE FLU NET t,S,E,C = solveFluNet(T,Ntime,a,b0,b1,g,k,warray[i],InitialConditions,N0,L,Nt,P,'omp') Tmaxarray[i,0] = t[np.argmax(C,0)[0]] arrayNs = np.zeros((Ntime,1)) for j in range(Ntime): #TAKE VALUES OF C OVER THRESHOLD arrayNs[j,0] = (C[j,:] > threshold).sum() #FIND MAX X Cmaxarray[i] = np.amax(C) #NUMBER OF Cs over THRESHOLD Nmaxarray[i] = max(arrayNs)/float(N) if display == True: plt.figure() plt.title('Omar Haque, analyze. Graph of Nmax against w') plt.xlabel('w') plt.ylabel('Nmax') plt.plot(warray,Nmaxarray) plt.show() plt.figure() plt.title('Omar Haque, analyze. Graph of Cmax against w') plt.xlabel('w') plt.ylabel('Cmax') plt.plot(warray,Cmaxarray) plt.show() plt.figure() plt.title('Omar Haque, analyze. Graph of Tmax against w') plt.xlabel('w') plt.ylabel('Tmax') plt.plot(warray,Tmaxarray) plt.show() return Cmaxarray,Tmaxarray,Nmaxarray def visualize(enet,C,threshold): """Optional, not for assessment: Create figure showing nodes with C > threshold. Use crude network visualization algorithm from homework 3 to display nodes. Contagious nodes should be red circles, all others should be black""" return None def performance(N0,L,Nt,T,Ntime,a,b0,b1,g,k,w): """function to analyze performance of python, fortran, and fortran+omp approaches Add input variables as needed, add comment clearly describing the functionality of this function including figures that are generated and trends shown in figures that you have submitted This function plots the time taken to complete solveFluNet, against N (total number of nodes), for the three RHS functions. The first (p24png) shows how the two Fortran functions are far superior than the python version, and it seems to grow linearly with the problem size. The second (p25.png) gives a log-log plot of the Fortran RHS with the Fortran+OpenMP RHS functions, against N. It is clear that there does not seem to be much of a difference between them, although the openMP version does seem to take over for larger N """ N = N0+Nt Nvalues = np.arange((N)) pythonTimes = np.zeros(N) fortranTimes = np.zeros(N) openMPTimes = np.zeros(N) for i in Nvalues: #Generate initial values InitialConditions, InfectedNode, P = initialize(N0,L,Nvalues[i],True) pythonTime1 = time.time() #solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,N0,L,Nt,P,verFlag): solveFluNet(T,Ntime,a,b0,b1,g,k,w,InitialConditions,N0,L,Nvalues[i],P,'python') pythonTime2 = time.time() fortranTime1 = time.time() solveFluNet(T,Ntime,a,b0,b1,g,k,w,InitialConditions,N0,L,Nvalues[i],P,'fortran') fortranTime2 = time.time() openMPtime1 = time.time() solveFluNet(T,Ntime,a,b0,b1,g,k,w,InitialConditions,N0,L,Nvalues[i],P,'omp') openMPtime2 = time.time() #solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,N0,L,Nt,verFlag): pythonTimes[i] = pythonTime2-pythonTime1 fortranTimes[i] = fortranTime2-fortranTime1 openMPTimes[i] = openMPtime2-openMPtime1 plt.figure() plt.title('Omar Haque, performance. Comparison of different RHS methods') plt.plot(Nvalues,pythonTimes,label='RHS from python') plt.plot(Nvalues,fortranTimes,label='RHS from fortran') plt.plot(Nvalues, openMPTimes,label='RHS using openMP') plt.xlabel('N') plt.ylabel('time taken for solveFluNet()') plt.legend(loc = 'best') plt.show() plt.figure() plt.title('Omar Haque, performance. Comparison of Fortran vs Fortran+OpenMP (logplot)') plt.plot(np.log(Nvalues),np.log(fortranTimes),label='RHS from fortran') plt.plot(np.log(Nvalues), np.log(openMPTimes),label='RHS using openMP') plt.xlabel('log(N)') plt.ylabel('log(time taken for solveFluNet)') plt.legend(loc = 'best') plt.show() if __name__ == '__main__': a,b0,b1,g,k,w = 45.6,750.0,0.5,73.0,1.0,0.1 #CODE FOR P21,P22,P23 N0,L,Nt = 5,2,500 T = 2 threshold = 0.1 warray = [0.0,0.01,0.1,0.2,0.5,1.0] display = True #def analyze(N0,L,Nt,T,Ntime,a,b0,b1,g,k,threshold,warray,display=False): analyze(N0,L,Nt,T,1000,a,b0,b1,1000,k,threshold,warray,True) #CODE FOR PERFORMANCE #I have chosen Nt = 200 as it covers the section where Fortran+OpenMP is faster than Fortran. # InitialConditions, InfectedNode, P = initialize(5,3,3,True) # N0,L,Nt = 5,2,200 # T = 2 # threshold = 0.1 # Ntime = 1000 # warray = [0,np.power(10,-2),0.1,0.2,0.5,1.0] # performance(N0,L,Nt,T,Ntime,a,b0,b1,g,k,w)

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class Solution(object): def containsNearbyDuplicate(self, nums, k): a = {} for i in range(len(nums)): if nums[i] in a: if(i - a[nums[i]] <= k): return True a[nums[i]] = i return False

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__author__ = 'Kalyan' notes = ''' Python has a good api to deal with text and binary files. We explore that in this module. You may notice how much this is easier than the corresponding c api :-). Use help(file.XXX) to find help on file method XXX (tell, seek etc.) ''' from placeholders import * import inspect import os def get_module_dir(): mod_file = inspect.getfile(inspect.currentframe()) mod_dir = os.path.dirname(mod_file) return mod_dir def get_temp_dir(): module_dir = get_module_dir() temp_dir = os.path.join(module_dir, "tempfiles") if not os.path.exists(temp_dir): os.mkdir(temp_dir) return temp_dir # open files generated as a part of the tests. Allow them to be in a different dir via DATA_DIR def open_temp_file(file, mode): data_dir = os.getenv("DATA_DIR", default=get_temp_dir()) out_file = os.path.join(data_dir, file) return open(out_file, mode) #opens a file from the module directory for reading. These are input files that are part of course material #and must be submitted along with this lesson. #Read: https://docs.python.org/3/library/functions.html#open def open_input_file(file, mode="rt"): mod_dir = get_module_dir() test_file = os.path.join(mod_dir, file) return open(test_file, mode) def test_file_readlines(): f = open_input_file("unit6_input_data.txt") lines = f.readlines() assert ['one\n', 'two\n', 'three\n', 'four\n', 'five\n']== lines def test_file_read(): f = open_input_file("unit6_input_data.txt") data = f.read() assert "one\ntwo\nthree\nfour\nfive\n" == data def test_file_end(): f = open_input_file("unit6_input_data.txt") s = f.read() # read till end. assert '' == f.read() assert '' == f.read() def test_file_read_binary(): f = open_input_file("unit6_input_data.txt", "rb") lines = f.readlines() assert [b"one\r\n",b"two\r\n",b"three\r\n",b"four\r\n",b"five\r\n"] == lines f.seek(0, 0) data = f.read() assert b"one\r\ntwo\r\nthree\r\nfour\r\nfive\r\n" == data def test_file_windows_newlines(): f = open_temp_file("newlines_tmp.txt", "wb") # what does encode do? why do you need it?, run this code without it and explore. f.write("one\r\ntwo\rthree\n".encode()) f.close() f = open_temp_file("newlines_tmp.txt", "rt") assert "one\ntwo\nthree\n" == f.read() f.close() f = open_temp_file("newlines_tmp.txt", "rb") data = f.read() assert not '' == f.read() #windows behavior : https://docs.python.org/3/tutorial/inputoutput.html#reading-and-writing-files def test_file_universal_newlines(): f = open_temp_file("newlines_tmp.txt", "wb") f.write(b"one\r\ntwo\rthree\n") f.close() f = open_temp_file("newlines_tmp.txt", "rU") assert "one\ntwo\nthree\n" == f.read() assert ('\r','\n','\r\n') == f.newlines def test_file_readline(): f = open_input_file("unit6_input_data.txt") lines = [] while True: line = f.readline() if not line: break lines.append(line) assert ['one\n', 'two\n', 'three\n', 'four\n', 'five\n'] == lines #files can be used as iterable objects! def test_file_iteration(): f = open_input_file("unit6_input_data.txt") lines = [] # this will not read the whole file into memory like readlines would. for x in f: lines.append(x) assert ['one\n', 'two\n', 'three\n', 'four\n', 'five\n'] == lines def test_file_tell(): tells = [] f = open_input_file("unit6_input_data.txt") while True: line = f.readline() tells.append(f.tell()) if not line: break assert [5, 10, 17, 23, 29, 29] == tells def test_file_readlines_tell(): tells = [] f = open_input_file("unit6_input_data.txt", "rb") for line in f.readlines(): tells.append(f.tell()) assert [29, 29, 29, 29, 29] == tells def test_file_iteration_tell(): tells = [] f = open_input_file("unit6_input_data.txt", "rb") for line in f: tells.append(f.tell()) assert [5,10,17,23,29] == tells # is there really no difference between readlines and iteration? # as an exercise do both this test and previous test in rt mode. discuss your # observations and findings on forums. def test_file_seek(): f = open_input_file("unit6_input_data.txt", "rb") assert 0 == f.tell() f.read() assert 29 == f.tell() assert b'' == f.read() f.seek(0, 0) assert b'one\r\ntwo\r\nthree\r\nfour\r\nfive\r\n' == f.read() f.seek(-3, 2) assert b'e\r\n' == f.read() f.seek(-2, 1) assert b'\r\n'== f.read() #windows has a few newlines quirks. def test_file_write_text(): f = open_temp_file("test_write.txt", "w") # same as "wt" f.write("one\ntwo\nthree\n") f.close() f = open_temp_file("test_write.txt", "rb") assert b"one\r\ntwo\r\nthree\r\n" == f.read() f = open_temp_file("test_write.txt", "rt") assert "one\ntwo\nthree\n" == f.read() def test_file_write_binary(): f = open_temp_file("test_write.txt", "wb") f.write(b"one\ntwo\nthree\n") f.close() f = open_temp_file("test_write.txt", "rb") assert b"one\ntwo\nthree\n" == f.read() f = open_temp_file("test_write.txt", "rt") assert "one\ntwo\nthree\n" == f.read() # It is generally a good practice to close files after their use is over def test_file_close(): f = open_input_file("unit6_input_data.txt") assert False == f.closed try: lines = [line.strip() for line in f.readlines()] finally: # putting it in finally so that it is closed even if an exception is raised f.close() assert True == f.closed # http://effbot.org/zone/python-with-statement.htm # you can close files with a 'with' statement irrespective of exceptions. # This is similar to using statement in c# def test_with_statement(): try: with open_input_file("unit6_input_data.txt") as f: assert False == f.closed raise Exception("some random exception") except Exception as ex: print(ex) pass assert True == f.closed def test_file_iteration1(): with open_input_file("unit6_input_data.txt") as f: lines = [] # we are walking through f as an iterator, so even if file is huge this code acts like a buffered reader!! for line in f: lines.append(line.strip()) assert ['one', 'two', 'three', 'four', 'five'] == lines lines = [] for line in f: lines.append(line.strip()) # what happened here? read the link i gave above. assert [] == lines # a common pattern to load files into an in memory list # after doing something on each line (for e.g. strip and upper in this case) # notice how little code gets so much work done! def test_file_iteration2(): def process(input): return input.strip().upper() with open_input_file("unit6_input_data.txt") as f: data = [process(line) for line in f] assert ["ONE","TWO","THREE","FOUR","FIVE"] == data three_things_i_learnt = """ -Usage of with statements -Difference between rb and rt and their uses - """

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# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from bigdl.util.common import * from zoo.pipeline.api.keras.base import ZooKerasCreator if sys.version >= '3': long = int unicode = str class AUC(JavaValue): """ Metric for binary(0/1) classification, support single label and multiple labels. # Arguments threshold_num: The number of thresholds. The quality of approximation may vary depending on threshold_num. >>> meter = AUC(20) creating: createAUC """ def __init__(self, threshold_num=200, bigdl_type="float"): JavaValue.__init__(self, None, bigdl_type, threshold_num) class Accuracy(ZooKerasCreator, JavaValue): """ Measures top1 accuracy for multi-class classification or accuracy for binary classification. # Arguments zero_based_label: Boolean. Whether target labels start from 0. Default is True. If False, labels start from 1. Note that this only takes effect for multi-class classification. For binary classification, labels ought to be 0 or 1. >>> acc = Accuracy() creating: createZooKerasAccuracy """ def __init__(self, zero_based_label=True, bigdl_type="float"): super(Accuracy, self).__init__(None, bigdl_type, zero_based_label) class Top5Accuracy(ZooKerasCreator, JavaValue): """ Measures top5 accuracy for multi-class classification. # Arguments zero_based_label: Boolean. Whether target labels start from 0. Default is True. If False, labels start from 1. >>> acc = Top5Accuracy() creating: createZooKerasTop5Accuracy """ def __init__(self, zero_based_label=True, bigdl_type="float"): super(Top5Accuracy, self).__init__(None, bigdl_type, zero_based_label)

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#!/Users/kevinnguyen/Desktop/ProjectX/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "austin_yam@yahoo.com" ]

austin_yam@yahoo.com

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/gramlogin/views.py

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import os import json import pprint from django.http import HttpResponse, HttpResponseForbidden from django.contrib.auth import authenticate from django.views.decorators.csrf import csrf_exempt from django.utils.crypto import get_random_string from django.shortcuts import render, redirect from django.contrib.auth import authenticate, login as auth_login from django.core.signing import TimestampSigner from django.contrib.auth.decorators import login_required from django.contrib.auth import logout as auth_logout from django.contrib.auth.models import User from django.core.urlresolvers import reverse import telegram BOT_TOKEN = os.environ['BOT_TOKEN'] BOT_WHTOKEN = os.environ['BOT_WHTOKEN'] bot = telegram.Bot(token=BOT_TOKEN) signer = TimestampSigner() def build_menu(buttons, n_cols, header_buttons=None, footer_buttons=None): menu = [buttons[i:i + n_cols] for i in range(0, len(buttons), n_cols)] if header_buttons: menu.insert(0, header_buttons) if footer_buttons: menu.append(footer_buttons) return menu @login_required(login_url='/login/') def index(request): auths = signer.sign('logout') context = { 'logout_url': reverse('logout') + '?auths=%s' % auths, } return render(request, 'index.html', context) def login(request): auths = request.GET.get('auths', None) or request.POST.get('auths', None) if auths is None: return render(request, 'login.html') if request.method == 'POST': user = authenticate(request, auths=auths) if user is None: return render(request, 'login.html', context={"message": "Login Fail"}) else: auth_login(request, user) return redirect(index) username = auths.split(':')[0] return render(request, 'login.html', context={'auths': auths,'username': username}) @login_required(login_url='/login/') def logout(request): try: auths = signer.unsign(request.GET.get('auths', '')) except Exception as e: print(e) return redirect(index) auth_logout(request) return redirect(login) @csrf_exempt def handle_bot(request, token): if token != BOT_WHTOKEN: return HttpResponseForbidden() if request.method == 'POST': data = json.loads(request.body.decode("utf-8")) print(json.dumps(data, indent=4)) update = telegram.Update.de_json(data, bot) print(update.message.chat.id, update.message.chat.username, update.message.text) chat_id = update.message.chat.id username = update.message.chat.username if not username: tg_faq = 'https://telegram.org/faq#usernames-and-t-me' bot.sendMessage(chat_id=chat_id, text='To login with Telegram, you must create a username. Refer %s for details.' % tg_faq) return HttpResponse() auths = signer.sign(username) user, created = User.objects.get_or_create(username=username) login_url = request.build_absolute_uri('/login/') + '?auths=%s' % auths button_list = [ telegram.InlineKeyboardButton("Login", url=login_url), ] reply_markup = telegram.InlineKeyboardMarkup(build_menu(button_list, n_cols=1)) bot.sendMessage(chat_id=chat_id, text="Click the button below to login. It only valid for 2 minutes. After that, you have to type 'Login' again.", reply_markup=reply_markup) return HttpResponse()

[ "kamal.mustafa@gmail.com" ]

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/testEmbedding.py

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from __future__ import print_function import sys # include subdirectories sys.path.insert(0, './lib/') sys.path.insert(0, './src/') import time import numpy as np import matplotlib.pyplot as plt import keras as k import tensorflow as tf assert k.backend.image_dim_ordering() == 'tf' import skimage.io as skio import skimage.transform as sktr import segmentVasculature as sv if __name__ == '__main__': VISUAL_OUTPUT = False # load example image imgStr = 'data/' + '20051020_55701_0100_PP.tif' img = skio.imread(imgStr) t0 = time.time() # create embedding in one step embVec, resEncCoordKept = sv.createOMIAembedding( img ) t1 = time.time() total = t1-t0 print ("-"*20,"Embeddings in ", total, " sec") #or run a visual test to see segmentation driving the embeddings #=========================================================== # load model segmentation model = sv.getTrainedModel() # create model encoding output modelEnc = sv.genEncModel() if modelEnc is None: print ('encoding layer not available') # segment resImg = sv.segmentImage( img, model ) #encoding (if available) if modelEnc is not None: resEnc, resEncCoord = sv.getImageEncoding( img, modelEnc ) # visual output imgOut = np.hstack((sktr.resize(img[:, :, 1], resImg.shape), resImg)) if VISUAL_OUTPUT: skio.imshow( imgOut ) sv.checkFiltering(resImg, resEnc, resEncCoord) plt.show() else: skio.imsave( 'data/testEmbOutput.png', imgOut ) # ===========================================================

[ "you@example.com" ]

you@example.com

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/day_wise_work_done/3_july/django_cache/store/migrations/0001_initial.py

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harsha444/toppr_training

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# Generated by Django 2.0.2 on 2018-07-03 05:00 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField(blank=True, null=True)), ('price', models.IntegerField(blank=True, null=True)), ('date_created', models.DateTimeField(auto_now_add=True)), ('date_modified', models.DateTimeField(auto_now=True)), ], ), ]

[ "sriharshashm@gmail.com" ]

sriharshashm@gmail.com

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/bank_account.py

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boitshepo97/bank-account

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refs/heads/master

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class BankAccount: def __init__(self,balance,interest_rate,monthly_fee,customer): self.balance =balance self.interest_rate = interest_rate self.monthly_fee = monthly_fee self.customer = customer def deposit(self,amount): self.balance += amount return self.balance def withdraw(self,amount): if self.balance <= 0: return 'insufficient funds' else: self.balance -= amount def transfer(self,amount): self.balance -= amount return self.balance def finish_month(self): self.balance = self.balance + self.balance*self.interest_rate/12 - self.monthly_fee return self.balance

[ "boitshepo.masemola@gmail.com" ]

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/week-1/single-number.py

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[]

no_license

johnsanc/wb-cohort3

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refs/heads/master

2020-06-10T04:14:56.489619

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class Solution: def singleNumber(self, nums: List[int]) -> int: num = 0 for n in nums: num ^= n return num

[ "johnny.ss@icloud.com" ]

johnny.ss@icloud.com

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#!/usr/bin/env python import serial from . import settings import time import binascii import os import logging import sys logging.basicConfig(filename=settings.logfile, level=getattr( logging, settings.log_level.upper())) class Device(): def __init__(self): self.ser = Device.connect() self.ser.timeout = settings.timeout @staticmethod def connect(): ''' returns a serial device, or raises an IOError ''' try: return serial.Serial(settings.port, settings.baudRate) except IOError as er: logging.error("Failed to connect to STR116 device") raise def write(self, data): data = data.encode(settings.encoding_type) try: self.ser.write(data) return True except IOError as err: logging.error("Can't write to the STR116 device (%s)" % (err)) raise def write_with_response(self, data): self.write(data) if self.ser.open: # This needs a small delay time.sleep(0.02) size = self.ser.inWaiting() if size: data = self.ser.read(size) return binascii.hexlify(data.encode(settings.encoding_type)) return None # ------------------------------------------------ def _write_message_with_response(data): # usart = serial.Serial(settings.port,settings.baudRate) # usart.timeout = settings.timeout # message_bytes = data.encode("utf8") try: usart.write(message_bytes) #print usart.open # True for opened if usart.open: time.sleep(0.02) size = usart.inWaiting() if size: data = usart.read(size) # print binascii.hexlify(data) else: print('no data') else: print('usart not open') except IOError as e : print(("Failed to write to the port. ({})".format(e))) return binascii.hexlify(data.encode('utf8')) def _get_checksum(data): checksum = sum(bytearray.fromhex(data)) checksumstripped = hex(checksum).replace('0x', '') return checksumstripped.zfill(2) def set_relay(relaynumber, onoff): #command to turn on relay is 0x08 0x17 #format is #MA0, MA1, 0x08, 0x17, CN, start number output (relaynumber), \ #number of outputs (usually 0x01), 00/01 (off/on), CS (calculated), MAE #need to do a checksum on 0x08, 0x17, CN, relaynumber, 0x01, 0x01 relaynumberhex = hex(relaynumber).replace('0x', '').zfill(2) str_to_checksum = '0817' + settings.CN + str(relaynumberhex) \ + '01' + str(onoff).zfill(2) CS = _get_checksum(str_to_checksum) bytestring = settings.MA0 + settings.MA1 + str_to_checksum \ + str(CS) + settings.MAE _write_message(bytestring) def get_relay(relaynumber): '''Get the status of the requested relay (true/false)''' str_to_checksum = '0714020010' CS = _get_checksum(str_to_checksum) # 2d bytestring = settings.MA0 + settings.MA1 + str_to_checksum \ + str(CS) + settings.MAE relaystatus = _write_message_with_response(bytestring)[6:-4] test = relaystatus[relaynumber*2:relaynumber*2+2] if int(test) > 0: return True else: return False def get_relays_status(): #command to get the status of all of the relays in an array. #format is #MA0, MA1, 0x07, 0x14, CN, start number output, number of outputs, CS, MAE #returns #SL0, SL1, BC, output first, output next,..., CS, SLE str_to_checksum = '0714' + settings.CN + '0010' CS = _get_checksum(str_to_checksum) bytestring = settings.MA0 + settings.MA1 + str_to_checksum \ + str(CS) + settings.MAE print(('relay status bytestring: ' + bytestring)) relaystatus = _write_message_with_response(bytestring)[6:-4] print(('relay status: ' + relaystatus)) totalrelays = len(relaystatus) n = 0 while(n < totalrelays): if str(relaystatus[n:n+2]) == '00': print(('relay ' + str(n/2).zfill(2) + ': off')) else: print(('relay ' + str(n/2).zfill(2) + ': on')) n += 2 def set_baudrate(): """ This function changes the baudrate on the str116 controller card. IMPORTANT! if you change the baudrate using this function, you MUST update the baudrate listed in the settings file, or your communications will not work. This is mainly a setup function that can be used to initialize the controller. """ #usart = serial.Serial (settings.port,settings.baudRate) #current settings d = {'300':0,'600':1,'1200':2,'2400':3,'4800':4,'9600':5,'19200':6, \ '38400':7,'57600':8,'115200':9} print(d) bytestring = "55AA083302AA55064277" #set baudrate to 19200 print(bytestring) #message_bytes = bytestring.decode("hex") #usart.write(message_bytes) rimsAddress = 1 switchAddress = 2 baudRate = 19200 timeout = 0.05 #master start byte MA0 = '55' #master1 byte MA1 = 'AA' #master end byte MAE = '77' #hex byte for controller number CN = '02'

[ "sweeney@tamu.edu" ]

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from PyQt5.Qt import QPalette from PyQt5.Qt import QColor class Colors: @classmethod def CLAMP(cls, x, l, u): if x < l: return l elif x > u: return u else: return x @classmethod def bg(cls, pal, widget): ''' @param: pal QPalette @param: widget QWidget @return: QColor ''' role = QPalette.NoRole if not widget: role = QPalette.Window elif widget.parentWidget(): role = widget.parentWidget().backgroundRole() else: role = widget.backgroundRole() return pal.color(role) @classmethod def contrast(cls, acolor, bcolor): ''' @brief: return contrast value of acolor and bcolor @param: acolor QColor @param: bcolor QColor @return: int ''' ar, ag, ab = acolor.getRgb() br, bg, bb = bcolor.getRgb() diff = 299 * (ar - br) + 587 * (ag - bg) + 114 * (ab - bb) if diff < 0: diff = -diff else: diff = 90 * diff // 100 perc = diff // 2550 diff = max(ar, br) + max(ag, bg) + max(ab, bb) - \ (min(ar, br) + min(ag, bg) + min(ab, bb)) perc += diff // 765 perc //= 2 return perc @classmethod def counterRole(cls, role): ''' @brief: return inverted role(color) of specified role @param: role QPalette.ColorRole @return: QPalette.ColorRole ''' if role == QPalette.ButtonText: # 8 return QPalette.Button elif role == QPalette.WindowText: # 10 return QPalette.Window elif role == QPalette.HighlightedText: # 13 return QPalette.Highlight elif role == QPalette.Window: # 10 return QPalette.WindowText elif role == QPalette.Base: # 9 return QPalette.Text elif role == QPalette.Text: # 6 return QPalette.Base elif role == QPalette.Highlight: # 12 return QPalette.HighlightedText elif role == QPalette.Button: # 1 return QPalette.ButtonText else: return QPalette.Window @classmethod def counterRoleWithDef(cls, from_, defFrom=QPalette.WindowText, defTo=QPalette.Window): ''' @param: from_ QPalette.ColorRole @param: to_ QPalette.ColorRole @param: defFrom QPalette.ColorRole @param: defTo QPalette.ColorRole @return: bool, (from_, to_) ''' if from_ == QPalette.WindowText: # 0 to_ = QPalette.Window elif from_ == QPalette.Window: # 10 to_ = QPalette.WindowText elif from_ == QPalette.Base: # 9 to_ = QPalette.Text elif from_ == QPalette.Text: # 6 to_ = QPalette.Base elif from_ == QPalette.Button: # 1 to_ = QPalette.ButtonText elif from_ == QPalette.ButtonText: # 8 to_ = QPalette.Button elif from_ == QPalette.Highlight: # 12 to_ = QPalette.HighlightedText elif from_ == QPalette.HighlightedText: # 13 to_ = QPalette.Highlight else: from_ = defFrom to_ = defTo return False, (from_, to_) return True, (from_, to_) @classmethod def emphasize(cls, color, value=10): ''' @param: color QColor @param: value int @return: QColor ''' ret = QColor() h, s, v, a = color.getHsv() if v < 75 + value: ret.setHsv(h, s, cls.CLAMP(85 + value, 85, 255), a) return ret if v > 200: if s > 30: h -= 5 if h < 0: h = 360 + h s = (s << 3) // 9 v += value ret.setHsv(h, cls.CLAMP(s, 30, 255), cls.CLAMP(v, 0, 255), a) return ret if v > 230: ret.setHsv(h, s, cls.CLAMP(v - value, 0, 255), a) return ret if v > 128: ret.setHsv(h, s, cls.CLAMP(v + value, 0, 255), a) else: ret.setHsv(h, s, cls.CLAMP(v - value, 0, 255), a) return ret @classmethod def haveContrast(cls, acolor, bcolor): ''' @brief: check whether two color are contrasting @param: acolor QColor @param: bcolor QColor @return: bool ''' ar, ag, ab = acolor.getRgb() br, bg, bb = bcolor.getRgb() diff = (299 * (ar - br) + 587 * (ag - bg) + 114 * (ab - bb)) if abs(diff) < 91001: return False diff = max(ar, br) + max(ag, bg) + max(ab, bb) - \ (min(ar, br) + min(ag, bg) + (ab, bb)) return diff > 300 @classmethod def light(cls, color, value): ''' @param: color QColor @param: value int @return: QColor ''' h, s, v, a = color.getHsv() ret = QColor() if v < 255 - value: ret.setHsv(h, s, cls.CLAMP(v + value, 0, 255), a) # value could be negative return ret # psychovisual uplightning, i.e. shift hue and lower saturation if s > 30: h -= (value * 5 / 20) if h < 0: h = 400 + h s = cls.CLAMP((s << 3) / 9, 30, 255) ret.setHsv(h, s, 255, a) return ret else: # hue shifting has no sense, half saturation (btw, white won't get brighter) ret.setHsv(h, s >> 1, 255, a) return ret @classmethod def mid(cls, color1, color2, w1=1, w2=1): ''' @param: color1 QColor @param: color2 QColor @param: w1 int @param: w2 int @return: QColor ''' sum0 = w1 + w2 if not sum0: return Qt.Black r = (w1 * color1.red() + w2 * color2.red()) / sum0 r = cls.CLAMP(r, 0, 255) g = (w1 * color1.green() + w2 * color2.green()) / sum0 g = cls.CLAMP(g, 0, 255) b = (w1 * color1.blue() + w2 * color2.blue()) / sum0 b = cls.CLAMP(b, 0, 255) a = (w1 * color1.alpha() + w2 * color2.alpha()) / sum0 a = cls.CLAMP(a, 0, 255) return QColor(r, g, b, a) @classmethod def value(cls, color): ''' @param: color QColor @return: int ''' v = color.red() if c.green() > v: v = c.green() if c.blue() > v: v = c.blue() return v

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# -*- coding: utf-8 -*- from fixture.orm import ORMFixture from model.contact import Contact from model.group import Group import random import pytest import allure def test_add_contact_to_group(app): db = ORMFixture(host="127.0.0.1", name="addressbook", user="root", password="") contact_for_test = None group_for_test = None if len(db.get_group_list()) == 0: group_for_test = Group(name="test_group") app.group.create(group_for_test) if len(db.get_contact_list()) == 0: contact_for_test = Contact(firstname = "test_contact") app.contact.create(contact_for_test) else: contact_for_test = random.choice(db.get_contact_list()) else: if len(db.get_contact_list()) == 0: contact_for_test = Contact(firstname = "test_contact") app.contact.create(contact_for_test) group_for_test = random.choice(db.get_group_list()) else: groups = db.get_group_list() contacts = db.get_contact_list() for c in contacts: if len(db.get_contacts_groups(c)) == len(groups): continue else: groups_without_contact= [g for g in groups if g not in db.get_contacts_groups(c)] group_for_test = random.choice(groups_without_contact) contact_for_test = c break if contact_for_test is None: contact_for_test = Contact(firstname="test_contact") group_for_test = random.choice(db.get_group_list()) with allure.step('Given contacts in groups list'): old_contact_list_in_group = db.get_contacts_in_group(group_for_test) with allure.step('When I add a contact %s to the gtoup %s' % (group_for_test , contact_for_test)): app.contact.add_contact_to_group(contact_for_test, group_for_test) with allure.step('Then the the contact %s is in this group %s ' % (group_for_test ,contact_for_test)): new_contact_list_in_group = db.get_contacts_in_group(group_for_test) assert len(new_contact_list_in_group) == len(old_contact_list_in_group)+1 old_contact_list_in_group.append(contact_for_test) assert sorted(old_contact_list_in_group, key = Contact.id_or_max) == sorted(new_contact_list_in_group, key = Contact.id_or_max)

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from django.forms import Form class BaseFormSet(object): """ A collection of instances of the same Form class. """ def __init__(self, data=None, files=None, auto_id='id_%s', prefix=None, initial=None, error_class=ErrorList, form_kwargs=None): """ :rtype: BaseFormSet[T <= Form] """ def __iter__(self): """ :rtype: collections.Iterator[T] """ pass def __getitem__(self, index): """ :rtype: T """ pass

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#Importing necessary modules import numpy as np import scipy.integrate import scipy.special import scipy.interpolate import matplotlib.pyplot as plt from Project_1 import fitfunc2, NLL2 #Reading the datafile and extracting the decay times and errors with open('lifetime-2018.txt', 'r') as f: lines = [np.float(element) for element in f.read().split()] dtimes = lines[::2] derrors = lines[1::2] #Range of tau and a values to plot 2D NLL function tau_arr1 = np.linspace(0.1, 0.9, 10) a_arr1 = np.linspace(0.001, 1, 10) #Plotting the 2D NLL function for tau ranging from 0.1 to 0.9, and a ranging #from 0.001 to 1. It can be seen from the graph that the minimum lies in the #range where tau is between about 0.40 and 0.42, and a is between 0.97 and 0.99 print('Generating 2D NLL function...') #Note: 2D NLL plot is interpolated for a smoother plot, not 100% indicative of minimum #but it gives a good enough estimate for the naked eye data = NLL2(tau_arr1, a_arr1) X, Y = np.meshgrid(tau_arr1, a_arr1) f = scipy.interpolate.interp2d(X, Y, data, kind='cubic') xnew = np.linspace(0.1, 0.9, 100) ynew = np.linspace(0.001, 1, 100) data1 = f(xnew,ynew) Xn, Yn = np.meshgrid(xnew, ynew) plt.figure(figsize=(15,8)) plt.pcolormesh(Xn, Yn, data1, cmap='RdBu', rasterized=True) cbar = plt.colorbar() cbar.ax.tick_params(labelsize=12) plt.xlabel('tau (ps)', fontsize=20) plt.ylabel('a', fontsize=20) plt.tick_params(axis='both', which='major', labelsize=20) plt.title('Negative Log Likelihood (NLL)', fontsize=20) #plt.savefig(r"C:\Users\Ivan\Desktop\Year 3\Computational Physics\Project\Graphs_2D\NLL_2D.pdf", fmt="pdf", transparent=True, rasterized=True, bbox_tight=True) print('Done')

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# -*- coding: utf-8 -*- #!/bin/python from __future__ import absolute_import # This will make sure the app is always imported when # Django starts so that shared_task will use this app. from .celery import app as celery_app #为了确保在django启动时加载了celery应用

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#!/bin/usr/python # Make H-R diagram for chose folder # M. Hoffman March 19 2016 # Try to make it so you can plot multiple folders or just the one import mesa as ms import numpy as np import matplotlib.pyplot as plt import os, re, shutil import datetime #from pylab import * Q = raw_input('Do you want a single H-R plot [S] per folder or all H-R paths on one diagram[A]? [S/A] :') # if not os.path.exists('HRs'): # os.mkdir('HRs') youarehere = os.getcwd() now = datetime.datetime.now() mo = str(now.month) da = str(now.day) hr = str(now.hour) mn = str(now.minute) #cols = ['voilet','r','orange','y','g','b','indigo','grey','k'] #nums = [0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1] #keys = dict(zip(nums,cols)) def ReadInls(inlist,value): inl = open(inlist,'r') for line in inl: if str(value)+'_scaling_' in line: a = line lastV = re.sub(' '+value+'\_scaling\_factor \= ','',a) Val = re.sub('d','e',lastV) V = float(Val) return V def MakeHRPlot(L,T,kind,M,savename): fig, ax = plt.subplots() if kind == 'l': ax.plot(T,L,color=M) if kind == 's': ax.scatter(T,L,s=40,c=M,cmap=plt.cm.gnuplot_r,vmin=min(M),vmax=max(M),edgecolor='none') fig.colorbar() cbar.set_label(Mass) ax.set_xlabel('Temperature') ax.set_xlim([max(T)+0.1,min(T)-0.1]) ax.xaxis.label.set_fontsize(18) ax.set_ylabel('Luminosity') ax.set_ylim([min(L)-0.1,max(L)+0.1]) ax.yaxis.label.set_fontsize(18) plt.savefig(savename) #FV = raw_input('Folder path name: ') FV = '/data/mhoffman/NUQ/fine_grid' X = [] Y = [] os.chdir(FV) print(os.getcwd()) for file in os.listdir(FV): os.chdir(file) file_cab = os.getcwd() for file in os.listdir(file_cab): matchc = re.match('\Ac([0-9]*)\Z',file) if matchc: os.chdir(file) print('Currently in...'+os.getcwd()) x = ReadInls('inlist_1.0','Blocker') y = ReadInls('inlist_1.0','Reimers') if x not in X: X.append(x) if y not in Y: Y.append(y) os.chdir(file_cab) os.chdir(FV) print X print Y print len(X) cmap = plt.get_cmap('jet') Colors = [cmap(i) for i in Y] print Colors keys = dict(zip(Y,Colors)) print keys if Q == 'S': os.chdir(FV) if not os.path.exists('HRs'): os.mkdir('HRs') today = mo+da+'_'+hr+mn os.chdir(FV+'/HRs') if not os.path.exists(today): os.mkdir(today) os.chdir(FV+'/HRs/'+today) if not os.path.exists('failed'): os.mkdir('failed') os.chdir(FV) for file in os.listdir(FV): matchf = re.match(reg,file) if matchf: os.chdir(file) file_cab = os.getcwd() for file in os.listdir(file_cab): matchc = re.match('\Ac([0-9]*)\Z',file) if matchc: os.chdir(file) print('Currently in...'+os.getcwd()) # Get Stellar Mass s = ms.history_data() Mass = s.get('star_mass') Temp = s.get('log_Teff') Lumi = s.get('log_L') lastn = len(Lumi) - 1 final_L = Lumi[lastn] B = ReadInls('inlist_1.0','Blocker') print B R = ReadInls('inlist_1.0','Reimers') print R # Make an HR plot os.chdir(FV+'/HRs/'+today) name = 'R'+str(R)+'_B'+str(B)+'.png' Sh = keys[R] #MakeHRPlot(Lumi,Temp,'l',Sh,name) if final_L > -0.999: os.chdir(FV+'/HRs/'+today+'/failed') MakeHRPlot(Lumi,Temp,'l',Sh,name) plt.close() os.chdir(file_cab) else: MakeHRPlot(Lumi,Temp,'l',Sh,name) plt.close() os.chdir(file_cab) os.chdir(FV) os.chdir(youarehere) if Q == 'A': Masses = [] Temperatures = [] Luminosities = [] Reimers = [] Blocker = [] Textrema = [] FV = input('Folder path name with \'\' : ') os.chdir(FV) if not os.path.exists('HRs'): os.mkdir('HRs') # for file in os.listdir(FV): # matchf = re.match('\Abloc\-rand\_reim\-[0-9]\.[0-9]\Z',file) # if matchf: # os.chdir(file) file_cab = os.getcwd() for file in os.listdir(file_cab): matchc = re.match('\Ac([0-9]*)\Z',file) if matchc: os.chdir(file) s = ms.history_data() Mass = s.get('star_mass') Temp = s.get('log_Teff') TM = max(Temp) Tm = min(Temp) Lumi = s.get('log_L') lastn = len(Lumi) - 1 final_L = Lumi[lastn] if final_L < 0: B = ReadInls('inlist_1.0','Blocker') R = ReadInls('inlist_1.0','Reimers') Ones = np.ones(lastn+1) Bl = B * Ones Rf = R * Ones Blocker.append(Bl) Reimers.append(Rf) Masses.append(Mass) Luminosities.append(Lumi) Temperatures.append(Temp) Textrema.append(TM) Textrema.append(Tm) else: print('Luminosity too low, excluding point!') os.chdir(file_cab) os.chdir(file_cab) os.chdir(FV) os.chdir(FV+'/HRs/') name = 'HR_'+mo+da+'_'+hr+mn+'.png' Color = [] for i in Reimers: row = [] for j in i: q = keys[j] row.append(q) Color.append(row) for i in range(len(Masses)): A = Luminosities[i] B = Temperatures[i] C = Color[i][0] plt.plot(B,A,color=C) plt.xlim(max(Textrema)+0.1,min(Textrema)-0.1) plt.show() # ptl.gcf() # plt.savefig('HR_'+mo+da+'_'+hr+mn+'.png') # plt.show()

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####################################################################### # # Example of how to hide a worksheet with XlsxWriter. # # Copyright 2013-2014, John McNamara, jmcnamara@cpan.org # import xlsxwriter workbook = xlsxwriter.Workbook('hide_sheet.xlsx') worksheet1 = workbook.add_worksheet() worksheet2 = workbook.add_worksheet() worksheet3 = workbook.add_worksheet() worksheet1.set_column('A:A', 30) worksheet2.set_column('A:A', 30) worksheet3.set_column('A:A', 30) # Hide Sheet2. It won't be visible until it is unhidden in Excel. worksheet2.hide() worksheet1.write('A1', 'Sheet2 is hidden') worksheet2.write('A1', "Now it's my turn to find you!") worksheet3.write('A1', 'Sheet2 is hidden') workbook.close()

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# Generated by Django 3.1.6 on 2021-02-16 10:27 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Post', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('text', models.TextField()), ('pub_date', models.DateTimeField(auto_now_add=True, verbose_name='date published')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='posts', to=settings.AUTH_USER_MODEL)), ], ), ]

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from django.conf.urls import url from django.contrib import admin from mentor001 import views urlpatterns = [ url(r'^$', views.index, name='home'), url(r'^admin/', admin.site.urls), ]

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import sys import logbook app_log = logbook.Logger('App') def main(): print("This is a test of how to put together a log") _var = input("Give me a number") try: _var = int(_var) * 1000 print(_var) except Exception as x: print(f"Oh that didn't work!: {x}") app_log.exception(x) finally: print("Thank's for stopping by") def init_logging(filename: str = None): level = logbook.TRACE if filename: logbook.TimedRotatingFileHandler(filename, level=level).push_application() else: logbook.StreamHandler(sys.stdout, level=level).push_application() msg = 'Logging initialized, level: {}, mode: {}'.format( level, "stdout mode" if not filename else 'file mode: ' + filename ) logger = logbook.Logger('Startup') logger.notice(msg) if __name__ == '__main__': init_logging('test.log') main()

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for number in range(1,1000000): print(number)

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#!/home/sharm493/energy-project-tensor-flow/env/energy-linux-no-gpu/bin/python3 # -*- coding: utf-8 -*- import re import sys from notebook.bundler.bundlerextensions import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# -*- coding: utf-8 -*- class Solution: def strStr(self, haystack, needle): n = len(needle) h = len(haystack) idx = -1 if n == 0 or needle == "": return 0 if n <= h: if n == h: if needle == haystack: return 0 else: i = 0 j = 0 for i in range(len(haystack)): if j < len(needle) and needle[j] != haystack[i]: j = 0 continue if j == len(needle) - 1: idx = i - j break j += 1 return idx return -1 s = Solution() print(s.strStr("aabaaabaaac","aabaaac"))

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class QueryConstructor: def __init__(self): self.table = None self.fields = None self.paramenters = None def noParametersQuery(self, table, *fields): query = "SELECT " for i in fields: query = query + i + ", " query = query[:-2] query = query + " FROM " + table return query def singleWhereQuery(self, table, parameter, operand, *fields): query = self.noParametersQuery(table, *fields) query = query + " WHERE " + parameter + " " + operand + " ?" return query

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from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input from tensorflow.keras.models import Model import numpy as np # See https://keras.io/api/applications/ for details class FeatureExtractor: def __init__(self): base_model = VGG16(weights='imagenet') self.model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output) def extract(self, img): """ Extract a deep feature from an input image Args: img: from PIL.Image.open(path) or tensorflow.keras.preprocessing.image.load_img(path) Returns: feature (np.ndarray): deep feature with the shape=(4096, ) """ img = img.resize((224, 224)) # VGG must take a 224x224 img as an input img = img.convert('RGB') # Make sure img is color x = image.img_to_array(img) # To np.array. Height x Width x Channel. dtype=float32 x = np.expand_dims(x, axis=0) # (H, W, C)->(1, H, W, C), where the first elem is the number of img x = preprocess_input(x) # Subtracting avg values for each pixel feature = self.model.predict(x)[0] # (1, 4096) -> (4096, ) return feature / np.linalg.norm(feature) # Normalize

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ozcanyarimdunya/mini-scrapy

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from bs4 import BeautifulSoup from base import BaseScrapper class TheHackerNewsScrapper(BaseScrapper): url = 'https://thehackernews.com/' next_selector = '.blog-pager-older-link-mobile' filename = 'news.json' max_result = 5 def scrap(self, soup: BeautifulSoup): print('Current page: {}/{}'.format(self.get_current_page(), '~'), end='\r') for post in soup.select('.body-post'): yield { 'url': post.select_one('.story-link').attrs.get('href'), 'img': post.select_one('img.home-img-src').attrs.get('data-src'), 'title': post.select_one('h2.home-title').get_text(strip=True), 'description': post.select_one('.home-desc').get_text(strip=True), } if __name__ == '__main__': TheHackerNewsScrapper()

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import deephyper_benchmark as dhb dhb.load("LCu/loglin2") from deephyper_benchmark.lib.lcu.loglin2 import hpo hp_problem = hpo.problem run = hpo.run

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# # Copyright (c) 2023 Airbyte, Inc., all rights reserved. # import base64 import logging from typing import Any, Iterable, List, Mapping, Optional, Set import pendulum import requests from airbyte_cdk.models import SyncMode from cached_property import cached_property from facebook_business.adobjects.abstractobject import AbstractObject from facebook_business.adobjects.adaccount import AdAccount as FBAdAccount from facebook_business.adobjects.adimage import AdImage from facebook_business.adobjects.user import User from .base_insight_streams import AdsInsights from .base_streams import FBMarketingIncrementalStream, FBMarketingReversedIncrementalStream, FBMarketingStream logger = logging.getLogger("airbyte") def fetch_thumbnail_data_url(url: str) -> Optional[str]: """Request thumbnail image and return it embedded into the data-link""" try: response = requests.get(url) if response.status_code == requests.status_codes.codes.OK: _type = response.headers["content-type"] data = base64.b64encode(response.content) return f"data:{_type};base64,{data.decode('ascii')}" else: logger.warning(f"Got {repr(response)} while requesting thumbnail image.") except requests.exceptions.RequestException as exc: logger.warning(f"Got {str(exc)} while requesting thumbnail image.") return None class AdCreatives(FBMarketingStream): """AdCreative is append only stream doc: https://developers.facebook.com/docs/marketing-api/reference/ad-creative """ entity_prefix = "adcreative" enable_deleted = False def __init__(self, fetch_thumbnail_images: bool = False, **kwargs): super().__init__(**kwargs) self._fetch_thumbnail_images = fetch_thumbnail_images @cached_property def fields(self) -> List[str]: """Remove "thumbnail_data_url" field because it is computed field and it's not a field that we can request from Facebook""" return [f for f in super().fields if f != "thumbnail_data_url"] def read_records( self, sync_mode: SyncMode, cursor_field: List[str] = None, stream_slice: Mapping[str, Any] = None, stream_state: Mapping[str, Any] = None, ) -> Iterable[Mapping[str, Any]]: """Read with super method and append thumbnail_data_url if enabled""" for record in super().read_records(sync_mode, cursor_field, stream_slice, stream_state): if self._fetch_thumbnail_images: thumbnail_url = record.get("thumbnail_url") if thumbnail_url: record["thumbnail_data_url"] = fetch_thumbnail_data_url(thumbnail_url) yield record def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_ad_creatives(params=params) class CustomConversions(FBMarketingStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/custom-conversion""" entity_prefix = "customconversion" enable_deleted = False def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_custom_conversions(params=params) class CustomAudiences(FBMarketingStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/custom-conversion""" entity_prefix = "customaudience" enable_deleted = False # The `rule` field is excluded from the list because it caused the error message "Please reduce the amount of data" for certain connections. # https://github.com/airbytehq/oncall/issues/2765 fields_exceptions = ["rule"] def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_custom_audiences(params=params) class Ads(FBMarketingIncrementalStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/adgroup""" entity_prefix = "ad" def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_ads(params=params) class AdSets(FBMarketingIncrementalStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/ad-campaign""" entity_prefix = "adset" def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_ad_sets(params=params) class Campaigns(FBMarketingIncrementalStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/ad-campaign-group""" entity_prefix = "campaign" def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_campaigns(params=params) class Activities(FBMarketingIncrementalStream): """doc: https://developers.facebook.com/docs/marketing-api/reference/ad-activity""" entity_prefix = "activity" cursor_field = "event_time" primary_key = None def list_objects(self, fields: List[str], params: Mapping[str, Any]) -> Iterable: return self._api.account.get_activities(fields=fields, params=params) def read_records( self, sync_mode: SyncMode, cursor_field: List[str] = None, stream_slice: Mapping[str, Any] = None, stream_state: Mapping[str, Any] = None, ) -> Iterable[Mapping[str, Any]]: """Main read method used by CDK""" loaded_records_iter = self.list_objects(fields=self.fields, params=self.request_params(stream_state=stream_state)) for record in loaded_records_iter: if isinstance(record, AbstractObject): yield record.export_all_data() # convert FB object to dict else: yield record # execute_in_batch will emmit dicts def _state_filter(self, stream_state: Mapping[str, Any]) -> Mapping[str, Any]: """Additional filters associated with state if any set""" state_value = stream_state.get(self.cursor_field) since = self._start_date if not state_value else pendulum.parse(state_value) potentially_new_records_in_the_past = self._include_deleted and not stream_state.get("include_deleted", False) if potentially_new_records_in_the_past: self.logger.info(f"Ignoring bookmark for {self.name} because of enabled `include_deleted` option") since = self._start_date return {"since": since.int_timestamp} class Videos(FBMarketingReversedIncrementalStream): """See: https://developers.facebook.com/docs/marketing-api/reference/video""" entity_prefix = "video" def list_objects(self, params: Mapping[str, Any]) -> Iterable: # Remove filtering as it is not working for this stream since 2023-01-13 return self._api.account.get_ad_videos(params=params, fields=self.fields) class AdAccount(FBMarketingStream): """See: https://developers.facebook.com/docs/marketing-api/reference/ad-account""" use_batch = False enable_deleted = False def get_task_permissions(self) -> Set[str]: """https://developers.facebook.com/docs/marketing-api/reference/ad-account/assigned_users/""" res = set() me = User(fbid="me", api=self._api.api) for business_user in me.get_business_users(): assigned_users = self._api.account.get_assigned_users(params={"business": business_user["business"].get_id()}) for assigned_user in assigned_users: if business_user.get_id() == assigned_user.get_id(): res.update(set(assigned_user["tasks"])) return res @cached_property def fields(self) -> List[str]: properties = super().fields # https://developers.facebook.com/docs/marketing-apis/guides/javascript-ads-dialog-for-payments/ # To access "funding_source_details", the user making the API call must have a MANAGE task permission for # that specific ad account. if "funding_source_details" in properties and "MANAGE" not in self.get_task_permissions(): properties.remove("funding_source_details") if "is_prepay_account" in properties and "MANAGE" not in self.get_task_permissions(): properties.remove("is_prepay_account") return properties def list_objects(self, params: Mapping[str, Any]) -> Iterable: """noop in case of AdAccount""" return [FBAdAccount(self._api.account.get_id())] class Images(FBMarketingReversedIncrementalStream): """See: https://developers.facebook.com/docs/marketing-api/reference/ad-image""" def list_objects(self, params: Mapping[str, Any]) -> Iterable: return self._api.account.get_ad_images(params=params, fields=self.fields) def get_record_deleted_status(self, record) -> bool: return record[AdImage.Field.status] == AdImage.Status.deleted class AdsInsightsAgeAndGender(AdsInsights): breakdowns = ["age", "gender"] class AdsInsightsCountry(AdsInsights): breakdowns = ["country"] class AdsInsightsRegion(AdsInsights): breakdowns = ["region"] class AdsInsightsDma(AdsInsights): breakdowns = ["dma"] class AdsInsightsPlatformAndDevice(AdsInsights): breakdowns = ["publisher_platform", "platform_position", "impression_device"] # FB Async Job fails for unknown reason if we set other breakdowns # my guess: it fails because of very large cardinality of result set (Eugene K) action_breakdowns = ["action_type"] class AdsInsightsActionType(AdsInsights): breakdowns = [] action_breakdowns = ["action_type"] class AdsInsightsActionCarouselCard(AdsInsights): action_breakdowns = ["action_carousel_card_id", "action_carousel_card_name"] class AdsInsightsActionConversionDevice(AdsInsights): breakdowns = ["device_platform"] action_breakdowns = ["action_type"] class AdsInsightsActionProductID(AdsInsights): breakdowns = ["product_id"] action_breakdowns = [] class AdsInsightsActionReaction(AdsInsights): action_breakdowns = ["action_reaction"] class AdsInsightsActionVideoSound(AdsInsights): action_breakdowns = ["action_video_sound"] class AdsInsightsActionVideoType(AdsInsights): action_breakdowns = ["action_video_type"] class AdsInsightsDeliveryDevice(AdsInsights): breakdowns = ["device_platform"] action_breakdowns = ["action_type"] class AdsInsightsDeliveryPlatform(AdsInsights): breakdowns = ["publisher_platform"] action_breakdowns = ["action_type"] class AdsInsightsDeliveryPlatformAndDevicePlatform(AdsInsights): breakdowns = ["publisher_platform", "device_platform"] action_breakdowns = ["action_type"] class AdsInsightsDemographicsAge(AdsInsights): breakdowns = ["age"] action_breakdowns = ["action_type"] class AdsInsightsDemographicsCountry(AdsInsights): breakdowns = ["country"] action_breakdowns = ["action_type"] class AdsInsightsDemographicsDMARegion(AdsInsights): breakdowns = ["dma"] action_breakdowns = ["action_type"] class AdsInsightsDemographicsGender(AdsInsights): breakdowns = ["gender"] action_breakdowns = ["action_type"]

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DiegoAlosilla/A

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import math import heapq #pack rodrigo def dijkstra(G, s): n = len(G) visited = [False]*n weights = [math.inf]*n path = [None]*n queue = [] weights[s] = 0 heapq.heappush(queue, (0, s)) while len(queue) > 0: g, u = heapq.heappop(queue) visited[u] = True for v, w in G[u]: if not visited[v]: f = g + w if f < weights[v]: weights[v] = f path[v] = u heapq.heappush(queue, (f, v)) return path, weights G = [[(1, 6), (3, 7)], [(2, 5), (3, 8), (4, -4)], [(1, -2), (4, 7)], [(2, -3), (4, 9)], [(0, 2)]]

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sumitkumar22299/logi-kart-

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from django.shortcuts import render from .models import Product, Contact from math import ceil # Create your views here. from django.http import HttpResponse def index(request): # products = Product.objects.all() # print(products) # n = len(products) # nSlides = n//4 + ceil((n/4)-(n//4)) allProds = [] catprods = Product.objects.values('category', 'id') cats = {item['category'] for item in catprods} for cat in cats: prod = Product.objects.filter(category=cat) n = len(prod) nSlides = n // 4 + ceil((n / 4) - (n // 4)) allProds.append([prod, range(1, nSlides), nSlides]) # params = {'no_of_slides':nSlides, 'range': range(1,nSlides),'product': products} # allProds = [[products, range(1, nSlides), nSlides], # [products, range(1, nSlides), nSlides]] params = {'allProds':allProds} return render(request, 'shop/index.html', params) def about(request): return render(request, 'shop/about.html') def contact(request): if request.method=="POST": name = request.POST.get('name', '') email = request.POST.get('email', '') phone = request.POST.get('phone', '') desc = request.POST.get('desc', '') contact = Contact(name=name, email=email, phone=phone, desc=desc) contact.save() return render(request, 'shop/contact.html') def tracker(request): return render(request, 'shop/tracker.html') def search(request): return render(request, 'shop/search.html') def productView(request, myid): # Fetch the product using the id product = Product.objects.filter(id=myid) return render(request, 'shop/prodView.html', {'product':product[0]}) def checkout(request): return render(request, 'shop/checkout.html')

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ZeroMin-K/Doit_Algorithm

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# input name and greet print("input name:", end=' ') name = input() print(f"Greeting {name}")

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class Employee: next_employee_number = 0 def __init__(self, name, title, dept, salary): employee_number = Employee.next_employee_number Employee.next_employee_number += 1 self.name = name self.title = title self.dept = dept self.salary = salary def __lt__(self, other): return self.salary < other.salary def __gt__(self, other): return self.salary > other.salary def __eq__(self, other): return self.salary == other.salary def getName(self): return self.name def getTitle(self): return self.title def getDept(self): return self.dept def getSalary(self): return self.salary def setName(self,newval): self.name = newval def setTitle(self,newval): self.title = newval def setDept(self,newval): self.dept = newval def setSalary(self,newval): self.salary = newval def giveRaise(self,percentage): self.salary = self.salary*(1.0+(percentage*1.0)/100.) def __str__(self): print "[Employee name: %s, %s, %s, $%0.2f]" % (self.name.upper(),self.title.upper(),self.dept.upper(),self.salary) ####################################### #if __name__=="__main__": # print "hi" # e = Employee("a",'woo',"wut",10000) # print e.next_employee_number # print e.getSalary() # e.giveRaise(10) # print e.getSalary() # e.__str__()

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from anytree import findall from devito.ir.stree.tree import (ScheduleTree, NodeIteration, NodeConditional, NodeSync, NodeExprs, NodeSection, NodeHalo, insert) from devito.ir.support import SEQUENTIAL, IterationSpace from devito.mpi import HaloScheme, HaloSchemeException from devito.parameters import configuration from devito.tools import Bunch, DefaultOrderedDict, flatten __all__ = ['stree_build'] def stree_build(clusters): """ Create a ScheduleTree from a ClusterGroup. """ # ClusterGroup -> ScheduleTree stree = stree_schedule(clusters) # Add in section nodes stree = stree_section(stree) # Add in halo update nodes stree = stree_make_halo(stree) return stree def stree_schedule(clusters): """ Arrange an iterable of Clusters into a ScheduleTree. """ stree = ScheduleTree() prev = None mapper = DefaultOrderedDict(lambda: Bunch(top=None, bottom=None)) def attach_metadata(cluster, d, tip): if d in cluster.guards: tip = NodeConditional(cluster.guards[d], tip) if d in cluster.syncs: tip = NodeSync(cluster.syncs[d], tip) return tip for c in clusters: pointers = list(mapper) index = 0 tip = stree for it0, it1 in zip(c.itintervals, pointers): if it0 != it1: break index += 1 d = it0.dim # The reused sub-trees might acquire some new sub-iterators mapper[it0].top.ispace = IterationSpace.union(mapper[it0].top.ispace, c.ispace.project([d])) # Different guards or syncops cannot be further nested if c.guards.get(d) != prev.guards.get(d) or \ c.syncs.get(d) != prev.syncs.get(d): tip = mapper[it0].top tip = attach_metadata(c, d, tip) mapper[it0].bottom = tip break else: tip = mapper[it0].bottom # Nested sub-trees, instead, will not be used anymore for it in pointers[index:]: mapper.pop(it) # Add in Iterations, Conditionals, and Syncs for it in c.itintervals[index:]: d = it.dim tip = NodeIteration(c.ispace.project([d]), tip, c.properties.get(d)) mapper[it].top = tip tip = attach_metadata(c, d, tip) mapper[it].bottom = tip # Add in Expressions NodeExprs(c.exprs, c.ispace, c.dspace, c.ops, c.traffic, tip) # Prepare for next iteration prev = c return stree def stree_make_halo(stree): """ Add NodeHalos to a ScheduleTree. A NodeHalo captures the halo exchanges that should take place before executing the sub-tree; these are described by means of a HaloScheme. """ # Build a HaloScheme for each expression bundle halo_schemes = {} for n in findall(stree, lambda i: i.is_Exprs): try: halo_schemes[n] = HaloScheme(n.exprs, n.ispace) except HaloSchemeException as e: if configuration['mpi']: raise RuntimeError(str(e)) # Split a HaloScheme based on where it should be inserted # For example, it's possible that, for a given HaloScheme, a Function's # halo needs to be exchanged at a certain `stree` depth, while another # Function's halo needs to be exchanged before some other nodes mapper = {} for k, hs in halo_schemes.items(): for f, v in hs.fmapper.items(): spot = k ancestors = [n for n in k.ancestors if n.is_Iteration] for n in ancestors: # Place the halo exchange right before the first # distributed Dimension which requires it if any(i.dim in n.dim._defines for i in v.halos): spot = n break mapper.setdefault(spot, []).append(hs.project(f)) # Now fuse the HaloSchemes at the same `stree` depth and perform the insertion for spot, halo_schemes in mapper.items(): insert(NodeHalo(HaloScheme.union(halo_schemes)), spot.parent, [spot]) return stree def stree_section(stree): """ Add NodeSections to a ScheduleTree. A NodeSection, or simply "section", defines a sub-tree with the following properties: * The root is a node of type NodeSection; * The immediate children of the root are nodes of type NodeIteration; * The Dimensions of the immediate children are either: * identical, OR * different, but all of type SubDimension; * The Dimension of the immediate children cannot be a TimeDimension. """ class Section(object): def __init__(self, node): self.parent = node.parent try: self.dim = node.dim except AttributeError: self.dim = None self.nodes = [node] def is_compatible(self, node): return self.parent == node.parent and self.dim.root == node.dim.root # Search candidate sections sections = [] for i in range(stree.height): # Find all sections at depth `i` section = None for n in findall(stree, filter_=lambda n: n.depth == i): if any(p in flatten(s.nodes for s in sections) for p in n.ancestors): # Already within a section continue elif n.is_Sync: # SyncNodes are self-contained sections.append(Section(n)) section = None elif n.is_Iteration: if n.dim.is_Time and SEQUENTIAL in n.properties: # If n.dim.is_Time, we end up here in 99.9% of the cases. # Sometimes, however, time is a PARALLEL Dimension (e.g., # think of `norm` Operators) section = None elif section is None or not section.is_compatible(n): section = Section(n) sections.append(section) else: section.nodes.append(n) else: section = None # Transform the schedule tree by adding in sections for i in sections: insert(NodeSection(), i.parent, i.nodes) return stree

[ "f.luporini12@imperial.ac.uk" ]

f.luporini12@imperial.ac.uk

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/Python/search/print_path.py

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[]

no_license

Marius-Juston/Advanced-Autonomous-Vehicule

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2022-11-15T04:52:19.713449

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# ----------- # User Instructions: # # Modify the the search function so that it returns # a shortest path as follows: # # [['>', 'v', ' ', ' ', ' ', ' '], # [' ', '>', '>', '>', '>', 'v'], # [' ', ' ', ' ', ' ', ' ', 'v'], # [' ', ' ', ' ', ' ', ' ', 'v'], # [' ', ' ', ' ', ' ', ' ', '*']] # # Where '>', '<', '^', and 'v' refer to right, left, # up, and down motions. Note that the 'v' should be # lowercase. '*' should mark the goal cell. # # You may assume that all test cases for this function # will have a path from init to goal. # ---------- from pprint import pprint grid = [[0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 0]] init = [0, 0] goal = [len(grid) - 1, len(grid[0]) - 1] cost = 1 delta = [[-1, 0], # go up [0, -1], # go left [1, 0], # go down [0, 1]] # go right delta_name = ['<', 'v', '>', '^'] def search(grid, init, goal, cost): # ---------------------------------------- # modify code below # ---------------------------------------- expand = [[' ' for _ in range(len(grid[0]))] for _ in range(len(grid))] index = 0 searched = {tuple(init): None} open = {(0, init[0], init[1]), } while len(open) > 0: cell = min(open, key=lambda x: x[0]) open.remove(cell) g, x, y = cell index += 1 for (d_x, d_y) in delta: x_n = x + d_x y_n = y + d_y if (x_n, y_n) not in searched and 0 <= x_n < len(grid) and 0 <= y_n < len(grid[0]) and grid[x_n][ y_n] == 0: path = (g + cost, x_n, y_n) open.add(path) searched[(x_n, y_n)] = (x, y) if x_n == goal[0] and y_n == goal[1]: expand[x_n][y_n] = '*' previous = (x_n, y_n) while not previous == tuple(init): next_point = searched[previous] index_x = (previous[0] - next_point[0]) index_y = (previous[1] - next_point[1]) if index_x > 0: expand[next_point[0]][next_point[1]] = 'v' elif index_x < 0: expand[next_point[0]][next_point[1]] = '^' elif index_y < 0: expand[next_point[0]][next_point[1]] = '<' else: expand[next_point[0]][next_point[1]] = '>' previous = next_point return expand return expand path = search(grid, init, goal, cost) pprint(path)

[ "Marius.juston@hotmail.fr" ]

Marius.juston@hotmail.fr

169a03b6a286b98dc2a18e031348a396fa9db442

ffb3027c11db1b51569414815bcae0066c00c37f

/scripts/hzz2l2q/DelPanj/forDaniel.py

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[]

no_license

syuvivida/usercode

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2020-06-05T19:41:20.506171

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import FWCore.ParameterSet.Config as cms isMergedJet = True ## Below are default values for the names of higgs collections ## and whether the analysis is a merged-jet analysis or not higgsCollectionEE = "hzzeejj:h" higgsCollectionMM = "hzzmmjj:h" MERGED = False if isMergedJet: higgsCollectionEE = "hzzee1j:h" higgsCollectionMM = "hzzmm1j:h" MERGED = True process = cms.Process("COMBINE") #runOnMC = False # to run on DATA runOnMC = True ## MessageLogger process.load("FWCore.MessageLogger.MessageLogger_cfi") process.source = cms.Source("PoolSource", fileNames = cms.untracked.vstring( 'file:/afs/cern.ch/work/s/syu/h2l2qSkimData.root' # 'file:/data4/syu/patsample/53X_MC/h2l2qSkimData_10_1_CL3.root' # 'file:/scratch/syu/update_PU/CMSSW_5_3_3_patch3/src/runJob/testData.root' ) ) process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1)) process.load("RecoBTag.PerformanceDB.PoolBTagPerformanceDB062012") process.load("RecoBTag.PerformanceDB.BTagPerformanceDB062012") process.options = cms.untracked.PSet( wantSummary = cms.untracked.bool(True)) process.MessageLogger.cerr.FwkReport.reportEvery = 1000 baseJetSel = cms.PSet( Jets=cms.InputTag("cleanPatJetsNoPUIsoLept") ) from DelPanj.TreeMaker.eSel2012HZZ_cff import * from DelPanj.TreeMaker.muSel2012HZZ_cff import * process.tree = cms.EDAnalyzer( 'TreeMaker', fillPUweightInfo_ = cms.bool(True), fillEventInfo_ = cms.bool(True), fillGenInfo_ = cms.bool(True), fillMuonInfo_ = cms.bool(True), fillElecInfo_ = cms.bool(True), fillElecIsoInfo_ = cms.bool(False), fillJetInfo_ = cms.bool(True), fillMetInfo_ = cms.bool(False), fillTrigInfo_ = cms.bool(False), fillPhotInfo_ = cms.bool(False), fillZZInfo_ = cms.bool(True), # hzzeejjTag = cms.InputTag("hzzeejj:h"), # hzzmmjjTag = cms.InputTag("hzzmmjj:h"), hzzeejjTag = cms.InputTag(higgsCollectionEE), hzzmmjjTag = cms.InputTag(higgsCollectionMM), merged_ = cms.bool(MERGED), genPartLabel = cms.InputTag("genParticles"), patMuons = cms.InputTag("userDataSelectedMuons"), patElectrons = cms.InputTag("userDataSelectedElectrons"), e2012IDSet = eSel2012HZZ, mu2012IDSet = muSel2012HZZ, eleRhoIso = cms.InputTag("kt6PFJets","rho"), muoRhoIso = cms.InputTag("kt6PFJetsCentralNeutral", "rho"), patMet = cms.InputTag("patMETs"), beamSpotLabel = cms.InputTag("offlineBeamSpot"), patJetPfAk05 = baseJetSel, outFileName = cms.string('outputFileName.root') ) process.counter_original = cms.EDAnalyzer('MyEventCounter', instance = cms.int32(1) ) process.TFileService = cms.Service("TFileService", fileName = cms.string("hzz2l2q.root"), closeFileFast = cms.untracked.bool(True) ) process.p = cms.Path( process.counter_original* process.tree##Trigger Applied. )

[ "" ]

b0484d6fe57864f6d725447f7223632ceda1c563

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/restapis/restapis/urls.py

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[]

no_license

kinjal1993/DjangoRestDemo

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refs/heads/main

2023-05-02T20:41:06.468483

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from django.contrib import admin from django.urls import path,include urlpatterns = [ path('admin/', admin.site.urls), path('', include('apis.urls')), ]

[ "kinjal.pathak20061993@gmail.com" ]

kinjal.pathak20061993@gmail.com

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/testing/test_base_processing.py

38b4f4e0439d15cd1ba431fbb3b2ddd51a170df0

[]

no_license

Ichinaru/slideSelector

7cc27fddc68f9baed7a9a671e65c8ca7260ab070

7e196ddd8ef50fe00c1d8c82091a55dfb935c6ee

refs/heads/master

2021-01-10T20:43:45.687687

2011-01-14T14:02:03

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__author__ = 'Administrator' import unittest from numpy import zeros_like from numpy.testing import assert_array_equal from src.processing.base_processing import BaseExporter from src.annotations import get_annotation_list annotation_folder_test = 'C:/Data' annotation_file_test = 'C:/Data/2.ndpi.ndpa' class TestBaseExporter(unittest.TestCase): def test_init(self): be = BaseExporter(annotation_file_test) self.assertEqual(be.image_file, 'C:/Data/2.ndpi') self.assertEqual(be.export_folder, 'C:/Data/2') fp = be.annotations fp_pl = [ann.point_list for ann in fp] fa = get_annotation_list(annotation_file_test) fa_pl = [ann.point_list for ann in fa] for a,b in zip(fp_pl, fa_pl): assert_array_equal(a,b) if __name__ == '__main__': unittest.main()

[ "xmoleslo@gmail.com" ]

xmoleslo@gmail.com

ee5333aa77f9785064d17219e0f4de854bba55a4

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/05_multiplication.py

dea80dedb0636d90e8f01678489f0bb73e7756d7

[]

no_license

jaymitchell/miniflow

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refs/heads/master

2021-01-19T11:32:25.311675

2017-02-19T19:30:45

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""" No need to change anything here! If all goes well, this should work after you modify the Add class in miniflow.py. """ from miniflow import * x, y, z = Input(), Input(), Input() f = Mul(x, y, z) feed_dict = {x: 4, y: 5, z: 10} graph = topological_sort(feed_dict) output = forward_pass(f, graph) # should output 19 print("{} + {} + {} = {} (according to miniflow)".format(feed_dict[x], feed_dict[y], feed_dict[z], output))

[ "jaybmitchell@gmail.com" ]

jaybmitchell@gmail.com

e799436d7ed93eb4507f35eac9b3e23398b0c220

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/tests/cmake/test_unquoted_args.py

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[ "MIT" ]

permissive

arjo129/ros_build_assistant

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refs/heads/master

2023-01-22T15:36:11.585367

2020-12-06T09:09:19

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from ros_build_assistant.parser.cmake.grammar import UnQuotedArgument, CombinatorState import unittest def parse_item(inp): ba = UnQuotedArgument() for x in inp: res, data = ba.next_char(x) return res,data class TestUnquotedArguments(unittest.TestCase): def test_unquoted_argument_default(self): res, data = parse_item("helloworld ") self.assertEqual(res, CombinatorState.FINISHED) self.assertDictEqual(data, {'body': 'helloworld', 'type': 'unquoted_argument'}) def test_unquoted_argument_with_embedded_quote(self): res, data = parse_item("hello\" \"world;") self.assertEqual(res, CombinatorState.FINISHED) self.assertDictEqual(data, {'body': 'hello\" \"world', 'type': 'unquoted_argument'}) def test_unquoted_argument_escaped(self): res, data = parse_item("hello\\ world ") self.assertEqual(res, CombinatorState.FINISHED) self.assertDictEqual(data, {'body': 'hello\\ world', 'type': 'unquoted_argument'}) def test_idempotency(self): inp = "hello\\ world " res, data = parse_item(inp) out = UnQuotedArgument().code_gen(data) self.assertEqual(inp[:-1], out)

[ "arjo129@gmail.com" ]

arjo129@gmail.com

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/apps/user/serializers.py

d2c34316e93d0ac1180700c216610f4fe4cba31c

[]

no_license

clq0528/callQ-backend-master

6223050ff1174fb7f0448e9a8fff06a94c4b2f45

de0b83b22464e735f209269d7cd009788d1785d4

refs/heads/master

2022-12-23T20:20:17.928538

2019-11-09T14:06:42

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2022-11-22T04:18:18

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019/10/9 15:18 # @Author : qinmin # @File : serializers.py import re from datetime import datetime,timedelta from rest_framework import serializers from rest_framework.validators import UniqueValidator from django.contrib.auth import get_user_model from backend.settings import REGEX_MOBILE from .models import VerifyCode User = get_user_model() class SmsSerializer(serializers.Serializer): mobile = serializers.CharField(max_length=11) def validate_mobile(self,mobile): """ 验证手机号码 :param mobile: :return: """ #手机是否注册 if User.objects.filter(mobile=mobile).count(): raise serializers.ValidationError("用户已经注册") #验证手机号码是否合法 if not re.match(REGEX_MOBILE,mobile): raise serializers.ValidationError("手机号码非法") #限制验证码发送频率 one_mintes_ago = datetime.now() - timedelta(hours=0,minutes=1,seconds=0) if VerifyCode.objects.filter(add_time__gt=one_mintes_ago,mobile=mobile).count(): raise serializers.ValidationError("请求过于频繁，请稍后再试") return mobile class UserDetailSerializer(serializers.ModelSerializer): """ 用户详情序列化类 """ class Meta: model = User fields = ("name","gender","birthday","email","mobile") class UserRegSerializer(serializers.ModelSerializer): code = serializers.CharField(required=True,write_only=True,max_length=6,min_length=6,label="验证码", error_messages={ "blank":"请输入验证码", "required":"请输入验证码", "max_length":"验证码格式错误", "min_length":"验证码格式错误" },help_text="验证码") username = serializers.CharField(label="用户名",help_text="用户名",required=True,allow_blank=False, validators=[UniqueValidator(queryset=User.objects.all(),message="用户已经存在")]) password = serializers.CharField( style={'input_type':'password'},help_text="密码",label="密码",write_only=True ) def create(self, validated_data): user = super(UserRegSerializer,self).create(validated_data=validated_data) user.set_password(validated_data["password"]) user.save() return user def validated_code(self,code): verify_records = VerifyCode.objects.filter(mobile=self.initial_data["username"]).order_by("-add_time") if verify_records: last_record = verify_records[0] five_mintes_ago = datetime.now() - timedelta(hours=0,minutes=5,seconds=0) if five_mintes_ago > last_record.add_time: raise serializers.ValidationError("验证码过期") if last_record.code != code: raise serializers.ValidationError("验证码错误") else: raise serializers.ValidationError("验证码错误") def validate(self, attrs): attrs["mobile"] = attrs["username"] del attrs["code"] return attrs class Meta: model = User fields = ("username","code","mobile","password")

[ "w-feng.jun@rootcloud.com" ]

w-feng.jun@rootcloud.com

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/assets/tuned/daemon/tuned/profiles/loader.py

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[ "GPL-2.0-or-later", "CC-BY-SA-3.0", "GPL-2.0-only", "Apache-2.0" ]

permissive

osherdp/cluster-node-tuning-operator

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refs/heads/master

2023-04-01T03:30:17.739249

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Apache-2.0

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import tuned.profiles.profile import tuned.profiles.variables from configobj import ConfigObj, ConfigObjError import tuned.consts as consts import os.path import collections import tuned.logs import re from tuned.profiles.exceptions import InvalidProfileException log = tuned.logs.get() class Loader(object): """ Profiles loader. """ __slots__ = ["_profile_locator", "_profile_merger", "_profile_factory", "_global_config", "_variables"] def __init__(self, profile_locator, profile_factory, profile_merger, global_config, variables): self._profile_locator = profile_locator self._profile_factory = profile_factory self._profile_merger = profile_merger self._global_config = global_config self._variables = variables def _create_profile(self, profile_name, config): return tuned.profiles.profile.Profile(profile_name, config) @classmethod def safe_name(cls, profile_name): return re.match(r'^[a-zA-Z0-9_.-]+$', profile_name) @property def profile_locator(self): return self._profile_locator def load(self, profile_names): if type(profile_names) is not list: profile_names = profile_names.split() profile_names = list(filter(self.safe_name, profile_names)) if len(profile_names) == 0: raise InvalidProfileException("No profile or invalid profiles were specified.") if len(profile_names) > 1: log.info("loading profiles: %s" % ", ".join(profile_names)) else: log.info("loading profile: %s" % profile_names[0]) profiles = [] processed_files = [] self._load_profile(profile_names, profiles, processed_files) if len(profiles) > 1: final_profile = self._profile_merger.merge(profiles) else: final_profile = profiles[0] final_profile.name = " ".join(profile_names) if "variables" in final_profile.units: self._variables.add_from_cfg(final_profile.units["variables"].options) del(final_profile.units["variables"]) # FIXME hack, do all variable expansions in one place self._expand_vars_in_devices(final_profile) self._expand_vars_in_regexes(final_profile) return final_profile def _expand_vars_in_devices(self, profile): for unit in profile.units: profile.units[unit].devices = self._variables.expand(profile.units[unit].devices) def _expand_vars_in_regexes(self, profile): for unit in profile.units: profile.units[unit].cpuinfo_regex = self._variables.expand(profile.units[unit].cpuinfo_regex) profile.units[unit].uname_regex = self._variables.expand(profile.units[unit].uname_regex) def _load_profile(self, profile_names, profiles, processed_files): for name in profile_names: filename = self._profile_locator.get_config(name, processed_files) if filename is None: raise InvalidProfileException("Cannot find profile '%s' in '%s'." % (name, list(reversed(self._profile_locator._load_directories)))) processed_files.append(filename) config = self._load_config_data(filename) profile = self._profile_factory.create(name, config) if "include" in profile.options: include_names = re.split(r"\b\s*[,;]\s*", self._variables.expand(profile.options.pop("include"))) self._load_profile(include_names, profiles, processed_files) profiles.append(profile) def _expand_profile_dir(self, profile_dir, string): return re.sub(r'(?<!\\)\$\{i:PROFILE_DIR\}', profile_dir, string) def _load_config_data(self, file_name): try: config_obj = ConfigObj(file_name, raise_errors = True, list_values = False, interpolation = False) except ConfigObjError as e: raise InvalidProfileException("Cannot parse '%s'." % file_name, e) config = collections.OrderedDict() for section in list(config_obj.keys()): config[section] = collections.OrderedDict() try: keys = list(config_obj[section].keys()) except AttributeError: raise InvalidProfileException("Error parsing section '%s' in file '%s'." % (section, file_name)) for option in keys: config[section][option] = config_obj[section][option] dir_name = os.path.dirname(file_name) # TODO: Could we do this in the same place as the expansion of other functions? for section in config: for option in config[section]: config[section][option] = self._expand_profile_dir(dir_name, config[section][option]) # TODO: HACK, this needs to be solved in a better way (better config parser) for unit_name in config: if "script" in config[unit_name] and config[unit_name].get("script", None) is not None: script_path = os.path.join(dir_name, config[unit_name]["script"]) config[unit_name]["script"] = [os.path.normpath(script_path)] return config

[ "jmencak@users.noreply.github.com" ]

jmencak@users.noreply.github.com

c6c02cdd6224c65352f710d16f9e022c41e3b450

a8c1c4306ff55219dd03f9370b844832a88d3ac8

/other/test/test_write_tfrecord.py

90615161d3bb3966f4b82ca27c6cd8222ae72598

[]

no_license

Peilun-Li/distributed-Keras

0e84abd4acfb28c620c38330b4be31abd8793b62

95ab392e103e07ec9d600c9c463d0737b2b0224b

refs/heads/master

2021-06-16T06:25:01.682433

2017-05-09T14:40:04

72,376,001

1

0

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UTF-8

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py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Converts MNIST data to TFRecords file format with Example protos.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets import mnist SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' # MNIST filenames TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' TEST_IMAGES = 't10k-images-idx3-ubyte.gz' TEST_LABELS = 't10k-labels-idx1-ubyte.gz' FLAGS = None def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) def convert_to(data_set, name): """Converts a dataset to tfrecords.""" images = data_set.images labels = data_set.labels num_examples = data_set.num_examples if images.shape[0] != num_examples: raise ValueError('Images size %d does not match label size %d.' % (images.shape[0], num_examples)) rows = images.shape[1] cols = images.shape[2] depth = images.shape[3] filename = os.path.join(FLAGS.directory, name + '.tfrecords') print('Writing', filename) writer = tf.python_io.TFRecordWriter(filename) for index in range(num_examples): image_raw = images[index].tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'height': _int64_feature(rows), 'width': _int64_feature(cols), 'depth': _int64_feature(depth), 'label': _int64_feature(int(labels[index])), 'image_raw': _bytes_feature(image_raw)})) writer.write(example.SerializeToString()) writer.close() def main(unused_argv): # Get the data. data_sets = mnist.read_data_sets(FLAGS.directory, dtype=tf.uint8, reshape=False, validation_size=FLAGS.validation_size) # Convert to Examples and write the result to TFRecords. convert_to(data_sets.train, 'train') convert_to(data_sets.validation, 'validation') convert_to(data_sets.test, 'test') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--directory', type=str, default='/tmp/data', help='Directory to download data files and write the converted result' ) parser.add_argument( '--validation_size', type=int, default=5000, help="""\ Number of examples to separate from the training data for the validation set.\ """ ) FLAGS, unparsed = parser.parse_known_args() tf.app.run()

[ "peilunli1994@gmail.com" ]

peilunli1994@gmail.com

03a733e629c37003b623c532ad69ab5714b4ebed

742f15ee3880306a946df7efee0020e42684b109

/out/oneoff/python/test/test_config_option.py

36c9d8ccaf041fe70916d5b260b8b0e7c020abf6

[]

no_license

potiuk/airflow-api-clients

d0196f80caf6e6f4ecfa6b7c9657f241218168ad

325ba127f1e9aa808091916d348102844e0aa6c5

refs/heads/master

2022-09-14T00:40:28.592508

2020-05-31T10:05:42

2020-05-31T10:15:55

268,128,082

0

null

2020-05-30T17:28:04

2020-05-30T17:28:03

null

UTF-8

Python

false

1,384

py

# coding: utf-8 """ Airflow API (Stable) Apache Airflow management API. # noqa: E501 The version of the OpenAPI document: 1.0.0 Contact: dev@airflow.apache.org Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import airflow_client from airflow_client.models.config_option import ConfigOption # noqa: E501 from airflow_client.rest import ApiException class TestConfigOption(unittest.TestCase): """ConfigOption unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test ConfigOption include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = airflow_client.models.config_option.ConfigOption() # noqa: E501 if include_optional : return ConfigOption( key = '0', value = '0' ) else : return ConfigOption( ) def testConfigOption(self): """Test ConfigOption""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

[ "kamil.bregula@polidea.com" ]

kamil.bregula@polidea.com

f9fb3d39e27b883fc6a3d2df45aeb86fa1e59f87

0247b4427dd37569202594dd7bea3e9a7f2abf92

/projects/django/fupload/fup_app/serializers.py

ef304e88f918671977a238b019e3cd50a7dffa1e

[]

no_license

DenisStepanoff/LP

0062caa26b1ded875752ebd897ae3881cdb56633

5a15df361c7754a09c12fc26a25b46b373bdcf11

refs/heads/master

2020-04-08T03:12:00.425492

2018-12-28T12:32:54

158,965,978

0

null

UTF-8

Python

false

243

py

from rest_framework import serializers from .models import File class FileSerializer(serializers.ModelSerializer): class Meta(): model = File #fields = ('file', 'remark', 'timestamp') fields = ('file', 'timestamp')

[ "dstepanov1630@gmail.com" ]

dstepanov1630@gmail.com

5ba84dbef3b4021c39c9648302274af505faf852

e96a1ff8bbe1af069a850dba378118129f5aa612

/Stat/Help_Pos.py

0c0816bba31bfcbf13b842c4524fc1f53dc3ac8b

[]

no_license

AaronWhy/Turiss

e51dac82a6d3511e5c2f1988929b7c330f67e3ee

6b1be44cd0993d0fd9580f18c56693c5c732bebf

refs/heads/master

2022-03-29T22:53:24.393133

2020-01-17T03:16:00

null

0

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UTF-8

Python

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py

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt import pandas as pd import seaborn as sns class Comment(object): def __init__(self, seq, score, help): super(Comment, self).__init__() self.seq = seq self.score = score self.help = help def getKey(x): return x.score * 100 + x.help max_iter = 100 init_comments = 100 GT_data = pd.read_csv("../data/res.csv") PR_data = pd.read_csv("../data/res.csv") size_of_data = len(list(GT_data['Score'])) # size_of_data comments in the pool # suppose we have list Seq, Score, and Help pos_error = [] help_error = [] for iter in range(max_iter): GT_list = [] PR_list = [] for i in range(init_comments): # randomly select init_comments comments index = np.random.randint(size_of_data) GT_list.append(Comment(GT_data['CleanedText'][index], GT_data['Score'][index], GT_data['NormalizedHelpfulness'][index])) GT_list.sort(key=getKey, reverse=True) PR_list = GT_list[:] index = np.random.randint(size_of_data) GT_add = Comment(GT_data['CleanedText'][index], GT_data['Score'][index], GT_data['NormalizedHelpfulness'][index]) GT_help = GT_data['NormalizedHelpfulness'][index] GT_list.append(GT_add) GT_list.sort(key=getKey, reverse=True) GT_position = GT_list.index(GT_add) GT_list.remove(GT_add) PR_add = Comment(PR_data['CleanedText'][index], GT_data['Score'][index], PR_data['helpfulness_preds'][index]) PR_help = PR_data['helpfulness_preds'][index] PR_list.append(PR_add) PR_list.sort(key=getKey, reverse=True) PR_position = PR_list.index(PR_add) PR_list.remove(PR_add) pos_error.append(GT_position-PR_position) help_error.append(GT_help-PR_help) # print(score_error) plt.figure() sns.kdeplot(help_error, pos_error, cmap="Blues", shade=True, shade_lowest=False) plt.xlabel('Helpfulness error') plt.ylabel('System error (position)') plt.show()

[ "racoon727@racoon727deMacBook-Pro.local" ]

racoon727@racoon727deMacBook-Pro.local

0981da73cff0fee83df38f71e17c2db6d0220801

976abd6cc78ba212850c6b1dc872d1bfbba9815d

/shortener/urls.py

30d70120022dad58c1feabb9fe8ca052eb9a2256

[]

no_license

Likael/DjangoShortener

162460a5f87f8ea911ea9b51532b4028c81c895b

748ba313fd382513ba44336684e7bcee1ca4044e

refs/heads/master

2023-04-16T14:45:23.336281

2021-04-24T22:35:07

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from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('create', views.create, name='create'), path('<str:pk>', views.go, name='go') ]

[ "glasekkamil@gmail.com" ]

glasekkamil@gmail.com

c877d9b518282909602e37d29fb09658411051d4

687928e5bc8d5cf68d543005bb24c862460edcfc

/nssrc/com/citrix/netscaler/nitro/resource/config/vpn/vpnvserver_vpnclientlessaccesspolicy_binding.py

c34cfbf4b0f4ccbe51d39eb85d542913ee1c45ce

[ "Apache-2.0", "LicenseRef-scancode-unknown-license-reference", "Python-2.0" ]

permissive

mbs91/nitro

c6c81665d6abd04de8b9f09554e5e8e541f4a2b8

be74e1e177f5c205c16126bc9b023f2348788409

refs/heads/master

2021-05-29T19:24:04.520762

2015-06-26T02:03:09

null

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# # Copyright (c) 2008-2015 Citrix Systems, Inc. # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response from nssrc.com.citrix.netscaler.nitro.service.options import options from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util class vpnvserver_vpnclientlessaccesspolicy_binding(base_resource) : """ Binding class showing the vpnclientlessaccesspolicy that can be bound to vpnvserver. """ def __init__(self) : self._policy = "" self._priority = 0 self._bindpoint = "" self._gotopriorityexpression = "" self._acttype = 0 self._name = "" self._secondary = False self._groupextraction = False self.___count = 0 @property def priority(self) : """The priority, if any, of the VPN virtual server policy. """ try : return self._priority except Exception as e: raise e @priority.setter def priority(self, priority) : """The priority, if any, of the VPN virtual server policy. """ try : self._priority = priority except Exception as e: raise e @property def gotopriorityexpression(self) : """Next priority expression. """ try : return self._gotopriorityexpression except Exception as e: raise e @gotopriorityexpression.setter def gotopriorityexpression(self, gotopriorityexpression) : """Next priority expression. """ try : self._gotopriorityexpression = gotopriorityexpression except Exception as e: raise e @property def policy(self) : """The name of the policy, if any, bound to the VPN virtual server. """ try : return self._policy except Exception as e: raise e @policy.setter def policy(self, policy) : """The name of the policy, if any, bound to the VPN virtual server. """ try : self._policy = policy except Exception as e: raise e @property def groupextraction(self) : """Binds the authentication policy to a tertiary chain which will be used only for group extraction. The user will not authenticate against this server, and this will only be called if primary and/or secondary authentication has succeeded. """ try : return self._groupextraction except Exception as e: raise e @groupextraction.setter def groupextraction(self, groupextraction) : """Binds the authentication policy to a tertiary chain which will be used only for group extraction. The user will not authenticate against this server, and this will only be called if primary and/or secondary authentication has succeeded. """ try : self._groupextraction = groupextraction except Exception as e: raise e @property def name(self) : """Name of the virtual server. Minimum length = 1. """ try : return self._name except Exception as e: raise e @name.setter def name(self, name) : """Name of the virtual server. Minimum length = 1 """ try : self._name = name except Exception as e: raise e @property def secondary(self) : """Binds the authentication policy as the secondary policy to use in a two-factor configuration. A user must then authenticate not only via a primary authentication method but also via a secondary authentication method. User groups are aggregated across both. The user name must be exactly the same for both authentication methods, but they can require different passwords. """ try : return self._secondary except Exception as e: raise e @secondary.setter def secondary(self, secondary) : """Binds the authentication policy as the secondary policy to use in a two-factor configuration. A user must then authenticate not only via a primary authentication method but also via a secondary authentication method. User groups are aggregated across both. The user name must be exactly the same for both authentication methods, but they can require different passwords. """ try : self._secondary = secondary except Exception as e: raise e @property def bindpoint(self) : """Bindpoint to which the policy is bound. Possible values = REQUEST, RESPONSE, ICA_REQUEST, OTHERTCP_REQUEST. """ try : return self._bindpoint except Exception as e: raise e @bindpoint.setter def bindpoint(self, bindpoint) : """Bindpoint to which the policy is bound. Possible values = REQUEST, RESPONSE, ICA_REQUEST, OTHERTCP_REQUEST """ try : self._bindpoint = bindpoint except Exception as e: raise e @property def acttype(self) : try : return self._acttype except Exception as e: raise e def _get_nitro_response(self, service, response) : """ converts nitro response into object and returns the object array in case of get request. """ try : result = service.payload_formatter.string_to_resource(vpnvserver_vpnclientlessaccesspolicy_binding_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.vpnvserver_vpnclientlessaccesspolicy_binding except Exception as e : raise e def _get_object_name(self) : """ Returns the value of object identifier argument """ try : if (self.name) : return str(self.name) return None except Exception as e : raise e @classmethod def add(cls, client, resource) : try : if resource and type(resource) is not list : updateresource = vpnvserver_vpnclientlessaccesspolicy_binding() updateresource.name = resource.name updateresource.policy = resource.policy updateresource.secondary = resource.secondary updateresource.groupextraction = resource.groupextraction updateresource.gotopriorityexpression = resource.gotopriorityexpression updateresource.bindpoint = resource.bindpoint return updateresource.update_resource(client) else : if resource and len(resource) > 0 : updateresources = [vpnvserver_vpnclientlessaccesspolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i].name = resource[i].name updateresources[i].policy = resource[i].policy updateresources[i].secondary = resource[i].secondary updateresources[i].groupextraction = resource[i].groupextraction updateresources[i].gotopriorityexpression = resource[i].gotopriorityexpression updateresources[i].bindpoint = resource[i].bindpoint return cls.update_bulk_request(client, updateresources) except Exception as e : raise e @classmethod def delete(cls, client, resource) : try : if resource and type(resource) is not list : deleteresource = vpnvserver_vpnclientlessaccesspolicy_binding() deleteresource.name = resource.name deleteresource.policy = resource.policy deleteresource.secondary = resource.secondary deleteresource.groupextraction = resource.groupextraction deleteresource.bindpoint = resource.bindpoint return deleteresource.delete_resource(client) else : if resource and len(resource) > 0 : deleteresources = [vpnvserver_vpnclientlessaccesspolicy_binding() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].name = resource[i].name deleteresources[i].policy = resource[i].policy deleteresources[i].secondary = resource[i].secondary deleteresources[i].groupextraction = resource[i].groupextraction deleteresources[i].bindpoint = resource[i].bindpoint return cls.delete_bulk_request(client, deleteresources) except Exception as e : raise e @classmethod def get(cls, service, name) : """ Use this API to fetch vpnvserver_vpnclientlessaccesspolicy_binding resources. """ try : obj = vpnvserver_vpnclientlessaccesspolicy_binding() obj.name = name response = obj.get_resources(service) return response except Exception as e: raise e @classmethod def get_filtered(cls, service, name, filter_) : """ Use this API to fetch filtered set of vpnvserver_vpnclientlessaccesspolicy_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = vpnvserver_vpnclientlessaccesspolicy_binding() obj.name = name option_ = options() option_.filter = filter_ response = obj.getfiltered(service, option_) return response except Exception as e: raise e @classmethod def count(cls, service, name) : """ Use this API to count vpnvserver_vpnclientlessaccesspolicy_binding resources configued on NetScaler. """ try : obj = vpnvserver_vpnclientlessaccesspolicy_binding() obj.name = name option_ = options() option_.count = True response = obj.get_resources(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e @classmethod def count_filtered(cls, service, name, filter_) : """ Use this API to count the filtered set of vpnvserver_vpnclientlessaccesspolicy_binding resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". """ try : obj = vpnvserver_vpnclientlessaccesspolicy_binding() obj.name = name option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(service, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e: raise e class Staaddresstype: IPV4 = "IPV4" IPV6 = "IPV6" class Bindpoint: REQUEST = "REQUEST" RESPONSE = "RESPONSE" ICA_REQUEST = "ICA_REQUEST" OTHERTCP_REQUEST = "OTHERTCP_REQUEST" class vpnvserver_vpnclientlessaccesspolicy_binding_response(base_response) : def __init__(self, length=1) : self.vpnvserver_vpnclientlessaccesspolicy_binding = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.vpnvserver_vpnclientlessaccesspolicy_binding = [vpnvserver_vpnclientlessaccesspolicy_binding() for _ in range(length)]

[ "bensassimaha@gmail.com" ]

bensassimaha@gmail.com

ebab06ad0e2a42021a16e2dcb7c1071bdf73b56e

8f0a99b4fb7bdd542cf77897b2db36536bf368a8

/btcexchange/urls.py

ebaba73b13ba05797ab9de5eec2373b67877daa5

[]

no_license

wasuaje/btcexchange

e9dc326cbe696b70e9c62eb614e4dd44439bcf83

b2f102fb5178ad428b80e66364dda6bdc0dbc215

refs/heads/main

2023-03-29T09:59:53.031999

2021-03-26T16:24:21

351,843,326

0

null

UTF-8

Python

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889

py

"""btcexchange URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.contrib import admin import app.views urlpatterns = [ url(r'^admin/', admin.site.urls), url('^', include('django.contrib.auth.urls')), url(r'^$', app.views.main, name='main'), ]

[ "wasuaje@gmail.com" ]

wasuaje@gmail.com

aab46026352578a4da1faea5beb7e1274ec2ccc6

d432e50b7cf01e3d9efaf6fd698e8bc966a1d1da

/loaded-api/flask/lib/python3.7/encodings/cp869.py

ff8973cc4cac060792ee89db50cc007d5efeb2df

[]

no_license

dylangrosz/Disarming_Loaded_Words

ab87a01c5ff3018891cdea7afa16fa3aca00cb45

d61129979b946c00277c50953b64b4161a59651c

refs/heads/master

2022-12-15T08:13:50.046628

2019-12-05T04:20:15

214,930,325

0

null

2022-12-08T06:55:39

2019-10-14T02:26:02

Python

UTF-8

Python

false

108

py

C:/usr/local/Cellar/python/3.7.4_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/encodings/cp869.py

[ "dylangrosz@gmail.com" ]

dylangrosz@gmail.com

07b32ab8ed6d8d8994575a024efc6cc37c3a066e

11f1d9a7d7a256f7dd5af472799dd5698a43c93a

/pythonbase/04_函数/hm_05_函数的参数.py

8232b7cdfc18e4bda1b09a342b30e5e524b4d081

[]

no_license

hejusl/My_Learn

13933172a198e19ef68de684d4e79aac5c9b2652

f327905b133e60b5c18d79303361adef63c97266

refs/heads/master

2020-04-13T17:52:42.553492

2019-01-08T12:23:22

163,358,517

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UTF-8

Python

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376

py

# !/usr/local/Cellar/python3/3.6.3/bin # coding=utf-8 # 是定义好函数之后，只表示这个函数封装了一段代码 # 如果不主动调用函数，函数是不主动执行的 def sum_2_num(num1, num2): """两个数字的求和""" # num1 = 10 # num2 = 20 result = num1 + num2 print('%d + %d = %d' % (num1, num2, result)) sum_2_num(50, 20)

[ "44864501@qq.com" ]

44864501@qq.com

0d699012b8eeb8b27f755199578dd7ae5f3a5d8d

43a1b4e79a80663e5237651f6630b2a87a7b6b24

/api/controllers/mutant_controller.py

2593fc1817d76a6de4b57f1fb19d76d523234dea

[]

no_license

gusdiazml/magneto-api

f5ba9ed0e21fe32c9f56d4103a7ae029f0360eaa

bfe342f7065b9f0eea8a86aa64d5faa64ba28ffe

refs/heads/master

2023-06-29T17:54:40.362011

2020-10-22T16:54:42

306,436,978

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null

2020-12-09T19:14:59

2020-10-22T19:17:28

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Python

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py

import re from api.models import Dna import datetime class MutantController: MUTANT_SEQUENCE_SIZE = 4 MINIMUM_MUTANTS_SEQUENCES = 1 def is_mutant(self, dna): is_mutant = False self._validate_dna(dna) sequences = self._decompose_dna(dna) count = self._count_mutant_sequences(sequences) if count > self.MINIMUM_MUTANTS_SEQUENCES: is_mutant = True dna_model = Dna() dna_model.sequences = dna dna_model.is_mutant = is_mutant dna_model.created_date = datetime.datetime.now() dna_model.updated_date = datetime.datetime.now() dna_model.save() return is_mutant def _validate_dna(self, dna): is_valid = False dna_size = len(dna) for seq in dna: if len(seq) != dna_size: raise ValueError("The DNA sequence is corrupt") x = re.findall("[^ATCG]", seq, re.IGNORECASE) if x: raise ValueError("The Nitrogen base corrupt") return is_valid def _decompose_dna(self, dna): sequences = [] sequences.extend(dna) sequences.extend(self._decompose_vertically(dna)) sequences.extend(self._decompose_diagonally(dna)) sequences.extend(self._decompose_diagonally_but_backwards(dna)) return sequences @staticmethod def _decompose_vertically(dna): dna_vertors_len = len(dna) vertical_sequences = [] for i in range(dna_vertors_len): temporal_vertical_seq = "" for sequences in dna: temporal_vertical_seq += sequences[i] vertical_sequences.append(temporal_vertical_seq) return vertical_sequences def _decompose_diagonally(self, dna): sequences = [] middle_diagonal = self._decompose_middle_diagonal(dna) down_diagonals = self._decompose_lower_diagonals(dna[1:]) upper_diagonals = self._decompose_upper_diagonals([x[1:] for x in dna[:-1]]) sequences.append(middle_diagonal) sequences.extend(down_diagonals) sequences.extend(upper_diagonals) return sequences def _decompose_middle_diagonal(self, dna): temp_sequence = "" for i, seq in enumerate(dna): temp_sequence += seq[i] return temp_sequence def _decompose_upper_diagonals(self, dna): diagonal_list = [self._decompose_middle_diagonal(dna)] if len(dna) > 1: diagonal_list.extend(self._decompose_upper_diagonals([x[1:] for x in dna[:-1]])) return diagonal_list def _decompose_lower_diagonals(self, dna): diagonal_list = [self._decompose_middle_diagonal(dna)] if len(dna) > 1: diagonal_list.extend(self._decompose_lower_diagonals(dna[1:])) return diagonal_list def _decompose_diagonally_but_backwards(self, dna): reversed_dna = self._reverse_sequences(dna) return self._decompose_diagonally(reversed_dna) def _reverse_sequences(self, sequences): reversed_sequences = [] for sequence in sequences: reversed_sequences.append("".join(reversed(sequence))) return reversed_sequences def _count_mutant_sequences(self, sequences): verified_mutant_sequences_count = 0 for sequence in sequences: verified_mutant_sequences_count += self._verify_mutant_sequence(sequence) return verified_mutant_sequences_count def _verify_mutant_sequence(self, sequence): found_base_count = 0 sequences_found = 0 last_base = None sequence_len = len(sequence) if sequence_len >= self.MUTANT_SEQUENCE_SIZE: for i, base in enumerate(sequence): if i == 0: last_base = base continue if last_base == base: found_base_count += 1 else: found_base_count = 0 if found_base_count == self.MUTANT_SEQUENCE_SIZE - 1: sequences_found += 1 found_base_count = 0 last_base = base return sequences_found @staticmethod def get_stats(): stats = Dna.get_stats() count_mutant_dna = 0 count_human_dna = 0 for stat in stats: if stat[0] == True: count_mutant_dna = stat[1] else: count_human_dna = stat[1] stats = { "count_mutant_dna": count_mutant_dna, "count_human_dna": count_human_dna, "ratio": MutantController._calculate_ratio(count_mutant_dna, count_human_dna) } return stats @staticmethod def _calculate_ratio(mutant_count, human_count): if human_count == 0 or human_count is None: ratio = 1 elif mutant_count == 0 or mutant_count is None: ratio = 0 else: ratio = mutant_count / human_count return ratio

[ "diazchg@gmail.com" ]

diazchg@gmail.com

928db3ff5ae561f17567082ff3595807d26cecbc

daccd137b963061d96c93ec6b38e4f9a69fb44f2

/decorators.py

e492accdcd2c10e3f31baef7b2abea88d7ca7ecf

[ "MIT" ]

permissive

Steven-Eardley/pibot_motor

6391ec7a3b7abef10068fd748c9477da300dc13e

8e3f96db47dd9b8687ad628f5a7f1129263df06a

refs/heads/master

2016-08-05T21:03:08.079362

2015-07-12T17:49:17

28,645,072

0

null

UTF-8

Python

false

437

py

from functools import wraps def expect_kb_interrupt(fn): @wraps(fn) def decorated_fn(*args, **kwargs): try: fn(*args, **kwargs) except KeyboardInterrupt: pass return decorated_fn def expect_attribute_errors(fn): @wraps(fn) def decorated_fn(*args, **kwargs): try: fn(*args, **kwargs) except AttributeError: pass return decorated_fn

[ "steve@eardley.xyz" ]

steve@eardley.xyz

ce10a2b1967ba47c01da55faf0f791e338de85d3

b733a463ba1a21ac4c2756e8552ff4b251ce6b55

/Inpainting/PIC-EC/color_domain/flist_train_split.py

a3eaff8e0ef110a8cb1051dc296ca9228a2b3e6b

[]

no_license

fengjiran/tensorflow_learning

003047cdb396572e3535043dfff8671befcd0f21

44dce5bb39c8d6421ea6181338b0ea4b0e5e9797

refs/heads/master

2023-04-04T07:53:55.707703

2022-03-30T09:35:52

100,839,372

3

0

null

2023-03-24T22:50:54

2017-08-20T04:58:21

Python

UTF-8

Python

false

1,595

py

import os import argparse import numpy as np parser = argparse.ArgumentParser() parser.add_argument('--path', type=str, help='path to the dataset') parser.add_argument('--train', type=int, default=28, help='number of train in a iter') parser.add_argument('--val', type=int, default=1, help='number of val in a iter') parser.add_argument('--test', type=int, default=1, help='number of test in a iter') parser.add_argument('--output', type=str, help='path to the three file lists') args = parser.parse_args() ext = {'.jpg', '.png', '.JPG'} total = args.train + args.val + args.test images_train = [] images_val = [] images_test = [] num = 1 for root, dirs, files in os.walk(args.path): print('loading ' + root) for file in files: if os.path.splitext(file)[1] in ext: path = os.path.join(root, file) if num % total > (args.val + args.test) or num % total == 0: images_train.append(path) elif num % total <= args.val and num % total > 0: images_val.append(path) else: images_test.append(path) num += 1 images_train.sort() images_val.sort() images_test.sort() print('train number =', len(images_train)) print('val number =', len(images_val)) print('test number =', len(images_test)) if not os.path.exists(args.output): os.mkdir(args.output) np.savetxt(os.path.join(args.output, 'train.flist'), images_train, fmt='%s') np.savetxt(os.path.join(args.output, 'val.flist'), images_val, fmt='%s') np.savetxt(os.path.join(args.output, 'test.flist'), images_test, fmt='%s')

[ "fengjiran@foxmail.com" ]

fengjiran@foxmail.com

88ee67d2de1dcfcdf033dd13c68e62b6f9761986

136be839cd958e5e7de33e46cebee1786e7b502e

/appNomas/migrations/0002_cliente.py

e4235091fa64a2c7860799338c5fb947bdb61b8b

[]

no_license

Joacaro/NomasAccidentes

623da00181fc12fe7f6d3d48dff4af86a30c8d8c

a791fdbdb251bee3e91bde8b6eb0186a59a416da

refs/heads/master

2023-06-09T10:28:00.522898

2021-07-02T05:00:12

381,887,090

0

null

UTF-8

Python

false

967

py

# Generated by Django 3.1.1 on 2021-07-01 03:14 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('appNomas', '0001_initial'), ] operations = [ migrations.CreateModel( name='Cliente', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rut', models.CharField(max_length=10, unique=True)), ('direccion', models.CharField(max_length=50)), ('nombres', models.CharField(max_length=50)), ('password', models.CharField(max_length=50)), ('correo', models.EmailField(max_length=50)), ('telefono', models.CharField(max_length=50)), ('last_login', models.DateTimeField(null=True)), ], options={ 'abstract': False, }, ), ]

[ "joa.caro@duocuc.cl" ]

joa.caro@duocuc.cl

fe512120422d5ec22a1f1710f9b6a8d6c042e47c

597c7a552afc38d6df556b277f6f74d2a1029438

/properties/tst/test_properties.py

469b985536691566494253f147ca002224db57f7

[]

no_license

hurenkam/tracker-py

80a5fa966ca486c82c2ccf28938101e0d4607fcb

b74101f33b301c9644f2a094cb18deb0a941265d

refs/heads/master

2021-01-25T12:01:57.006662

2009-09-27T23:18:21

33,602,904

0

null

UTF-8

Python

false

2,598

py

from properties import * from e32 import ao_sleep as Sleep sid = 1 def Wait(text = None): if text != None: print text, Sleep(1) def RunSidTests(): global sid print "Test GetSid... ", sid = GetSid() #print sid, #if sid == 4028634395L: # emulator sid if sid == 537013993L: # on-device sid print "ok!" else: print "failed!" def RunEIntTests(): global sid print "Test EInt... " Wait("Define...") Property.Define(sid,1,Property.EInt) Wait("Create...") n = Property() Wait("Attach...") n.Attach(sid,1,Property.EInt) Wait("Set...") n.Set(5) Wait("Get...") i = n.Get() Wait("Delete...") Property.Delete(sid,1) Wait() if i == 5: print "ok." else: print "failed! (i=%i)" % i del n del i def RunETextTests(): global sid print "Test EText... " Wait("Define...") Property.Define(sid,1,Property.EText) Wait("Create...") t = Property() Wait("Attach...") t.Attach(sid,1,Property.EText) Wait("Set...") t.Set("Hello world!") Wait("Get...") s = t.Get(32) Wait("Delete...") Property.Delete(sid,1) Wait() if s == "Hello world!": print "ok." else: print "failed! (s=\"%s\")" % s del t del s cbcount = 0 setcount = 0 result = [ ] should = [ 1,-1,0x7fffffff,-0x80000000,0x10 ] def RunSubscribeTests(): def Callback(): global result global p r = p.Get() #print r, result.append(r) global p global sid global result print "Test Subscribe & Callback... " Wait("Define...") Property.Define(sid,0x10,Property.EInt) p = Property() Wait("Attach...") p.Attach(sid,0x10,Property.EInt) Wait("Set(0)...") p.Set(0) Wait("Subscribe...") p.Subscribe(Callback) Wait("Set(1)...") p.Set(1) Wait("Set(-1)...") p.Set(-1) Wait("Set(0x7fffffff)...") p.Set(0x7fffffff) Wait("Set(-0x80000000)...") p.Set(-0x80000000) Wait("Set(0x10)...") p.Set(0x10) Wait("Cancel...") p.Cancel() Wait("Set(0)...") p.Set(0) if result == should: print "ok!" else: print "failed!" #, result, should print "Running tests for properties module..." RunSidTests() RunEIntTests() RunETextTests() RunSubscribeTests() print Property.__dict__.keys() print PowerState.__dict__.keys() print SystemAgent.__dict__.keys() print "Done!"

[ "mark.hurenkamp@xs4all.nl@ef54a0f8-d952-0410-8fc1-d1a2e12a3fa0" ]

mark.hurenkamp@xs4all.nl@ef54a0f8-d952-0410-8fc1-d1a2e12a3fa0

330d36924cf7df5102ada150cb056163cd33e9a3

f0d671a7be0136f14158c78385ceaf284358d9d7

/data_analysis/ipython_log.py

57dc224cc77430125280541fdf7fec8f5d671229

[]

no_license

epicarts/python3_practice

3d0bd30d53976606402159d6a507c872284cd5e7

e092d3adacd44fd9722050267b07630022cd11df

refs/heads/master

2021-07-10T06:43:30.651712

2020-07-01T14:55:54

143,696,860

0

null

UTF-8

Python

false

2,391

py

# IPython log file import numpy as np import numpy as np def f(x, y, z): return (x + y) / z def f(x, y, z): return (x + y) / z result = f(5, 6, 7.5) a = np.random.randn(100, 100) get_ipython().run_line_magic('timeit', 'np.dot(a, a)') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_cell_magic('automagic', '', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('quickref', '') get_ipython().run_line_magic('', '') get_ipython().run_line_magic('quickref', '') get_ipython().run_line_magic('quickref', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('quickref', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('timeit', 'np.dot(a, a)') a = np.random.randn(10, 10) get_ipython().run_line_magic('timeit', 'np.dot(a, a)') get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('timeit', 'np.dot(a, a)') get_ipython().run_line_magic('who', '') get_ipython().run_line_magic('who', '') get_ipython().run_line_magic('who_is', '') get_ipython().run_line_magic('who_ls', '') 2 ** 27 _ __ foo = 'bar' _ __ foo _i27 get_ipython().run_line_magic('automagic', '') _i27 _i27 get_ipython().run_line_magic('automagic', '') get_ipython().run_line_magic('run', 'test1') get_ipython().run_line_magic('run', 'test1.py') get_ipython().run_line_magic('run', '/') _i27| _i27 _i2 _i21 _i23 _i5 get_ipython().run_line_magic('logstart', '') get_ipython().run_line_magic('logstop', '')

[ "0505zxc@gmail.com" ]

0505zxc@gmail.com

351523efcc8b35f6d0ea6c19f56d447f2a7e4ac0

99697559d046cdd04dd9068bd518e4da4177aaa2

/Finish/M545_Boundary_of_Binary_Tree.py

88c576130740e0f831bb5332c6a40362d95be6cf

[]

no_license

Azurisky/Leetcode

3e3621ef15f2774cfdfac8c3018e2e4701760c3b

8fa215fb0d5b2e8f6a863756c874d0bdb2cffa04

refs/heads/master

2020-03-18T22:46:35.780864

2018-10-07T05:45:30

135,364,168

0

null

UTF-8

Python

false

1,307

py

# Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def boundaryOfBinaryTree(self, root): """ :type root: TreeNode :rtype: List[int] """ def dfs_leftmost(node): if not node or not node.left and not node.right: return boundary.append(node.val) if node.left: dfs_leftmost(node.left) else: dfs_leftmost(node.right) def dfs_leaves(node): if not node: return dfs_leaves(node.left) if node != root and not node.left and not node.right: boundary.append(node.val) dfs_leaves(node.right) def dfs_rightmost(node): if not node or not node.left and not node.right: return if node.right: dfs_rightmost(node.right) else: dfs_rightmost(node.left) boundary.append(node.val) if not root: return [] boundary = [root.val] dfs_leftmost(root.left) dfs_leaves(root) dfs_rightmost(root.right) return boundary

[ "andrew0704us@gmail.com" ]

andrew0704us@gmail.com

c496121f8836365b61237c1ab508a1fd4c2bf41d

5e239849b5588d1924f44a69f0eeac040c0454bd

/core/serializers.py

c2bf36123c0bdbe5a996af81ca76a5a5b3ccf269

[]

no_license

mare-alta/backend

53be33244efeab12b1e548d499bc942807275d8f

d76fc822c6bdf3f2127887fab6885d8b967ac501

refs/heads/master

2022-12-10T15:31:01.419861

2019-12-08T15:45:49

226,612,261

0

null

2021-09-22T18:05:45

2019-12-08T04:05:45

Python

UTF-8

Python

false

547

py

from rest_framework import serializers from .models import * class LevelSerializer(serializers.ModelSerializer): class Meta: model = Level fields = '__all__' class ComplaintSerializer(serializers.ModelSerializer): class Meta: model = Complaint fields = '__all__' class AnswerSerializer(serializers.ModelSerializer): class Meta: model = Answer fields = '__all__' class ImageSerializer(serializers.ModelSerializer): class Meta: model = Image fields = '__all__'

[ "thales.gibbon@gmail.com" ]

thales.gibbon@gmail.com