xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

76959b57c9321fc06a543414d55880c4bcc1b129

249

py

Python

zeromq_rpc/client_pushpull.py

cr0hn/TestingBench

37975343cf9ccb019e8dc42404b5b321285b04b3

[ "BSD-3-Clause" ]

5

2018-05-10T19:50:29.000Z

2018-05-10T20:07:08.000Z

zeromq_rpc/client_pushpull.py

cr0hn/TestingBench

37975343cf9ccb019e8dc42404b5b321285b04b3

[ "BSD-3-Clause" ]

null

zeromq_rpc/client_pushpull.py

cr0hn/TestingBench

37975343cf9ccb019e8dc42404b5b321285b04b3

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import zerorpc class TestPuller(object): def test(self, msg): print(msg.split('.', 1)[0]) def main(): p = zerorpc.Puller(TestPuller()) p.connect('tcp://127.0.0.1:8080') p.run() if __name__ == '__main__': main()

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0.4

0.1

0

null

0

null

0

1

0

7696820fd42b35f43ad696c61bc30700ea9558d4

744

py

Python

textts.py

maanksa/text-to-speach-using-python

3708d1253b8bbce85074804331e21394b04d1e08

[ "Apache-2.0" ]

null

textts.py

maanksa/text-to-speach-using-python

3708d1253b8bbce85074804331e21394b04d1e08

[ "Apache-2.0" ]

null

textts.py

maanksa/text-to-speach-using-python

3708d1253b8bbce85074804331e21394b04d1e08

[ "Apache-2.0" ]

1

2021-07-16T07:19:53.000Z

from ibm_watson import TextToSpeechV1 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator url= 'https://api.us-south.text-to-speech.watson.cloud.ibm.com/instances/b5ca51fb-56d7-4e2b-827d-3ea50ad17dc3' apikey= 'J-PsrlP1DwzglNm-kwiEsNuAjHXmGdpKe-roAbUjjjra' # Setup Service authenticator = IAMAuthenticator(apikey) tts = TextToSpeechV1(authenticator=authenticator) tts.set_service_url(url) with open ('churchill.txt','r') as f: text = f.readlines() text = [line.replace('\n', '') for line in text] text = ''.join(str(line) for line in text) with open('./churchill.mp3', 'wb') as audio_file: res = tts.synthesize(text, accept='audio/mp3', voice='en-US_AllisonV3Voice').get_result() audio_file.write(res.content)

41.333333

111

0.752688

102

744

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0.627451

0.025455

0.061818

0.047273

0

0.034639

0.107527

744

18

112

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0

0.142857

0

null

0

null

0

1

0

769908792b127abeed255023abcee6228e8ccf59

598

py

Python

teamwake.py

chibacchi01/discordpy-startup

c3e5d9f6b9c73a0b304e08a061c36984623f354f

[ "MIT" ]

null

teamwake.py

chibacchi01/discordpy-startup

c3e5d9f6b9c73a0b304e08a061c36984623f354f

[ "MIT" ]

null

teamwake.py

chibacchi01/discordpy-startup

c3e5d9f6b9c73a0b304e08a061c36984623f354f

[ "MIT" ]

null

import random import sys def doTeamwake(lst): team = list(lst) random.shuffle(team) #print(len(team)) team1 = team[0:len(team)//2] team2 = team[len(team)//2:len(team)] #print(team) return team1,team2 def makeResult(team,team1,team2): mes1 = 'チーム' + team1[0] + ': \n' mes2 = 'チーム' + team2[0] + ': \n' for i in range (len(team1)): mes1 += team1[i] + '\n' for i in range (len(team2)): if(i == len(team2) - 1): mes2 += team2[i] else: mes2 += team2[i] + '\n' result = (mes1 + '\n' + mes2) return result

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null

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1

0

769aad2c61d0547afe4d66a3ecb918c9dbccb772

6,637

py

Python

notebooks_for_development/teff_retrieval_plots.py

mwanakijiji/rrlyrae_metallicity

1aa867eb9c96dba433271207efdf758cc7849360

[ "MIT" ]

null

notebooks_for_development/teff_retrieval_plots.py

mwanakijiji/rrlyrae_metallicity

1aa867eb9c96dba433271207efdf758cc7849360

[ "MIT" ]

15

2019-11-05T17:43:00.000Z

2022-01-12T16:29:59.000Z

notebooks_for_development/teff_retrieval_plots.py

mwanakijiji/rrlyrae_metallicity

1aa867eb9c96dba433271207efdf758cc7849360

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[ ]: # This makes plots showing the effective temperature retrievals based on synthetic spectra # produced by R.W. # Created from parent restacking_scraped_data.ipynb 2021 March 17 by E.S. # In[1]: import pandas as pd #from astropy.io import fits from astropy.io.fits import getdata import matplotlib import matplotlib.pyplot as plt import numpy as np # In[2]: # name of csv file with EWs as produced by pipeline ew_good_data_poststack_file_name = "/Users/bandari/Documents/git.repos/rrlyrae_metallicity/rrlyrae_metallicity/ew_products/20210225_restacked_ew_info_good_only.csv" # read in df_poststack = pd.read_csv(ew_good_data_poststack_file_name) # In[49]: def line_fit(x_data_pass, y_data_pass): # remove the stuff outside of 6000-7250 K #x_data_rrl = x_data_pass.where(np.logical_and(x_data_pass>=5900,x_data_pass<=7350)) #y_data_rrl = x_data_pass.where(np.logical_and(x_data_pass>=5900,x_data_pass<=7350)) x_data_rrl = x_data_pass[np.where(np.logical_and(y_data_pass>=5900,y_data_pass<=7350))] y_data_rrl = y_data_pass[np.where(np.logical_and(y_data_pass>=5900,y_data_pass<=7350))] coeff, cov = np.polyfit(x_data_rrl, y_data_rrl, 1, full=False, cov=True) m = coeff[0] b = coeff[1] err_m = np.sqrt(np.diag(cov))[0] err_b = np.sqrt(np.diag(cov))[1] print("---------") print("Note stuff outside of 6000-7350 K is not being considered") print("m:") print(m) print("err_m:") print(err_m) print("b:") print(b) print("err_b:") print(err_b) return m, b # In[44]: # plot: how do Balmer lines scale with Teff? plt.clf() plt.title("Scaling of lines with Hdelta") plt.scatter(df_poststack["Teff"],df_poststack["EW_Hbeta"], s=3, label="Hbeta") plt.scatter(df_poststack["Teff"],np.add(df_poststack["EW_Hgamma"],6), s=3, label="Hgamma+6") plt.scatter(df_poststack["Teff"],np.add(df_poststack["EW_Hdelta"],12), s=3, label="Hdel+12") plt.scatter(df_poststack["Teff"],np.add(df_poststack["EW_Balmer"],18), s=3, label="Net Balmer+18") plt.scatter(df_poststack["Teff"],np.add(df_poststack["EW_Heps"],24), s=3, label="Heps+24") #plt.ylim([0,70]) plt.xlabel("Teff (K)") plt.ylabel("EW (Angstr)") plt.title("Balmer EW trend with Teff") plt.legend(ncol=5) plt.show() #plt.savefig("junk_balmer_rescalings.pdf") # In[55]: y_data_metalrich = df_poststack["Teff"].where(df_poststack["FeH"] > -2.9).dropna().values.astype(float) # In[61]: x_data_Balmer_metalrich = df_poststack["EW_Balmer"].where(df_poststack["FeH"] > -2.9).dropna() # In[62]: x_data_Balmer_metalrich # In[56]: y_data_metalrich # In[63]: # find linear trends of {net Balmer, Hdelta, and Hgamma} EW with Teff, entire Teff range y_data = df_poststack["Teff"].values.astype(float) # fit a straight line: net Balmer x_data_Balmer = df_poststack["EW_Balmer"].values.astype(float) m_Balmer, b_Balmer = line_fit(x_data_Balmer,y_data) # same, except that [Fe/H] = -3 is neglected x_data_Balmer_metalrich = df_poststack["EW_Balmer"].where(df_poststack["FeH"] > -2.9).dropna().values.astype(float) y_data_metalrich = df_poststack["Teff"].where(df_poststack["FeH"] > -2.9).dropna().values.astype(float) m_Balmer_metalrich, b_Balmer_metalrich = line_fit(x_data_Balmer_metalrich,y_data_metalrich) # fit a straight line: Hdelta x_data_Hdelta = df_poststack["EW_Hdelta"].values.astype(float) m_Hdelta, b_Hdelta = line_fit(x_data_Hdelta,y_data) # fit a straight line: Hgamma x_data_Hgamma = df_poststack["EW_Hgamma"].values.astype(float) m_Hgamma, b_Hgamma = line_fit(x_data_Hgamma,y_data) # In[67]: # calculate retrieved Teff and add new columns to DataFrame to make the plotting easier df_poststack["Teff_retrieved_Balmer"] = np.add(np.multiply(df_poststack["EW_Balmer"],m_Balmer),b_Balmer) df_poststack["Teff_retrieved_Hdelta"] = np.add(np.multiply(df_poststack["EW_Hdelta"],m_Hdelta),b_Hdelta) df_poststack["Teff_retrieved_Hgamma"] = np.add(np.multiply(df_poststack["EW_Hgamma"],m_Hgamma),b_Hgamma) df_poststack["Teff_retrieved_Balmer_metalrich"] = np.add(np.multiply(df_poststack["EW_Balmer"],m_Balmer_metalrich),b_Balmer_metalrich) colormap = "Reds" # array of metallicities feh_values = np.sort(df_poststack["FeH"].drop_duplicates().values) norm = matplotlib.colors.Normalize(vmin=np.min(feh_values),vmax=np.max(feh_values)) # retrieved Balmer values # retrieved Balmer values plt.clf() colormap="Reds" norm = matplotlib.colors.Normalize(vmin=np.min(feh_values),vmax=np.max(feh_values)) f, (a0, a1) = plt.subplots(nrows=2, ncols=1, gridspec_kw={'height_ratios': [2, 1]}, sharex=True) a0.axvspan(6000, 7250, color='y', alpha=0.5, lw=0,zorder=0) # RRLs in instability strip (Catelan 2015) a1.axvspan(6000, 7250, color='y', alpha=0.5, lw=0,zorder=0) a0.plot(df_poststack["Teff"],df_poststack["Teff"],zorder=1,linestyle="--",color="k") a1.plot([np.min(df_poststack["Teff"]),np.max(df_poststack["Teff"])],[0,0],zorder=1,linestyle="--",color="k") a0.scatter(df_poststack["Teff"], df_poststack["Teff_retrieved_Balmer"], c=df_poststack["FeH"], cmap=colormap, norm=norm, edgecolor="k",zorder=2) a1.scatter(df_poststack["Teff"], np.subtract(df_poststack["Teff_retrieved_Balmer_metalrich"],df_poststack["Teff"]), c=df_poststack["FeH"], cmap=colormap, norm=norm, edgecolor="k",zorder=2) ''' # annotation to check the color mapping for t in range(0,len(df_poststack["FeH"])): plt.annotate(str(df_poststack["FeH"][t]), (df_poststack["Teff"][t],df_poststack["Teff_retrieved_Balmer"][t])) ''' # kludge to add legend while mapping colors correctly for i in range(0,len(feh_values)): # indices reversed to get the order descending in the legend a0.scatter([0], [0], cmap=colormap, norm=norm, c=feh_values[-i-1], edgecolor="k", label="[Fe/H]="+str(feh_values[-i-1])) print(feh_values[i]) a0.set_ylabel("Retrieved T$_{eff}$") a1.set_xlabel("Injected T$_{eff}$") a1.set_ylabel("Retrieved T$_{eff}$ - Injected T$_{eff}$\n(based on trend for [Fe/H] $\geq$ -2.5)") f.canvas.draw() # need before legend to render a0.set_xlim([5500,8000]) a0.set_ylim([5500,8500]) a0.legend(loc="lower right") plt.show() print("USE NOTEBOOK VERSION OF THIS! OTHERWISE THE LEGEND DOESN'T HAVE RIGHT HANDLES!") #plt.savefig("junk.pdf") #import ipdb; ipdb.set_trace() f.savefig("junk.pdf") # In[ ]: # calculate BIC to find best model # In[ ]: ''' def pred_teff(EW_pass,m_pass,b_pass): teff_pass = np.add(np.multiply(EW_pass,m_pass),b_pass) return teff_pass # In[ ]: Teff_model = pred_teff(df_poststack["EW_Balmer"]) '''

28.363248

164

0.717644

1,099

6,637

4.1101

0.250227

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0.076378

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0

0.033408

0.120536

6,637

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0

null

0

null

0

1

0

769c61429859f2722eb0d1cae80d73e5e4f5da1f

5,704

py

Python

models/lstm/model.py

ErikHumphrey/sustain-seq2seq

c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4

[ "Apache-2.0" ]

4

2019-05-09T19:47:48.000Z

2020-04-11T13:58:31.000Z

models/lstm/model.py

ErikHumphrey/sustain-seq2seq

c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4

[ "Apache-2.0" ]

null

models/lstm/model.py

ErikHumphrey/sustain-seq2seq

c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4

[ "Apache-2.0" ]

4

2018-12-05T01:52:22.000Z

2019-11-01T01:01:52.000Z

import sys, os sys.path.insert(0, '../..') from collections import OrderedDict import torch import torch.nn as nn from models.components.encodersdecoders.EncoderDecoder import EncoderDecoder class MyEncoderDecoder(EncoderDecoder): def __init__(self, src_lookup, tgt_lookup, encoder, decoder, dec_transfer_hidden, device): super().__init__(src_lookup, tgt_lookup, encoder, decoder, device) self.dec_transfer_hidden = dec_transfer_hidden if dec_transfer_hidden == True: assert encoder.num_layers == decoder.num_layers, "For transferring the last hidden state from encoder to decoder, both must have the same number of layers." # Transform h from encoder's [num_layers * 2, batch_size, enc_hidden_dim/2] to decoder's [num_layers * 1, batch_size, dec_hidden_dim], same for c; batch_size = 1 (last timestep only) self.h_state_linear = nn.Linear(int(encoder.hidden_dim * encoder.num_layers/1), decoder.hidden_dim * decoder.num_layers * 1) self.c_state_linear = nn.Linear(int(encoder.hidden_dim * encoder.num_layers/1), decoder.hidden_dim * decoder.num_layers * 1) self.to(self.device) def forward(self, x_tuple, y_tuple, teacher_forcing_ratio=0.): """ Args: x (tensor): The input of the decoder. Shape: [batch_size, seq_len_enc]. y (tensor): The input of the decoder. Shape: [batch_size, seq_len_dec]. Returns: The output of the Encoder-Decoder with attention. Shape: [batch_size, seq_len_dec, n_class]. """ x, x_lenghts, x_mask = x_tuple[0], x_tuple[1], x_tuple[2] y, y_lenghts, y_mask = y_tuple[0], y_tuple[1], y_tuple[2] batch_size = x.shape[0] # Calculates the output of the encoder encoder_dict = self.encoder.forward(x_tuple) enc_output = encoder_dict["output"] enc_states = encoder_dict["states"] # enc_states is a tuple of size ( h=[enc_num_layers*2, batch_size, enc_hidden_dim/2], c=[same-as-h] ) if self.dec_transfer_hidden == True: dec_states = self.transfer_hidden_from_encoder_to_decoder(enc_states) else: hidden = Variable(next(self.parameters()).data.new(batch_size, self.decoder.num_layers, self.decoder.hidden_dim), requires_grad=False) cell = Variable(next(self.parameters()).data.new(batch_size, self.decoder.num_layers, self.decoder.hidden_dim), requires_grad=False) dec_states = ( hidden.zero_(), cell.zero_() ) # Calculates the output of the decoder. encoder_dict = self.decoder.forward(x_tuple, y_tuple, enc_output, dec_states, teacher_forcing_ratio) output = encoder_dict["output"] attention_weights = encoder_dict["attention_weights"] # Creates a BOS tensor that must be added to the beginning of the output. [batch_size, 1, dec_vocab_size] bos_tensor = torch.zeros(batch_size, 1, self.decoder.vocab_size).to(self.device) # Marks the corresponding BOS position with a probability of 1. bos_tensor[:, :, self.tgt_bos_token_id] = 1 # Concatenates the BOS tensor with the output. [batch_size, dec_seq_len-1, dec_vocab_size] -> [batch_size, dec_seq_len, dec_vocab_size] output = torch.cat((bos_tensor, output), dim=1) return output, attention_weights def run_batch(self, X_tuple, y_tuple, criterion=None, tf_ratio=.0, aux_loss_weight = 0.5): (x_batch, x_batch_lenghts, x_batch_mask) = X_tuple (y_batch, y_batch_lenghts, y_batch_mask) = y_tuple if hasattr(self.decoder.attention, 'reset_coverage'): self.decoder.attention.reset_coverage(x_batch.size()[0], x_batch.size()[1]) output, attention_weights = self.forward((x_batch, x_batch_lenghts, x_batch_mask), (y_batch, y_batch_lenghts, y_batch_mask), tf_ratio) if criterion is not None: loss = criterion(output.view(-1, self.decoder.vocab_size), y_batch.contiguous().flatten()) else: loss = 0 return output, loss, attention_weights, {} def transfer_hidden_from_encoder_to_decoder(self, enc_states): batch_size = enc_states[0].shape[1] # Reshapes the shape of the hidden and cell state of the encoder LSTM layers. Permutes the batch_size to # the first dimension, and reshapes them to a 2-D tensor. # [enc_num_layers * 2, batch_size, enc_hidden_dim] -> [batch_size, enc_num_layers * enc_hidden_dim * 2]. enc_states = (enc_states[0].permute(1, 0, 2).reshape(batch_size, -1), enc_states[1].permute(1, 0, 2).reshape(batch_size, -1)) # Transforms the hidden and the cell state of the encoder lstm layer to correspond to the decoder lstm states dimensions. # [batch_size, enc_num_layers * enc_hidden_dim * 2] -> [batch_size, dec_num_layers * dec_hidden_dim]. dec_states = (torch.tanh(self.h_state_linear(enc_states[0])), torch.tanh(self.c_state_linear(enc_states[1]))) # Reshapes the states to have the correct shape for the decoder lstm states dimension. Reshape the states from # 2-D to 3-D sequence. Permutes the batch_size to the second dimension. # [batch_size, dec_num_layers * dec_hidden_dim] -> [dec_num_layers, batch_size, dec_hidden_dim]. dec_states = (dec_states[0].reshape(batch_size, self.decoder.num_layers, self.decoder.hidden_dim).permute(1, 0, 2), dec_states[1].reshape(batch_size, self.decoder.num_layers, self.decoder.hidden_dim).permute(1, 0, 2)) return dec_states

54.846154

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0.677069

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4.394673

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null

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null

0

1

0

76a0ced258b379bc8d32233ac40e74815c8cb7f3

468

py

Python

bot/cogs/dota.py

tsalmela/Rankaisijabot

7feb3522a2770314fe98d40a0bf361b6b60a7386

[ "MIT" ]

1

2021-02-06T16:50:02.000Z

bot/cogs/dota.py

tsalmela/Rankaisijabot

7feb3522a2770314fe98d40a0bf361b6b60a7386

[ "MIT" ]

1

2022-01-20T09:50:32.000Z

bot/cogs/dota.py

tsalmela/Rankaisijabot

7feb3522a2770314fe98d40a0bf361b6b60a7386

[ "MIT" ]

1

2022-01-20T08:42:41.000Z

import discord from discord.ext import commands class Dota(commands.Cog, name="dota"): def __init__(self, bot): self.bot = bot @commands.command(name="dotaukkoja", aliases=["ukkoja"]) async def ukkoja(self, ctx): await ctx.send(file=discord.File("images/dota_ukkoja.png")) @commands.command(name="ei") async def ei(self, ctx): await ctx.send(file=discord.File("images/ei.png")) def setup(bot): bot.add_cog(Dota(bot))

27.529412

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67

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0.045902

0.12459

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17

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27.529412

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null

0

null

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1

0

76a41b6ac26a0b5b24f85bacc5004a2ef1d5c340

6,555

py

Python

src/theory/compute_parameters.py

RPGroup-PBoC/chann_cap

f2a826166fc2d47c424951c616c46d497ed74b39

[ "MIT" ]

2

2020-08-21T04:06:12.000Z

2022-02-09T07:36:58.000Z

src/theory/compute_parameters.py

RPGroup-PBoC/chann_cap

f2a826166fc2d47c424951c616c46d497ed74b39

[ "MIT" ]

null

src/theory/compute_parameters.py

RPGroup-PBoC/chann_cap

f2a826166fc2d47c424951c616c46d497ed74b39

[ "MIT" ]

2

2020-04-29T17:43:28.000Z

2020-09-09T00:20:16.000Z

import pickle # Our numerical workhorses import numpy as np import pandas as pd # Import matplotlib stuff for plotting import matplotlib.pyplot as plt import matplotlib.cm as cm # Seaborn, useful for graphics import seaborn as sns # Import the utils for this project import ccutils # Define mRNA rate # gm = 0.00284 # s**-1 # http://bionumbers.hms.harvard.edu/bionumber.aspx?id=105717&ver=3&trm=lacZ%20mRNA%20lifetime&org= gm = 1 / (3 * 60) # Define cell volume Vcell = 2.15 # fL # Define diffusion limiting rate k0 = 2.7E-3 # ============================================================================= # Single promoter # ============================================================================= # Load the flat-chain with open('../../data/mcmc/lacUV5_constitutive_mRNA_prior.pkl', 'rb') as file: unpickler = pickle.Unpickler(file) gauss_flatchain = unpickler.load() gauss_flatlnprobability = unpickler.load() # Generate a Pandas Data Frame with the mcmc chain index = ['kp_on', 'kp_off', 'rm'] # Generate a data frame out of the MCMC chains df_mcmc = pd.DataFrame(gauss_flatchain, columns=index) # reasign the index with the new entries index = df_mcmc.columns # map value of the parameters max_idx = np.argmax(gauss_flatlnprobability, axis=0) kpon, kpoff, rm = df_mcmc.iloc[max_idx, :] # ea range kpon_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 0], 0.95) kpoff_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 1], 0.95) rm_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 2], 0.95) # Print results print('Single gene copy parameters: ') print(""" The most probable parameters for the model ------------------------------------------ kp_on = {0:.1f} -{1:0.1f} +{2:0.1f} kp_off = {3:.1f} -{4:0.1f} +{5:0.1f} rm = {6:.1f} -{7:0.1f} +{8:0.1f} """.format(kpon, np.abs(kpon-kpon_hpd[0]), np.abs(kpon-kpon_hpd[1]),\ kpoff, np.abs(kpoff-kpoff_hpd[0]), np.abs(kpoff-kpoff_hpd[1]),\ rm, np.abs(rm-rm_hpd[0]), np.abs(rm-rm_hpd[1]))) # Print results print(""" The most probable parameters for the model in seconds^-1 -------------------------------------------------------- kp_on = {0:.3f} -{1:0.3f} +{2:0.3f} s^-1 kp_off = {3:.3f} -{4:0.3f} +{5:0.3f} s^-1 rm = {6:.3f} -{7:0.3f} +{8:0.3f} s^-1 """.format(kpon * gm, np.abs(kpon-kpon_hpd[0]) * gm, np.abs(kpon-kpon_hpd[1]) * gm, kpoff * gm, np.abs(kpoff-kpoff_hpd[0]) * gm, np.abs(kpoff-kpoff_hpd[1]) * gm, rm * gm, np.abs(rm-rm_hpd[0]) * gm, np.abs(rm-rm_hpd[1]) * gm)) # ============================================================================= # Double promoter # ============================================================================= # Load the flat-chain with open('../../data/mcmc/lacUV5_constitutive_mRNA_double_expo.pkl', 'rb') as file: unpickler = pickle.Unpickler(file) gauss_flatchain = unpickler.load() gauss_flatlnprobability = unpickler.load() # Generate a Pandas Data Frame with the mcmc chain index = ['kp_on', 'kp_off', 'rm'] # Generate a data frame out of the MCMC chains df_mcmc = pd.DataFrame(gauss_flatchain, columns=index) # rerbsine the index with the new entries index = df_mcmc.columns # map value of the parameters max_idx = np.argmax(gauss_flatlnprobability, axis=0) kpon_double, kpoff_double, rm_double = df_mcmc.iloc[max_idx, :] # ea range kpon_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 0], 0.95) kpoff_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 1], 0.95) rm_hpd = ccutils.stats.hpd(df_mcmc.iloc[:, 2], 0.95) # Print results print('Two-promoter model') print(""" The most probable parameters for the model ------------------------------------------ kp_on = {0:.1f} -{1:0.1f} +{2:0.1f} kp_off = {3:.1f} -{4:0.1f} +{5:0.1f} rm = {6:.1f} -{7:0.1f} +{8:0.1f} """.format(kpon_double, np.abs(kpon_double-kpon_hpd[0]), np.abs(kpon_double-kpon_hpd[1]), kpoff_double, np.abs(kpoff_double-kpoff_hpd[0]), np.abs(kpoff_double-kpoff_hpd[1]), rm_double, np.abs(rm_double-rm_hpd[0]), np.abs(rm_double-rm_hpd[1]))) # Print results print(""" The most probable parameters for the model in seconds^-1 -------------------------------------------------------- kp_on = {0:.3f} -{1:0.3f} +{2:0.3f} s^-1 kp_off = {3:.2f} -{4:0.2f} +{5:0.2f} s^-1 rm = {6:.1f} -{7:0.1f} +{8:0.1f} s^-1 """.format(kpon_double * gm, np.abs(kpon_double-kpon_hpd[0]) * gm, np.abs(kpon_double-kpon_hpd[1]) * gm, kpoff_double * gm, np.abs(kpoff_double-kpoff_hpd[0]) * gm, np.abs(kpoff_double-kpoff_hpd[1]) * gm, rm_double * gm, np.abs(rm_double-rm_hpd[0]) * gm, np.abs(rm_double-rm_hpd[1]) * gm)) # ============================================================================= # Repressor rates # ============================================================================= # Define binding energies of the different operators energies = {'Oid': -17, 'O1': -15.3, 'O2': -13.9, 'O3': -9.7} # Compute the rates for each repressor kr_offs = {key: ccutils.model.kr_off_fun(value, k0, kpon_double, kpoff_double, Vcell=Vcell) for key, value in energies.items()} # Print repressor rates print(""" The most probable parameters for the repressor in seconds^-1 ------------------------------------------------------------ """) for key, value in kr_offs.items(): print('kr_off {0:s} = {1:.5f} s^-1'.format(key, value)) # ============================================================================= # Compute probability of each of the states # ============================================================================= def prob_promoter(kr_on, kr_off, kp_on, kp_off, rm): ''' Computes the probability of the three promoter states for a regulated promoter ''' P_B = (kr_off * kp_on) / (kp_off * kr_off + kp_off * kr_on + kr_off * kp_on) P_E = (kp_off * kr_off) / (kp_off * kr_off + kp_off * kr_on + kr_off * kp_on) P_R = (kp_off * kr_on) / (kp_off * kr_off + kp_off * kr_on + kr_off * kp_on) return {'P_B': P_B, 'P_E': P_E, 'P_R': P_R} # O1 R = 22 kr_on = 1 / Vcell / 0.6022 * k0 * R probs_O1 = prob_promoter(kr_on, kr_offs['O1'], kpon_double, kpoff_double, rm_double) print(''' Probability of each promoter state for O1 - R{:d} ------------------------------------------------- '''.format(R)) for key, value in probs_O1.items(): print('State {0:s} = {1:.5f}'.format(key, value))

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null

0

null

0

1

0

76a675361f75929eaef0ac54e1cad4d801933fa3

1,340

py

Python

syne_tune/blackbox_repository/serialize.py

awslabs/syne-tune

1dd8e157477b86db01047a9a7821780ea04389bc

[ "ECL-2.0", "Apache-2.0" ]

97

2021-11-18T17:14:30.000Z

2022-03-29T00:33:12.000Z

syne_tune/blackbox_repository/serialize.py

awslabs/syne-tune

1dd8e157477b86db01047a9a7821780ea04389bc

[ "ECL-2.0", "Apache-2.0" ]

54

2021-11-18T17:14:12.000Z

2022-03-22T08:11:48.000Z

syne_tune/blackbox_repository/serialize.py

awslabs/syne-tune

1dd8e157477b86db01047a9a7821780ea04389bc

[ "ECL-2.0", "Apache-2.0" ]

9

2021-11-29T11:47:32.000Z

2022-02-24T15:28:11.000Z

from pathlib import Path from typing import Optional, Dict import json import syne_tune.config_space as sp def serialize_configspace( path: str, configuration_space: Dict, fidelity_space: Optional[Dict] = None ): path = Path(path) with open(path / "configspace.json", "w") as f: json.dump({k: sp.to_dict(v) for k, v in configuration_space.items()}, f) if fidelity_space is not None: with open(path / "fidelityspace.json", "w") as f: json.dump({k: sp.to_dict(v) for k, v in fidelity_space.items()}, f) def deserialize_configspace(path: str): def open_if_exists(name): config_path = Path(path) / name if config_path.exists(): with open(config_path, "r") as file: cs_space = json.load(file) return {k: sp.from_dict(v) for k, v in cs_space.items()} else: return None configuration_space = open_if_exists("configspace.json") fidelity_space = open_if_exists("fidelityspace.json") return configuration_space, fidelity_space def serialize_metadata(path: str, metadata): with open(path / "metadata.json", "w") as f: json.dump(metadata, f) def deserialize_metadata(path: str): with open(Path(path) / "metadata.json", "r") as f: metadata = json.load(f) return metadata

31.162791

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null

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null

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0

76a770e1dff189dc92f8b5b60eccb33c71ae9285

3,356

py

Python

src/trainer.py

alcaster/Wassa2018

216fa943c3f4489d320e73c0ff46ff0b6e1f4c5a

[ "MIT" ]

null

src/trainer.py

alcaster/Wassa2018

216fa943c3f4489d320e73c0ff46ff0b6e1f4c5a

[ "MIT" ]

null

src/trainer.py

alcaster/Wassa2018

216fa943c3f4489d320e73c0ff46ff0b6e1f4c5a

[ "MIT" ]

null

import logging from typing import Optional from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn from paths import OUTPUTS from src.data import one_hot_labels_fn, get_data_fn, labels_map from src.net import Model from src.utils.types import path import tensorflow as tf log = logging.getLogger(__name__) class NetworkTrainer: def __init__(self): self.params = Params() self.config = Config() def train(self, train_path: path, test_path: path, model_name: Optional[str] = None): if model_name: self.config.checkpoint_path = self.config.checkpoint_path / model_name self.config.checkpoint_path.mkdir(exist_ok=True, parents=True) run_config = tf.estimator.RunConfig( save_checkpoints_steps=self.config.save_checkpoints_steps, save_summary_steps=self.config.save_summary_steps) def model_fn(features, labels, mode): model = Model(features, labels, self.params, mode) return tf.estimator.EstimatorSpec( mode, {'label': model.prediction}, model.loss, model.train_op, {"acc": model.acc} ) train_spec = tf.estimator.TrainSpec(self.create_input_fn(train_path), max_steps=self.params.max_steps) eval_spec = tf.estimator.EvalSpec(self.create_input_fn(test_path), steps=100) estimator = tf.estimator.Estimator(model_fn, self.config.checkpoint_path, run_config) tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) def export(self): assert self.config.checkpoint_path is not None model_dir = str(self.config.checkpoint_path) def model_fn(features, labels, mode): sentence = features['sentence'] model = Model(sentence, labels, self.params, mode) return tf.estimator.EstimatorSpec( mode, {'label': model.prediction} ) estimator = tf.estimator.Estimator(model_fn, model_dir) sentence = tf.placeholder(tf.string, [None], 'sentence') serving_input_receiver_fn = build_raw_serving_input_receiver_fn( {'sentence': sentence} ) estimator.export_saved_model( str(self.config.checkpoint_path / 'exported'), serving_input_receiver_fn ) log.info("Export complete") def create_input_fn(self, src: path): def input_fn(): dataset = (tf.data.Dataset.from_generator(get_data_fn(src), output_types=(tf.string, tf.uint8)) .shuffle(buffer_size=300) .map(one_hot_labels_fn(self.params.num_classes), num_parallel_calls=8) .batch(self.params.batch_size) .repeat() .prefetch(1) ) return dataset return input_fn class Params: def __init__(self): self.num_classes = len(labels_map) self.learning_rate = 1e-4 self.batch_size = 32 self.max_steps = 40_000 class Config: def __init__(self): self.checkpoint_path = OUTPUTS.path self.save_checkpoints_steps = 500 self.save_summary_steps = 100

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5.058524

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null

0

null

0

1

0

76a9dbfc53f8c5660ceebd58e3b28042193f92c9

5,920

py

Python

setup.py

ankane/isotree-1

67940e7afaf8332bf2ca95722cec90c56280b04f

[ "BSD-2-Clause" ]

1

2020-09-18T17:21:49.000Z

setup.py

ankane/isotree-1

67940e7afaf8332bf2ca95722cec90c56280b04f

[ "BSD-2-Clause" ]

null

setup.py

ankane/isotree-1

67940e7afaf8332bf2ca95722cec90c56280b04f

[ "BSD-2-Clause" ]

null

try: from setuptools import setup from setuptools.extension import Extension except: from distutils.core import setup from distutils.extension import Extension import numpy as np from Cython.Distutils import build_ext from sys import platform import sys, os, re from os import environ has_cereal = True try: import cycereal cereal_dir = cycereal.get_cereal_include_dir() except: has_cereal = False cereal_dir = "." ## <- placeholder ## https://stackoverflow.com/questions/724664/python-distutils-how-to-get-a-compiler-that-is-going-to-be-used class build_ext_subclass( build_ext ): def build_extensions(self): c = self.compiler.compiler_type # TODO: add entries for intel's ICC if c == 'msvc': # visual studio for e in self.extensions: e.extra_compile_args = ['/openmp', '/O2', '/std:c++14'] ### Note: MSVC never implemented C++11 elif (c == "clang") or (c == "clang++"): for e in self.extensions: e.extra_compile_args = ['-fopenmp', '-O3', '-march=native', '-std=c++17'] e.extra_link_args = ['-fopenmp'] ### Note: when passing C++11 to CLANG, it complains about C++17 features in CYTHON_FALLTHROUGH else: # gcc for e in self.extensions: e.extra_compile_args = ['-fopenmp', '-O3', '-march=native', '-std=c++11'] e.extra_link_args = ['-fopenmp'] ### when testing with clang: # e.extra_compile_args = ['-fopenmp=libiomp5', '-O3', '-march=native', '-std=c++11'] # e.extra_link_args = ['-fopenmp=libiomp5'] # e.extra_compile_args = ['-fopenmp=libiomp5', '-O2', '-march=native', '-std=c++11', '-stdlib=libc++', '-lc++abi'] # e.extra_link_args = ['-fopenmp=libiomp5', '-lc++abi'] # e.extra_compile_args = ['-O2', '-march=native', '-std=c++11'] ### for testing (run with `LD_PRELOAD=libasan.so python script.py`) # e.extra_compile_args = ["-std=c++11", "-fsanitize=address", "-static-libasan", "-ggdb"] # e.extra_link_args = ["-fsanitize=address", "-static-libasan"] ### when testing for oneself # e.extra_compile_args += ["-Wno-sign-compare", "-Wno-switch", "-Wno-maybe-uninitialized"] ## Note: apple will by default alias 'gcc' to 'clang', and will ship its own "special" ## 'clang' which has no OMP support and nowadays will purposefully fail to compile when passed ## '-fopenmp' flags. If you are using mac, and have an OMP-capable compiler, ## comment out the code below, or set 'use_omp' to 'True'. if not use_omp: for e in self.extensions: e.extra_compile_args = [arg for arg in e.extra_compile_args if arg != '-fopenmp'] e.extra_link_args = [arg for arg in e.extra_link_args if arg != '-fopenmp'] build_ext.build_extensions(self) use_omp = (("enable-omp" in sys.argv) or ("-enable-omp" in sys.argv) or ("--enable-omp" in sys.argv)) if use_omp: sys.argv = [a for a in sys.argv if a not in ("enable-omp", "-enable-omp", "--enable-omp")] if environ.get('ENABLE_OMP') is not None: use_omp = True if platform[:3] != "dar": use_omp = True ### Shorthand for apple computer: ### uncomment line below # use_omp = True setup( name = "isotree", packages = ["isotree"], version = '0.2.6', description = 'Isolation-Based Outlier Detection, Distance, and NA imputation', author = 'David Cortes', author_email = 'david.cortes.rivera@gmail.com', url = 'https://github.com/david-cortes/isotree', keywords = ['isolation-forest', 'anomaly', 'outlier'], cmdclass = {'build_ext': build_ext_subclass}, ext_modules = [Extension( "isotree._cpp_interface", sources=["isotree/cpp_interface.pyx", "src/dealloc.cpp", "src/merge_models.cpp", "src/serialize.cpp", "src/sql.cpp"], include_dirs=[np.get_include(), ".", "./src", cereal_dir], language="c++", install_requires = ["numpy", "pandas>=0.24.0", "cython", "scipy"], define_macros = [("_USE_MERSENNE_TWISTER", None), ("_ENABLE_CEREAL", None) if has_cereal else ("NO_CEREAL", None), ("_FOR_PYTHON", None), ("PY_GEQ_3_3", None) if (sys.version_info[0] >= 3 and sys.version_info[1] >= 3) else ("PY_LT_3_3", None)] )] ) if not use_omp: import warnings apple_msg = "\n\n\nMacOS detected. Package will be built without multi-threading capabilities, " apple_msg += "due to Apple's lack of OpenMP support in default clang installs. In order to enable it, " apple_msg += "install the package directly from GitHub: https://www.github.com/david-cortes/isotree\n" apple_msg += "Using 'python setup.py install enable-omp'. " apple_msg += "You'll also need an OpenMP-capable compiler.\n\n\n" warnings.warn(apple_msg) if not has_cereal: import warnings msg = "\n\nWarning: cereal library not found. Package will be built " msg += "without serialization (importing/exporting models) capabilities. " msg += "In order to enable cereal, install package 'cycereal' and reinstall " msg += "'isotree' by downloading the source files and running " msg += "'python setup.py install'.\n" warnings.warn(msg)

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0

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0.170455

0

null

0

null

0

1

0

76ab9b88c9f7dc5c6592c990a9aea52a667da955

7,025

py

Python

Mengascini-Spina/Sistemi-Digitali-M/Models/BaseModel.py

mattpoggi/SistemiDigitaliM20-21

202e520a571a2bb961851763f37e9293c3af400d

[ "MIT" ]

9

2021-02-07T22:53:34.000Z

2022-03-14T21:47:30.000Z

Mengascini-Spina/Sistemi-Digitali-M/Models/BaseModel.py

mattpoggi/SistemiDigitaliM20-21

202e520a571a2bb961851763f37e9293c3af400d

[ "MIT" ]

null

Mengascini-Spina/Sistemi-Digitali-M/Models/BaseModel.py

mattpoggi/SistemiDigitaliM20-21

202e520a571a2bb961851763f37e9293c3af400d

[ "MIT" ]

18

2021-02-07T18:30:47.000Z

2022-01-22T16:57:40.000Z

import time from abc import ABC, abstractmethod from contextlib import redirect_stdout from pathlib import Path from tensorflow.python.keras.backend import clear_session from tensorflow.python.keras.models import Model import tensorflow as tf from Generators import DataGenerator class BaseModel(ABC): """ Base class defining the endpoint to use to interact with a model """ def __init__(self, model_name: str, log_dir: Path = False, verbose: bool = True): """ :param model_name: name of the model, used for logging and saving it :param log_dir: path of the dir in which to save the model and the tensorboard log :param verbose: boolean indicating if it is necessary to print extensive information in the console """ # check if the log folder is a valid folder if log_dir is not None or log_dir != False: assert (log_dir.is_dir()) self.logs = True else: self.logs = False # the verbose parameter controls how many log info will be printed in the console self.verbose = verbose # save the time of creation of this class, it will help us to uniquelly identify this specific train run self.str_time = time.strftime("%b%d%Y%H%M%S", time.gmtime()) # save the model name and the directory in which to save the Logs self.name = model_name self.checkpoint_path = None if self.logs: self.parent_log_dir = log_dir # create the path of the log folder for this train run self.log_dir = self.parent_log_dir / "models" / self.name / self.str_time # create the log folder if not self.log_dir.is_dir(): self.log_dir.mkdir(parents=True, exist_ok=True) # tensorboard has its own log directory self.tensorboard_log_dir = self.parent_log_dir / "tensorboard" / self.name / self.str_time #set the path to use to save a checkpoint self.checkpoint_path = self.log_dir / 'best_model.h5' # generating a unique name for the model depending on the time of its creation self.name_with_time = self.name + " " + self.str_time @abstractmethod def build_model(self, input_shape, output_shape) -> Model: """ Function in charge of defining the model structure :param input_shape: tuple containing the shape of the data this model will recive as input :param output_shape: tuple containing the shape of the output produced by this model :return: Keras Sequential Model """ raise NotImplementedError @property @abstractmethod def input_shape(self) -> tuple: """ This property returns the input shape of the model :return: tuple """ raise NotImplementedError @property @abstractmethod def output_shape(self) -> tuple: """ This property returns the output shape of the model :return: """ raise NotImplementedError def _get_callbacks(self) -> list: """ Function defining all the callbacks for the given model and returning them as a list. In particular by default each model uses the following 3 callbacks - early stopping -> to stop the train early if the model has not improved in the past 10 epochs - checkpoint -> to save the model each time we find better weights - tensorboard -> to save the model Logs and be able to confront the models :return: list(keras.Callbacks) """ callbacks = [] if self.logs: callbacks += [ tf.keras.callbacks.ModelCheckpoint(self.checkpoint_path, monitor='val_accuracy', save_best_only=True,verbose=self.verbose), tf.keras.callbacks.TensorBoard(log_dir=self.tensorboard_log_dir), ] return callbacks def _on_before_train(self): """ Set of actions to do right before the training phase :return: """ self.training_start_time = time.time() if self.verbose: print("Model structure:") print(self.model.summary()) print("The training phase of the model {} has started at:{}".format(self.name, self.training_start_time)) def _on_after_train(self): """ Set of actions to do right after the training phase :return: """ self.training_time = time.time() - self.training_start_time if self.verbose: print("The model:{} has completed the training phase in: {}".format(self.name, self.training_time)) def train_model(self, training_data: DataGenerator, validation_data: DataGenerator, epochs: int, loss_function, optimizer=None, save_model: bool = False, save_summary: bool = True): """ Function in charge of training the model defined in the given class :param training_data: DataGenerator class, generating the training data :param validation_data: Datagenerator class, generating the validation data :param optimizer: optimizer to use during training (tf.keras.optimizers.Adam(0.0001)), :param loss_function: loss function to use :param epochs: number of epochs to run :param save_model: should the model be saved at the end of the training phase? :param save_summary: save the summary of the model into the log folder :return: """ if optimizer == None: optimizer = tf.keras.optimizers.Adam(0.0001) # get the structure of the model as defined by the build function self.model = self.build_model(self.input_shape, self.output_shape) # compile the model self.model.compile(optimizer=optimizer, loss=loss_function, metrics=['accuracy']) # save the summary of the model if required if save_summary & self.logs: with open(self.log_dir / 'summary.txt', 'w') as f: with redirect_stdout(f): self.model.summary() # execute "on before train" operations self._on_before_train() # train the model history = self.model.fit(training_data, steps_per_epoch=len(training_data), epochs=epochs, validation_data=validation_data, validation_steps=len(validation_data), callbacks=self._get_callbacks(), workers=4, shuffle=True) # execute "on after train" operations self._on_after_train() model_path = None # save the final model if save_model & self.logs: model_path = self.log_dir / "final-model.h5" self.model.save(model_path) if self.verbose: print("Model saved: {}".format(model_path)) clear_session() return history.history,model_path,self.checkpoint_path

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null

0

null

0

1

0

76ac32571cb948c9489cc7293946eee1deb0de5c

834

py

Python

scripts/create_erc20pool.py

mikaelaakko/staking-pool-ERC20-ERC721

b5e3fe6c5c3051ba25a696afe28d01868c1069b9

[ "MIT" ]

1

2022-02-18T14:37:51.000Z

scripts/create_erc20pool.py

mikaelaakko/staking-pool-ERC20-ERC721

b5e3fe6c5c3051ba25a696afe28d01868c1069b9

[ "MIT" ]

null

scripts/create_erc20pool.py

mikaelaakko/staking-pool-ERC20-ERC721

b5e3fe6c5c3051ba25a696afe28d01868c1069b9

[ "MIT" ]

null

from brownie import ( StakingPoolFactory, StripERC20, WETH, accounts, network, config, ) from scripts.helper_functions import get_account strip_address = "0x0Ff63FbbDEe379B4FDA592Ea869188643Ab4c478" weth_address = "0x55eD4d3A07e41D446A4213C797057b10A53B9e79" week_seconds = 604800 def deploy_factory(duration): account = get_account() strip = StripERC20.at(strip_address) weth = WETH.at(weth_address) factory = StakingPoolFactory[-1] staking_factory_contract = factory.createERC20StakingPool( strip, weth, duration, {"from": account} ) print(f"Pool {staking_factory_contract} created!") def main(): name = "Strip staking pool" symbol = "SSP" name_hex = "0x5374726970207374616B696E6720706F6F6C" symbol_hex = "0x535350" deploy_factory(week_seconds)

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834

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1

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false

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0.148148

0.037037

0

null

0

null

0

1

0

76ad26393ad43403e265611493e5f79435924719

16,954

py

Python

generator/generate.py

glynnc/liblcf

301371de7d8e39f30c464ace355252b58beb71ee

[ "MIT" ]

null

generator/generate.py

glynnc/liblcf

301371de7d8e39f30c464ace355252b58beb71ee

[ "MIT" ]

null

generator/generate.py

glynnc/liblcf

301371de7d8e39f30c464ace355252b58beb71ee

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import division import sys import os import re gen_dir = os.path.dirname(os.path.abspath(__file__)) csv_dir = os.path.join(gen_dir, "csv") tmpl_dir = os.path.join(gen_dir, "templates") dest_dir = os.path.abspath(os.path.join(gen_dir, "..", "src", "generated")) class Template(object): def __init__(self, filename): with open(os.path.join(tmpl_dir, filename), 'r') as f: name = None value = None for line in f: if line[0] == '@': if name is not None: setattr(self, name, value) name = line[1:].rstrip('\r\n') value = '' else: value += line if name is not None: setattr(self, name, value) copy = Template('copyright.tmpl') reader = Template('reader.tmpl') ctor = Template('constructor.tmpl') decl = Template('declaration.tmpl') decl2 = Template('declaration.tmpl') chunk = Template('chunks.tmpl') freader = Template('flag_reader.tmpl') decl2.enum_header = decl.enum2_header decl2.enum_tmpl = decl.enum2_tmpl decl2.enum_footer = decl.enum2_footer cpp_types = { 'Boolean': 'bool', 'Double': 'double', 'Integer': 'int', 'UInt8': 'uint8_t', 'UInt16': 'uint16_t', 'UInt32': 'uint32_t', 'Int16': 'int16_t', 'String': 'std::string', } def flags_def(struct_name): f = ['\t\t\tbool %s;\n' % name for name in flags[struct_name]] return 'struct Flags {\n' + ''.join(f) + '\t\t}' def cpp_type(ty, prefix = True, expand_flags = None): if ty in cpp_types: return cpp_types[ty] m = re.match(r'Array<(.*):(.*)>', ty) if m: return 'std::vector<%s>' % cpp_type(m.group(1), prefix, expand_flags) m = re.match(r'(Vector|Array)<(.*)>', ty) if m: return 'std::vector<%s>' % cpp_type(m.group(2), prefix, expand_flags) m = re.match(r'Ref<(.*):(.*)>', ty) if m: return cpp_type(m.group(2), prefix, expand_flags) m = re.match(r'Ref<(.*)>', ty) if m: return 'int' m = re.match(r'Enum<(.*)>', ty) if m: return 'int' m = re.match(r'(.*)_Flags$', ty) if m: if expand_flags: return flags_def(expand_flags) else: ty = m.expand(r'\1::Flags') if prefix: ty = 'RPG::' + ty return ty if prefix: ty = 'RPG::' + ty return ty int_types = { 'UInt8': 'uint8_t', 'UInt16': 'uint16_t', 'UInt32': 'uint32_t', 'Int16': 'int16_t', } def struct_headers(ty, header_map): if ty == 'String': return ['<string>'] if ty in int_types: return ['"reader_types.h"'] if ty in cpp_types: return [] m = re.match(r'Ref<(.*):(.*)>', ty) if m: return struct_headers(m.group(2), header_map) if re.match(r'Ref<(.*)>', ty): return [] if re.match(r'Enum<(.*)>', ty): return [] if re.match(r'(.*)_Flags$', ty): return [] m = re.match(r'Array<(.*):(.*)>', ty) if m: return ['<vector>'] + struct_headers(m.group(1), header_map) m = re.match(r'(Vector|Array)<(.*)>', ty) if m: return ['<vector>'] + struct_headers(m.group(2), header_map) header = header_map.get(ty) if header is not None: return ['"rpg_%s.h"' % header] if ty in ['Parameters', 'Equipment', 'EventCommand', 'MoveCommand', 'Rect', 'TreeMap']: return ['"rpg_%s.h"' % ty.lower()] return [] def get_structs(filename = 'structs.csv'): result = [] with open(os.path.join(csv_dir, filename), 'r') as f: for line in f: sline = line.strip() if not sline: continue if sline.startswith("#"): continue data = sline.split(',') filetype, structname, hasid = data hasid = bool(int(hasid)) if hasid else None filename = structname.lower() result.append((filetype, filename, structname, hasid)) return result def get_fields(filename = 'fields.csv'): result = {} with open(os.path.join(csv_dir, filename), 'r') as f: for line in f: sline = line.strip() if not sline: continue if sline.startswith("#"): continue data = sline.split(',', 6) struct, fname, issize, ftype, code, dfl, comment = data issize = issize.lower() == 't' code = int(code, 16) if code else None if struct not in result: result[struct] = [] result[struct].append((fname, issize, ftype, code, dfl, comment)) return result def get_enums(filename = 'enums.csv'): enums = {} fields = {} with open(os.path.join(csv_dir, filename), 'r') as f: for line in f: sline = line.strip() if not sline: continue if sline.startswith("#"): continue data = sline.split(',') sname, ename, name, num = data num = int(num) if (sname, ename) not in fields: if sname not in enums: enums[sname] = [] enums[sname].append(ename) fields[sname, ename] = [] fields[sname, ename].append((name, num)) return enums, fields def get_flags(filename = 'flags.csv'): result = {} with open(os.path.join(csv_dir, filename), 'r') as f: for line in f: sline = line.strip() if not sline: continue if sline.startswith("#"): continue data = sline.split(',') struct, fname = data if struct not in result: result[struct] = [] result[struct].append(fname) return result def get_setup(filename = 'setup.csv'): result = {} with open(os.path.join(csv_dir, filename), 'r') as f: for line in f: sline = line.strip() if not sline: continue if sline.startswith("#"): continue data = sline.split(',') struct, method, headers = data headers = headers.split(' ') if headers else [] if struct not in result: result[struct] = [] result[struct].append((method, headers)) return result def get_headers(structs, sfields, setup): header_map = dict([(struct_name, filename) for filetype, filename, struct_name, hasid in structs]) result = {} for filetype, filename, struct_name, hasid in structs: if struct_name not in sfields: continue headers = set() for field in sfields[struct_name]: fname, issize, ftype, code, dfl, comment = field if not ftype: continue headers.update(struct_headers(ftype, header_map)) if struct_name in setup: for method, hdrs in setup[struct_name]: headers.update(hdrs) result[struct_name] = sorted(x for x in headers if x[0] == '<') + sorted(x for x in headers if x[0] == '"') return result def write_enums(sname, f): for ename in enums[sname]: dcl = decl2 if (sname, ename) in [('MoveCommand','Code'),('EventCommand','Code')] else decl evars = dict(ename = ename) f.write(dcl.enum_header % evars) ef = efields[sname, ename] n = len(ef) for i, (name, num) in enumerate(ef): comma = '' if i == n - 1 else ',' vars = dict(ename = ename, name = name, num = num, comma = comma) f.write(dcl.enum_tmpl % vars) f.write(dcl.enum_footer % evars) f.write('\n') def write_setup(sname, f): for method, headers in setup[sname]: f.write('\t\t%s;\n' % method) def generate_reader(f, struct_name, vars): f.write(copy.header) f.write(reader.header % vars) for field in sfields[struct_name]: fname, issize, ftype, code, dfl, comment = field if not ftype: continue fvars = dict( ftype = cpp_type(ftype), fname = fname) if issize: f.write(reader.size_tmpl % fvars) else: f.write(reader.typed_tmpl % fvars) f.write(reader.footer % vars) def write_flags(f, sname, fname): for name in flags[sname]: fvars = dict( fname = fname, name = name) f.write(ctor.flags % fvars) def generate_ctor(f, struct_name, hasid, vars): f.write(copy.header) f.write(ctor.header % vars) if hasid: f.write(ctor.tmpl % dict(fname = 'ID', default = '0')) for field in sfields[struct_name]: fname, issize, ftype, code, dfl, comment = field if not ftype: continue if issize: continue if ftype.endswith('_Flags'): write_flags(f, struct_name, fname) continue if dfl == '': continue if ftype.startswith('Vector'): continue if ftype.startswith('Array'): continue if ftype == 'Boolean': dfl = dfl.lower() elif ftype == 'String': dfl = '"' + dfl[1:-1] + '"' if '|' in dfl: # dfl = re.sub(r'(.*)\|(.*)', r'\1', dfl) dfl = -1 fvars = dict( fname = fname, default = dfl) f.write(ctor.tmpl % fvars) if struct_name in setup and any('Init()' in method for method, hdrs in setup[struct_name]): f.write('\n\tInit();\n') f.write(ctor.footer % vars) def needs_ctor(struct_name, hasid): if hasid: return True for field in sfields[struct_name]: fname, issize, ftype, code, dfl, comment = field if not ftype: continue if issize: continue if ftype.endswith('_Flags'): return True if dfl != '': return True return False def generate_header(f, struct_name, hasid, vars): f.write(copy.header) f.write(decl.header1 % vars) if headers[struct_name]: f.write(decl.header2) for header in headers[struct_name]: f.write(decl.header_tmpl % dict(header = header)) f.write(decl.header3 % vars) if struct_name in enums: write_enums(struct_name, f) needs_blank = False if needs_ctor(struct_name, hasid): f.write(decl.ctor % vars) needs_blank = True if struct_name in setup: write_setup(struct_name, f) needs_blank = True if needs_blank: f.write('\n') if hasid: f.write(decl.tmpl % dict(ftype = 'int', fname = 'ID')) for field in sfields[struct_name]: fname, issize, ftype, code, dfl, comment = field if not ftype: continue if issize: continue fvars = dict( ftype = cpp_type(ftype, False, struct_name), fname = fname) f.write(decl.tmpl % fvars) f.write(decl.footer % vars) def generate_chunks(f, struct_name, vars): f.write(chunk.header % vars) mwidth = max(len(field[0] + ('_size' if field[1] else '')) for field in sfields[struct_name]) + 1 mwidth = (mwidth + 3) // 4 * 4 # print struct_name, mwidth sf = sfields[struct_name] n = len(sf) for i, field in enumerate(sf): fname, issize, ftype, code, dfl, comment = field if issize: fname += '_size' pad = mwidth - len(fname) ntabs = (pad + 3) // 4 tabs = '\t' * ntabs comma = ' ' if i == n - 1 else ',' fvars = dict( fname = fname, tabs = tabs, code = code, comma = comma, comment = comment) f.write(chunk.tmpl % fvars) f.write(chunk.footer % vars) def generate_struct(filetype, filename, struct_name, hasid): if struct_name not in sfields: return vars = dict( filetype = filetype, filename = filename, typeupper = filetype.upper(), structname = struct_name, structupper = struct_name.upper(), idtype = ['NoID','WithID'][hasid]) filepath = os.path.join(dest_dir, '%s_%s.cpp' % (filetype, filename)) with open(filepath, 'w') as f: generate_reader(f, struct_name, vars) if needs_ctor(struct_name, hasid): filepath = os.path.join(dest_dir, 'rpg_%s.cpp' % filename) with open(filepath, 'w') as f: generate_ctor(f, struct_name, hasid, vars) filepath = os.path.join(dest_dir, 'rpg_%s.h' % filename) with open(filepath, 'w') as f: generate_header(f, struct_name, hasid, vars) filepath = os.path.join(dest_dir, '%s_chunks.h' % filetype) with open(filepath, 'a') as f: generate_chunks(f, struct_name, vars) def generate_rawstruct(filename, struct_name): vars = dict( filename = filename, structname = struct_name, structupper = struct_name.upper()) if needs_ctor(struct_name, False): filepath = os.path.join(dest_dir, 'rpg_%s.cpp' % filename) with open(filepath, 'w') as f: generate_ctor(f, struct_name, False, vars) filepath = os.path.join(dest_dir, 'rpg_%s.h' % filename) with open(filepath, 'w') as f: generate_header(f, struct_name, False, vars) def generate_flags(filetype, filename, struct_name): maxsize = (len(flags[struct_name]) + 7) // 8 maxwidth = max(len(fname) for fname in flags[struct_name]) maxwidth = (maxwidth + 2 + 3) // 4 * 4 vars = dict( filetype = filetype, filename = filename, structname = struct_name, structupper = struct_name.upper(), maxsize = maxsize ) filepath = os.path.join(dest_dir, '%s_%s_flags.cpp' % (filetype, filename)) with open(filepath, 'w') as f: f.write(copy.header) f.write(freader.header % vars) for fname in flags[struct_name]: width = len(fname) pad1 = maxwidth - width - 2 tabs1 = (pad1 + 3) // 4 pad2 = maxwidth - width - 2 tabs2 = (pad2 + 3) // 4 fvars = dict( fname = fname, pad1 = '\t' * tabs1, pad2 = '\t' * tabs2) f.write(freader.tmpl % fvars) f.write(freader.footer % vars) def generate(): for filetype in ['ldb','lmt','lmu','lsd']: vars = dict( filetype = filetype, typeupper = filetype.upper()) filepath = os.path.join(dest_dir, '%s_chunks.h' % filetype) with open(filepath, 'w') as f: f.write(copy.header) f.write(chunk.file_header % vars) for filetype, filename, struct_name, hasid in structs: if hasid is not None: generate_struct(filetype, filename, struct_name, hasid) else: generate_rawstruct(filename, struct_name) if struct_name in flags: generate_flags(filetype, filename, struct_name) for filetype in ['ldb','lmt','lmu','lsd']: filepath = os.path.join(dest_dir, '%s_chunks.h' % filetype) with open(filepath, 'a') as f: f.write(chunk.file_footer) def list_files_struct(filetype, filename, struct_name, hasid): if struct_name not in sfields: return print('%s_%s.cpp' % (filetype, filename)) if needs_ctor(struct_name, hasid): print('rpg_%s.cpp' % filename) print('rpg_%s.h' % filename) def list_files_rawstruct(filename, struct_name): if needs_ctor(struct_name, False): print('rpg_%s.cpp' % filename) print('rpg_%s.h' % filename) def list_files_flags(filetype, filename, struct_name): print('%s_%s_flags.cpp' % (filetype, filename)) def list_files(): for filetype in ['ldb','lmt','lmu','lsd']: print('%s_chunks.h' % filetype) for filetype, filename, struct_name, hasid in structs: if hasid is not None: list_files_struct(filetype, filename, struct_name, hasid) else: list_files_rawstruct(filename, struct_name) if struct_name in flags: list_files_flags(filetype, filename, struct_name) def main(argv): if not os.path.exists(dest_dir): os.mkdir(dest_dir) global structs, sfields, enums, efields, flags, setup, headers structs = get_structs() sfields = get_fields() enums, efields = get_enums() flags = get_flags() setup = get_setup() headers = get_headers(structs, sfields, setup) if argv[1:] == ['-l']: list_files() else: generate() if __name__ == '__main__': main(sys.argv)

31.051282

115

0.548543

2,112

16,954

4.283144

0.100852

0.077382

0.019898

0.03449

0.561132

0.522773

0.4392

0.37652

0.354742

0.298032

0

0.007884

0.319158

16,954

545

116

31.108257

0.775795

0.005073

0

0.474684

0

0.064157

0

1

0.056962

false

0

0.008439

0

0.139241

0.014768

0

null

0

null

0

1

0

76af6ec2112ed2e11ec23b213a1caad36b51f515

4,597

py

Python

application.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

9

2020-03-30T08:27:57.000Z

2020-04-11T12:37:28.000Z

application.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

145

2020-03-25T20:41:24.000Z

2020-04-15T17:39:10.000Z

application.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

4

2020-03-22T09:57:27.000Z

2020-04-15T18:10:48.000Z

import os import time from uptime import uptime from tkinter import Tk from data.configurations import ConfigurationManager from graphics.panes import MasterFrame from graphics.themes import Theme from graphics.calibrate.screen import calc_calibration_line from graphics.constants import SCREEN_WIDTH, SCREEN_HEIGHT from graphics.snackbar.default_config_snackbar import DefaultConfigSnackbar class Application(object): """The Inhalator application""" TEXT_SIZE = 10 HARDWARE_SAMPLE_RATE = 33 # HZ __instance = None # shared instance def __new__(cls, *args, **kwargs): if cls.__instance is None: cls.__instance = object.__new__(cls) return cls.__instance @classmethod def instance(cls): return cls.__instance def __init__(self, measurements, events, arm_wd_event, drivers, sampler, simulation=False, fps=10, sample_rate=70, record_sensors=False): self.should_run = True self.drivers = drivers self.arm_wd_event = arm_wd_event self.sampler = sampler self.simulation = simulation self.events = events self.frame_interval = 1 / fps self.sample_interval = 1 / sample_rate self.last_sample_update_ts = 0 self.last_gui_update_ts = 0 self.root = Tk() self.theme = Theme.choose_theme() # TODO: Make this configurable self.root.protocol("WM_DELETE_WINDOW", self.exit) # Catches Alt-F4 self.root.title("Inhalator") self.root.geometry(f'{SCREEN_WIDTH}x{SCREEN_HEIGHT}') if os.uname()[1] == 'raspberrypi': # on production we don't want to see the ugly cursor self.root.config(cursor="none") # We want fullscreen only for the raspberry-pi self.root.attributes("-fullscreen", True) self.master_frame = MasterFrame(self.root, measurements=measurements, events=events, drivers=drivers, record_sensors=record_sensors) self.config = ConfigurationManager.config() if ConfigurationManager.loaded_from_defaults: DefaultConfigSnackbar(self.root).show() # Load sensors calibrations differential_pressure_driver = self.drivers.differential_pressure differential_pressure_driver.set_calibration_offset(self.config.calibration.dp_offset) oxygen_driver = self.drivers.a2d oxygen_driver.set_oxygen_calibration( *calc_calibration_line( self.config.calibration.oxygen_point1, self.config.calibration.oxygen_point2)) def exit(self): self.root.quit() self.should_run = False def render(self): self.master_frame.render() self.events.alerts_queue.initial_uptime = uptime() def gui_update(self): self.root.update() self.root.update_idletasks() self.master_frame.update() def sample(self): self.sampler.sampling_iteration() @property def next_render(self): return self.frame_interval - (time.time() - self.last_gui_update_ts) @property def next_sample(self): return self.sample_interval - (time.time() - self.last_sample_update_ts) def run(self): self.render() while self.should_run: try: time_now = time.time() if (time_now - self.last_gui_update_ts) >= self.frame_interval: self.gui_update() self.last_gui_update_ts = time_now if (time_now - self.last_sample_update_ts) >= self.sample_interval: self.sample() self.last_sample_update_ts = time_now self.arm_wd_event.set() except KeyboardInterrupt: break self.exit() def run_iterations(self, max_iterations, fast_forward=True, render=True): if render: self.render() for _ in range(max_iterations): try: if self.next_sample > 0 and not fast_forward: time.sleep(max(self.next_sample, 0)) self.sample() self.last_sample_update_ts = time.time() if self.next_render <= 0: self.gui_update() self.last_gui_update_ts = time.time() self.arm_wd_event.set() except KeyboardInterrupt: break

34.56391

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0.614096

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5.221359

0.300971

0.032726

0.018594

0.037189

0.151357

0.087021

0.026776

0

0.006268

0.305852

4,597

132

95

34.825758

0.836415

0.0459

0

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0

0.018519

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0

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1

0.105769

false

0

0.096154

0.028846

0.278846

0

null

0

null

0

1

0

76b0222ce19734afd389fa8ad840fc2aee9b9b1f

440

py

Python

functions/save.py

notsys/minecraft-checker

da91ac6c8b7c8f7e1a7843dba87ad056c88c37de

[ "MIT" ]

null

functions/save.py

notsys/minecraft-checker

da91ac6c8b7c8f7e1a7843dba87ad056c88c37de

[ "MIT" ]

null

functions/save.py

notsys/minecraft-checker

da91ac6c8b7c8f7e1a7843dba87ad056c88c37de

[ "MIT" ]

null

from tabulate import tabulate import os import functions.menu as menu def save(y,available,blocked,upcoming,taken): menu.menu() print(f'{taken} taken | {available} available | {blocked} blocked | {upcoming} upcoming\n') headers = [f'{available} available', f'{blocked} blocked', f'{upcoming} upcoming'] r = tabulate(y, headers=headers,tablefmt="psql") f = open("result.txt", "w") f.write(r) f.close()

29.333333

95

0.661364

58

440

5.017241

0.465517

0.09622

0

0.184091

440

15

96

29.333333

0.810585

0

0.090909

0.346939

0

1

0.090909

false

0

0.272727

0

0.363636

0.090909

0

null

0

null

0

1

0

76b032c0e71e5f826f28b65e0f1e154015953c98

547

py

Python

samples/s9.py

AndreiHondrari/python_exploration

cb4ac0b92ddc48c322201ba31cd6e7c5ee6af06d

[ "MIT" ]

3

2019-05-04T12:19:09.000Z

2019-08-30T07:12:31.000Z

samples/s9.py

AndreiHondrari/python_exploration

cb4ac0b92ddc48c322201ba31cd6e7c5ee6af06d

[ "MIT" ]

null

samples/s9.py

AndreiHondrari/python_exploration

cb4ac0b92ddc48c322201ba31cd6e7c5ee6af06d

[ "MIT" ]

null

# prng sequence guessing from collections import namedtuple N = input() sequences = {} TsPair = namedtuple("TimestampsPair", ['t1', 't2']) if N < 10: for i in range(N): timestamps = raw_input() timestamps = map(int, timestamps.split(' ')) timestamps = TsPair(t1=timestamps[0], t2=timestamps[1]) if timestamps[0] - timestamps[1] <= 10**6: numbers = [] for j in range(2): numbers.append(input()) sequences[timestamps] = numbers print(sequences)

19.535714

63

0.572212

60

547

5.2

0.55

0.089744

0

0.036269

0.294333

547

27

64

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0.772021

0.040219

0

0.036468

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0.066667

0

0.066667

0

null

0

null

0

1

0

76b107c2aa48ea70becd8b93119d8cbb71c32586

3,268

py

Python

tests/tracking/default_experiment/test_databricks_notebook_experiment_provider.py

adamreeve/mlflow

d0d307f7f7b49f013727191a672ae2139bf37343

[ "Apache-2.0" ]

1

2022-01-11T02:51:17.000Z

tests/tracking/default_experiment/test_databricks_notebook_experiment_provider.py

adamreeve/mlflow

d0d307f7f7b49f013727191a672ae2139bf37343

[ "Apache-2.0" ]

null

tests/tracking/default_experiment/test_databricks_notebook_experiment_provider.py

adamreeve/mlflow

d0d307f7f7b49f013727191a672ae2139bf37343

[ "Apache-2.0" ]

null

from unittest import mock from mlflow.exceptions import MlflowException from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE from mlflow.tracking import MlflowClient from mlflow.tracking.default_experiment.databricks_notebook_experiment_provider import ( DatabricksNotebookExperimentProvider, DatabricksRepoNotebookExperimentProvider, ) from mlflow.utils.mlflow_tags import MLFLOW_EXPERIMENT_SOURCE_TYPE, MLFLOW_EXPERIMENT_SOURCE_ID def test_databricks_notebook_default_experiment_in_context(): with mock.patch("mlflow.utils.databricks_utils.is_in_databricks_notebook") as in_notebook_mock: assert DatabricksNotebookExperimentProvider().in_context() == in_notebook_mock.return_value def test_databricks_notebook_default_experiment_id(): with mock.patch("mlflow.utils.databricks_utils.get_notebook_id") as patch_notebook_id: assert ( DatabricksNotebookExperimentProvider().get_experiment_id() == patch_notebook_id.return_value ) def test_databricks_repo_notebook_default_experiment_in_context(): with mock.patch( "mlflow.utils.databricks_utils.is_in_databricks_repo_notebook" ) as in_repo_notebook_mock: in_repo_notebook_mock.return_value = True assert DatabricksRepoNotebookExperimentProvider().in_context() with mock.patch( "mlflow.utils.databricks_utils.is_in_databricks_repo_notebook" ) as not_in_repo_notebook_mock: not_in_repo_notebook_mock.return_value = False assert not DatabricksRepoNotebookExperimentProvider().in_context() def test_databricks_repo_notebook_default_experiment_gets_id_by_request(): with mock.patch( "mlflow.utils.databricks_utils.get_notebook_id" ) as notebook_id_mock, mock.patch( "mlflow.utils.databricks_utils.get_notebook_path" ) as notebook_path_mock, mock.patch.object( MlflowClient, "create_experiment" ) as create_experiment_mock: notebook_id_mock.return_value = 1234 notebook_path_mock.return_value = "/Repos/path" create_experiment_mock.return_value = "experiment_id" returned_id = DatabricksRepoNotebookExperimentProvider().get_experiment_id() assert returned_id == "experiment_id" tags = {MLFLOW_EXPERIMENT_SOURCE_TYPE: "REPO_NOTEBOOK", MLFLOW_EXPERIMENT_SOURCE_ID: 1234} create_experiment_mock.assert_called_once_with("/Repos/path", None, tags) def test_databricks_repo_notebook_default_experiment_uses_fallback_notebook_id(): with mock.patch( "mlflow.utils.databricks_utils.get_notebook_id" ) as notebook_id_mock, mock.patch( "mlflow.utils.databricks_utils.get_notebook_path" ) as notebook_path_mock, mock.patch.object( MlflowClient, "create_experiment" ) as create_experiment_mock: DatabricksRepoNotebookExperimentProvider._resolved_repo_notebook_experiment_id = None notebook_id_mock.return_value = 1234 notebook_path_mock.return_value = "/Repos/path" create_experiment_mock.side_effect = MlflowException( message="not enabled", error_code=INVALID_PARAMETER_VALUE ) returned_id = DatabricksRepoNotebookExperimentProvider().get_experiment_id() assert returned_id == 1234

45.388889

99

0.779988

374

3,268

6.355615

0.165775

0.055532

0.050484

0.067312

0.56037

0.541018

0.491796

0.444257

0.376104

0

0.006151

0.154223

3,268

71

100

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0.853835

0

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0

0.1

0

0.183333

0

null

0

null

0

1

0

76b258efcb378ef005eb36ab0863fee39f30dbca

873

py

Python

pullfaces.py

dinuka-rp/Python-Face_recognition

8e5d39b54d979868a6a6cf4c2b71e10b8dadd181

[ "MIT" ]

1

2019-10-23T06:33:11.000Z

pullfaces.py

dinuka-rp/Python-Face_recognition

8e5d39b54d979868a6a6cf4c2b71e10b8dadd181

[ "MIT" ]

2

2021-06-08T20:27:35.000Z

2021-09-08T01:22:09.000Z

pullfaces.py

DinDev3/Python-Face_recognition

337f17f85173fda3a5d91896ddc5be70b33ed2fc

[ "MIT" ]

null

# This program identifies multiple faces in an image , displays and saves them from PIL import Image #implementing the Pillow library (Imaging library) import face_recognition image = face_recognition.load_image_file('./img/groups/team.jpg') face_locations = face_recognition.face_locations(image) #get locations of faces in image for face_location in face_locations: top, right, bottom, left = face_location #assigning co-ordinates of one face location to seperate variables face_image = image[top:bottom,left:right] #gives a face image in a form of an array pil_image = Image.fromarray(face_image) # pil_image.show() #to display the identified faces in an image pil_image.save(f'{top}.jpg') #saving the faces identified in an image with the top co-ordinate of the face given as the file name

54.5625

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0.729668

130

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0.461538

0.033762

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0.045016

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873

16

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54.5625

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0

0.222222

0

null

0

null

0

1

0

76b677133fcbae61e2dea9e942a4312c344c21e4

8,692

py

Python

models.py

efratkohen/Project

d95d20a1be8fe0e0918b3e699c640f36704639f8

[ "MIT" ]

1

2020-07-25T11:27:17.000Z

models.py

efratkohen/Project

d95d20a1be8fe0e0918b3e699c640f36704639f8

[ "MIT" ]

null

models.py

efratkohen/Project

d95d20a1be8fe0e0918b3e699c640f36704639f8

[ "MIT" ]

null

from enum import Enum from keras import backend as K, Sequential, Input, Model from keras.callbacks import EarlyStopping, ModelCheckpoint, TerminateOnNaN from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers import ( Permute, Dense, multiply, LSTM, Bidirectional, Conv1D, MaxPooling1D, Flatten, TimeDistributed, RepeatVector, Dropout, GRU, AveragePooling1D, ) from matplotlib import pyplot, pyplot as plt import enum class ModelType(enum.Enum): SIMPLE_LSTM = 1 STACKED_LSTM = 2 BIDRECTIONAL_LSTM = 3 CNN = 4 CNN_LSTM = 5 LSTM_AUTOENCODER = 6 DEEP_CNN = 7 GRU = 8 GRU_CNN = 9 def attention_block(inputs, time_steps): x = Permute((2, 1))(inputs) x = Dense(time_steps, activation="softmax")(x) x = Permute((2, 1), name="attention_prob")(x) x = multiply([inputs, x]) return x def get_activation(model, layer_name, inputs): layer = [l for l in model.layers if l.name == layer_name][0] func = K.function([model.input], [layer.output]) return func([inputs])[0] def make_model( model_type, Xtrain, Ytrain, opt="adam", loss_func="mse", summary=False, binary=False ): if binary: LAST_ACTIVATION = "sigmoid" else: LAST_ACTIVATION = "linear" print(model_type) if model_type is ModelType.SIMPLE_LSTM: print(model_type) # Single cell LSTM model = Sequential() model.add( LSTM( units=100, activation="relu", name="first_lstm", recurrent_dropout=0.1, input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(Dense(1, activation=LAST_ACTIVATION)) elif model_type is ModelType.STACKED_LSTM: # Stacked LSTM model = Sequential() model.add( LSTM( 100, activation="relu", return_sequences=True, recurrent_dropout=0.1, input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add( LSTM(50, activation="relu", return_sequences=True, recurrent_dropout=0.1) ) model.add(LSTM(30, activation="relu", recurrent_dropout=0.2)) model.add(Dense(1, activation=LAST_ACTIVATION)) elif model_type is ModelType.BIDRECTIONAL_LSTM: # Bidirectional LSTM model = Sequential() model.add(Bidirectional(LSTM(100, return_sequences=True, activation="relu"))) model.add(Bidirectional(LSTM(50, return_sequences=True, activation="relu"))) model.add(Bidirectional(LSTM(20, activation="relu"))) model.add(Dense(1, activation=LAST_ACTIVATION)) elif model_type is ModelType.CNN: model = Sequential() model.add( Conv1D( filters=128, kernel_size=2, activation="relu", name="extractor", input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(Dropout(0.5)) model.add(MaxPooling1D(pool_size=2)) model.add(Conv1D(filters=64, kernel_size=2, activation="relu")) model.add(Dropout(0.5)) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(50, activation="relu")) model.add(Dense(1, activation=LAST_ACTIVATION)) elif model_type is ModelType.CNN_LSTM: model = Sequential() model.add( Conv1D( filters=256, kernel_size=5, padding="same", activation="relu", input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(Conv1D(filters=256, kernel_size=5, padding="same", activation="relu")) model.add(MaxPooling1D(pool_size=4)) model.add(Conv1D(filters=256, kernel_size=5, padding="same", activation="relu")) model.add(MaxPooling1D(pool_size=4)) model.add(LSTM(100)) model.add(Dropout(0.5)) model.add(Dense(100)) model.add(Dense(1, activation=LAST_ACTIVATION)) elif model_type is ModelType.LSTM_AUTOENCODER: model = Sequential() model.add( Conv1D( filters=128, kernel_size=2, activation="relu", name="extractor", input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(Dropout(0.3)) model.add(MaxPooling1D(pool_size=2)) model.add( Bidirectional( LSTM( 50, activation="relu", input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) ) model.add(RepeatVector(10)) model.add(Bidirectional(LSTM(50, activation="relu"))) model.add(Dense(1)) elif model_type is ModelType.DEEP_CNN: model = Sequential() model.add( TimeDistributed( Conv1D(filters=64, kernel_size=2, activation="relu"), input_shape=(None, Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(TimeDistributed(Conv1D(filters=64, kernel_size=2, activation="relu"))) model.add(TimeDistributed(Dropout(0.5))) model.add(TimeDistributed(MaxPooling1D(pool_size=2))) model.add(TimeDistributed(Flatten())) model.add(LSTM(100)) model.add(Dropout(0.5)) model.add(Dense(100, activation="relu")) model.add(Dense(1, activation="softmax")) elif model_type is ModelType.GRU: model = Sequential() model.add( GRU( 75, return_sequences=True, input_shape=(Xtrain.shape[1], Xtrain.shape[2]), ) ) model.add(GRU(units=30, return_sequences=True)) model.add(GRU(units=30)) model.add(Dense(units=1, activation=LAST_ACTIVATION)) elif model_type is ModelType.GRU_CNN: inp_seq = Input(shape=(Xtrain.shape[1], Xtrain.shape[2])) x = Bidirectional(GRU(100, return_sequences=True))(inp_seq) x = AveragePooling1D(2)(x) x = Conv1D(100, 3, activation="relu", padding="same", name="extractor")(x) x = Flatten()(x) x = Dense(16, activation="relu")(x) x = Dropout(0.5)(x) out = Dense(1, activation=LAST_ACTIVATION)(x) model = Model(inp_seq, out) else: print("ERROR ", model_type) return None model.compile(loss=loss_func, optimizer=opt) # fit network if summary: model.summary() return model def fit( Xtrain, Ytrain, Xtest, Ytest, model, epochs=20, batch_size=32, graph=False, binary=False, pos_weight=1, verbose=1, ): min_es = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10) checkpoint_es = ModelCheckpoint( filepath="C:\\Users\\nitza\\Local\\WWTP\\models\\model.{epoch:02d}-{val_loss:.5f}.h5" ) nan_es = TerminateOnNaN() history = model.fit( Xtrain, Ytrain, epochs=epochs, batch_size=batch_size, validation_data=(Xtest, Ytest), verbose=verbose, shuffle=True, use_multiprocessing=False, callbacks=[nan_es, min_es], ) # plot history if graph: pyplot.plot(history.history["loss"], label="train") pyplot.plot(history.history["val_loss"], label="test") plt.legend() plt.show() return model def evaluate(model, Xtest, Ytest, scalers, binary=False): Yhat = model.predict(Xtest) if not binary: Yhat = scalers[-1].inverse_transform(Yhat) Ytest = scalers[-1].inverse_transform(Ytest) return Yhat, Ytest def var_importance(names, model, X, Y, size, minimum): res = dict() orig_out = model.predict(X) for i in range(min(size, len(names))): new_x = X.copy() perturbation = np.random.normal(0.0, 0.2, size=new_x.shape[:2]) new_x[:, :, i] = new_x[:, :, i] + perturbation perturbed_out = model.predict(new_x) f1_orig = metrics.calc_rmse(Y, orig_out, graph=False) f1_pertubed = metrics.calc_rmse(Y, perturbed_out, graph=False) effect = f1_orig - f1_pertubed effect = -effect res[names[i]] = effect print(f"Variable {names[i]}, perturbation effect: {effect:.4f}") return res var_table = var_importance( mdfs[-1].columns[:], model, Xtrain, Ytrain, Xtrain.shape[2] - 1, 0.002 )

29.665529

93

0.580994

1,016

8,692

4.855315

0.200787

0.077843

0.023718

0.044598

0.443341

0.412933

0.385364

0.362254

0.342996

0.29252

0

0.033442

0.294754

8,692

292

94

29.767123

0.771289

0.008399

0

0.301587

0

0.003968

0.040757

0.008593

0

1

0.02381

false

0

0.039683

0

0.130952

0.015873

0

null

0

null

0

1

0

76b732fabc69f97a04fe1ec88757dc0068f33d8f

1,076

py

Python

examples/run.py

haloship/rec-sys-dynamics

886095eca8c71cc2f30d64f0b1da9a0a8f2f37f5

[ "MIT" ]

null

examples/run.py

haloship/rec-sys-dynamics

886095eca8c71cc2f30d64f0b1da9a0a8f2f37f5

[ "MIT" ]

null

examples/run.py

haloship/rec-sys-dynamics

886095eca8c71cc2f30d64f0b1da9a0a8f2f37f5

[ "MIT" ]

null

import path_resolver import argparse from src.analysis.cluster import movielens, cluster, analysis, post_process from src.analysis.simulate import simulate parser = argparse.ArgumentParser( description="Example script to replicate results obtained on user dynamics in recommender systems" ) parser.add_argument('algo', help= "Name of algorithm: 'ease', 'cosin', or 'mf'") parser.add_argument('dataset', help= "Name of dataset: \ 'All_Neutral',\ '1_Biased_Communities_Control', \ '2_Biased_Communities_Control', \ 'Biased_Neutral_Control'") args = parser.parse_args() # FOR All_Neutral run = simulate(str(args.algo), str(args.dataset)) run_output = run.run_dynamics(n_i=10, n_u=0, n_r=30, steps=5, n_clusters = 2) # save the plot_counts() and plot_percent pngs analyse = analysis(run_output[1]) analyse.rename_cluster(1,1000) analyse.plot_counts(show=False, loc=run.run_name+'/counts.png') analyse.plot_percent(show=False, loc=run.run_name+'/percent.png')

39.851852

102

0.695167

144

1,076

4.993056

0.527778

0.025035

0.041725

0.061196

0

0.017261

0.192379

1,076

27

103

39.851852

0.810127

0.055762

0

0.246548

0.076923

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

76ba2a92260fc79d4f8b4eedd73dd3c7c4ae63ad

2,404

py

Python

src/scite/scite/scripts/commandsdoc.py

segafan/wme1_jankavan_tlc_edition-repo

72163931f348d5a2132577930362d297cc375a26

[ "MIT" ]

3

2021-03-28T00:11:48.000Z

2022-01-12T13:10:52.000Z

src/scite/scite/scripts/commandsdoc.py

segafan/wme1_jankavan_tlc_edition-repo

72163931f348d5a2132577930362d297cc375a26

[ "MIT" ]

null

src/scite/scite/scripts/commandsdoc.py

segafan/wme1_jankavan_tlc_edition-repo

72163931f348d5a2132577930362d297cc375a26

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import with_statement import os, sys scintillaDirectory = os.path.join("..", "..", "scintilla", "include") sys.path.append(scintillaDirectory) import Face def cell(s): return "<td>%s</td>" % s def faceFeatures(out): out.write("<h2>Scintilla key commands</h2>\n") out.write("<table>\n") out.write("<thead>%s%s%s</thead>\n" % (cell("Command"), cell("Name"), cell("Explanation"))) face = Face.Face() face.ReadFromFile(os.path.join(scintillaDirectory, "Scintilla.iface")) texts = [] for name in face.features: #~ print name f = face.features[name] if f["FeatureType"] == "fun" and \ f["ReturnType"] == "void" and \ not (f["Param1Type"] or f["Param2Type"]): texts.append([name, f["Value"], " ".join(f["Comment"])]) texts.sort() for t in texts: out.write("<tr>%s%s%s</tr>\n" % (cell(t[1]), cell(t[0]), cell(t[2]))) out.write("</table>\n") def menuFeatures(out): out.write("<h2>SciTE menu commands</h2>\n") out.write("<table>\n") out.write("<thead>%s%s</thead>\n" % (cell("Command"), cell("Menu text"))) with open(os.path.join("..", "win32", "SciTERes.rc"), "rt") as f: for l in f: l = l.strip() if l.startswith("MENUITEM") and "SEPARATOR" not in l: l = l.replace("MENUITEM", "").strip() text, symbol = l.split('",', 1) symbol = symbol.strip() text = text[1:].replace("&", "").replace("...", "") if "\\t" in text: text = text.split("\\t",1)[0] if text: out.write("<tr><td>%s</td><td>%s</td></tr>\n" % (symbol, text)) out.write("</table>\n") startFile = """ <?xml version="1.0"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml">  <style type="text/css"> table { border: 1px solid #1F1F1F; border-collapse: collapse; } td { border: 1px solid; border-color: #E0E0E0 #000000; padding: 1px 5px 1px 5px; } th { border: 1px solid #1F1F1F; padding: 1px 5px 1px 5px; } thead { background-color: #000000; color: #FFFFFF; } </style> <body> """ if __name__ == "__main__": with open(os.path.join("..", "doc", "CommandValues.html"), "w") as out: out.write(startFile) menuFeatures(out) faceFeatures(out) out.write("</body>\n</html>\n")

33.388889

93

0.596506

343

2,404

4.142857

0.355685

0.067558

0.028149

0.039409

0.138635

0.086559

0.056298

0

0.030638

0.171797

2,404

71

94

33.859155

0.683074

0.013727

0

0.065574

0

0.032787

0.438398

0.046147

0

1

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false

0

0.04918

0.016393

0.114754

0

null

0

null

0

1

0

76bbe9ed61ac92be46c58eb48d6f9f31baf4a00f

829

py

Python

tests/unit/refund_tests.py

LyntServices/gopay-python-api

0bee7da29f0ed9a414142bf5787255e190421da0

[ "MIT" ]

null

tests/unit/refund_tests.py

LyntServices/gopay-python-api

0bee7da29f0ed9a414142bf5787255e190421da0

[ "MIT" ]

null

tests/unit/refund_tests.py

LyntServices/gopay-python-api

0bee7da29f0ed9a414142bf5787255e190421da0

[ "MIT" ]

null

import unittest import gopay from utils import Utils class TestRefund(unittest.TestCase): """ TestRefund class To execute test for certain method properly it is necessary to add prefix 'test' to its name. """ def setUp(self): self.payments = gopay.payments({ 'goid': Utils.GO_ID, 'clientId': Utils.CLIENT_ID, 'clientSecret': Utils.CLIENT_SECRET, 'isProductionMode': False }) def refund_payment(self): payment_id = 3049525986 response = self.payments.refund_payment(payment_id, 1900) if "error_code" not in str(response.json): print('Response: ' + str(response.json)) print('Payment id: ' + str(response.json['id'])) else: print('Error: ' + str(response.json))

25.90625

97

0.600724

93

829

5.268817

0.526882

0.089796

0.122449

0.081633

0

0.02385

0.291918

829

31

98

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0.810903

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0.105263

false

0

0.157895

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0.315789

0.157895

0

null

0

null

0

1

0

76bd559ffa8e901eb2ded58ab32ff20f30927954

5,459

py

Python

texar/data/data/large_file_test.py

lunayach/texar-pytorch

ac3e334e491f524dd01654b07af030fa20c88b34

[ "Apache-2.0" ]

null

texar/data/data/large_file_test.py

lunayach/texar-pytorch

ac3e334e491f524dd01654b07af030fa20c88b34

[ "Apache-2.0" ]

null

texar/data/data/large_file_test.py

lunayach/texar-pytorch

ac3e334e491f524dd01654b07af030fa20c88b34

[ "Apache-2.0" ]

null

import contextlib import resource import time import unittest from typing import List, Optional, Tuple import gc import numpy as np import torch from texar.data.data.data_base import DataBase, DataSource from texar.data.data.data_iterators import DataIterator from texar.data.data.dataset_utils import Batch from texar.data.data.text_data_base import TextLineDataSource from texar.data.vocabulary import Vocab from texar.utils.utils import AnyDict RawExample = str Example = Tuple[np.ndarray, np.ndarray] @contextlib.contextmanager def work_in_progress(msg): print(msg + "... ", flush=True) begin_time = time.time() yield time_consumed = time.time() - begin_time print(f"done. ({time_consumed:.2f}s)", flush=True) class ParallelData(DataBase[RawExample, Example]): def __init__(self, source: DataSource[RawExample], src_vocab_path: str, tgt_vocab_path: str, hparams: AnyDict, device: Optional[torch.device] = None): # hparams.update(parallelize_processing=False) self.src_vocab = Vocab(src_vocab_path) self.tgt_vocab = Vocab(tgt_vocab_path) self.device = device super().__init__(source, hparams) def process(self, raw_example: RawExample) -> Example: src, tgt = raw_example.strip().split('\t') src = self.src_vocab.map_tokens_to_ids_py(src.split()) tgt = self.tgt_vocab.map_tokens_to_ids_py(tgt.split()) return src, tgt def collate(self, examples: List[Example]) -> Batch: src_pad_length = max(len(src) for src, _ in examples) tgt_pad_length = max(len(tgt) for _, tgt in examples) batch_size = len(examples) src_indices = np.zeros((batch_size, src_pad_length), dtype=np.int64) tgt_indices = np.zeros((batch_size, tgt_pad_length), dtype=np.int64) for b_idx, (src, tgt) in enumerate(examples): src_indices[b_idx, :len(src)] = src tgt_indices[b_idx, :len(tgt)] = tgt src_indices = torch.from_numpy(src_indices).to(device=self.device) tgt_indices = torch.from_numpy(tgt_indices).to(device=self.device) return Batch(batch_size, src=src_indices, tgt=tgt_indices) def wrap_progress(func): from tqdm import tqdm return lambda: tqdm(func(), leave=False) def get_process_memory(): return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024 / 1024 @unittest.skip("Manual test only") class LargeFileTest(unittest.TestCase): def setUp(self) -> None: self.source = TextLineDataSource( '../../Downloads/en-es.bicleaner07.txt.gz', compression_type='gzip') self.source.__iter__ = wrap_progress( # type: ignore self.source.__iter__) self.num_workers = 3 self.batch_size = 64 def _test_modes_with_workers(self, lazy_mode: str, cache_mode: str, num_workers: int): from tqdm import tqdm gc.collect() mem = get_process_memory() with work_in_progress(f"Data loading with lazy mode '{lazy_mode}' " f"and cache mode '{cache_mode}' " f"with {num_workers} workers"): print(f"Memory before: {mem:.2f} MB") with work_in_progress("Construction"): data = ParallelData(self.source, '../../Downloads/src.vocab', '../../Downloads/tgt.vocab', {'batch_size': self.batch_size, 'lazy_strategy': lazy_mode, 'cache_strategy': cache_mode, 'num_parallel_calls': num_workers, 'shuffle': False, 'allow_smaller_final_batch': False, 'max_dataset_size': 100000}) print(f"Memory after construction: {mem:.2f} MB") iterator = DataIterator(data) with work_in_progress("Iteration"): for batch in tqdm(iterator, leave=False): self.assertEqual(batch.batch_size, self.batch_size) gc.collect() print(f"Memory after iteration: {mem:.2f} MB") with work_in_progress("2nd iteration"): for batch in tqdm(iterator, leave=False): self.assertEqual(batch.batch_size, self.batch_size) def _test_modes(self, lazy_mode: str, cache_mode: str): self._test_modes_with_workers(lazy_mode, cache_mode, self.num_workers) self._test_modes_with_workers(lazy_mode, cache_mode, 1) def test_none_processed(self): self._test_modes('none', 'processed') def test_process_loaded(self): self._test_modes('process', 'loaded') def test_process_processed(self): self._test_modes('process', 'processed') def test_all_none(self): self._test_modes('all', 'none') def test_all_loaded(self): self._test_modes('all', 'loaded') def test_all_processed(self): self._test_modes('all', 'processed') def _test_all_combinations(self): self.test_none_processed() self.test_process_loaded() self.test_process_processed() self.test_all_none() self.test_all_loaded() self.test_all_processed()

38.174825

78

0.616596

660

5,459

4.824242

0.237879

0.035176

0.032663

0.032035

0.238693

0.123744

0.110553

0.077889

0.052136

0

0.007372

0.279355

5,459

142

79

38.443662

0.801983

0.010441

0

0.068376

0

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0.025375

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false

0

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0.042735

0

null

0

null

0

1

0

76be1e3d498d76e899af09c79f635e387363d63b

1,473

py

Python

cap2/api.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

9

2020-07-10T15:45:12.000Z

2022-01-19T10:44:13.000Z

cap2/api.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

14

2020-06-15T16:04:54.000Z

2022-03-12T01:05:47.000Z

cap2/api.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

5

2021-01-05T01:26:48.000Z

2022-01-23T11:20:49.000Z

import luigi from .pipeline.databases import MODULES as DB_MODULES from .constants import ( STAGES, STAGES_GROUP, ) def run_db_stage(config_path='', cores=1, **kwargs): """Run the database stage of the pipeline.""" instances = [] for module in DB_MODULES: instances.append( module( config_filename=config_path, cores=cores ) ) luigi.build(instances, local_scheduler=True, **kwargs) def run_stage(samples, stage_name, config_path='', cores=1, workers=1, **kwargs): """Run a subpipeline on a list of samples. stage_name can be one of `qc`, `pre`, `reads`.""" modules = STAGES[stage_name] group_modules = STAGES_GROUP.get(stage_name, []) run_modules( samples, modules, group_modules=group_modules, config_path=config_path, cores=cores, workers=workers, **kwargs ) def run_modules(samples, modules, group_modules=[], config_path='', cores=1, workers=1, **kwargs): """Run a set of modules for a list of samples.""" instances = [] for sample in samples: for module in modules: instance = module.from_sample(sample, config_path, cores=cores) instances.append(instance) for grp_module in group_modules: instances.append(grp_module.from_samples('all', samples, config_path)) luigi.build(instances, local_scheduler=True, workers=workers, **kwargs)

28.882353

98

0.644263

182

1,473

5.032967

0.28022

0.087336

0.098253

0.052402

0.257642

0.233624

0.074236

0

0.0045

0.245757

1,473

50

99

29.46

0.819982

0.115411

0

0.055556

0

0.002333

0

1

0.083333

false

0

0.083333

0

0.166667

0

null

0

null

0

1

0

76be3939c12479026243425b0c57784692e21da7

1,375

py

Python

src/models.py

antble/CompSci-Project-1

3629e85752f70cc987de96a665bb2c25ce80a00f

[ "Apache-2.0" ]

null

src/models.py

antble/CompSci-Project-1

3629e85752f70cc987de96a665bb2c25ce80a00f

[ "Apache-2.0" ]

null

src/models.py

antble/CompSci-Project-1

3629e85752f70cc987de96a665bb2c25ce80a00f

[ "Apache-2.0" ]

null

import numpy as np from src.utils import statistics from sklearn import linear_model ''' OLS Regression ''' def ols_model(train_data, X_test, *args): X_train, y_train = train_data beta = np.linalg.pinv(X_train.T @ X_train) @ X_train.T @ y_train y_predict = X_test @ beta return y_predict, beta def ols_model_skl(train_data, X_test, *args): X_train, y_train = train_data ols = linear_model.LinearRegression(fit_intercept=False) ols.fit(X_train, y_train) y_predict = ols.predict(X_test) return y_predict, ols.coef_ ''' Ridge regression ''' def ridge_model(train_data, X_test, lmb=0): X_train, y_train = train_data p = (X_train.T @ X_train).shape identity_matrix = np.eye(p[0], p[1]) ridge_beta = np.linalg.pinv(X_train.T @ X_train + lmb*identity_matrix) @ X_train.T @ y_train y_predict = X_test @ ridge_beta return y_predict, ridge_beta def ridge_model_skl(train_data, X_test, lmb): X_train, y_train = train_data ridge = linear_model.Ridge(lmb, fit_intercept=False) ridge.fit(X_train, y_train) return ridge.predict(X_test), ridge.coef_ ''' LASSO Regression ''' def lasso_model_skl(train_data, X_test, lmb=0): X_train, y_train = train_data lasso = linear_model.Lasso(lmb, fit_intercept=False, tol=1e-2) lasso.fit(X_train, y_train) return lasso.predict(X_test), lasso.coef_

25.943396

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0.714182

231

1,375

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0.293275

0.171996

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1,375

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0

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0

0.419355

0

null

0

null

0

1

0

76c05d501d2db1028d5ed0985cbecc07cbbeee94

3,652

py

Python

dataset/pipa.py

tugrabatin/backdoors101

af12c08280fe59380f74c05e2737eb2e92a80fdf

[ "MIT" ]

179

2020-11-08T18:57:35.000Z

2022-03-29T00:51:36.000Z

dataset/pipa.py

tugrabatin/backdoors101

af12c08280fe59380f74c05e2737eb2e92a80fdf

[ "MIT" ]

15

2020-11-24T01:20:13.000Z

2022-03-03T03:45:55.000Z

dataset/pipa.py

tugrabatin/backdoors101

af12c08280fe59380f74c05e2737eb2e92a80fdf

[ "MIT" ]

46

2020-11-30T02:36:02.000Z

2022-03-20T02:39:08.000Z

from __future__ import print_function, division import torch import torch.utils.data as data from torchvision.datasets.folder import default_loader class Annotations: photoset_id = None photo_id = None xmin = None ymin = None width = None height = None identity_id = None subset_id = None people_on_photo = 0 def __repr__(self): return f'photoset: {self.photoset_id}, photo id: {self.photo_id}, ' \ f'identity: {self.identity_id}, subs: {self.subset_id}, ' \ f'{self.people_on_photo}' class PipaDataset(data.Dataset): """Face Landmarks dataset.""" def __init__(self, data_path, train=True, transform=None): """ Args: data_path (string): Directory with all the data. train (bool): train or test dataset. transform (callable, optional): Optional transform to be applied on a sample. """ self.directory = data_path try: if train: self.data_list = torch.load(f'{self.directory}/train_split.pt') else: self.data_list = torch.load(f'{self.directory}/test_split.pt') self.photo_list = torch.load(f'{self.directory}/photo_list.pt') self.target_identities = torch.load( f'{self.directory}/target_identities.pt') except FileNotFoundError: raise FileNotFoundError( 'Please download the archive: https://drive.google.com/' 'file/d/1IAsTDl6kw4u8kk7Ikyf8K2A4RSPv9izz') self.transform = transform self.loader = default_loader self.labels = torch.tensor( [self.get_label(x)[0] for x in range(len(self))]) self.metadata = [self.get_label(x) for x in range(len(self))] def __len__(self): return len(self.data_list) def get_label(self, idx): photo_id, identities = self.data_list[idx] target = len(identities) - 1 if target > 4: target = 4 target_identity = 0 for pos, z in enumerate(self.target_identities): if z in identities: target_identity = pos + 1 return target, target_identity, photo_id, idx def __getitem__(self, idx): photo_id, identities = self.data_list[idx] x = self.photo_list[photo_id][0] if x.subset_id == 1: path = 'train' else: path = 'test' target = len(identities) - 1 # more than 5 people nobody cares if target > 4: target = 4 target_identity = 0 for pos, z in enumerate(self.target_identities): if z in identities: target_identity = pos + 1 # get image sample = self.loader( f'{self.directory}/{path}/{x.photoset_id}_{x.photo_id}.jpg') crop = self.get_crop(photo_id) sample = sample.crop(crop) if self.transform is not None: sample = self.transform(sample) return sample, target, target_identity, (photo_id, idx) def get_crop(self, photo_id): ids = self.photo_list[photo_id] left = 100000 upper = 100000 right = 0 lower = 0 for x in ids: left = min(x.xmin, left) upper = min(x.ymin, upper) right = max(x.xmin + x.width, right) lower = max(x.ymin + x.height, lower) diff = (right - left) - (lower - upper) if diff >= 0: lower += diff else: right -= diff return left, upper, right, lower

30.949153

79

0.570646

445

3,652

4.51236

0.267416

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0.02988

0.027888

0.270916

0.239542

0.208167

0.175299

0.140438

0.101594

0

0.015944

0.33023

3,652

117

80

31.213675

0.804988

0.070099

0

0.215909

0

0.125975

0.073785

0

1

0.068182

false

0

0.045455

0.022727

0.295455

0.011364

0

null

0

null

0

1

0

76c3514a5ebad4003ba1794c25c1144967d20722

5,422

py

Python

src/exabgp/bgp/message/update/nlri/bgpls/tlvs/node.py

pierky/exabgp

34be537ae5906c0830b31da1152ae63108ccf911

[ "BSD-3-Clause" ]

1,560

2015-01-01T08:53:05.000Z

2022-03-29T20:22:43.000Z

src/exabgp/bgp/message/update/nlri/bgpls/tlvs/node.py

pierky/exabgp

34be537ae5906c0830b31da1152ae63108ccf911

[ "BSD-3-Clause" ]

818

2015-01-01T17:38:40.000Z

2022-03-30T07:29:24.000Z

src/exabgp/bgp/message/update/nlri/bgpls/tlvs/node.py

pierky/exabgp

34be537ae5906c0830b31da1152ae63108ccf911

[ "BSD-3-Clause" ]

439

2015-01-06T21:20:41.000Z

2022-03-19T23:24:25.000Z

# encoding: utf-8 """ node.py Created by Evelio Vila on 2016-11-26. eveliovila@gmail.com Copyright (c) 2009-2017 Exa Networks. All rights reserved. License: 3-clause BSD. (See the COPYRIGHT file) """ from struct import unpack from exabgp.protocol.ip import IP from exabgp.protocol.iso import ISO from exabgp.bgp.message.notification import Notify # +--------------------+-------------------+----------+ # | Sub-TLV Code Point | Description | Length | # +--------------------+-------------------+----------+ # | 512 | Autonomous System | 4 | # | 513 | BGP-LS Identifier | 4 | # | 514 | OSPF Area-ID | 4 | # | 515 | IGP Router-ID | Variable | # +--------------------+-------------------+----------+ # https://tools.ietf.org/html/rfc7752#section-3.2.1.4 # ================================================================== NODE-DESC-SUB-TLVs NODE_TLVS = { 512: 'autonomous-system', 513: 'bgp-ls-id', 514: 'ospf-area-id', 515: 'igp-rid', } # TODO # 3.2.1.5. Multi-Topology ID class NodeDescriptor(object): def __init__(self, node_id, dtype, psn=None, dr_id=None, packed=None): self.node_id = node_id self.dtype = dtype self.psn = psn self.dr_id = dr_id self._packed = packed @classmethod def unpack(cls, data, igp): dtype, dlength = unpack('!HH', data[0:4]) if dtype not in NODE_TLVS.keys(): raise Exception("Unknown Node Descriptor Sub-TLV") # OSPF Area-ID if dtype == 514: return ( cls(node_id=IP.unpack(data[4 : 4 + dlength]), dtype=dtype, packed=data[: 4 + dlength]), data[4 + dlength :], ) # IGP Router-ID: The TLV size in combination with the protocol # identifier enables the decoder to determine the type # of the node: sec 3.2.1.4. elif dtype == 515: # OSPFv{2,3} non-pseudonode if (igp == 3 or igp == 6) and dlength == 4: r_id = IP.unpack(data[4 : 4 + 4]) return cls(node_id=r_id, dtype=dtype, packed=data[: 4 + dlength]), data[4 + 4 :] # OSPFv{2,3} LAN pseudonode if (igp == 3 or igp == 6) and dlength == 8: r_id = IP.unpack(data[4 : 4 + 4]) dr_id = IP.unpack(data[8 : 4 + 8]) return cls(node_id=r_id, dtype=dtype, psn=None, dr_id=dr_id, packed=data[: 4 + dlength]), data[4 + 8 :] # IS-IS non-pseudonode if (igp == 1 or igp == 2) and dlength == 6: return ( cls(node_id=ISO.unpack_sysid(data[4 : 4 + 6]), dtype=dtype, packed=data[: 4 + dlength]), data[4 + 6 :], ) # IS-IS LAN pseudonode = ISO Node-ID + PSN # Unpack ISO address if (igp == 1 or igp == 2) and dlength == 7: iso_node = ISO.unpack_sysid(data[4 : 4 + 6]) psn = unpack('!B', data[4 + 6 : 4 + 7])[0] return cls(node_id=iso_node, dtype=dtype, psn=psn, packed=data[: 4 + dlength]), data[4 + 7 :] elif dtype == 512 and dlength == 4: # ASN return ( cls(node_id=unpack('!L', data[4 : 4 + dlength])[0], dtype=dtype, packed=data[: 4 + dlength]), data[4 + 4 :], ) elif dtype == 513 and dlength == 4: # BGP-LS return ( cls(node_id=unpack('!L', data[4 : 4 + dlength])[0], dtype=dtype, packed=data[: 4 + dlength]), data[4 + 4 :], ) else: raise Notify(3, 5, 'could not decode Local Node descriptor') def json(self, compact=None): ospf = None designated = None psn = None router_id = None asn = None bgpls_id = None if self.dtype == 514: ospf = '"ospf-area-id": "%s"' % self.node_id if self.dr_id is not None: designated = '"designated-router-id": "%s"' % self.dr_id if self.psn is not None: psn = '"psn": "%s"' % self.psn if self.dtype == 515: router_id = '"router-id": "%s"' % self.node_id if self.dtype == 512: asn = '"autonomous-system": %d' % self.node_id if self.dtype == 513: bgpls_id = '"bgp-ls-identifier": "%d"' % self.node_id content = ', '.join(d for d in [ospf, designated, psn, router_id, asn, bgpls_id] if d) return content def __eq__(self, other): return isinstance(other, NodeDescriptor) and self.node_id == other.node_id def __neq__(self, other): return self.node_id != other.node_id def __lt__(self, other): raise RuntimeError('Not implemented') def __le__(self, other): raise RuntimeError('Not implemented') def __gt__(self, other): raise RuntimeError('Not implemented') def __ge__(self, other): raise RuntimeError('Not implemented') def __str__(self): return self.json() def __repr__(self): return self.__str__() def __len__(self): return len(self._packed) def __hash__(self): return hash(str(self)) def pack(self): return self._packed

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null

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76c76b3318aabc52c57cd61148edf9a8a15fc12b

17,314

py

Python

TwitterWebsiteSearch/TwitterClient.py

dtuit/TwitterWebsiteSearch

9e7cdee9fd82139b8a8b540be0103eca6b82b0e2

[ "MIT" ]

5

2017-08-29T04:07:19.000Z

2021-04-25T15:16:34.000Z

TwitterWebsiteSearch/TwitterClient.py

dtuit/TwitterWebsiteSearch

9e7cdee9fd82139b8a8b540be0103eca6b82b0e2

[ "MIT" ]

null

TwitterWebsiteSearch/TwitterClient.py

dtuit/TwitterWebsiteSearch

9e7cdee9fd82139b8a8b540be0103eca6b82b0e2

[ "MIT" ]

3

2016-08-20T23:25:14.000Z

2017-08-29T04:07:20.000Z

import requests from requests import Request, Session from requests.packages.urllib3.util import Retry from requests.adapters import HTTPAdapter from datetime import datetime, timezone from time import sleep import lxml import lxml.html as lh from urllib.parse import quote, urlsplit import re from operator import itemgetter from copy import deepcopy #tmp import # from lxml import etree from lxml.etree import strip_elements # import logging # logging.basicConfig(level=logging.DEBUG) # import time # def timing(f): # def wrap(*args): # time1 = time.time() # ret = f(*args) # time2 = time.time() # print("{} function took {:0.3f} ms".format(f.__name__, (time2-time1)*1000.0)) # return ret # return wrap class TwitterClient(): FIDDLER_DEBUG = False @staticmethod def init_default_session(retrys=5,backoff_factor=0.1): session = Session() session.headers.update( {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36', 'Accept-Encoding' : 'gzip, deflate, sdch, br', 'Accept': 'application/json, text/javascript, */*; q=0.01', 'Accept-Language': 'en-GB,en-US;q=0.8,en;q=0.6', 'X-Requested-With': 'XMLHttpRequest'}) retries = Retry(total=retrys, backoff_factor=backoff_factor, status_forcelist=[ 500, 502, 503, 504 ]) session.mount('https://', HTTPAdapter(max_retries=retries)) if TwitterClient.FIDDLER_DEBUG: proxies = {'http': 'http://127.0.0.1:8888', 'https': 'https://127.0.0.1:8888'} session.proxies.update(proxies) return session def __init__(self, session=None, timeout=12, continue_on_empty_result=True): if session is None: session = self.init_default_session() self.session = session self.timeout = timeout self.continue_on_empty_result = continue_on_empty_result self.search_url = 'https://twitter.com/i/search/timeline' self.user_url = 'https://twitter.com/i/profiles/show/{username}/timeline/tweets' def search_query(self, queryBuilder, raw_query_str=None): if raw_query_str is None: raw_query_str = queryBuilder.build() request = self._prepare_request(self.search_url, raw_query_str) resp = self._execute_request(request) resp_json = resp.json() # Extract Results tweets = [] if resp_json is not None and resp_json['items_html'] is not None: tweets = self.parse_tweets(resp_json['items_html']) next_query = deepcopy(queryBuilder) next_query.max_position = resp_json.get('min_position') #switch the labels because twitter mislabels them next_query.min_position = resp_json.get('max_position') next_query.reset_error_state = False min_id = max_id = None if len(tweets) > 0: min_id = tweets[0]['id_str'] max_id = tweets[1]['id_str'] retval = { '_request': request, '_response_raw': resp, '_response_json': resp_json, 'refresh_query': queryBuilder, 'next_query': next_query, 'tweets': tweets, 'min_id': min_id, 'max_id': max_id } return retval def user_query(self, user): raise NotImplementedError def get_search_iterator(self, search_query): # determine if this is the first query or a continuation. search_query.autoset_reset_error_state() result = self.search_query(search_query) next_query = result['next_query'] yield result while True: if len(result['tweets']) == 0: if not self.continue_on_empty_result: print('No tweets returned terminating program') break else: break # TODO remimplement result = self.search_query(next_query) next_query = result['next_query'] yield result # def binary_search_ # def get_search_iterator(self, queryBuilder): # qb = qb_prev = deepcopy(queryBuilder) # result = self.search_query(qb) # prev_min_tweetId = None # yield result # while True: # if len(result['tweets']) == 0: # if not self.continue_on_empty_result: # print('No tweets returned terminating program') # break # else: # # Sometimes the API stops returning tweets even when there are more # # we can try to find these tweets by modifying the max_position parameter. # int_minId = int(qb.min_tweetId) # for x in range(8, len(qb.min_tweetId)): #TODO impl something more sophisticated # qb.min_tweetId = int_minId - 10**x # result = self.search(qb) # if len(result['tweets']) > 0: # break # else: # print('No tweets returned terminating program') # # if we didnt find any point to continue from, break. # break # if qb.max_tweetId is None: # qb.max_tweetId = result['tweets'][0]['id_str'] # # In a high volume search query like 'a' must use the max_tweet_id provided by the result, # # otherwise the same results will be returned many times. (only happens during the first ~10 pages of results) # res_min_pos = result['response_json'].get('min_position') # if res_min_pos is not None: # split = res_min_pos.split('-') # qb.max_tweetId = split[2] # prev_min_tweetId = qb.min_tweetId # qb.min_tweetId = result['tweets'][-1]['id_str'] # # If the current request returns the same tweets as the last # # the query is configured wrong # # TODO create more accurate metric # if prev_min_tweetId is qb.min_tweetId: # break # result = self.search_query(qb) # yield result def _execute_request(self, prepared_request): try: if TwitterClient.FIDDLER_DEBUG: result = self.session.send(prepared_request, timeout=self.timeout, verify=False) else: result = self.session.send(prepared_request, timeout=self.timeout) return result except requests.exceptions.Timeout as e: raise def _prepare_request(self, url, payload_str): req = Request('GET', url, params=payload_str, cookies={}) return self.session.prepare_request(req) @staticmethod def _encode_max_postion_param(min, max): return "TWEET-{0}-{1}".format(min, max) def parse_tweets(self, items_html): try: html = lh.fromstring(items_html) except lxml.etree.ParserError as e: return [] tweets = [] for li in html.cssselect('li.js-stream-item'): # Check if is a tweet type element if 'data-item-id' not in li.attrib: continue tweet = self._parse_tweet(li) if tweet is not None: tweets.append(tweet) return tweets def _parse_tweet(self, tweetElement): ''' Parses the attributes of a tweet from the tweetElement into a dict returns None if there is an error in the tweet ''' li = tweetElement tweet = { 'created_at' : None, 'id_str' : li.get('data-item-id'), 'text' : None, 'lang' : None, 'entities': { 'hashtags': [], 'symbols':[], 'user_mentions':[], 'urls':[], }, 'user' : { 'id_str' : None, 'name' : None, 'screen_name': None, 'profile_image_url': None, 'verified': False }, 'retweet_count' : 0, 'favorite_count' : 0, 'is_quote_status' : False, 'in_reply_to_user_id': None, 'in_reply_to_screen_name' : None, 'contains_photo': False, 'contains_video': False, 'contains_card': False } content_div = li.cssselect('div.tweet') if len(content_div) > 0: content_div = content_div[0] tweet['user']['id_str'] = content_div.get('data-user-id') tweet['user']['name'] = content_div.get('data-name') tweet['user']['screen_name'] = content_div.get('data-screen-name') reply_a = content_div.cssselect('div.tweet-context a.js-user-profile-link') # tweet-context can be used by many functions, incl follow, reply, retweet only extract reply atm if len(reply_a) > 0: if len(content_div.cssselect('div.tweet-context span.Icon--reply')) > 0: # check if actually a reply tweet['in_reply_to_user_id'] = reply_a[0].get('data-user-id') tweet['in_reply_to_screen_name'] = reply_a[0].get('href').strip('/') user_img = content_div.cssselect('img.avatar') if len(user_img) > 0: tweet['user']['profile_image_url'] = user_img[0].get('src') text_p = content_div.cssselect('p.tweet-text, p.js-tweet-text') if len(text_p) > 0: text_p = text_p[0] self._parse_tweet_text(text_p, tweet) tweet['lang'] = text_p.get('lang') self._parse_tweet_entites(text_p, tweet['entities']) else: # there is no tweet text, unknown if this occurs return None verified_span = content_div.cssselect('span.Icon--verified') if len(verified_span) > 0: tweet['user']['verified'] = True date_span = content_div.cssselect('span._timestamp') if len(date_span) > 0: timestamp = int(date_span[0].get('data-time-ms'))/1000 tweet['created_at'] = datetime.fromtimestamp(timestamp, tz=timezone.utc).strftime('%a %b %d %H:%M:%S %z %Y') #Retweet and Favoritte counts counts = li.cssselect('span.ProfileTweet-action--retweet, span.ProfileTweet-action--favorite') if len(counts) > 0: for c in counts: classes = c.get('class').split(' ') if 'ProfileTweet-action--retweet' in classes: tweet['retweet_count'] = int(c[0].get('data-tweet-stat-count')) elif 'ProfileTweet-action--favorite' in classes: tweet['favorite_count'] = int(c[0].get('data-tweet-stat-count')) #Extract Quoted Status quoted_tweet_context = content_div.cssselect('div.QuoteTweet-innerContainer') if len(quoted_tweet_context) > 0: quoted_tweet_context = quoted_tweet_context[0] tweet['is_quote_status'] = True tweet['quoted_status_id_str'] = quoted_tweet_context.get('data-item-id') tweet['quoted_status'] = { 'id_str': None, 'text': None, 'user': { 'id_str' : None, 'name' : None, 'screen_name' : None, }, 'entities' : { 'hashtags' : [], 'symbols' :[], 'user_mentions':[], 'urls':[] } } qtweet = tweet['quoted_status'] qtweet['id_str'] = quoted_tweet_context.get('data-item-id') qtweet['user']['id_str'] = quoted_tweet_context.get('data-user-id') qtweet['user']['screen_name'] = quoted_tweet_context.get('data-screen-name') qt_user_name = quoted_tweet_context.cssselect('b.QuoteTweet-fullname') if len(qt_user_name) > 0: qtweet['user']['name'] = qt_user_name[0].text_content() qt_text = quoted_tweet_context.cssselect('div.QuoteTweet-text.tweet-text') if len(qt_text) > 0: qt_text = qt_text[0] self._parse_tweet_text(qt_text, qtweet) self._parse_tweet_entites(qt_text, qtweet['entities']) # Extract Media entities tweet_media_context = content_div.cssselect('div.AdaptiveMedia-container') if len(tweet_media_context) > 0: tweet_media_context = tweet_media_context[0] tweet['entities']['media'] = [] photo_found = False tweet_media_photos = tweet_media_context.cssselect('div.AdaptiveMedia-photoContainer') for elm in tweet_media_photos: tweet['contains_photo'] = photo_found = True photo = { 'media_url' : elm.get('data-image-url'), 'type' : 'photo' } tweet['entities']['media'].append(photo) if not photo_found: tweet_media_video = tweet_media_context.cssselect('div.AdaptiveMedia-videoContainer') if len(tweet_media_video) > 0: tweet['contains_video'] = True video = { 'type' : 'video', 'video_type' : re.search(re.compile(r"PlayableMedia--([a-zA-Z]*)"), tweet_media_video[0].cssselect('div[class*="PlayableMedia--"]')[0].get('class')).group(1), 'media_url' : 'https://twitter.com/i/videos/tweet/' + tweet['id_str'], 'video_thumbnail' : re.search(re.compile(r"background-image:url\(\'(.*)\'"),tweet_media_video[0].cssselect('div.PlayableMedia-player')[0].get('style')).group(1) } tweet['entities']['media'].append(video) return tweet def _parse_tweet_text(self, text_element, tweet): #hacky way to include Emojis for emoj in text_element.cssselect('img.Emoji'): emoj.tail = emoj.get('alt') + emoj.tail if emoj.tail else emoj.get('alt') #Modify Urls so they are correct for url in text_element.cssselect('a.twitter-timeline-link'): is_truncated = u'\u2026' in url.text_content() url_disp = url.cssselect('span.js-display-url') if len(url_disp) > 0: url_disp_text = url_disp[0].text_content() if is_truncated: url_disp_text = url_disp_text + u'\u2026' url.attrib['xtract-display-url'] = url_disp_text # store for later extraction elif 'pic.twitter.com' in url.text: url.attrib['xtract-display-url'] = url.text strip_elements(url, ['*']) url.text = url.attrib['href'] tmp = str(text_element.text_content()) for m in re.finditer(r'(?<!\s)(?<!\\n)(http|https)://', tmp): #add a space before urls where required tmp = tmp[:m.start()] + ' ' + tmp[m.start():] tweet['text'] = tmp def _parse_tweet_entites(self, element, entities): tags = element.cssselect('a.twitter-hashtag, a.twitter-cashtag, a.twitter-atreply, a.twitter-timeline-link') if len(tags) > 0: for tag in tags: classes = tag.get('class').split(' ') if 'twitter-hashtag' in classes: entities['hashtags'].append(tag.text_content().strip(' \n#')) elif 'twitter-cashtag' in classes: entities['symbols'].append(tag.text_content().strip(' \n$')) elif 'twitter-atreply' in classes: mentioned_user = { 'id_str' : tag.get('data-mentioned-user-id'), 'screen_name' : tag.get('href').strip('/') if tag.get('href') is not None else None } entities['user_mentions'].append(mentioned_user) elif 'twitter-timeline-link' in classes: url = { 'url': tag.get('href'), 'expanded_url' : tag.get('data-expanded-url'), 'display_url' : tag.get('xtract-display-url') } entities['urls'].append(url) if __name__ == "__main__": import TwitterQuery # TwitterClient.FIDDLER_DEBUG = True x = TwitterClient(timeout=None) try: gen = x.get_search_iterator(TwitterQuery.SearchQuery('apple filter:replies')) for res in gen: print(len(res['tweets'])) except requests.exceptions.Timeout as e: print(e) def get_ids(tweets): return [tweet['id_str'] for tweet in tweets]

40.171694

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17,314

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0

null

0

null

0

1

0

76c82b69f1006320dc0fddf50db8de80c24e26c9

532

py

Python

sumupto.py

SasiGV/pands-problem-set

3ddcb21b1103ab88d734e1281188a772573c839e

[ "Apache-2.0" ]

null

sumupto.py

SasiGV/pands-problem-set

3ddcb21b1103ab88d734e1281188a772573c839e

[ "Apache-2.0" ]

null

sumupto.py

SasiGV/pands-problem-set

3ddcb21b1103ab88d734e1281188a772573c839e

[ "Apache-2.0" ]

null

#Sasikala Varatharajan - G00376470 #Program to calculate sum of numbers from 1 to the number entered #Get input from User n=int(input("Please enter a positive integer ")) #Create an empty intergar called Sum and set initialize it to zero sum = 0 if n > 0: #Create a For loop and add all numbers starting from 1 upto the number entered for num in range (0, n+1, 1): sum = sum + num #Print the output value print ("Sum of numbers 1 to", n, "is: ", sum) else: print ("Oops, It is a negative number")

31.294118

82

0.680451

91

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0.027624

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0

0.039702

0.242481

532

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83

31.294118

0.858561

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0

0.345679

0

1

0

false

0

0.25

0

null

0

null

0

1

0

76c8506b592c49671db60b7ecb7cdfe6fc993e2a

1,661

py

Python

https/views.py

bridgesign/Mini-tweet

97e13afda73b816c953bd93baba31c4686621fdd

[ "Apache-2.0" ]

null

https/views.py

bridgesign/Mini-tweet

97e13afda73b816c953bd93baba31c4686621fdd

[ "Apache-2.0" ]

null

https/views.py

bridgesign/Mini-tweet

97e13afda73b816c953bd93baba31c4686621fdd

[ "Apache-2.0" ]

null

from . import handler import os from .settings import static from . import settings import json from api.parse import parser from . import token import psycopg2 def static_handler(request): split = request.headers['url'].split('/') filename, ftype = split[-1], split[-2] path = os.path.join(settings.static, ftype, filename) if os.path.isfile(path): with open(path, 'rb') as fp: data = fp.read() else: return handler.httpresponse(request, settings.NOT_FOUND_TEMPLATE, 404) h = handler.httpresponse(request, data, content_type=settings.ext_to_type[filename.split('.')[-1]]) h.cache_control = ["public", "max-age=3600"] return h def api_handler(request): if request.headers['method']=='POST': conn = psycopg2.connect(**settings.DBSETTINGS) cur = conn.cursor() try: if 'token' in request.headers['cookie']: ctx = token.validate_token(request.headers['cookie']['token']) else: ctx = {} # Connecting to DB in thread safe manner p = parser({'conn':conn, 'cur':cur}) response = p.parse(ctx, json.loads(request.body)) h = handler.httpresponse(request, json.dumps(response), content_type='application/json') except: h = handler.httpresponse(request, settings.BAD_REQUEST_TEMPLATE, 400) cur.close() conn.close() return h else: return handler.httpresponse(request, settings.BAD_REQUEST_TEMPLATE, 400) def index(request): path = os.path.join('templates','index.html') with open(path, 'rb') as fp: data = fp.read() return handler.httpresponse(request, data) patterns = ( ('^(?![\s\S])', index), ('index(\.html|\.htm)?', index), ('static/(image|css|js)/.*', static_handler), ('api', api_handler) )

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0.084137

0.192813

0.145486

0.04908

0

0.012579

0.138471

1,661

56

101

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0

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0

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0

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false

0

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0

0.32

0

null

0

null

0

1

0

76c89cbd402fd7fb8d66be683d4508aeece89775

2,209

py

Python

oandapy-master/oandapybot-master/logic/candle.py

cdibble2011/OANDA

68327d6d65dd92952d7a1dc49fe29efca766d900

[ "MIT" ]

null

oandapy-master/oandapybot-master/logic/candle.py

cdibble2011/OANDA

68327d6d65dd92952d7a1dc49fe29efca766d900

[ "MIT" ]

null

oandapy-master/oandapybot-master/logic/candle.py

cdibble2011/OANDA

68327d6d65dd92952d7a1dc49fe29efca766d900

[ "MIT" ]

null

# Candle sticks import datetime import time from logic import Indicator, ValidateDatapoint class Candle(Indicator): # Opening price Open = 0.0 # Closing price Close = 0.0 # Highest price High = 0.0 # Lowest price Low = 0.0 # Open timestamp OpenTime = datetime.datetime.fromtimestamp(time.time()); # Close timestamp CloseTime = OpenTime; def __init__(self, openTime, closeTime): if (isinstance(openTime, datetime.datetime)): self.OpenTime = openTime if (isinstance(closeTime, datetime.datetime)): self.CloseTime = closeTime self._is_closed = False # Returns true if candle stick accumulated enough data to represent the # time span between Opening and Closing timestamps def SeenEnoughData(self): return self._is_closed def AmounOfDataStillMissing(self): if (self.SeenEnoughData()): return 0 return 1 def Update(self, data): if ( self.CloseTime < self.OpenTime ): self._resetPrice(0.0) self._is_closed = False return if (not ValidateDatapoint(data)): return _current_timestamp = data["now"] _price = data["value"] if (_current_timestamp >= self.CloseTime): self._is_closed = True if (_current_timestamp <= self.CloseTime and _current_timestamp >= self.OpenTime): self._updateData(_price) def _resetPrice(self, price): self.High = price self.Low = price self.Open = price self.Close = price # Update the running timestamps of the data def _updateData(self, price): # If this is the first datapoint, initialize the values if ( self.High == 0.0 and self.Low == 0.0 and self.Open == 0.0 and self.Close == 0.0): self._resetPrice(price) self._is_closed = False return # Update the values in case the current datapoint is a current High/Low self.Close = price self.High = max(price,self.High) self.Low = min(price,self.Low)

26.939024

94

0.595292

250

2,209

5.144

0.272

0.013997

0.046656

0.039658

0.083981

0

0.013378

0.323223

2,209

81

95

27.271605

0.846823

0.176098

0

0.166667

0

0.004427

0

1

0.125

false

0

0.0625

0.020833

0.458333

0

null

0

null

0

1

0

76caeb17e7fcca41625bbccedc33d81d11c076ce

1,703

py

Python

aoc2020/day15.py

rfrazier716/aoc_2020

90c35d16910d28ec2f6de15d4b758977d0ff6f7b

[ "MIT" ]

null

aoc2020/day15.py

rfrazier716/aoc_2020

90c35d16910d28ec2f6de15d4b758977d0ff6f7b

[ "MIT" ]

null

aoc2020/day15.py

rfrazier716/aoc_2020

90c35d16910d28ec2f6de15d4b758977d0ff6f7b

[ "MIT" ]

null

from collections import defaultdict def memory_generator(starting_numbers): # make a dictionary that keeps track of number that were called and when they were called memory_dict = defaultdict(int, zip(starting_numbers,range(1,len(starting_numbers)+1))) next_num = 0 turn = len(memory_dict) + 1 # now constantly loop over the last value, check if it's been called before, and if so # this turns value is the difference from when it was last called while True: yield next_num # return the next number # now update the dictionary so you can generate the number after last_called_turn = memory_dict[next_num] # if it's never been called added it to the dict and set the next num to zero if not last_called_turn: memory_dict[next_num]=turn next_num = 0 # next number is always zero since it's never been called else: temp = turn - memory_dict[next_num] # temporarily store what the next number will be memory_dict[next_num]= turn # update the memory dict with the turn next_num = temp # update the next num to be the difference from when the number was last called turn+=1 # incrememnt the turn def find_nth_memory_result(input_array, search_number): gen = memory_generator(input_array) for _ in range(search_number-1-len(input_array)): next(gen) return next(gen) if __name__ == "__main__": input_array = [1,2,16,19,18,0] part1_answer = find_nth_memory_result(input_array, 2020) print(f"Part1 Solution: {part1_answer}") part2_answer = find_nth_memory_result(input_array, 30000000) print(f"Part1 Solution: {part2_answer}")

48.657143

107

0.704052

263

1,703

4.357414

0.380228

0.061082

0.048866

0.059337

0.212042

0.140489

0.115183

0

0.025974

0.231356

1,703

35

108

48.657143

0.849503

0.373459

0

0.148148

0

0.064394

0

1

0.074074

false

0

0.037037

0

0.148148

0.074074

0

null

0

null

0

1

0

76cd82a03ec0619f89a6f670da436df7f60a8e00

864

py

Python

src/make_simple_polygon.py

wreck-count/Fast-Simple-Polygon-Triangulation

9b54f8cd87512d9ca2ee6e8208571c91595bfcae

[ "MIT" ]

1

2021-06-29T08:28:32.000Z

src/make_simple_polygon.py

wreck-count/Fast-Simple-Polygon-Triangulation

9b54f8cd87512d9ca2ee6e8208571c91595bfcae

[ "MIT" ]

null

src/make_simple_polygon.py

wreck-count/Fast-Simple-Polygon-Triangulation

9b54f8cd87512d9ca2ee6e8208571c91595bfcae

[ "MIT" ]

null

import geopandas as gpd from shapely.geometry import Polygon lat_point_list = [50.854457, 52.518172, 50.072651, 48.853033, 50.854457] lon_point_list = [4.377184, 13.407759, 14.435935, 2.349553, 4.377184] polygon_geom = Polygon(zip(lon_point_list, lat_point_list)) crs = {'init': 'epsg:4326'} polygon = gpd.GeoDataFrame(index=[0], crs=crs, geometry=[polygon_geom]) print(polygon.geometry) polygon.to_file(filename='polygon.geojson', driver='GeoJSON') polygon.to_file(filename='polygon.shp', driver="ESRI Shapefile") import random from shapely.geometry import Point def generate_random(number, polygon): points = [] minx, miny, maxx, maxy = polygon.bounds while len(points) < number: pnt = Point(random.uniform(minx, maxx), random.uniform(miny, maxy)) if polygon.contains(pnt): points.append(pnt) return points

34.56

78

0.71875

121

864

5.024793

0.520661

0.059211

0.0625

0.082237

0.092105

0

0.111565

0.149306

864

25

79

34.56

0.715646

0

0.069364

0

1

0.05

false

0

0.2

0

0.3

0.05

0

null

0

null

0

1

0

4f113307e1dde4f1936705a49826b0a68aefcbf5

6,678

py

Python

openmv_filesys/astro_sensor.py

morrowsend/OpenMV-Astrophotography-Gear

abcc1755ab85b32c8fbdba67d24350f296513544

[ "MIT" ]

1

2021-06-29T02:19:25.000Z

openmv_filesys/astro_sensor.py

morrowsend/OpenMV-Astrophotography-Gear

abcc1755ab85b32c8fbdba67d24350f296513544

[ "MIT" ]

null

openmv_filesys/astro_sensor.py

morrowsend/OpenMV-Astrophotography-Gear

abcc1755ab85b32c8fbdba67d24350f296513544

[ "MIT" ]

1

2021-02-07T02:00:58.000Z

import micropython micropython.opt_level(2) import sensor, image, pyb, time, gc import exclogger class AstroCam(object): def __init__(self, pixfmt = sensor.GRAYSCALE, simulate = None): self.pixfmt = pixfmt self.gain = -2 self.shutter = -2 self.framesize = sensor.QQCIF self.flip = False self.fileseq = 1 self.img = None self.has_error = False self.wait_init = 0 self.snap_started = False self.simulate = False if simulate is not None: sensor.shutdown(True) gc.collect() #print("about to load simulation file, checking memory") #micropython.mem_info(False) print("loading simulation file ...", end="") self.img = image.Image(simulate, copy_to_fb = True) print(" done, alloc and converting ...", end="") self.img = sensor.alloc_extra_fb(self.img.width(), self.img.height(), sensor.RGB565).replace(self.img).to_grayscale() print(" done!") self.simulate = True self.snap_started = False self.width = self.img.width() self.height = self.img.height() def init(self, gain_db = 0, shutter_us = 500000, framesize = sensor.WQXGA2, force_reset = True, flip = False): if self.simulate: self.shutter = shutter_us self.gain = gain_db self.snap_started = False return if force_reset or self.has_error or self.gain != gain_db or self.shutter != shutter_us or self.framesize != framesize or self.flip != flip: sensor.reset() sensor.set_pixformat(self.pixfmt) sensor.set_framesize(framesize) if flip: # upside down camera sensor.set_vflip(True) sensor.set_hmirror(True) self.flip = flip self.framesize = framesize if shutter_us < 0: sensor.set_auto_exposure(True) else: if shutter_us > 500000: sensor.__write_reg(0x3037, 0x08) # slow down PLL if shutter_us > 1000000: pyb.delay(100) sensor.__write_reg(0x3037, 0x18) # slow down PLL if shutter_us > 1500000: pyb.delay(100) sensor.__write_reg(0x3036, 80) # slow down PLL # warning: doesn't work well, might crash pyb.delay(200) sensor.set_auto_exposure(False, shutter_us) self.shutter = shutter_us if gain_db < 0: sensor.set_auto_gain(True) else: sensor.set_auto_gain(False, gain_db) self.gain = gain_db self.wait_init = 2 self.width = sensor.width() self.height = sensor.height() def check_init(self): if self.wait_init > 0: if self.snap_started == False: self.snapshot_start() elif self.snapshot_check(): self.snapshot_finish() self.wait_init -= 1 return False return True def snapshot(self, filename = None): if self.simulate: pyb.delay(self.shutter // 1000) self.snap_started = False return self.img try: if self.snap_started == True: self.img = self.snapshot_finish() else: self.img = sensor.snapshot() if filename == "auto": filename = "%u_%u_%u.jpg" % (self.fileseq, round(self.gain), self.shutter) self.fileseq += 1 if filename is not None: self.img.save(filename, quality = 100) return self.img except RuntimeError as exc: exclogger.log_exception(exc) self.has_error = True return None def snapshot_start(self): if self.snap_started == True: return if self.simulate: self.sim_t = pyb.millis() self.snap_started = True return try: sensor.snapshot_start() self.snap_started = True except RuntimeError as exc: exclogger.log_exception(exc) self.has_error = True def snapshot_check(self): if self.snap_started == False: return False if self.simulate: dt = pyb.elapsed_millis(self.sim_t) if dt > (self.shutter // 1000): return True else: return False return sensor.snapshot_check() def snapshot_finish(self): if self.snap_started == False: return None if self.snap_started == False: return False if self.simulate: while self.snapshot_check() == False: gc.collect() self.snap_started = False return self.img try: self.img = sensor.snapshot_finish() self.has_error = False except RuntimeError as exc: exclogger.log_exception(exc) self.img = None self.has_error = True self.snap_started = False return self.img def test_gather(self, shots = 2, gain_start = 0, gain_step = 16, gain_limit = 128, shutter_start = 500000, shutter_step = 250000, shutter_limit = 1500000): shot = 0 rnd = pyb.rng() % 1000 gain = gain_start shutter = shutter_start while True: self.init(gain_db = gain, shutter_us = shutter, framesize = sensor.WQXGA2, force_reset = False, flip = True) fn = "%u_%u_%u_%u_%u.jpg" % (rnd, self.fileseq, shot, round(self.gain), self.shutter) print(fn + " ... ", end="") self.snapshot(filename = fn) print("done") shot += 1 if shot >= shots: shot = 0 gain += gain_step if gain > gain_limit: gain = gain_start shutter += shutter_step if shutter > shutter_limit: return def test_view(self): self.init(gain_db = -1, shutter_us = -1, framesize = sensor.WQXGA2, force_reset = True, flip = True) clock = time.clock() while True: clock.tick() self.snapshot() print("%u - %0.2f" % (self.fileseq, clock.fps())) if __name__ == "__main__": cam = AstroCam() cam.test_view() #cam.test_gather()

36.097297

159

0.529799

747

6,678

4.564926

0.198126

0.034897

0.061584

0.058651

0.286217

0.183578

0.143695

0.100587

0.079472

0.064516

0

0.029908

0.379155

6,678

184

160

36.293478

0.792571

0.029949

0

0.413174

0

0.01932

0

0.004019

0

1

0.053892

false

0

0.017964

0

0.179641

0.035928

0

null

0

null

0

1

0

4f12094bbfecc319d7f0685f3ac1415baa8332b9

1,843

py

Python

blueprints/docker.py

LucasBiason/python521

ed418d5335f84fcbfdb1251bc571710c48b429bc

[ "MIT" ]

null

blueprints/docker.py

LucasBiason/python521

ed418d5335f84fcbfdb1251bc571710c48b429bc

[ "MIT" ]

2

2021-05-25T13:26:42.000Z

2021-05-25T13:26:43.000Z

blueprints/docker.py

LucasBiason/python521

ed418d5335f84fcbfdb1251bc571710c48b429bc

[ "MIT" ]

null

import flask import docker from services import decorators blueprint = flask.Blueprint('docker', __name__) connection = docker.DockerClient() @blueprint.route('/docker', methods=[ 'GET' ]) @decorators.login_required @decorators.loggingroutes def get_docker(): try: lista_dockers = connection.containers.list(all=True) except Exception as msg: ## Dentro do container não vai rodar pois não colocamos o Docker dentro # ('Connection aborted.', FileNotFoundError(2, 'No such file or directory')) print(msg) lista_dockers = [] context = { 'page': 'docker', 'route': { 'is_public': False }, 'containers': lista_dockers } return flask.render_template('docker.html', context=context) @blueprint.route('/docker/start/<string:short_id>/', methods=[ 'GET' ]) @decorators.login_required @decorators.loggingroutes def start_docker(short_id): container = connection.containers.get(short_id) if container and container.status != 'running': container.start() flask.flash("Container Iniciado", "success") elif not container: flask.flash("Container não Encontrado", "danger") else: flask.flash("Container já está iniciado", "danger") return flask.redirect('/docker') @blueprint.route('/docker/stop/<string:short_id>/', methods=[ 'GET' ]) @decorators.login_required @decorators.loggingroutes def stop_docker(short_id): container = connection.containers.get(short_id) if container and container.status == 'running': container.stop() flask.flash("Container Encerrado", "success") elif not container: flask.flash("Container não Encontrado", "danger") else: flask.flash("Container já está encerrado", "danger") return flask.redirect('/docker')

30.213115

84

0.675529

202

1,843

6.059406

0.376238

0.034314

0.093137

0.061275

0.522059

0.471405

0.423203

0

0.000678

0.199132

1,843

60

85

30.716667

0.828591

0.077591

0

0.333333

0

0.19705

0.037168

0

1

0.0625

false

0

0.0625

0

0.1875

0.104167

0

null

0

null

0

1

0

4f1335bba0da53d7e660db8abd330c6d402f948e

1,620

py

Python

tools/csv_embed_distances.py

hypraptive/bearid

03e200e6ee5c236344b0dc7da05cf39ef39a7f5b

[ "MIT" ]

33

2017-04-25T22:59:10.000Z

2022-02-24T21:19:55.000Z

tools/csv_embed_distances.py

hypraptive/bearid

03e200e6ee5c236344b0dc7da05cf39ef39a7f5b

[ "MIT" ]

5

2017-04-12T22:55:15.000Z

2020-03-08T02:02:54.000Z

tools/csv_embed_distances.py

hypraptive/bearid

03e200e6ee5c236344b0dc7da05cf39ef39a7f5b

[ "MIT" ]

3

2020-11-25T14:31:04.000Z

2021-06-21T23:13:40.000Z

#! /usr/bin/python3 import sys import argparse import xml_utils as u import datetime import os from argparse import RawTextHelpFormatter from collections import defaultdict ##------------------------------------------------------------ ## generate csv of distances between all permutations of two ## embedding files. ## ## usage: ## csv_embed_distances.py -out e_dist.csv e_test.xml e_train.xml ##------------------------------------------------------------ def main (argv) : parser = argparse.ArgumentParser(description='\nGenerate CSV of distances for all permutations of two input embedding files.\n\n \tExample: ' + os.path.basename (argv[0]) + ' -out e_dist.csv embed1.xml embed2.xml', formatter_class=RawTextHelpFormatter) parser.add_argument ('embed1') parser.add_argument ('embed2') parser.add_argument ('-db', '--db', help='db of images info.') parser.add_argument ('-out', '--output', default="e_dist.csv", help='specify csv output file.') parser.add_argument ('-v', '--verbosity', type=int, default=1, choices=[0, 1, 2, 3], help='') # help="increase output verbosity" u.set_argv (argv) args = parser.parse_args() u.set_verbosity (args.verbosity) u.set_argv (argv) u.set_filetype ('embeds') verbose = 0 if verbose > 0: print("files: ", args.files) if os.path.exists (args.output) : u.current_datetime () csv_filename = 'e_dist_' + u.current_datetime () + '.csv' print ('CSV file exists, writing to ' + csv_filename) args.output = csv_filename u.gen_embed_dist_csv ([args.embed1], [args.embed2], args.output, args.db) if __name__ == "__main__": main (sys.argv)

33.75

216

0.659877

217

1,620

4.75576

0.396313

0.043605

0.082364

0.03876

0.040698

0

0.010691

0.133951

1,620

47

217

34.468085

0.724875

0.199383

0

0.058824

0

0.225781

0

1

0.029412

false

0

0.205882

0

0.235294

0.058824

0

null

0

null

0

1

0

4f14b3f40c82ae119234be850bf3cbe591a95186

294

py

Python

calculate_time.py

Tips-Lee/Interview

62c9eb8cefe49eb8b1beabe30ec5def66427ea60

[ "MIT" ]

1

2020-04-26T00:56:03.000Z

calculate_time.py

Tips-Lee/Interview

62c9eb8cefe49eb8b1beabe30ec5def66427ea60

[ "MIT" ]

null

calculate_time.py

Tips-Lee/Interview

62c9eb8cefe49eb8b1beabe30ec5def66427ea60

[ "MIT" ]

null

import time class cal_time: def __init__(self, func): self.f = func def __call__(self, *args, **kwargs): start = time.time() ans = self.f(*args, **kwargs) end = time.time() t = end - start print('total time: %f' % t) return ans

21

40

0.517007

38

294

3.763158

0.5

0.06993

0

0.346939

294

14

41

21

0.744792

0

0.047458

0

1

0.181818

false

0

0.090909

0

0.454545

0.090909

0

null

0

null

0

1

0

4f184275b62d014ce34231a10c787a12374b4a75

1,133

py

Python

tests/conftest.py

lycantropos/rene

c73c616f3e360b994e92c950a3616a8ccb1136b9

[ "MIT" ]

null

tests/conftest.py

lycantropos/rene

c73c616f3e360b994e92c950a3616a8ccb1136b9

[ "MIT" ]

null

tests/conftest.py

lycantropos/rene

c73c616f3e360b994e92c950a3616a8ccb1136b9

[ "MIT" ]

null

import os import platform from datetime import timedelta import pytest from hypothesis import (HealthCheck, settings) is_pypy = platform.python_implementation() == 'PyPy' on_ci = bool(os.getenv('CI', False)) max_examples = (-(-settings.default.max_examples // 5) if is_pypy and on_ci else settings.default.max_examples) settings.register_profile('default', max_examples=max_examples, suppress_health_check=[HealthCheck.too_slow]) if on_ci: @pytest.hookimpl(tryfirst=True) def pytest_runtest_call(item: pytest.Item) -> None: set_deadline = settings(deadline=((timedelta(hours=1) / (max_examples * len(item.session.items))))) item.obj = set_deadline(item.obj) @pytest.hookimpl(trylast=True) def pytest_sessionfinish(session: pytest.Session, exitstatus: pytest.ExitCode) -> None: if exitstatus == pytest.ExitCode.NO_TESTS_COLLECTED: session.exitstatus = pytest.ExitCode.OK

35.40625

75

0.61165

120

1,133

5.583333

0.475

0.098507

0.080597

0.077612

0

0.002503

0.294793

1,133

31

76

36.548387

0.836045

0

0.011474

0

1

0.076923

false

0

0.192308

0

0.269231

0

null

0

null

0

1

0

4f1b8955bf3b066b5a24e9aae941aaa3fcfedc5f

3,748

py

Python

algorithms/refinement/parameterisation/scan_varying_goniometer_parameters.py

TiankunZhou/dials

bd5c95b73c442cceb1c61b1690fd4562acf4e337

[ "BSD-3-Clause" ]

2

2021-03-17T11:25:46.000Z

2021-11-18T04:20:54.000Z

algorithms/refinement/parameterisation/scan_varying_goniometer_parameters.py

TiankunZhou/dials

bd5c95b73c442cceb1c61b1690fd4562acf4e337

[ "BSD-3-Clause" ]

null

algorithms/refinement/parameterisation/scan_varying_goniometer_parameters.py

TiankunZhou/dials

bd5c95b73c442cceb1c61b1690fd4562acf4e337

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import, division, print_function from scitbx import matrix from dials.algorithms.refinement.parameterisation.goniometer_parameters import ( GoniometerMixin, ) from dials.algorithms.refinement.parameterisation.scan_varying_model_parameters import ( GaussianSmoother, ScanVaryingModelParameterisation, ScanVaryingParameterSet, ) class ScanVaryingGoniometerParameterisation( ScanVaryingModelParameterisation, GoniometerMixin ): """A scan-varying parameterisation for the setting rotation of a goniometer with angles expressed in mrad.""" def __init__( self, goniometer, t_range, num_intervals, beam=None, experiment_ids=None ): if experiment_ids is None: experiment_ids = [0] # The state of a scan varying goniometer parameterisation is a matrix # '[S](t)', expressed as a function of image number 't' # in a sequential scan. # # The initial state is a snapshot of the setting matrix # at the point of initialisation '[S0]', which is independent of # image number. # # Future states are composed by rotations around two axes orthogonal to the # initial spindle axis direction. # # [S](t) = [G2](t)[G1](t)[S0] # Set up the smoother smoother = GaussianSmoother(t_range, num_intervals) nv = smoother.num_values() # Set up the initial state e_lab = matrix.col(goniometer.get_rotation_axis()) istate = matrix.sqr(goniometer.get_setting_rotation()) self._S_at_t = istate # Factory function to provide to _build_p_list def parameter_type(value, axis, ptype, name): return ScanVaryingParameterSet(value, nv, axis, ptype, name) # Build the parameter list p_list = self._build_p_list(e_lab, beam, parameter_type=parameter_type) # Set up the base class ScanVaryingModelParameterisation.__init__( self, goniometer, istate, p_list, smoother, experiment_ids=experiment_ids ) return def compose(self, t): """calculate state and derivatives for model at image number t""" # Extract setting matrix from the initial state iS0 = self._initial_state # extract parameter sets from the internal list gamma1_set, gamma2_set = self._param # extract angles and other data at time t using the smoother gamma1, gamma1_weights, gamma1_sumweights = self._smoother.value_weight( t, gamma1_set ) gamma2, gamma2_weights, gamma2_sumweights = self._smoother.value_weight( t, gamma2_set ) # calculate derivatives of angles wrt underlying parameters. dgamma1_dp = gamma1_weights * (1.0 / gamma1_sumweights) dgamma2_dp = gamma2_weights * (1.0 / gamma2_sumweights) self._S_at_t, dS_dval = self._compose_core( iS0, gamma1, gamma2, gamma1_axis=gamma1_set.axis, gamma2_axis=gamma2_set.axis, ) # calculate derivatives of state wrt underlying smoother parameters dS_dp1 = [None] * dgamma1_dp.size for (i, v) in dgamma1_dp: dS_dp1[i] = dS_dval[0] * v dS_dp2 = [None] * dgamma2_dp.size for (i, v) in dgamma2_dp: dS_dp2[i] = dS_dval[1] * v # store derivatives as list-of-lists self._dstate_dp = [dS_dp1, dS_dp2] return def get_state(self): """Return setting matrix [S] at image number t""" # only a single goniometer is parameterised here, so no multi_state_elt # argument is allowed return self._S_at_t

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null

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4f1b9b470f4aa8fbe3dda5465263cc78a43c4fc1

26,022

py

Python

src/model_apply.py

hadao211/routing-challenge

e2863a3b48c4aa387538a06d3c705c219942e134

[ "MIT" ]

null

src/model_apply.py

hadao211/routing-challenge

e2863a3b48c4aa387538a06d3c705c219942e134

[ "MIT" ]

null

src/model_apply.py

hadao211/routing-challenge

e2863a3b48c4aa387538a06d3c705c219942e134

[ "MIT" ]

1

2021-08-21T11:53:20.000Z

from os import path import json, time, numpy as np, pandas as pd, datetime, copy, re, traceback, multiprocessing as mp from scipy.spatial import distance # function for solving instance def meta_zone(zone): m = re.match('[A-Z]+(?=-)', zone) higher_meta = m.group(0) if m else zone m_ = re.match('[A-Z]-\d+(?=.)', zone) meta = m_.group(0) if m_ else zone return meta, higher_meta # function for solving instance def solve(k,route, travel_time, package, timer_start, penalty, sz_weight, last_zone_weight, max_allowed_dist): print("start "+str(k)) try: n = len(travel_time) points = np.zeros((n,2)) # coords of nodes stops = route["stops"] # stop dictionary nodes = [k for k,v in travel_time.items()] # node ids of nodes zones_l = [stops[k]["zone_id"] for k,v in travel_time.items()] # zone ids of nodes types = [stops[k]["type"] for k,v in travel_time.items()] # stop types of nodes tt = np.zeros((n+1,n+1)) # convert travel matrix from dict to np array # add artificial node n+1 as end node of last path tt[n, :] = 999999 tt[:, n] = 0 tt[n,n] = 999999 # fill tt and node coords for i in range(n): tt[i,0:n] = list(travel_time[nodes[i]].values()) tt[i,i] = 999999 points[i,0] = stops[nodes[i]]["lat"] points[i,1] = stops[nodes[i]]["lng"] # impute missing zone data ################################################################### nans = [i for i in range(len(zones_l)) if pd.isna(zones_l[i])] for i in nans: if types[i] == "Station": zones_l[i] = "Start" else: closest = min([tt[i,j] for j in range(n) if tt[i,j] > 0 and pd.isna(zones_l[j]) == False]) closest = np.where(tt[i,:] == closest)[0][0] zones_l[i] = zones_l[closest] # 'assign' nodes to clusters ################################################################## zones = list(set(zones_l)) # list of unique zone ids clusters = {} # dict that holds all nodes for a zone for i in zones: clusters[i] = [j for j in range(n) if zones_l[j] == i] # data per node ############################################################################### a = [] # lower TW b = [] # upper TW s = [] # service times dateFormat = '%Y-%m-%d %H:%M:%S' start_time = str(route['date_YYYY_MM_DD']+" "+str(route['departure_time_utc'])) start_time_obj = datetime.datetime.strptime(start_time, dateFormat) start_time = start_time = (start_time_obj.hour * 60 + start_time_obj.minute) * 60 + start_time_obj.second unlimited_a = 0 # lower TW for unrestricted nodes unlimited_b = start_time+2*24*3600 # upper TW for unrestricted nodes: starting time of tour + 2 days # loop over stops for i in nodes: s_tmp = 0.0 if len(package[i])==0: # set parameters for depots a.append(start_time) b.append(unlimited_b) s.append(s_tmp) else: # set parameters for regular stops a_stop = [unlimited_a] # in case all TW entries of package are nans b_stop = [unlimited_b] # loop over packages of stop for p in package[i].values(): s_tmp += p['planned_service_time_seconds'] # add service times if isinstance(p['time_window']['start_time_utc'], str): a_package = datetime.datetime.strptime(p['time_window']['start_time_utc'], dateFormat) b_package = datetime.datetime.strptime(p['time_window']['end_time_utc'], dateFormat) # check if time windows stretch til next day # for TW=[a,_] if int(a_package.day) != int(start_time_obj.day): a_stop.append(((24+a_package.hour) * 60 + a_package.minute) * 60 + a_package.second) else: a_stop.append((a_package.hour * 60 + a_package.minute) * 60 + a_package.second) # for TW=[_,b] if int(b_package.day) != int(start_time_obj.day): b_stop.append(((24+b_package.hour) * 60 + b_package.minute) * 60 + b_package.second) else: b_stop.append((b_package.hour * 60 + b_package.minute) * 60 + b_package.second) # set most narrow TW s.append(s_tmp) a.append(max(a_stop)) b.append(min(b_stop)) # determine centroids of each zone for meta tour centroids = [] # not actual centroids but node of cluster closest to centroid ctrs = np.zeros((len(zones), 2)) # holds coords of the actual centroids # find cluster node nearest to centroid cou = 0 # for i in zones: if len(clusters[i]) > 1: ctr = [0,0] # centroid of zone i ctr[0] = np.mean(points[clusters[i], 0]) ctr[1] = np.mean(points[clusters[i], 1]) ctrs[cou, :] = ctr # cou+=1 # dist = distance.cdist([ctr], points[clusters[i],:]) min_ind = dist.argmin() centroids.append(clusters[i][min_ind]) else: centroids.append(clusters[i][0]) ctrs[cou, :] = points[clusters[i][0],:]# cou +=1 s = np.array(s) a = np.array(a) b = np.array(b) # solving routine ############################################################################# ############################################################################################### # determine meta tour #print("starting") shortest_distances_clusters={} for i in clusters.keys(): shortest_distances_clusters[i]={} for j in clusters.keys(): if i !=j: best_dist=9999999 best_from = None best_to = None for x in clusters[i]: for y in clusters[j]: dist=tt[int(x)][int(y)] if dist<best_dist: best_dist=dist best_from=x best_to=y shortest_distances_clusters[i][j]={"dist":best_dist,"from":best_from,"to":best_to} cluster_order=[("Start",clusters["Start"])] remaining_cluster=copy.deepcopy(list(clusters.keys())) remaining_cluster.remove("Start") while len(remaining_cluster)>0: best_dist=999999 best_ind=None for ind,r in enumerate(remaining_cluster): last=cluster_order[-1][0] dist=shortest_distances_clusters[last][r]["dist"] if dist <=best_dist: best_dist=dist best_ind=ind cluster_order.append((remaining_cluster[best_ind],clusters[remaining_cluster[best_ind]])) remaining_cluster.pop(best_ind) ######################################################################### def two_opt_global_clusters(cluster_order, sz_weight, last_zone_weight): improved = True distance = shortest_distances_clusters start_time = time.time() tour=cluster_order while improved: if time.time() - start_time >= 10: return tour min_i = 9999 min_j = 9999 change = 0 improved = False min_change = 0 num_cities = len(tour) # Find the best move for i in range(num_cities - 2): for j in range(i + 2, num_cities - 1): # change = dist(i, j,tour) + dist(i+1, j+1,tour) - dist(i, i+1,tour) - dist(j, j+1,tour) dist_change = distance[tour[i][0]][tour[j][0]]["dist"] + distance[tour[i + 1][0]][tour[j + 1][0]]["dist"] - \ distance[tour[i][0]][ tour[i + 1][0]]["dist"] - distance[tour[j][0]][tour[j + 1][0]]["dist"] ########################################################## curr_mz_violation = sum( [ meta_zone(tour[i][0]) != meta_zone(tour[j][0]), meta_zone(tour[i+1][0]) != meta_zone(tour[j+1][0]) ] ) prev_mz_violation = sum( [ meta_zone(tour[i][0]) != meta_zone(tour[i+1][0]), meta_zone(tour[j][0]) != meta_zone(tour[j+1][0]) ] ) meta_zone_change = curr_mz_violation - prev_mz_violation if i != 0: last_zone_change = sum( [tour[i][-1] != tour[j][-1], tour[i+1][-1] != tour[j+1][-1]] ) \ - sum( [tour[i][-1] != tour[i+1][-1], tour[j][-1] != tour[j+1][-1]] ) else: last_zone_change = 0 if meta_zone_change != 0: change = dist_change*(1-sz_weight) + meta_zone_change*100*sz_weight else: change = dist_change*(1-last_zone_weight) + last_zone_change*100*last_zone_weight ########################################################### if change < min_change and change < -0.00000001: improved = True min_change = change min_i, min_j = i, j if min_change < 0: tour[min_i + 1:min_j + 1] = tour[min_i + 1:min_j + 1][::-1] return cluster_order ########################################################################## # apply 2-opts on the clusters cluster_order=two_opt_global_clusters(cluster_order, sz_weight, last_zone_weight) curr_dist = sum([shortest_distances_clusters[cluster_order[i][0]][cluster_order[i+1][0]]["dist"] for i in range(len(cluster_order)-1)]) # looping improvement between distance optimization and zone id rules new_cluster_order = copy.deepcopy(cluster_order) changed= True start_time = time.time() while (time.time() - start_time <= 20) and changed: changed=False for i in range(1, len(new_cluster_order)-1): n1 = new_cluster_order[i][0] n2 = new_cluster_order[i+1][0] rem = [(idx, r[0]) for idx,r in list(enumerate(new_cluster_order))[i+2:]] # improve following zone id if meta_zone(n1)[0] == meta_zone(n2)[0]: # last zone rule if n1[-1] != n2[-1]: tmp = [idx for idx, r in rem if meta_zone(n1)[0] == meta_zone(r)[0] and n1[-1] == r[-1]] if len(tmp) != 0: new_cluster_order = new_cluster_order[:i+1] \ + [new_cluster_order[j] for j in tmp] \ + [new_cluster_order[idx] for idx in range(i+1,len(new_cluster_order)) if idx not in tmp] changed=True else: # super zone rule tmp = [idx for idx, r in rem if meta_zone(n1)[0] == meta_zone(r)[0]] if len(tmp) != 0: new_cluster_order = new_cluster_order[:i+1] \ + [new_cluster_order[j] for j in tmp] \ + [new_cluster_order[idx] for idx in range(i+1,len(new_cluster_order)) if idx not in tmp] changed=True if changed: new_dist = sum([shortest_distances_clusters[new_cluster_order[i][0]][new_cluster_order[i+1][0]]["dist"] for i in range(len(new_cluster_order)-1)]) # if distance increased < maximum allowed increased => exit if new_dist - curr_dist <= max_allowed_dist: cluster_order = copy.deepcopy(new_cluster_order) changed=False else: # improve distance new_cluster_order = two_opt_global_clusters(new_cluster_order, sz_weight, last_zone_weight) meta_tour = [] for i in cluster_order: meta_tour.append(zones.index(i[0])) # solve cluster paths ######################################################################### # find closest cluster nodes between 2 neighboring clusters in meta tour connections = [0] # numbers are not actual node ids but position of node in clusters dictionary for i in range(len(meta_tour)-1): tt_sel = tt[clusters[ zones[meta_tour[i] ] ], : ] tt_sel = tt_sel[: , clusters[ zones[meta_tour[i+1] ] ] ] j = np.where( tt_sel == np.min(tt_sel) ) # if j is already used, find second closest if len(clusters[ zones[meta_tour[i] ] ])>1 and \ clusters[zones[meta_tour[i]]] [connections[-1]] == clusters[zones[meta_tour[i]]] [j[0][0]]: tt_sel[j[0][0],:] = 999999 j = np.where( tt_sel == np.min(tt_sel) ) connections.append(j[0][0]) connections.append(j[1][0]) # add artificial node as destination of path of the last cluster if n not in clusters[ zones[ meta_tour[-1]] ]: clusters[ zones[ meta_tour[-1]] ].append(n) if len(connections)<len(zones)*2: connections.append( len(clusters[ zones[ meta_tour[-1]] ])-1) # solve path within clusters ################################################################################################################ # function to determine a score for given tour def tour_score(tt, a, b, s, tour, start_t, penalty):#, meta): # global penalty wait = 0 delay = 0 t = start_t + s[tour[0]] t_seq = [t] for i in range(1, len(tour)): t += round(s[tour[i]] + tt[tour[i-1], tour[i]],2) t = max(t, a[tour[i]]) wait += max(a[tour[i]]-t, 0) delay += max(t-b[tour[i]],0) t_seq.append(t) return (1-penalty)*t+penalty*(wait+delay), t_seq, wait, delay t_final = [start_time] # contains point in time when service is finished at node; for all nodes big_tour = [clusters[ zones[ meta_tour[0] ] ][0] ] # contains final tour; KEEP IN MIND: entries are the index of a node in nodes if len(a) == n: a = np.append(a,a[big_tour[0]]) b = np.append(b,b[big_tour[0]]) s = np.append(s,s[big_tour[0]]) for i in range(1, len(meta_tour)): cn = clusters[ zones[meta_tour[i] ] ] # list of clusters nodes, makes following code more concise if len(cn) == 1: # 1 node clusters big_tour.append(cn[connections[i*2]]) t_final.append(max(a[big_tour[-1]], t_final[-1] + tt[big_tour[-2], big_tour[-1]] + s[big_tour[-1]])) elif len(cn) == 2: # 2 node clusters big_tour.append(cn[connections[i*2]] ) t_final.append(max(a[big_tour[-1]], t_final[-1] + tt[big_tour[-2], big_tour[-1]] + s[big_tour[-1]])) big_tour.append(cn[connections[i*2+1]] ) t_final.append(max(a[big_tour[-1]], t_final[-1] + tt[big_tour[-2], big_tour[-1]] + s[big_tour[-1]] )) else: # larger clusters => farthest insertion # farthest insertion sub_tour = [connections[i*2], connections[i*2+1]] # contains path: KEEP IN MIND: entrier are the index of a node in cn # distance matrix of nodes within cluster tt_s = tt[cn, :] tt_s = tt_s[:, cn] # parameter of nodes within cluster a_s = a[cn] b_s = b[cn] s_s = s[cn] for i in range(len(cn)): tt_s[i,i] = -1000 tt_sel = copy.deepcopy(tt_s) tt_sel[: , sub_tour ]= -1000 test = copy.deepcopy(tt_sel) rem = list(set([i for i in range(len(cn))])-set(sub_tour)) for i in range(len(cn)-2): # determine farthest centroid far = np.max(tt_sel[sub_tour,:]) far = np.where(tt_sel[sub_tour, :] == far)[1][0] tt_sel[:,far] = -1000 # insert at best position best = 9999999999 b_ind = -1 for j in range(1, len(sub_tour)): new_tour = copy.deepcopy(sub_tour) new_tour.insert(j, far) new, t_seq, _, _ = tour_score(tt_s, a_s, b_s, s_s, new_tour, max(a[cn[sub_tour[0]]] ,t_final[-1] + tt[big_tour[-1], cn[sub_tour[0]]]), penalty) if new < best: b_ind = j best_tour = new_tour best = new tseq = t_seq # update rem and sub tour/path rem.remove(far) sub_tour = copy.deepcopy(best_tour) t_final += tseq sub_tour2 = [cn[index] for index in sub_tour] # convert to real node ids (index of node in nodes) big_tour += sub_tour2 # add to big tour big_tour.pop() # pop artificial node t_final.pop() # pop artificial node final_score, t_final_val, ff_wait, ff_delay = tour_score(tt, a, b, s, big_tour, start_time, penalty) # convert to result format out = {} for i in range(len(big_tour)): out[nodes[big_tour[i]]] = i return k,out except Exception as e: print(e) print("EXCEPTION CAUGHT!!!!!!!!") return k,{} # Preprocessing ############################################################################### ############################################################################################### if __name__ == "__main__": # Get Directory BASE_DIR = path.dirname(path.dirname(path.abspath(__file__))) # Read input data print('Reading Input Data') # Model Build output try: model_path=path.join(BASE_DIR, 'data/model_build_outputs/model.json') with open(model_path, newline='') as in_file: model_build_out = json.load(in_file) except Exception: # in case build process crashed completely, use fallback parameters print("no model file, use fallback solution instead") model_build_out={0: { 'sz_weight': {'best': 0.9}, 'lz_weight': {'best': 0}, 'penalty': {'best': 0}, 'max_dist': {'best': 300}}, 1: { 'sz_weight': {'best': 0.8}, 'lz_weight': {'best': 0}, 'penalty': {'best': 0}, 'max_dist': {'best': 300}}, 2: { 'sz_weight': {'best': 0.8}, 'lz_weight': {'best': 0}, 'penalty': {'best': 0}, 'max_dist': {'best': 300} } } #check the model output for i in range(3): if i not in model_build_out: # check set model_build_out.update({i: { 'sz_weight': {'best': 0.9 if i == 0 else 0.8}, 'lz_weight': {'best': 0}, 'penalty': {'best': 0}, 'max_dist': {'best': 300}} }) else: params = ['sz_weight', 'lz_weight', 'penalty', 'max_dist'] default_vals = [0.9 if i == 1 else 0.8, 0, 0, 300] for j in range(len(params)): # check params if params[j] not in model_build_out[i]: model_build_out[i].update({params[j]: {'best': default_vals[j]}}) else: if 'best' not in model_build_out[i][params[j]]: model_build_out[i][params[j]].update({'best': default_vals[j]}) print(model_build_out) # Prediction Routes (Model Apply input) prediction_routes_path = path.join(BASE_DIR, 'data/model_apply_inputs/new_route_data.json') with open(prediction_routes_path, newline='') as in_file: prediction_routes = json.load(in_file) # Prediction Travel Times prediction_travel_times_path = path.join(BASE_DIR, 'data/model_apply_inputs/new_travel_times.json') with open(prediction_travel_times_path, newline='') as in_file: prediction_travel_times = json.load(in_file) # Prediction Travel Times prediction_packages_path = path.join(BASE_DIR, 'data/model_apply_inputs/new_package_data.json') with open(prediction_packages_path, newline='') as in_file: prediction_packages = json.load(in_file) ################################################################################################### output = {} # for actual tour output count = 1 # load data ################################################################################### # read parameter weight_dict = {} for key, route in prediction_routes.items(): weight_dict[key] = {} n_sz = len(set([meta_zone(route['stops'][s]['zone_id'])[0] for s in route['stops'] if str(route['stops'][s]['zone_id']) != 'nan'])) if n_sz == 1: sz_weight = model_build_out[0]['sz_weight']['best'] lz_weight = model_build_out[0]['lz_weight']['best'] penalty = model_build_out[0]['penalty']['best'] max_dist = model_build_out[0]['max_dist']['best'] elif n_sz == 2: sz_weight = model_build_out[1]['sz_weight']['best'] lz_weight = model_build_out[1]['lz_weight']['best'] penalty = model_build_out[1]['penalty']['best'] max_dist = model_build_out[1]['max_dist']['best'] else: sz_weight = model_build_out[2]['sz_weight']['best'] lz_weight = model_build_out[2]['lz_weight']['best'] penalty = model_build_out[2]['penalty']['best'] max_dist = model_build_out[2]['max_dist']['best'] weight_dict[key].update({ 'sz_weight': sz_weight, 'lz_weight': lz_weight, 'penalty': penalty, 'max_dist': max_dist }) # start solving ################################################################################### timer_start = time.time() args = [(key, prediction_routes[key], copy.deepcopy(prediction_travel_times[key]), prediction_packages[key], timer_start, weight_dict[key]['penalty'], weight_dict[key]['sz_weight'], weight_dict[key]['lz_weight'], weight_dict[key]['max_dist']) for key in list(prediction_routes.keys())] pool = mp.Pool(mp.cpu_count()) result = pool.starmap(solve, args) for key,out in result: output[key] = {} output[key]["proposed"] = {} output[key]['proposed'] = out print("Finish solving") # Write output data output_path=path.join(BASE_DIR, 'data/model_apply_outputs/proposed_sequences.json') with open(output_path, 'w') as out_file: json.dump(output, out_file) print("Success: The '{}' file has been saved".format(output_path)) print(f"model apply done after {time.time()-timer_start}") with open(path.join(BASE_DIR, 'data/model_apply_outputs/runningtime_apply.json'), 'w') as f: f.write(str(time.time()-timer_start)) print('Done!')

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false

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0.025907

0

null

0

null

0

1

0

4f1c65e273012bb93c3cad39c1fe3b14f2e4cc5f

7,465

py

Python

tests/osquery/osquery_load.py

synthetic-intelligence/zentral

774104cea90b7f3d6f2aac655859c1b1f034f8dd

[ "Apache-2.0" ]

null

tests/osquery/osquery_load.py

synthetic-intelligence/zentral

774104cea90b7f3d6f2aac655859c1b1f034f8dd

[ "Apache-2.0" ]

null

tests/osquery/osquery_load.py

synthetic-intelligence/zentral

774104cea90b7f3d6f2aac655859c1b1f034f8dd

[ "Apache-2.0" ]

null

import random import requests import string import uuid as uuid_mod SYSTEM_INFOS = [ {'cpu_brand': 'Apple M1', 'cpu_logical_cores': '8', 'cpu_physical_cores': '8', 'cpu_subtype': 'ARM64E', 'cpu_type': 'arm64e', 'hardware_model': 'MacBook Air (M1, 2020)', 'physical_memory': '17179869184'}, {'cpu_brand': 'Intel(R) Core(TM) i7-1068NG7 CPU @ 2.30GHz', 'cpu_logical_cores': '8', 'cpu_physical_cores': '4', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookPro16,2', 'physical_memory': '17179869184'}, {'cpu_brand': 'Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz', 'cpu_logical_cores': '16', 'cpu_physical_cores': '8', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookPro16,1', 'physical_memory': '34359738368'}, {'cpu_brand': 'Intel(R) Core(TM) i5-8210Y CPU @ 1.60GHz', 'cpu_logical_cores': '4', 'cpu_physical_cores': '2', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookAir8,2', 'physical_memory': '17179869184'}, {'cpu_brand': 'Intel(R) Core(TM) i5-1030NG7 CPU @ 1.10GHz', 'cpu_logical_cores': '8', 'cpu_physical_cores': '4', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookAir9,1', 'physical_memory': '17179869184'}, {'cpu_brand': 'Intel(R) Core(TM) i5-8279U CPU @ 2.40GHz', 'cpu_logical_cores': '8', 'cpu_physical_cores': '4', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookPro15,2', 'physical_memory': '17179869184'}, {'cpu_brand': 'Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz', 'cpu_logical_cores': '8', 'cpu_physical_cores': '4', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookPro15,2', 'physical_memory': '17179869184'}, {'cpu_brand': 'Apple M1 Max', 'cpu_logical_cores': '10', 'cpu_physical_cores': '10', 'cpu_subtype': 'ARM64E', 'cpu_type': 'arm64e', 'hardware_model': 'MacBook Pro (16-inch, 2021)', 'physical_memory': '68719476736'}, {'cpu_brand': 'Apple M1 Pro', 'cpu_logical_cores': '8', 'cpu_physical_cores': '8', 'cpu_subtype': 'ARM64E', 'cpu_type': 'arm64e', 'hardware_model': 'MacBook Pro (14-inch, 2021)', 'physical_memory': '34359738368'}, {'cpu_brand': 'Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz', 'cpu_logical_cores': '12', 'cpu_physical_cores': '6', 'cpu_subtype': 'Intel x86-64h Haswell', 'cpu_type': 'x86_64h', 'hardware_model': 'MacBookPro15,1', 'physical_memory': '34359738368'}, ] OS_VERSIONS = [ (12, 3, 1, "macOS", "21E258"), (12, 2, 1, "macOS", "21D62"), (11, 6, 5, "macOS", "20G527"), (11, 6, 0, "macOS", "20G165"), ] FIREFOX_CHOICES = [ ("10022.4.19", "100.0"), ("9922.4.11", "99.0.1"), ("9922.3.30", "99.0"), ] def make_random_word_function(): with open("/usr/share/dict/words", "r", encoding="utf-8") as f: word_list = list(set(w.strip().lower() for w in f.readlines() if w.strip() and len(w) > 3)) def random_word_function(): return random.choice(word_list) return random_word_function def random_serial_number(prefix=""): return (prefix + random.choice(string.ascii_uppercase) + "".join(random.sample(string.ascii_uppercase + string.digits, max(0, 9 - len(prefix)))) + "".join(random.sample(string.ascii_uppercase, 2))) def random_uuid(): return str(uuid_mod.uuid4()).upper() def random_os_version(cpu_type): choices = OS_VERSIONS if "arm" in cpu_type.lower(): choices = [t for t in choices if t[0] >= 12] os_version_t = random.choice(choices) os_version = dict(zip(("major", "minor", "patch", "name", "build"), os_version_t)) os_version["table_name"] = "os_version" return os_version def random_system_info(computer_name, serial_number): system_info = random.choice(SYSTEM_INFOS) system_info["computer_name"] = computer_name system_info["hardware_serial"] = serial_number system_info["table_name"] = "system_info" return system_info def random_firefox_version(): version, version_str = random.choice(FIREFOX_CHOICES) return { "bundle_id": "org.mozilla.firefox", "bundle_name": "Firefox", "bundle_version": version, "bundle_version_str": version_str, "bundle_path": "/Applications/Firefox.app", "table_name": "apps" } def random_inventory_result(node_key, computer_name, serial_number, uuid, osquery_version): system_info = random_system_info(computer_name, serial_number) os_version = random_os_version(system_info["cpu_type"]) app = random_firefox_version() return { "node_key": node_key, "log_type": "result", "action": "snapshot", "data": [ {"snapshot": [ system_info, os_version, app, ], "hostIdentifier": serial_number, "calendarTime": "", "unixTime": 0, "epoch": 0, "counter": 0, "numerics": False, "name": "ztl-inv", "decorations": { "serial_number": serial_number, "version": osquery_version, }} ] } def enroll(base_url, enrollment_secret, computer_name, serial_number, uuid, osquery_version): enroll_payload = { "host_identifier": computer_name, "enroll_secret": enrollment_secret, "platform_type": "21", "host_details": {"system_info": {"hardware_serial": serial_number, "uuid": uuid}, "osquery_info": {"version": osquery_version}}, } response = requests.post(f"{base_url}/osquery/enroll", json=enroll_payload, headers={'user-agent': f"osquery/{osquery_version}"}) response.raise_for_status() return response.json()["node_key"] def post_inventory_result(base_url, node_key, computer_name, serial_number, uuid, osquery_version): inventory_result = random_inventory_result(node_key, computer_name, serial_number, uuid, osquery_version) response = requests.post(f"{base_url}/osquery/log", json=inventory_result, headers={'user-agent': f"osquery/{osquery_version}"}) response.raise_for_status() def iter_machines(num=10, prefix=""): random_word_function = make_random_word_function() for i in range(num): yield ("-".join(random_word_function() for _ in range(3)), random_serial_number(prefix), random_uuid()) if __name__ == "__main__": import sys base_url, enrollment_secret = sys.argv[1:] osquery_version = "5.2.2" for computer_name, serial_number, uuid in iter_machines(2000, prefix="DEMO"): print(computer_name, serial_number, uuid) node_key = enroll(base_url, enrollment_secret, computer_name, serial_number, uuid, osquery_version) print("Enrollment OK") post_inventory_result(base_url, node_key, computer_name, serial_number, uuid, osquery_version) print("Inventory OK")

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null

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4f1d966244bdd8238e88c699c4861240b6a65462

3,155

py

Python

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/learn-python3/micropython/rccar/main.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

5

2021-06-02T23:44:25.000Z

2021-12-27T16:21:57.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/learn-python3/micropython/rccar/main.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

22

2021-05-31T01:33:25.000Z

2021-10-18T18:32:39.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/learn-python3/micropython/rccar/main.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

3

2021-06-19T03:37:47.000Z

2021-08-31T00:49:51.000Z

#!/usr/bin/env pybricks-micropython import struct, threading from pybricks import ev3brick as brick from pybricks.ev3devices import ( Motor, TouchSensor, ColorSensor, InfraredSensor, UltrasonicSensor, GyroSensor, ) from pybricks.parameters import ( Port, Stop, Direction, Button, Color, SoundFile, ImageFile, Align, ) from pybricks.tools import print, wait, StopWatch from pybricks.robotics import DriveBase from devices import detectJoystick class Robot: def __init__(self): self.motor = Motor(Port.B) self.ultrasonic = UltrasonicSensor(Port.S4) self.active = True self.speed = 0 self.colors = [None, Color.GREEN, Color.YELLOW, Color.RED] def setSpeed(self, acc): if acc < 0: self.speed = max(-3, self.speed - 1) elif acc > 0: self.speed = min(3, self.speed + 1) else: self.speed = 0 if self.speed != 0: self.motor.run(self.speed * 90) else: self.motor.stop() brick.light(self.colors[abs(self.speed)]) def inactive(self): self.active = False self.setSpeed(0) brick.sound.beep() def autoStopLoop(robot): while robot.active: if robot.speed > 0 and robot.ultrasonic.distance() < 200: robot.setSpeed(0) wait(100) def joystickLoop(robot, eventFile): FORMAT = "llHHI" EVENT_SIZE = struct.calcsize(FORMAT) with open(eventFile, "rb") as infile: while True: event = infile.read(EVENT_SIZE) _, _, t, c, v = struct.unpack(FORMAT, event) # button A, B: if t == 1 and v == 1: if c == 305: # press A: robot.setSpeed(1) elif c == 304: # press B: robot.setSpeed(-1) elif c == 307: # press X: return robot.inactive() elif t == 3: if c == 1: # Left stick & vertical: speed = 0 if v < 32768: # up: speed = 1 elif v > 32768: # down: speed = -1 robot.setSpeed(speed) def buttonLoop(robot): while True: if not any(brick.buttons()): wait(10) else: if Button.LEFT in brick.buttons(): robot.setSpeed(-1) elif Button.RIGHT in brick.buttons(): robot.setSpeed(1) elif Button.CENTER in brick.buttons(): robot.setSpeed(0) elif Button.UP in brick.buttons(): return robot.inactive() wait(500) def main(): brick.sound.beep() joystickEvent = detectJoystick(["Controller"]) robot = Robot() t = threading.Thread(target=autoStopLoop, args=(robot,)) t.start() if joystickEvent: joystickLoop(robot, joystickEvent) else: buttonLoop(robot) main()

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null

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null

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4f1f0cf6edffe04fb3dbf21bea18886f3b13bfaf

3,160

py

Python

0.3.7/src/SoundPrintCollector.py

RockmanZheng/Digital-Speech-Recognizer

6cf0b9edc4d040458f5172e811fd5e266ab284f1

[ "Apache-2.0" ]

15

2017-06-13T01:14:34.000Z

2020-12-27T14:37:24.000Z

0.3.7/src/SoundPrintCollector.py

RockmanZheng/Digital-Speech-Recognizer

6cf0b9edc4d040458f5172e811fd5e266ab284f1

[ "Apache-2.0" ]

2

2017-11-13T13:04:24.000Z

2018-07-15T16:44:17.000Z

0.3.7/src/SoundPrintCollector.py

RockmanZheng/Digital-Speech-Recognizer

6cf0b9edc4d040458f5172e811fd5e266ab284f1

[ "Apache-2.0" ]

15

2017-08-03T07:33:22.000Z

2022-01-23T04:16:57.000Z

# Prompt the user to utter scripts # Record sound print into wave file import pyaudio import wave import sys import Utility def Collect(output_file,instruction=''): CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 44100 RECORD_SECONDS = 5 if instruction != '': raw_input(instruction) p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) print("* recording. Press <crtl>+<c> to complete the recording.") frames = [] while True: try: data = stream.read(CHUNK) frames.append(data) except KeyboardInterrupt: break print("* done recording.") stream.stop_stream() stream.close() p.terminate() wf = wave.open(output_file, 'wb') wf.setnchannels(CHANNELS) wf.setsampwidth(p.get_sample_size(FORMAT)) wf.setframerate(RATE) wf.writeframes(b''.join(frames)) wf.close() MAIN_DIR = '../' WAVE_FOLDER = MAIN_DIR + 'wav/' SINGLE_FOLDER = WAVE_FOLDER + 'single/' CONFIG_DIR = MAIN_DIR + 'config/' DICT_DIR = MAIN_DIR + 'dict/' # EMBEDDED_FOLDER = WAVE_FOLDER+'Embedded/' # if len(sys.argv)<3: if len(sys.argv) < 2: # sys.exit("Usage: SoundPrintCollector.py <dict> <transcipts> <config>") sys.exit("Usage: SoundPrintCollector.py <dict> <config>") num_repeat_key = 'NUMREPEAT' # Get configuration # num_repeat = Utility.ParseConfig(sys.argv[3],num_repeat_key) # num_repeat = Utility.ParseConfig(sys.argv[2],num_repeat_key) num_repeat = Utility.ParseConfig(CONFIG_DIR + sys.argv[2] + '.conf',num_repeat_key) if num_repeat != '': num_repeat = int(num_repeat) else: num_repeat = 1 # Get word list # dict_file = open(sys.argv[1]) # model_id = [] # words = [] # for line in dict_file: # tokens = line.strip().split() # words.append(tokens[1]) # model_id.append(tokens[0]) # dict_file.close() words,model_id = Utility.GetDictionary(DICT_DIR + sys.argv[1] + '.txt') # # Get scripts # scripts_file = open(sys.argv[2]) # script_ids = [] # scripts = [] # for line in scripts_file: # tokens = line.strip().split() # script_ids.append(tokens[0]) # script = '' # for i in range(1,len(tokens)): # script += tokens[i]+' ' # scripts.append(script) # num_scripts = len(scripts) # total_num = len(words)*num_repeat+num_scripts total_num = len(words) * num_repeat # Collect sound print for single model for i in range(len(words)): for k in range(num_repeat): total_num -= 1 print(str(total_num) + ' transcript(s) remaining.') if words[i].find('!') > -1: instruction = 'Press <Enter> to record background noise.\n' + words[i] else: instruction = 'Get ready to speek the following script and press <Enter> to start record.\n Remember to leave 3 seconds of blank before and after the utterance.' + words[i] Collect(SINGLE_FOLDER + model_id[i] + '-' + str(k) + '.wav',instruction) # # Collect sound print for embedded model # for i in range(num_scripts): # total_num -= 1 # print str(total_num)+'transcript(s) remaining.' # instruction = 'Get ready to speek the following script and press <Enter> to start record.\n'+scripts[i] # Collect(EMBEDDED_FOLDER+script_ids[i]+'.wav',instruction)

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null

0

null

0

1

0

4f22f0d4d5c89564faf5aad5ae6f9de88a11a4aa

907

py

Python

scripts/WindowsVersion/Linker.py

Lyniat/AutomatedWallpaperChanger

76093f4f9bd20d8fdfd497f6dfbe93d22b17feac

[ "MIT" ]

null

scripts/WindowsVersion/Linker.py

Lyniat/AutomatedWallpaperChanger

76093f4f9bd20d8fdfd497f6dfbe93d22b17feac

[ "MIT" ]

null

scripts/WindowsVersion/Linker.py

Lyniat/AutomatedWallpaperChanger

76093f4f9bd20d8fdfd497f6dfbe93d22b17feac

[ "MIT" ]

1

2021-07-19T17:32:04.000Z

import winshell import os from pathlib import Path class Linker: """Creates the shortcuts for the Desktop as well as the Autostart""" def __init__(self, install_path, autostart_path): # Gets the paths self.install_path = install_path self.autostart_path = autostart_path # Gets desktop path self.desktop_path = Path(winshell.desktop()) # Creates shortcut for autostart self.create_shortcut(self.autostart_path) # Creates shortcut for desktop self.create_shortcut(self.desktop_path) def create_shortcut(self, path): """Makes a shortcut of the .exe in the specified folder""" with winshell.shortcut(os.path.join(path, "AWC.lnk")) as link: link.path = os.path.join(self.install_path, "Launcher.exe") link.working_directory = self.install_path if __name__ == "__main__": ...

29.258065

72

0.669239

115

907

5.043478

0.365217

0.094828

0.103448

0.072414

0

0.242558

907

30

73

30.233333

0.84425

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0

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0

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0.125

false

0

0.1875

0

0.375

0

null

0

null

0

1

0

4f24e0bab05f02affd8c7615a133de4f38e4ed8f

1,428

py

Python

baselines/bao/bao/classifier/factory.py

armancohan/flex

2a005fd18f522d2667421f170568df1164a73c3a

[ "Apache-2.0" ]

63

2021-07-01T23:40:55.000Z

2022-03-15T21:56:57.000Z

baselines/bao/bao/classifier/factory.py

armancohan/flex

2a005fd18f522d2667421f170568df1164a73c3a

[ "Apache-2.0" ]

1

2022-03-04T11:15:55.000Z

2022-03-28T09:33:54.000Z

baselines/bao/bao/classifier/factory.py

armancohan/flex

2a005fd18f522d2667421f170568df1164a73c3a

[ "Apache-2.0" ]

3

2021-07-31T05:06:14.000Z

2022-02-28T12:45:06.000Z

import torch from .nn import NN from .proto import PROTO from .r2d2 import R2D2 from .lrd2 import LRD2 from .mlp import MLP from .routing import ROUTING from ..dataset.utils import tprint def get_classifier(ebd_dim, args): tprint("Building classifier") if args.classifier == 'nn': model = NN(ebd_dim, args) elif args.classifier == 'proto': model = PROTO(ebd_dim, args) elif args.classifier == 'r2d2': model = R2D2(ebd_dim, args) elif args.classifier == 'lrd2': model = LRD2(ebd_dim, args) elif args.classifier == 'routing': model = ROUTING(ebd_dim, args) elif args.classifier == 'mlp': # detach top layer from rest of MLP if args.mode == 'finetune': top_layer = MLP.get_top_layer(args, args.n_train_class) model = MLP(ebd_dim, args, top_layer=top_layer) # if not finetune, train MLP as a whole else: model = MLP(ebd_dim, args) else: raise ValueError('Invalid classifier. ' 'classifier can only be: nn, proto, r2d2, mlp.') if args.snapshot != '': # load pretrained models tprint("Loading pretrained classifier from {}".format( args.snapshot + '.clf' )) model.load_state_dict(torch.load(args.snapshot + '.clf')) if args.cuda != -1: return model.cuda(args.cuda) else: return model

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false

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0

null

0

null

0

1

0

4f25539a7c0c30ccf2e316ab7f7df71a729fa981

2,747

py

Python

models/gauss.py

tangym/autoapi

adc3ce02a803dd989be787ff21568231103d8625

[ "Apache-2.0" ]

null

models/gauss.py

tangym/autoapi

adc3ce02a803dd989be787ff21568231103d8625

[ "Apache-2.0" ]

null

models/gauss.py

tangym/autoapi

adc3ce02a803dd989be787ff21568231103d8625

[ "Apache-2.0" ]

null

import json import collections import numpy as np from scipy.stats import multivariate_normal # import matplotlib.pyplot as plt def parse_json_parameters(func): def inner(*args, **kwargs): print(args, kwargs) args = [json.loads(value) if type(value) is str else value for value in args] kwargs = {key: json.loads(kwargs[key]) if type(kwargs[key]) is str else kwargs[key] for key in kwargs} return func(*args, **kwargs) return inner def parse_json(x): try: return json.loads(x) except: return x # @parse_json_parameters def generate_gauss_sample(mu='0', sigma='1', n='1'): mu, sigma, n = parse_json(mu), parse_json(sigma), parse_json(n) try: return np.random.multivariate_normal(mu, sigma, n).tolist() except ValueError as ve: return np.random.normal(mu, sigma, n).tolist() # @parse_json_parameters def gauss_pdf(mu='0', sigma='1', x='0'): mu, sigma, x = parse_json(mu), parse_json(sigma), parse_json(x) return multivariate_normal(mu, sigma).pdf(x).tolist() # def plot_dataset(class1, class2): # for sample in class1: # plt.plot(sample[0], sample[1], 'r.') # for sample in class2: # plt.plot(sample[0], sample[1], 'g.') # plt.savefig('{}-{}.pdf'.format(len(class1), len(class2))) # def decide(class1, class2): # def inner(sample): # return 2 * class1.pdf(sample) - class2.pdf(sample) # return inner # def plot_roc(): # with open('dataset.txt') as f: # dataset = json.load(f) # # Training model # class1_samples = np.matrix(dataset['class1']['samples']).T # class2_samples = np.matrix(dataset['class2']['samples']).T # class1 = mnormal(mean=np.asarray(np.mean(class1_samples, axis=1)).reshape(-1), cov=np.cov(class1_samples)) # class2 = mnormal(mean=np.asarray(np.mean(class2_samples, axis=1)).reshape(-1), cov=np.cov(class2_samples)) # g = decide(class1, class2) # # Counting TP and FP # samples = [(g(sample), 'class1') for sample in dataset['class1']['samples']] + [(g(sample), 'class2') for sample in dataset['class2']['samples']] # samples.sort(key=lambda e: e[0], reverse=True) # samples = [sample[1] for sample in samples] # tp = np.cumsum([sample=='class1' for sample in samples]) # tp = [i/len(dataset['class1']['samples']) for i in tp] # fp = np.cumsum([sample=='class2' for sample in samples]) # fp = [i/len(dataset['class2']['samples']) for i in fp] # # Plot # plt.plot(fp, tp) # plt.plot([0,1], [0,1], '--') # plt.xlabel('False Positive Rate') # plt.ylabel('True Positive Rate') # plt.savefig('roc.pdf')

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null

0

null

0

1

0

4f25a23bcc4d4f91ead0bc8861399e70bae9cc51

5,873

py

Python

dxm/lib/DxAlgorithm/DxAlgorithm.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

5

2018-08-23T15:47:05.000Z

2022-01-19T23:38:18.000Z

dxm/lib/DxAlgorithm/DxAlgorithm.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

59

2018-10-15T10:37:00.000Z

2022-03-22T20:49:25.000Z

dxm/lib/DxAlgorithm/DxAlgorithm.py

experiortec/dxm-toolkit

b2ab6189e163c62fa8d7251cd533d2a36430d44a

[ "Apache-2.0" ]

12

2019-03-08T19:59:13.000Z

2021-12-16T03:28:04.000Z

# # 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. # # Copyright (c) 2018 by Delphix. All rights reserved. # # Author : Marcin Przepiorowski # Date : April 2018 import logging import pickle import json from dxm.lib.DxLogging import print_error from dxm.lib.DxLogging import print_message from dxm.lib.masking_api.api.sync_api import SyncApi from dxm.lib.masking_api.rest import ApiException from dxm.lib.masking_api.genericmodel import GenericModel class DxAlgorithm(object): swagger_types = { 'algorithm_name': 'str', 'algorithm_type': 'str', 'created_by': 'str', 'description': 'str', 'algorithm_extension': 'dict', 'framework_id': 'int', 'plugin_id': 'int', 'fields': 'dict' } swagger_map = { 'algorithm_name': 'algorithmName', 'algorithm_type': 'algorithmType', 'created_by': 'createdBy', 'description': 'description', 'algorithm_extension': 'algorithmExtension', 'framework_id': 'frameworkId', 'plugin_id': 'pluginId', 'fields' : 'fields' } def __init__(self, engine): """ Constructor :param engine: DxMaskingEngine object """ #Algorithm.__init__(self) self.__engine = engine self.__logger = logging.getLogger() self.__domain_name = None self.__sync = None self.__logger.debug("creating DxAlgorithm object") self.__api = SyncApi self.__apiexc = ApiException self.__obj = None @property def obj(self): if self.__obj is not None: return self.__obj else: return None def from_alg(self, alg): """ Set obj properties with a Algorithm object :param column: Algorithm object """ self.__obj = alg self.__obj.swagger_map = self.swagger_map self.__obj.swagger_types = self.swagger_types @property def domain_name(self): return self.__domain_name @domain_name.setter def domain_name(self, domain): self.__domain_name = domain @property def sync(self): return self.__sync @sync.setter def sync(self, sync): self.__sync = sync @property def algorithm_name(self): if self.obj is not None and hasattr(self.obj,'algorithm_name'): return self.obj.algorithm_name else: return None @property def algorithm_type(self): if self.obj is not None and hasattr(self.obj,'algorithm_type'): return self.obj.algorithm_type else: return None @property def created_by(self): if self.obj is not None and hasattr(self.obj,'created_by'): return self.obj.created_by else: return None @property def description(self): if self.obj is not None and hasattr(self.obj,'description'): return self.obj.description else: return None @property def algorithm_extension(self): if self.obj is not None and hasattr(self.obj,'algorithm_extension'): return self.obj.algorithm_extension else: return None @property def framework_id(self): if self.obj is not None and hasattr(self.obj,'framework_id'): return self.obj.framework_id else: return None @property def plugin_id(self): if self.obj is not None and hasattr(self.obj,'plugin_id'): return self.obj.plugin_id else: return None @property def fields(self): if self.obj is not None and hasattr(self.obj,'fields'): return self.obj.fields else: return None # def export(self, path=None): # """ # Export algorithm into file # :param path: path to save algorithm # """ # api_sync = SyncApi(self.__engine.api_client) # self.__logger.debug("Export input %s" % self.sync) # export_list = [] # export_list.append(self.sync) # api_response = api_sync.export(export_list) # self.__logger.debug("Export response %s" % str(api_response)) # # # binary_file = open('{0}.alg'.format(self.algorithm_name), mode='wb') # # json.dump(api_response.blob, binary_file) # # binary_file.close() # # binary_file = open('{0}.alg_bin '.format(self.algorithm_name), mode='wb') # pickle.dump(api_response, binary_file) # binary_file.close() # # # def importalg(self, path=None): # """ # Import algorithm from file # :param path: path to save algorithm # """ # # binary_file = open('{0}.alg_bin'.format("EU_LAST_NAME"), mode='rb') # algobj = pickle.load(binary_file) # binary_file.close() # # # api_sync = SyncApi(self.__engine.api_client) # self.__logger.debug("Import input %s" % self.sync) # api_response = api_sync.import_object(algobj, force_overwrite=True) # self.__logger.debug("Import response %s" % str(api_response)) # # # binary_file = open('{0}.alg'.format(self.algorithm_name), mode='wb') # # json.dump(api_response.blob, binary_file) # # binary_file.close()

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null

0

null

0

1

0

4f27d366d10504ccd771920410823b0f81358bab

15,577

py

Python

tfsnippet/variational/estimators.py

QianLiGui/tfsnippet

63adaf04d2ffff8dec299623627d55d4bacac598

[ "MIT" ]

63

2018-06-06T11:56:40.000Z

2022-03-22T08:00:59.000Z

tfsnippet/variational/estimators.py

QianLiGui/tfsnippet

63adaf04d2ffff8dec299623627d55d4bacac598

[ "MIT" ]

39

2018-07-04T12:40:53.000Z

2022-02-09T23:48:44.000Z

tfsnippet/variational/estimators.py

QianLiGui/tfsnippet

63adaf04d2ffff8dec299623627d55d4bacac598

[ "MIT" ]

34

2018-06-25T09:59:22.000Z

2022-02-23T12:46:33.000Z

from contextlib import contextmanager import tensorflow as tf from tfsnippet.ops import log_mean_exp, convert_to_tensor_and_cast from tfsnippet.utils import (add_name_arg_doc, get_static_shape, get_dimension_size, is_tensor_object, assert_deps) from .utils import _require_multi_samples __all__ = [ 'sgvb_estimator', 'iwae_estimator', 'nvil_estimator', 'vimco_estimator', ] @add_name_arg_doc def sgvb_estimator(values, axis=None, keepdims=False, name=None): """ Derive the gradient estimator for :math:`\\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})}\\big[f(\\mathbf{x},\\mathbf{z})\\big]`, by SGVB (Kingma, D.P. and Welling, M., 2013) algorithm. .. math:: \\nabla \\, \\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})}\\big[f(\\mathbf{x},\\mathbf{z})\\big] = \\nabla \\, \\mathbb{E}_{q(\\mathbf{\\epsilon})}\\big[f(\\mathbf{x},\\mathbf{z}(\\mathbf{\\epsilon}))\\big] = \\mathbb{E}_{q(\\mathbf{\\epsilon})}\\big[\\nabla f(\\mathbf{x},\\mathbf{z}(\\mathbf{\\epsilon}))\\big] Args: values: Values of the target function given `z` and `x`, i.e., :math:`f(\\mathbf{z},\\mathbf{x})`. axis: The sampling axes to be reduced in outputs. If not specified, no axis will be reduced. keepdims (bool): When `axis` is specified, whether or not to keep the reduced axes? (default :obj:`False`) Returns: tf.Tensor: The surrogate for optimizing the original target. Maximizing/minimizing this surrogate via gradient descent will effectively maximize/minimize the original target. """ values = tf.convert_to_tensor(values) with tf.name_scope(name, default_name='sgvb_estimator', values=[values]): estimator = values if axis is not None: estimator = tf.reduce_mean(estimator, axis=axis, keepdims=keepdims) return estimator @add_name_arg_doc def iwae_estimator(log_values, axis, keepdims=False, name=None): """ Derive the gradient estimator for :math:`\\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\Big[\\log \\frac{1}{K} \\sum_{k=1}^K f\\big(\\mathbf{x},\\mathbf{z}^{(k)}\\big)\\Big]`, by IWAE (Burda, Y., Grosse, R. and Salakhutdinov, R., 2015) algorithm. .. math:: \\begin{aligned} &\\nabla\\,\\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\Big[\\log \\frac{1}{K} \\sum_{k=1}^K f\\big(\\mathbf{x},\\mathbf{z}^{(k)}\\big)\\Big] = \\nabla \\, \\mathbb{E}_{q(\\mathbf{\\epsilon}^{(1:K)})}\\Bigg[\\log \\frac{1}{K} \\sum_{k=1}^K w_k\\Bigg] = \\mathbb{E}_{q(\\mathbf{\\epsilon}^{(1:K)})}\\Bigg[\\nabla \\log \\frac{1}{K} \\sum_{k=1}^K w_k\\Bigg] = \\\\ & \\quad \\mathbb{E}_{q(\\mathbf{\\epsilon}^{(1:K)})}\\Bigg[\\frac{\\nabla \\frac{1}{K} \\sum_{k=1}^K w_k}{\\frac{1}{K} \\sum_{i=1}^K w_i}\\Bigg] = \\mathbb{E}_{q(\\mathbf{\\epsilon}^{(1:K)})}\\Bigg[\\frac{\\sum_{k=1}^K w_k \\nabla \\log w_k}{\\sum_{i=1}^K w_i}\\Bigg] = \\mathbb{E}_{q(\\mathbf{\\epsilon}^{(1:K)})}\\Bigg[\\sum_{k=1}^K \\widetilde{w}_k \\nabla \\log w_k\\Bigg] \\end{aligned} Args: log_values: Log values of the target function given `z` and `x`, i.e., :math:`\\log f(\\mathbf{z},\\mathbf{x})`. axis: The sampling axes to be reduced in outputs. keepdims (bool): When `axis` is specified, whether or not to keep the reduced axes? (default :obj:`False`) Returns: tf.Tensor: The surrogate for optimizing the original target. Maximizing/minimizing this surrogate via gradient descent will effectively maximize/minimize the original target. """ _require_multi_samples(axis, 'iwae estimator') log_values = tf.convert_to_tensor(log_values) with tf.name_scope(name, default_name='iwae_estimator', values=[log_values]): estimator = log_mean_exp(log_values, axis=axis, keepdims=keepdims) return estimator @add_name_arg_doc def nvil_estimator(values, latent_log_joint, baseline=None, center_by_moving_average=True, decay=0.8, axis=None, keepdims=False, batch_axis=None, name=None): """ Derive the gradient estimator for :math:`\\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})}\\big[f(\\mathbf{x},\\mathbf{z})\\big]`, by NVIL (Mnih and Gregor, 2014) algorithm. .. math:: \\begin{aligned} \\nabla \\, \\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})} \\big[f(\\mathbf{x},\\mathbf{z})\\big] &= \\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})}\\Big[ \\nabla f(\\mathbf{x},\\mathbf{z}) + f(\\mathbf{x},\\mathbf{z})\\,\\nabla\\log q(\\mathbf{z}|\\mathbf{x})\\Big] \\\\ &= \\mathbb{E}_{q(\\mathbf{z}|\\mathbf{x})}\\Big[ \\nabla f(\\mathbf{x},\\mathbf{z}) + \\big(f(\\mathbf{x},\\mathbf{z}) - C_{\\psi}(\\mathbf{x})-c\\big)\\,\\nabla\\log q(\\mathbf{z}|\\mathbf{x})\\Big] \\end{aligned} where :math:`C_{\\psi}(\\mathbf{x})` is a learnable network with parameter :math:`\\psi`, and `c` is a learnable constant. They would be learnt by minimizing :math:`\\mathbb{E}_{ q(\\mathbf{z}|\\mathbf{x}) }\\Big[\\big(f(\\mathbf{x},\\mathbf{z}) - C_{\\psi}(\\mathbf{x})-c\\big)^2 \\Big]`. Args: values: Values of the target function given `z` and `x`, i.e., :math:`f(\\mathbf{z},\\mathbf{x})`. latent_log_joint: Values of :math:`\\log q(\\mathbf{z}|\\mathbf{x})`. baseline: Values of the baseline function :math:`C_{\\psi}(\\mathbf{x})` given input `x`. If this is not specified, then this method will degenerate to the REINFORCE algorithm, with only a moving average estimated constant baseline `c`. center_by_moving_average (bool): Whether or not to use the moving average to maintain an estimation of `c` in above equations? decay: The decaying factor for moving average. axis: The sampling axes to be reduced in outputs. If not specified, no axis will be reduced. keepdims (bool): When `axis` is specified, whether or not to keep the reduced axes? (default :obj:`False`) batch_axis: The batch axes to be reduced when computing expectation over `x`. If not specified, all axes will be treated as batch axes, except the sampling axes. Returns: (tf.Tensor, tf.Tensor): The `(surrogate, baseline cost)`. `surrogate` is the surrogate for optimizing the original target. Maximizing/minimizing this surrogate via gradient descent will effectively maximize/minimize the original target. `baseline cost` is the cost to be minimized for training baseline. It will be :obj:`None` if `baseline` is :obj:`None`. """ if baseline is None and not center_by_moving_average: raise ValueError('`baseline` is not specified, thus ' '`center_by_moving_average` must be False.') values = tf.convert_to_tensor(values) # f(x,z) latent_log_joint = tf.convert_to_tensor(latent_log_joint) # log q(z|x) if baseline is not None: baseline = tf.convert_to_tensor(baseline) dtype = values.dtype @contextmanager def mk_scope(): if center_by_moving_average: with tf.variable_scope(None, default_name=name or 'nvil_estimator'): yield else: ns_values = [values, latent_log_joint] if baseline is not None: ns_values += [baseline] with tf.name_scope(name or 'nvil_estimator', values=ns_values): yield with mk_scope(): l_signal = values baseline_cost = None # compute the baseline cost if baseline is not None: # baseline_cost = E[(f(x,z)-C(x)-c)^2] with tf.name_scope('baseline_cost'): baseline_cost = tf.square( tf.stop_gradient(l_signal) - baseline) if axis is not None: baseline_cost = tf.reduce_mean( baseline_cost, axis, keepdims=keepdims) l_signal = l_signal - baseline # estimate `c` by moving average if center_by_moving_average: with tf.name_scope('center_by_moving_average'): batch_center = tf.reduce_mean( l_signal, axis=batch_axis, keepdims=True) moving_mean_shape = get_static_shape(batch_center) if None in moving_mean_shape: raise ValueError( 'The shape of `values` after `batch_axis` having been ' 'reduced must be static: values {}, batch_axis {}'. format(values, batch_axis) ) moving_mean = tf.get_variable( 'moving_mean', shape=moving_mean_shape, initializer=tf.constant_initializer(0.), trainable=False, dtype=dtype ) decay = convert_to_tensor_and_cast(1. - decay, dtype) moving_mean = moving_mean.assign( moving_mean - (moving_mean - batch_center) * decay) l_signal = l_signal - moving_mean # compute the nvil cost with tf.name_scope('cost'): cost = tf.stop_gradient(l_signal) * latent_log_joint + values if axis is not None: cost = tf.reduce_mean(cost, axis, keepdims=keepdims) return cost, baseline_cost def _vimco_replace_diag(x, y, axis): assert(isinstance(axis, int)) assert(get_static_shape(x) is not None) assert(get_static_shape(y) is not None) rank = len(get_static_shape(x)) assert(rank >= 2) assert(len(get_static_shape(y)) == rank) assert(-rank <= axis < -1) k = get_static_shape(x)[axis] assert(get_static_shape(x)[axis + 1] == k) assert(get_static_shape(y)[axis] == k) assert(get_static_shape(y)[axis + 1] == 1) if k is None: k = tf.shape(x)[axis] diag_mask = tf.reshape( tf.eye(k, k, dtype=x.dtype), tf.stack([1] * (rank + axis) + [k, k] + [1] * (-axis - 2), axis=0) ) return x * (1 - diag_mask) + y * diag_mask def _vimco_control_variate(log_f, axis): assert(isinstance(axis, int)) assert(get_static_shape(log_f) is not None) rank = len(get_static_shape(log_f)) assert(rank >= 1) assert(-rank <= axis <= -1) K = get_dimension_size(log_f, axis=axis) K_f = tf.cast(K, dtype=log_f.dtype) mean_except_k = ( (tf.reduce_mean(log_f, axis=axis, keepdims=True) - log_f / K_f) * (K_f / (K_f - 1)) ) mean_except_k = tf.expand_dims(mean_except_k, axis=axis) x_expand = tf.expand_dims(log_f, axis=axis - 1) tile_rep = [1] * (rank + axis) + [K] + [1] * (-axis) x_tiled = tf.tile(x_expand, tile_rep) merged = _vimco_replace_diag(x_tiled, mean_except_k, axis=axis - 1) return log_mean_exp(merged, axis=axis) @add_name_arg_doc def vimco_estimator(log_values, latent_log_joint, axis=None, keepdims=False, name=None): """ Derive the gradient estimator for :math:`\\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\Big[\\log \\frac{1}{K} \\sum_{k=1}^K f\\big(\\mathbf{x},\\mathbf{z}^{(k)}\\big)\\Big]`, by VIMCO (Minh and Rezende, 2016) algorithm. .. math:: \\begin{aligned} &\\nabla\\,\\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\Big[\\log \\frac{1}{K} \\sum_{k=1}^K f\\big(\\mathbf{x},\\mathbf{z}^{(k)}\\big)\\Big] \\\\ &\\quad = \\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\bigg[{\\sum_{k=1}^K \\hat{L}(\\mathbf{z}^{(k)}|\\mathbf{z}^{(-k)}) \\, \\nabla \\log q(\\mathbf{z}^{(k)}|\\mathbf{x})}\\bigg] + \\mathbb{E}_{q(\\mathbf{z}^{(1:K)}|\\mathbf{x})}\\bigg[{\\sum_{k=1}^K \\widetilde{w}_k\\,\\nabla\\log f(\\mathbf{x},\\mathbf{z}^{(k)})}\\bigg] \\end{aligned} where :math:`w_k = f\\big(\\mathbf{x},\\mathbf{z}^{(k)}\\big)$, $\\widetilde{w}_k = w_k / \\sum_{i=1}^K w_i`, and: .. math:: \\begin{aligned} \\hat{L}(\\mathbf{z}^{(k)}|\\mathbf{z}^{(-k)}) &= \\hat{L}(\\mathbf{z}^{(1:K)}) - \\log \\frac{1}{K} \\bigg(\\hat{f}(\\mathbf{x},\\mathbf{z}^{(-k)})+\\sum_{i \\neq k} f(\\mathbf{x},\\mathbf{z}^{(i)})\\bigg) \\\\ \\hat{L}(\\mathbf{z}^{(1:K)}) &= \\log \\frac{1}{K} \\sum_{k=1}^K f(\\mathbf{x},\\mathbf{z}^{(k)}) \\\\ \\hat{f}(\\mathbf{x},\\mathbf{z}^{(-k)}) &= \\exp\\big(\\frac{1}{K-1} \\sum_{i \\neq k} \\log f(\\mathbf{x},\\mathbf{z}^{(i)})\\big) \\end{aligned} Args: log_values: Log values of the target function given `z` and `x`, i.e., :math:`\\log f(\\mathbf{z},\\mathbf{x})`. latent_log_joint: Values of :math:`\\log q(\\mathbf{z}|\\mathbf{x})`. axis: The sampling axes to be reduced in outputs. keepdims (bool): When `axis` is specified, whether or not to keep the reduced axes? (default :obj:`False`) Returns: tf.Tensor: The surrogate for optimizing the original target. Maximizing/minimizing this surrogate via gradient descent will effectively maximize/minimize the original target. """ _require_multi_samples(axis, 'vimco_estimator') # check axis and rank if get_static_shape(log_values) is None: raise ValueError('vimco_estimator only supports `log_values` with ' 'deterministic ndims.') rank = len(get_static_shape(log_values)) try: axis = int(axis) except TypeError: raise TypeError('vimco_estimator only supports integer `axis`: ' 'got {!r}'.format(axis)) if not (-rank <= axis < rank): raise ValueError('`axis` out of range: rank {} vs axis {}'. format(rank, axis)) # prepare for the computation log_values = tf.convert_to_tensor(log_values) # log f(x,z) latent_log_joint = tf.convert_to_tensor(latent_log_joint) # log q(z|x) with tf.name_scope(name, default_name='vimco_estimator', values=[log_values, latent_log_joint]): # check whether or not the sampling axis has more than 1 sample sample_size = get_dimension_size(log_values, axis=axis) err_msg = ('VIMCO requires sample size >= 2: ' 'sample axis is {}'.format(axis)) if is_tensor_object(sample_size): with assert_deps([ tf.assert_greater_equal( sample_size, 2, message=err_msg ) ]): log_values = tf.identity(log_values) else: if sample_size < 2: raise ValueError(err_msg) # the variance reduction term if axis >= 0: axis -= rank control_variate = _vimco_control_variate(log_values, axis=axis) # the final estimator true_term = log_mean_exp(log_values, axis=axis, keepdims=True) fake_term = tf.reduce_sum( latent_log_joint * tf.stop_gradient(true_term - control_variate), axis=axis, keepdims=keepdims ) if not keepdims: true_term = tf.squeeze(true_term, axis=axis) estimator = true_term + fake_term return estimator

44.002825

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4.049203

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0.033098

0.035644

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44.127479

0.738946

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false

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0.106509

0

null

0

null

0

1

0

4f283edba2f0f21c8d22c3a228847a20f6f6b797

438

py

Python

code_all/day06/demo06.py

testcg/python

4db4bd5d0e44af807d2df80cf8c8980b40cc03c4

[ "MIT" ]

null

code_all/day06/demo06.py

testcg/python

4db4bd5d0e44af807d2df80cf8c8980b40cc03c4

[ "MIT" ]

null

code_all/day06/demo06.py

testcg/python

4db4bd5d0e44af807d2df80cf8c8980b40cc03c4

[ "MIT" ]

null

""" 字典dict基本操作创建遍历删除 """ # 列表擅长存储单一维度的信息 # 字典擅长存储多个维度的信息 # 1. 创建 # 语法1：字典名 = {键1:值1,键2:值2} dict_gsx = { "name": "郭世鑫", "age": 26, "sex": "女" } dict_wz = { "name": "王志", "age": 22, "sex": "男" } dict_llt = { "name": "刘兰涛", "age": 25, "sex": "女" } # 语法2：字典名 = {键1:值1,键2:值2} # 对于可迭代对象元素的格式要求：一分为二 list01 = ["悟空", ("猪", "八戒"), ["唐", "三藏"]] dict02 = dict(list01) print(dict02)

14.129032

41

0.461187

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438

3.491228

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null

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null

0

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0

4f29cb0ce214325b80d6f5c9cbf743c296c6bd71

8,279

py

Python

tests/fullscale/viscoelasticity/nofaults-2d/axialstrain_genmaxwell_soln.py

cehanagan/pylith

cf5c1c34040460a82f79b6eb54df894ed1b1ee93

[ "MIT" ]

93

2015-01-08T16:41:22.000Z

2022-02-25T13:40:02.000Z

tests/fullscale/viscoelasticity/nofaults-2d/axialstrain_genmaxwell_soln.py

sloppyjuicy/pylith

ac2c1587f87e45c948638b19560813d4d5b6a9e3

[ "MIT" ]

277

2015-02-20T16:27:35.000Z

2022-03-30T21:13:09.000Z

tests/fullscale/viscoelasticity/nofaults-2d/axialstrain_genmaxwell_soln.py

sloppyjuicy/pylith

ac2c1587f87e45c948638b19560813d4d5b6a9e3

[ "MIT" ]

71

2015-03-24T12:11:08.000Z

2022-03-03T04:26:02.000Z

# ---------------------------------------------------------------------- # # Brad T. Aagaard, U.S. Geological Survey # Charles A. Williams, GNS Science # Matthew G. Knepley, University at Buffalo # # This code was developed as part of the Computational Infrastructure # for Geodynamics (http://geodynamics.org). # # Copyright (c) 2010-2021 University of California, Davis # # See LICENSE.md for license information. # # ---------------------------------------------------------------------- # # @file tests/fullscale/viscoelasticity/nofaults-2d/axialstrain_genmaxwell_soln.py # # @brief Analytical solution to axial strain relaxation problem for a generalized Maxwell viscoelastic material. # # 2-D axial strain solution for linear generalized Maxwell viscoelastic material. # # Uy=0 # ---------- # | | # Ux=0 | | Ux=U0 # | | # | | # ---------- # Uy=0 # # Dirichlet boundary conditions # Ux(-4000,y) = 0 # Ux(+4000,y) = U0 # Uy(x,-4000) = 0 # Uy(x,+4000) = 0 # import numpy # Physical properties. p_density = 2500.0 p_vs = 3464.1016 p_vp = 6000.0 p_viscosity_1 = 9.46728e17 p_viscosity_2 = 4.73364e17 p_viscosity_3 = 1.893456e18 p_shear_ratio_1 = 0.25 p_shear_ratio_2 = 0.25 p_shear_ratio_3 = 0.25 # Applied displacement. U0 = 1.0 # Derived properties. p_mu = p_density*p_vs*p_vs p_lambda = p_density*p_vp*p_vp - 2.0*p_mu p_youngs = p_mu*(3.0*p_lambda + 2.0*p_mu)/(p_lambda + p_mu) p_poissons = 0.5*p_lambda/(p_lambda + p_mu) p_shear_ratio_0 = 1.0 - p_shear_ratio_1 - p_shear_ratio_2 - p_shear_ratio_3 p_tau_1 = p_viscosity_1/(p_mu*p_shear_ratio_1) p_tau_2 = p_viscosity_2/(p_mu*p_shear_ratio_2) p_tau_3 = p_viscosity_3/(p_mu*p_shear_ratio_3) # Time information. year = 60.0*60.0*24.0*365.25 dt = 0.025*year startTime = dt endTime = 1.0*year numSteps = 40 timeArray = numpy.linspace(startTime, endTime, num=numSteps, dtype=numpy.float64) # Uniform strain field (plane strain). e0 = U0/8000.0 exx = e0*numpy.ones(numSteps, dtype=numpy.float64) eyy = numpy.zeros(numSteps, dtype=numpy.float64) ezz = numpy.zeros(numSteps, dtype=numpy.float64) exy = numpy.zeros(numSteps, dtype=numpy.float64) # Deviatoric strains. eMean = (exx + eyy + ezz)/3.0 eDevxx = exx - eMean eDevyy = eyy - eMean eDevzz = ezz - eMean eDevxy = exy # Deviatoric stresses. timeFac1 = numpy.exp(-timeArray/p_tau_1) timeFac2 = numpy.exp(-timeArray/p_tau_2) timeFac3 = numpy.exp(-timeArray/p_tau_3) sDevxx = 2.0*p_mu*eDevxx*(p_shear_ratio_0 + p_shear_ratio_1*timeFac1 + p_shear_ratio_2*timeFac2 + p_shear_ratio_3*timeFac3) sDevyy = 2.0*p_mu*eDevyy*(p_shear_ratio_0 + p_shear_ratio_1*timeFac1 + p_shear_ratio_2*timeFac2 + p_shear_ratio_3*timeFac3) sDevzz = 2.0*p_mu*eDevzz*(p_shear_ratio_0 + p_shear_ratio_1*timeFac1 + p_shear_ratio_2*timeFac2 + p_shear_ratio_3*timeFac3) sDevxy = numpy.zeros_like(sDevxx) # Total stresses. sMean = e0*(3.0*p_lambda + 2.0*p_mu)/3.0 sxx = sDevxx + sMean syy = sDevyy + sMean szz = sDevzz + sMean sxy = sDevxy # Get viscous strains from initial deviatoric strains (strain rate = 0). eVisxx_1 = eDevxx*timeFac1 eVisyy_1 = eDevyy*timeFac1 eViszz_1 = eDevzz*timeFac1 eVisxy_1 = eDevxy eVisxx_2 = eDevxx*timeFac2 eVisyy_2 = eDevyy*timeFac2 eViszz_2 = eDevzz*timeFac2 eVisxy_2 = eDevxy eVisxx_3 = eDevxx*timeFac3 eVisyy_3 = eDevyy*timeFac3 eViszz_3 = eDevzz*timeFac3 eVisxy_3 = eDevxy # ---------------------------------------------------------------------- class AnalyticalSoln(object): """Analytical solution to axial extension problem. """ SPACE_DIM = 2 TENSOR_SIZE = 4 def __init__(self): self.fields = { "displacement": self.displacement, "density": self.density, "shear_modulus": self.shear_modulus, "bulk_modulus": self.bulk_modulus, "shear_modulus_ratio": self.shear_modulus_ratio, "maxwell_time": self.maxwell_time, "cauchy_strain": self.strain, "cauchy_stress": self.stress, "viscous_strain": self.viscous_strain, "initial_amplitude": self.initial_displacement, } return def getField(self, name, mesh_entity, pts): field = self.fields[name](pts) return field def displacement(self, locs): """Compute displacement field at locations. """ (npts, dim) = locs.shape disp = numpy.zeros((numSteps, npts, self.SPACE_DIM), dtype=numpy.float64) disp[:,:, 0] = numpy.dot(exx.reshape(numSteps, 1), (locs[:, 0] + 4000.0).reshape(1, npts)) return disp def initial_displacement(self, locs): """Compute initial displacement field at locations. """ (npts, dim) = locs.shape disp = numpy.zeros((1, npts, self.SPACE_DIM), dtype=numpy.float64) disp[0,:, 0] = e0*(locs[:, 0] + 4000.0).reshape(1, npts) return disp def density(self, locs): """Compute density field at locations. """ (npts, dim) = locs.shape density = p_density * numpy.ones((1, npts, 1), dtype=numpy.float64) return density def shear_modulus(self, locs): """Compute shear modulus field at locations. """ (npts, dim) = locs.shape shear_modulus = p_mu * numpy.ones((1, npts, 1), dtype=numpy.float64) return shear_modulus def bulk_modulus(self, locs): """Compute bulk modulus field at locations. """ (npts, dim) = locs.shape bulk_modulus = (p_lambda + 2.0 / 3.0 * p_mu) * numpy.ones((1, npts, 1), dtype=numpy.float64) return bulk_modulus def maxwell_time(self, locs): """Compute Maxwell time field at locations. """ (npts, dim) = locs.shape maxwell_time = numpy.zeros((1, npts, 3), dtype=numpy.float64) maxwell_time[0,:, 0] = p_tau_1 maxwell_time[0,:, 1] = p_tau_2 maxwell_time[0,:, 2] = p_tau_3 return maxwell_time def shear_modulus_ratio(self, locs): """Compute shear modulus ratio field at locations. """ (npts, dim) = locs.shape shear_modulus_ratio = numpy.zeros((1, npts, 3), dtype=numpy.float64) shear_modulus_ratio[0,:, 0] = p_shear_ratio_1 shear_modulus_ratio[0,:, 1] = p_shear_ratio_2 shear_modulus_ratio[0,:, 2] = p_shear_ratio_3 return shear_modulus_ratio def strain(self, locs): """Compute strain field at locations. """ (npts, dim) = locs.shape strain = numpy.zeros((numSteps, npts, self.TENSOR_SIZE), dtype=numpy.float64) strain[:,:, 0] = exx.reshape(numSteps, 1) strain[:,:, 1] = eyy.reshape(numSteps, 1) strain[:,:, 2] = ezz.reshape(numSteps, 1) strain[:,:, 3] = exy.reshape(numSteps, 1) return strain def stress(self, locs): """Compute stress field at locations. """ (npts, dim) = locs.shape stress = numpy.zeros((numSteps, npts, self.TENSOR_SIZE), dtype=numpy.float64) stress[:,:, 0] = sxx.reshape(numSteps, 1) stress[:,:, 1] = syy.reshape(numSteps, 1) stress[:,:, 2] = szz.reshape(numSteps, 1) stress[:,:, 3] = sxy.reshape(numSteps, 1) return stress def viscous_strain(self, locs): """Compute viscous strain field at locations. """ (npts, dim) = locs.shape viscous_strain = numpy.zeros((numSteps, npts, 3*self.TENSOR_SIZE), dtype=numpy.float64) viscous_strain[:,:, 0] = eVisxx_1.reshape(numSteps, 1) viscous_strain[:,:, 1] = eVisyy_1.reshape(numSteps, 1) viscous_strain[:,:, 2] = eViszz_1.reshape(numSteps, 1) viscous_strain[:,:, 3] = eVisxy_1.reshape(numSteps, 1) viscous_strain[:,:, 4] = eVisxx_2.reshape(numSteps, 1) viscous_strain[:,:, 5] = eVisyy_2.reshape(numSteps, 1) viscous_strain[:,:, 6] = eViszz_2.reshape(numSteps, 1) viscous_strain[:,:, 7] = eVisxy_2.reshape(numSteps, 1) viscous_strain[:,:, 8] = eVisxx_3.reshape(numSteps, 1) viscous_strain[:,:, 9] = eVisyy_3.reshape(numSteps, 1) viscous_strain[:,:, 10] = eViszz_3.reshape(numSteps, 1) viscous_strain[:,:, 11] = eVisxy_3.reshape(numSteps, 1) return viscous_strain # End of file

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0

null

0

1

0

4f2bc4c42f2c78df859c0e4146c39f512955ca96

2,486

py

Python

scripts/tsne_embed.py

Imperssonator/afm-cnn

67f757cb38cf595b32f768f26d4a6d646fbb1b36

[ "MIT" ]

null

scripts/tsne_embed.py

Imperssonator/afm-cnn

67f757cb38cf595b32f768f26d4a6d646fbb1b36

[ "MIT" ]

null

scripts/tsne_embed.py

Imperssonator/afm-cnn

67f757cb38cf595b32f768f26d4a6d646fbb1b36

[ "MIT" ]

null

#!/usr/bin/env python import os import h5py import click import numpy as np import warnings import pandas as pd from sklearn.decomposition import PCA, KernelPCA from sklearn.metrics.pairwise import chi2_kernel, additive_chi2_kernel import ntsne # needed with slurm to see local python library under working dir import sys sys.path.append(os.path.join(os.getcwd(), 'code')) #import models #from models import Base, User, Collection, Sample, Micrograph, dbpath #from sqlalchemy import create_engine #from sqlalchemy.orm import sessionmaker # #engine = create_engine('sqlite:///data/microstructures.sqlite') #Base.metadata.bind = engine #DBSession = sessionmaker(bind=engine) #db = DBSession() def load_representations(datafile): # grab image representations from hdf5 file keys, features = [], [] with h5py.File(datafile, 'r') as f: for key in f: keys.append(key) features.append(f[key][...]) return np.array(keys), np.array(features) def stash_tsne_embeddings(resultsfile, keys, embeddings, perplexity): with h5py.File(resultsfile) as f: g = f.create_group('perplexity-{}'.format(perplexity)) for idx, key in enumerate(keys): # add t-SNE map point for each record g[key] = embeddings[idx] return @click.command() @click.argument('datafile', type=click.Path()) @click.option('--kernel', '-k', type=click.Choice(['linear', 'chi2']), default='linear') @click.option('--n-repeats', '-r', type=int, default=1) @click.option('--seed', '-s', type=int, default=None) def tsne_embed(datafile, kernel, n_repeats, seed): # datafile = './data/full/features/vgg16_block5_conv3-vlad-32.h5' print('working') resultsfile = datafile.replace('features', 'tsne') keys, features = load_representations(datafile) if kernel == 'linear': x_pca = PCA(n_components=50).fit_transform(features) elif kernel == 'chi2': gamma = -1 / np.mean(additive_chi2_kernel(features)) with warnings.catch_warnings(): warnings.simplefilter("once", DeprecationWarning) x_pca = KernelPCA(n_components=50, kernel=chi2_kernel, gamma=gamma).fit_transform(features) perplexity = [10, 20, 30, 40, 50, 60] for p in perplexity: x_tsne = ntsne.best_tsne(x_pca, perplexity=p, theta=0.1, n_repeats=n_repeats) stash_tsne_embeddings(resultsfile, keys, x_tsne, p) if __name__ == '__main__': tsne_embed()

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0

null

0

null

0

1

0

4f2beebf2938dcf71d6f556607fd862e60478c1e

1,325

py

Python

src/brewlog/forms/widgets.py

zgoda/brewlog

13a930b328f81d01a2be9aca07d3b14703b80faa

[ "BSD-3-Clause" ]

3

2019-03-11T04:30:06.000Z

2020-01-26T03:21:52.000Z

src/brewlog/forms/widgets.py

zgoda/brewlog

13a930b328f81d01a2be9aca07d3b14703b80faa

[ "BSD-3-Clause" ]

23

2019-02-06T20:37:37.000Z

2020-06-01T07:08:35.000Z

src/brewlog/forms/widgets.py

zgoda/brewlog

13a930b328f81d01a2be9aca07d3b14703b80faa

[ "BSD-3-Clause" ]

null

from html import escape from markupsafe import Markup from wtforms.compat import text_type from wtforms.widgets.core import html_params EMPTY_HINTS = [ ('', ''), ] def textarea_with_hints(field, **kwargs): kwargs.setdefault('id', field.id) hints = kwargs.pop('hints', EMPTY_HINTS) if not EMPTY_HINTS[0] in hints: hints = EMPTY_HINTS + hints obj_id = kwargs['id'] hint_elem_id = f'{obj_id}_hints' if len(hints) > 1: hint = [f'<select {html_params(id=hint_elem_id)} class="form-control">'] for hint_value, hint_label in hints: hint.append( f'<option value="{escape(hint_value)}">{escape(hint_label)}</option>' ) hint.append('</select>') hint = Markup(''.join(hint)) else: hint = '' textarea = Markup( f'<textarea {html_params(name=field.name, **kwargs)}>' f'{escape(text_type(field._value()))}</textarea>' ) if hint: script = f""" <script type="text/javascript"> $("#{hint_elem_id}").change(function() {{ var value = $(this).val(); $("#{obj_id}").val(value); }}); </script> """ else: script = '' items = [i for i in [hint, textarea, script] if i] return Markup('<br />'.join(items))

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1,325

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0

null

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null

0

1

0

4f2df52d0ccad9f803247d4c4e720cd7ff4d0a44

4,466

py

Python

buildnotifylib/preferences.py

johnjohndoe/buildnotify

ea1cec69eba6011f4a91cdf20aa4f55f28141c90

[ "MIT" ]

27

2017-11-13T09:31:22.000Z

2022-03-09T11:00:15.000Z

buildnotifylib/preferences.py

johnjohndoe/buildnotify

ea1cec69eba6011f4a91cdf20aa4f55f28141c90

[ "MIT" ]

159

2017-09-14T16:22:51.000Z

2022-03-21T06:01:39.000Z

buildnotifylib/preferences.py

johnjohndoe/buildnotify

ea1cec69eba6011f4a91cdf20aa4f55f28141c90

[ "MIT" ]

10

2018-06-12T02:13:18.000Z

2021-03-12T11:04:51.000Z

from typing import Optional, List, Tuple from PyQt5.QtCore import QStringListModel from PyQt5.QtWidgets import QDialog, QWidget from buildnotifylib.config import Config, Preferences from buildnotifylib.generated.preferences_ui import Ui_Preferences from buildnotifylib.server_configuration_dialog import ServerConfigurationDialog class PreferencesDialog(QDialog): def __init__(self, conf: Config, parent: QWidget = None): QDialog.__init__(self, parent) self.conf = conf self.ui = Ui_Preferences() self.ui.setupUi(self) self.checkboxes = dict(successfulBuild=self.ui.successfulBuildsCheckbox, brokenBuild=self.ui.brokenBuildsCheckbox, fixedBuild=self.ui.fixedBuildsCheckbox, stillFailingBuild=self.ui.stillFailingBuildsCheckbox, connectivityIssues=self.ui.connectivityIssuesCheckbox, lastBuildTimeForProject=self.ui.showLastBuildTimeCheckbox) self.set_values_from_config() # Connect up the buttons. self.ui.addButton.clicked.connect(self.add_server) self.ui.removeButton.clicked.connect(self.remove_element) self.ui.buttonBox.accepted.connect(self.accept) self.ui.configureProjectButton.clicked.connect(self.configure_projects) def set_values_from_config(self): self.ui.cctrayPathList.setModel(QStringListModel(self.conf.get_urls())) self.ui.cctrayPathList.clicked.connect(lambda _: self.item_selection_changed(True)) self.ui.cctrayPathList.doubleClicked.connect(self.configure_projects) self.ui.removeButton.clicked.connect(lambda _: self.item_selection_changed(False)) for key, checkbox in self.checkboxes.items(): checkbox.setChecked(self.conf.get_value(str(key))) self.ui.pollingIntervalSpinBox.setValue(self.conf.get_interval_in_seconds()) self.ui.scriptCheckbox.setChecked(self.conf.get_custom_script_enabled()) self.ui.scriptLineEdit.setText(self.conf.get_custom_script()) self.ui.sortBuildByLastBuildTime.setChecked(self.conf.get_sort_by_last_build_time()) self.ui.sortBuildByName.setChecked(self.conf.get_sort_by_name()) self.ui.showLastBuildLabelCheckbox.setChecked(self.conf.get_show_last_build_label()) def item_selection_changed(self, status): self.ui.configureProjectButton.setEnabled(status) def add_server(self): server_config = ServerConfigurationDialog(None, self.conf, self).open() if server_config is not None: self.conf.save_server_config(server_config) urls = self.ui.cctrayPathList.model().stringList() urls.append(server_config.url) self.ui.cctrayPathList.setModel(QStringListModel(urls)) def remove_element(self): index = self.ui.cctrayPathList.selectionModel().currentIndex() urls = self.ui.cctrayPathList.model().stringList() urls.pop(index.row()) self.ui.cctrayPathList.setModel(QStringListModel(urls)) def configure_projects(self): url = self.ui.cctrayPathList.selectionModel().currentIndex().data() if not url: return server_config = ServerConfigurationDialog(url, self.conf, self).open() if server_config is not None: self.conf.save_server_config(server_config) def get_urls(self) -> List[str]: return [str(url) for url in self.ui.cctrayPathList.model().stringList()] def get_interval_in_seconds(self) -> int: return self.ui.pollingIntervalSpinBox.value() def get_selections(self) -> List[Tuple[str, bool]]: return [(key, checkbox.isChecked()) for (key, checkbox) in list(self.checkboxes.items())] def open(self) -> Optional[Preferences]: # type: ignore if self.exec_() == QDialog.Accepted: return Preferences( urls=self.get_urls(), interval=self.get_interval_in_seconds(), custom_script_text=self.ui.scriptLineEdit.text(), custom_script_checked=self.ui.scriptCheckbox.isChecked(), sort_by_build_time=self.ui.sortBuildByLastBuildTime.isChecked(), sort_by_name=self.ui.sortBuildByName.isChecked(), selections=self.get_selections(), show_last_build_label=self.ui.showLastBuildLabelCheckbox.isChecked() ) return None

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null

0

null

0

1

0

4f30b973a7b4217f88644328ff0a2c4461e056e4

5,082

py

Python

src/test/python/tranquilitybase/gcpdac/integration/solution/test_integration_solution.py

tranquilitybase-io/tb-gcp-dac

1d65afced1ab7427262dcdf98ee544370201439a

[ "Apache-2.0" ]

2

2020-04-23T16:50:26.000Z

2021-05-09T11:30:42.000Z

src/test/python/tranquilitybase/gcpdac/integration/solution/test_integration_solution.py

tranquilitybase-io/tb-gcp-dac

1d65afced1ab7427262dcdf98ee544370201439a

[ "Apache-2.0" ]

156

2020-04-08T14:08:47.000Z

2021-07-01T14:48:15.000Z

src/test/python/tranquilitybase/gcpdac/integration/solution/test_integration_solution.py

tranquilitybase-io/tb-gcp-dac

1d65afced1ab7427262dcdf98ee544370201439a

[ "Apache-2.0" ]

2

2020-06-24T11:19:58.000Z

2020-06-24T13:27:22.000Z

import json import unittest import requests from celery import states from time import sleep from unittest import TestCase from src.test.python.tranquilitybase.gcpdac import local_test_runner from tranquilitybase.gcpdac.integration.solution.solution_config import get_solutionId, get_payload, \ processed_environments class UserFlowTests(unittest.TestCase): def test_solution(self): solutionTest_methods = SolutionTest_methods() solution_payload = solutionTest_methods.create_solution(get_payload()) solutionTest_methods.delete_solution() # check solution values after deleting solution solutionTest_methods.check_values(solution_response=json.loads(solution_payload), solution_input=get_payload()) class SolutionTest_methods(): def delete_solution(self): taskid = SolutionUtils.delete_solution_task(get_solutionId()) print("Deleting a solution") print("Celery task id {}".format(taskid)) status = '' max_tries = 10 try_count = 0 while status != states.SUCCESS and status != states.FAILURE: try_count = try_count+1 if try_count >= max_tries: break print("Checking task {}".format(taskid)) status, payload = SolutionUtils.delete_solution_task_result(taskid) print('Status {}'.format(status)) print('Payload {}'.format(payload)) sleep(10) TestCase().assertEqual(states.SUCCESS, status) def create_solution(self, solution_input): taskid = SolutionUtils.create_solution_task(solution_input) print("Creating a solution") print("Celery task id {}".format(taskid)) status = '' payload = {} max_tries = 10 try_count = 0 while status != states.SUCCESS and status != states.FAILURE: try_count = try_count+1 if try_count >= max_tries: break print("Checking task {}".format(taskid)) status, payload = SolutionUtils.create_solution_task_result(taskid) print('Status {}'.format(status)) sleep(10) TestCase().assertEqual(states.SUCCESS, status) print('Payload {}'.format(payload)) return payload def check_values(self, solution_response, solution_input): TestCase().assertFalse("billing_account_id" in solution_response) environment_projects = solution_response["environment_projects"]["value"] for environment_project in environment_projects: labels = environment_project["labels"] self.check_common_project_labels(labels) if 'environment' not in labels: TestCase().fail("No environment label") environment_label = labels['environment'] if environment_label not in processed_environments: TestCase().fail("Invalid environment label") workspace_project = solution_response["workspace_project"]["value"] self.check_common_project_labels(workspace_project["labels"]) solution_folder = solution_response["solution_folder"]["value"] display_name = solution_folder["display_name"] TestCase().assertEqual(solution_input['name'], display_name) def check_common_project_labels(self, labels): if 'cost-code' not in labels: TestCase().fail("No cost-code label") if 'business-unit' not in labels: TestCase().fail("No business-unit label") if 'team' not in labels: TestCase().fail("No team label") class SolutionUtils: @staticmethod def create_solution_task(payload): endpoint_url = f"http://{local_test_runner.houston_url()}/solution_async/" data = json.dumps(payload, indent=4) resp = requests.post(endpoint_url, headers=local_test_runner.headers, data=data) resp_json = resp.json() task_id = resp_json['taskid'] return task_id @staticmethod def create_solution_task_result(taskId): endpoint_url = f"http://{local_test_runner.houston_url()}/result/create/{taskId}" resp = requests.get(endpoint_url, headers=local_test_runner.headers) resp_json = resp.json() status = resp_json['status'] payload = resp_json.get('payload', None) return status, payload @staticmethod def delete_solution_task(solutionId): url = '{}/solution_async/{}'.format(local_test_runner.houston_url(), solutionId) resp = requests.delete(url, headers=local_test_runner.headers) resp_json = resp.json() task_id = resp_json['taskid'] return task_id @staticmethod def delete_solution_task_result(taskId): url = '{}/solution_async/result/delete/{}'.format(local_test_runner.houston_url(), taskId) resp = requests.get(url, headers=local_test_runner.headers) resp_json = resp.json() status = resp_json['status'] payload = resp_json.get('payload', None) return status, payload

36.042553

119

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null

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null

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4f31fdc18b197872d19cefe205514ca154af965e

447

py

Python

PySpace/json/json1.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

PySpace/json/json1.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

PySpace/json/json1.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # 文件名：json1.py """ JSON (JavaScript Object Notation) 是一种轻量级的数据交换格式。它基于ECMAScript的一个子集。 Python3 中可以使用 json 模块来对 JSON 数据进行编解码，它包含了两个函数： json.dumps(): 对数据进行编码。 json.loads(): 对数据进行解码。在json的编解码过程中，python 的原始类型与json类型会相互转换，具体的转化对照如下： """ import json # Python字典类型转换为JSON对象 data = { 'no':1, 'name':'Runoob', 'url':'http://www.runoob.com' } json_str = json.dumps(data) print("Python原始数据：",repr(data)) print("JSON对象：",json_str)

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null

0

null

0

1

0

4f3260b90cbd388828d1e9d27dbf8dcf71f7a00b

9,617

py

Python

test.py

andygikling/aiozyre

3e5d4ecad0c35e7382d3d16c6147bdbc2d445b75

[ "BSD-3-Clause" ]

4

2020-01-07T10:23:27.000Z

2021-03-24T08:19:33.000Z

test.py

andygikling/aiozyre

3e5d4ecad0c35e7382d3d16c6147bdbc2d445b75

[ "BSD-3-Clause" ]

3

2020-05-24T04:45:10.000Z

2020-09-11T18:15:56.000Z

test.py

andygikling/aiozyre

3e5d4ecad0c35e7382d3d16c6147bdbc2d445b75

[ "BSD-3-Clause" ]

1

2020-05-23T15:05:30.000Z

import faulthandler faulthandler.enable(all_threads=True) try: import tracemalloc except ImportError: # Not available in pypy pass else: tracemalloc.start() import asyncio import sys import unittest from pprint import pformat from aiozyre import Node, Stopped class AIOZyreTestCase(unittest.TestCase): __slots__ = ('nodes', 'loop') def setUp(self): self.nodes = {} self.loop = asyncio.new_event_loop() asyncio.set_event_loop(self.loop) def tearDown(self) -> None: self.loop.close() def test_cluster(self): self.loop.run_until_complete(self.create_cluster()) try: self.assert_received_message('soup', event='ENTER', name='salad') except AssertionError: self.assert_received_message('soup', event='ENTER', name='lacroix') self.assert_received_message('soup', event='JOIN', name='salad', group='foods') self.assert_received_message('soup', event='JOIN', name='lacroix', group='drinks') self.assert_received_message('soup', event='SHOUT', name='salad', group='foods', blob=b'Hello foods from salad') self.assert_received_message('soup', event='SHOUT', name='lacroix', group='drinks', blob=b'Hello drinks from lacroix') try: self.assert_received_message('salad', event='ENTER', name='soup') except AssertionError: self.assert_received_message('salad', event='ENTER', name='lacroix') self.assert_received_message('salad', event='JOIN', name='soup', group='foods') self.assert_received_message('salad', event='JOIN', name='soup', group='drinks') self.assert_received_message('salad', event='JOIN', name='lacroix', group='drinks') self.assert_received_message('salad', event='SHOUT', name='soup', group='foods', blob=b'Hello foods from soup') try: self.assert_received_message('lacroix', event='ENTER', name='salad') except AssertionError: self.assert_received_message('lacroix', event='ENTER', name='soup') self.assert_received_message('lacroix', event='JOIN', name='salad', group='foods') self.assert_received_message('lacroix', event='JOIN', name='soup', group='drinks') self.assert_received_message('lacroix', event='JOIN', name='soup', group='foods') self.assert_received_message('lacroix', event='SHOUT', name='soup', group='drinks', blob=b'Hello drinks from soup') self.assertEqual(self.nodes['soup']['own_groups'], {'foods', 'drinks'}) self.assertEqual(self.nodes['soup']['peer_groups'], {'foods', 'drinks'}) self.assertEqual(self.nodes['soup']['peer_header_value_types'], {'pamplemousse', 'caesar'}) self.assertEqual(self.nodes['soup']['peers'], {self.nodes['salad']['uuid'], self.nodes['lacroix']['uuid']}) self.assertEqual(self.nodes['soup']['peers_by_group'], { 'foods': {self.nodes['salad']['uuid']}, 'drinks': {self.nodes['lacroix']['uuid']} }) self.assertEqual(self.nodes['salad']['own_groups'], {'foods'}) self.assertEqual(self.nodes['salad']['peer_groups'], {'foods', 'drinks'}) self.assertEqual(self.nodes['salad']['peer_header_value_types'], {'pamplemousse', 'tomato bisque'}) self.assertEqual(self.nodes['salad']['peers'], {self.nodes['lacroix']['uuid'], self.nodes['soup']['uuid']}) self.assertEqual(self.nodes['salad']['peers_by_group'], { 'foods': {self.nodes['soup']['uuid']}, 'drinks': {self.nodes['lacroix']['uuid'], self.nodes['soup']['uuid']} }) self.assertEqual(self.nodes['lacroix']['own_groups'], {'drinks'}) self.assertEqual(self.nodes['lacroix']['peer_groups'], {'foods', 'drinks'}) self.assertEqual(self.nodes['lacroix']['peer_header_value_types'], {'tomato bisque', 'caesar'}) self.assertEqual(self.nodes['lacroix']['peers'], {self.nodes['salad']['uuid'], self.nodes['soup']['uuid']}) self.assertEqual(self.nodes['lacroix']['peers_by_group'], { 'foods': {self.nodes['salad']['uuid'], self.nodes['soup']['uuid']}, 'drinks': {self.nodes['soup']['uuid']} }) def test_start_stop(self): self.loop.run_until_complete(self.start_stop()) self.assert_received_message('fizz', blob=b'Hello #1 from buzz') self.assert_received_message('fizz', blob=b'Hello #2 from buzz') def test_timeout(self): self.loop.run_until_complete(self.timeout()) def assert_received_message(self, node_name, **kwargs): match = False for msg in self.nodes[node_name]['messages']: if set(kwargs.items()).issubset(set(msg.to_dict().items())): match = True break self.assertTrue(match, '%s not in %s' % (pformat(kwargs), pformat(self.nodes[node_name]['messages']))) async def create_cluster(self): print('starting nodes...') soup = await self.start('soup', groups=['foods', 'drinks'], headers={'type': 'tomato bisque'}) salad = await self.start('salad', groups=['foods'], headers={'type': 'caesar'}) lacroix = await self.start('lacroix', groups=['drinks'], headers={'type': 'pamplemousse'}) print('setting up listeners...') self.listen(soup, salad, lacroix) print('sending messages...') await asyncio.wait([ self.create_task(soup.shout('drinks', b'Hello drinks from soup')), self.create_task(soup.shout('foods', b'Hello foods from soup')), self.create_task(salad.shout('foods', b'Hello foods from salad')), self.create_task(lacroix.shout('drinks', b'Hello drinks from lacroix')), ]) print('collecting peer data...') await asyncio.wait([ self.create_task(self.collect_peer_info('soup')), self.create_task(self.collect_peer_info('salad')), self.create_task(self.collect_peer_info('lacroix')), ]) # Give nodes some time to receive the messages print('Receiving messages...') await asyncio.sleep(5) print('Stopping nodes...') await asyncio.wait([ self.create_task(self.nodes[node]['node'].stop()) for node in self.nodes ]) async def timeout(self): fizz = await self.start('fizz') try: with self.assertRaises(asyncio.TimeoutError): await fizz.recv(timeout=0) finally: await fizz.stop() async def start_stop(self): fizz = await self.start('fizz', groups=['test']) buzz = await self.start('buzz', groups=['test']) self.listen(fizz) await buzz.whisper(fizz.uuid, b'Hello #1 from buzz') # Give some time to receive messages await asyncio.sleep(3) await fizz.stop() await buzz.stop() # Restart and send a new message await fizz.start() await buzz.start() self.listen(fizz) await buzz.whisper(fizz.uuid, b'Hello #2 from buzz') # Give some time to receive messages await asyncio.sleep(3) await fizz.stop() await buzz.stop() async def start(self, name, groups=None, headers=None) -> Node: node = Node( name, groups=groups, headers=headers, endpoint='inproc://{}'.format(name), gossip_endpoint='inproc://gossip', # verbose=True, evasive_timeout_ms=30000, expired_timeout_ms=30000, ) await node.start() self.nodes[node.name] = {'node': node, 'messages': [], 'uuid': node.uuid} return node def listen(self, *nodes): for node in nodes: # Intentionally don't wait for these, they stop themselves self.create_task(self._listen(node)) async def _listen(self, node): name = node.name print('%s: listener started' % node.name) while True: try: msg = await node.recv() except Stopped: print('%s: listener stopped' % node.name) break else: self.nodes[name]['messages'].append(msg) async def collect_peer_info(self, name): node = self.nodes[name]['node'] print('%s: collecting peer header values "type"...'% name) self.nodes[name]['peer_header_value_types'] = peer_header_value_types = set() for peer in self.nodes.values(): if peer['node'].name != name: peer_header_value_types.add(await node.peer_header_value(peer['node'].uuid, 'type')) print('%s: collecting peers...' % name) self.nodes[name]['peers'] = await node.peers() print('%s: collecting peer groups...' % name) self.nodes[name]['peer_groups'] = await node.peer_groups() print('%s: collecting own groups...' % name) self.nodes[name]['own_groups'] = await node.own_groups() print('%s: collecting peers by group...' % name) self.nodes[name]['peers_by_group'] = peers_by_group = {} for group in {'drinks', 'foods'}: peers_by_group[group] = await node.peers_by_group(group) def create_task(self, coro): if sys.version_info[:2] >= (3, 8): return asyncio.create_task(coro) else: return self.loop.create_task(coro) if __name__ == '__main__': unittest.main()

41.632035

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0.202103

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0.005376

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0.112903

0.075269

0

null

0

null

0

1

0

4f33fdc73c452aedf8ae2eda011018c277164092

2,045

py

Python

markets/_bittrex_market_generator.py

lax1089/crypto-arbitrage-trader

7bad71d60490035568418c3bfa6291b2865ef3f0

[ "MIT" ]

9

2018-06-13T09:04:24.000Z

2021-11-23T00:10:25.000Z

markets/_bittrex_market_generator.py

lax1089/crypto-arbitrage-trader

7bad71d60490035568418c3bfa6291b2865ef3f0

[ "MIT" ]

null

markets/_bittrex_market_generator.py

lax1089/crypto-arbitrage-trader

7bad71d60490035568418c3bfa6291b2865ef3f0

[ "MIT" ]

1

2022-03-30T06:32:20.000Z

import urllib.request import urllib.error import urllib.parse import json from ._bittrex_base_market import BittrexBaseMarket class BittrexMarketGenerator(): def __init__(self): print("initialized bittrex market gen") def get_market_json(self): url = 'https://bittrex.com/api/v1.1/public/getmarkets' req = urllib.request.Request(url, headers={ "Content-Type": "application/x-www-form-urlencoded", "Accept": "*/*", "User-Agent": "curl/7.24.0 (x86_64-apple-darwin12.0)"}) res = urllib.request.urlopen(req) market_json = json.loads(res.read().decode('utf8')) return market_json def get_market_summary_json(self): url = 'https://bittrex.com/api/v1.1/public/getmarketsummaries' req = urllib.request.Request(url, headers={ "Content-Type": "application/x-www-form-urlencoded", "Accept": "*/*", "User-Agent": "curl/7.24.0 (x86_64-apple-darwin12.0)"}) res = urllib.request.urlopen(req) market_summary_json = json.loads(res.read().decode('utf8')) return market_summary_json def get_markets(self, market_json): markets = list() for market in market_json['result']: market_name = market['MarketName'] curr1 = market['MarketCurrency'] curr2 = market['BaseCurrency'] markets.append(BittrexBaseMarket(curr1, curr2, market_name, 0.0025, 0.0025, 0)) return markets def update_markets(self, markets): market_summary_json = self.get_market_summary_json() for market in markets: for i in market_summary_json['result']: if i['MarketName'] == market.code: market.bid = i['Bid'] market.ask = i['Ask']

35.877193

92

0.548655

215

2,045

5.065116

0.353488

0.071625

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0.029385

0.416896

0.339761

0

0.031641

0.335452

2,045

57

93

35.877193

0.769684

0

0.27907

0

0.203015

0.058291

0

1

0.116279

false

0

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0.325581

0.023256

0

null

0

null

0

1

0

4f39f1dd19feed7138be131f4bb62e922c66bb51

1,798

py

Python

section_11_(api)/using_requests.py

govex/python-lessons

e692f48b6db008a45df0b941dee1e580f5a6c800

[ "MIT" ]

5

2019-10-25T20:47:22.000Z

2021-12-07T06:37:22.000Z

section_11_(api)/using_requests.py

govex/python-lessons

e692f48b6db008a45df0b941dee1e580f5a6c800

[ "MIT" ]

null

section_11_(api)/using_requests.py

govex/python-lessons

e692f48b6db008a45df0b941dee1e580f5a6c800

[ "MIT" ]

1

2021-07-20T18:56:15.000Z

import requests title = raw_input("Enter your movie: ") url = 'http://bechdeltest.com/api/v1/getMoviesByTitle?title={0}'.format(title).replace(" ","+").replace("'","'") print(url) response = requests.get(url).json() print(response) # Search for 'matrix' gives the following JSON response (this is printed at line 11): # [ # { # u'rating': u'3', # u'submitterid': u'1', # u'imdbid': u'0234215', # u'title': u'Matrix Reloaded, The', # u'dubious': u'0', # u'visible': u'1', # u'year': u'2003', # u'date': u'2008-07-21 00:00:00', # u'id': u'58' # }, # { # u'rating': u'3', # u'submitterid': u'1', # u'imdbid': u'0242653', # u'title': u'Matrix Revolutions, The', # u'dubious': u'0', # u'visible': u'1', # u'year': u'2003', # u'date': u'2008-07-21 00:00:00', # u'id': u'59' # }, # { # u'rating': u'1', # u'submitterid': u'7916', # u'imdbid': u'0303678', # u'title': u'Armitage: Dual Matrix', # u'dubious': u'1', # u'visible': u'1', # u'year': u'2002', # u'date': u'2013-08-01 15:26:03', # u'id': u'4429' # }, # { # u'rating': u'3', # u'submitterid': u'1', # u'imdbid': u'0133093', # u'title': u'Matrix, The', # u'dubious': u'0', # u'visible': u'1', # u'year': u'1999', # u'date': u'2008-07-20 00:00:00', # u'id': u'36' # } # ] # Which is then looped through for movie in response: print(movie['title'], movie['rating']) # And printed: # Matrix Reloaded, The 3 # Matrix Revolutions, The 3 # Armitage: Dual Matrix 1 # Matrix, The 3

24.297297

116

0.471635

247

1,798

3.42915

0.319838

0.021251

0.031877

0.047226

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0.27863

0

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117

24.630137

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null

0

null

0

1

0

4f3d0e62173f1ad9185d6e8fe2379194edc0dad5

3,888

py

Python

torrents/alcazar_event_processor.py

2600box/harvest

57264c15a3fba693b4b58d0b6d4fbf4bd5453bbd

[ "Apache-2.0" ]

9

2019-03-26T14:50:00.000Z

2020-11-10T16:44:08.000Z

torrents/alcazar_event_processor.py

2600box/harvest

57264c15a3fba693b4b58d0b6d4fbf4bd5453bbd

[ "Apache-2.0" ]

22

2019-03-02T23:16:13.000Z

2022-02-27T10:36:36.000Z

torrents/alcazar_event_processor.py

2600box/harvest

57264c15a3fba693b4b58d0b6d4fbf4bd5453bbd

[ "Apache-2.0" ]

5

2019-04-24T00:51:30.000Z

2020-11-06T18:31:49.000Z

import time from itertools import chain from Harvest.utils import get_logger from torrents.alcazar_client import update_torrent_from_alcazar, \ create_or_update_torrent_from_alcazar from torrents.models import Torrent, Realm, TorrentInfo from torrents.signals import torrent_removed logger = get_logger(__name__) class AlcazarEventProcessor: @classmethod def _process_removed_events(cls, realm, removed_info_hashes): removed_torrents_qs = Torrent.objects.filter(realm=realm, info_hash__in=removed_info_hashes) removed_info_hashes = list(removed_torrents_qs.values_list('info_hash', flat=True)) logger.debug('Matched {} Torrent objects for deletion.'.format(len(removed_info_hashes))) removed_torrents_qs.delete() for removed_info_hash in removed_info_hashes: torrent_removed.send_robust(cls, realm=realm, info_hash=removed_info_hash) @classmethod def _process_added_torrents(cls, realm, added_torrent_states): # Short-circuit to avoid any queries if not added_torrent_states: return info_hashes = [state['info_hash'] for state in added_torrent_states] torrent_info_ids = { item[0]: item[1] for item in TorrentInfo.objects.filter( realm=realm, info_hash__in=info_hashes, is_deleted=False, ).values_list('info_hash', 'id') } for added_state in added_torrent_states: create_or_update_torrent_from_alcazar( realm=realm, torrent_info_id=torrent_info_ids.get(added_state['info_hash']), torrent_state=added_state, ) @classmethod def _process_events(cls, realm, events): cls._process_removed_events(realm, events['removed']) updated_info_hashes = [state['info_hash'] for state in chain(events['added'], events['updated'])] existing_torrents = { t.info_hash: t for t in Torrent.objects.filter(realm=realm, info_hash__in=updated_info_hashes)} added_torrents_states = [] logger.debug('Matched {} Torrent objects for updating.', len(existing_torrents)) num_updated = 0 for updated_state in chain(events['added'], events['updated']): torrent = existing_torrents.get(updated_state['info_hash']) if not torrent: added_torrents_states.append(updated_state) else: if update_torrent_from_alcazar(torrent, updated_state): num_updated += 1 logger.debug('Actually updated {} in DB.', num_updated) logger.debug('Matched {} new states for adding.', len(added_torrents_states)) cls._process_added_torrents(realm, added_torrents_states) @classmethod def _process(cls, events): realms = {realm.name: realm for realm in Realm.objects.all()} for realm_name, batch in events.items(): realm = realms.get(realm_name) if not realm: realm, _ = Realm.objects.get_or_create(name=realm_name) logger.debug('Processing events for realm {}.', realm_name) cls._process_events(realm, batch) @classmethod def process(cls, events): start = time.time() logger.debug('Processing events.') retries_remaining = 3 while True: try: cls._process(events) break except Exception: if retries_remaining > 0: logger.warning('Exception during alcazar event processing. Retrying.') retries_remaining -= 1 else: logger.exception('Exhausted event processing retries.') raise logger.debug('Completed alcazar update in {:.3f}.', time.time() - start)

38.117647

107

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448

3,888

5.279018

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0.043975

0.044397

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0.25074

0.207188

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0.061734

0

0.002825

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3,888

101

108

38.49505

0.832274

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0

0.111111

0

0.103063

0

1

0.061728

false

0

0.074074

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0.160494

0

null

0

null

0

1

0

4f3d91e7f95020f3e5f0281618902c8deef3aec6

8,452

py

Python

Session10/Day2/dsfp_mh_mcmc.py

hsnee/LSSTC-DSFP-Sessions

5d90992179c80efbd63e9ecc95fe0fef7a0d83c1

[ "MIT" ]

10

2016-08-01T16:47:14.000Z

2019-11-12T10:56:55.000Z

Session10/Day2/dsfp_mh_mcmc.py

hsnee/LSSTC-DSFP-Sessions

5d90992179c80efbd63e9ecc95fe0fef7a0d83c1

[ "MIT" ]

2

2017-04-26T16:05:10.000Z

2019-09-06T20:15:34.000Z

Session10/Day2/dsfp_mh_mcmc.py

hsnee/LSSTC-DSFP-Sessions

5d90992179c80efbd63e9ecc95fe0fef7a0d83c1

[ "MIT" ]

10

2017-04-21T23:38:13.000Z

2021-06-08T04:06:35.000Z

import numpy as np import matplotlib.pyplot as plt def hastings_ratio(theta_1, theta_0, y, x, y_unc): ''' Calculate the Hastings ratio Parameters ---------- theta_1 : tuple proposed new posterior position theta_0 : tuple current posterior position y : arr-like, shape (n_samples) Array of observational measurements x : arr-like, shape (n_samples) Array of positions where y is measured y_unc : arr-like, shape (n_samples) Array of uncertainties on y Returns ------- h_ratio : float The Hastings ratio ''' lnpost1 = lnposterior(theta_1, y_obs, x, y_unc) lnpost0 = lnposterior(theta_0, y_obs, x, y_unc) h_ratio = np.exp(lnpost1)/np.exp(lnpost0) return h_ratio def propose_jump(theta, cov): ''' Generate a proposed new position for MCMC chain Parameters ---------- theta : 1-D array_like, of length N current position of the MCMC chain cov : 1-D or 2-D array_like, of shape (N,) or (N, N) Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling. 1-D inputs for cov require the standard deviation along each axis of the N-dimensional Gaussian. Returns ------- proposed_position : 1-D array_like, of length N ''' if np.shape(theta) == np.shape(cov): cov = np.diag(np.array(cov)**2) proposed_position = np.random.multivariate_normal(theta, cov) return proposed_position def mh_mcmc(theta_0, cov, nsteps, y, x, y_unc): ''' Metropolis-Hastings MCMC algorithm Parameters ---------- theta_0 : 1-D array_like of shape N starting position for the MCMC chain cov : 1-D or 2-D array_like, of shape (N,) or (N, N) Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling. 1-D inputs for cov require the standard deviation along each axis of the N-dimensional Gaussian. nsteps : int Number of steps to take in the MCMC chain y : arr-like, shape (n_samples) Array of observational measurements x : arr-like, shape (n_samples) Array of positions where y is measured y_unc : arr-like, shape (n_samples) Array of uncertainties on y Returns ------- (positions, lnpost_at_pos, acceptance_ratio) : tuple positions : 2-D array_like of shape (nsteps+1, N) Position of the MCMC chain at every step lnpost_at_pos : 1-D array_like of shape nsteps+1 log-posterior value at the position of the MCMC chain acceptance_ratio : 1-D array_like of shape nsteps+1 acceptance ratio of all previous steps in the chain ''' positions = np.zeros((nsteps+1, len(theta_0))) lnpost_at_pos = -np.inf*np.ones(nsteps+1) acceptance_ratio = np.zeros_like(lnpost_at_pos) accepted = 0 positions[0] = theta_0 lnpost_at_pos[0] = lnposterior(theta_0, y, x, y_unc) for step_num in np.arange(1, nsteps+1): proposal = propose_jump(positions[step_num-1], cov) H = hastings_ratio(proposal, positions[step_num-1], y, x, y_unc) R = np.random.uniform() if H > R: accepted += 1 positions[step_num] = proposal lnpost_at_pos[step_num] = lnposterior(proposal, y, x, y_unc) acceptance_ratio[step_num] = float(accepted)/step_num else: positions[step_num] = positions[step_num-1] lnpost_at_pos[step_num] = lnpost_at_pos[step_num-1] acceptance_ratio[step_num] = float(accepted)/step_num return (positions, lnpost_at_pos, acceptance_ratio) def plot_post(theta_0, cov, nsteps, y, x, y_unc): ''' Plot posterior trace from MH MCMC Parameters ---------- theta_0 : 1-D array_like of shape N starting position for the MCMC chain cov : 1-D or 2-D array_like, of shape (N,) or (N, N) Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling. 1-D inputs for cov require the standard deviation along each axis of the N-dimensional Gaussian. nsteps : int Number of steps to take in the MCMC chain y : arr-like, shape (n_samples) Array of observational measurements x : arr-like, shape (n_samples) Array of positions where y is measured y_unc : arr-like, shape (n_samples) Array of uncertainties on y ''' pos, lnpost, acc = mh_mcmc(theta_0, cov, nsteps, y_obs, x, y_unc) fig, (ax1, ax2) = plt.subplots(1,2,figsize=(9,4)) ax1.plot(pos[:,0], pos[:,1], 'o-', alpha=0.3) ax1.plot(2.3, 15, '*', ms=30, mfc='Crimson', mec='0.8', mew=2, alpha=0.7) ax1.set_xlabel('m', fontsize=14) ax1.set_ylabel('b', fontsize=14) ax2.plot(pos[:,0], pos[:,1], 'o-', alpha=0.3) cax = ax2.scatter(pos[:,0], pos[:,1], c = lnpost, zorder=10) ax2.plot(2.3, 15, '*', ms=30, mfc='Crimson', mec='0.8', mew=2, alpha=0.7, zorder=20) ax2.set_xlabel('m', fontsize=14) ax2.set_ylabel('b', fontsize=14) cbar = fig.colorbar(cax) cbar.ax.set_ylabel(r'$\log \; \pi (\theta)$', fontsize=12) fig.tight_layout() return def plot_mh_summary(theta_0, cov, nsteps, y, x, y_unc): ''' Plot the posterior, draws from the posterior, and 1-d chains Parameters ---------- theta_0 : 1-D array_like of shape N starting position for the MCMC chain cov : 1-D or 2-D array_like, of shape (N,) or (N, N) Covariance matrix of the distribution. It must be symmetric and positive-semidefinite for proper sampling. 1-D inputs for cov require the standard deviation along each axis of the N-dimensional Gaussian. nsteps : int Number of steps to take in the MCMC chain y : arr-like, shape (n_samples) Array of observational measurements x : arr-like, shape (n_samples) Array of positions where y is measured y_unc : arr-like, shape (n_samples) Array of uncertainties on y ''' pos, lnpost, acc = mh_mcmc(theta_0, cov, nsteps, y_obs, x, y_unc) fig = plt.figure(figsize=(7.5,6)) ax1 = plt.subplot2grid((4,5), (0, 0), colspan=2, rowspan=2) ax2 = plt.subplot2grid((4,5), (2, 0), colspan=2, rowspan=2) ax3 = plt.subplot2grid((4,5), (0, 2), colspan=3) ax4 = plt.subplot2grid((4,5), (1, 2), colspan=3, sharex=ax3) ax5 = plt.subplot2grid((4,5), (2, 2), colspan=3, sharex=ax3) ax6 = plt.subplot2grid((4,5), (3, 2), colspan=3, sharex=ax3) # posterior ax1.hexbin(pos[:,0], pos[:,1], gridsize=50, mincnt=1, bins='log') ax1.plot(2.3, 15, '*', ms=30, mfc='Crimson', mec='0.8', mew=2, alpha=0.7) ylims = ax1.get_ylim() xlims = ax1.get_xlim() ax1.plot([2.3, 2.3], ylims, 'Crimson', alpha=0.3) ax1.plot(xlims, [15, 15], 'Crimson', alpha=0.3) ax1.set_ylim(ylims) ax1.set_xlim(xlims) ax1.set_xlabel('m') ax1.set_ylabel('b') ax1.xaxis.set_ticks_position('top') ax1.xaxis.set_label_position('top') ax1.tick_params(top=True, bottom=False) # posterior draws ax2.errorbar(x, y_obs, y_unc, fmt='o') # ax2.plot([0,100], # b_true + m_true*np.array([0,100]), # '--', color='DarkOrange', lw=2, zorder=-10) for draw in np.random.choice(len(pos), 10, replace=False): ax2.plot([0,100], pos[draw,1] + pos[draw,0]*np.array([0,100]), 'DarkOrange', alpha=0.4) ax2.set_xlabel('x') ax2.set_ylabel('y') ax3.plot(pos[:,0]) ax3.set_ylabel('m') ax4.plot(pos[:,1]) ax4.set_ylabel('b') ax5.plot(lnpost) ax5.set_ylabel('$\ln \; \pi$') ax6.plot(acc) ax6.set_ylabel('acceptance') ax6.set_xlabel('step number') plt.setp(ax3.get_xticklabels(), visible=False) plt.setp(ax4.get_xticklabels(), visible=False) plt.setp(ax5.get_xticklabels(), visible=False) fig.tight_layout() fig.subplots_adjust(top=0.93, left=0.09, right=0.99, hspace=0.07, wspace=0.75)

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null

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null

0

1

0

4f3ff67dbb0740f2004b391228e54493135364ff

8,677

py

Python

src/hub/dataload/sources/chembl/chembl_dump.py

biothings/mychem.info

3d0f66ae9c1e9e6fa78a32868f440d162660e2aa

[ "Apache-2.0" ]

10

2017-07-24T11:45:27.000Z

2022-02-14T13:42:36.000Z

src/hub/dataload/sources/chembl/chembl_dump.py

biothings/mychem.info

3d0f66ae9c1e9e6fa78a32868f440d162660e2aa

[ "Apache-2.0" ]

92

2017-06-22T16:49:20.000Z

2022-03-24T20:50:01.000Z

src/hub/dataload/sources/chembl/chembl_dump.py

biothings/mychem.info

3d0f66ae9c1e9e6fa78a32868f440d162660e2aa

[ "Apache-2.0" ]

11

2017-06-12T18:31:35.000Z

2022-01-31T02:56:52.000Z

import glob import json import os import os.path import itertools import biothings import config biothings.config_for_app(config) from config import DATA_ARCHIVE_ROOT from biothings.hub.dataload.dumper import HTTPDumper from biothings.utils.common import iter_n class ChemblDumper(HTTPDumper): SRC_NAME = "chembl" SRC_ROOT_FOLDER = os.path.join(DATA_ARCHIVE_ROOT, SRC_NAME) SRC_VERSION_URL = "https://www.ebi.ac.uk/chembl/api/data/status.json" """ As the code is written, we have: - 1,961,462 "molecule" json objects - 5,134 "mechanism" json objects - 37,259 "drug_indication" json objects - 13,382 "target" json objects - 14,342 "binding_site" json objects """ SRC_DATA_URLS = { # primary data source "molecule": "https://www.ebi.ac.uk/chembl/api/data/molecule.json", # supplementary data sources to `molecule` "drug_indication": "https://www.ebi.ac.uk/chembl/api/data/drug_indication.json", "mechanism": "https://www.ebi.ac.uk/chembl/api/data/mechanism.json", # Used to join with `mechanism` by `target_chembl_id` "target": "https://www.ebi.ac.uk/chembl/api/data/target.json", # used to join with `mechanism` by `site_id` "binding_site": "https://www.ebi.ac.uk/chembl/api/data/binding_site.json" } SCHEDULE = "0 12 * * *" SLEEP_BETWEEN_DOWNLOAD = 0.1 MAX_PARALLEL_DUMP = 5 # HUB_MAX_WORKERS // 2 # number of documents in each download job, i.e. number of documents in each .part* file TO_DUMP_DOWNLOAD_SIZE = 1000 # number of .part* files to be merged together after download POST_DUMP_MERGE_SIZE = 100 def get_total_count_of_documents(self, src_data_name): """ Get the total count of documents from the first page of the url specified by `src_data_name`. `total_count` is a member of the `page_meta` member of the root json object. Args: src_data_name (str): must be a key to self.__class__.SRC_DATA_URLS Returns: int: the total count of documents """ if src_data_name not in self.__class__.SRC_DATA_URLS: raise KeyError("Cannot recognize src_data_name={}. Must be one of {{{}}}". format(src_data_name, ", ".join(self.__class__.SRC_DATA_URLS.keys()))) data = self.load_json_from_file(self.__class__.SRC_DATA_URLS[src_data_name]) return data["page_meta"]["total_count"] def load_json_from_file(self, file) -> dict: """ Read the content of `file` and return the json object Args: file (str): could either be an URL ("remotefile") or a path to a local text file ("localfile") Returns: object: the json object read from the `file` """ """ Note that: - `json.loads(string)` deserializes string - `json.load(file)` deserializes a file object """ if file.startswith("http://") or file.startswith("https://"): # file is an URL data = json.loads(self.client.get(file).text) else: # file is a local path data = json.load(open(file)) return data def remote_is_better(self, remotefile, localfile): remote_data = self.load_json_from_file(remotefile) assert "chembl_db_version" in remote_data assert remote_data["status"] == "UP" # API is working correctly self.release = remote_data["chembl_db_version"] if localfile is None: # ok we have the release, we can't compare further so we need to download return True local_data = self.load_json_from_file(localfile) self.logger.info("ChEMBL DB version: remote=={}, local=={}". format(remote_data["chembl_db_version"], local_data["chembl_db_version"])) # comparing strings should work since it's formatted as "ChEMBL_xxx" if remote_data["chembl_db_version"] > local_data["chembl_db_version"]: return True else: return False def create_todump_list(self, force=False, **kwargs): version_filename = os.path.basename(self.__class__.SRC_VERSION_URL) try: current_localfile = os.path.join(self.current_data_folder, version_filename) if not os.path.exists(current_localfile): current_localfile = None except TypeError: # current data folder doesn't even exist current_localfile = None remote_better = self.remote_is_better(self.__class__.SRC_VERSION_URL, current_localfile) self.logger.info("ChEMBL Dump: force=={}, current_localfile=={}, remote_better=={}". format(force, current_localfile, remote_better)) if force or current_localfile is None or remote_better: new_localfile = os.path.join(self.new_data_folder, version_filename) self.to_dump.append({"remote": self.__class__.SRC_VERSION_URL, "local": new_localfile}) """ Now we need to scroll the API endpoints. Let's get the total number of records and generate URLs for each call to parallelize the downloads for each type of source data, i.e. "molecule", "mechanism", "drug_indication", "target" and "binding_site". The partition size is set to 1000 json objects (represented by `TO_DUMP_DOWNLOAD_SIZE`). E.g. suppose for "molecule" data we have a `total_count` of 2500 json objects, and then we'll have, in the process of iteration: - (part_index, part_start) = (0, 0) - (part_index, part_start) = (1, 1000) - (part_index, part_start) = (2, 2000) Therefore we would download 3 files, i.e. "molecule.part0", "molecule.part1", and "molecule.part2". """ part_size = self.__class__.TO_DUMP_DOWNLOAD_SIZE for src_data_name in self.__class__.SRC_DATA_URLS: total_count = self.get_total_count_of_documents(src_data_name) for part_index, part_start in enumerate(range(0, total_count, part_size)): remote = self.__class__.SRC_DATA_URLS[src_data_name] + \ "?limit=" + str(part_size) + \ "&offset=" + str(part_start) local = os.path.join(self.new_data_folder, "{}.part{}".format(src_data_name, part_index)) self.to_dump.append({"remote": remote, "local": local}) def post_dump(self, *args, **kwargs): """ In the post-dump phase, for each type of source data, we merge each chunk of 100 .part* files into one .*.json file. (This way we won't have a small number of huge files nor a pile of small files.) E.g. as the code is written, we have 1,961,462 "molecule" json objects. Therefore we would download 1,962 files, i.e. "molecule.part0", ..., "molecule.part1961". For each chunk of 100 such files, e.g. "molecule.part0", ..., "molecule.part99", we merge them into one json file, e.g. "molecule.100.json". We'll also remove metadata (useless now) """ self.logger.info("Merging JSON documents in '%s'" % self.new_data_folder) chunk_size = self.__class__.POST_DUMP_MERGE_SIZE for src_data_name in self.__class__.SRC_DATA_URLS: part_files = glob.iglob(os.path.join(self.new_data_folder, "{}.part*".format(src_data_name))) for chunk, cnt in iter_n(part_files, chunk_size, with_cnt=True): outfile = os.path.join(self.new_data_folder, "{}.{}.json".format(src_data_name, cnt)) """ For each "molecule" json object, we only fetch the value associated with the "molecules" key. This rule also applies to "mechanism", "drug_indication", "target" and "binding_site" json objects. """ data_key = src_data_name + "s" merged_value = itertools.chain.from_iterable(self.load_json_from_file(f)[data_key] for f in chunk) merged_data = {data_key: list(merged_value)} json.dump(merged_data, open(outfile, "w")) self.logger.info("Merged %s %s files" % (src_data_name, cnt)) # now we can delete the part files self.logger.info("Deleting part files") part_files = glob.iglob(os.path.join(self.new_data_folder, "{}.part*".format(src_data_name))) for f in part_files: os.remove(f) self.logger.info("Post-dump merge done")

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1,181

8,677

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0

0.322581

0

null

0

null

0

1

0

4f419842dbdfdc91028cbda728bbb96e7a5880e7

2,487

py

Python

data/convert2json_bar.py

A-mberr/project

60ac3455d48a45450336a50a4de79d6c11d1cb42

[ "Unlicense" ]

null

data/convert2json_bar.py

A-mberr/project

60ac3455d48a45450336a50a4de79d6c11d1cb42

[ "Unlicense" ]

null

data/convert2json_bar.py

A-mberr/project

60ac3455d48a45450336a50a4de79d6c11d1cb42

[ "Unlicense" ]

null

# Name: Amber Nobel # Student number: 11819359 import pandas as pd countries_in_eu = [ 'Albania', 'Andorra', 'Austria', 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria', 'Croatia', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Latvia', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'The former Yugoslav republic of Macedonia', 'Malta', 'Republic of Moldova', 'Monaco', 'Montenegro', 'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania', 'Russia', 'San Marino', 'Serbia and Montenegro', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine', 'United Kingdom of Great Britain and Northern Ireland' ] codes = { "Albania": "AL", "Andorra": "AD", "Austria": "AT", "Azerbaijan": "AZ", "Belarus": "BY", "Belgium": "BE", "Bosnia and Herzegovina": "BA", "Bulgaria": "BG", "Croatia": "HR", "Cyprus": "CY", "Czechia": "CZ", "Denmark": "DK", "Estonia": "EE", "Finland": "FI", "France": "FR", "Georgia": "GE", "Germany": "DE", "Greece": "GR", "Hungary": "HU", "Iceland": "IS", "Ireland": "IE", "Italy": "IT", "Kazakhstan": "KZ", "Kosovo": "XK", "Latvia": "LV", "Liechtenstein": "LI", "Lithuania": "LT", "Luxembourg": "LU", "The former Yugoslav republic of Macedonia": "MK", "Malta": "MT", "Republic of Moldova": "MD", "Monaco": "MC", "Montenegro": "ME", "Netherlands": "NL", "Norway": "NO", "Poland": "PL", "Portugal": "PT", "Romania": "RO", "Russia": "RU", "San Marino": "SM", "Serbia": "RS", "Slovakia": "SK", "Slovenia": "SI", "Spain": "ES", "Sweden": "SE", "Switzerland": "CH", "Turkey": "TR", "Ukraine": "UA", "United Kingdom of Great Britain and Northern Ireland": "GB", "Vatican City": "VA" } vacc_types = [ "Hib", "Pneu", "DTP", "Hepb", ] for vacc_type in vacc_types: csvFilePath = "vacc_eu_{}.csv".format(vacc_type) df = pd.read_csv(csvFilePath, header=1) # selects only countries from Europe df = df[df['Country'].isin(countries_in_eu)] # changes country name to country code df["Country"] = df['Country'].map(codes).fillna(df['Country']) df = df.set_index('Country') df = df.apply(pd.to_numeric, errors='ignore') df.to_json('vacc_bar_{}.json'.format(vacc_type), orient='index')

26.178947

84

0.564536

273

2,487

5.080586

0.593407

0.028839

0.018745

0.036049

0.116799

0.064888

0

0.004649

0.221552

2,487

94

85

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0.045838

0

0.46875

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1

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false

0

0.012658

0

0.012658

0

null

0

null

0

1

0

4f429c50b35eaa47c1a01acf58c137d77a1df5c6

720

py

Python

exercicio_py/ex0018_matrizes/main_v7.py

danielle8farias/Exercicios-Python-3

f2fe9b6ca63536df1d83fd10162cfc04de36b830

[ "MIT" ]

null

exercicio_py/ex0018_matrizes/main_v7.py

danielle8farias/Exercicios-Python-3

f2fe9b6ca63536df1d83fd10162cfc04de36b830

[ "MIT" ]

null

exercicio_py/ex0018_matrizes/main_v7.py

danielle8farias/Exercicios-Python-3

f2fe9b6ca63536df1d83fd10162cfc04de36b830

[ "MIT" ]

null

######## # autora: danielle8farias@gmail.com # repositório: https://github.com/danielle8farias # Descrição: Dado uma matriz, o programa retorna a sua transposta. ######## import sys sys.path.append('/home/danielle8farias/hello-world-python3/meus_modulos') from mensagem import ler_cabecalho, criar_rodape, criar_linha from funcoes_matriz import ler_matriz, imprimir_matriz, matriz_transposta ler_cabecalho('matriz:') arquivo = 'matriz_exemplo.txt' matriz = ler_matriz(arquivo) n_linhas = len (matriz) n_colunas = len(matriz[0]) imprimir_matriz(matriz, n_linhas, n_colunas) matriz_T = matriz_transposta(matriz) ler_cabecalho('matriz transposta:') imprimir_matriz(matriz_T, n_colunas, n_linhas) print() criar_rodape()

30

73

0.788889

97

720

5.608247

0.474227

0.066176

0.110294

0

0.007634

0.090278

720

23

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31.304348

0.822901

0.204167

0

0.175407

0.097649

0

1

0

false

0

0.2

0

0.2

0.066667

0

null

0

null

0

1

0

4f4429e5d4d3c544a3a02ea4f09154ef7c36601f

6,294

py

Python

tests/test_halo_exchange.py

BuildJet/distdl

28b0dcf2c0a762de924cc310398a2eab9c35297f

[ "BSD-2-Clause" ]

null

tests/test_halo_exchange.py

BuildJet/distdl

28b0dcf2c0a762de924cc310398a2eab9c35297f

[ "BSD-2-Clause" ]

null

tests/test_halo_exchange.py

BuildJet/distdl

28b0dcf2c0a762de924cc310398a2eab9c35297f

[ "BSD-2-Clause" ]

null

import os import numpy as np import pytest import torch from adjoint_test import check_adjoint_test_tight from distdl.nn.mixins.conv_mixin import ConvMixin from distdl.nn.mixins.halo_mixin import HaloMixin from distdl.nn.mixins.pooling_mixin import PoolingMixin use_cuda = 'USE_CUDA' in os.environ class MockConvLayer(HaloMixin, ConvMixin): pass class MockPoolLayer(HaloMixin, PoolingMixin): pass adjoint_parametrizations = [] # Main functionality adjoint_parametrizations.append( pytest.param( np.arange(0, 9), [1, 1, 3, 3], # P_x_ranks, P_x_shape [1, 1, 10, 7], # x_global_shape torch.float32, # dtype [1, 1, 3, 3], # kernel_size [1, 1, 1, 1], # stride [0, 0, 0, 0], # padding [1, 1, 1, 1], # dilation MockConvLayer, # MockKernelStyle 9, # passed to comm_split_fixture, required MPI ranks id="conv-same_padding-float32", marks=[pytest.mark.mpi(min_size=9)] ) ) # Main functionality adjoint_parametrizations.append( pytest.param( np.arange(0, 9), [1, 1, 3, 3], # P_x_ranks, P_x_shape [1, 1, 10, 7], # x_global_shape torch.float64, # dtype [1, 1, 3, 3], # kernel_size [1, 1, 1, 1], # stride [0, 0, 0, 0], # padding [1, 1, 1, 1], # dilation MockConvLayer, # MockKernelStyle 9, # passed to comm_split_fixture, required MPI ranks id="conv-same_padding-float64", marks=[pytest.mark.mpi(min_size=9)] ) ) adjoint_parametrizations.append( pytest.param( np.arange(0, 3), [1, 1, 3], # P_x_ranks, P_x_shape [1, 1, 10], # x_global_shape torch.float32, # dtype [2], # kernel_size [2], # stride [0], # padding [1], # dilation MockConvLayer, # MockKernelStyle 3, # passed to comm_split_fixture, required MPI ranks id="conv-same_padding-float32", marks=[pytest.mark.mpi(min_size=3)] ) ) adjoint_parametrizations.append( pytest.param( np.arange(0, 3), [1, 1, 3], # P_x_ranks, P_x_shape [1, 1, 10], # x_global_shape torch.float64, # dtype [2], # kernel_size [2], # stride [0], # padding [1], # dilation MockConvLayer, # MockKernelStyle 3, # passed to comm_split_fixture, required MPI ranks id="conv-same_padding-float64", marks=[pytest.mark.mpi(min_size=3)] ) ) @pytest.mark.parametrize("P_x_ranks, P_x_shape," "x_global_shape," "dtype," "kernel_size," "stride," "padding," "dilation," "MockKernelStyle," "comm_split_fixture", adjoint_parametrizations, indirect=["comm_split_fixture"]) def test_halo_exchange_adjoint(barrier_fence_fixture, comm_split_fixture, P_x_ranks, P_x_shape, x_global_shape, dtype, kernel_size, stride, padding, dilation, MockKernelStyle): import numpy as np import torch from distdl.backends.mpi.partition import MPIPartition from distdl.nn.halo_exchange import HaloExchange from distdl.nn.padnd import PadNd from distdl.utilities.slicing import compute_subshape from distdl.utilities.torch import zero_volume_tensor device = torch.device('cuda' if use_cuda else 'cpu') # Isolate the minimum needed ranks base_comm, active = comm_split_fixture if not active: return P_world = MPIPartition(base_comm) P_x_base = P_world.create_partition_inclusive(P_x_ranks) P_x = P_x_base.create_cartesian_topology_partition(P_x_shape) x_global_shape = np.asarray(x_global_shape) kernel_size = np.asarray(kernel_size) stride = np.asarray(stride) padding = np.asarray(padding) dilation = np.asarray(dilation) halo_shape = None recv_buffer_shape = None send_buffer_shape = None if P_x.active: mockup_layer = MockKernelStyle() exchange_info = mockup_layer._compute_exchange_info(x_global_shape, kernel_size, stride, padding, dilation, P_x.active, P_x.shape, P_x.index) halo_shape = exchange_info[0] recv_buffer_shape = exchange_info[1] send_buffer_shape = exchange_info[2] pad_layer = PadNd(halo_shape, value=0) pad_layer = pad_layer.to(device) halo_layer = HaloExchange(P_x, halo_shape, recv_buffer_shape, send_buffer_shape) halo_layer = halo_layer.to(device) x = zero_volume_tensor(x_global_shape[0], device=device) if P_x.active: x_local_shape = compute_subshape(P_x.shape, P_x.index, x_global_shape) x = torch.randn(*x_local_shape, device=device).to(dtype) x = pad_layer.forward(x) x.requires_grad = True dy = zero_volume_tensor(x_global_shape[0], device=device) if P_x.active: dy = torch.randn(*x.shape, device=device).to(dtype) x_clone = x.clone() dy_clone = dy.clone() # x_clone is be modified in place by halo_layer, but we assign y to # reference it for clarity y = halo_layer(x_clone) # dy_clone is modified in place by halo_layer-adjoint, but we assign dx to # reference it for clarity y.backward(dy_clone) dx = dy_clone x = x.detach() dx = dx.detach() dy = dy.detach() y = y.detach() check_adjoint_test_tight(P_world, x, dx, y, dy) P_world.deactivate() P_x_base.deactivate() P_x.deactivate()

32.78125

84

0.560375

754

6,294

4.421751

0.185676

0.016797

0.043191

0.016797

0.494001

0.459208

0.391722

0.390522

0

0.026895

0.350175

6,294

191

85

32.95288

0.788264

0.132031

0

0.393548

0

0.045211

0.018454

0

1

0.006452

false

0.012903

0.096774

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0.122581

0

null

0

null

0

1

0

4f4614f1c0ed9b06d2d2365fd147912b5bfcb7c3

1,489

py

Python

algorithms/logistic_regression/logitsticRegresision.py

krasen86/ML_algorithms

54dd289ec82b36119bd5680833d0faab67058eb3

[ "MIT" ]

null

algorithms/logistic_regression/logitsticRegresision.py

krasen86/ML_algorithms

54dd289ec82b36119bd5680833d0faab67058eb3

[ "MIT" ]

null

algorithms/logistic_regression/logitsticRegresision.py

krasen86/ML_algorithms

54dd289ec82b36119bd5680833d0faab67058eb3

[ "MIT" ]

null

import numpy as np class LogisticRegression: def __init__(self, learningRate=0.001, numberOfIterations=1000): self.learningRate = learningRate self.numberOfIterations = numberOfIterations self.weights = None self.bias = None def fit(self, samples, labels): # initialize parameters numberOfSamples, numberOfFeatures = samples.shape self.weights = np.zeros(numberOfFeatures) self.bias = 0 # gradient descent for _ in range(self.numberOfIterations): # approximate the labels with liniear combination of weights and samples with bias linearModel = np.dot(samples, self.weights) + self.bias # apply sigmoid predicted = self._sigmoid(linearModel) # compute gradients delivaritiveWeights = (1/numberOfSamples) * np.dot(samples.T, predicted - labels) delivaritiveBias = (1/numberOfSamples) * np.sum(predicted - labels) # update parameters based on the gradient self.weights -= self.learningRate * delivaritiveWeights self.bias -= self.learningRate * delivaritiveBias def predict(self, samples): linearModel = np.dot(samples, self.weights) + self.bias predicted = self._sigmoid(linearModel) labelPredictedClasses = [1 if i > 0.5 else 0 for i in predicted] return labelPredictedClasses def _sigmoid(self, x): return 1/(1 + np.exp(-x))

38.179487

94

0.650101

152

1,489

6.315789

0.407895

0.057292

0.0375

0.047917

0.0875

0

0.015668

0.271323

1,489

39

95

38.179487

0.869124

0.128946

0

0.16

0

1

0.16

false

0

0.04

0.32

0

null

0

null

0

1

0

4f469ad81dc702dbbdb599c1d178b52955ddb183

5,432

py

Python

mars/optimization/logical/common/head.py

perfumescent/mars

9bf9bb990587cb9f091d108ed7f725fb429a80e8

[ "Apache-2.0" ]

null

mars/optimization/logical/common/head.py

perfumescent/mars

9bf9bb990587cb9f091d108ed7f725fb429a80e8

[ "Apache-2.0" ]

null

mars/optimization/logical/common/head.py

perfumescent/mars

9bf9bb990587cb9f091d108ed7f725fb429a80e8

[ "Apache-2.0" ]

null

# Copyright 1999-2021 Alibaba Group Holding Ltd. # # 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 typing import List from ....core import OperandType, TileableType, CHUNK_TYPE from ....dataframe.base.value_counts import DataFrameValueCounts from ....dataframe.datasource.core import HeadOptimizedDataSource from ....dataframe.sort.core import DataFrameSortOperand from ....dataframe.utils import parse_index from ....utils import implements from ..core import OptimizationRule, OptimizationRecord, OptimizationRecordType class HeadPushDown(OptimizationRule): @implements(OptimizationRule.match) def match(self, op: OperandType) -> bool: node = op.outputs[0] input_node = self._graph.predecessors(node)[0] successors = self._graph.successors(input_node) return self._all_successor_head_pushdown(successors) def _all_successor_head_pushdown(self, successors: List[TileableType]): for succ in successors: rule_types = self._optimizer_cls.get_rule_types(type(succ.op)) if rule_types is None: return False for rule_type in rule_types: if not issubclass(rule_type, HeadPushDown): return False rule = rule_type(self._graph, self._records, self._optimizer_cls) if not rule._can_push_down(succ.op): return False return True def _can_push_down(self, op: OperandType) -> bool: input_nodes = self._graph.predecessors(op.outputs[0]) accept_types = ( HeadOptimizedDataSource, DataFrameSortOperand, DataFrameValueCounts, ) if ( len(input_nodes) == 1 and op.can_be_optimized() and isinstance(input_nodes[0].op, accept_types) and input_nodes[0] not in self._graph.results ): return True return False def apply(self, op: OperandType): node = op.outputs[0] input_node = self._graph.predecessors(node)[0] nrows = input_node.op.nrows or 0 head = op.indexes[0].stop new_input_op = input_node.op.copy() new_input_op._key = input_node.op.key new_input_op._nrows = nrows = max(nrows, head) new_input_params = input_node.params.copy() new_input_params["shape"] = (nrows,) + input_node.shape[1:] pandas_index = node.index_value.to_pandas()[:nrows] new_input_params["index_value"] = parse_index(pandas_index, node) new_input_params.update(input_node.extra_params) new_entity = ( new_input_op.new_tileable if not isinstance(node, CHUNK_TYPE) else new_input_op.new_chunk ) new_input_node = new_entity(input_node.inputs, kws=[new_input_params]).data if ( new_input_node.op.nrows == head and self._graph.count_successors(input_node) == 1 ): new_input_node._key = node.key new_input_node._id = node.id # just remove the input data self._graph.add_node(new_input_node) for succ in self._graph.successors(node): self._graph.add_edge(new_input_node, succ) for pred in self._graph.predecessors(input_node): self._graph.add_edge(pred, new_input_node) self._graph.remove_node(input_node) self._graph.remove_node(node) # mark optimization record # the input node is removed self._records.append_record( OptimizationRecord(input_node, None, OptimizationRecordType.delete) ) self._records.append_record( OptimizationRecord(node, new_input_node, OptimizationRecordType.replace) ) new_node = new_input_node else: self._replace_node(input_node, new_input_node) new_op = op.copy() new_op._key = op.key params = node.params.copy() params.update(node.extra_params) new_entity = ( new_op.new_tileable if not isinstance(node, CHUNK_TYPE) else new_op.new_chunk ) new_node = new_entity([new_input_node], kws=[params]).data self._replace_node(node, new_node) # mark optimization record self._records.append_record( OptimizationRecord( input_node, new_input_node, OptimizationRecordType.replace ) ) self._records.append_record( OptimizationRecord(node, new_node, OptimizationRecordType.replace) ) # check node if it's in result try: i = self._graph.results.index(node) self._graph.results[i] = new_node except ValueError: pass

39.362319

88

0.631443

642

5,432

5.087227

0.267913

0.079914

0.044091

0.027557

0.195958

0.177281

0.119412

0.0594

0

0.006219

0.28958

5,432

137

89

39.649635

0.840114

0.12905

0

0.205607

0

0.003396

0

1

0.037383

false

0.009346

0.074766

0

0.186916

0

null

0

null

0

1

0

4f49a3fb21d094950777761f4be4e543e9c25599

823

py

Python

altair/vegalite/v2/examples/cumulative_wiki_donations.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

null

altair/vegalite/v2/examples/cumulative_wiki_donations.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

null

altair/vegalite/v2/examples/cumulative_wiki_donations.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

null

""" Cumulative Wikipedia Donations ============================== This chart shows cumulative donations to Wikipedia over the past 10 years. This chart was inspired by https://www.reddit.com/r/dataisbeautiful/comments/7guwd0/cumulative_wikimedia_donations_over_the_past_10/ but using lines instead of areas. Data comes from https://frdata.wikimedia.org/. """ import altair as alt data = "https://frdata.wikimedia.org/donationdata-vs-day.csv" chart = alt.Chart(data).mark_line().encode( alt.X( 'date:T', timeUnit='monthdate', axis=alt.Axis(format='%B', title='Month') ), alt.Y( 'max(ytdsum):Q', stack=None, axis=alt.Axis(title='Cumulative Donations') ), alt.Color('date:O', timeUnit='year', legend=alt.Legend(title='Year')), alt.Order('data:O', timeUnit='year') )

32.92

241

0.669502

111

823

4.900901

0.603604

0.033088

0.040441

0.047794

0

0.008559

0.148238

823

24

242

34.291667

0.767475

0.426488

0

0.142857

0

0.282328

0

1

0

false

0

0.071429

0

0.071429

0

null

0

null

0

1

0

4f4ae7fe0c623e0a6063b5d4970978fb4fb9660a

542

py

Python

modules/hash_extract.py

TURROKS/IOC-Parser

2c6d0fd049fbf3ba00766459ad19cde10aa8e6a8

[ "Apache-2.0" ]

null

modules/hash_extract.py

TURROKS/IOC-Parser

2c6d0fd049fbf3ba00766459ad19cde10aa8e6a8

[ "Apache-2.0" ]

null

modules/hash_extract.py

TURROKS/IOC-Parser

2c6d0fd049fbf3ba00766459ad19cde10aa8e6a8

[ "Apache-2.0" ]

null

import modules.common as common import iocextract # function reads inp file, extracts hashes using a regex string def main(inp, out): for line in inp.readlines(): for new_hash in iocextract.extract_hashes(line): if new_hash not in common.Hashes: common.Hashes.append(new_hash) print(new_hash + ', ') else: print(new_hash + ' Already in List') out.write('#####HASHES#####\n\n') for item in common.Hashes: out.write('"' + item + '", \n')

25.809524

63

0.581181

70

542

4.414286

0.5

0.113269

0.090615

0

0.298893

542

21

64

25.809524

0.813158

0.112546

0

0.091667

0

1

0.076923

false

0

0.153846

0

0.230769

0.153846

0

null

0

null

0

1

0

4f51ab0338418834dbcbbb94011e6959c76bbe20

29,534

py

Python

openslides/motions/models.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

openslides/motions/models.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

openslides/motions/models.py

rolandgeider/OpenSlides

331141c17cb23da26e377d4285efdb4a50753a59

[ "MIT" ]

null

from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.db import models from django.db.models import Max from django.utils import formats from django.utils.translation import ugettext as _ from django.utils.translation import ugettext_lazy, ugettext_noop from jsonfield import JSONField from openslides.agenda.models import Item from openslides.core.config import config from openslides.core.models import Tag from openslides.mediafiles.models import Mediafile from openslides.poll.models import ( BaseOption, BasePoll, BaseVote, CollectDefaultVotesMixin, ) from openslides.utils.models import RESTModelMixin from openslides.utils.search import user_name_helper from .access_permissions import ( CategoryAccessPermissions, MotionAccessPermissions, WorkflowAccessPermissions, ) from .exceptions import WorkflowError class Motion(RESTModelMixin, models.Model): """ The Motion Class. This class is the main entry point to all other classes related to a motion. """ access_permissions = MotionAccessPermissions() active_version = models.ForeignKey( 'MotionVersion', on_delete=models.SET_NULL, null=True, related_name="active_version") """ Points to a specific version. Used be the permitted-version-system to deside which version is the active version. Could also be used to only choose a specific version as a default version. Like the sighted versions on Wikipedia. """ state = models.ForeignKey( 'State', on_delete=models.SET_NULL, null=True) # TODO: Check whether null=True is necessary. """ The related state object. This attribute is to get the current state of the motion. """ identifier = models.CharField(max_length=255, null=True, blank=True, unique=True) """ A string as human readable identifier for the motion. """ identifier_number = models.IntegerField(null=True) """ Counts the number of the motion in one category. Needed to find the next free motion identifier. """ category = models.ForeignKey( 'Category', on_delete=models.SET_NULL, null=True, blank=True) """ ForeignKey to one category of motions. """ attachments = models.ManyToManyField(Mediafile, blank=True) """ Many to many relation to mediafile objects. """ parent = models.ForeignKey( 'self', on_delete=models.SET_NULL, null=True, blank=True, related_name='amendments') """ Field for amendments to reference to the motion that should be altered. Null if the motion is not an amendment. """ tags = models.ManyToManyField(Tag, blank=True) """ Tags to categorise motions. """ submitters = models.ManyToManyField(settings.AUTH_USER_MODEL, related_name='motion_submitters', blank=True) """ Users who submit this motion. """ supporters = models.ManyToManyField(settings.AUTH_USER_MODEL, related_name='motion_supporters', blank=True) """ Users who support this motion. """ class Meta: default_permissions = () permissions = ( ('can_see', 'Can see motions'), ('can_create', 'Can create motions'), ('can_support', 'Can support motions'), ('can_manage', 'Can manage motions'), ) ordering = ('identifier', ) verbose_name = ugettext_noop('Motion') def __str__(self): """ Return the title of this motion. """ return self.title # TODO: Use transaction def save(self, use_version=None, *args, **kwargs): """ Save the motion. 1. Set the state of a new motion to the default state. 2. Ensure that the identifier is not an empty string. 3. Save the motion object. 4. Save the version data. 5. Set the active version for the motion if a new version object was saved. The version data is *not* saved, if 1. the django-feature 'update_fields' is used or 2. the argument use_version is False (differ to None). The argument use_version is choose the version object into which the version data is saved. * If use_version is False, no version data is saved. * If use_version is None, the last version is used. * Else the given version is used. To create and use a new version object, you have to set it via the use_version argument. You have to set the title, text and reason into this version object before giving it to this save method. The properties motion.title, motion.text and motion.reason will be ignored. """ if not self.state: self.reset_state() # Solves the problem, that there can only be one motion with an empty # string as identifier. if not self.identifier and isinstance(self.identifier, str): self.identifier = None super(Motion, self).save(*args, **kwargs) if 'update_fields' in kwargs: # Do not save the version data if only some motion fields are updated. return if use_version is False: # We do not need to save the version. return elif use_version is None: use_version = self.get_last_version() # Save title, text and reason into the version object. for attr in ['title', 'text', 'reason']: _attr = '_%s' % attr data = getattr(self, _attr, None) if data is not None: setattr(use_version, attr, data) delattr(self, _attr) # If version is not in the database, test if it has new data and set # the version_number. if use_version.id is None: if not self.version_data_changed(use_version): # We do not need to save the version. return version_number = self.versions.aggregate(Max('version_number'))['version_number__max'] or 0 use_version.version_number = version_number + 1 # Necessary line if the version was set before the motion got an id. use_version.motion = use_version.motion use_version.save() # Set the active version of this motion. This has to be done after the # version is saved in the database. # TODO: Move parts of these last lines of code outside the save method # when other versions than the last ones should be edited later on. if self.active_version is None or not self.state.leave_old_version_active: # TODO: Don't call this if it was not a new version self.active_version = use_version self.save(update_fields=['active_version']) def version_data_changed(self, version): """ Compare the version with the last version of the motion. Returns True if the version data (title, text, reason) is different, else returns False. """ if not self.versions.exists(): # If there is no version in the database, the data has always changed. return True last_version = self.get_last_version() for attr in ['title', 'text', 'reason']: if getattr(last_version, attr) != getattr(version, attr): return True return False def set_identifier(self): """ Sets the motion identifier automaticly according to the config value if it is not set yet. """ # The identifier is already set or should be set manually if config['motions_identifier'] == 'manually' or self.identifier: # Do not set an identifier. return # The motion is an amendment elif self.is_amendment(): motions = self.parent.amendments.all() # The motions should be counted per category elif config['motions_identifier'] == 'per_category': motions = Motion.objects.filter(category=self.category) # The motions should be counted over all. else: motions = Motion.objects.all() number = motions.aggregate(Max('identifier_number'))['identifier_number__max'] or 0 if self.is_amendment(): parent_identifier = self.parent.identifier or '' prefix = '%s %s ' % (parent_identifier, config['motions_amendments_prefix']) elif self.category is None or not self.category.prefix: prefix = '' else: prefix = '%s ' % self.category.prefix number += 1 identifier = '%s%d' % (prefix, number) while Motion.objects.filter(identifier=identifier).exists(): number += 1 identifier = '%s%d' % (prefix, number) self.identifier = identifier self.identifier_number = number def get_title(self): """ Get the title of the motion. The title is taken from motion.version. """ try: return self._title except AttributeError: return self.get_active_version().title def set_title(self, title): """ Set the title of the motion. The title will be saved in the version object, when motion.save() is called. """ self._title = title title = property(get_title, set_title) """ The title of the motion. Is saved in a MotionVersion object. """ def get_text(self): """ Get the text of the motion. Simular to get_title(). """ try: return self._text except AttributeError: return self.get_active_version().text def set_text(self, text): """ Set the text of the motion. Simular to set_title(). """ self._text = text text = property(get_text, set_text) """ The text of a motin. Is saved in a MotionVersion object. """ def get_reason(self): """ Get the reason of the motion. Simular to get_title(). """ try: return self._reason except AttributeError: return self.get_active_version().reason def set_reason(self, reason): """ Set the reason of the motion. Simular to set_title(). """ self._reason = reason reason = property(get_reason, set_reason) """ The reason for the motion. Is saved in a MotionVersion object. """ def get_new_version(self, **kwargs): """ Return a version object, not saved in the database. The version data of the new version object is populated with the data set via motion.title, motion.text, motion.reason if these data are not given as keyword arguments. If the data is not set in the motion attributes, it is populated with the data from the last version object if such object exists. """ if self.pk is None: # Do not reference the MotionVersion object to an unsaved motion new_version = MotionVersion(**kwargs) else: new_version = MotionVersion(motion=self, **kwargs) if self.versions.exists(): last_version = self.get_last_version() else: last_version = None for attr in ['title', 'text', 'reason']: if attr in kwargs: continue _attr = '_%s' % attr data = getattr(self, _attr, None) if data is None and last_version is not None: data = getattr(last_version, attr) if data is not None: setattr(new_version, attr, data) return new_version def get_active_version(self): """ Returns the active version of the motion. If no active version is set by now, the last_version is used. """ if self.active_version: return self.active_version else: return self.get_last_version() def get_last_version(self): """ Return the newest version of the motion. """ try: return self.versions.order_by('-version_number')[0] except IndexError: return self.get_new_version() def is_submitter(self, user): """ Returns True if user is a submitter of this motion, else False. """ return user in self.submitters.all() def is_supporter(self, user): """ Returns True if user is a supporter of this motion, else False. """ return user in self.supporters.all() def create_poll(self): """ Create a new poll for this motion. Return the new poll object. """ if self.state.allow_create_poll: poll = MotionPoll.objects.create(motion=self) poll.set_options() return poll else: raise WorkflowError('You can not create a poll in state %s.' % self.state.name) @property def workflow(self): """ Returns the id of the workflow of the motion. """ # TODO: Rename to workflow_id return self.state.workflow.pk def set_state(self, state): """ Set the state of the motion. 'state' can be the id of a state object or a state object. """ if type(state) is int: state = State.objects.get(pk=state) if not state.dont_set_identifier: self.set_identifier() self.state = state def reset_state(self, workflow=None): """ Set the state to the default state. 'workflow' can be a workflow, an id of a workflow or None. If the motion is new and workflow is None, it chooses the default workflow from config. """ if type(workflow) is int: workflow = Workflow.objects.get(pk=workflow) if workflow is not None: new_state = workflow.first_state elif self.state: new_state = self.state.workflow.first_state else: new_state = (Workflow.objects.get(pk=config['motions_workflow']).first_state or Workflow.objects.get(pk=config['motions_workflow']).states.all()[0]) self.set_state(new_state) def get_agenda_title(self): """ Return a simple title string for the agenda. Returns only the motion title so that you have only agenda item number and title in the agenda. """ return str(self) def get_agenda_list_view_title(self): """ Return a title string for the agenda list view. Returns only the motion title so that you have agenda item number, title and motion identifier in the agenda. Note: It has to be the same return value like in JavaScript. """ if self.identifier: string = '%s (%s %s)' % (self.title, _(self._meta.verbose_name), self.identifier) else: string = '%s (%s)' % (self.title, _(self._meta.verbose_name)) return string @property def agenda_item(self): """ Returns the related agenda item. """ content_type = ContentType.objects.get_for_model(self) return Item.objects.get(object_id=self.pk, content_type=content_type) @property def agenda_item_id(self): """ Returns the id of the agenda item object related to this object. """ return self.agenda_item.pk def get_allowed_actions(self, person): """ Return a dictonary with all allowed actions for a specific person. The dictonary contains the following actions. * see * update / edit * delete * create_poll * support * unsupport * change_state * reset_state NOTE: If you update this function please also update the 'isAllowed' function on client side in motions/site.js. """ # TODO: Remove this method and implement these things in the views. actions = { 'see': (person.has_perm('motions.can_see') and (not self.state.required_permission_to_see or person.has_perm(self.state.required_permission_to_see) or self.is_submitter(person))), 'update': (person.has_perm('motions.can_manage') or (self.is_submitter(person) and self.state.allow_submitter_edit)), 'delete': person.has_perm('motions.can_manage'), 'create_poll': (person.has_perm('motions.can_manage') and self.state.allow_create_poll), 'support': (self.state.allow_support and config['motions_min_supporters'] > 0 and not self.is_submitter(person) and not self.is_supporter(person)), 'unsupport': (self.state.allow_support and self.is_supporter(person)), 'change_state': person.has_perm('motions.can_manage'), 'reset_state': person.has_perm('motions.can_manage')} actions['edit'] = actions['update'] return actions def write_log(self, message_list, person=None): """ Write a log message. The message should be in English and translatable, e. g. motion.write_log(message_list=[ugettext_noop('Message Text')]) """ MotionLog.objects.create(motion=self, message_list=message_list, person=person) def is_amendment(self): """ Returns True if the motion is an amendment. A motion is a amendment if amendments are activated in the config and the motion has a parent. """ return config['motions_amendments_enabled'] and self.parent is not None def get_search_index_string(self): """ Returns a string that can be indexed for the search. """ return " ".join(( self.title or '', self.text or '', self.reason or '', str(self.category) if self.category else '', user_name_helper(self.submitters.all()), user_name_helper(self.supporters.all()), " ".join(tag.name for tag in self.tags.all()))) class MotionVersion(RESTModelMixin, models.Model): """ A MotionVersion object saves some date of the motion. """ motion = models.ForeignKey( Motion, on_delete=models.CASCADE, related_name='versions') """The motion to which the version belongs.""" version_number = models.PositiveIntegerField(default=1) """An id for this version in realation to a motion. Is unique for each motion. """ title = models.CharField(max_length=255) """The title of a motion.""" text = models.TextField() """The text of a motion.""" reason = models.TextField(null=True, blank=True) """The reason for a motion.""" creation_time = models.DateTimeField(auto_now=True) """Time when the version was saved.""" class Meta: default_permissions = () unique_together = ("motion", "version_number") def __str__(self): """Return a string, representing this object.""" counter = self.version_number or ugettext_lazy('new') return "Motion %s, Version %s" % (self.motion_id, counter) @property def active(self): """Return True, if the version is the active version of a motion. Else: False.""" return self.active_version.exists() def get_root_rest_element(self): """ Returns the motion to this instance which is the root REST element. """ return self.motion class Category(RESTModelMixin, models.Model): """ Model for categories of motions. """ access_permissions = CategoryAccessPermissions() name = models.CharField(max_length=255) """Name of the category.""" prefix = models.CharField(blank=True, max_length=32) """Prefix of the category. Used to build the identifier of a motion. """ class Meta: default_permissions = () ordering = ['prefix'] def __str__(self): return self.name class MotionLog(RESTModelMixin, models.Model): """Save a logmessage for a motion.""" motion = models.ForeignKey( Motion, on_delete=models.CASCADE, related_name='log_messages') """The motion to witch the object belongs.""" message_list = JSONField() """ The log message. It should be a list of strings in English. """ person = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.SET_NULL, null=True) """A user object, who created the log message. Optional.""" time = models.DateTimeField(auto_now=True) """The Time, when the loged action was performed.""" class Meta: default_permissions = () ordering = ['-time'] def __str__(self): """ Return a string, representing the log message. """ time = formats.date_format(self.time, 'DATETIME_FORMAT') time_and_messages = '%s ' % time + ''.join(map(_, self.message_list)) if self.person is not None: return _('%(time_and_messages)s by %(person)s') % {'time_and_messages': time_and_messages, 'person': self.person} return time_and_messages def get_root_rest_element(self): """ Returns the motion to this instance which is the root REST element. """ return self.motion class MotionVote(RESTModelMixin, BaseVote): """Saves the votes for a MotionPoll. There should allways be three MotionVote objects for each poll, one for 'yes', 'no', and 'abstain'.""" option = models.ForeignKey( 'MotionOption', on_delete=models.CASCADE) """The option object, to witch the vote belongs.""" class Meta: default_permissions = () def get_root_rest_element(self): """ Returns the motion to this instance which is the root REST element. """ return self.option.poll.motion class MotionOption(RESTModelMixin, BaseOption): """Links between the MotionPollClass and the MotionVoteClass. There should be one MotionOption object for each poll.""" poll = models.ForeignKey( 'MotionPoll', on_delete=models.CASCADE) """The poll object, to witch the object belongs.""" vote_class = MotionVote """The VoteClass, to witch this Class links.""" class Meta: default_permissions = () def get_root_rest_element(self): """ Returns the motion to this instance which is the root REST element. """ return self.poll.motion class MotionPoll(RESTModelMixin, CollectDefaultVotesMixin, BasePoll): """The Class to saves the vote result for a motion poll.""" motion = models.ForeignKey( Motion, on_delete=models.CASCADE, related_name='polls') """The motion to witch the object belongs.""" option_class = MotionOption """The option class, witch links between this object the the votes.""" vote_values = ['Yes', 'No', 'Abstain'] """The possible anwers for the poll. 'Yes, 'No' and 'Abstain'.""" class Meta: default_permissions = () def __str__(self): """ Representation method only for debugging purposes. """ return 'MotionPoll for motion %s' % self.motion def set_options(self): """Create the option class for this poll.""" # TODO: maybe it is possible with .create() to call this without poll=self # or call this in save() self.get_option_class()(poll=self).save() def get_percent_base_choice(self): return config['motions_poll_100_percent_base'] def get_slide_context(self, **context): return super(MotionPoll, self).get_slide_context(poll=self) def get_root_rest_element(self): """ Returns the motion to this instance which is the root REST element. """ return self.motion class State(RESTModelMixin, models.Model): """ Defines a state for a motion. Every state belongs to a workflow. All states of a workflow are linked together via 'next_states'. One of these states is the first state, but this is saved in the workflow table (one-to-one relation). In every state you can configure some handling of a motion. See the following fields for more information. """ name = models.CharField(max_length=255) """A string representing the state.""" action_word = models.CharField(max_length=255) """An alternative string to be used for a button to switch to this state.""" workflow = models.ForeignKey( 'Workflow', on_delete=models.CASCADE, related_name='states') """A many-to-one relation to a workflow.""" next_states = models.ManyToManyField('self', symmetrical=False) """A many-to-many relation to all states, that can be choosen from this state.""" css_class = models.CharField(max_length=255, default='primary') """ A css class string for showing the state name in a coloured label based on bootstrap, e.g. 'danger' (red), 'success' (green), 'warning' (yellow), 'default' (grey). Default value is 'primary' (blue). """ required_permission_to_see = models.CharField(max_length=255, blank=True) """ A permission string. If not empty, the user has to have this permission to see a motion in this state. To use this feature change the database entry of a state object and add your favourite permission string. You can do this e. g. by editing the definitions in create_builtin_workflows() in openslides/motions/signals.py. """ allow_support = models.BooleanField(default=False) """If true, persons can support the motion in this state.""" allow_create_poll = models.BooleanField(default=False) """If true, polls can be created in this state.""" allow_submitter_edit = models.BooleanField(default=False) """If true, the submitter can edit the motion in this state.""" versioning = models.BooleanField(default=False) """ If true, editing the motion will create a new version by default. This behavior can be changed by the form and view, e. g. via the MotionDisableVersioningMixin. """ leave_old_version_active = models.BooleanField(default=False) """If true, new versions are not automaticly set active.""" dont_set_identifier = models.BooleanField(default=False) """ Decides if the motion gets an identifier. If true, the motion does not get an identifier if the state change to this one, else it does. """ class Meta: default_permissions = () def __str__(self): """Returns the name of the state.""" return self.name def save(self, **kwargs): """Saves a state in the database. Used to check the integrity before saving. """ self.check_next_states() super(State, self).save(**kwargs) def get_action_word(self): """Returns the alternative name of the state if it exists.""" return self.action_word or self.name def check_next_states(self): """Checks whether all next states of a state belong to the correct workflow.""" # No check if it is a new state which has not been saved yet. if not self.id: return for state in self.next_states.all(): if not state.workflow == self.workflow: raise WorkflowError('%s can not be next state of %s because it does not belong to the same workflow.' % (state, self)) def get_root_rest_element(self): """ Returns the workflow to this instance which is the root REST element. """ return self.workflow class Workflow(RESTModelMixin, models.Model): """ Defines a workflow for a motion. """ access_permissions = WorkflowAccessPermissions() name = models.CharField(max_length=255) """A string representing the workflow.""" first_state = models.OneToOneField( State, on_delete=models.SET_NULL, related_name='+', null=True) """A one-to-one relation to a state, the starting point for the workflow.""" class Meta: default_permissions = () def __str__(self): """Returns the name of the workflow.""" return self.name def save(self, **kwargs): """Saves a workflow in the database. Used to check the integrity before saving. """ self.check_first_state() super(Workflow, self).save(**kwargs) def check_first_state(self): """Checks whether the first_state itself belongs to the workflow.""" if self.first_state and not self.first_state.workflow == self: raise WorkflowError( '%s can not be first state of %s because it ' 'does not belong to it.' % (self.first_state, self))

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4f550e9bf9a276fbb780fa8bc882a77497808503

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py

Python

src/gam/var.py

GAM-team/GAM

b45ad5dcafc217690afc3c2e7086c1895f036172

[ "Apache-2.0" ]

102

2022-01-15T22:08:37.000Z

2022-03-31T16:02:20.000Z

src/gam/var.py

GAM-team/GAM

b45ad5dcafc217690afc3c2e7086c1895f036172

[ "Apache-2.0" ]

29

2022-01-14T20:16:51.000Z

2022-03-25T15:56:33.000Z

src/gam/var.py

GAM-team/GAM

b45ad5dcafc217690afc3c2e7086c1895f036172

[ "Apache-2.0" ]

30

2022-01-14T22:18:10.000Z

2022-03-31T17:31:40.000Z

"""Variables common across modules""" # pylint: disable=too-many-lines import os import ssl import string import sys import platform import re GAM_AUTHOR = 'Jay Lee <jay0lee@gmail.com>' GAM_VERSION = '6.22' GAM_LICENSE = 'Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0)' GAM_URL = 'https://jaylee.us/gam' GAM_INFO = ( f'GAM {GAM_VERSION} - {GAM_URL} / {GAM_AUTHOR} / ' f'Python {platform.python_version()} {sys.version_info.releaselevel} / ' f'{platform.platform()} {platform.machine()}') GAM_RELEASES = 'https://github.com/GAM-team/GAM/releases' GAM_WIKI = 'https://github.com/GAM-team/GAM/wiki' GAM_ALL_RELEASES = 'https://api.github.com/repos/GAM-team/GAM/releases' GAM_LATEST_RELEASE = GAM_ALL_RELEASES + '/latest' GAM_PROJECT_FILEPATH = 'https://raw.githubusercontent.com/GAM-team/GAM/master/src/' true_values = ['on', 'yes', 'enabled', 'true', '1'] false_values = ['off', 'no', 'disabled', 'false', '0'] usergroup_types = [ 'user', 'users', 'group', 'group_ns', 'group_susp', 'group_inde', 'ou', 'org', 'ou_ns', 'org_ns', 'ou_susp', 'org_susp', 'ou_and_children', 'ou_and_child', 'ou_and_children_ns', 'ou_and_child_ns', 'ou_and_children_susp', 'ou_and_child_susp', 'query', 'queries', 'license', 'licenses', 'licence', 'licences', 'file', 'csv', 'csvfile', 'all', 'cros', 'cros_sn', 'crosquery', 'crosqueries', 'crosfile', 'croscsv', 'croscsvfile' ] ERROR_PREFIX = 'ERROR: ' WARNING_PREFIX = 'WARNING: ' UTF8 = 'utf-8' UTF8_SIG = 'utf-8-sig' FN_ENABLEDASA_TXT = 'enabledasa.txt' FN_EXTRA_ARGS_TXT = 'extra-args.txt' FN_LAST_UPDATE_CHECK_TXT = 'lastupdatecheck.txt' MY_CUSTOMER = 'my_customer' # See https://support.google.com/drive/answer/37603 MAX_GOOGLE_SHEET_CELLS = 10000000 MAX_LOCAL_GOOGLE_TIME_OFFSET = 30 SKUS = { '1010010001': { 'product': '101001', 'aliases': ['identity', 'cloudidentity'], 'displayName': 'Cloud Identity' }, '1010050001': { 'product': '101005', 'aliases': ['identitypremium', 'cloudidentitypremium'], 'displayName': 'Cloud Identity Premium' }, '1010350001': { 'product': '101035', 'aliases': ['cloudsearch'], 'displayName': 'Google Cloud Search', }, '1010310002': { 'product': '101031', 'aliases': ['gsefe', 'e4e', 'gsuiteenterpriseeducation'], 'displayName': 'Google Workspace for Education Plus - Legacy' }, '1010310003': { 'product': '101031', 'aliases': ['gsefes', 'e4es', 'gsuiteenterpriseeducationstudent'], 'displayName': 'Google Workspace for Education Plus - Legacy (Student)' }, '1010310005': { 'product': '101031', 'aliases': ['gwes', 'workspaceeducationstandard'], 'displayName': 'Google Workspace for Education Standard' }, '1010310006': { 'product': '101031', 'aliases': ['gwesstaff', 'workspaceeducationstandardstaff'], 'displayName': 'Google Workspace for Education Standard (Staff)' }, '1010310007': { 'product': '101031', 'aliases': ['gwesstudent', 'workspaceeducationstandardstudent'], 'displayName': 'Google Workspace for Education Standard (Extra Student)' }, '1010310008': { 'product': '101031', 'aliases': ['gwep', 'workspaceeducationplus'], 'displayName': 'Google Workspace for Education Plus' }, '1010310009': { 'product': '101031', 'aliases': ['gwepstaff', 'workspaceeducationplusstaff'], 'displayName': 'Google Workspace for Education Plus (Staff)' }, '1010310010': { 'product': '101031', 'aliases': ['gwepstudent', 'workspaceeducationplusstudent'], 'displayName': 'Google Workspace for Education Plus (Extra Student)' }, '1010330003': { 'product': '101033', 'aliases': ['gvstarter', 'voicestarter', 'googlevoicestarter'], 'displayName': 'Google Voice Starter' }, '1010330004': { 'product': '101033', 'aliases': ['gvstandard', 'voicestandard', 'googlevoicestandard'], 'displayName': 'Google Voice Standard' }, '1010330002': { 'product': '101033', 'aliases': ['gvpremier', 'voicepremier', 'googlevoicepremier'], 'displayName': 'Google Voice Premier' }, '1010360001': { 'product': '101036', 'aliases': ['meetdialing','googlemeetglobaldialing'], 'displayName': 'Google Meet Global Dialing' }, '1010370001': { 'product': '101037', 'aliases': ['gwetlu', 'workspaceeducationupgrade'], 'displayName': 'Google Workspace for Education: Teaching and Learning Upgrade' }, 'Google-Apps': { 'product': 'Google-Apps', 'aliases': ['standard', 'free'], 'displayName': 'G Suite Legacy' }, 'Google-Apps-For-Business': { 'product': 'Google-Apps', 'aliases': ['gafb', 'gafw', 'basic', 'gsuitebasic'], 'displayName': 'G Suite Basic' }, 'Google-Apps-For-Government': { 'product': 'Google-Apps', 'aliases': ['gafg', 'gsuitegovernment', 'gsuitegov'], 'displayName': 'G Suite Government' }, 'Google-Apps-For-Postini': { 'product': 'Google-Apps', 'aliases': [ 'gams', 'postini', 'gsuitegams', 'gsuitepostini', 'gsuitemessagesecurity' ], 'displayName': 'G Suite Message Security' }, 'Google-Apps-Lite': { 'product': 'Google-Apps', 'aliases': ['gal', 'gsl', 'lite', 'gsuitelite'], 'displayName': 'G Suite Lite' }, 'Google-Apps-Unlimited': { 'product': 'Google-Apps', 'aliases': ['gau', 'gsb', 'unlimited', 'gsuitebusiness'], 'displayName': 'G Suite Business' }, '1010020027': { 'product': 'Google-Apps', 'aliases': ['wsbizstart', 'workspacebusinessstarter'], 'displayName': 'Workspace Business Starter' }, '1010020028': { 'product': 'Google-Apps', 'aliases': ['wsbizstan', 'workspacebusinessstandard'], 'displayName': 'Workspace Business Standard' }, '1010020025': { 'product': 'Google-Apps', 'aliases': ['wsbizplus', 'workspacebusinessplus'], 'displayName': 'Workspace Business Plus' }, '1010060001': { 'product': 'Google-Apps', 'aliases': [ 'gsuiteessentials', 'essentials', 'd4e', 'driveenterprise', 'drive4enterprise', 'wsess', 'workspaceesentials' ], 'displayName': 'Google Workspace Essentials' }, '1010060003': { 'product': 'Google-Apps', 'aliases': ['wsentess', 'workspaceenterpriseessentials'], 'displayName': 'Workspace Enterprise Essentials' }, '1010020026': { 'product': 'Google-Apps', 'aliases': ['wsentstan', 'workspaceenterprisestandard'], 'displayName': 'Workspace Enterprise Standard' }, '1010020020': { 'product': 'Google-Apps', 'aliases': ['gae', 'gse', 'enterprise', 'gsuiteenterprise', 'wsentplus', 'workspaceenterpriseplus'], 'displayName': 'Workspace Enterprise Plus' }, '1010020029': { 'product': 'Google-Apps', 'aliases': ['wes', 'workspaceenterprisestarter'], 'displayName': 'Workspace Enterprise Starter' }, '1010020030': { 'product': 'Google-Apps', 'aliases': ['workspacefrontline', 'workspacefrontlineworker'], 'displayName': 'Workspace Frontline' }, '1010340002': { 'product': '101034', 'aliases': ['gsbau', 'businessarchived', 'gsuitebusinessarchived'], 'displayName': 'G Suite Business Archived' }, '1010340001': { 'product': '101034', 'aliases': ['gseau', 'enterprisearchived', 'gsuiteenterprisearchived'], 'displayName': 'Google Workspace Enterprise Plus Archived' }, 'Google-Drive-storage-20GB': { 'product': 'Google-Drive-storage', 'aliases': ['drive20gb', '20gb', 'googledrivestorage20gb'], 'displayName': 'Google Drive Storage 20GB' }, 'Google-Drive-storage-50GB': { 'product': 'Google-Drive-storage', 'aliases': ['drive50gb', '50gb', 'googledrivestorage50gb'], 'displayName': 'Google Drive Storage 50GB' }, 'Google-Drive-storage-200GB': { 'product': 'Google-Drive-storage', 'aliases': ['drive200gb', '200gb', 'googledrivestorage200gb'], 'displayName': 'Google Drive Storage 200GB' }, 'Google-Drive-storage-400GB': { 'product': 'Google-Drive-storage', 'aliases': ['drive400gb', '400gb', 'googledrivestorage400gb'], 'displayName': 'Google Drive Storage 400GB' }, 'Google-Drive-storage-1TB': { 'product': 'Google-Drive-storage', 'aliases': ['drive1tb', '1tb', 'googledrivestorage1tb'], 'displayName': 'Google Drive Storage 1TB' }, 'Google-Drive-storage-2TB': { 'product': 'Google-Drive-storage', 'aliases': ['drive2tb', '2tb', 'googledrivestorage2tb'], 'displayName': 'Google Drive Storage 2TB' }, 'Google-Drive-storage-4TB': { 'product': 'Google-Drive-storage', 'aliases': ['drive4tb', '4tb', 'googledrivestorage4tb'], 'displayName': 'Google Drive Storage 4TB' }, 'Google-Drive-storage-8TB': { 'product': 'Google-Drive-storage', 'aliases': ['drive8tb', '8tb', 'googledrivestorage8tb'], 'displayName': 'Google Drive Storage 8TB' }, 'Google-Drive-storage-16TB': { 'product': 'Google-Drive-storage', 'aliases': ['drive16tb', '16tb', 'googledrivestorage16tb'], 'displayName': 'Google Drive Storage 16TB' }, 'Google-Vault': { 'product': 'Google-Vault', 'aliases': ['vault', 'googlevault'], 'displayName': 'Google Vault' }, 'Google-Vault-Former-Employee': { 'product': 'Google-Vault', 'aliases': ['vfe', 'googlevaultformeremployee'], 'displayName': 'Google Vault Former Employee' }, 'Google-Chrome-Device-Management': { 'product': 'Google-Chrome-Device-Management', 'aliases': ['chrome', 'cdm', 'googlechromedevicemanagement'], 'displayName': 'Google Chrome Device Management' } } PRODUCTID_NAME_MAPPINGS = { '101001': 'Cloud Identity Free', '101005': 'Cloud Identity Premium', '101031': 'G Suite Workspace for Education', '101033': 'Google Voice', '101034': 'G Suite Archived', '101035': 'Cloud Search', '101036': 'Google Meet Global Dialing', '101037': 'G Suite Workspace for Education', 'Google-Apps': 'Google Workspace', 'Google-Chrome-Device-Management': 'Google Chrome Device Management', 'Google-Drive-storage': 'Google Drive Storage', 'Google-Vault': 'Google Vault', } # Legacy APIs that use v1 discovery. Newer APIs should all use v2. V1_DISCOVERY_APIS = { 'drive', 'oauth2', } API_NAME_MAPPING = { 'directory': 'admin', 'directory_beta': 'admin', 'reports': 'admin', 'datatransfer': 'admin', 'drive3': 'drive', 'calendar': 'calendar-json', 'cloudidentity_beta': 'cloudidentity', } API_VER_MAPPING = { 'accesscontextmanager': 'v1', 'alertcenter': 'v1beta1', 'driveactivity': 'v2', 'calendar': 'v3', 'cbcm': 'v1.1beta1', 'chromemanagement': 'v1', 'chromepolicy': 'v1', 'classroom': 'v1', 'cloudidentity': 'v1', 'cloudidentity_beta': 'v1beta1', 'cloudresourcemanager': 'v3', 'contactdelegation': 'v1', 'datatransfer': 'datatransfer_v1', 'directory': 'directory_v1', 'directory_beta': 'directory_v1.1beta1', 'drive': 'v2', 'drive3': 'v3', 'gmail': 'v1', 'groupssettings': 'v1', 'iam': 'v1', 'iap': 'v1', 'licensing': 'v1', 'oauth2': 'v2', 'pubsub': 'v1', 'reports': 'reports_v1', 'reseller': 'v1', 'servicemanagement': 'v1', 'serviceusage': 'v1', 'sheets': 'v4', 'siteVerification': 'v1', 'storage': 'v1', 'vault': 'v1', 'versionhistory': 'v1', } USERINFO_EMAIL_SCOPE = 'https://www.googleapis.com/auth/userinfo.email' API_SCOPE_MAPPING = { 'alertcenter': ['https://www.googleapis.com/auth/apps.alerts',], 'driveactivity': [ 'https://www.googleapis.com/auth/drive.activity', 'https://www.googleapis.com/auth/drive', ], 'calendar': ['https://www.googleapis.com/auth/calendar',], 'cloudidentity': ['https://www.googleapis.com/auth/cloud-identity'], 'cloudidentity_beta': ['https://www.googleapis.com/auth/cloud-identity'], 'drive': ['https://www.googleapis.com/auth/drive',], 'drive3': ['https://www.googleapis.com/auth/drive',], 'gmail': [ 'https://mail.google.com/', 'https://www.googleapis.com/auth/gmail.settings.basic', 'https://www.googleapis.com/auth/gmail.settings.sharing', ], 'sheets': ['https://www.googleapis.com/auth/spreadsheets',], } ADDRESS_FIELDS_PRINT_ORDER = [ 'contactName', 'organizationName', 'addressLine1', 'addressLine2', 'addressLine3', 'locality', 'region', 'postalCode', 'countryCode', ] ADDRESS_FIELDS_ARGUMENT_MAP = { 'contact': 'contactName', 'contactname': 'contactName', 'name': 'organizationName', 'organizationname': 'organizationName', 'address': 'addressLine1', 'address1': 'addressLine1', 'addressline1': 'addressLine1', 'address2': 'addressLine2', 'addressline2': 'addressLine2', 'address3': 'addressLine3', 'addressline3': 'addressLine3', 'city': 'locality', 'locality': 'locality', 'state': 'region', 'region': 'region', 'zipcode': 'postalCode', 'postal': 'postalCode', 'postalcode': 'postalCode', 'country': 'countryCode', 'countrycode': 'countryCode', } SERVICE_NAME_TO_ID_MAP = { 'Calendar': '435070579839', 'Currents': '553547912911', 'Drive and Docs': '55656082996', 'Google Data Studio': '810260081642', } SERVICE_NAME_CHOICES_MAP = { 'calendar': 'Calendar', 'currents': 'Currents', 'datastudio': 'Google Data Studio', 'google data studio': 'Google Data Studio', 'drive': 'Drive and Docs', 'drive and docs': 'Drive and Docs', 'googledrive': 'Drive and Docs', 'gdrive': 'Drive and Docs', } PRINTJOB_ASCENDINGORDER_MAP = { 'createtime': 'CREATE_TIME', 'status': 'STATUS', 'title': 'TITLE', } PRINTJOB_DESCENDINGORDER_MAP = { 'CREATE_TIME': 'CREATE_TIME_DESC', 'STATUS': 'STATUS_DESC', 'TITLE': 'TITLE_DESC', } PRINTJOBS_DEFAULT_JOB_LIMIT = 0 PRINTJOBS_DEFAULT_MAX_RESULTS = 100 CALENDAR_REMINDER_METHODS = [ 'email', 'sms', 'popup', ] CALENDAR_NOTIFICATION_METHODS = [ 'email', 'sms', ] CALENDAR_NOTIFICATION_TYPES_MAP = { 'eventcreation': 'eventCreation', 'eventchange': 'eventChange', 'eventcancellation': 'eventCancellation', 'eventresponse': 'eventResponse', 'agenda': 'agenda', } DEVICE_ORDERBY_CHOICES_MAP = { 'createtime': 'create_time', 'devicetype': 'device_type', 'lastsynctime': 'last_sync_time', 'model': 'model', 'osversion': 'os_version', 'serialnumber': 'serial_number' } DRIVEFILE_FIELDS_CHOICES_MAP = { 'alternatelink': 'alternateLink', 'appdatacontents': 'appDataContents', 'cancomment': 'canComment', 'canreadrevisions': 'canReadRevisions', 'contentrestrictions': 'contentRestrictions', 'copyable': 'copyable', 'copyrequireswriterpermission': 'copyRequiresWriterPermission', 'createddate': 'createdDate', 'createdtime': 'createdDate', 'description': 'description', 'driveid': 'driveId', 'editable': 'editable', 'explicitlytrashed': 'explicitlyTrashed', 'fileextension': 'fileExtension', 'filesize': 'fileSize', 'foldercolorrgb': 'folderColorRgb', 'fullfileextension': 'fullFileExtension', 'headrevisionid': 'headRevisionId', 'iconlink': 'iconLink', 'id': 'id', 'lastmodifyinguser': 'lastModifyingUser', 'lastmodifyingusername': 'lastModifyingUserName', 'lastviewedbyme': 'lastViewedByMeDate', 'lastviewedbymedate': 'lastViewedByMeDate', 'lastviewedbymetime': 'lastViewedByMeDate', 'lastviewedbyuser': 'lastViewedByMeDate', 'linksharemetadata': 'linkShareMetadata', 'md5': 'md5Checksum', 'md5checksum': 'md5Checksum', 'md5sum': 'md5Checksum', 'mime': 'mimeType', 'mimetype': 'mimeType', 'modifiedbyme': 'modifiedByMeDate', 'modifiedbymedate': 'modifiedByMeDate', 'modifiedbymetime': 'modifiedByMeDate', 'modifiedbyuser': 'modifiedByMeDate', 'modifieddate': 'modifiedDate', 'modifiedtime': 'modifiedDate', 'name': 'title', 'originalfilename': 'originalFilename', 'ownedbyme': 'ownedByMe', 'ownernames': 'ownerNames', 'owners': 'owners', 'parents': 'parents', 'permissions': 'permissions', 'resourcekey': 'resourceKey', 'quotabytesused': 'quotaBytesUsed', 'quotaused': 'quotaBytesUsed', 'shareable': 'shareable', 'shared': 'shared', 'sharedwithmedate': 'sharedWithMeDate', 'sharedwithmetime': 'sharedWithMeDate', 'sharinguser': 'sharingUser', 'shortcutdetails': 'shortcutDetails', 'spaces': 'spaces', 'thumbnaillink': 'thumbnailLink', 'title': 'title', 'userpermission': 'userPermission', 'version': 'version', 'viewedbyme': 'labels(viewed)', 'viewedbymedate': 'lastViewedByMeDate', 'viewedbymetime': 'lastViewedByMeDate', 'viewerscancopycontent': 'labels(restricted)', 'webcontentlink': 'webContentLink', 'webviewlink': 'webViewLink', 'writerscanshare': 'writersCanShare', } DRIVEFILE_LABEL_CHOICES_MAP = { 'restricted': 'restricted', 'restrict': 'restricted', 'starred': 'starred', 'star': 'starred', 'trashed': 'trashed', 'trash': 'trashed', 'viewed': 'viewed', 'view': 'viewed', } DRIVEFILE_ORDERBY_CHOICES_MAP = { 'createddate': 'createdDate', 'folder': 'folder', 'lastviewedbyme': 'lastViewedByMeDate', 'lastviewedbymedate': 'lastViewedByMeDate', 'lastviewedbyuser': 'lastViewedByMeDate', 'modifiedbyme': 'modifiedByMeDate', 'modifiedbymedate': 'modifiedByMeDate', 'modifiedbyuser': 'modifiedByMeDate', 'modifieddate': 'modifiedDate', 'name': 'title', 'quotabytesused': 'quotaBytesUsed', 'quotaused': 'quotaBytesUsed', 'recency': 'recency', 'sharedwithmedate': 'sharedWithMeDate', 'starred': 'starred', 'title': 'title', 'viewedbymedate': 'lastViewedByMeDate', } DELETE_DRIVEFILE_FUNCTION_TO_ACTION_MAP = { 'delete': 'purging', 'trash': 'trashing', 'untrash': 'untrashing', } DRIVEFILE_LABEL_CHOICES_MAP = { 'restricted': 'restricted', 'restrict': 'restricted', 'starred': 'starred', 'star': 'starred', 'trashed': 'trashed', 'trash': 'trashed', 'viewed': 'viewed', 'view': 'viewed', } APPLICATION_VND_GOOGLE_APPS = 'application/vnd.google-apps.' MIMETYPE_GA_DOCUMENT = f'{APPLICATION_VND_GOOGLE_APPS}document' MIMETYPE_GA_DRAWING = f'{APPLICATION_VND_GOOGLE_APPS}drawing' MIMETYPE_GA_FOLDER = f'{APPLICATION_VND_GOOGLE_APPS}folder' MIMETYPE_GA_FORM = f'{APPLICATION_VND_GOOGLE_APPS}form' MIMETYPE_GA_FUSIONTABLE = f'{APPLICATION_VND_GOOGLE_APPS}fusiontable' MIMETYPE_GA_MAP = f'{APPLICATION_VND_GOOGLE_APPS}map' MIMETYPE_GA_PRESENTATION = f'{APPLICATION_VND_GOOGLE_APPS}presentation' MIMETYPE_GA_SCRIPT = f'{APPLICATION_VND_GOOGLE_APPS}script' MIMETYPE_GA_SITES = f'{APPLICATION_VND_GOOGLE_APPS}sites' MIMETYPE_GA_SPREADSHEET = f'{APPLICATION_VND_GOOGLE_APPS}spreadsheet' MIMETYPE_GA_SHORTCUT = f'{APPLICATION_VND_GOOGLE_APPS}shortcut' MIMETYPE_GA_3P_SHORTCUT = f'{APPLICATION_VND_GOOGLE_APPS}drive-sdk' MIMETYPE_CHOICES_MAP = { 'gdoc': MIMETYPE_GA_DOCUMENT, 'gdocument': MIMETYPE_GA_DOCUMENT, 'gdrawing': MIMETYPE_GA_DRAWING, 'gfolder': MIMETYPE_GA_FOLDER, 'gdirectory': MIMETYPE_GA_FOLDER, 'gform': MIMETYPE_GA_FORM, 'gfusion': MIMETYPE_GA_FUSIONTABLE, 'gpresentation': MIMETYPE_GA_PRESENTATION, 'gscript': MIMETYPE_GA_SCRIPT, 'gshortcut': MIMETYPE_GA_SHORTCUT, 'g3pshortcut': MIMETYPE_GA_3P_SHORTCUT, 'gsite': MIMETYPE_GA_SITES, 'gsheet': MIMETYPE_GA_SPREADSHEET, 'gspreadsheet': MIMETYPE_GA_SPREADSHEET, 'shortcut': MIMETYPE_GA_SHORTCUT, } DFA_CONVERT = 'convert' DFA_LOCALFILEPATH = 'localFilepath' DFA_LOCALFILENAME = 'localFilename' DFA_LOCALMIMETYPE = 'localMimeType' DFA_OCR = 'ocr' DFA_OCRLANGUAGE = 'ocrLanguage' DFA_PARENTQUERY = 'parentQuery' NON_DOWNLOADABLE_MIMETYPES = [ MIMETYPE_GA_FORM, MIMETYPE_GA_FUSIONTABLE, MIMETYPE_GA_MAP ] GOOGLEDOC_VALID_EXTENSIONS_MAP = { MIMETYPE_GA_DRAWING: ['.jpeg', '.jpg', '.pdf', '.png', '.svg'], MIMETYPE_GA_DOCUMENT: [ '.docx', '.html', '.odt', '.pdf', '.rtf', '.txt', '.zip' ], MIMETYPE_GA_PRESENTATION: ['.pdf', '.pptx', '.odp', '.txt'], MIMETYPE_GA_SPREADSHEET: ['.csv', '.ods', '.pdf', '.xlsx', '.zip'], } MACOS_CODENAMES = { 10: { 6: 'Snow Leopard', 7: 'Lion', 8: 'Mountain Lion', 9: 'Mavericks', 10: 'Yosemite', 11: 'El Capitan', 12: 'Sierra', 13: 'High Sierra', 14: 'Mojave', 15: 'Catalina', 16: 'Big Sur' }, 11: 'Big Sur', 12: 'Monterey', } _MICROSOFT_FORMATS_LIST = [{ 'mime': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'ext': '.docx' }, { 'mime': 'application/vnd.openxmlformats-officedocument.wordprocessingml.template', 'ext': '.dotx' }, { 'mime': 'application/vnd.openxmlformats-officedocument.presentationml.presentation', 'ext': '.pptx' }, { 'mime': 'application/vnd.openxmlformats-officedocument.presentationml.template', 'ext': '.potx' }, { 'mime': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'ext': '.xlsx' }, { 'mime': 'application/vnd.openxmlformats-officedocument.spreadsheetml.template', 'ext': '.xltx' }, { 'mime': 'application/msword', 'ext': '.doc' }, { 'mime': 'application/msword', 'ext': '.dot' }, { 'mime': 'application/vnd.ms-powerpoint', 'ext': '.ppt' }, { 'mime': 'application/vnd.ms-powerpoint', 'ext': '.pot' }, { 'mime': 'application/vnd.ms-excel', 'ext': '.xls' }, { 'mime': 'application/vnd.ms-excel', 'ext': '.xlt' }] DOCUMENT_FORMATS_MAP = { 'csv': [{ 'mime': 'text/csv', 'ext': '.csv' }], 'doc': [{ 'mime': 'application/msword', 'ext': '.doc' }], 'dot': [{ 'mime': 'application/msword', 'ext': '.dot' }], 'docx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'ext': '.docx' }], 'dotx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.wordprocessingml.template', 'ext': '.dotx' }], 'epub': [{ 'mime': 'application/epub+zip', 'ext': '.epub' }], 'html': [{ 'mime': 'text/html', 'ext': '.html' }], 'jpeg': [{ 'mime': 'image/jpeg', 'ext': '.jpeg' }], 'jpg': [{ 'mime': 'image/jpeg', 'ext': '.jpg' }], 'mht': [{ 'mime': 'message/rfc822', 'ext': 'mht' }], 'odp': [{ 'mime': 'application/vnd.oasis.opendocument.presentation', 'ext': '.odp' }], 'ods': [{ 'mime': 'application/x-vnd.oasis.opendocument.spreadsheet', 'ext': '.ods' }, { 'mime': 'application/vnd.oasis.opendocument.spreadsheet', 'ext': '.ods' }], 'odt': [{ 'mime': 'application/vnd.oasis.opendocument.text', 'ext': '.odt' }], 'pdf': [{ 'mime': 'application/pdf', 'ext': '.pdf' }], 'png': [{ 'mime': 'image/png', 'ext': '.png' }], 'ppt': [{ 'mime': 'application/vnd.ms-powerpoint', 'ext': '.ppt' }], 'pot': [{ 'mime': 'application/vnd.ms-powerpoint', 'ext': '.pot' }], 'potx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.presentationml.template', 'ext': '.potx' }], 'pptx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.presentationml.presentation', 'ext': '.pptx' }], 'rtf': [{ 'mime': 'application/rtf', 'ext': '.rtf' }], 'svg': [{ 'mime': 'image/svg+xml', 'ext': '.svg' }], 'tsv': [{ 'mime': 'text/tab-separated-values', 'ext': '.tsv' }, { 'mime': 'text/tsv', 'ext': '.tsv' }], 'txt': [{ 'mime': 'text/plain', 'ext': '.txt' }], 'xls': [{ 'mime': 'application/vnd.ms-excel', 'ext': '.xls' }], 'xlt': [{ 'mime': 'application/vnd.ms-excel', 'ext': '.xlt' }], 'xlsx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'ext': '.xlsx' }], 'xltx': [{ 'mime': 'application/vnd.openxmlformats-officedocument.spreadsheetml.template', 'ext': '.xltx' }], 'zip': [{ 'mime': 'application/zip', 'ext': '.zip' }], 'ms': _MICROSOFT_FORMATS_LIST, 'microsoft': _MICROSOFT_FORMATS_LIST, 'micro$oft': _MICROSOFT_FORMATS_LIST, 'openoffice': [{ 'mime': 'application/vnd.oasis.opendocument.presentation', 'ext': '.odp' }, { 'mime': 'application/x-vnd.oasis.opendocument.spreadsheet', 'ext': '.ods' }, { 'mime': 'application/vnd.oasis.opendocument.spreadsheet', 'ext': '.ods' }, { 'mime': 'application/vnd.oasis.opendocument.text', 'ext': '.odt' }], } REFRESH_PERM_ERRORS = [ 'invalid_grant: reauth related error (rapt_required)', # no way to reauth today 'invalid_grant: Token has been expired or revoked.', ] DNS_ERROR_CODES_MAP = { 1: 'DNS Query Format Error', 2: 'Server failed to complete the DNS request', 3: 'Domain name does not exist', 4: 'Function not implemented', 5: 'The server refused to answer for the query', 6: 'Name that should not exist, does exist', 7: 'RRset that should not exist, does exist', 8: 'Server not authoritative for the zone', 9: 'Name not in zone' } EMAILSETTINGS_OLD_NEW_OLD_FORWARD_ACTION_MAP = { 'ARCHIVE': 'archive', 'DELETE': 'trash', 'KEEP': 'leaveInInBox', 'MARK_READ': 'markRead', 'archive': 'ARCHIVE', 'trash': 'DELETE', 'leaveInInbox': 'KEEP', 'markRead': 'MARK_READ', } EMAILSETTINGS_IMAP_EXPUNGE_BEHAVIOR_CHOICES_MAP = { 'archive': 'archive', 'deleteforever': 'deleteForever', 'trash': 'trash', } EMAILSETTINGS_IMAP_MAX_FOLDER_SIZE_CHOICES = [ '0', '1000', '2000', '5000', '10000' ] EMAILSETTINGS_POP_ENABLE_FOR_CHOICES_MAP = { 'allmail': 'allMail', 'fromnowon': 'fromNowOn', 'mailfromnowon': 'fromNowOn', 'newmail': 'fromNowOn', } EMAILSETTINGS_FORWARD_POP_ACTION_CHOICES_MAP = { 'archive': 'archive', 'delete': 'trash', 'keep': 'leaveInInbox', 'leaveininbox': 'leaveInInbox', 'markread': 'markRead', 'trash': 'trash', } RT_PATTERN = re.compile(r'(?s){RT}.*?{(.+?)}.*?{/RT}') RT_OPEN_PATTERN = re.compile(r'{RT}') RT_CLOSE_PATTERN = re.compile(r'{/RT}') RT_STRIP_PATTERN = re.compile(r'(?s){RT}.*?{/RT}') RT_TAG_REPLACE_PATTERN = re.compile(r'{(.*?)}') LOWERNUMERIC_CHARS = string.ascii_lowercase + string.digits ALPHANUMERIC_CHARS = LOWERNUMERIC_CHARS + string.ascii_uppercase URL_SAFE_CHARS = ALPHANUMERIC_CHARS + '-._~' FILTER_ADD_LABEL_TO_ARGUMENT_MAP = { 'IMPORTANT': 'important', 'STARRED': 'star', 'TRASH': 'trash', } FILTER_REMOVE_LABEL_TO_ARGUMENT_MAP = { 'IMPORTANT': 'notimportant', 'UNREAD': 'markread', 'INBOX': 'archive', 'SPAM': 'neverspam', } FILTER_CRITERIA_CHOICES_MAP = { 'excludechats': 'excludeChats', 'from': 'from', 'hasattachment': 'hasAttachment', 'haswords': 'query', 'musthaveattachment': 'hasAttachment', 'negatedquery': 'negatedQuery', 'nowords': 'negatedQuery', 'query': 'query', 'size': 'size', 'subject': 'subject', 'to': 'to', } FILTER_ACTION_CHOICES = [ 'archive', 'forward', 'important', 'label', 'markread', 'neverspam', 'notimportant', 'star', 'trash', ] VAULT_MATTER_ACTIONS = [ 'reopen', 'undelete', 'close', 'delete', ] CROS_ARGUMENT_TO_PROPERTY_MAP = { 'activetimeranges': [ 'activeTimeRanges.activeTime', 'activeTimeRanges.date' ], 'annotatedassetid': ['annotatedAssetId',], 'annotatedlocation': ['annotatedLocation',], 'annotateduser': ['annotatedUser',], 'asset': ['annotatedAssetId',], 'assetid': ['annotatedAssetId',], 'autoupdateexpiration': ['autoUpdateExpiration',], 'bootmode': ['bootMode',], 'cpustatusreports': ['cpuStatusReports',], 'devicefiles': ['deviceFiles',], 'deviceid': ['deviceId',], 'dockmacaddress': ['dockMacAddress',], 'diskvolumereports': ['diskVolumeReports',], 'ethernetmacaddress': ['ethernetMacAddress',], 'ethernetmacaddress0': ['ethernetMacAddress0',], 'firmwareversion': ['firmwareVersion',], 'lastenrollmenttime': ['lastEnrollmentTime',], 'lastknownnetwork': ['lastKnownNetwork'], 'lastsync': ['lastSync',], 'location': ['annotatedLocation',], 'macaddress': ['macAddress',], 'manufacturedate': ['manufactureDate',], 'meid': ['meid',], 'model': ['model',], 'notes': ['notes',], 'ordernumber': ['orderNumber',], 'org': ['orgUnitPath',], 'orgunitid': ['orgUnitId',], 'orgunitpath': ['orgUnitPath',], 'osversion': ['osVersion',], 'ou': ['orgUnitPath',], 'platformversion': ['platformVersion',], 'recentusers': ['recentUsers.email', 'recentUsers.type'], 'serialnumber': ['serialNumber',], 'status': ['status',], 'supportenddate': ['supportEndDate',], 'systemramtotal': ['systemRamTotal',], 'systemramfreereports': ['systemRamFreeReports',], 'tag': ['annotatedAssetId',], 'timeranges': ['activeTimeRanges.activeTime', 'activeTimeRanges.date'], 'times': ['activeTimeRanges.activeTime', 'activeTimeRanges.date'], 'tpmversioninfo': ['tpmVersionInfo',], 'user': ['annotatedUser',], 'users': ['recentUsers.email', 'recentUsers.type'], 'willautorenew': ['willAutoRenew',], } CROS_BASIC_FIELDS_LIST = [ 'deviceId', 'annotatedAssetId', 'annotatedLocation', 'annotatedUser', 'lastSync', 'notes', 'serialNumber', 'status' ] CROS_SCALAR_PROPERTY_PRINT_ORDER = [ 'orgUnitId', 'orgUnitPath', 'annotatedAssetId', 'annotatedLocation', 'annotatedUser', 'lastSync', 'notes', 'serialNumber', 'status', 'model', 'firmwareVersion', 'platformVersion', 'osVersion', 'bootMode', 'meid', 'dockMacAddress', 'ethernetMacAddress', 'ethernetMacAddress0', 'macAddress', 'systemRamTotal', 'lastEnrollmentTime', 'orderNumber', 'manufactureDate', 'supportEndDate', 'autoUpdateExpiration', 'tpmVersionInfo', 'willAutoRenew', ] CROS_RECENT_USERS_ARGUMENTS = ['recentusers', 'users'] CROS_ACTIVE_TIME_RANGES_ARGUMENTS = ['timeranges', 'activetimeranges', 'times'] CROS_DEVICE_FILES_ARGUMENTS = ['devicefiles', 'files'] CROS_CPU_STATUS_REPORTS_ARGUMENTS = [ 'cpustatusreports', ] CROS_DISK_VOLUME_REPORTS_ARGUMENTS = [ 'diskvolumereports', ] CROS_SYSTEM_RAM_FREE_REPORTS_ARGUMENTS = [ 'systemramfreereports', ] CROS_LISTS_ARGUMENTS = CROS_ACTIVE_TIME_RANGES_ARGUMENTS + \ CROS_RECENT_USERS_ARGUMENTS + \ CROS_DEVICE_FILES_ARGUMENTS + \ CROS_CPU_STATUS_REPORTS_ARGUMENTS + \ CROS_DISK_VOLUME_REPORTS_ARGUMENTS + \ CROS_SYSTEM_RAM_FREE_REPORTS_ARGUMENTS CROS_START_ARGUMENTS = ['start', 'startdate', 'oldestdate'] CROS_END_ARGUMENTS = ['end', 'enddate'] # From https://www.chromium.org/chromium-os/tpm_firmware_update CROS_TPM_VULN_VERSIONS = [ '41f', '420', '628', '8520', ] CROS_TPM_FIXED_VERSIONS = [ '422', '62b', '8521', ] COLLABORATIVE_INBOX_ATTRIBUTES = [ 'whoCanAddReferences', 'whoCanAssignTopics', 'whoCanEnterFreeFormTags', 'whoCanMarkDuplicate', 'whoCanMarkFavoriteReplyOnAnyTopic', 'whoCanMarkFavoriteReplyOnOwnTopic', 'whoCanMarkNoResponseNeeded', 'whoCanModifyTagsAndCategories', 'whoCanTakeTopics', 'whoCanUnassignTopic', 'whoCanUnmarkFavoriteReplyOnAnyTopic', 'favoriteRepliesOnTop', ] GROUP_SETTINGS_LIST_ATTRIBUTES = { # ACL choices 'whoCanAdd', 'whoCanApproveMembers', 'whoCanApproveMessages', 'whoCanAssignTopics', 'whoCanAssistContent', 'whoCanBanUsers', 'whoCanContactOwner', 'whoCanDeleteAnyPost', 'whoCanDeleteTopics', 'whoCanDiscoverGroup', 'whoCanEnterFreeFormTags', 'whoCanHideAbuse', 'whoCanInvite', 'whoCanJoin', 'whoCanLeaveGroup', 'whoCanLockTopics', 'whoCanMakeTopicsSticky', 'whoCanMarkDuplicate', 'whoCanMarkFavoriteReplyOnAnyTopic', 'whoCanMarkFavoriteReplyOnOwnTopic', 'whoCanMarkNoResponseNeeded', 'whoCanModerateContent', 'whoCanModerateMembers', 'whoCanModifyMembers', 'whoCanModifyTagsAndCategories', 'whoCanMoveTopicsIn', 'whoCanMoveTopicsOut', 'whoCanPostAnnouncements', 'whoCanPostMessage', 'whoCanTakeTopics', 'whoCanUnassignTopic', 'whoCanUnmarkFavoriteReplyOnAnyTopic', 'whoCanViewGroup', 'whoCanViewMembership', # Miscellaneous choices 'default_sender', 'messageModerationLevel', 'replyTo', 'spamModerationLevel', } GROUP_SETTINGS_BOOLEAN_ATTRIBUTES = { 'allowExternalMembers', 'allowGoogleCommunication', 'allowWebPosting', 'archiveOnly', 'enableCollaborativeInbox', 'favoriteRepliesOnTop', 'includeCustomFooter', 'includeInGlobalAddressList', 'isArchived', 'membersCanPostAsTheGroup', 'sendMessageDenyNotification', 'showInGroupDirectory', } # # Global variables # # The following GM_XXX constants are arbitrary but must be unique # Most errors print a message and bail out with a return code # Some commands want to set a non-zero return code but not bail GM_SYSEXITRC = 'sxrc' # Path to gam GM_GAM_PATH = 'gpth' # Python source, PyInstaller or StaticX? GM_GAM_TYPE = 'gtyp' # Are we on Windows? GM_WINDOWS = 'wndo' # Encodings GM_SYS_ENCODING = 'syen' # Extra arguments to pass to GAPI functions GM_EXTRA_ARGS_DICT = 'exad' # Current API services GM_CURRENT_API_SERVICES = 'caps' # Current API user GM_CURRENT_API_USER = 'capu' # Current API scope GM_CURRENT_API_SCOPES = 'scoc' # Values retrieved from oauth2service.json GM_OAUTH2SERVICE_JSON_DATA = 'oajd' GM_OAUTH2SERVICE_ACCOUNT_CLIENT_ID = 'oaci' # Full path to enabledasa.txt GM_ENABLEDASA_TXT = 'enda' # File containing time of last GAM update check GM_LAST_UPDATE_CHECK_TXT = 'lupc' # Dictionary mapping OrgUnit ID to Name GM_MAP_ORGUNIT_ID_TO_NAME = 'oi2n' # Dictionary mapping Role ID to Name GM_MAP_ROLE_ID_TO_NAME = 'ri2n' # Dictionary mapping Role Name to ID GM_MAP_ROLE_NAME_TO_ID = 'rn2i' # Dictionary mapping User ID to Name GM_MAP_USER_ID_TO_NAME = 'ui2n' # GAM cache directory. If no_cache is True, this variable will be set to None GM_CACHE_DIR = 'gacd' # Reset GAM cache directory after discovery GM_CACHE_DISCOVERY_ONLY = 'gcdo' # Dictionary mapping Building ID to Name GM_MAP_BUILDING_ID_TO_NAME = 'bi2n' # Dictionary mapping Building Name to ID GM_MAP_BUILDING_NAME_TO_ID = 'bn2i' # _DEFAULT_CHARSET = UTF8 _FN_CLIENT_SECRETS_JSON = 'client_secrets.json' _FN_OAUTH2SERVICE_JSON = 'oauth2service.json' _FN_OAUTH2_TXT = 'oauth2.txt' # GM_Globals = { GM_SYSEXITRC: 0, GM_GAM_PATH: None, GM_GAM_TYPE: None, GM_WINDOWS: os.name == 'nt', GM_SYS_ENCODING: _DEFAULT_CHARSET, GM_EXTRA_ARGS_DICT: { 'prettyPrint': False }, GM_CURRENT_API_SERVICES: {}, GM_CURRENT_API_USER: None, GM_CURRENT_API_SCOPES: [], GM_OAUTH2SERVICE_JSON_DATA: None, GM_OAUTH2SERVICE_ACCOUNT_CLIENT_ID: None, GM_ENABLEDASA_TXT: '', GM_LAST_UPDATE_CHECK_TXT: '', GM_MAP_ORGUNIT_ID_TO_NAME: {}, GM_MAP_ROLE_ID_TO_NAME: None, GM_MAP_ROLE_NAME_TO_ID: None, GM_MAP_USER_ID_TO_NAME: None, GM_CACHE_DIR: None, GM_CACHE_DISCOVERY_ONLY: True, GM_MAP_BUILDING_ID_TO_NAME: None, GM_MAP_BUILDING_NAME_TO_ID: None, } # # Global variables defined by environment variables/signal files # # Automatically generate gam batch command if number of users specified in gam # users xxx command exceeds this number # Default: 0, don't automatically generate gam batch commands GC_AUTO_BATCH_MIN = 'auto_batch_min' # When processing items in batches, how many should be processed in each batch GC_BATCH_SIZE = 'batch_size' # GAM cache directory. If no_cache is specified, this variable will be set to None GC_CACHE_DIR = 'cache_dir' # GAM cache discovery only. If no_cache is False, only API discovery calls will be cached GC_CACHE_DISCOVERY_ONLY = 'cache_discovery_only' # Character set of batch, csv, data files GC_CHARSET = 'charset' # Path to client_secrets.json GC_CLIENT_SECRETS_JSON = 'client_secrets_json' # GAM config directory containing client_secrets.json, oauth2.txt, # oauth2service.json, extra_args.txt GC_CONFIG_DIR = 'config_dir' # custmerId from gam.cfg or retrieved from Google GC_CUSTOMER_ID = 'customer_id' # Admin email address, required when enable_dasa is true, overrides oauth2.txt value otherwise GC_ADMIN_EMAIL = 'admin_email' # If debug_level > 0: extra_args[u'prettyPrint'] = True, # httplib2.debuglevel = gam_debug_level, appsObj.debug = True GC_DEBUG_LEVEL = 'debug_level' # ID Token decoded from OAuth 2.0 refresh token response. Includes hd (domain) # and email of authorized user GC_DECODED_ID_TOKEN = 'decoded_id_token' # Domain obtained from gam.cfg or oauth2.txt GC_DOMAIN = 'domain' # Google Drive download directory GC_DRIVE_DIR = 'drive_dir' # Enable Delegated Admin Service Accounts GC_ENABLE_DASA = 'enabledasa' # If no_browser is True, writeCSVfile won't open a browser when todrive is set # and doRequestOAuth prints a link and waits for the verification code when # oauth2.txt is being created GC_NO_BROWSER = 'no_browser' # If no_tdemail is True, writeCSVfile won't send an email GC_NO_TDEMAIL = 'no_tdemail' # oauth_browser forces usage of web server OAuth flow that proved problematic. GC_OAUTH_BROWSER = 'oauth_browser' # Disable GAM API caching GC_NO_CACHE = 'no_cache' # Disable Short URLs GC_NO_SHORT_URLS = 'no_short_urls' # Disable GAM update check GC_NO_UPDATE_CHECK = 'no_update_check' # Number of threads for gam batch GC_NUM_THREADS = 'num_threads' # Path to oauth2.txt GC_OAUTH2_TXT = 'oauth2_txt' # Path to oauth2service.json GC_OAUTH2SERVICE_JSON = 'oauth2service_json' # Default section to use for processing GC_SECTION = 'section' # Add (n/m) to end of messages if number of items to be processed exceeds this number GC_SHOW_COUNTS_MIN = 'show_counts_min' # Enable/disable "Getting ... " messages GC_SHOW_GETTINGS = 'show_gettings' # GAM config directory containing json discovery files GC_SITE_DIR = 'site_dir' # CSV Columns GAM should show on CSV output GC_CSV_HEADER_FILTER = 'csv_header_filter' # CSV Columns GAM should not show on CSV output GC_CSV_HEADER_DROP_FILTER = 'csv_header_drop_filter' # CSV Rows GAM should filter GC_CSV_ROW_FILTER = 'csv_row_filter' # CSV Rows GAM should filter/drop GC_CSV_ROW_DROP_FILTER = 'csv_row_drop_filter' # Minimum TLS Version required for HTTPS connections GC_TLS_MIN_VERSION = 'tls_min_ver' # Maximum TLS Version used for HTTPS connections GC_TLS_MAX_VERSION = 'tls_max_ver' # Path to certificate authority file for validating TLS hosts GC_CA_FILE = 'ca_file' TLS_MIN = 'TLSv1_3' if hasattr(ssl.SSLContext(), 'minimum_version') else None GC_Defaults = { GC_ADMIN_EMAIL: '', GC_AUTO_BATCH_MIN: 0, GC_BATCH_SIZE: 50, GC_CACHE_DIR: '', GC_CACHE_DISCOVERY_ONLY: True, GC_CHARSET: _DEFAULT_CHARSET, GC_CLIENT_SECRETS_JSON: _FN_CLIENT_SECRETS_JSON, GC_CONFIG_DIR: '', GC_CUSTOMER_ID: MY_CUSTOMER, GC_DEBUG_LEVEL: 0, GC_DECODED_ID_TOKEN: '', GC_DOMAIN: '', GC_DRIVE_DIR: '', GC_ENABLE_DASA: False, GC_NO_BROWSER: False, GC_NO_TDEMAIL: False, GC_NO_CACHE: False, GC_NO_SHORT_URLS: False, GC_NO_UPDATE_CHECK: False, GC_NUM_THREADS: 25, GC_OAUTH_BROWSER: False, GC_OAUTH2_TXT: _FN_OAUTH2_TXT, GC_OAUTH2SERVICE_JSON: _FN_OAUTH2SERVICE_JSON, GC_SECTION: '', GC_SHOW_COUNTS_MIN: 0, GC_SHOW_GETTINGS: True, GC_SITE_DIR: '', GC_CSV_HEADER_FILTER: '', GC_CSV_HEADER_DROP_FILTER: '', GC_CSV_ROW_FILTER: '', GC_CSV_ROW_DROP_FILTER: '', GC_TLS_MIN_VERSION: TLS_MIN, GC_TLS_MAX_VERSION: None, GC_CA_FILE: None, } GC_Values = {} GC_TYPE_BOOLEAN = 'bool' GC_TYPE_CHOICE = 'choi' GC_TYPE_DIRECTORY = 'dire' GC_TYPE_EMAIL = 'emai' GC_TYPE_FILE = 'file' GC_TYPE_HEADERFILTER = 'heaf' GC_TYPE_INTEGER = 'inte' GC_TYPE_LANGUAGE = 'lang' GC_TYPE_ROWFILTER = 'rowf' GC_TYPE_STRING = 'stri' GC_VAR_TYPE = 'type' GC_VAR_LIMITS = 'lmit' GC_VAR_INFO = { GC_ADMIN_EMAIL: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_AUTO_BATCH_MIN: { GC_VAR_TYPE: GC_TYPE_INTEGER, GC_VAR_LIMITS: (0, None) }, GC_BATCH_SIZE: { GC_VAR_TYPE: GC_TYPE_INTEGER, GC_VAR_LIMITS: (1, 1000) }, GC_CACHE_DIR: { GC_VAR_TYPE: GC_TYPE_DIRECTORY }, GC_CACHE_DISCOVERY_ONLY: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_CHARSET: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_CLIENT_SECRETS_JSON: { GC_VAR_TYPE: GC_TYPE_FILE }, GC_CONFIG_DIR: { GC_VAR_TYPE: GC_TYPE_DIRECTORY }, GC_CUSTOMER_ID: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_DEBUG_LEVEL: { GC_VAR_TYPE: GC_TYPE_INTEGER, GC_VAR_LIMITS: (0, None) }, GC_DECODED_ID_TOKEN: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_DOMAIN: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_DRIVE_DIR: { GC_VAR_TYPE: GC_TYPE_DIRECTORY }, GC_ENABLE_DASA: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NO_BROWSER: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NO_TDEMAIL: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NO_CACHE: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NO_SHORT_URLS: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NO_UPDATE_CHECK: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_NUM_THREADS: { GC_VAR_TYPE: GC_TYPE_INTEGER, GC_VAR_LIMITS: (1, None) }, GC_OAUTH_BROWSER: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_OAUTH2_TXT: { GC_VAR_TYPE: GC_TYPE_FILE }, GC_OAUTH2SERVICE_JSON: { GC_VAR_TYPE: GC_TYPE_FILE }, GC_SECTION: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_SHOW_COUNTS_MIN: { GC_VAR_TYPE: GC_TYPE_INTEGER, GC_VAR_LIMITS: (0, None) }, GC_SHOW_GETTINGS: { GC_VAR_TYPE: GC_TYPE_BOOLEAN }, GC_SITE_DIR: { GC_VAR_TYPE: GC_TYPE_DIRECTORY }, GC_CSV_HEADER_FILTER: { GC_VAR_TYPE: GC_TYPE_HEADERFILTER }, GC_CSV_HEADER_DROP_FILTER: { GC_VAR_TYPE: GC_TYPE_HEADERFILTER }, GC_CSV_ROW_FILTER: { GC_VAR_TYPE: GC_TYPE_ROWFILTER }, GC_CSV_ROW_DROP_FILTER: { GC_VAR_TYPE: GC_TYPE_ROWFILTER }, GC_TLS_MIN_VERSION: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_TLS_MAX_VERSION: { GC_VAR_TYPE: GC_TYPE_STRING }, GC_CA_FILE: { GC_VAR_TYPE: GC_TYPE_FILE }, } # Google API constants NEVER_TIME = '1970-01-01T00:00:00.000Z' NEVER_TIME_NOMS = '1970-01-01T00:00:00Z' ROLE_MANAGER = 'MANAGER' ROLE_MEMBER = 'MEMBER' ROLE_OWNER = 'OWNER' PROJECTION_CHOICES_MAP = { 'basic': 'BASIC', 'full': 'FULL', } SORTORDER_CHOICES_MAP = { 'ascending': 'ASCENDING', 'descending': 'DESCENDING', } # CLEAR_NONE_ARGUMENT = [ 'clear', 'none', ] # MESSAGE_API_ACCESS_CONFIG = 'API access is configured in your Control Panel' \ ' under: Security-Show more-Advanced' \ ' settings-Manage API client access' MESSAGE_API_ACCESS_DENIED = 'API access Denied.\n\nPlease make sure the Client' \ ' ID: {0} is authorized for the API Scope(s): {1}' MESSAGE_GAM_EXITING_FOR_UPDATE = 'GAM is now exiting so that you can' \ ' overwrite this old version with the' \ ' latest release' MESSAGE_GAM_OUT_OF_MEMORY = 'GAM has run out of memory. If this is a large' \ ' G Suite instance, you should use a 64-bit' \ ' version of GAM on Windows or a 64-bit version' \ ' of Python on other systems.' MESSAGE_HEADER_NOT_FOUND_IN_CSV_HEADERS = 'Header "{0}" not found in CSV' \ ' headers of "{1}".' MESSAGE_HIT_CONTROL_C_TO_UPDATE = '\n\nHit CTRL+C to visit the GAM website' \ ' and download the latest release or wait' \ ' 15 seconds continue with this boring old' \ ' version. GAM won\'t bother you with this ' \ ' announcement for 1 week or you can create' \ ' a file named noupdatecheck.txt in the same' \ ' location as gam.py or gam.exe and GAM' \ ' won\'t ever check for updates.' MESSAGE_INVALID_JSON = 'The file {0} has an invalid format.' MESSAGE_NO_DISCOVERY_INFORMATION = 'No online discovery doc and {0} does not' \ ' exist locally' MESSAGE_NO_TRANSFER_LACK_OF_DISK_SPACE = 'Cowardly refusing to perform' \ ' migration due to lack of target' \ ' drive space. Source size: {0}mb' \ ' Target Free: {1}mb' MESSAGE_RESULTS_TOO_LARGE_FOR_GOOGLE_SPREADSHEET = 'Results are too large for' \ ' Google Spreadsheets.' \ ' Uploading as a regular' \ ' CSV file.' MESSAGE_SERVICE_NOT_APPLICABLE = 'Service not applicable for this address:' \ ' {0}. Please make sure service is enabled' \ ' for user and run\n\ngam user <user> check' \ ' serviceaccount\n\nfor further instructions' MESSAGE_INSTRUCTIONS_OAUTH2SERVICE_JSON = 'Please run\n\ngam create project\n' \ 'gam user <user> check ' \ 'serviceaccount\n\nto create and' \ ' configure a service account.' MESSAGE_UPDATE_GAM_TO_64BIT = 'You\'re running a 32-bit version of GAM on a' \ ' 64-bit version of Windows, upgrade to a' \ ' windows-x86_64 version of GAM' MESSAGE_YOUR_SYSTEM_TIME_DIFFERS_FROM_GOOGLE_BY = 'Your system time differs' \ ' from %s by %s' shared_drive_values = ['teamdrive', 'teamdrives', 'shareddrive', 'shareddrives'] USER_ADDRESS_TYPES = ['home', 'work', 'other'] USER_EMAIL_TYPES = ['home', 'work', 'other'] USER_EXTERNALID_TYPES = [ 'account', 'customer', 'login_id', 'network', 'organization' ] USER_GENDER_TYPES = ['female', 'male', 'unknown'] USER_IM_TYPES = ['home', 'work', 'other'] USER_KEYWORD_TYPES = ['occupation', 'outlook', 'mission'] USER_LOCATION_TYPES = ['default', 'desk'] USER_ORGANIZATION_TYPES = ['domain_only', 'school', 'unknown', 'work'] USER_PHONE_TYPES = [ 'assistant', 'callback', 'car', 'company_main', 'grand_central', 'home', 'home_fax', 'isdn', 'main', 'mobile', 'other', 'other_fax', 'pager', 'radio', 'telex', 'tty_tdd', 'work', 'work_fax', 'work_mobile', 'work_pager' ] USER_RELATION_TYPES = [ 'admin_assistant', 'assistant', 'brother', 'child', 'domestic_partner', 'dotted_line_manager', 'exec_assistant', 'father', 'friend', 'manager', 'mother', 'parent', 'partner', 'referred_by', 'relative', 'sister', 'spouse' ] USER_WEBSITE_TYPES = [ 'app_install_page', 'blog', 'ftp', 'home', 'home_page', 'other', 'profile', 'reservations', 'resume', 'work' ] WEBCOLOR_MAP = { 'aliceblue': '#f0f8ff', 'antiquewhite': '#faebd7', 'aqua': '#00ffff', 'aquamarine': '#7fffd4', 'azure': '#f0ffff', 'beige': '#f5f5dc', 'bisque': '#ffe4c4', 'black': '#000000', 'blanchedalmond': '#ffebcd', 'blue': '#0000ff', 'blueviolet': '#8a2be2', 'brown': '#a52a2a', 'burlywood': '#deb887', 'cadetblue': '#5f9ea0', 'chartreuse': '#7fff00', 'chocolate': '#d2691e', 'coral': '#ff7f50', 'cornflowerblue': '#6495ed', 'cornsilk': '#fff8dc', 'crimson': '#dc143c', 'cyan': '#00ffff', 'darkblue': '#00008b', 'darkcyan': '#008b8b', 'darkgoldenrod': '#b8860b', 'darkgray': '#a9a9a9', 'darkgrey': '#a9a9a9', 'darkgreen': '#006400', 'darkkhaki': '#bdb76b', 'darkmagenta': '#8b008b', 'darkolivegreen': '#556b2f', 'darkorange': '#ff8c00', 'darkorchid': '#9932cc', 'darkred': '#8b0000', 'darksalmon': '#e9967a', 'darkseagreen': '#8fbc8f', 'darkslateblue': '#483d8b', 'darkslategray': '#2f4f4f', 'darkslategrey': '#2f4f4f', 'darkturquoise': '#00ced1', 'darkviolet': '#9400d3', 'deeppink': '#ff1493', 'deepskyblue': '#00bfff', 'dimgray': '#696969', 'dimgrey': '#696969', 'dodgerblue': '#1e90ff', 'firebrick': '#b22222', 'floralwhite': '#fffaf0', 'forestgreen': '#228b22', 'fuchsia': '#ff00ff', 'gainsboro': '#dcdcdc', 'ghostwhite': '#f8f8ff', 'gold': '#ffd700', 'goldenrod': '#daa520', 'gray': '#808080', 'grey': '#808080', 'green': '#008000', 'greenyellow': '#adff2f', 'honeydew': '#f0fff0', 'hotpink': '#ff69b4', 'indianred': '#cd5c5c', 'indigo': '#4b0082', 'ivory': '#fffff0', 'khaki': '#f0e68c', 'lavender': '#e6e6fa', 'lavenderblush': '#fff0f5', 'lawngreen': '#7cfc00', 'lemonchiffon': '#fffacd', 'lightblue': '#add8e6', 'lightcoral': '#f08080', 'lightcyan': '#e0ffff', 'lightgoldenrodyellow': '#fafad2', 'lightgray': '#d3d3d3', 'lightgrey': '#d3d3d3', 'lightgreen': '#90ee90', 'lightpink': '#ffb6c1', 'lightsalmon': '#ffa07a', 'lightseagreen': '#20b2aa', 'lightskyblue': '#87cefa', 'lightslategray': '#778899', 'lightslategrey': '#778899', 'lightsteelblue': '#b0c4de', 'lightyellow': '#ffffe0', 'lime': '#00ff00', 'limegreen': '#32cd32', 'linen': '#faf0e6', 'magenta': '#ff00ff', 'maroon': '#800000', 'mediumaquamarine': '#66cdaa', 'mediumblue': '#0000cd', 'mediumorchid': '#ba55d3', 'mediumpurple': '#9370db', 'mediumseagreen': '#3cb371', 'mediumslateblue': '#7b68ee', 'mediumspringgreen': '#00fa9a', 'mediumturquoise': '#48d1cc', 'mediumvioletred': '#c71585', 'midnightblue': '#191970', 'mintcream': '#f5fffa', 'mistyrose': '#ffe4e1', 'moccasin': '#ffe4b5', 'navajowhite': '#ffdead', 'navy': '#000080', 'oldlace': '#fdf5e6', 'olive': '#808000', 'olivedrab': '#6b8e23', 'orange': '#ffa500', 'orangered': '#ff4500', 'orchid': '#da70d6', 'palegoldenrod': '#eee8aa', 'palegreen': '#98fb98', 'paleturquoise': '#afeeee', 'palevioletred': '#db7093', 'papayawhip': '#ffefd5', 'peachpuff': '#ffdab9', 'peru': '#cd853f', 'pink': '#ffc0cb', 'plum': '#dda0dd', 'powderblue': '#b0e0e6', 'purple': '#800080', 'red': '#ff0000', 'rosybrown': '#bc8f8f', 'royalblue': '#4169e1', 'saddlebrown': '#8b4513', 'salmon': '#fa8072', 'sandybrown': '#f4a460', 'seagreen': '#2e8b57', 'seashell': '#fff5ee', 'sienna': '#a0522d', 'silver': '#c0c0c0', 'skyblue': '#87ceeb', 'slateblue': '#6a5acd', 'slategray': '#708090', 'slategrey': '#708090', 'snow': '#fffafa', 'springgreen': '#00ff7f', 'steelblue': '#4682b4', 'tan': '#d2b48c', 'teal': '#008080', 'thistle': '#d8bfd8', 'tomato': '#ff6347', 'turquoise': '#40e0d0', 'violet': '#ee82ee', 'wheat': '#f5deb3', 'white': '#ffffff', 'whitesmoke': '#f5f5f5', 'yellow': '#ffff00', 'yellowgreen': '#9acd32', } # Gmail label colors LABEL_COLORS = [ '#000000', '#076239', '#0b804b', '#149e60', '#16a766', '#1a764d', '#1c4587', '#285bac', '#2a9c68', '#3c78d8', '#3dc789', '#41236d', '#434343', '#43d692', '#44b984', '#4a86e8', '#653e9b', '#666666', '#68dfa9', '#6d9eeb', '#822111', '#83334c', '#89d3b2', '#8e63ce', '#999999', '#a0eac9', '#a46a21', '#a479e2', '#a4c2f4', '#aa8831', '#ac2b16', '#b65775', '#b694e8', '#b9e4d0', '#c6f3de', '#c9daf8', '#cc3a21', '#cccccc', '#cf8933', '#d0bcf1', '#d5ae49', '#e07798', '#e4d7f5', '#e66550', '#eaa041', '#efa093', '#efefef', '#f2c960', '#f3f3f3', '#f691b3', '#f6c5be', '#f7a7c0', '#fad165', '#fb4c2f', '#fbc8d9', '#fcda83', '#fcdee8', '#fce8b3', '#fef1d1', '#ffad47', '#ffbc6b', '#ffd6a2', '#ffe6c7', '#ffffff', ] # Valid language codes LANGUAGE_CODES_MAP = { 'ach': 'ach', 'af': 'af', 'ag': 'ga', 'ak': 'ak', 'am': 'am', 'ar': 'ar', 'az': 'az', 'be': 'be', 'bem': 'bem', 'bg': 'bg', 'bn': 'bn', 'br': 'br', 'bs': 'bs', 'ca': 'ca', 'chr': 'chr', 'ckb': 'ckb', 'co': 'co', 'crs': 'crs', 'cs': 'cs', 'cy': 'cy', 'da': 'da', 'de': 'de', 'ee': 'ee', 'el': 'el', 'en': 'en', 'en-gb': 'en-GB', 'en-us': 'en-US', 'eo': 'eo', 'es': 'es', 'es-419': 'es-419', 'et': 'et', 'eu': 'eu', 'fa': 'fa', 'fi': 'fi', 'fo': 'fo', 'fr': 'fr', 'fr-ca': 'fr-ca', 'fy': 'fy', 'ga': 'ga', 'gaa': 'gaa', 'gd': 'gd', 'gl': 'gl', 'gn': 'gn', 'gu': 'gu', 'ha': 'ha', 'haw': 'haw', 'he': 'he', 'hi': 'hi', 'hr': 'hr', 'ht': 'ht', 'hu': 'hu', 'hy': 'hy', 'ia': 'ia', 'id': 'id', 'ig': 'ig', 'in': 'in', 'is': 'is', 'it': 'it', 'iw': 'iw', 'ja': 'ja', 'jw': 'jw', 'ka': 'ka', 'kg': 'kg', 'kk': 'kk', 'km': 'km', 'kn': 'kn', 'ko': 'ko', 'kri': 'kri', 'ku': 'ku', 'ky': 'ky', 'la': 'la', 'lg': 'lg', 'ln': 'ln', 'lo': 'lo', 'loz': 'loz', 'lt': 'lt', 'lua': 'lua', 'lv': 'lv', 'mfe': 'mfe', 'mg': 'mg', 'mi': 'mi', 'mk': 'mk', 'ml': 'ml', 'mn': 'mn', 'mo': 'mo', 'mr': 'mr', 'ms': 'ms', 'mt': 'mt', 'my': 'my', 'ne': 'ne', 'nl': 'nl', 'nn': 'nn', 'no': 'no', 'nso': 'nso', 'ny': 'ny', 'nyn': 'nyn', 'oc': 'oc', 'om': 'om', 'or': 'or', 'pa': 'pa', 'pcm': 'pcm', 'pl': 'pl', 'ps': 'ps', 'pt-br': 'pt-BR', 'pt-pt': 'pt-PT', 'qu': 'qu', 'rm': 'rm', 'rn': 'rn', 'ro': 'ro', 'ru': 'ru', 'rw': 'rw', 'sd': 'sd', 'sh': 'sh', 'si': 'si', 'sk': 'sk', 'sl': 'sl', 'sn': 'sn', 'so': 'so', 'sq': 'sq', 'sr': 'sr', 'sr-me': 'sr-ME', 'st': 'st', 'su': 'su', 'sv': 'sv', 'sw': 'sw', 'ta': 'ta', 'te': 'te', 'tg': 'tg', 'th': 'th', 'ti': 'ti', 'tk': 'tk', 'tl': 'tl', 'tn': 'tn', 'to': 'to', 'tr': 'tr', 'tt': 'tt', 'tum': 'tum', 'tw': 'tw', 'ug': 'ug', 'uk': 'uk', 'ur': 'ur', 'uz': 'uz', 'vi': 'vi', 'wo': 'wo', 'xh': 'xh', 'yi': 'yi', 'yo': 'yo', 'zh-cn': 'zh-CN', 'zh-hk': 'zh-HK', 'zh-tw': 'zh-TW', 'zu': 'zu', } # maxResults exception values for API list calls. Should only be listed if: # - discovery doc does not specify maximum value (we use maximum value if it # exists, not this) # - actual max API returns with maxResults=<bigNum> > default API returns # when maxResults isn't specified (we should use default otherwise by not # setting maxResults) MAX_RESULTS_API_EXCEPTIONS = { 'calendar.acl.list': 250, 'calendar.calendarList.list': 250, 'calendar.events.list': 2500, 'calendar.settings.list': 250, 'directory.chromeosdevices.list': 200, 'drive.files.list': 1000, } ONE_KILO_BYTES = 1000 ONE_MEGA_BYTES = 1000000 ONE_GIGA_BYTES = 1000000000 DELTA_DATE_PATTERN = re.compile(r'^([+-])(\d+)([dwy])$') DELTA_DATE_FORMAT_REQUIRED = '(+|-)<Number>(d|w|y)' DELTA_TIME_PATTERN = re.compile(r'^([+-])(\d+)([mhdwy])$') DELTA_TIME_FORMAT_REQUIRED = '(+|-)<Number>(m|h|d|w|y)' HHMM_FORMAT = '%H:%M' HHMM_FORMAT_REQUIRED = 'hh:mm' YYYYMMDD_FORMAT = '%Y-%m-%d' YYYYMMDD_FORMAT_REQUIRED = 'yyyy-mm-dd' YYYYMMDDTHHMMSS_FORMAT_REQUIRED = 'yyyy-mm-ddThh:mm:ss[.fff](Z|(+|-(hh:mm)))' YYYYMMDD_PATTERN = re.compile(r'^[0-9]{4}-[0-9]{2}-[0-9]{2}$') UID_PATTERN = re.compile(r'u?id: ?(.+)', re.IGNORECASE)

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0

1

0

4f55819c7fde2558b6072ee4e0544797f9dd0ca1

6,037

py

Python

whoville/cloudbreak/models/user_profile_response.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

whoville/cloudbreak/models/user_profile_response.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

whoville/cloudbreak/models/user_profile_response.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

# coding: utf-8 """ Cloudbreak API Cloudbreak is a powerful left surf that breaks over a coral reef, a mile off southwest the island of Tavarua, Fiji. Cloudbreak is a cloud agnostic Hadoop as a Service API. Abstracts the provisioning and ease management and monitoring of on-demand clusters. SequenceIQ's Cloudbreak is a RESTful application development platform with the goal of helping developers to build solutions for deploying Hadoop YARN clusters in different environments. Once it is deployed in your favourite servlet container it exposes a REST API allowing to span up Hadoop clusters of arbitary sizes and cloud providers. Provisioning Hadoop has never been easier. Cloudbreak is built on the foundation of cloud providers API (Amazon AWS, Microsoft Azure, Google Cloud Platform, Openstack), Apache Ambari, Docker lightweight containers, Swarm and Consul. For further product documentation follow the link: <a href=\"http://hortonworks.com/apache/cloudbreak/\">http://hortonworks.com/apache/cloudbreak/</a> OpenAPI spec version: 2.7.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class UserProfileResponse(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'credential': 'CredentialResponse', 'owner': 'str', 'account': 'str', 'ui_properties': 'dict(str, object)' } attribute_map = { 'credential': 'credential', 'owner': 'owner', 'account': 'account', 'ui_properties': 'uiProperties' } def __init__(self, credential=None, owner=None, account=None, ui_properties=None): """ UserProfileResponse - a model defined in Swagger """ self._credential = None self._owner = None self._account = None self._ui_properties = None if credential is not None: self.credential = credential if owner is not None: self.owner = owner if account is not None: self.account = account if ui_properties is not None: self.ui_properties = ui_properties @property def credential(self): """ Gets the credential of this UserProfileResponse. :return: The credential of this UserProfileResponse. :rtype: CredentialResponse """ return self._credential @credential.setter def credential(self, credential): """ Sets the credential of this UserProfileResponse. :param credential: The credential of this UserProfileResponse. :type: CredentialResponse """ self._credential = credential @property def owner(self): """ Gets the owner of this UserProfileResponse. :return: The owner of this UserProfileResponse. :rtype: str """ return self._owner @owner.setter def owner(self, owner): """ Sets the owner of this UserProfileResponse. :param owner: The owner of this UserProfileResponse. :type: str """ self._owner = owner @property def account(self): """ Gets the account of this UserProfileResponse. :return: The account of this UserProfileResponse. :rtype: str """ return self._account @account.setter def account(self, account): """ Sets the account of this UserProfileResponse. :param account: The account of this UserProfileResponse. :type: str """ self._account = account @property def ui_properties(self): """ Gets the ui_properties of this UserProfileResponse. :return: The ui_properties of this UserProfileResponse. :rtype: dict(str, object) """ return self._ui_properties @ui_properties.setter def ui_properties(self, ui_properties): """ Sets the ui_properties of this UserProfileResponse. :param ui_properties: The ui_properties of this UserProfileResponse. :type: dict(str, object) """ self._ui_properties = ui_properties def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, UserProfileResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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null

0

null

0

1

0

4f5b50b3cdb081de40cbc7892b3382fb3bcb5e33

1,243

py

Python

templatepython/examples/fizzbuzz.py

humayun-argn/python-template

100f61fc4f092cee2b27c0855c4830325e0267a1

[ "MIT" ]

16

2020-07-23T13:40:54.000Z

2022-02-18T23:02:09.000Z

templatepython/examples/fizzbuzz.py

humayun-argn/python-template

100f61fc4f092cee2b27c0855c4830325e0267a1

[ "MIT" ]

2

2020-07-11T10:19:44.000Z

2020-11-01T05:55:36.000Z

templatepython/examples/fizzbuzz.py

humayun-argn/python-template

100f61fc4f092cee2b27c0855c4830325e0267a1

[ "MIT" ]

3

2021-05-18T18:05:15.000Z

2021-12-27T13:24:44.000Z

#!/usr/bin/env python3 """ FizzBuzz https://medium.freecodecamp.org/a-software-engineering-survival-guide-fe3eafb47166 https://medium.freecodecamp.org/coding-interviews-for-dummies-5e048933b82b This solution uses the following syntax features: Modulo (%, remainder) Strict equality (==) Addition assignment (+=) """ from typing import List, Union def fizzbuzz_print() -> None: """Print 1-100 --- - Multiples of 3: Fizz - Multiples of 5: Buzz - Multiples of 3 and 5: FizzBuzz """ for i in range(1, 101): out = "" if i % 3 == 0: out += "Fizz" if i % 5 == 0: out += "Buzz" print(out or i) def fizzbuzz_list() -> List[Union[int, str]]: """Create a list 1-100 --- - Multiples of 3 and 5: FizzBuzz - Multiples of 3: Fizz - Multiples of 5: Buzz - Else: integer """ out: List[Union[int, str]] = [] for i in range(100): if i % 3 == 0 and i % 5 == 0: out.insert(i, "FizzBuzz") elif i % 3 == 0: out.insert(i, "Fizz") elif i % 5 == 0: out.insert(i, "Buzz") else: out.insert(i, i) return out if __name__ == "__main__": print(fizzbuzz_list())

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null

0

null

0

1

0

4f5b7d77d537ad078c5197d7ca0feffbbbacd00d

3,774

py

Python

src/utils/glsl/generate_hardcode.py

Time-Coder/glass

c249f11ba906d0e8d40dac52f1cfc99506d362cd

[ "MIT" ]

1

2021-06-05T13:34:58.000Z

src/utils/glsl/generate_hardcode.py

Time-Coder/glass

c249f11ba906d0e8d40dac52f1cfc99506d362cd

[ "MIT" ]

null

src/utils/glsl/generate_hardcode.py

Time-Coder/glass

c249f11ba906d0e8d40dac52f1cfc99506d362cd

[ "MIT" ]

null

import os import pathlib import copy def delete_comments(content): while True: pos_start = content.find("/*") if pos_start == -1: break pos_end = content.find("*/", pos_start+2) if pos_end == -1: pos_end = len(content)-2 content = content[:pos_start] + content[pos_end+2:] while True: pos_start = content.find("//") if pos_start == -1: break pos_end = content.find("\n", pos_start + 2) if pos_end == -1: pos_end = len(content) content = content[:pos_start] + content[pos_end:] return content class Node: def __init__(self): self.content = "" self.in_edges = set() def skip_space(content, pos): if pos < 0: return 0 if pos >= len(content): return len(content) while pos < len(content): if content[pos] not in ' \t': break pos += 1 return pos def to_hard_code(filename): content = delete_comments(open(filename).read()) lines = content.split("\n") name = filename.replace("/", "_").replace("\\", "_").replace(".", "_").replace("glsl", "shader") result = Node() for line in lines: line = line.rstrip() if line == "": continue pos_start = line.find("#include") if pos_start == -1: line = '"' + line.replace('\\', '\\\\').replace('"', '\\"') + '\\n"' else: pos_start = skip_space(line, pos_start+len("#include")) start_sign = line[pos_start] pos_start += 1 if start_sign == '"': end_sign = '"' elif start_sign == '<': end_sign = '>' pos_end = line.find(end_sign, pos_start) if pos_end == -1: raise SyntexError("Error format in #include") file_name = line[pos_start:pos_end] file_name = os.path.relpath(os.path.dirname(filename) + "/" + file_name) var_name = file_name.replace("/", "_").replace("\\", "_").replace(".", "_").replace("glsl", "shader") line = '+ glass::' + var_name + ' +' result.in_edges.add(var_name) result.content += line + '\n' return result def generate_hardcode_dict(folder_name): files = all_files(".") result = {} for file in files: var_name = file.replace("/", "_").replace("\\", "_").replace(".", "_").replace("glsl", "shader") result[var_name] = to_hard_code(file) return result def extname(filename): return pathlib.Path(filename).suffix def all_files(folder = "."): result = [] files = os.listdir(folder) for file in files: file = folder + "/" + file if os.path.isfile(file) and extname(file) == ".glsl": if len(file)>=2 and (file[:2] == "./" or file[:2] == ".\\"): file = file[2:] result.append(file) elif os.path.isdir(file): result.extend(all_files(file)) return result result_dict = generate_hardcode_dict(".") result_dict_copy = copy.deepcopy(result_dict) order_list = [] while True: pop_key = None for key in result_dict: if len(result_dict[key].in_edges) == 0: order_list.append(key) for sub_key in result_dict: if key in result_dict[sub_key].in_edges: result_dict[sub_key].in_edges.remove(key) pop_key = key break if pop_key != None: result_dict.pop(pop_key) else: break for key in result_dict: print(result_dict[key].in_edges) out_file = open("../shaders.cpp", "w") out_file.write("#include \"glass/utils/shaders.h\"\n") out_file.write("\nusing namespace std;\n") for var_name in order_list: out_file.write("string glass::" + var_name + " = \n") out_file.write(result_dict_copy[var_name].content + ";\n\n") out_file.close() out_file = open("../../../include/glass/utils/shaders.h", "w") out_file.write("#ifndef __SHADERS_H__\n") out_file.write("#define __SHADERS_H__\n") out_file.write("#include <string>\n") out_file.write("namespace glass\n") out_file.write("{\n") for var_name in order_list: out_file.write("extern std::string " + var_name + ";\n") out_file.write("};\n") out_file.write("#endif") out_file.close()

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0.044886

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0.26025

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3,774

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0.00813

0

null

0

null

0

1

0

4f5cee69e3b4b126cfc5e418c2c152018ba7ad45

8,696

py

Python

pytorch3d/implicitron/models/renderer/ray_sampler.py

janEbert/pytorch3d

accdac80fb29e82f72d4e8e73135ba8fd790b6c0

[ "MIT", "BSD-3-Clause" ]

null

pytorch3d/implicitron/models/renderer/ray_sampler.py

janEbert/pytorch3d

accdac80fb29e82f72d4e8e73135ba8fd790b6c0

[ "MIT", "BSD-3-Clause" ]

null

pytorch3d/implicitron/models/renderer/ray_sampler.py

janEbert/pytorch3d

accdac80fb29e82f72d4e8e73135ba8fd790b6c0

[ "MIT", "BSD-3-Clause" ]

null

# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from dataclasses import field from typing import Optional, Tuple import torch from pytorch3d.implicitron.tools import camera_utils from pytorch3d.implicitron.tools.config import Configurable from pytorch3d.renderer import NDCMultinomialRaysampler, RayBundle from pytorch3d.renderer.cameras import CamerasBase from .base import EvaluationMode, RenderSamplingMode class RaySampler(Configurable, torch.nn.Module): """ Samples a fixed number of points along rays which are in turn sampled for each camera in a batch. This class utilizes `NDCMultinomialRaysampler` which allows to either randomly sample rays from an input foreground saliency mask (`RenderSamplingMode.MASK_SAMPLE`), or on a rectangular image grid (`RenderSamplingMode.FULL_GRID`). The sampling mode can be set separately for training and evaluation by setting `self.sampling_mode_training` and `self.sampling_mode_training` accordingly. The class allows two modes of sampling points along the rays: 1) Sampling between fixed near and far z-planes: Active when `self.scene_extent <= 0`, samples points along each ray with approximately uniform spacing of z-coordinates between the minimum depth `self.min_depth` and the maximum depth `self.max_depth`. This sampling is useful for rendering scenes where the camera is in a constant distance from the focal point of the scene. 2) Adaptive near/far plane estimation around the world scene center: Active when `self.scene_extent > 0`. Samples points on each ray between near and far planes whose depths are determined based on the distance from the camera center to a predefined scene center. More specifically, `min_depth = max( (self.scene_center-camera_center).norm() - self.scene_extent, eps )` and `max_depth = (self.scene_center-camera_center).norm() + self.scene_extent`. This sampling is ideal for object-centric scenes whose contents are centered around a known `self.scene_center` and fit into a bounding sphere with a radius of `self.scene_extent`. Similar to the sampling mode, the sampling parameters can be set separately for training and evaluation. Settings: image_width: The horizontal size of the image grid. image_height: The vertical size of the image grid. scene_center: The xyz coordinates of the center of the scene used along with `scene_extent` to compute the min and max depth planes for sampling ray-points. scene_extent: The radius of the scene bounding sphere centered at `scene_center`. If `scene_extent <= 0`, the raysampler samples points between `self.min_depth` and `self.max_depth` depths instead. sampling_mode_training: The ray sampling mode for training. This should be a str option from the RenderSamplingMode Enum sampling_mode_evaluation: Same as above but for evaluation. n_pts_per_ray_training: The number of points sampled along each ray during training. n_pts_per_ray_evaluation: The number of points sampled along each ray during evaluation. n_rays_per_image_sampled_from_mask: The amount of rays to be sampled from the image grid min_depth: The minimum depth of a ray-point. Active when `self.scene_extent > 0`. max_depth: The maximum depth of a ray-point. Active when `self.scene_extent > 0`. stratified_point_sampling_training: if set, performs stratified random sampling along the ray; otherwise takes ray points at deterministic offsets. stratified_point_sampling_evaluation: Same as above but for evaluation. """ image_width: int = 400 image_height: int = 400 scene_center: Tuple[float, float, float] = field( default_factory=lambda: (0.0, 0.0, 0.0) ) scene_extent: float = 0.0 sampling_mode_training: str = "mask_sample" sampling_mode_evaluation: str = "full_grid" n_pts_per_ray_training: int = 64 n_pts_per_ray_evaluation: int = 64 n_rays_per_image_sampled_from_mask: int = 1024 min_depth: float = 0.1 max_depth: float = 8.0 # stratified sampling vs taking points at deterministic offsets stratified_point_sampling_training: bool = True stratified_point_sampling_evaluation: bool = False def __post_init__(self): super().__init__() self.scene_center = torch.FloatTensor(self.scene_center) self._sampling_mode = { EvaluationMode.TRAINING: RenderSamplingMode(self.sampling_mode_training), EvaluationMode.EVALUATION: RenderSamplingMode( self.sampling_mode_evaluation ), } self._raysamplers = { EvaluationMode.TRAINING: NDCMultinomialRaysampler( image_width=self.image_width, image_height=self.image_height, n_pts_per_ray=self.n_pts_per_ray_training, min_depth=self.min_depth, max_depth=self.max_depth, n_rays_per_image=self.n_rays_per_image_sampled_from_mask if self._sampling_mode[EvaluationMode.TRAINING] == RenderSamplingMode.MASK_SAMPLE else None, unit_directions=True, stratified_sampling=self.stratified_point_sampling_training, ), EvaluationMode.EVALUATION: NDCMultinomialRaysampler( image_width=self.image_width, image_height=self.image_height, n_pts_per_ray=self.n_pts_per_ray_evaluation, min_depth=self.min_depth, max_depth=self.max_depth, n_rays_per_image=self.n_rays_per_image_sampled_from_mask if self._sampling_mode[EvaluationMode.EVALUATION] == RenderSamplingMode.MASK_SAMPLE else None, unit_directions=True, stratified_sampling=self.stratified_point_sampling_evaluation, ), } def forward( self, cameras: CamerasBase, evaluation_mode: EvaluationMode, mask: Optional[torch.Tensor] = None, ) -> RayBundle: """ Args: cameras: A batch of `batch_size` cameras from which the rays are emitted. evaluation_mode: one of `EvaluationMode.TRAINING` or `EvaluationMode.EVALUATION` which determines the sampling mode that is used. mask: Active for the `RenderSamplingMode.MASK_SAMPLE` sampling mode. Defines a non-negative mask of shape `(batch_size, image_height, image_width)` where each per-pixel value is proportional to the probability of sampling the corresponding pixel's ray. Returns: ray_bundle: A `RayBundle` object containing the parametrizations of the sampled rendering rays. """ sample_mask = None if ( # pyre-fixme[29] self._sampling_mode[evaluation_mode] == RenderSamplingMode.MASK_SAMPLE and mask is not None ): sample_mask = torch.nn.functional.interpolate( mask, # pyre-fixme[6]: Expected `Optional[int]` for 2nd param but got # `List[int]`. size=[self.image_height, self.image_width], mode="nearest", )[:, 0] if self.scene_extent > 0.0: # Override the min/max depth set in initialization based on the # input cameras. min_depth, max_depth = camera_utils.get_min_max_depth_bounds( cameras, self.scene_center, self.scene_extent ) # pyre-fixme[29]: # `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self, # torch.Tensor), Named(item, typing.Any)], typing.Any], torch.Tensor], # torch.Tensor, torch.nn.Module]` is not a function. ray_bundle = self._raysamplers[evaluation_mode]( cameras=cameras, mask=sample_mask, min_depth=float(min_depth[0]) if self.scene_extent > 0.0 else None, max_depth=float(max_depth[0]) if self.scene_extent > 0.0 else None, ) return ray_bundle

45.528796

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null

0

null

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1

0

4f5fb52afdee604b9fbf7559c2c35f9daa937ae9

2,871

py

Python

planning_launch/launch/mission_planning/mission_planning.launch.py

tier4/autoware_launcher.iv.universe

6cd7bef7f97da75621aef424fa190a6e9ec3a300

[ "Apache-2.0" ]

5

2020-09-25T08:53:20.000Z

2021-08-11T14:27:17.000Z

planning_launch/launch/mission_planning/mission_planning.launch.py

tier4/autoware_launcher.iv.universe

6cd7bef7f97da75621aef424fa190a6e9ec3a300

[ "Apache-2.0" ]

46

2020-11-06T14:47:52.000Z

2021-08-12T06:53:29.000Z

planning_launch/launch/mission_planning/mission_planning.launch.py

tier4/autoware_launcher.iv.universe

6cd7bef7f97da75621aef424fa190a6e9ec3a300

[ "Apache-2.0" ]

25

2020-09-30T16:38:53.000Z

2021-08-11T14:38:21.000Z

# Copyright 2021 Tier IV, Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless 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. import launch from launch.actions import DeclareLaunchArgument from launch.substitutions import LaunchConfiguration from launch_ros.actions import ComposableNodeContainer from launch_ros.descriptions import ComposableNode def generate_launch_description(): container = ComposableNodeContainer( name='mission_planning_container', namespace='', package='rclcpp_components', executable='component_container', composable_node_descriptions=[ ComposableNode( package='mission_planner', plugin='mission_planner::MissionPlannerLanelet2', name='mission_planner', remappings=[ ('input/vector_map', '/map/vector_map'), ('input/goal_pose', '/planning/mission_planning/goal'), ('input/checkpoint', '/planning/mission_planning/checkpoint'), ('output/route', '/planning/mission_planning/route'), ('debug/route_marker', '/planning/mission_planning/route_marker'), ], parameters=[ { 'map_frame': 'map', 'base_link_frame': 'base_link', } ], extra_arguments=[{ 'use_intra_process_comms': LaunchConfiguration('use_intra_process') }], ), ComposableNode( package='mission_planner', plugin='mission_planner::GoalPoseVisualizer', name='goal_pose_visualizer', remappings=[ ('input/route', '/planning/mission_planning/route'), ('output/goal_pose', '/planning/mission_planning/echo_back_goal_pose'), ], extra_arguments=[{ 'use_intra_process_comms': LaunchConfiguration('use_intra_process') }], ) ], ) return launch.LaunchDescription([ DeclareLaunchArgument('use_intra_process', default_value='false', description='use ROS2 component container communication'), container ])

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null

0

null

0

1

0

4f5fcfd6021a2de97a5ebce4f9a1bbfac3341807

1,268

py

Python

dongfangtoutiao.py

lixueyuan/-

0b3fcaa9bd9eea2453c5f6859823f92d2459469e

[ "Apache-2.0" ]

null

dongfangtoutiao.py

lixueyuan/-

0b3fcaa9bd9eea2453c5f6859823f92d2459469e

[ "Apache-2.0" ]

null

dongfangtoutiao.py

lixueyuan/-

0b3fcaa9bd9eea2453c5f6859823f92d2459469e

[ "Apache-2.0" ]

null

import requests from urllib.parse import urlencode import json import re def get_page_detail(pageNumber,keyWord): data = { #分页加载默认一页五条数据 'callback': 'jQuery18308665374998856634_1526290762213', 'type': keyWord, 'pgnum': pageNumber, } #包装参数 params = urlencode(data) #爬取的主网址 base = 'http://pcflow.dftoutiao.com/toutiaopc_jrtt/newspool' url = base + '?' + params print(url) try: response = requests.get(url) if response.status_code == 200: token = re.findall(r"{.*}", response.text) return token return None except ConnectionError: print('connection error') return None def parse_page_index(html): data = json.loads(html[0]) if data and 'data' in data.keys(): for item in data.get('data'): print(item.get('miniimg')) print(item.get('topic')) print(item.get('source')) # for imagedata in item.get('miniimg'): # # print(imagedata) # print(imagedata.get('src')) # # print(item.get('miniimg')) def main(): html = get_page_detail('1','yule') if html: parse_page_index(html) if __name__ == '__main__': main()

25.877551

64

0.576498

140

1,268

5.085714

0.485714

0.049157

0.067416

0.050562

0

0.042506

0.294953

1,268

49

65

25.877551

0.753915

0.111987

0

0.055556

0

0.150268

0.035778

0

1

0.083333

false

0

0.111111

0

0.277778

0.138889

0

null

0

null

0

1

0

4f604712379f982d1fbd6ff6a8bf8fb8bf5c46db

858

py

Python

d03p1.py

emaballarin/AoC-2021

19ccb275eaf83a22e3e80e9a6aec11fa6dd923fc

[ "MIT" ]

1

2021-12-01T11:27:45.000Z

d03p1.py

emaballarin/AoC-2021

19ccb275eaf83a22e3e80e9a6aec11fa6dd923fc

[ "MIT" ]

null

d03p1.py

emaballarin/AoC-2021

19ccb275eaf83a22e3e80e9a6aec11fa6dd923fc

[ "MIT" ]

null

#!/usr/bin/env python3 from functools import partial as fpartial import numpy as np from util.transforms import binarray2int from util.specifics import binary_abundance def solve() -> int: # Transposition eases operating over rows w.r.t. over columns data_in = np.genfromtxt("./data/d03/p1/input", delimiter=1).transpose() # Original (i.e. pre-transposition) number of rows data_len = len(data_in[0]) # Number of 1s is sum at that position; number of 0s is number of elements - number of 1s gamma = np.apply_along_axis( fpartial(binary_abundance, col_len=data_len), 1, data_in ) # (Binary complement, done with integers) epsilon = 1 - gamma consumption = binarray2int(epsilon) * binarray2int(gamma) return consumption def main() -> None: print(solve()) if __name__ == "__main__": main()

25.235294

93

0.699301

120

858

4.85

0.6

0.068729

0.034364

0

0.020468

0.202797

858

33

94

26

0.830409

0.300699

0

0.045378

0

1

0.117647

false

0

0.235294

0

0.411765

0.058824

0

null

0

null

0

1

0

4f6806332e710a076973a5dac22b128dd421ede3

6,034

py

Python

boardfarm/devices/friendly_acs_soap.py

superice119/boardfarm

c525b4da94bf745d30c4a9f675aa4a7ae184b1fd

[ "BSD-3-Clause-Clear" ]

null

boardfarm/devices/friendly_acs_soap.py

superice119/boardfarm

c525b4da94bf745d30c4a9f675aa4a7ae184b1fd

[ "BSD-3-Clause-Clear" ]

null

boardfarm/devices/friendly_acs_soap.py

superice119/boardfarm

c525b4da94bf745d30c4a9f675aa4a7ae184b1fd

[ "BSD-3-Clause-Clear" ]

null

import os import xmltodict from boardfarm.lib.bft_logging import LoggerMeta from zeep import Client from zeep.wsse.username import UsernameToken if "BFT_DEBUG" in os.environ: import logging.config logging.config.dictConfig({ 'version': 1, 'formatters': { 'verbose': { 'format': '%(name)s: %(message)s' } }, 'handlers': { 'console': { 'level': 'DEBUG', 'class': 'logging.StreamHandler', 'formatter': 'verbose', }, }, 'loggers': { 'zeep.transports': { 'level': 'DEBUG', 'propagate': True, 'handlers': ['console'], }, } }) class FriendlyACS(): __metaclass__ = LoggerMeta log = "" log_calls = "" model = "friendly_acs_soap" def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs self.username = self.kwargs['username'] self.password = self.kwargs['password'] self.ipaddr = self.kwargs['ipaddr'] self.wsdl = "http://" + self.kwargs[ 'ipaddr'] + "/ftacsws/acsws.asmx?WSDL" self.client = Client(wsdl=self.wsdl, wsse=UsernameToken(self.username, self.password)) self.port = self.kwargs.get('port', '80') self.log = "" name = "acs_server" def __str__(self): return "FriendlyACS" def close(self): pass def get(self, cpeid, param, source=0): # source = 0 (CPE), source = 1 (DB) ret = self.client.service.FTGetDeviceParameters(devicesn=cpeid, source=source, arraynames=[param]) if None == ret['Params']: return None else: return ret['Params']['ParamWSDL'][0]['Value'] def set(self, cpeid, attr, value): array_of_param = self.client.get_type( '{http://www.friendly-tech.com}ArrayOfParam') arr = array_of_param([{'Name': attr, 'Value': value}]) # TODO: investigate push, endsession, reprovision, priority to make sure they are what we want self.client.service.FTSetDeviceParameters(devicesn=cpeid, \ arrayparams=arr, \ push=True, \ endsession=False, \ priority=0) def rpc(self, cpeid, name, content): ''' Invoke custom RPC on specific CM''' ret = self.client.service.FTRPCInvoke(devicesn=cpeid, rpcname=name, soapcontent=content) return xmltodict.parse(ret['Response']) def rpc_GetParameterAttributes(self, cpeid, name): content = '<cwmp:GetParameterAttributes xmlns:cwmp="urn:dslforum-org:cwmp-1-0"> <ParameterNames arrayType-="xsd:string[1]"> <string>%s</string> </ParameterNames> </cwmp:GetParameterAttributes>' % name ret = self.rpc(cpeid, name, content) return ret['cwmp:GetParameterAttributesResponse']['ParameterList'][ 'ParameterAttributeStruct'] def rpc_GetParameterValues(self, cpeid, name): content = '<cwmp:GetParameterValues xmlns:cwmp="urn:dslforum-org:cwmp-1-0"> <ParameterNames arrayType="xsd:string[1]"> <string>%s</string> </ParameterNames> </cwmp:GetParameterValues>' % name ret = self.rpc(cpeid, name, content) return ret['cwmp:GetParameterValuesResponse']['ParameterList'][ 'ParameterValueStruct']['Value']['#text'] def getcurrent(self, cpeid, param, source=0): self.client.service.FTGetDeviceParameters(devicesn=cpeid, source=source, arraynames=[param + '.']) def rpc_SetParameterAttributes(self, cpeid, name, set_value): content = '<cwmp:SetParameterAttributes xmlns:cwmp="urn:dslforum-org:cwmp-1-0"> <ParameterList arrayType="cwmp:SetParameterAttributesStruct[1]"> <SetParameterAttributesStruct> <Name>%s</Name> <NotificationChange>1</NotificationChange> <Notification>%s</Notification> <AccessListChange>0</AccessListChange> <AccessList></AccessList> </SetParameterAttributesStruct> </ParameterList> </cwmp:SetParameterAttributes>' % ( name, set_value) self.rpc(cpeid, name, content) def rpc_AddObject(self, cpeid, obj_name): content = '<cwmp:AddObject xmlns:cwmp="urn:dslforum-org:cwmp-1-0"> <ObjectName>%s.</ObjectName> <ParameterKey></ParameterKey> </cwmp:AddObject>' % obj_name self.rpc(cpeid, obj_name, content) def rpc_DeleteObject(self, cpeid, obj_name): content = '<cwmp:DeleteObject xmlns:cwmp="urn:dslforum-org:cwmp-1-0"> <ObjectName>%s.</ObjectName> <ParameterKey></ParameterKey> </cwmp:DeleteObject>' % obj_name self.rpc(cpeid, obj_name, content) def is_online(self, cpeid): ret = self.client.service.FTCPEStatus(devicesn=cpeid) return ret['Online'] def delete_cpe(self, cpeid): print("WARN: not impl for this class") pass if __name__ == '__main__': import sys if ':' in sys.argv[1]: ip = sys.argv[1].split(':')[0] port = sys.argv[1].split(':')[1] else: ip = sys.argv[1] port = 80 acs = FriendlyACS(ipaddr=ip, port=port, username=sys.argv[2], password=sys.argv[3]) ret = acs.rpc_GetParameterAttributes('DEAP815610DA', 'Device.WiFi.SSID.1.SSID') print(ret['Notification']) ret = acs.get('DEAP815610DA', 'Device.DeviceInfo.SoftwareVersion') print(ret) ret = acs.get('DEAP815610DA', 'Device.WiFi.SSID.1.SSID') print(ret)

37.478261

424

0.560988

582

6,034

5.735395

0.297251

0.029658

0.02876

0.029958

0.289395

0.244757

0.22858

0.196525

0.174955

0

0.012931

0.307922

6,034

160

425

37.7125

0.786398

0.026516

0

0.114754

0

0.040984

0.292533

0.168428

0

0.00625

0

1

0.114754

false

0.040984

0.057377

0.008197

0.278689

0.032787

0

null

0

null

0

1

0

4f68bad39e47426834db4d7281d8f521915704a1

1,550

py

Python

seisgen/util_SPECFEM3D/ibool_reader.py

Liang-Ding/seisgen

59688b88ecfb52c22824f5fe60b17c7a7e37f3b0

[ "MIT" ]

5

2021-11-22T23:54:01.000Z

2021-12-06T06:17:45.000Z

seisgen/util_SPECFEM3D/ibool_reader.py

Liang-Ding/seisgen

59688b88ecfb52c22824f5fe60b17c7a7e37f3b0

[ "MIT" ]

null

seisgen/util_SPECFEM3D/ibool_reader.py

Liang-Ding/seisgen

59688b88ecfb52c22824f5fe60b17c7a7e37f3b0

[ "MIT" ]

null

# ------------------------------------------------------------------- # ibool reader. # # Author: Liang Ding # Email: myliang.ding@mail.utoronto.ca # ------------------------------------------------------------------- from seisgen.util_SPECFEM3D import NGLLX, NGLLY, NGLLZ, CONSTANT_INDEX_27_GLL from scipy.io import FortranFile import numpy as np def read_ibool_by_scipy(ibool_file, NSPEC): ''' Read the ibool file in the folder */model3D/ ''' f = FortranFile(ibool_file, 'r') ibool = f.read_reals(dtype='int32') f.close() ibool = np.reshape(ibool, (NSPEC, NGLLX * NGLLY * NGLLZ)) # The index in *.ibool files starts from 1. ibool = ibool - 1 return ibool def DEnquire_Element(ibool_file, index_element, NSPEC): ''' Read the index of the 27 GLL points where the SGT been stored in the selected element.''' ibool = read_ibool_by_scipy(ibool_file, NSPEC) if ibool.__len__() <= index_element: return np.zeros(27) else: NGLLX_N3 = 3 NGLLY_N3 = 3 NGLLZ_N3 = 3 # The global index in slice of selected GLL points. gll_array = ibool[index_element][CONSTANT_INDEX_27_GLL] # sort the index. gll_points = [] gll_array = np.reshape(gll_array, [NGLLZ_N3, NGLLY_N3, NGLLX_N3]) for i in range(NGLLX_N3): for j in range(NGLLY_N3): for k in range(NGLLZ_N3): gll_points.append(gll_array[k, j, i]) gll_points = np.asarray(gll_points) return gll_points

29.245283

97

0.582581

204

1,550

4.210784

0.362745

0.073341

0.034924

0.041909

0.069849

0

0.022222

0.245161

1,550

52

98

29.807692

0.711966

0.287742

0

0.005607

0

1

0.074074

false

0

0.111111

0

0.296296

0

null

0

null

0

1

0

4f69217e425b162dc5a97da49f5c472c66b388fd

5,882

py

Python

GasGrid/OtherTools/Data Collection Tools/nts_data_collect.py

mdhillmancmcl/TheWorldAvatar-CMCL-Fork

011aee78c016b76762eaf511c78fabe3f98189f4

[ "MIT" ]

21

2021-03-08T01:58:25.000Z

2022-03-09T15:46:16.000Z

GasGrid/OtherTools/Data Collection Tools/nts_data_collect.py

mdhillmancmcl/TheWorldAvatar-CMCL-Fork

011aee78c016b76762eaf511c78fabe3f98189f4

[ "MIT" ]

63

2021-05-04T15:05:30.000Z

2022-03-23T14:32:29.000Z

GasGrid/OtherTools/Data Collection Tools/nts_data_collect.py

mdhillmancmcl/TheWorldAvatar-CMCL-Fork

011aee78c016b76762eaf511c78fabe3f98189f4

[ "MIT" ]

15

2021-03-08T07:52:03.000Z

2022-03-29T04:46:20.000Z

from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions as ec import io from tabulate import tabulate import os import numpy as np import bs4 as bs import pandas as pd from tqdm import tqdm import time import re def get_data(from_date,to_date,data): ''' DESCRIPTION: get_data will obtain data between two defined dates from the national grid's online database. Taking advantage of the Selenium library for interaction with webpages and Beautiful Soup for the extraction of html tables INPUTS: from_date: A date that the start of data to be obtained is required in the form 'DD,MM,YYYY' to_date: A date that the end of data to be obtained is required in the form 'DD,MM,YYYY' data: An array containing values of consecutuve folder numbers to click on/obtain data from OUTPUTS: table: A pandas dataframe containing all information specified NOTES: the 'data' array is currently the least 'flexible' aspect of this code, for example it is obtained through manual interaction with the online database, however once obtained for the data needed it can be stored and reused. For now is relatively fine as only certain information is required from the database. ''' #--- opening the database in a firefox window ---# driver = webdriver.Firefox() driver.get("https://mip-prd-web.azurewebsites.net/DataItemExplorer") wait = WebDriverWait(driver,10) #--- clicking through the file tree ---# base_text = '/html/body/div[1]/div/div[2]/div[2]/div/div' end_text = '/span/span[1]' add_text = '' for i in range(len(data)): if i == len(data)-1: end_text = '/span/span[2]' add_text += '/ul/li['+data[i]+']' complete_text = base_text + add_text + end_text wait.until(ec.visibility_of_element_located\ ((By.XPATH,complete_text))).click() #--- inserting dates required ---# wait.until(ec.element_to_be_clickable((By.ID,"applicableForRadioButton"))).click() wait.until(ec.element_to_be_clickable((By.ID,"FromDateTime"))).clear() wait.until(ec.element_to_be_clickable((By.ID,"FromDateTime"))).send_keys(from_date) wait.until(ec.element_to_be_clickable((By.ID,"ToDateTime"))).clear() wait.until(ec.element_to_be_clickable((By.ID,"ToDateTime"))).send_keys(to_date) wait.until(ec.visibility_of_element_located((By.ID,"viewReportButton"))).click() #--- creation of pandas dataframe ---# header = [] for i in range(6): header.append(wait.until(ec.visibility_of_element_located\ ((By.XPATH,"/html/body/div[1]/div[2]/table/thead/tr/th["+str(i+1)+"]"))).text) html = driver.page_source soup = bs.BeautifulSoup(html,'lxml') driver.quit() table = [] for tr in soup.find_all('tr')[1:]: tds = tr.find_all('td') row = [] for i in tds: row.append(i.text) table.append(row) table = pd.DataFrame(table) table.columns = header return table ''' EXAMPLE CODE FOR get_data ''' # actual_flows = ['14','4'] # comp_weather_var_actual = ['18','1','1'] # comp_weather_table = get_data('01/10/2020','20/10/2020',comp_weather_var_actual) # print(comp_weather_table) def real_time_intakes(): ''' DESCRIPTION: Calls the National Grid online publication of incoming flows to the NTS and produces two numpy tables, one with zonal intakes and one with terminal intakes. Units are mcm/day. ''' #--- opening intakes webpage ---# os.system('cls' if os.name == 'nt' else 'clear') while True: driver = webdriver.Firefox() try: driver.get("https://mip-prd-web.azurewebsites.net/InstantaneousView") except: print('ONLINE DATABASE UNAVAILABLE, CHECK NETWORK CONNECTION') break wait = WebDriverWait(driver,10) html = driver.page_source #--- converting all the information to a table ---# #--- as all data presented in a large html table ---# soup = bs.BeautifulSoup(html,'lxml') driver.quit() table = [] for tr in soup.find_all('tr')[1:]: tds = tr.find_all('td') row = [] for i in tds: row.append(i.text) table.append(row) table = pd.DataFrame(table) #--- ontaining only the required values ---# table = table.to_numpy()[4:,1:] zone_names = table[1:29,0] latest_zone_value = table[1:29,6] latest_zone_value[6] = latest_zone_value[6][1:] latest_zone_value = latest_zone_value.astype(np.float) zone_supply = np.concatenate(([zone_names],[latest_zone_value]),axis=0).T zone_supply_pd = pd.DataFrame(zone_supply) terminal_names = table[47:59,0] latest_terminal_value = table[47:59,6].astype(np.float) terminal_supply = np.concatenate(([terminal_names],[latest_terminal_value]),axis=0).T terminal_supply_pd = pd.DataFrame(terminal_supply) overall_df = pd.concat((zone_supply_pd,terminal_supply_pd),axis=1,ignore_index=True) header = ['Zone Supply','Instananeous Flow (mcm/day)','Terminal Supply','Instananeous Flow (mcm/day)'] print(tabulate(overall_df,headers=header,showindex="never")) overall_df.to_excel('Intakedata.xlsx') for i in reversed(range(120)): print('TIME FOR NEXT UPDATE: ',i,' SECONDS',end='\r') time.sleep(1) os.system('cls' if os.name == 'nt' else 'clear') return ''' EXAMPLE CODE FOR real_time_intakes() ''' real_time_intakes()

38.444444

110

0.66032

819

5,882

4.60928

0.312576

0.019073

0.023311

0.023841

0.267815

0.227815

0.196821

0.148344

0

0.015234

0.218803

5,882

153

111

38.444444

0.806311

0.276947

0

0.297872

0

0.021277

0.131806

0.0271

0

1

0.021277

false

0

0.159574

0

0.202128

0.031915

0

null

0

null

0

1

0

4f6cf0a69c1e3cc460fbf95605b3932048d1765d

57,972

py

Python

jupyterhub/src/jupyterhub_config.py

kskels/workshop-spawner

71f27b82ee1c65d7a61b41e784ec40b43d25ddd5

[ "Apache-2.0" ]

null

jupyterhub/src/jupyterhub_config.py

kskels/workshop-spawner

71f27b82ee1c65d7a61b41e784ec40b43d25ddd5

[ "Apache-2.0" ]

null

jupyterhub/src/jupyterhub_config.py

kskels/workshop-spawner

71f27b82ee1c65d7a61b41e784ec40b43d25ddd5

[ "Apache-2.0" ]

null

# This file provides common configuration for the different ways that # the deployment can run. Configuration specific to the different modes # will be read from separate files at the end of this configuration # file. import os import json import string import yaml import threading import time import requests import wrapt from tornado import gen from kubernetes.client.rest import ApiException from kubernetes.client.configuration import Configuration from kubernetes.config.incluster_config import load_incluster_config from kubernetes.client.api_client import ApiClient from openshift.dynamic import DynamicClient, Resource from openshift.dynamic.exceptions import ResourceNotFoundError # The workshop name and configuration type are passed in through the # environment. The applicaton name should be the value used for the # deployment, and more specifically, must match the name of the route. workshop_name = os.environ.get('WORKSHOP_NAME') application_name = os.environ.get('APPLICATION_NAME') if not workshop_name: workshop_name = 'homeroom' if not application_name: application_name = workshop_name print('INFO: Workshop name is %r.' % workshop_name) print('INFO: Application name is %r.' % application_name) configuration_type = os.environ.get('CONFIGURATION_TYPE', 'hosted-workshop') print('INFO: Configuration type is %r.' % configuration_type) homeroom_link = os.environ.get('HOMEROOM_LINK') print('INFO: Homeroom link is %r.' % homeroom_link) homeroom_name = os.environ.get('HOMEROOM_NAME') print('INFO: Homeroom name is %r.' % homeroom_name) # Work out the service account name and name of the namespace that the # deployment is in. service_account_path = '/var/run/secrets/kubernetes.io/serviceaccount' service_account_name = '%s-spawner' % application_name print('INFO: Service account name is %r.' % service_account_name) with open(os.path.join(service_account_path, 'namespace')) as fp: namespace = fp.read().strip() print('INFO: Namespace is %r.' % namespace) full_service_account_name = 'system:serviceaccount:%s:%s' % ( namespace, service_account_name) print('INFO: Full service account name is %r.' % full_service_account_name) # Determine the Kubernetes REST API endpoint and cluster information, # including working out the address of the internal image regstry. kubernetes_service_host = os.environ['KUBERNETES_SERVICE_HOST'] kubernetes_service_port = os.environ['KUBERNETES_SERVICE_PORT'] kubernetes_server_url = 'https://%s:%s' % (kubernetes_service_host, kubernetes_service_port) kubernetes_server_version_url = '%s/version' % kubernetes_server_url with requests.Session() as session: response = session.get(kubernetes_server_version_url, verify=False) kubernetes_server_info = json.loads(response.content.decode('UTF-8')) image_registry = 'image-registry.openshift-image-registry.svc:5000' if kubernetes_server_info['major'] == '1': if kubernetes_server_info['minor'] in ('10', '10+', '11', '11+'): image_registry = 'docker-registry.default.svc:5000' # Initialise the client for the REST API used doing configuration. # # XXX Currently have a workaround here for OpenShift 4.0 beta versions # which disables verification of the certificate. If don't use this the # Python openshift/kubernetes clients will fail. We also disable any # warnings from urllib3 to get rid of the noise in the logs this creates. load_incluster_config() import urllib3 urllib3.disable_warnings() instance = Configuration() instance.verify_ssl = False Configuration.set_default(instance) api_client = DynamicClient(ApiClient()) try: image_stream_resource = api_client.resources.get( api_version='image.openshift.io/v1', kind='ImageStream') except ResourceNotFoundError: image_stream_resource = None try: route_resource = api_client.resources.get( api_version='route.openshift.io/v1', kind='Route') except ResourceNotFoundError: route_resource = None ingress_resource = api_client.resources.get( api_version='networking.k8s.io/v1', kind='Ingress') # Create a background thread to dynamically calculate back link to the # Homeroom workshop picker if no explicit link is provided, but group is. def watch_for_homeroom(): global homeroom_link while True: if route_resource is not None: try: route = route_resource.get(namespace=namespace, name=homeroom_name) scheme = 'http' if route.metadata.annotations: if route.metadata.annotations['homeroom/index'] == homeroom_name: if route.tls and route.tls.termination: scheme = 'https' link = '%s://%s' % (scheme, route.spec.host) if link != homeroom_link: print('INFO: Homeroom link set to %s.' % link) homeroom_link = link except ApiException as e: if e.status != 404: print('ERROR: Error looking up homeroom route. %s' % e) except Exception as e: print('ERROR: Error looking up homeroom route. %s' % e) try: ingress = ingress_resource.get(namespace=namespace, name=homeroom_name) scheme = 'http' if ingress.metadata.annotations: if ingress.metadata.annotations['homeroom/index'] == homeroom_name: if ingress.tls: scheme = 'https' link = '%s://%s' % (scheme, ingress.spec.rules[0].host) if link != homeroom_link: print('INFO: Homeroom link set to %s.' % link) homeroom_link = link except ApiException as e: if e.status != 404: print('ERROR: Error looking up homeroom ingress. %s' % e) except Exception as e: print('ERROR: Error looking up homeroom ingress. %s' % e) time.sleep(15) if not homeroom_link and homeroom_name: thread = threading.Thread(target=watch_for_homeroom) thread.daemon = True thread.start() # Workaround bug in minishift where a service cannot be contacted from a # pod which backs the service. For further details see the minishift issue # https://github.com/minishift/minishift/issues/2400. # # What these workarounds do is monkey patch the JupyterHub proxy client # API code, and the code for creating the environment for local service # processes, and when it sees something which uses the service name as # the target in a URL, it replaces it with localhost. These work because # the proxy/service processes are in the same pod. It is not possible to # change hub_connect_ip to localhost because that is passed to other # pods which need to contact back to JupyterHub, and so it must be left # as the service name. @wrapt.patch_function_wrapper('jupyterhub.proxy', 'ConfigurableHTTPProxy.add_route') def _wrapper_add_route(wrapped, instance, args, kwargs): def _extract_args(routespec, target, data, *_args, **_kwargs): return (routespec, target, data, _args, _kwargs) routespec, target, data, _args, _kwargs = _extract_args(*args, **kwargs) old = 'http://%s:%s' % (c.JupyterHub.hub_connect_ip, c.JupyterHub.hub_port) new = 'http://127.0.0.1:%s' % c.JupyterHub.hub_port if target.startswith(old): target = target.replace(old, new) return wrapped(routespec, target, data, *_args, **_kwargs) @wrapt.patch_function_wrapper('jupyterhub.spawner', 'LocalProcessSpawner.get_env') def _wrapper_get_env(wrapped, instance, args, kwargs): env = wrapped(*args, **kwargs) target = env.get('JUPYTERHUB_API_URL') old = 'http://%s:%s' % (c.JupyterHub.hub_connect_ip, c.JupyterHub.hub_port) new = 'http://127.0.0.1:%s' % c.JupyterHub.hub_port if target and target.startswith(old): target = target.replace(old, new) env['JUPYTERHUB_API_URL'] = target return env # Define all the defaults for the JupyterHub instance for our setup. c.JupyterHub.port = 8080 c.JupyterHub.hub_ip = '0.0.0.0' c.JupyterHub.hub_port = 8081 c.JupyterHub.hub_connect_ip = '%s-spawner' % application_name c.ConfigurableHTTPProxy.api_url = 'http://127.0.0.1:8082' c.Spawner.start_timeout = 180 c.Spawner.http_timeout = 60 c.KubeSpawner.port = 10080 c.KubeSpawner.common_labels = { 'app': '%s' % application_name } c.KubeSpawner.extra_labels = { 'spawner': configuration_type, 'class': 'session', 'user': '{username}' } c.KubeSpawner.uid = os.getuid() c.KubeSpawner.fs_gid = os.getuid() c.KubeSpawner.extra_annotations = { "alpha.image.policy.openshift.io/resolve-names": "*" } c.KubeSpawner.cmd = ['start-singleuser.sh'] c.KubeSpawner.pod_name_template = '%s-user-{username}' % application_name c.JupyterHub.admin_access = False if os.environ.get('JUPYTERHUB_COOKIE_SECRET'): c.JupyterHub.cookie_secret = os.environ[ 'JUPYTERHUB_COOKIE_SECRET'].encode('UTF-8') else: c.JupyterHub.cookie_secret_file = '/opt/app-root/data/cookie_secret' c.JupyterHub.db_url = '/opt/app-root/data/database.sqlite' c.JupyterHub.authenticator_class = 'tmpauthenticator.TmpAuthenticator' c.JupyterHub.spawner_class = 'kubespawner.KubeSpawner' c.JupyterHub.logo_file = '/opt/app-root/src/images/HomeroomIcon.png' c.Spawner.environment = dict() c.JupyterHub.services = [] c.KubeSpawner.init_containers = [] c.KubeSpawner.extra_containers = [] c.JupyterHub.extra_handlers = [] # Determine amount of memory to allocate for workshop environment. def convert_size_to_bytes(size): multipliers = { 'k': 1000, 'm': 1000**2, 'g': 1000**3, 't': 1000**4, 'ki': 1024, 'mi': 1024**2, 'gi': 1024**3, 'ti': 1024**4, } size = str(size) for suffix in multipliers: if size.lower().endswith(suffix): return int(size[0:-len(suffix)]) * multipliers[suffix] else: if size.lower().endswith('b'): return int(size[0:-1]) try: return int(size) except ValueError: raise RuntimeError('"%s" is not a valid memory specification. Must be an integer or a string with suffix K, M, G, T, Ki, Mi, Gi or Ti.' % size) c.Spawner.mem_limit = convert_size_to_bytes( os.environ.get('WORKSHOP_MEMORY', '512Mi')) # Override the image details with that for the terminal or dashboard # image being used. The default is to assume that a image stream with # the same name as the application name is being used. The call to the # function resolve_image_name() is to try and resolve to image registry # when using image stream. This is to workaround issue that many # clusters do not have image policy controller configured correctly. # # Note that we set the policy that images will always be pulled to the # node each time when the image name is not explicitly provided. This is # so that during development, changes to the terminal image will always # be picked up. Someone developing a new image need only update the # 'latest' tag on the image using 'oc tag'. # # Check for TERMINAL_IMAGE is for backward compatibility. Should use # WORKSHOP_IMAGE now. workshop_image = os.environ.get('WORKSHOP_IMAGE') if not workshop_image: workshop_image = os.environ.get('TERMINAL_IMAGE') if not workshop_image: c.KubeSpawner.image_pull_policy = 'Always' workshop_image = '%s-session:latest' % application_name def resolve_image_name(name): # If no image stream resource we are on plain Kubernetes. if image_stream_resource is None: return name # If the image name contains a slash, we assume it is already # referring to an image on some image registry. Even if it does # not contain a slash, it may still be hosted on docker.io. if name.find('/') != -1: return name # Separate actual source image name and tag for the image from the # name. If the tag is not supplied, default to 'latest'. parts = name.split(':', 1) if len(parts) == 1: source_image, tag = parts, 'latest' else: source_image, tag = parts # See if there is an image stream in the current project with the # target name. try: image_stream = image_stream_resource.get(namespace=namespace, name=source_image) except ApiException as e: if e.status not in (403, 404): raise return name # If we get here then the image stream exists with the target name. # We need to determine if the tag exists. If it does exist, we # extract out the full name of the image including the reference # to the image registry it is hosted on. if image_stream.status.tags: for entry in image_stream.status.tags: if entry.tag == tag: registry_image = image_stream.status.dockerImageRepository if registry_image: return '%s:%s' % (registry_image, tag) # Use original value if can't find a matching tag. return name c.KubeSpawner.image = resolve_image_name(workshop_image) # Work out hostname for the exposed route of the JupyterHub server. This # is tricky as we need to use the REST API to query it. This is used # when needing to do OAuth. public_hostname = os.environ.get('PUBLIC_HOSTNAME') public_protocol = os.environ.get('PUBLIC_PROTOCOL') route_name = '%s-spawner' % application_name if not public_hostname: if route_resource is not None: routes = route_resource.get(namespace=namespace) for route in routes.items: if route.metadata.name == route_name: if not public_protocol: public_protocol = route.spec.tls and 'https' or 'http' public_hostname = route.spec.host break if not public_hostname: ingresses = ingress_resource.get(namespace=namespace) for ingresses in ingresses.items: if ingresses.metadata.name == route_name: if not public_protocol: public_protocol = ingresses.spec.tls and 'https' or 'http' public_hostname = ingresses.spec.rules[0].host break if not public_hostname: raise RuntimeError('Cannot calculate external host name for the spawner.') c.Spawner.environment['JUPYTERHUB_ROUTE'] = '%s://%s' % (public_protocol, public_hostname) # Work out the subdomain under which applications hosted in the cluster # are hosted. Calculate this from the route for the spawner route if # not supplied explicitly. cluster_subdomain = os.environ.get('CLUSTER_SUBDOMAIN') if not cluster_subdomain: cluster_subdomain = '.'.join(public_hostname.split('.')[1:]) c.Spawner.environment['CLUSTER_SUBDOMAIN'] = cluster_subdomain # The terminal image will normally work out what versions of OpenShift # and Kubernetes command line tools should be used, based on the version # of OpenShift which is being used. Allow these to be overridden if # necessary. if os.environ.get('OC_VERSION'): c.Spawner.environment['OC_VERSION'] = os.environ.get('OC_VERSION') if os.environ.get('ODO_VERSION'): c.Spawner.environment['ODO_VERSION'] = os.environ.get('ODO_VERSION') if os.environ.get('KUBECTL_VERSION'): c.Spawner.environment['KUBECTL_VERSION'] = os.environ.get('KUBECTL_VERSION') # Common functions for creating projects, injecting resources etc. namespace_resource = api_client.resources.get( api_version='v1', kind='Namespace') service_account_resource = api_client.resources.get( api_version='v1', kind='ServiceAccount') secret_resource = api_client.resources.get( api_version='v1', kind='Secret') cluster_role_resource = api_client.resources.get( api_version='rbac.authorization.k8s.io/v1', kind='ClusterRole') role_binding_resource = api_client.resources.get( api_version='rbac.authorization.k8s.io/v1', kind='RoleBinding') limit_range_resource = api_client.resources.get( api_version='v1', kind='LimitRange') resource_quota_resource = api_client.resources.get( api_version='v1', kind='ResourceQuota') service_resource = api_client.resources.get( api_version='v1', kind='Service') namespace_template = string.Template(""" { "kind": "Namespace", "apiVersion": "v1", "metadata": { "name": "${name}", "labels": { "app": "${application_name}", "spawner": "${configuration}", "class": "session", "user": "${username}" }, "annotations": { "spawner/requestor": "${requestor}", "spawner/namespace": "${namespace}", "spawner/deployment": "${deployment}", "spawner/account": "${account}", "spawner/session": "${session}" }, "ownerReferences": [ { "apiVersion": "v1", "kind": "ClusterRole", "blockOwnerDeletion": false, "controller": true, "name": "${owner}", "uid": "${uid}" } ] } } """) service_account_template = string.Template(""" { "kind": "ServiceAccount", "apiVersion": "v1", "metadata": { "name": "${name}", "labels": { "app": "${application_name}", "spawner": "${configuration}", "class": "session", "user": "${username}" } } } """) role_binding_template = string.Template(""" { "kind": "RoleBinding", "apiVersion": "rbac.authorization.k8s.io/v1", "metadata": { "name": "${name}-${tag}", "labels": { "app": "${application_name}", "spawner": "${configuration}", "class": "session", "user": "${username}" } }, "subjects": [ { "kind": "ServiceAccount", "namespace": "${namespace}", "name": "${name}" } ], "roleRef": { "apiGroup": "rbac.authorization.k8s.io", "kind": "ClusterRole", "name": "${role}" } } """) resource_budget_mapping = { "small": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "small" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "1", "memory": "1Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "1", "memory": "1Gi" }, "default": { "cpu": "250m", "memory": "256Mi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "1Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "small" } }, "spec": { "hard": { "limits.cpu": "1", "limits.memory": "1Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "small" } }, "spec": { "hard": { "limits.cpu": "1", "limits.memory": "1Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "small" } }, "spec": { "hard": { "persistentvolumeclaims": "3", "replicationcontrollers": "10", "secrets": "20", "services": "5" } } }, }, "medium": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "medium" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "2", "memory": "2Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "2", "memory": "2Gi" }, "default": { "cpu": "500m", "memory": "512Mi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "5Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "medium" } }, "spec": { "hard": { "limits.cpu": "2", "limits.memory": "2Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "medium" } }, "spec": { "hard": { "limits.cpu": "2", "limits.memory": "2Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "medium" } }, "spec": { "hard": { "persistentvolumeclaims": "6", "replicationcontrollers": "15", "secrets": "25", "services": "10" } } }, }, "large": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "large" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "4", "memory": "4Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "4", "memory": "4Gi" }, "default": { "cpu": "500m", "memory": "1Gi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "10Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "large" } }, "spec": { "hard": { "limits.cpu": "4", "limits.memory": "4Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "large" } }, "spec": { "hard": { "limits.cpu": "4", "limits.memory": "4Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "large" } }, "spec": { "hard": { "persistentvolumeclaims": "12", "replicationcontrollers": "25", "secrets": "35", "services": "20" } } } }, "x-large": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "x-large" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "8", "memory": "8Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "8", "memory": "8Gi" }, "default": { "cpu": "500m", "memory": "2Gi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "20Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "x-large" } }, "spec": { "hard": { "limits.cpu": "8", "limits.memory": "8Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "x-large" } }, "spec": { "hard": { "limits.cpu": "8", "limits.memory": "8Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "x-large" } }, "spec": { "hard": { "persistentvolumeclaims": "18", "replicationcontrollers": "35", "secrets": "45", "services": "30" } } } }, "xx-large": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "xx-large" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "12", "memory": "12Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "12", "memory": "12Gi" }, "default": { "cpu": "500m", "memory": "2Gi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "20Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "xx-large" } }, "spec": { "hard": { "limits.cpu": "12", "limits.memory": "12Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "xx-large" } }, "spec": { "hard": { "limits.cpu": "12", "limits.memory": "12Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "xx-large" } }, "spec": { "hard": { "persistentvolumeclaims": "24", "replicationcontrollers": "45", "secrets": "55", "services": "40" } } } }, "xxx-large": { "resource-limits" : { "kind": "LimitRange", "apiVersion": "v1", "metadata": { "name": "resource-limits", "annotations": { "resource-budget": "xxx-large" } }, "spec": { "limits": [ { "type": "Pod", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "16", "memory": "16Gi" } }, { "type": "Container", "min": { "cpu": "50m", "memory": "32Mi" }, "max": { "cpu": "16", "memory": "16Gi" }, "default": { "cpu": "500m", "memory": "2Gi" }, "defaultRequest": { "cpu": "50m", "memory": "128Mi" } }, { "type": "PersistentVolumeClaim", "min": { "storage": "1Gi" }, "max": { "storage": "20Gi" } } ] } }, "compute-resources" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources", "annotations": { "resource-budget": "xxx-large" } }, "spec": { "hard": { "limits.cpu": "16", "limits.memory": "16Gi" }, "scopes": [ "NotTerminating" ] } }, "compute-resources-timebound" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "compute-resources-timebound", "annotations": { "resource-budget": "xxx-large" } }, "spec": { "hard": { "limits.cpu": "16", "limits.memory": "16Gi" }, "scopes": [ "Terminating" ] } }, "object-counts" : { "kind": "ResourceQuota", "apiVersion": "v1", "metadata": { "name": "object-counts", "annotations": { "resource-budget": "xxx-large" } }, "spec": { "hard": { "persistentvolumeclaims": "30", "replicationcontrollers": "55", "secrets": "65", "services": "50" } } } } } service_template = string.Template(""" { "kind": "Service", "apiVersion": "v1", "metadata": { "name": "${name}", "labels": { "app": "${application_name}", "spawner": "${configuration}", "class": "session", "user": "${username}" }, "ownerReferences": [ { "apiVersion": "v1", "kind": "ServiceAccount", "blockOwnerDeletion": false, "controller": true, "name": "${name}", "uid": "${uid}" } ] }, "spec": { "type": "ClusterIP", "selector": { "app": "${application_name}", "spawner": "${configuration}", "user": "${username}" }, "ports": [] } } """) route_template = string.Template(""" { "apiVersion": "route.openshift.io/v1", "kind": "Route", "metadata": { "name": "${name}-${port}", "labels": { "app": "${application_name}", "spawner": "${configuration}", "class": "session", "user": "${username}", "port": "${port}" }, "ownerReferences": [ { "apiVersion": "v1", "kind": "ServiceAccount", "blockOwnerDeletion": false, "controller": true, "name": "${name}", "uid": "${uid}" } ] }, "spec": { "host": "${host}", "port": { "targetPort": "${port}-tcp" }, "to": { "kind": "Service", "name": "${name}", "weight": 100 } } } """) @gen.coroutine def create_service_account(spawner, pod): short_name = spawner.user.name user_account_name = '%s-%s' % (application_name, short_name) owner_uid = None print('INFO: Create service account "%s".' % user_account_name) while True: try: text = service_account_template.safe_substitute( configuration=configuration_type, namespace=namespace, name=user_account_name, application_name=application_name, username=short_name) body = json.loads(text) service_account_object = service_account_resource.create( namespace=namespace, body=body) owner_uid = service_account_object.metadata.uid except ApiException as e: if e.status != 409: print('ERROR: Error creating service account. %s' % e) raise else: print('WARNING: Service account %s exists.' % user_account_name) break except Exception as e: print('ERROR: Error creating service account. %s' % e) raise else: break # If we didn't create a service account object as one already existed, # we need to query the existing one to get the uid to use as owner. if owner_uid is None: try: service_account_object = service_account_resource.get( namespace=namespace, name=user_account_name) owner_uid = service_account_object.metadata.uid except Exception as e: print('ERROR: Error getting service account. %s' % e) raise print('INFO: Service account id is %s.' % owner_uid) return owner_uid @gen.coroutine def create_project_namespace(spawner, pod, project_name): short_name = spawner.user.name user_account_name = '%s-%s' % (application_name, short_name) try: text = namespace_template.safe_substitute( configuration=configuration_type, name=project_name, application_name=application_name, requestor=full_service_account_name, namespace=namespace, deployment=application_name, account=user_account_name, session=pod.metadata.name, owner=project_owner.metadata.name, uid=project_owner.metadata.uid, username=short_name) body = json.loads(text) namespace_resource.create(body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating project. %s' % e) raise except Exception as e: print('ERROR: Error creating project. %s' % e) raise @gen.coroutine def setup_project_namespace(spawner, pod, project_name, role, budget): short_name = spawner.user.name user_account_name = '%s-%s' % (application_name, short_name) # Wait for project namespace to exist before continuing. for _ in range(30): try: project = namespace_resource.get(name=project_name) except ApiException as e: if e.status == 404: yield gen.sleep(0.1) continue print('ERROR: Error querying project. %s' % e) raise else: break else: # If can't verify project created, carry on anyway. print('ERROR: Could not verify project creation. %s' % project_name) raise Exception('Could not verify project creation. %s' % project_name) project_uid = project.metadata.uid # Create role binding in the project so the spawner service account can # delete project when done. Will fail if the project hasn't actually # been created yet. try: text = role_binding_template.safe_substitute( configuration=configuration_type, namespace=namespace, name=service_account_name, tag='admin', role='admin', application_name=application_name, username=short_name) body = json.loads(text) role_binding_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating role binding for spawner. %s' % e) raise except Exception as e: print('ERROR: Error creating rolebinding for spawner. %s' % e) raise # Create role binding in the project so the users service account # can create resources in it. try: text = role_binding_template.safe_substitute( configuration=configuration_type, namespace=namespace, name=user_account_name, tag=role, role=role, application_name=application_name, username=short_name) body = json.loads(text) role_binding_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating role binding for user. %s' % e) raise except Exception as e: print('ERROR: Error creating rolebinding for user. %s' % e) raise # Create role binding in the project so the users service account # can perform additional actions declared through additional policy # rules for a specific workshop session. try: text = role_binding_template.safe_substitute( configuration=configuration_type, namespace=namespace, name=user_account_name, tag='session-rules', role=application_name+'-session-rules', application_name=application_name, username=short_name) body = json.loads(text) role_binding_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating role binding for extras. %s' % e) raise except Exception as e: print('ERROR: Error creating rolebinding for extras. %s' % e) raise # Determine what project namespace resources need to be used. if budget != 'unlimited': if budget not in resource_budget_mapping: budget = 'default' elif not resource_budget_mapping[budget]: budget = 'default' if budget not in ('default', 'unlimited'): budget_item = resource_budget_mapping[budget] resource_limits_definition = budget_item['resource-limits'] compute_resources_definition = budget_item['compute-resources'] compute_resources_timebound_definition = budget_item['compute-resources-timebound'] object_counts_definition = budget_item['object-counts'] # Delete any limit ranges applied to the project that may conflict # with the limit range being applied. For the case of unlimited, we # delete any being applied but don't replace it. if budget != 'default': try: limit_ranges = limit_range_resource.get( namespace=project_name) except ApiException as e: print('ERROR: Error querying limit ranges. %s' % e) raise for limit_range in limit_ranges.items: try: limit_range_resource.delete(namespace=project_name, name=limit_range.metadata.name) except ApiException as e: print('ERROR: Error deleting limit range. %s' % e) raise # Create limit ranges for the project namespace so any deployments # will have default memory/cpu min and max values. if budget not in ('default', 'unlimited'): try: body = resource_limits_definition limit_range_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating limit range. %s' % e) raise # Delete any resource quotas applied to the project namespace that # may conflict with the resource quotas being applied. if budget != 'default': try: resource_quotas = resource_quota_resource.get(namespace=project_name) except ApiException as e: print('ERROR: Error querying resource quotas. %s' % e) raise for resource_quota in resource_quotas.items: try: resource_quota_resource.delete(namespace=project_name, name=resource_quota.metadata.name) except ApiException as e: print('ERROR: Error deleting resource quota. %s' % e) raise # Create resource quotas for the project so there is a maximum for # what resources can be used. if budget not in ('default', 'unlimited'): try: body = compute_resources_definition resource_quota_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating compute resources quota. %s' % e) raise try: body = compute_resources_timebound_definition resource_quota_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating compute resources timebound quota. %s' % e) raise try: body = object_counts_definition resource_quota_resource.create(namespace=project_name, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating object counts quota. %s' % e) raise # Return the project UID for later use as owner UID if needed. return project_uid extra_resources = {} extra_resources_loader = None if os.path.exists('/opt/app-root/resources/extra_resources.yaml'): with open('/opt/app-root/resources/extra_resources.yaml') as fp: extra_resources = fp.read().strip() extra_resources_loader = yaml.safe_load if os.path.exists('/opt/app-root/resources/extra_resources.json'): with open('/opt/app-root/resources/extra_resources.json') as fp: extra_resources = fp.read().strip() extra_resources_loader = json.loads def _namespaced_resources(): api_groups = api_client.resources.parse_api_groups() for api in api_groups.values(): for domain, items in api.items(): for version, group in items.items(): try: for kind in group.resources: if domain: version = '%s/%s' % (domain, version) resource = api_client.resources.get(api_version=version, kind=kind) if type(resource) == Resource and resource.namespaced: yield (version, resource.kind) except Exception: pass namespaced_resources = set(_namespaced_resources()) @gen.coroutine def create_extra_resources(spawner, pod, project_name, owner_uid, user_account_name, short_name): if not extra_resources: return template = string.Template(extra_resources) text = template.safe_substitute(spawner_namespace=namespace, project_namespace=project_name, image_registry=image_registry, service_account=user_account_name, username=short_name, application_name=application_name) data = extra_resources_loader(text) if isinstance(data, dict) and data.get('kind') == 'List': data = data['items'] for body in data: try: kind = body['kind'] api_version = body['apiVersion'] if not (api_version, kind) in namespaced_resources: body['metadata']['ownerReferences'] = [dict( apiVersion='v1', kind='Namespace', blockOwnerDeletion=False, controller=True, name=project_name, uid=owner_uid)] if kind.lower() == 'namespace': annotations = body['metadata'].setdefault('annotations', {}) annotations['spawner/requestor'] = full_service_account_name annotations['spawner/namespace'] = namespace annotations['spawner/deployment'] = application_name annotations['spawner/account'] = user_account_name annotations['spawner/session'] = pod.metadata.name resource = api_client.resources.get(api_version=api_version, kind=kind) target_namespace = body['metadata'].get('namespace', project_name) resource.create(namespace=target_namespace, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating resource %s. %s' % (body, e)) raise else: print('WARNING: Resource already exists %s.' % body) except Exception as e: print('ERROR: Error creating resource %s. %s' % (body, e)) raise if kind.lower() == 'namespace': annotations = body['metadata'].get('annotations', {}) role = annotations.get('session/role', 'admin') default_budget = os.environ.get('RESOURCE_BUDGET', 'default') budget = annotations.get('session/budget', default_budget) yield setup_project_namespace(spawner, pod, body['metadata']['name'], role, budget) @gen.coroutine def expose_service_ports(spawner, pod, owner_uid): short_name = spawner.user.name user_account_name = '%s-%s' % (application_name, short_name) # Can't do this for now if deployed to plain Kubernetes. if route_resource is None: return exposed_ports = os.environ.get('EXPOSED_PORTS', '') if exposed_ports: exposed_ports = exposed_ports.split(',') try: text = service_template.safe_substitute( configuration=configuration_type, name=user_account_name, application_name=application_name, username=short_name, uid=owner_uid) body = json.loads(text) for port in exposed_ports: body['spec']['ports'].append(dict(name='%s-tcp' % port, protocol="TCP", port=int(port), targetPort=int(port))) service_resource.create(namespace=namespace, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating service. %s' % e) raise except Exception as e: print('ERROR: Error creating service. %s' % e) raise for port in exposed_ports: try: host = '%s-%s.%s' % (user_account_name, port, cluster_subdomain) text = route_template.safe_substitute(configuration=configuration_type, name=user_account_name, application_name=application_name, port='%s' % port, username=short_name, uid=owner_uid, host=host) body = json.loads(text) route_resource.create(namespace=namespace, body=body) except ApiException as e: if e.status != 409: print('ERROR: Error creating route. %s' % e) raise except Exception as e: print('ERROR: Error creating route. %s' % e) raise @gen.coroutine def wait_on_service_account(user_account_name): for _ in range(10): try: service_account = service_account_resource.get( namespace=namespace, name=user_account_name) # Hope that all secrets added at same time and don't have # to check names to verify api token secret added. if service_account.secrets: for item in service_account.secrets: try: secret = secret_resource.get(namespace=namespace, name=item['name']) except Exception as e: print('WARNING: Error fetching secret. %s' % e) yield gen.sleep(0.1) break else: break else: yield gen.sleep(0.1) continue except Exception as e: print('ERROR: Error fetching service account. %s' % e) raise else: # If can't verify after multiple attempts, continue on anyway. print('WARNING: Could not verify account. %s' % user_account_name) # Load configuration corresponding to the configuration type. c.Spawner.environment['DEPLOYMENT_TYPE'] = 'spawner' c.Spawner.environment['CONFIGURATION_TYPE'] = configuration_type config_root = '/opt/app-root/src/configs' config_file = '%s/%s.py' % (config_root, configuration_type) if os.path.exists(config_file): with open(config_file) as fp: exec(compile(fp.read(), config_file, 'exec'), globals()) # Load configuration provided via the environment. environ_config_file = '/opt/app-root/configs/jupyterhub_config.py' if os.path.exists(environ_config_file): with open(environ_config_file) as fp: exec(compile(fp.read(), environ_config_file, 'exec'), globals())

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null

0

null

0

1

0

4f6d55643f17e1e6df4acdca8f4b81328ac69205

1,005

py

Python

bbox_annotation_convert.py

lafius/YoLOGO

5dc888773a7a6442e270ed8111494042a1386de9

[ "MIT" ]

3

2019-05-21T11:00:29.000Z

2020-12-08T09:47:59.000Z

bbox_annotation_convert.py

lafius/YoLOGO

5dc888773a7a6442e270ed8111494042a1386de9

[ "MIT" ]

null

bbox_annotation_convert.py

lafius/YoLOGO

5dc888773a7a6442e270ed8111494042a1386de9

[ "MIT" ]

1

2021-01-29T03:25:55.000Z

import os from collections import defaultdict SOURCE_DIR = os.getcwd() classes = [] with open('class.txt', "r") as f: for line in f.readlines(): classes.append(line.split()[-1]) f.close() def convertAnnotation(filename, newAnnotation): with open(filename, "r") as f: listeAnnotation = f.readlines() for i in range(1, len(listeAnnotation)): bbox = listeAnnotation[i].split() newAnnotation[filename].append(bbox[0] + "," + bbox[1] + "," + bbox[2] + "," + bbox[3] + "," + str(classes.index(bbox[4]))) f.close() if __name__ == '__main__': new_Annotation = defaultdict(list) dir = os.listdir(SOURCE_DIR + "/Labels/") for filename in dir: convertAnnotation(SOURCE_DIR + "/Labels/" + filename, new_Annotation) with open('annotation.txt', "w") as f: for fileName in new_Annotation: f.write(SOURCE_DIR + "/Images/" + fileName.split("/")[-1] + " " + " ".join(new_Annotation[fileName]) + "\n") f.close()

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null

0

1

0

4f6ee92d3e4ebb7dafcda9e91ea9d426dbaf292c

5,379

py

Python

c3dm/config.py

facebookresearch/c3dm

cac38418e41f75f1395422200b8d7bdf6725aa43

[ "MIT" ]

15

2020-12-04T16:40:21.000Z

2021-11-06T01:35:16.000Z

c3dm/config.py

facebookresearch/c3dm

cac38418e41f75f1395422200b8d7bdf6725aa43

[ "MIT" ]

2

2021-03-16T09:05:22.000Z

2021-12-23T12:43:37.000Z

c3dm/config.py

facebookresearch/c3dm

cac38418e41f75f1395422200b8d7bdf6725aa43

[ "MIT" ]

2

2021-04-08T00:50:29.000Z

2021-11-06T01:35:06.000Z

# Copyright (c) Facebook, Inc. and its affiliates. import argparse import inspect import copy import os import yaml import ast import numpy as np from tools.attr_dict import nested_attr_dict from tools.utils import auto_init_args def convert_to_stringval(cfg_,squeeze=None,stringify_vals=False): out = {} convert_to_stringval_rec( [('ROOT',cfg_)], out, squeeze=squeeze,stringify_vals=stringify_vals) return out def convert_to_stringval_rec( flds, output, squeeze=None, stringify_vals=False): for k,v in flds[-1][1].items(): if isinstance(v,dict): flds_cp = copy.deepcopy(flds) flds_cp.append( (k,v) ) convert_to_stringval_rec( flds_cp, output, squeeze=squeeze, stringify_vals=stringify_vals) else: valname = [] ; valname_full = [] for f in flds[1:]: valname_full.append(squeeze_string(f[0],squeeze)) valname_full.append(squeeze_string(k,squeeze)) valname_full = ".".join(valname_full) if stringify_vals: output[valname_full] = str(v) else: output[valname_full] = v def squeeze_key_string(f,squeeze_inter,squeeze_tail): keys = f.split('.') tail = keys[-1] inter = keys[0:-1] nkeys = len(keys) if nkeys > 1: take_from_each = int(np.floor(float(squeeze_inter-nkeys)/float(nkeys-1))) take_from_each = max(take_from_each,1) for keyi in range(nkeys-1): s = inter[keyi] s = s[0:min(take_from_each,len(s))] inter[keyi] = s tail = squeeze_string(tail,squeeze_tail) inter.append(tail) out = ".".join( inter ) return out def squeeze_string(f,squeeze): if squeeze is None or squeeze > len(f): return f; idx = np.round(np.linspace(0,len(f)-1,squeeze)) idx = idx.astype(int).tolist() f_short = [ f[i] for i in idx ] f_short = str("").join(f_short) return f_short def get_default_args(C): # returns dict of keyword args of a callable C sig = inspect.signature(C) kwargs = {} for pname,defval in dict(sig.parameters).items(): if defval.default==inspect.Parameter.empty: print('skipping %s' % pname) continue else: kwargs[pname] = copy.deepcopy(defval.default) return kwargs def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def arg_as_list(s): v = ast.literal_eval(s) if type(v) is not list: raise argparse.ArgumentTypeError("Argument \"%s\" is not a list" % (s)) return v def get_arg_parser(cfg_constructor): dargs = (get_default_args(cfg_constructor) if inspect.isclass(cfg_constructor) else cfg_constructor) dargs_full_name = convert_to_stringval(dargs,stringify_vals=False) parser = argparse.ArgumentParser( description='Auto-initialized argument parser' ) for darg, val in dargs_full_name.items(): tp = type(val) if val is not None else str if tp==bool: parser.add_argument( '--%s' % darg, dest=darg, help=darg, default=val, type=str2bool, ) elif tp == list: parser.add_argument( '--%s' % darg, type=arg_as_list, default=val, help=darg) else: parser.add_argument( '--%s' % darg, dest=darg, help=darg, default=val, type=tp, ) return parser def set_config_from_config(cfg,cfg_set): # cfg_set ... dict with nested options cfg_dot_separated = convert_to_stringval(cfg_set,stringify_vals=False) set_config(cfg,cfg_dot_separated) def set_config_rec(cfg,tgt_key,val,check_only=False): if len(tgt_key) > 1: k = tgt_key.pop(0) if k not in cfg: #raise ValueError('no such config key %s' % k ) cfg[k] = {} set_config_rec(cfg[k],tgt_key,val,check_only=check_only) else: if check_only: assert cfg[tgt_key[0]]==val else: cfg[tgt_key[0]] = val def set_config(cfg,cfg_set): # cfg_set ... dict with .-separated options for cfg_key,cfg_val in cfg_set.items(): # print('setting %s = %s' % (cfg_key,str(cfg_val)) ) cfg_key_split = [ k for k in cfg_key.split('.') if len(k) > 0 ] set_config_rec(cfg,copy.deepcopy(cfg_key_split),cfg_val) set_config_rec(cfg,cfg_key_split,cfg_val,check_only=True) def set_config_from_file(cfg,cfg_filename): # set config from yaml file with open(cfg_filename, 'r') as f: yaml_cfg = yaml.load(f) set_config_from_config(cfg,yaml_cfg) def dump_config(cfg): cfg_filename = os.path.join(cfg.exp_dir,'expconfig.yaml') with open(cfg_filename, 'w') as yaml_file: yaml.dump(cfg, yaml_file, default_flow_style=False)

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null

0

null

0

1

0

4f700623aae7a95a1ed534501dc5e986a608fd04

2,324

py

Python

cave/build/tools/headup/Source/FileTypeConfig.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

null

cave/build/tools/headup/Source/FileTypeConfig.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

null

cave/build/tools/headup/Source/FileTypeConfig.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

1

2021-10-30T00:03:05.000Z

## # This software was developed and / or modified by Raytheon Company, # pursuant to Contract DG133W-05-CQ-1067 with the US Government. # # U.S. EXPORT CONTROLLED TECHNICAL DATA # This software product contains export-restricted data whose # export/transfer/disclosure is restricted by U.S. law. Dissemination # to non-U.S. persons whether in the United States or abroad requires # an export license or other authorization. # # Contractor Name: Raytheon Company # Contractor Address: 6825 Pine Street, Suite 340 # Mail Stop B8 # Omaha, NE 68106 # 402.291.0100 # # See the AWIPS II Master Rights File ("Master Rights File.pdf") for # further licensing information. ## # SOFTWARE HISTORY # # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 3 Mar 2010 #3771 jelkins Initial Creation. from ConfigParser import ConfigParser from ConfigParser import NoOptionError from os import pathsep from os import listdir from os.path import join class FileTypeConfig(ConfigParser): """ Handles file type configurations """ def __init__(self,defaultConfig = None,configDirectories = None, fileType = None): self.fileType = fileType dConf = {"space":" "} if defaultConfig != None: dConf.update(defaultConfig) ConfigParser.__init__(self,dConf) if configDirectories != None: self.loadConfig(configDirectories) def isAvailable(self,fileType = None): if fileType == None: fileType = self.fileType return self.has_section(fileType) def loadConfig(self,configDirectories): for path in configDirectories.split(pathsep): for file in listdir(path): if ".cfg" in file: self.read(join(path,file)) def _getConfig(self,configKey,getterFunction,varDict=None): result = None try: if varDict != None: result = getterFunction(self.fileType,configKey,vars=varDict) else: result = getterFunction(self.fileType,configKey) except NoOptionError: pass return result def getConfig(self,configKey,varDict=None): return self._getConfig(configKey,self.get,varDict) def getBooleanConfig(self,configKey): return self._getConfig(configKey,self.getboolean)

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false

0.026316

0.131579

0.052632

0.421053

0

null

0

null

0

1

0

4f70634b62de82ee85628c44e5f54c1ae0ba85ac

577

py

Python

var/spack/repos/builtin/packages/r-rodbc/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

2

2018-11-27T03:39:44.000Z

2021-09-06T15:50:35.000Z

var/spack/repos/builtin/packages/r-rodbc/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

1

2019-01-11T20:11:52.000Z

var/spack/repos/builtin/packages/r-rodbc/package.py

HaochengLIU/spack

26e51ff1705a4d6234e2a0cf734f93f7f95df5cb

[ "ECL-2.0", "Apache-2.0", "MIT" ]

1

2020-10-14T14:20:17.000Z

# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class RRodbc(RPackage): """An ODBC database interface.""" homepage = "https://cran.rstudio.com/web/packages/RODBC/" url = "https://cran.rstudio.com/src/contrib/RODBC_1.3-13.tar.gz" list_url = "https://cran.r-project.org/src/contrib/Archive/RODBC/" version('1.3-13', 'c52ef9139c2ed85adc53ad6effa7d68e') depends_on('unixodbc')

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577

18

74

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0.762677

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0

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0.091168

0

1

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false

0

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0

0.714286

0

null

0

null

0

1

0

4f72a5836ba28dfe89a013c0b38cf2e69cd03926

862

py

Python

pythonFiles/pydev/launcher.py

lee-vius/python-preview

1d953a8d08698693042ce763ec7861224661032f

[ "MIT" ]

1

2021-02-25T05:47:14.000Z

pythonFiles/pydev/launcher.py

lee-vius/python-preview

1d953a8d08698693042ce763ec7861224661032f

[ "MIT" ]

null

pythonFiles/pydev/launcher.py

lee-vius/python-preview

1d953a8d08698693042ce763ec7861224661032f

[ "MIT" ]

null

import os import os.path import sys import traceback sys.stdout.write('&info&succeeded to launch script') sys.stdout.flush() try: import debugger except: traceback.print_exc() print('Press Enter to close...') try: raw_input() except NameError: input() sys.exit(1) #======================================================================================================================= # 1. Debugger port to connect to. # 2. GUID for the debug session. # 3. Startup script name. #======================================================================================================================= port_num = int(sys.argv[1]) debug_id = sys.argv[2] del sys.argv[1:3] # filename = sys.argv[0] sys.path[0] = '' current_pid = os.getpid() del sys, os print(current_pid) debugger.debug(port_num, debug_id, current_pid)

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null

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null

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0

4f72ddd81a2967ee26f9ff43caf15bf5a947f6a1

11,398

py

Python

framework/UI/FitnessView.py

rinelson456/raven

1114246136a2f72969e75b5e99a11b35500d4eef

[ "Apache-2.0" ]

159

2017-03-24T21:07:06.000Z

2022-03-20T13:44:40.000Z

framework/UI/FitnessView.py

rinelson456/raven

1114246136a2f72969e75b5e99a11b35500d4eef

[ "Apache-2.0" ]

1,667

2017-03-27T14:41:22.000Z

2022-03-31T19:50:06.000Z

framework/UI/FitnessView.py

rinelson456/raven

1114246136a2f72969e75b5e99a11b35500d4eef

[ "Apache-2.0" ]

95

2017-03-24T21:05:03.000Z

2022-03-08T17:30:22.000Z

# Copyright 2017 Battelle Energy Alliance, LLC # # 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. """ A view widget for visualizing the R^2 fitness of the local stepwise regression results. """ #For future compatibility with Python 3 from __future__ import division, print_function, absolute_import #End compatibility block for Python 3 try: from PySide import QtCore as qtc from PySide import QtGui as qtg from PySide import QtGui as qtw from PySide import QtSvg as qts except ImportError as e: from PySide2 import QtCore as qtc from PySide2 import QtGui as qtg from PySide2 import QtWidgets as qtw from PySide2 import QtSvg as qts from .BaseTopologicalView import BaseTopologicalView import math import numpy as np class FitnessView(BaseTopologicalView): """ A view widget for visualizing the R^2 fitness of the local stepwise regression results. """ def __init__(self, parent=None, amsc=None, title=None): """ Initialization method that can optionally specify the parent widget, an AMSC object to reference, and a title for this widget. @ In, parent, an optional QWidget that will be the parent of this widget @ In, amsc, an optional AMSC_Object specifying the underlying data object for this widget to use. @ In, title, an optional string specifying the title of this widget. """ super(FitnessView, self).__init__(parent,amsc,title) def Reinitialize(self, parent=None, amsc=None, title=None): """ Reinitialization method that resets this widget and can optionally specify the parent widget, an AMSC object to reference, and a title for this widget. @ In, parent, an optional QWidget that will be the parent of this widget @ In, amsc, an optional AMSC_Object specifying the underlying data object for this widget to use. @ In, title, an optional string specifying the title of this widget. """ # Try to apply a new layout, if one already exists then make sure to grab # it for updating if self.layout() is None: self.setLayout(qtw.QVBoxLayout()) layout = self.layout() self.clearLayout(layout) self.padding = 2 ## General Graphics View/Scene setup self.scene = qtw.QGraphicsScene() self.scene.setSceneRect(0,0,100,100) self.gView = qtw.QGraphicsView(self.scene) self.gView.setRenderHints(qtg.QPainter.Antialiasing | qtg.QPainter.SmoothPixmapTransform) self.gView.setHorizontalScrollBarPolicy(qtc.Qt.ScrollBarAlwaysOff) self.gView.setVerticalScrollBarPolicy(qtc.Qt.ScrollBarAlwaysOff) self.font = qtg.QFont('sans-serif', 12) ## Defining the right click menu self.rightClickMenu = qtw.QMenu() self.fillAction = self.rightClickMenu.addAction('Fill viewport') self.fillAction.setCheckable(True) self.fillAction.setChecked(True) self.fillAction.triggered.connect(self.updateScene) self.showNumberAction = self.rightClickMenu.addAction('Show Numeric Values') self.showNumberAction.setCheckable(True) self.showNumberAction.setChecked(True) self.showNumberAction.triggered.connect(self.updateScene) captureAction = self.rightClickMenu.addAction('Capture') captureAction.triggered.connect(self.saveImage) self.gView.scale(self.gView.width()/self.scene.width(), self.gView.height()/self.scene.height()) layout.addWidget(self.gView) self.updateScene() def saveImage(self, filename=None): """ Saves the current display of this view to a static image by loading a file dialog box. @ In, filename, string, optional parameter specifying where this image will be saved. If None, then a dialog box will prompt the user for a name and location. @ Out, None """ if filename is None: dialog = qtw.QFileDialog(self) dialog.setFileMode(qtw.QFileDialog.AnyFile) dialog.setAcceptMode(qtw.QFileDialog.AcceptSave) dialog.exec_() if dialog.result() == qtw.QFileDialog.Accepted: filename = dialog.selectedFiles()[0] else: return self.scene.clearSelection() self.scene.setSceneRect(self.scene.itemsBoundingRect()) if filename.endswith('.svg'): svgGen = qts.QSvgGenerator() svgGen.setFileName(filename) svgGen.setSize(self.scene.sceneRect().size().toSize()) svgGen.setViewBox(self.scene.sceneRect()) svgGen.setTitle("Screen capture of " + self.__class__.__name__) svgGen.setDescription("Generated from RAVEN.") painter = qtg.QPainter(svgGen) else: image = qtg.QImage(self.scene.sceneRect().size().toSize(), qtg.QImage.Format_ARGB32) image.fill(qtc.Qt.transparent) painter = qtg.QPainter(image) self.scene.render(painter) if not filename.endswith('.svg'): image.save(filename,quality=100) del painter def contextMenuEvent(self,event): """ An event handler triggered when the user right-clicks on this view that will force the context menu to appear. @ In, event, a QContextMenuEvent specifying the context of this event. """ self.rightClickMenu.popup(event.globalPos()) def resizeEvent(self,event): """ An event handler triggered when the user resizes this view. @ In, event, a QResizeEvent specifying the context of this event. """ super(FitnessView, self).resizeEvent(event) self.gView.scale(self.gView.width()/self.scene.width(), self.gView.height()/self.scene.height()) self.updateScene() def selectionChanged(self): """ An event handler triggered when the user changes the selection of the data. """ self.updateScene() def persistenceChanged(self): """ An event handler triggered when the user changes the persistence setting of the data. """ self.updateScene() def modelsChanged(self): """ An event handler triggered when the user requests a new set of local models. """ self.updateScene() def updateScene(self): """ A method for drawing the scene of this view. """ self.scene.clear() if self.fillAction.isChecked(): self.scene.setSceneRect(0,0,100*float(self.gView.width())/float(self.gView.height()),100) else: self.scene.setSceneRect(0,0,100,100) width = self.scene.width() height = self.scene.height() plotWidth = width - 2*self.padding plotHeight = height - 2*self.padding axisPen = qtg.QPen(qtc.Qt.black) names = self.amsc.GetNames()[:-1] if not self.amsc.FitsSynced(): txtItem = self.scene.addSimpleText('Rebuild Local Models',self.font) txtItem.setFlag(qtw.QGraphicsItem.ItemIgnoresTransformations) txtItem.setPos(0,0) txtItem.setFlag(qtw.QGraphicsItem.ItemIsMovable) txtItem.setFlag(qtw.QGraphicsItem.ItemIsSelectable) self.scene.changed.connect(self.scene.invalidate) self.gView.fitInView(self.scene.sceneRect(),qtc.Qt.KeepAspectRatio) return selection = self.amsc.GetSelectedSegments() colorMap = self.amsc.GetColors() ## Check if they selected any extrema if selection is None or len(selection) == 0: selection = [] selectedExts = self.amsc.GetSelectedExtrema() allSegments = self.amsc.GetCurrentLabels() for minMaxPair in allSegments: for extIdx in selectedExts: if extIdx in minMaxPair: selection.append(minMaxPair) ## Okay, well then we will just plot everything we have for the current ## level if len(selection) == 0: selection = allSegments selectionCount = len(selection) if selectionCount > 0: axisHeight = plotHeight/float(selectionCount) axisWidth = plotWidth/float(selectionCount) dimCount = len(names) fitErrorData = {} for j,extPair in enumerate(selection): fitErrorData[extPair] = self.amsc.ComputePerDimensionFitErrors(extPair) maxValue = 1 j = 0 for extPair in selection: retValue = fitErrorData[extPair] if retValue is not None: indexOrder,rSquared,fStatistic = retValue myColor = colorMap[extPair] myPen = qtg.QPen(qtg.QColor('#000000')) brushColor = qtg.QColor(myColor) brushColor.setAlpha(127) myBrush = qtg.QBrush(brushColor) vals = rSquared w = axisWidth / dimCount self.font.setPointSizeF(np.clip(w-2*self.padding,2,18)) for i,val in enumerate(vals): name = names[indexOrder[i]] if val > 0: barExtent = (val/maxValue)*plotHeight else: barExtent = 0 x = j*axisWidth + i*axisWidth/float(dimCount)+self.padding y = height-self.padding h = -barExtent if self.showNumberAction.isChecked(): numTxtItem = self.scene.addSimpleText('%.3g' % val, self.font) fm = qtg.QFontMetrics(numTxtItem.font()) fontHeight = fm.height() fontWidth = fm.width(numTxtItem.text()) numTxtItem.setPos(x+(w-fontHeight)/2.,y-plotHeight+fontWidth) #numTxtItem.rotate(285) #XXX not in qt5 numTxtItem.setFlag(qtw.QGraphicsItem.ItemIsMovable) numTxtItem.setFlag(qtw.QGraphicsItem.ItemIsSelectable) numTxtItem.setZValue(2) myRect = self.scene.addRect(x,y,w,h,myPen,myBrush) myRect.setToolTip(str(val)) myRect.setAcceptHoverEvents(True) txtItem = self.scene.addSimpleText(' ' + name,self.font) fm = qtg.QFontMetrics(txtItem.font()) fontHeight = fm.height() fontWidth = fm.width(name) txtItem.setPos(x+(w-fontHeight)/2.,y) #txtItem.rotate(270) #XXX not in qt5 txtItem.setFlag(qtw.QGraphicsItem.ItemIsMovable) txtItem.setFlag(qtw.QGraphicsItem.ItemIsSelectable) txtItem.setZValue(2) x = j*axisWidth+self.padding y = height-self.padding w = axisWidth h = -plotHeight self.scene.addRect(x,y,w,h,axisPen) j += 1 self.scene.changed.connect(self.scene.invalidate) self.gView.fitInView(self.scene.sceneRect(),qtc.Qt.KeepAspectRatio) def test(self): """ A test function for performing operations on this class that need to be automatically tested such as simulating mouse and keyboard events, and other internal operations. For this class in particular, we will test: - Building the models (which allows the actual plot to be displayed) - Saving the view buffer in svg and png formats. - Triggering the resize event. @ In, None @ Out, None """ self.amsc.BuildModels() self.amsc.ClearSelection() self.saveImage(self.windowTitle()+'.svg') self.saveImage(self.windowTitle()+'.png') self.resizeEvent(qtg.QResizeEvent(qtc.QSize(1,1),qtc.QSize(100,100))) super(FitnessView, self).test()

36.415335

95

0.683716

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11,398

5.559771

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0.020729

0.014806

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null

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null

0

1

0

4f76066515b21780cdfe4cdebbc95c9f722c5365

2,391

py

Python

13/main.py

DVRodri8/advent-of-code-2019

8c1e1a0766b067fbe282dd482bc258275c5a3364

[ "MIT" ]

null

13/main.py

DVRodri8/advent-of-code-2019

8c1e1a0766b067fbe282dd482bc258275c5a3364

[ "MIT" ]

null

13/main.py

DVRodri8/advent-of-code-2019

8c1e1a0766b067fbe282dd482bc258275c5a3364

[ "MIT" ]

null

from time import sleep from intMachine import intMachine from os import system from time import sleep class Arcade(): def __init__(self, program, ws=[], animation=False): self.__computer = intMachine(program) self.__mblock = [ ' ', '#', 'B', '-', 'O'] self.__screen = [['%' for i in range(23)] for i in range(37)] self.__score = 0 self.__ws = ws[:] self.__ANIMATION = animation def __nextFrame(self): output = self.__computer.run() return [output[i:i+3] for i in range(0,len(output),3)] def __updateScreen(self): for t in self.__nextFrame(): x,y,c = t if x==-1 and y == 0: self.__score = c else: self.__screen[x][y] = self.__mblock[c] def __print(self): system("clear") print("score:",self.__score) for i in range(len(self.__screen[0])): for j in range(len(self.__screen)): print(self.__screen[j][i], end='') print() sleep(0.01) def __interact(self): if len(self.__ws) > 0: i = self.__ws.pop(0) else: i = input() d=0 if i=='a': d=-1 elif i=='d': d=1 self.__computer.appendStdin(d) def print(self): self.__updateScreen() self.__print() def run(self): while True: self.__updateScreen() if self.__ANIMATION: self.__print() self.__interact() if self.__computer.isHalted(): break else: self.__computer.run() if not self.__ANIMATION: print(self.__score) with open("input", "r") as f: program = list(map(int, f.readline().strip().split(","))) # Part 1 G=intMachine(program) output = G.run() print(len([i for i in output[2::3] if i==2])) # Part 2 # My way to find which part of memory store the board # in order to don't wait for inputs raise an Exception on # io instructions on the intMachine class ''' zeros = [] for i,v in enumerate(program): if v==0: zeros.append(i) for i in zeros: break p = program[:] p[i] = 3 try: Arcade(p).print() except: print("no") print(i) sleep(0.0001) ''' # Replace floor space with wall for i in range(1416, 1416+36): program[i]=1 G = Arcade(program, ['n']*5000, True) G.run()

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0

0.027862

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2,391

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1

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false

0

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0.220339

0.152542

0

null

0

null

0

1

0

4f76c2ea5bf6266807ac181b18a05763f4cc86ed

1,103

py

Python

test_set_finder.py

SaarLevy/set-finder

02bdcf6a484f73fb808cc467142f18304036a97c

[ "MIT" ]

null

test_set_finder.py

SaarLevy/set-finder

02bdcf6a484f73fb808cc467142f18304036a97c

[ "MIT" ]

null

test_set_finder.py

SaarLevy/set-finder

02bdcf6a484f73fb808cc467142f18304036a97c

[ "MIT" ]

null

import unittest import set_finder from card import Card, Color, Suit, Fill class Test_Set_Finder(unittest.TestCase): def test_values_consistent(self): self.assertTrue(set_finder.values_consistent(1, 1, 1)) self.assertTrue(set_finder.values_consistent(1, 2, 3)) self.assertFalse(set_finder.values_consistent(1, 1, 2)) def test_check_set(self): a1 = Card(Color.red, Suit.diamond, Fill.striped, 1) a2 = Card(Color.red, Suit.diamond, Fill.striped, 2) a3 = Card(Color.red, Suit.diamond, Fill.striped, 3) self.assertTrue(set_finder.check_set(a1, a2, a3)) b1 = Card(Color.red, Suit.diamond, Fill.striped, 1) b2 = Card(Color.green, Suit.wave, Fill.solid, 1) b3 = Card(Color.purple, Suit.circle, Fill.blank, 1) self.assertTrue(set_finder.check_set(b1, b2, b3)) c1 = Card(Color.red, Suit.diamond, Fill.striped, 1) c2 = Card(Color.red, Suit.diamond, Fill.striped, 2) c3 = Card(Color.green, Suit.circle, Fill.solid, 1) self.assertFalse(set_finder.check_set(c1, c2, c3))

35.580645

63

0.659112

163

1,103

4.343558

0.245399

0.127119

0.101695

0.135593

0.538136

0.535311

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0

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1,103

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false

0

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0.285714

0

null

0

null

0

1

0

4f7af1d7d5c5fb2278ca5fd3846a86c199882fb7

8,660

py

Python

mac-platform-tools/systrace/catapult/devil/devil/android/app_ui.py

NBPS-Robotics/FTC-Code-Team-9987---2022

180538f3ebd234635fa88f96ae7cf7441df6a246

[ "MIT" ]

2

2022-01-14T23:19:48.000Z

2022-02-08T23:40:26.000Z

mac-platform-tools/systrace/catapult/devil/devil/android/app_ui.py

NBPS-Robotics/FTC-Code-Team-9987---2022

180538f3ebd234635fa88f96ae7cf7441df6a246

[ "MIT" ]

7

2022-02-15T01:11:37.000Z

2022-03-02T12:46:13.000Z

mac-platform-tools/systrace/catapult/devil/devil/android/app_ui.py

NBPS-Robotics/FTC-Code-Team-9987---2022

180538f3ebd234635fa88f96ae7cf7441df6a246

[ "MIT" ]

1

2022-03-04T01:03:53.000Z

# Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Provides functionality to interact with UI elements of an Android app.""" import collections import re from xml.etree import ElementTree as element_tree from devil.android import decorators from devil.android import device_temp_file from devil.utils import geometry from devil.utils import timeout_retry _DEFAULT_SHORT_TIMEOUT = 10 _DEFAULT_SHORT_RETRIES = 3 _DEFAULT_LONG_TIMEOUT = 30 _DEFAULT_LONG_RETRIES = 0 # Parse rectangle bounds given as: '[left,top][right,bottom]'. _RE_BOUNDS = re.compile( r'\[(?P<left>\d+),(?P<top>\d+)\]\[(?P<right>\d+),(?P<bottom>\d+)\]') class _UiNode(object): def __init__(self, device, xml_node, package=None): """Object to interact with a UI node from an xml snapshot. Note: there is usually no need to call this constructor directly. Instead, use an AppUi object (below) to grab an xml screenshot from a device and find nodes in it. Args: device: A device_utils.DeviceUtils instance. xml_node: An ElementTree instance of the node to interact with. package: An optional package name for the app owning this node. """ self._device = device self._xml_node = xml_node self._package = package def _GetAttribute(self, key): """Get the value of an attribute of this node.""" return self._xml_node.attrib.get(key) @property def bounds(self): """Get a rectangle with the bounds of this UI node. Returns: A geometry.Rectangle instance. """ d = _RE_BOUNDS.match(self._GetAttribute('bounds')).groupdict() return geometry.Rectangle.FromDict({k: int(v) for k, v in d.items()}) def Tap(self, point=None, dp_units=False): """Send a tap event to the UI node. Args: point: An optional geometry.Point instance indicating the location to tap, relative to the bounds of the UI node, i.e. (0, 0) taps the top-left corner. If ommited, the center of the node is tapped. dp_units: If True, indicates that the coordinates of the point are given in device-independent pixels; otherwise they are assumed to be "real" pixels. This option has no effect when the point is ommited. """ if point is None: point = self.bounds.center else: if dp_units: point = (float(self._device.pixel_density) / 160) * point point += self.bounds.top_left x, y = (str(int(v)) for v in point) self._device.RunShellCommand(['input', 'tap', x, y], check_return=True) def Dump(self): """Get a brief summary of the child nodes that can be found on this node. Returns: A list of lines that can be logged or otherwise printed. """ summary = collections.defaultdict(set) for node in self._xml_node.iter(): package = node.get('package') or '(no package)' label = node.get('resource-id') or '(no id)' text = node.get('text') if text: label = '%s[%r]' % (label, text) summary[package].add(label) lines = [] for package, labels in sorted(summary.iteritems()): lines.append('- %s:' % package) for label in sorted(labels): lines.append(' - %s' % label) return lines def __getitem__(self, key): """Retrieve a child of this node by its index. Args: key: An integer with the index of the child to retrieve. Returns: A UI node instance of the selected child. Raises: IndexError if the index is out of range. """ return type(self)(self._device, self._xml_node[key], package=self._package) def _Find(self, **kwargs): """Find the first descendant node that matches a given criteria. Note: clients would usually call AppUi.GetUiNode or AppUi.WaitForUiNode instead. For example: app = app_ui.AppUi(device, package='org.my.app') app.GetUiNode(resource_id='some_element', text='hello') would retrieve the first matching node with both of the xml attributes: resource-id='org.my.app:id/some_element' text='hello' As the example shows, if given and needed, the value of the resource_id key is auto-completed with the package name specified in the AppUi constructor. Args: Arguments are specified as key-value pairs, where keys correnspond to attribute names in xml nodes (replacing any '-' with '_' to make them valid identifiers). At least one argument must be supplied, and arguments with a None value are ignored. Returns: A UI node instance of the first descendant node that matches ALL the given key-value criteria; or None if no such node is found. Raises: TypeError if no search arguments are provided. """ matches_criteria = self._NodeMatcher(kwargs) for node in self._xml_node.iter(): if matches_criteria(node): return type(self)(self._device, node, package=self._package) return None def _NodeMatcher(self, kwargs): # Auto-complete resource-id's using the package name if available. resource_id = kwargs.get('resource_id') if (resource_id is not None and self._package is not None and ':id/' not in resource_id): kwargs['resource_id'] = '%s:id/%s' % (self._package, resource_id) criteria = [(k.replace('_', '-'), v) for k, v in kwargs.items() if v is not None] if not criteria: raise TypeError('At least one search criteria should be specified') return lambda node: all(node.get(k) == v for k, v in criteria) class AppUi(object): # timeout and retry arguments appear unused, but are handled by decorator. # pylint: disable=unused-argument def __init__(self, device, package=None): """Object to interact with the UI of an Android app. Args: device: A device_utils.DeviceUtils instance. package: An optional package name for the app. """ self._device = device self._package = package @property def package(self): return self._package @decorators.WithTimeoutAndRetriesDefaults(_DEFAULT_SHORT_TIMEOUT, _DEFAULT_SHORT_RETRIES) def _GetRootUiNode(self, timeout=None, retries=None): """Get a node pointing to the root of the UI nodes on screen. Note: This is currently implemented via adb calls to uiatomator and it is *slow*, ~2 secs per call. Do not rely on low-level implementation details that may change in the future. TODO(crbug.com/567217): Swap to a more efficient implementation. Args: timeout: A number of seconds to wait for the uiautomator dump. retries: Number of times to retry if the adb command fails. Returns: A UI node instance pointing to the root of the xml screenshot. """ with device_temp_file.DeviceTempFile(self._device.adb) as dtemp: output = self._device.RunShellCommand( ['uiautomator', 'dump', dtemp.name], single_line=True, check_return=True) if output.startswith('ERROR:'): raise RuntimeError( 'uiautomator dump command returned error: {}'.format(output)) xml_node = element_tree.fromstring( self._device.ReadFile(dtemp.name, force_pull=True)) return _UiNode(self._device, xml_node, package=self._package) def ScreenDump(self): """Get a brief summary of the nodes that can be found on the screen. Returns: A list of lines that can be logged or otherwise printed. """ return self._GetRootUiNode().Dump() def GetUiNode(self, **kwargs): """Get the first node found matching a specified criteria. Args: See _UiNode._Find. Returns: A UI node instance of the node if found, otherwise None. """ # pylint: disable=protected-access return self._GetRootUiNode()._Find(**kwargs) @decorators.WithTimeoutAndRetriesDefaults(_DEFAULT_LONG_TIMEOUT, _DEFAULT_LONG_RETRIES) def WaitForUiNode(self, timeout=None, retries=None, **kwargs): """Wait for a node matching a given criteria to appear on the screen. Args: timeout: A number of seconds to wait for the matching node to appear. retries: Number of times to retry in case of adb command errors. For other args, to specify the search criteria, see _UiNode._Find. Returns: The UI node instance found. Raises: device_errors.CommandTimeoutError if the node is not found before the timeout. """ def node_found(): return self.GetUiNode(**kwargs) return timeout_retry.WaitFor(node_found)

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0

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8,660

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1

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false

0

0.072917

0.020833

0.385417

0

null

0

null

0

1

0

4f7bc1d2e2ff7ffa0eea88735806bb111db8a6c9

9,277

py

Python

event_monitor/common/lockutils.py

wangyu2014/galaxy

e34473ea8b903d31dac6c74c826a91319b681bf6

[ "BSD-2-Clause" ]

null

event_monitor/common/lockutils.py

wangyu2014/galaxy

e34473ea8b903d31dac6c74c826a91319b681bf6

[ "BSD-2-Clause" ]

null

event_monitor/common/lockutils.py

wangyu2014/galaxy

e34473ea8b903d31dac6c74c826a91319b681bf6

[ "BSD-2-Clause" ]

null

from contextlib import contextmanager from threading import Condition from threading import current_thread from threading import Lock from time import time class RWLock(object): def __init__(self, lock=None): """Initialize this read-write lock.""" # Condition variable, used to signal waiters of a change in object # state. if lock is None: self.__condition = Condition(Lock()) else: self.__condition = Condition(lock) # Initialize with no writers. self.__writer = None self.__upgradewritercount = 0 self.__pendingwriters = [] # Initialize with no readers. self.__readers = {} def acquire_read(self, blocking=True, timeout=None): """Acquire a read lock for the current thread, waiting at most timeout seconds or doing a non-blocking check in case timeout is <= 0. In case timeout is None, the call to acquire_read blocks until the lock request can be serviced. In case the timeout expires before the lock could be serviced, a RuntimeError is thrown.""" if not blocking: endtime = -1 elif timeout is not None: endtime = time() + timeout else: endtime = None me = current_thread() self.__condition.acquire() try: if self.__writer is me: # If we are the writer, grant a new read lock, always. self.__writercount += 1 return while True: if self.__writer is None: # Only test anything if there is no current writer. if self.__upgradewritercount or self.__pendingwriters: if me in self.__readers: # Only grant a read lock if we already have one # in case writers are waiting for their turn. # This means that writers can't easily get starved # (but see below, readers can). self.__readers[me] += 1 return # No, we aren't a reader (yet), wait for our turn. else: # Grant a new read lock, always, in case there are # no pending writers (and no writer). self.__readers[me] = self.__readers.get(me, 0) + 1 return if timeout is not None: remaining = endtime - time() if remaining <= 0: # Timeout has expired, signal caller of this. raise RuntimeError("Acquiring read lock timed out") self.__condition.wait(remaining) else: self.__condition.wait() finally: self.__condition.release() def acquire_write(self, timeout=None): """Acquire a write lock for the current thread, waiting at most timeout seconds or doing a non-blocking check in case timeout is <= 0. In case the write lock cannot be serviced due to the deadlock condition mentioned above, a ValueError is raised. In case timeout is None, the call to acquire_write blocks until the lock request can be serviced. In case the timeout expires before the lock could be serviced, a RuntimeError is thrown.""" if timeout is not None: endtime = time() + timeout me, upgradewriter = current_thread(), False self.__condition.acquire() try: if self.__writer is me: # If we are the writer, grant a new write lock, always. self.__writercount += 1 return elif me in self.__readers: # If we are a reader, no need to add us to pendingwriters, # we get the upgradewriter slot. if self.__upgradewritercount: # If we are a reader and want to upgrade, and someone # else also wants to upgrade, there is no way we can do # this except if one of us releases all his read locks. # Signal this to user. if timeout is not None: raise RuntimeError( "Write lock upgrade would deadlock until timeout") else: raise ValueError( "Inevitable dead lock, denying write lock") upgradewriter = True self.__upgradewritercount = self.__readers.pop(me) else: # We aren't a reader, so add us to the pending writers queue # for synchronization with the readers. self.__pendingwriters.append(me) while True: if not self.__readers and self.__writer is None: # Only test anything if there are no readers and writers. if self.__upgradewritercount: if upgradewriter: # There is a writer to upgrade, and it's us. Take # the write lock. self.__writer = me self.__writercount = self.__upgradewritercount + 1 self.__upgradewritercount = 0 return # There is a writer to upgrade, but it's not us. # Always leave the upgrade writer the advance slot, # because he presumes he'll get a write lock directly # from a previously held read lock. elif self.__pendingwriters[0] is me: # If there are no readers and writers, it's always # fine for us to take the writer slot, removing us # from the pending writers queue. # This might mean starvation for readers, though. self.__writer = me self.__writercount = 1 self.__pendingwriters = self.__pendingwriters[1:] return if timeout is not None: remaining = endtime - time() if remaining <= 0: # Timeout has expired, signal caller of this. if upgradewriter: # Put us back on the reader queue. No need to # signal anyone of this change, because no other # writer could've taken our spot before we got # here (because of remaining readers), as the test # for proper conditions is at the start of the # loop, not at the end. self.__readers[me] = self.__upgradewritercount self.__upgradewritercount = 0 else: # We were a simple pending writer, just remove us # from the FIFO list. self.__pendingwriters.remove(me) raise RuntimeError("Acquiring write lock timed out") self.__condition.wait(remaining) else: self.__condition.wait() finally: self.__condition.release() def release(self): """Release the currently held lock. In case the current thread holds no lock, a ValueError is thrown.""" me = current_thread() self.__condition.acquire() try: if self.__writer is me: # We are the writer, take one nesting depth away. self.__writercount -= 1 if not self.__writercount: # No more write locks; take our writer position away and # notify waiters of the new circumstances. self.__writer = None self.__condition.notifyAll() elif me in self.__readers: # We are a reader currently, take one nesting depth away. self.__readers[me] -= 1 if not self.__readers[me]: # No more read locks, take our reader position away. del self.__readers[me] if not self.__readers: # No more readers, notify waiters of the new # circumstances. self.__condition.notifyAll() else: raise ValueError("Trying to release unheld lock") finally: self.__condition.release() @property @contextmanager def readlock(self): self.acquire_read() try: yield finally: self.release() @property @contextmanager def writelock(self): self.acquire_write() try: yield finally: self.release()

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9,277

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0.325889

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0

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9,277

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0.877532

0.327584

0

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1

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false

0

0.03876

0

0.139535

0

null

0

null

0

1

0

4f7ff0be57aebe2550e805caa1e135470f309ea7

1,891

py

Python

app/database/scraper/parsers/CourseParser.py

gilltan97/uoft-course-api

b66b8409fb81d151ef470c71aeb3e5aad24691dd

[ "MIT" ]

null

app/database/scraper/parsers/CourseParser.py

gilltan97/uoft-course-api

b66b8409fb81d151ef470c71aeb3e5aad24691dd

[ "MIT" ]

null

app/database/scraper/parsers/CourseParser.py

gilltan97/uoft-course-api

b66b8409fb81d151ef470c71aeb3e5aad24691dd

[ "MIT" ]

null

from utils import Scraper from parsers import Helpers import bs4 as BeautifulSoup class CourseParser: """ Course parser which parses data from web service called Course Finder located at: http://coursefinder.utoronto.ca/. -- references -- :see: https://docs.python.org/3.4/library/re.html """ host_url = 'http://coursefinder.utoronto.ca/course-search/search/' def __init__(self, course_code): self.course_code = course_code def retrieve_html(self): """ Search for course at host url and retrieve the html data with the given course code """ scraper = Scraper.Scraper() url = CourseParser.host_url + 'courseInquiry' params = { 'methodToCall': 'start', 'viewId': 'CourseDetails-InquiryView', 'courseId': '{}'.format(self.course_code) } return scraper.get_data(url, params=params, json=False, write=False) def parse_html(self): """ Parse the HTML content and create a JSON file from that content """ soup = BeautifulSoup.BeautifulSoup(self.retrieve_html(), 'html.parser') if soup.find(id='u19')['data-headerfor'] != 'correctPage': print("Course not found.") return None return { 'code': self.course_code[:-5], 'name': Helpers.get_name(soup), 'division': Helpers.get_division(soup), 'department': Helpers.get_department(soup), 'prerequisites': Helpers.get_prerequisites(soup), 'exclusion': Helpers.get_exclusion(soup), 'level': Helpers.get_courselevel(soup), 'campus': Helpers.get_campus(soup), 'breadth requirements': Helpers.get_breadth(soup), 'term': Helpers.get_term(soup), 'sections': Helpers.get_sections(soup) }

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1,891

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1,891

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false

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0.029412

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null

0

null

0

1

0

4f81b80b0e19ba69b31b17aae322062d35934abf

1,005

py

Python

ML/Projects/spam_classifier_naive_bayes/build_vocabulary.py

xuyannus/Machine-Learning-Collection

6d5dcd18d4e40f90e77355d56a2902e4c617ecbe

[ "MIT" ]

3,094

2020-09-20T04:34:31.000Z

2022-03-31T23:59:46.000Z

ML/Projects/spam_classifier_naive_bayes/build_vocabulary.py

xkhainguyen/Machine-Learning-Collection

425d196e9477dbdbbd7cc0d19d29297571746ab5

[ "MIT" ]

79

2020-09-24T08:54:17.000Z

2022-03-30T14:45:08.000Z

ML/Projects/spam_classifier_naive_bayes/build_vocabulary.py

xkhainguyen/Machine-Learning-Collection

425d196e9477dbdbbd7cc0d19d29297571746ab5

[ "MIT" ]

1,529

2020-09-20T16:21:21.000Z

2022-03-31T21:16:25.000Z

# -*- coding: utf-8 -*- """ We want go through each word in all emails, check if the word is an actual english word by comparing with nltk.corpus words and if it is then add it to our vocabulary. """ import pandas as pd import nltk from nltk.corpus import words vocabulary = {} data = pd.read_csv("data/emails.csv") nltk.download("words") set_words = set(words.words()) def build_vocabulary(curr_email): idx = len(vocabulary) for word in curr_email: if word.lower() not in vocabulary and word.lower() in set_words: vocabulary[word] = idx idx += 1 if __name__ == "__main__": for i in range(data.shape[0]): curr_email = data.iloc[i, :][0].split() print( f"Current email is {i}/{data.shape[0]} and the \ length of vocab is curr {len(vocabulary)}" ) build_vocabulary(curr_email) # Write dictionary to vocabulary.txt file file = open("vocabulary.txt", "w") file.write(str(vocabulary)) file.close()

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0

0.006502

0.234826

1,005

41

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0

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false

0

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0

0.166667

0.041667

0

null

0

null

0

1

0

4f81bfe97d1be0be08bfd049718d6a4355de5593

5,356

py

Python

Ch16/pftrack.py

quietcoolwu/MLCode

b4bdb7ee3468da597e5d16cfb58728e3c29ca889

[ "Xnet", "X11" ]

null

Ch16/pftrack.py

quietcoolwu/MLCode

b4bdb7ee3468da597e5d16cfb58728e3c29ca889

[ "Xnet", "X11" ]

null

Ch16/pftrack.py

quietcoolwu/MLCode

b4bdb7ee3468da597e5d16cfb58728e3c29ca889

[ "Xnet", "X11" ]

null

# Code from Chapter 16 of Machine Learning: An Algorithmic Perspective (2nd Edition) # by Stephen Marsland (http://stephenmonika.net) # You are free to use, change, or redistribute the code in any way you wish for # non-commercial purposes, but please maintain the name of the original author. # This code comes with no warranty of any kind. # Stephen Marsland, 2014 # 2D particle filter tracking. Euclidean distance based import numpy as np import pylab as pl def systematic(w, N): # Systematic resampling # One too many to make sure it is >1 samples = np.random.rand(N + 1) indices = np.arange(N + 1) u = (samples + indices) / N cumw = np.cumsum(w) Ncopies = np.zeros((N)) keep = np.zeros((N)) # ni copies of particle xi where ni = number of u between ws[i-1] and ws[i] j = 0 for i in range(N): while ((u[j] < cumw[i]) & (j < N)): keep[j] = i Ncopies[i] += 1 j += 1 return keep def pf(x0, xdot, sigma, T, N, width): # Sample x0 from prior p(x0) particles = np.zeros((N, 2, T + 1)) x = np.zeros((2, T + 1)) x[:, 0] = x0 particles[:, :, 0] = x0 particlepred = np.zeros((N, 2, T)) particlepred[:, :, 0] = x0 + np.random.uniform(-width, width, (N, 2)) print(particlepred[:, :, 0]) weights = np.ones((N, T)) # Main loop for t in range(0, T): # importance sampling particlepred[:, :, t] = particles[:, :, t] + np.random.uniform(-width, width, (N, 2)) # print particlepred[:,:,t] print(x[:, t]) print(x[:, t] - particlepred[:, :, t]) weights[:, t] = np.sum((x[:, t] - particlepred[:, :, t]) ** 2 + 1e-99, axis=1) print(weights[:, t]) weights[:, t] = 1. / np.sum((x[:, t] - particlepred[:, :, t]) ** 2 + 1e-99, axis=1) print(weights[:, t]) # weights[:,t] = np.sum(1./np.sqrt(sigma) * np.exp(-0.5/sigma * (x[:,t] - particlepred[:,:,t])**2) + 1e-99,axis=1) weights[:, t] /= np.sum(weights[:, t]) print(weights[:, t]) # selection resample = False if 1. / sum(weights[:, t] ** 2) < N / 2.: print("Resampling") resample = True sys = True if resample: if sys: keep = systematic(weights[:, t], N) else: # Residual resampling # Add a little bit because of a rounding error! Ncopies = np.floor(weights[:, t] * N + 1e-10) keep = np.zeros((N)) j = 0 for i in range(N): keep[j:j + Ncopies[i]] = i j += Ncopies[i] Nleft = int(N - np.sum(Ncopies)) # Rest by systematic resampling if Nleft > 0: print("sys resample") probs = (weights[:, t] * N - Ncopies) / Nleft extrakeep = systematic(probs, Nleft) keep[j:] = extrakeep else: keep = list(range(N)) print(keep) # output for i in range(N): particles[i, :, t + 1] = particlepred[keep[i], :, t] # print "here" print(x[:, t]) print(particlepred[:, :, t]) # x[:,t+1] = x[:,t] + xdot*np.random.uniform(-1,1,(1,2)) x[:, t + 1] = x[:, t] + xdot # + np.random.uniform(-1,1,(1,2)) # print particles[:,:,t] return particles, x, weights def pf_demo(): x0 = np.array([10, 12]) xdot = np.array([10, 8]) np.random.seed(3) T = 15 N = 30 sigma = 1.0 [particles, x, weights] = pf(x0, xdot, sigma, T, N, 15) x = x[:, :T] particles = particles[:, :, :T] # print particles # print x dfilt = x[[0, 1], :] - particles[[0, 1], :] mse_filt = np.sqrt(np.sum(dfilt ** 2)) # plot_track(x,y,xfilt,Pfilt) plot_position(x, particles, T) def plot_position(x, particles, T): pl.ion() pl.figure() colours = pl.cm.gray(np.linspace(0, 1, T)) # for t in [0,5,10,14]: for t in range(T): # print particles[:,:,t] pl.plot(x[0, t], x[1, t], 'x', color=colours[t], ms=10.) pl.plot(particles[:, 0, t], particles[:, 1, t], 'o', color=colours[t]) # pl.plot(particles[:,0,5],particles[:,1,5],'go') # pl.plot(particles[:,0,10],particles[:,1,10],'co') # pl.plot(particles[:,0,14],particles[:,1,14],'ko') pl.xlim((0, 150)) pl.ylim((0, 150)) def plot_track(x, y, Kx, P): fig = pl.figure() ax = fig.add_subplot(111, aspect='equal') pl.plot(x[0, :], x[1, :], 'ks-') pl.plot(y[0, :], y[1, :], 'k*') pl.plot(Kx[0, :], Kx[1, :], 'kx:') obs_size, T = np.shape(y) from matplotlib.patches import Ellipse # Axes of ellipse are eigenvectors of covariance matrix, lengths are square roots of eigenvalues ellsize = np.zeros((obs_size, T)) ellangle = np.zeros((T)) for t in range(T): [evals, evecs] = np.linalg.eig(P[:2, :2, t]) ellsize[:, t] = np.sqrt(evals) ellangle[t] = np.angle(evecs[0, 0] + 0.j * evecs[0, 1]) ells = [Ellipse(xy=[Kx[0, t], Kx[1, t]], width=ellsize[0, t], height=ellsize[1, t], angle=ellangle[t]) for t in range(T)] for e in ells: ax.add_artist(e) e.set_alpha(0.1) e.set_facecolor([0.7, 0.7, 0.7])

31.692308

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3.485934

0.286445

0.035216

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0.099413

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0

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0.307879

5,356

168

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31.880952

0.692204

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0

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false

0

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0

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0.102804

0

null

0

null

0

1

0

4f831b7eadeddc95c309456c4f686e40797eef19

5,599

py

Python

tools/extract_ppi.py

RenskeW/cwl-epitope

475d0716b07c58b40ed01b4a49d5a14b15d641cc

[ "AAL" ]

null

tools/extract_ppi.py

RenskeW/cwl-epitope

475d0716b07c58b40ed01b4a49d5a14b15d641cc

[ "AAL" ]

null

tools/extract_ppi.py

RenskeW/cwl-epitope

475d0716b07c58b40ed01b4a49d5a14b15d641cc

[ "AAL" ]

null

""" This script combines multiple BioDL datasets and extracts the relevant information. In addition, it uses the UniProt mapping_dict tool to find pdb identifiers for each UniProt identifier in the dataset. @author: Renske de Wit @dateCreated: 2022-05-27 Inputs: - biodl_training_....csv - biodl_test_....csv Outputs: - directory with fasta files for each protein in the combined bioDL dataset (and which maps to a UniProt identifier) """ import argparse import pandas as pd import urllib.parse import urllib.request from pathlib import Path import os def parse_args(): """ Parses arguments from the command line. """ parser = argparse.ArgumentParser(description='Combines features into 1 file for every fasta sequence, stores files in 1 output directory.') # Arguments parser.add_argument('train_biodl_path', help='Path to biodl training dataset.') parser.add_argument('test_biodl_path', help='Path to biodl testing dataset.') parser.add_argument('--outdir', help='Path to output directory.', default="./fasta_files") return parser.parse_args() def merge_datasets(df1, df2): """ Merge testing and training set into one dataframe. """ assert sorted(list(df1.columns)) == sorted(list(df2.columns)) new_df = pd.concat([df1, df2], axis=0, ignore_index=True) assert len(new_df) == len(df1) + len(df2) return new_df def use_uniprot_mapping_tool(uniprot_ids): """ Queries UniProt API to return PDB ids associated with query UniProt IDs. Code adapted from the example at https://www.uniprot.org/help/api_idmapping. """ url = 'https://www.uniprot.org/uploadlists/' query = " ".join(uniprot_ids) params = { 'from': 'ACC+ID', 'to': 'PDB_ID', 'format': 'tab', 'query': query } data = urllib.parse.urlencode(params) data = data.encode('utf-8') req = urllib.request.Request(url, data) with urllib.request.urlopen(req) as f: response = f.read() return response def response_to_dictionary(response): """ Converts the response obtained from UniProt mapping tool to a dictionary. Output: Dictionary with Uniprot IDs as keys and a lists of PDB ids as values (single UniProt IDs may map to different PDB ids). """ list = response.decode('utf-8').split(sep="\n") # output: ['From\tTo', uniprot1\tpdb1, uniprot2\tpdb2, ... ] split = [a.split('\t') for a in list[1:]] # output: [ [uniprot1, pdb1], [uniprot2, pdb2], ... ] mapping_dict = {} for id in split: if len(id) == 2: if id[0] not in mapping_dict.keys(): mapping_dict[id[0]] = [id[1]] else: mapping_dict[id[0]].append(id[1]) return mapping_dict def map_identifiers(dataset): """ Uses the UniProt mapping_dict tool to map pdb identifiers to every UniProt ID in the dataset. """ uniprot_ids = [i for i in dataset["uniprot_id"]] response = use_uniprot_mapping_tool(uniprot_ids) # Convert response to dictionary mapping = response_to_dictionary(response) # Arbitrary & not necessarily correct choice: map the first pdb id to the uniprot id. dataset.insert(len(dataset.columns), "pdb_id", "") for i in dataset.index: uniprot_id = dataset.loc[i, "uniprot_id"] try: pdb_id = mapping[uniprot_id][0] dataset.loc[i, "pdb_id"] = pdb_id except KeyError: dataset = dataset.drop(index = i) # drop rows which do not map to any pdb id return dataset, response.decode('utf-8') def write_fasta_files(dataset, out_dir): """ Writes a fasta file for every pdb id in the dataset, which includes which residues are PPI residues. """ for i in dataset.index: pdb_id = dataset.loc[i, "pdb_id"] sequence = dataset.loc[i, "sequence"] domain = dataset.loc[i, "domain"] # Remove commas from domain and sequence sequence = "".join(sequence.split(sep=",")) domain = "".join(domain.split(sep=",")) assert len(sequence) == len(domain) # Write fasta file filename = f"{pdb_id}.fasta" out_path = Path(out_dir) / filename with open(out_path, 'w') as f: f.writelines(f">{pdb_id}\n{sequence}\n{domain}") def main(): args = parse_args() train_biodl_path = args.train_biodl_path test_biodl_path = args.test_biodl_path out_dir = args.outdir # Create output directory if not os.path.exists(out_dir): # maybe introduce some safeguards here to avoid overwriting existing files os.mkdir(out_dir) # Read input data data_train = pd.read_csv(train_biodl_path) data_test = pd.read_csv(test_biodl_path) # Extract columns of interest from both dataframes relevant_columns = [ "domain", "sequence", "uniprot_id" ] data_train_slim = data_train[relevant_columns] data_test_slim = data_test[relevant_columns] # Merge the two datasets combined_data = merge_datasets(data_train_slim, data_test_slim) # Map the UniProt identifiers in the dataset to their associated pdb ids (obtained from UniProt mapping tool) mapped_dataset, uniprot_response = map_identifiers(combined_data) # Write each protein sequence together with its interface residues to a separate .fasta file. write_fasta_files(mapped_dataset, out_dir) # Write UniProt response to a file as well with open(Path(out_dir).parent / 'uniprot_mapping.tsv', 'w') as f: f.write(uniprot_response) if __name__ == "__main__": main()

32.552326

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4.684685

0.289575

0.016484

0.01511

0.010714

0.082418

0.047253

0.017033

0

0.00874

0.223433

5,599

172

144

32.552326

0.828427

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0

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0

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0.008505

0

0.034483

1

0.08046

false

0

0.068966

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0.206897

0

null

0

null

0

1

0

4f8a17ff768229051455475e41d5e2e581a55eab

12,664

py

Python

python_api/renderer/raysampler.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

3

2022-03-04T09:16:20.000Z

2022-03-19T02:57:01.000Z

python_api/renderer/raysampler.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

2

2022-03-08T10:54:47.000Z

2022-03-11T08:58:18.000Z

python_api/renderer/raysampler.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

null

import torch import numpy as np from collections import namedtuple from python_api.renderer.raymarching import near_far_from_aabb from python_api.renderer.rays import Rays, RaysWithDepth from python_api.utils import FunctionRegistry SamplerResult = namedtuple( 'SamplerResult', ('xyzs', 'views', 'z_vals', 'deltas') ) SamplerResultWithBound = namedtuple( 'SamplerResultWithBound', ('xyzs', 'views', 'z_vals', 'deltas', 'nears', 'fars') ) # Hierarchical sampling (section 5.2) def sample_pdf(bins, weights, N_samples, det=False, pytest=False): # Get pdf device = weights.device weights = weights + 1e-5 # prevent nans pdf = weights / torch.sum(weights, -1, keepdim=True) cdf = torch.cumsum(pdf, -1) cdf = torch.cat([torch.zeros_like(cdf[...,:1]), cdf], -1) # (batch, len(bins)) # Take uniform samples if det: u = torch.linspace(0., 1., steps=N_samples, device=device) u = u.expand(list(cdf.shape[:-1]) + [N_samples]) else: u = torch.rand(list(cdf.shape[:-1]) + [N_samples], device=device) # Pytest, overwrite u with numpy's fixed random numbers if pytest: np.random.seed(0) new_shape = list(cdf.shape[:-1]) + [N_samples] if det: u = np.linspace(0., 1., N_samples) u = np.broadcast_to(u, new_shape) else: u = np.random.rand(*new_shape) u = torch.Tensor(u) # Invert CDF u = u.contiguous() inds = torch.searchsorted(cdf, u, right=True) below = torch.max(torch.zeros_like(inds-1), inds-1) above = torch.min((cdf.shape[-1]-1) * torch.ones_like(inds), inds) inds_g = torch.stack([below, above], -1) # (batch, N_samples, 2) # cdf_g = tf.gather(cdf, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2) # bins_g = tf.gather(bins, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2) matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) denom = (cdf_g[...,1]-cdf_g[...,0]) denom = torch.where(denom<1e-5, torch.ones_like(denom), denom) t = (u-cdf_g[...,0])/denom samples = bins_g[...,0] + t * (bins_g[...,1]-bins_g[...,0]) return samples def delta_from_zval(z_vals, rays_d, delta_inf=1e10): # Convert these values using volume rendering (Section 4) deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples_-1) delta_inf = delta_inf * torch.ones_like(deltas[:, :1]) # (N_rays, 1) the last delta is infinity deltas = torch.cat([deltas, delta_inf], -1) # (N_rays, N_samples_) deltas = deltas * torch.norm(rays_d[...,None,:], dim=-1) return deltas def uniform_sampler(rays: Rays, N_samples: int, lindisp: bool, perturb: bool): near, far = rays.near, rays.far rays_o, rays_d = rays.origins, rays.directions N_rays = rays_o.shape[0] device = rays_o.device t_vals = torch.linspace(0., 1., steps=N_samples, device=device) if not lindisp: z_vals = near * (1.-t_vals) + far * (t_vals) else: z_vals = 1./(1./near * (1.-t_vals) + 1./far * (t_vals)) z_vals = z_vals.expand([N_rays, N_samples]) if perturb > 0.: # get intervals between samples mids = .5 * (z_vals[...,1:] + z_vals[...,:-1]) upper = torch.cat([mids, z_vals[...,-1:]], -1) lower = torch.cat([z_vals[...,:1], mids], -1) # stratified samples in those intervals t_rand = torch.rand(z_vals.shape, device=device) z_vals = lower + (upper - lower) * t_rand pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples, 3] # Convert these values using volume rendering (Section 4) deltas = delta_from_zval(z_vals, rays_d) views = rays.viewdirs[...,None,:].expand(pts.shape) return SamplerResult(pts, views, z_vals, deltas) def _importance_sampler(rays: Rays, samples: SamplerResult, # samples from last pass weights, N_importance, use_norm_dir, delta_inf, perturb, concat_input_sample, stop_grad): rays_o = rays.origins if use_norm_dir: rays_d = rays.viewdirs else: rays_d = rays.directions z_vals = samples.z_vals z_vals_mid = .5 * (z_vals[...,1:] + z_vals[...,:-1]) z_samples = sample_pdf(z_vals_mid, weights[...,1:-1], N_importance, det=(perturb==0.)) if stop_grad: z_samples = z_samples.detach() if concat_input_sample: z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1) else: z_vals, _ = torch.sort(z_samples, -1) pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples + N_importance, 3] # Convert these values using volume rendering (Section 4) deltas = delta_from_zval(z_vals, rays_d, delta_inf) views = rays.viewdirs[...,None,:].expand(pts.shape) return SamplerResult(pts, views, z_vals, deltas) def importance_sampler(rays: Rays, samples: SamplerResult, # samples from last pass weights, N_importance, use_norm_dir, perturb): return _importance_sampler(rays, samples, weights, N_importance, use_norm_dir, 1e10, perturb, True, True) def importance_sampler_mipnerf360(rays: Rays, samples: SamplerResult, # samples from last pass weights, N_importance, use_norm_dir, perturb, concat_input_sample, stop_grad): return _importance_sampler(rays, samples, weights, N_importance, use_norm_dir, 1e10, perturb, concat_input_sample, stop_grad) def ngp_importance_sampler(rays: Rays, samples: SamplerResultWithBound, # samples from last pass weights, primitive, N_importance, perturb): if isinstance(samples, SamplerResultWithBound): sample_dist = (samples.fars - samples.nears) / samples.z_vals.shape[-1] use_norm_dir = True else: sample_dist = (rays.far - rays.near) / samples.z_vals.shape[-1] use_norm_dir = False sample_ret = _importance_sampler(rays, samples, weights, N_importance, use_norm_dir, sample_dist, perturb, True, True) aabb = primitive.geometry.aabb pts = torch.min(torch.max(sample_ret.xyzs, aabb[:3]), aabb[3:]) # a manual clip. return sample_ret._replace(xyzs=pts) def ngp_uniform_sampler(rays: Rays, primitive, num_steps, min_near, perturb): rays_o, rays_d = rays.origins, rays.viewdirs # rays.directions isn't normalized device = rays_o.device prefix = rays_o.shape[:-1] rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # N = B * N, in fact device = rays_o.device # choose aabb aabb = primitive.geometry.aabb # sample steps nears, fars = near_far_from_aabb(rays_o, rays_d, aabb, min_near) nears.unsqueeze_(-1) fars.unsqueeze_(-1) #print(f'nears = {nears.min().item()} ~ {nears.max().item()}, fars = {fars.min().item()} ~ {fars.max().item()}') z_vals = torch.linspace(0.0, 1.0, num_steps, device=device).unsqueeze(0) # [1, T] z_vals = z_vals.expand((N, num_steps)) # [N, T] z_vals = nears + (fars - nears) * z_vals # [N, T], in [nears, fars] # same perturb strategy with ori nerf, which guarantees z_vals in [near, far] # if perturb > 0.: # # get intervals between samples # mids = .5 * (z_vals[...,1:] + z_vals[...,:-1]) # upper = torch.cat([mids, z_vals[...,-1:]], -1) # lower = torch.cat([z_vals[...,:1], mids], -1) # # stratified samples in those intervals # t_rand = torch.rand(z_vals.shape, device=device) # z_vals = lower + (upper - lower) * t_rand # perturb z_vals sample_dist = (fars - nears) / (num_steps - 1) if perturb: z_vals = z_vals + (torch.rand(z_vals.shape, device=device) - 0.5) * sample_dist #z_vals = z_vals.clamp(nears, fars) # avoid out of bounds xyzs. # generate xyzs pts = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(-1) # [N, 1, 3] * [N, T, 1] -> [N, T, 3] pts = torch.min(torch.max(pts, aabb[:3]), aabb[3:]) # a manual clip. # Convert these values using volume rendering (Section 4) deltas = delta_from_zval(z_vals, rays_d, sample_dist) views = rays.viewdirs[...,None,:].expand(pts.shape) return SamplerResultWithBound(pts, views, z_vals, deltas, nears, fars) def ngp_sampler_with_depth(rays: RaysWithDepth, primitive, num_steps, min_near, perturb, epsilon): rays_o, rays_d = rays.origins, rays.viewdirs # rays.directions isn't normalized device = rays_o.device prefix = rays_o.shape[:-1] rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # N = B * N, in fact device = rays_o.device # choose aabb aabb = primitive.geometry.aabb # sample steps nears, fars = near_far_from_aabb(rays_o, rays_d, aabb, min_near) nears.unsqueeze_(-1) fars.unsqueeze_(-1) #print(f'nears = {nears.min().item()} ~ {nears.max().item()}, fars = {fars.min().item()} ~ {fars.max().item()}') z_vals = torch.linspace(0.0, 1.0, num_steps, device=device).unsqueeze(0) # [1, T] z_vals = z_vals.expand((N, num_steps)) # [N, T] z_vals = nears + (fars - nears) * z_vals # [N, T], in [nears, fars] # perturb z_vals sample_dist = (fars - nears) / num_steps if perturb: z_vals = z_vals + (torch.rand(z_vals.shape, device=device) - 0.5) * sample_dist #z_vals = z_vals.clamp(nears, fars) # avoid out of bounds xyzs. # sample from depth prior, N(d_prior, epsilon) extra_z_vals = torch.zeros_like(z_vals) valid_depth = rays.depth[rays.mask] m = torch.distributions.Normal(valid_depth, epsilon*torch.ones_like(valid_depth)) depth_samples = m.sample(torch.Size([num_steps])) extra_z_vals[rays.mask.squeeze()] = depth_samples.T # sample from m = torch.distributions.Uniform(nears[~rays.mask], fars[~rays.mask]) pad_samples = m.sample(torch.Size([num_steps])) extra_z_vals[~rays.mask.squeeze()] = pad_samples.T z_vals, _ = torch.sort(torch.cat([z_vals, extra_z_vals], -1), -1) # generate xyzs pts = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(-1) # [N, 1, 3] * [N, T, 1] -> [N, T, 3] pts = torch.min(torch.max(pts, aabb[:3]), aabb[3:]) # a manual clip. # Convert these values using volume rendering (Section 4) deltas = delta_from_zval(z_vals, rays_d, sample_dist) views = rays.viewdirs[...,None,:].expand(pts.shape) return SamplerResult(pts, views, z_vals, deltas) def sparsity_sampler(rays: Rays, primitive, n_sp): device = rays.origins.device bound = primitive.geometry.bound sp_points = torch.empty((n_sp, 3), device=device) sp_points.uniform_(-bound, bound) return SamplerResult(sp_points, None, None, None) raysampler = FunctionRegistry( uniform_sampler=uniform_sampler, importance_sampler=importance_sampler, importance_sampler_mipnerf360=importance_sampler_mipnerf360, instant_ngp_sampler=ngp_uniform_sampler, ngp_uniform_sampler=ngp_uniform_sampler, ngp_importance_sampler=ngp_importance_sampler, ngp_sampler_with_depth=ngp_sampler_with_depth, sparsity_sampler=sparsity_sampler )

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null

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null

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4f8a224b51e6689597f171d5894a74164eaa371f

754

py

Python

patchalerts/games/fortnite.py

silentdot/PatchAlerts

eee0ece48a39dcf16f6b4d6ea3335e56e1082d78

[ "MIT" ]

8

2019-07-08T05:59:50.000Z

2020-08-26T23:13:15.000Z

patchalerts/games/fortnite.py

silentdot/PatchAlerts

eee0ece48a39dcf16f6b4d6ea3335e56e1082d78

[ "MIT" ]

1

2020-05-27T21:10:54.000Z

2020-05-28T02:23:03.000Z

patchalerts/games/fortnite.py

silentdot/PatchAlerts

eee0ece48a39dcf16f6b4d6ea3335e56e1082d78

[ "MIT" ]

4

2018-11-20T07:30:25.000Z

2020-06-24T20:23:39.000Z

from util import loader from wrappers.update import Update from games.base_class import Game class Fortnite(Game): def __init__(self): super().__init__("Fortnite", homepage='https://www.epicgames.com/fortnite/') def scan(self): soup = loader.soup("https://www.epicgames.com/fortnite/en-US/patch-notes/") # Follow redirect to latest. _title = soup.find(attrs={'property': "og:title"})['content'] _desc = soup.find(attrs={'class': "patch-notes-text"}).get_text('\n') _img = soup.find(attrs={'property': "og:image"})['content'] _url = loader.get_redirect() yield Update(game=self, update_name=_title, post_url=_url, desc=_desc, image=_img, color="#1c237a") if __name__ == "__main__": lol = Fortnite() for u in lol.scan(): print(u)

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4f8b3b75523296d05c8c3a2f6a1a0602de5b94e6

469

py

Python

tests/test_base_model.py

jpscaletti/sqlalchemy-wrapper

049d150ca95e24e532bb1b64a9454683dc8e62c6

[ "BSD-3-Clause" ]

39

2016-01-01T02:44:15.000Z

2018-12-10T10:32:28.000Z

tests/test_base_model.py

jpscaletti/sqlalchemy-wrapper

049d150ca95e24e532bb1b64a9454683dc8e62c6

[ "BSD-3-Clause" ]

10

2016-01-09T15:05:30.000Z

2018-02-14T21:15:40.000Z

tests/test_base_model.py

jpscaletti/sqlalchemy-wrapper

049d150ca95e24e532bb1b64a9454683dc8e62c6

[ "BSD-3-Clause" ]

13

2015-12-02T23:20:19.000Z

2018-01-15T06:57:08.000Z

def test_fill(dbs, TestModelA): obj = dbs.create(TestModelA, title="Remember") obj.fill(title="lorem ipsum") dbs.commit() updated = dbs.first(TestModelA) assert updated.title == "lorem ipsum" def test_repr(dbs, TestModelA): obj = dbs.create(TestModelA, title="Hello world") dbs.commit() repr = str(obj) assert f"<TestModelA #{id(obj)}" in repr assert f"\n id = {obj.id}" in repr assert "\n title = 'Hello world'" in repr

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null

0

null

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4f8d5d0caa86ba142e07a4811bdd1e8a34863f11

3,485

py

Python

sharpy/plans/acts/protoss/act_defensive_cannons.py

eladyaniv01/sharpy-sc2

91119cc3e3fce683c2dbe9687c616c9cc0461b06

[ "MIT" ]

null

sharpy/plans/acts/protoss/act_defensive_cannons.py

eladyaniv01/sharpy-sc2

91119cc3e3fce683c2dbe9687c616c9cc0461b06

[ "MIT" ]

null

sharpy/plans/acts/protoss/act_defensive_cannons.py

eladyaniv01/sharpy-sc2

91119cc3e3fce683c2dbe9687c616c9cc0461b06

[ "MIT" ]

null

from typing import Optional from sharpy.general.zone import Zone from sc2 import UnitTypeId from sc2.position import Point2 from sc2.unit import Unit from sharpy.plans.acts.act_base import ActBase class ActDefensiveCannons(ActBase): """Act of starting to build new buildings up to specified count""" def __init__(self, to_count_pre_base: int, additional_batteries: int = 0, to_base_index: Optional[int] = None): self.to_base_index = to_base_index self.additional_batteries = additional_batteries assert to_count_pre_base is not None and isinstance(to_count_pre_base, int) and (to_count_pre_base > 0 or additional_batteries > 0) self.to_count_per_base = to_count_pre_base super().__init__() async def execute(self) -> bool: map_center = self.ai.game_info.map_center pending_cannon_count = self.pending_build(UnitTypeId.PHOTONCANNON) pending_battery_count = self.pending_build(UnitTypeId.SHIELDBATTERY) all_ready = True # Go through zones so that furthest expansions are fortified first zones = self.knowledge.expansion_zones for i in range(0, len(zones)): zone = zones[i] # Filter out zones that aren't ours and own zones that we are about to lose. if zone.our_townhall is None or zone.known_enemy_power.ground_power > zone.our_power.ground_presence: continue if self.to_base_index is not None and i != self.to_base_index: # Defenses are not ordered to that base continue closest_pylon: Unit = None pylons = zone.our_units(UnitTypeId.PYLON) if pylons.exists: closest_pylon = pylons.closest_to(zone.center_location) available_minerals = self.ai.minerals - self.knowledge.reserved_minerals can_afford_cannon = available_minerals >= 150 can_afford_battery = available_minerals >= 100 if closest_pylon is None or closest_pylon.distance_to(zone.center_location) > 10: # We need a pylon, but only if one isn't already on the way if not self.pending_build(UnitTypeId.PYLON) and can_afford_battery: await self.ai.build(UnitTypeId.PYLON, near=zone.center_location.towards(map_center, 4)) all_ready = False continue if zone.our_photon_cannons.amount + pending_cannon_count < self.to_count_per_base: all_ready = False if closest_pylon.is_ready and can_afford_cannon: pos = self.defense_position(zone, closest_pylon) await self.ai.build(UnitTypeId.PHOTONCANNON, near=pos) if zone.our_batteries.amount + pending_battery_count < self.additional_batteries: all_ready = False if closest_pylon.is_ready and can_afford_battery: pos = self.defense_position(zone, closest_pylon) await self.ai.build(UnitTypeId.SHIELDBATTERY, near=pos) return all_ready def defense_position(self, zone: Zone, pylon: Unit): position: Point2 = pylon.position path = zone.paths.get(self.knowledge.enemy_main_zone.zone_index, None) if path and path.distance > 50: target_pos = path.get_index(10) return position.towards(target_pos, 3) return position.towards(zone.center_location, -2)

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null

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null

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4f8f018df0b0fd5b16b4fc6fe2287fa62ce54ad9

5,004

py

Python

project/image_patch/__init__.py

delldu/ImagePatch

aaeadba9fe9f40e9bf900468f100a06bafc8231f

[ "MIT" ]

1

2020-07-22T04:18:28.000Z

project/image_patch/__init__.py

delldu/Patch

aaeadba9fe9f40e9bf900468f100a06bafc8231f

[ "MIT" ]

null

project/image_patch/__init__.py

delldu/Patch

aaeadba9fe9f40e9bf900468f100a06bafc8231f

[ "MIT" ]

null

"""Image/Video Patch Package.""" # coding=utf-8 # # /************************************************************************************ # *** # *** Copyright Dell 2021, 2022(18588220928@163.com) All Rights Reserved. # *** # *** File Author: Dell, 2021年 12月 14日星期二 00:22:28 CST # *** # ************************************************************************************/ # __version__ = "1.0.0" import os from tqdm import tqdm import torch import redos import todos from . import patch import pdb PATCH_ZEROPAD_TIMES = 128 def get_model(): """Create model.""" model_path = "models/image_patch.pth" cdir = os.path.dirname(__file__) checkpoint = model_path if cdir == "" else cdir + "/" + model_path model = patch.ImagePatchModel(4, 3) todos.model.load(model, checkpoint) device = todos.model.get_device() model = model.to(device) model.eval() model = torch.jit.script(model) todos.data.mkdir("output") if not os.path.exists("output/image_patch.torch"): model.save("output/image_patch.torch") return model, device def model_forward(model, device, input_tensor): # zeropad for model H, W = input_tensor.size(2), input_tensor.size(3) if H % PATCH_ZEROPAD_TIMES != 0 or W % PATCH_ZEROPAD_TIMES != 0: input_tensor = todos.data.zeropad_tensor(input_tensor, times=PATCH_ZEROPAD_TIMES) output_tensor = todos.model.forward(model, device, input_tensor) output_tensor = output_tensor[:, :, 0:H, 0:W] mask = torch.ones_like(output_tensor) return torch.cat((output_tensor, mask[:, 0:1, :, :]), dim=1) def image_client(name, input_files, output_dir): redo = redos.Redos(name) cmd = redos.image.Command() image_filenames = todos.data.load_files(input_files) for filename in image_filenames: output_file = f"{output_dir}/{os.path.basename(filename)}" context = cmd.patch(filename, output_file) redo.set_queue_task(context) print(f"Created {len(image_filenames)} tasks for {name}.") def image_server(name, host="localhost", port=6379): # load model model, device = get_model() def do_service(input_file, output_file, targ): print(f" clean {input_file} ...") try: input_tensor = todos.data.load_rgba_tensor(input_file) output_tensor = model_forward(model, device, input_tensor) todos.data.save_tensor(output_tensor, output_file) return True except Exception as e: print("exception: ", e) return False return redos.image.service(name, "image_patch", do_service, host, port) def image_predict(input_files, output_dir): # Create directory to store result todos.data.mkdir(output_dir) # load model model, device = get_model() # load files image_filenames = todos.data.load_files(input_files) # start predict progress_bar = tqdm(total=len(image_filenames)) for filename in image_filenames: progress_bar.update(1) # orig input input_tensor = todos.data.load_rgba_tensor(filename) # pytorch recommand clone.detach instead of torch.Tensor(input_tensor) orig_tensor = input_tensor.clone().detach() predict_tensor = model_forward(model, device, input_tensor) output_file = f"{output_dir}/{os.path.basename(filename)}" todos.data.save_tensor([orig_tensor, predict_tensor], output_file) def video_service(input_file, output_file, targ): # load video video = redos.video.Reader(input_file) if video.n_frames < 1: print(f"Read video {input_file} error.") return False # Create directory to store result output_dir = output_file[0 : output_file.rfind(".")] todos.data.mkdir(output_dir) # load model model, device = get_model() print(f" clean {input_file}, save to {output_file} ...") progress_bar = tqdm(total=video.n_frames) def clean_video_frame(no, data): # print(f"frame: {no} -- {data.shape}") progress_bar.update(1) input_tensor = todos.data.frame_totensor(data) # keep tensor 1x4xHxW output_tensor = model_forward(model, device, input_tensor) temp_output_file = "{}/{:06d}.png".format(output_dir, no) todos.data.save_tensor(output_tensor, temp_output_file) video.forward(callback=clean_video_frame) redos.video.encode(output_dir, output_file) # delete temp files for i in range(video.n_frames): temp_output_file = "{}/{:06d}.png".format(output_dir, i) os.remove(temp_output_file) return True def video_client(name, input_file, output_file): cmd = redos.video.Command() context = cmd.patch(input_file, output_file) redo = redos.Redos(name) redo.set_queue_task(context) print(f"Created 1 video tasks for {name}.") def video_server(name, host="localhost", port=6379): return redos.video.service(name, "video_patch", video_service, host, port)

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