xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

033ac5b42cb933ad3ddd01f9391bd47273e14726

8,611

py

Python

examples/inference/python/test/ls_quant_gpt2.py

hexisyztem/lightseq

25265dabaaee42ee9e7b7ec43c8c04fb90292733

[ "Apache-2.0" ]

106

2019-12-06T09:02:58.000Z

2020-09-09T07:12:21.000Z

examples/inference/python/test/ls_quant_gpt2.py

hexisyztem/lightseq

25265dabaaee42ee9e7b7ec43c8c04fb90292733

[ "Apache-2.0" ]

null

examples/inference/python/test/ls_quant_gpt2.py

hexisyztem/lightseq

25265dabaaee42ee9e7b7ec43c8c04fb90292733

[ "Apache-2.0" ]

15

2019-12-09T05:44:28.000Z

2020-09-04T03:43:56.000Z

import time import torch from torch import nn from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config import lightseq.inference as lsi from lightseq.training.ops.pytorch.quantization import ( qat_mode, QuantLinear, TensorQuantizer, weight_quant_config, ) from lightseq.training.ops.pytorch.torch_transformer_layers import ( TransformerDecoderLayer, ) from export.util import parse_args def ls_gpt2(model, inputs, generation_method="topk"): torch.cuda.synchronize() start_time = time.perf_counter() results = None if generation_method == "topk" or generation_method == "topp": results = model.sample(inputs) elif generation_method == "ppl": results = model.ppl(inputs)[0] torch.cuda.synchronize() end_time = time.perf_counter() return results, end_time - start_time def compute_hf_ppl(model, inputs): max_length = 512 stride = 512 end_loc = 0 nlls = [] for i in range(0, inputs.size(1), stride): begin_loc = max(i + stride - max_length, 0) end_loc = min(i + stride, inputs.size(1)) trg_len = end_loc - i input_ids = inputs[:, begin_loc:end_loc].to("cuda:0") target_ids = input_ids.clone() target_ids[:, :-trg_len] = -100 with torch.no_grad(): outputs = model(input_ids, labels=target_ids) neg_log_likelihood = outputs[0] * trg_len nlls.append(neg_log_likelihood) ppl = torch.stack(nlls).sum() / end_loc return ppl.cpu().numpy() def hf_gpt2(model, inputs, tokenizer, generation_method="topk"): inputs = inputs.to("cuda:0") torch.cuda.synchronize() start_time = time.perf_counter() results = None if generation_method == "topk" or generation_method == "topp": results = model.generate( inputs, max_length=50, pad_token_id=tokenizer.eos_token_id ) elif generation_method == "ppl": results = compute_hf_ppl(model, inputs) torch.cuda.synchronize() end_time = time.perf_counter() return results, end_time - start_time def ls_generate(model, tokenizer, inputs): print("=========lightseq=========") print("lightseq generating...") ls_res_ids, ls_time = ls_gpt2(model, inputs) ls_res = tokenizer.batch_decode(ls_res_ids, skip_special_tokens=True) print(f"lightseq time: {ls_time}s") print("lightseq results:") for sent in ls_res: print(sent) def hf_generate(model, tokenizer, inputs): print("=========huggingface=========") print("huggingface generating...") hf_res_ids, hf_time = hf_gpt2(model, inputs, tokenizer) hf_res = tokenizer.batch_decode(hf_res_ids, skip_special_tokens=True) print(f"huggingface time: {hf_time}s") print("huggingface results:") for sent in hf_res: print(sent) def ls_ppl(model, tokenizer, inputs): print("=========lightseq=========") print("lightseq calculating ppl...") ls_ppl, ls_time = ls_gpt2(model, inputs, "ppl") print(f"lightseq time: {ls_time}s") print("lightseq results:") print(ls_ppl) def hf_ppl(model, tokenizer, inputs): print("=========huggingface=========") print("huggingface calculating ppl...") hf_ppl, hf_time = hf_gpt2(model, inputs, tokenizer, "ppl") print(f"huggingface time: {hf_time}s") print("huggingface results:") print(hf_ppl) def warmup( ls_tokenizer, hf_tokenizer, ls_model, hf_model, sentences, generation_method ): ls_inputs = ls_tokenizer(sentences, return_tensors="pt", padding=True)["input_ids"] hf_inputs = hf_tokenizer(sentences, return_tensors="pt", padding=True)["input_ids"] if generation_method == "topk" or generation_method == "topp": ls_generate(ls_model, ls_tokenizer, ls_inputs) # hf_generate(hf_model, hf_tokenizer, hf_inputs) elif generation_method == "ppl": ls_ppl(ls_model, ls_tokenizer, ls_inputs) hf_ppl(hf_model, hf_tokenizer, hf_inputs) class GptEmbedding(nn.Embedding): def __init__(self, *args, **kwargs): super(GptEmbedding, self).__init__(*args, **kwargs) self.emb_quant = TensorQuantizer(weight_quant_config) def forward(self, input_ids): x = super(GptEmbedding, self).forward(input_ids) x = self.emb_quant(x) return x def gen_gpt_enc_config(config): gpt_enc_config = TransformerDecoderLayer.get_config( max_batch_tokens=8192, max_seq_len=config.max_position_embeddings, hidden_size=config.hidden_size, intermediate_size=4 * config.hidden_size, nhead=config.num_attention_heads, attn_prob_dropout_ratio=config.attn_pdrop, activation_dropout_ratio=config.resid_pdrop, hidden_dropout_ratio=config.resid_pdrop, pre_layer_norm=True, fp16=True, local_rank=0, nlayer=config.num_hidden_layers, activation_fn="gelu", has_cross_attn=False, ) return gpt_enc_config class LSHFGptEncoderLayer(TransformerDecoderLayer): def __init__(self, *args, **kwargs): super(LSHFGptEncoderLayer, self).__init__(*args, **kwargs) def forward(self, hidden_states, attention_mask=None, *args, **kwargs): if attention_mask is not None: ls_attention_mask = attention_mask.squeeze() else: ls_attention_mask = torch.zeros(hidden_states.size()[:2]) output = super().forward(hidden_states, ls_attention_mask) return output def inject_ls_layer(model, config): model.transformer.wte = GptEmbedding(config.vocab_size, config.hidden_size) model.transformer.wte.apply(qat_mode) for i in range(config.num_hidden_layers): gpt_enc_config = gen_gpt_enc_config(config) model.transformer.h[i] = LSHFGptEncoderLayer(gpt_enc_config).cuda() model.transformer.h[i].apply(qat_mode) q_lm_head = QuantLinear(config.n_embd, config.vocab_size, bias=False) q_lm_head.weight = model.transformer.wte.weight q_lm_head.weight_quant = model.transformer.wte.emb_quant model.lm_head = q_lm_head def main(): args = parse_args() if args.generation_method not in ["topk", "topp", "ppl"]: args.generation_method = "topk" model_name = ".".join(args.model.split(".")[:-1]) ckpt_path = f"{model_name}.bin" print("initializing gpt2 config...") config = GPT2Config.from_pretrained("gpt2") print("initializing gpt2 tokenizer...") ls_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") # lightseq use len(tokenizer) as pad_token in default ls_tokenizer.add_special_tokens({"pad_token": "[PAD]"}) print(f"lightseq tokenizer pad token id: {ls_tokenizer.pad_token_id}") hf_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") # use EOS as PAD for huggingface to avoid warning according to https://huggingface.co/blog/how-to-generate while avoid reshaping the model embedding hf_tokenizer.pad_token = hf_tokenizer.eos_token print(f"huggingface tokenizer pad token id: {hf_tokenizer.pad_token_id}") print("creating huggingface model...") hf_model = GPT2LMHeadModel.from_pretrained("gpt2", config=config) inject_ls_layer(hf_model, config) state_dict = torch.load(ckpt_path, map_location="cpu") hf_model.load_state_dict(state_dict, strict=False) hf_model.to("cuda:0") hf_model.eval() print("creating lightseq model...") ls_model = lsi.QuantGpt(args.model, max_batch_size=16) # lightseq gpt perplexity supports batch infer with different lengths, # but sampling doesn't support sentences = [ "I love you, but you say that", "I love you, but you say that", "I love you, but you say that", "I love you, but you say that", ] print("====================START warmup====================") warmup( ls_tokenizer, hf_tokenizer, ls_model, hf_model, sentences, args.generation_method, ) print("====================END warmup====================") print("tokenizing the sentences...") ls_inputs = ls_tokenizer(sentences, return_tensors="pt", padding=True)["input_ids"] hf_inputs = hf_tokenizer(sentences, return_tensors="pt", padding=True)["input_ids"] if args.generation_method == "topk" or args.generation_method == "topp": ls_generate(ls_model, ls_tokenizer, ls_inputs) # hf_generate(hf_model, hf_tokenizer, hf_inputs) elif args.generation_method == "ppl": ls_ppl(ls_model, ls_tokenizer, ls_inputs) hf_ppl(hf_model, hf_tokenizer, hf_inputs) if __name__ == "__main__": main()

34.170635

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8,611

4.929338

0.205725

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0.405915

0.335329

0.306659

0.259481

0.24569

0

0.007518

0.196725

8,611

251

153

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0.789215

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false

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0.040609

0

0.15736

0.147208

0

null

0

null

0

1

0

033c4e6e47b886b0874ca7cd97804e2f650bcb80

623

py

Python

algorithms/153. Find Minimum in Rotated Sorted Array.py

vuzway9132/leetcode

e51a9ce7a6bb3e35c0fcb8c8f4f6cd5763708dbf

[ "MIT" ]

1

2020-12-02T13:54:30.000Z

algorithms/153. Find Minimum in Rotated Sorted Array.py

vuzway9132/leetcode

e51a9ce7a6bb3e35c0fcb8c8f4f6cd5763708dbf

[ "MIT" ]

null

algorithms/153. Find Minimum in Rotated Sorted Array.py

vuzway9132/leetcode

e51a9ce7a6bb3e35c0fcb8c8f4f6cd5763708dbf

[ "MIT" ]

null

""" 1. Clarification 2. Possible solutions - Cheat - Binary search II 3. Coding 4. Tests """ # T=O(n), S=O(1) class Solution: def findMin(self, nums: List[int]) -> int: if not nums: return int(-inf) return min(nums) # T=O(lgn), S=O(1) class Solution: def findMin(self, nums: List[int]) -> int: if not nums: return int(-inf) left, right = 0, len(nums) - 1 while left < right: mid = left + (right - left) // 2 if nums[mid] < nums[right]: right = mid else: left = mid + 1 return nums[left]

20.766667

46

0.507223

86

623

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0.085443

0.018987

0.050633

0.411392

0

0.024938

0.35634

623

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false

0

0.4

0

null

0

null

0

1

0

033cec1f9ccce81f5733fb9f495e2a48d763be85

1,766

py

Python

Web/gRPC/python_practical_example/quote_service/test_cowsay_client.py

MasuqaT-NET/BlogSamples

b424b51e1c01e65f952099cfd1fa05f9ef405432

[ "MIT" ]

18

2018-01-03T23:07:26.000Z

2021-12-30T11:44:43.000Z

Web/gRPC/python_practical_example/quote_service/test_cowsay_client.py

MasuqaT-NET/BlogSamples

b424b51e1c01e65f952099cfd1fa05f9ef405432

[ "MIT" ]

null

Web/gRPC/python_practical_example/quote_service/test_cowsay_client.py

MasuqaT-NET/BlogSamples

b424b51e1c01e65f952099cfd1fa05f9ef405432

[ "MIT" ]

6

2018-08-09T05:17:13.000Z

2020-05-07T09:45:33.000Z

import time from unittest import TestCase import grpc_testing from grpc import StatusCode from grpc.framework.foundation import logging_pool from cowsay_client import CowsayClient from cowsay_pb2 import DESCRIPTOR as COWSAY_DESCRIPTOR, QuoteRequest, QuoteResponse from cowsay_pb2_grpc import CowsayStub target_service = COWSAY_DESCRIPTOR.services_by_name['Cowsay'] class TestCowsayClient(TestCase): def setUp(self): self._client_execution_thread_pool = logging_pool.pool(1) self._fake_time = grpc_testing.strict_fake_time(time.time()) self._real_time = grpc_testing.strict_real_time() self._fake_time_channel = grpc_testing.channel(COWSAY_DESCRIPTOR.services_by_name.values(), self._fake_time) self._real_time_channel = grpc_testing.channel(COWSAY_DESCRIPTOR.services_by_name.values(), self._real_time) def tearDown(self): self._client_execution_thread_pool.shutdown(wait=False) def test_get_quote(self): arguments = ('cow', 'foo') def run(scenario, channel): stub = CowsayStub(channel) client = CowsayClient(stub) return client.get_quote(*scenario) f = self._client_execution_thread_pool.submit(run, arguments, self._real_time_channel) invocation_metadata, request, rpc = self._real_time_channel.take_unary_unary( target_service.methods_by_name['GetQuote']) self.assertEqual(QuoteRequest(message='foo', animal=QuoteRequest.COW), request) self.assertIn(('z', 'y'), invocation_metadata) rpc.send_initial_metadata([('abc', 'def')]) rpc.terminate(QuoteResponse(output='foo2'), [('uvw', 'xyz')], StatusCode.OK, '') result = f.result() self.assertEqual('foo2', result)

36.791667

116

0.725934

215

1,766

5.637209

0.367442

0.039604

0.049505

0.064356

0.216997

0.168317

0.113861

0

0.003427

0.173839

1,766

47

117

37.574468

0.827279

0

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false

0

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0

0.424242

0

null

0

null

0

1

0

03401c816f8f4430f92c4b87cef1da6e317a54e9

5,140

py

Python

scripts/alpha_diversity_stats.py

dcdanko/MetaSUB_CAP

db5672b0206afb3ffe3204b0577a4a5f84b9bcd4

[ "MIT" ]

20

2017-11-02T13:36:16.000Z

2021-07-23T12:44:28.000Z

scripts/alpha_diversity_stats.py

dcdanko/MetaSUB_CAP

db5672b0206afb3ffe3204b0577a4a5f84b9bcd4

[ "MIT" ]

30

2018-02-22T18:25:02.000Z

2019-11-06T15:03:34.000Z

scripts/alpha_diversity_stats.py

dcdanko/MetaSUB_CAP

db5672b0206afb3ffe3204b0577a4a5f84b9bcd4

[ "MIT" ]

9

2018-04-26T22:12:08.000Z

2020-08-06T01:04:54.000Z

#! /usr/bin/env python3 import sys import math import argparse as ap from json import dumps as jdumps from random import choices class LevelNotFoundException(Exception): pass def checkLevel(taxon, level): if level == 'species': return ('s__' in taxon) and ('t__' not in taxon) elif level == 'genus': return ('g__' in taxon) and ('s__' not in taxon) raise LevelNotFoundException() class Sample: def __init__(self, tool, level): self.tool = tool self.level = level self.abunds = {} self._total = None def addLine(self, line): taxon, abund = line.split() if checkLevel(taxon, self.level): self.abunds[taxon] = float(abund) @classmethod def parseMPA(ctype, tool, mpaFile, level): sample = Sample(tool, level) with open(mpaFile) as mF: for line in mF: sample.addLine(line) return sample def subset(self, n): if n == self.total(): return self brkpoints = [0] rmap = {} for i, (key, val) in enumerate(self.abunds.items()): brkpoints.append(brkpoints[i] + val) rmap[i] = key i = 0 outAbunds = {} indices = range(int(self.total())) indices = sorted(choices(indices, k=n)) for ind in indices: while ind >= brkpoints[i + 1]: i += 1 key = rmap[i] try: outAbunds[key] += 1 except KeyError: outAbunds[key] = 1 outSamp = Sample(self.tool, self.level) outSamp.abunds = outAbunds return outSamp def total(self): if self._total is None: self._total = sum(self.abunds.values()) return self._total def richness(self): return len(self.abunds) def shannonIndex(self): H = 0 for count in self.abunds.values(): p = count / self.total() assert p <= 1 H += p * math.log(p) if H < 0: H *= -1 return H def ginisimpson(self): H = 0 for count in self.abunds.values(): p = count / self.total() assert p <= 1 H += p * p H = 1 - H return H def chao1(self): sings, doubs = 0, 1 # give doubles a pseudocount to avoid div by zero for val in self.abunds.values(): if val == 1: sings += 1 elif val == 2: doubs += 1 est = (sings * sings) / (2 * doubs) return self.richness() + est def getSubsets(N): vals = [1, 5, 10, 100, 500, 1000, 10 * 1000] vals = [el * 1000 for el in vals] out = [] for val in vals: if val < N: out.append(val) else: out.append(N) break return out def handleCounts(tool, fname): obj = { 'species': { 'richness': {}, 'shannon_index': {}, 'gini-simpson': {}, 'chao1': {} }, 'genus': { 'richness': {}, 'shannon_index': {}, 'gini-simpson': {}, 'chao1': {} } } for level in obj.keys(): sample = Sample.parseMPA(tool, fname, level) for subsetSize in getSubsets(sample.total()): subsample = sample.subset(subsetSize) key = str(subsetSize) if subsample == sample: key = 'all_reads' obj[level]['shannon_index'][key] = subsample.shannonIndex() obj[level]['richness'][key] = subsample.richness() obj[level]['gini-simpson'][key] = subsample.ginisimpson() obj[level]['chao1'][key] = subsample.chao1() return obj def handleProportions(tool, fname): obj = { 'species': { 'richness': {}, 'shannon_index': {}, 'gini-simpson': {} }, 'genus': { 'richness': {}, 'shannon_index': {}, 'gini-simpson': {} } } for level in obj.keys(): sample = Sample.parseMPA(tool, fname, level) key = 'all_reads' obj[level]['richness'][key] = sample.richness() obj[level]['shannon_index'][key] = sample.shannonIndex() obj[level]['gini-simpson'][key] = sample.ginisimpson() return obj def main(): args = parseArgs() outobj = {} for mpaFilePair in args.mpa_files: tool, mpaFile = mpaFilePair.split(',') if tool.lower() == 'kraken': outobj['kraken'] = handleCounts(tool, mpaFile) elif tool.lower() == 'metaphlan2': outobj['metaphlan2'] = handleProportions(tool, mpaFile) else: sys.stderr.write('tool {} unsupported'.format(tool)) sys.stdout.write(jdumps(outobj)) def parseArgs(): parser = ap.ArgumentParser() parser.add_argument('mpa_files', nargs='+', help='pairs of tool_name,mpa_file') args = parser.parse_args() return args if __name__ == '__main__': main()

26.091371

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4.642729

0.281867

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0.037123

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0

0.015834

0.361089

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0

0.01227

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0.092025

false

0.006135

0.030675

0.006135

0.220859

0

null

0

null

0

1

0

0343d8a19a30189bafdda72fb63bd75d656dfc89

1,061

py

Python

image_collection/migrations/0004_auto_20160113_0458.py

bitmazk/django-image-collection

73b05ff825d74bdab64609b531d5305f9332b702

[ "MIT" ]

null

image_collection/migrations/0004_auto_20160113_0458.py

bitmazk/django-image-collection

73b05ff825d74bdab64609b531d5305f9332b702

[ "MIT" ]

null

image_collection/migrations/0004_auto_20160113_0458.py

bitmazk/django-image-collection

73b05ff825d74bdab64609b531d5305f9332b702

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import image_collection.models class Migration(migrations.Migration): dependencies = [ ('image_collection', '0003_auto_20160113_0445'), ] operations = [ migrations.RemoveField( model_name='imageslide', name='link', ), migrations.AddField( model_name='imageslide', name='external_link', field=models.URLField(help_text='E.g. "http://www.example.com/my-page/". Enter absolute URL, that the image should link to.', verbose_name='external link', blank=True), preserve_default=True, ), migrations.AddField( model_name='imageslide', name='internal_link', field=image_collection.models.RelativeURLField(help_text='E.g. "/my-page/". Enter slug of internal pager, that the image should link to.', verbose_name='internal link', blank=True), preserve_default=True, ), ]

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null

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null

0

1

0

0344447ad9ea2ba9e5f78647ef0e90f1b7786110

7,913

py

Python

hw1/hollygrimm_behavior_cloner.py

andyk/homework

6f31240e7b16bb94992a87fc764839591bd034af

[ "MIT" ]

null

hw1/hollygrimm_behavior_cloner.py

andyk/homework

6f31240e7b16bb94992a87fc764839591bd034af

[ "MIT" ]

null

hw1/hollygrimm_behavior_cloner.py

andyk/homework

6f31240e7b16bb94992a87fc764839591bd034af

[ "MIT" ]

null

# Copying Holly Grimm's solution https://github.com/hollygrimm/cs294-homework/blob/master/hw1/bc.py # Copy and pasting and merging it into a copy of my behavior_cloner.py code. import argparse import pickle import os import sys import tensorflow.compat.v1 as tf import numpy as np from sklearn.model_selection import train_test_split import mlflow.tensorflow import gym from gym import wrappers from tqdm import tqdm #Imports copied from hollygrimm's solution import logging from hollygrimm_model import Model # The following doesn't seem to work with the way Holly Grimm builds her tensorflow model. mlflow.tensorflow.autolog() def config_logging(log_file): if os.path.exists(log_file): os.remove(log_file) logger = logging.getLogger() logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(message)s') fh = logging.FileHandler(log_file) fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) logger.addHandler(fh) return logger def create_model(session, obs_samples, num_observations, num_actions, logger, optimizer, learning_rate, restore, checkpoint_dir): model = Model(obs_samples, num_observations, num_actions, checkpoint_dir, logger, optimizer, learning_rate) if restore: model.load(session) else: logger.info("Created model with fresh parameters") session.run(tf.global_variables_initializer()) return model def bc(expert_data_filename, env_name, restore, results_dir, max_timesteps=None, optimizer='adam', num_epochs=100, learning_rate=.001, batch_size=32, keep_prob=1): # Reset TF env tf.reset_default_graph() # Create a gym env. env = gym.make(env_name) max_steps = max_timesteps or env.spec.max_episode_steps with open(expert_data_filename, 'rb') as f: data = pickle.loads(f.read()) obs = np.stack(data['observations'], axis=0) actions = np.squeeze(np.stack(data['actions'], axis=0)) x_train, x_test, y_train, y_test = train_test_split(obs, actions, test_size=0.2) num_samples = len(x_train) min_val_loss = sys.maxsize with tf.Session() as session: model = create_model(session, x_train, x_train.shape[1], y_train.shape[1], logger, optimizer, learning_rate, restore, results_dir) file_writer = tf.summary.FileWriter(results_dir, session.graph) #file_writer = tf.summary.FileWriter(results_dir, session.graph) for epoch in tqdm(range(num_epochs)): perm = np.random.permutation(x_train.shape[0]) obs_samples = x_train[perm] action_samples = y_train[perm] loss = 0. for k in range(0, obs_samples.shape[0], batch_size): batch_loss, training_scalar = model.update(session, obs_samples[k:k + batch_size], action_samples[k:k + batch_size], keep_prob) loss += batch_loss file_writer.add_summary(training_scalar, epoch) min_val_loss, validation_scalar = validate(model, logger, session, x_test, y_test, epoch, batch_size, min_val_loss, results_dir) file_writer.add_summary(validation_scalar, epoch) # Test the updated model after each epoch of training the DNN. new_exp = model.test_run(session, env, max_steps) tqdm.write( "Epoch %3d; Loss %f; Reward %f; Steps %d" % (epoch, loss / num_samples, new_exp['reward'], new_exp['steps'])) # Write a video of the final gym test results. env = wrappers.Monitor(env, results_dir, force=True) results = [] for _ in tqdm(range(10)): results.append(model.test_run(session, env, max_steps)['reward']) logger.info("Reward mean and std dev with behavior cloning: %f(%f)" % (np.mean(results), np.std(results))) mlflow.log_params({"reward_mean": np.mean(results), "reward_std": np.std(results)}) return np.mean(results), np.std(results) def validate(model, logger, session, x_test, y_test, num_epoch, batch_size, min_loss, checkpoint_dir): avg_loss = [] # for k in range(0, x_test.shape[0], batch_size): loss, validation_scalar = model.validate(session, x_test, y_test) avg_loss.append(loss) new_loss = sum(avg_loss) / len(avg_loss) logger.info("Finished epoch %d, average validation loss = %f" % (num_epoch, new_loss)) if new_loss < min_loss: # Only save model if val loss dropped model.save(session) min_loss = new_loss return min_loss, validation_scalar if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('expert_run_id', type=str) parser.add_argument('--num_epochs', type=int, default=100) parser.add_argument('--batch_size', type=int, default=32) parser.add_argument("--restore", type=bool, default=False) args = parser.parse_args() for k, v in vars(args).items(): mlflow.log_param(k, v) if not os.path.exists('results'): os.makedirs('results') log_file = os.path.join(os.getcwd(), 'results', 'train_out.log') logger = config_logging(log_file) #env_models = [('Ant-v1', 'data/Ant-v1_data_250_rollouts.pkl', 'experts/Ant-v1.pkl', 250), # ('HalfCheetah-v1', 'data/HalfCheetah-v1_data_10_rollouts.pkl', 'experts/HalfCheetah-v1.pkl', 10), # ('Hopper-v1', 'data/Hopper-v1_data_10_rollouts.pkl', 'experts/Hopper-v1.pkl', 10), # ('Humanoid-v1', 'data/Humanoid-v1_data_250_rollouts.pkl', 'experts/Humanoid-v1.pkl', 250), # ('Reacher-v1', 'data/Reacher-v1_data_250_rollouts.pkl', 'experts/Reacher-v1.pkl', 250), # ('Walker2d-v1', 'data/Walker2d-v1_data_10_rollouts.pkl','experts/Walker2d-v1.pkl', 10) # ] #for env_name, rollout_data, expert_policy_file, num_rollouts in env_models : # =================================================== # read in dataset from expert policy rollouts. mlflow_c = mlflow.tracking.MlflowClient() expert_data_file_base = mlflow_c.download_artifacts(args.expert_run_id, "") expert_data_file_rel_path = mlflow_c.list_artifacts(args.expert_run_id, "expert_data_file")[ 0].path expert_data_filename = expert_data_file_base + "/" + expert_data_file_rel_path print("opening {0}".format(expert_data_filename)) env_name = mlflow_c.get_run(args.expert_run_id).data.params["envname"] bc_results_dir = os.path.join(os.getcwd(), 'results', env_name, 'bc') bc_reward_mean, bc_reward_std = bc(expert_data_filename, env_name, args.restore, bc_results_dir, batch_size=args.batch_size, num_epochs=args.num_epochs) logger.info('Behavior Cloning mean & std rewards: %f(%f))' % (bc_reward_mean, bc_reward_std)) print("logging 'results' directory to mlflow.") mlflow.log_artifacts('results') # Commenting out dagger for now. #da_results_dir = os.path.join(os.getcwd(), 'results', env_name, 'da') #if not os.path.exists(da_results_dir): # os.makedirs(da_results_dir) #_,_, da_mean,da_std = dagger(rollout_data, expert_policy_file, env_name, args.restore, da_results_dir, num_rollouts) #results.append((env_name, ex_mean, ex_std, bc_mean, bc_std, da_mean, da_std)) #for env_name, ex_mean, ex_std, bc_mean, bc_std, da_mean, da_std in results : # logger.info('Env: %s, Expert: %f(%f), Behavior Cloning: %f(%f), Dagger: %f(%f)'% # (env_name, ex_mean, ex_std, bc_mean, bc_std, da_mean, da_std))

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7,913

4.5

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null

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null

0

1

0

03470a7c37dd728524a1ea76f0cbdccac1e546fa

4,397

py

Python

tree/generate.py

xi-studio/DiscreteNN

85468da14bddfe4cbe2e07071454cdbc52ef915f

[ "MIT" ]

1

2019-08-15T09:43:21.000Z

tree/generate.py

xi-studio/DiscreteNN

85468da14bddfe4cbe2e07071454cdbc52ef915f

[ "MIT" ]

null

tree/generate.py

xi-studio/DiscreteNN

85468da14bddfe4cbe2e07071454cdbc52ef915f

[ "MIT" ]

null

from __future__ import print_function import argparse import torch import torch.utils.data from torch import nn, optim from torch.nn import functional as F from torchvision import datasets, transforms from torchvision.utils import save_image import numpy as np parser = argparse.ArgumentParser(description='VAE MNIST Example') parser.add_argument('--batch-size', type=int, default=128, metavar='N', help='input batch size for training (default: 128)') parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)') parser.add_argument('--no-cuda', action='store_true', default=False, help='enables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if args.cuda else "cpu") kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} train_loader = torch.utils.data.DataLoader( datasets.MNIST('./data', train=True, download=True, transform=transforms.ToTensor()), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST('./data', train=False, transform=transforms.ToTensor()), batch_size=args.batch_size, shuffle=False, **kwargs) norm = torch.nn.functional.normalize class GLU(nn.Module): def __init__(self, c1, c2): super(GLU, self).__init__() self.s = nn.Linear(c1, c2) self.g = nn.Linear(c1, c2) def forward(self, x): s = torch.sigmoid(self.s(x)) g = torch.relu(self.g(x)) output = s * g return output class Encoder(nn.Module): def __init__(self): super(Encoder, self).__init__() self.fc1 = nn.Linear(784, 400) self.fc2 = nn.Linear(400, 50) def forward(self, x): x = torch.relu(self.fc1(x)) phase = torch.sigmoid(self.fc2(x)) return phase class Decoder(nn.Module): def __init__(self): super(Decoder, self).__init__() self.fc1 = GLU(100, 400) self.fc2 = nn.Linear(400, 784) def forward(self, x): x = self.fc1(x) x = torch.sigmoid(self.fc2(x)) return x class Key(nn.Module): def __init__(self): super(Key, self).__init__() self.fc1 = nn.Linear(10, 50) self.fc2 = nn.Linear(50, 50) def forward(self, x): x = torch.relu(self.fc1(x)) w = torch.sigmoid(self.fc2(x)) return w class VAE(nn.Module): def __init__(self): super(VAE, self).__init__() self.e = Encoder() self.d = Decoder() self.amplitude = Key() def forward(self, x, c, t): x = x.view(-1, 784) N = x.shape[0] w = self.amplitude(c) phase = self.e(x) w = w.view(N, 50, 1) phase = phase.view(N, 50, 1) w = w.repeat(1, 1, 100) phase = phase.repeat(1, 1, 100) x = torch.sin(2 * np.pi * w * t + np.pi * phase ) x = x.sum(dim=1) x = x.view(N, 100) noise = torch.randn_like(x) x = noise + x x = self.d(x) return x, w, phase model = VAE().to(device) model.load_state_dict(torch.load('checkpoints/mnist/fft_400.pt')) def test(): model.eval() with torch.no_grad(): t = torch.arange(100) t = t.type(torch.FloatTensor) t = t.to(device) c = torch.zeros(64, 10).to(device) c[:, 4] =1 data = torch.rand(64, 1, 28, 28).to(device) rx, w, phase= model(data, c, t) img = rx.view(64, 1, 28, 28) save_image(img.cpu(), 'images/sample_4.png', nrow=8) # for i in range(100): # rx, w, phase= model(data, c, t) # img = rx.view(1, 1, 28, 28) # save_image(img.cpu(), # 'images/sample_t_%d.png' % i, nrow=1) # data = rx # if __name__ == "__main__": test()

28.185897

83

0.56766

611

4,397

3.9509

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0.03314

0.035211

0.031069

0.314416

0.299917

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0.168186

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null

0

null

0

1

0

0347e93cbe7693fc7a1ee576744efea58d5d82da

4,760

py

Python

wepy/io/yahoo.py

Jul13/wepy

3f6acc7ecb4c9bcadf366d7ed1752660838d9dd7

[ "Apache-2.0" ]

null

wepy/io/yahoo.py

Jul13/wepy

3f6acc7ecb4c9bcadf366d7ed1752660838d9dd7

[ "Apache-2.0" ]

null

wepy/io/yahoo.py

Jul13/wepy

3f6acc7ecb4c9bcadf366d7ed1752660838d9dd7

[ "Apache-2.0" ]

null

# Author: Gheorghe Postelnicu from datetime import date import pandas as pd from io import BytesIO from urllib.request import urlopen class Yahoo(object): # Taken from http://www.jarloo.com/yahoo_finance/ yahoo_query_params = { 'ticker': 's', 'average_daily_volume': 'a2', 'dividend_yield': 'y', 'dividend_per_share': 'd', 'earnings_per_share': 'e', 'est_eps_yr': 'e7', 'est_eps_next_yr': 'e8', 'ex_dividend_date': 'q', 'market_cap': 'j1', 'price_earnings_ratio': 'r', 'short_ratio': 's7', 'volume': 'v', '52w_low': 'j', '52w_high': 'k' } def __init__(self, chunk_size=500): self.chunk_size = chunk_size self.market_cap_pattern = '(\d+[\.]\d+)([MB])' @staticmethod def _convert_market_cap(str_value): if type(str_value) != str: return -1. last_char = str_value[-1] if last_char in ['B', 'M']: base = float(str_value[:-1]) multiplier = 10. ** 9 if last_char == 'B' else 10. ** 6 return base * multiplier return float(str_value) def _fetch_fields(self, symbols, fields): def chunker(symbols_): i = 0 while i < len(symbols_): count_chunk = min(self.chunk_size, len(symbols_) - i) yield symbols_[i:(i + count_chunk)] i += count_chunk dfs = [] for chunk in chunker(symbols): request = 'http://download.finance.yahoo.com/d/quotes.csv?s={}&f={}'.format(','.join(chunk), fields) raw_dat = urlopen(request).read() df = pd.read_csv(BytesIO(raw_dat), header=None) dfs.append(df) ret = pd.concat(dfs) return ret def batch_snapshot(self, tickers): """ Retrieves financial information for a batch of stock symbols. Args: tickers (list<str>): list of stock symbols Returns: pandas.Dataframe: dataframe with one row per symbol. """ ret = self._fetch_fields(tickers, ''.join(Yahoo.yahoo_query_params.values())) ret.columns = Yahoo.yahoo_query_params.keys() for col in ['ex_dividend_date']: ret[col] = pd.to_datetime(ret[col]) ret['market_cap'] = [self._convert_market_cap(mc) for mc in ret.market_cap] return ret @staticmethod def _history_call(ticker, from_date, to_date, params): base_url = 'http://ichart.finance.yahoo.com/table.csv' params.update({'s': ticker, 'a': from_date.month - 1, 'b': from_date.day, 'c': from_date.year, 'd': to_date.month - 1, 'e': to_date.day, 'f': to_date.year }) url = '{}?{}'.format(base_url, '&'.join('{}={}'.format(k, params[k]) for k in params)) raw_dat = urlopen(url).read() df = pd.read_csv(BytesIO(raw_dat), parse_dates=[0]) return df def historic_close(self, tickers, from_date=date(2010, 1, 1), to_date=date.today(), join_type='outer'): """ Extracts the adjusted close for a set of tickers. Args: tickers (list(str)): stock symbol from_date (date): start date to_date (date): end date join_type (str): type of join Returns: Dataframe indexed by date with one column by stock ticker. """ def fetch_adj_close(ticker, from_date_, to_date_): dat = self._single_historic_ohlc(ticker, from_date_, to_date_) dat['Date'] = pd.to_datetime(dat.Date, infer_datetime_format=True) dat.set_index('Date', inplace=True) dat.sort_index(inplace=True) ret = dat[['Adj Close']] ret.columns = [ticker] return ret dats = [fetch_adj_close(ticker, from_date_=from_date, to_date_=to_date) for ticker in tickers] return dats[0].join(dats[1:], how=join_type) def _single_historic_ohlc(self, ticker, from_date=date(2010, 1, 1), to_date=date.today()): return self._history_call(ticker, from_date, to_date, {'g': 'd'}) def historic_dividends(self, ticker, from_date=date(2010, 1, 1), to_date=date.today()): """ Extracts the dividend payout history for an individual stock. Args: ticker (str): stock symbol from_date (date): start date to_date (date): end date Returns: pandas.DataFrame: dataframe with dates and dividends. """ return self._history_call(ticker, from_date, to_date, {'g': 'v'})

36.335878

112

0.563655

602

4,760

4.225914

0.302326

0.04717

0.035377

0.033019

0.216195

0.188679

0.154874

0.142689

0.120676

0

0.013674

0.308613

4,760

130

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36.615385

0.759344

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0

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0

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0

1

0.116279

false

0

0.046512

0.011628

0.302326

0

null

0

null

0

1

0

034a4ded8103d6d6dff5e4e2de1713b5fd8b65e6

1,627

py

Python

physalia/fixtures/models.py

luiscruz/physalia

364951d94e02b60092785db46a8c7a7299ffe2a4

[ "MIT" ]

13

2017-02-14T10:35:43.000Z

2021-12-11T17:33:36.000Z

physalia/fixtures/models.py

luiscruz/physalia

364951d94e02b60092785db46a8c7a7299ffe2a4

[ "MIT" ]

3

2020-02-27T12:07:21.000Z

2021-07-25T12:52:36.000Z

physalia/fixtures/models.py

luiscruz/physalia

364951d94e02b60092785db46a8c7a7299ffe2a4

[ "MIT" ]

3

2019-10-06T14:01:58.000Z

2020-03-13T15:40:30.000Z

"""Fixtures for models module.""" from physalia.models import Measurement import numpy def create_measurement(use_case='login', app_pkg='com.package', duration=2, energy_consumption=30): """Fake data for measurement.""" return Measurement( 1485634263.096069, # timestamp use_case, # use_case app_pkg, # application package '1.0.0', # version 'Nexus 5X', # device model duration, # duration energy_consumption # energy consumption ) def create_random_sample(mean, std, app_pkg='com.package', use_case='login', count=30, seed=1): """Create a sample of measurements.""" # pylint: disable=too-many-arguments if seed is not None: numpy.random.seed(seed) energy_consumptions = numpy.random.normal(loc=mean, scale=std, size=count) return [ create_measurement( energy_consumption=energy_consumptions[i], app_pkg=app_pkg, use_case=use_case ) for i in range(count) ] def create_random_samples(count=30, seed=1): """Create a sample of measurements.""" if seed is not None: numpy.random.seed(seed) sample_a = create_random_sample(10.0, 1.0, count=count, seed=None) sample_b = create_random_sample(12.0, 1.0, count=count, seed=None) return sample_a, sample_b

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178

1,627

4.797753

0.376404

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0.063232

0.037471

0.220141

0.17096

0

0.037791

0.365704

1,627

48

71

33.895833

0.789729

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0

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0

1

0.078947

false

0

0.052632

0

0.210526

0

null

0

null

0

1

0

0354caf01e13d2c8ffb045f382e909588ad189a5

786

py

Python

setup.py

rrosajp/xdcc

e8ffa143cd48745824d077a686bfc0b3f0af6193

[ "MIT" ]

7

2020-06-03T06:24:23.000Z

2022-03-09T13:00:54.000Z

setup.py

thiagotps/xdcc

e8ffa143cd48745824d077a686bfc0b3f0af6193

[ "MIT" ]

3

2020-09-26T12:52:43.000Z

2022-01-22T23:17:19.000Z

setup.py

rrosajp/xdcc

e8ffa143cd48745824d077a686bfc0b3f0af6193

[ "MIT" ]

4

2020-09-26T01:17:00.000Z

2022-02-06T19:22:04.000Z

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="xdcc", version="0.0.3", author="Thiago T. P. Silva", author_email="thiagoteodoro501@gmail.com", description="A simple XDCC downloader written in python3", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/thiagotps/xdcc", packages=setuptools.find_packages(), install_requires = ['irc'], keywords="irc xdcc", entry_points={"console_scripts": ["xdcc=xdcc.__main__:main"]}, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.7', )

30.230769

66

0.661578

91

786

5.538462

0.714286

0.119048

0.075397

0.119048

0

0.015674

0.188295

786

25

67

31.44

0.774295

0

0.398219

0.062341

0

1

0

false

0

0.043478

0

0.043478

0

null

0

null

0

1

0

035543c446ab93f942c4299a0b653ef67467158d

9,319

py

Python

draw-tsp-path.py

wenderlemes/gcc218_trabalho_pratico

e57aab3c1ebcbe92683052994de646d0f76e8eb8

[ "Apache-2.0", "CC-BY-4.0", "MIT" ]

null

draw-tsp-path.py

wenderlemes/gcc218_trabalho_pratico

e57aab3c1ebcbe92683052994de646d0f76e8eb8

[ "Apache-2.0", "CC-BY-4.0", "MIT" ]

null

draw-tsp-path.py

wenderlemes/gcc218_trabalho_pratico

e57aab3c1ebcbe92683052994de646d0f76e8eb8

[ "Apache-2.0", "CC-BY-4.0", "MIT" ]

null

"""Modified code from https://developers.google.com/optimization/routing/tsp#or-tools """ # Copyright Matthew Mack (c) 2020 under CC-BY 4.0: https://creativecommons.org/licenses/by/4.0/ from __future__ import print_function import math from ortools.constraint_solver import routing_enums_pb2 from ortools.constraint_solver import pywrapcp from PIL import Image, ImageDraw import os import time import copy from itertools import permutations # Change these file names to the relevant files. ORIGINAL_IMAGE = "images/brother-1024-stipple.png" IMAGE_TSP = "images/brother-1024-stipple.tsp" # Change the number of points according to the base tsp file you are using. NUMBER_OF_POINTS = 1024 NUMBER_OF_PARTITIONS = 8 INITIAL_VERTEX = 0 def create_data_model(): """Stores the data for the problem.""" # Extracts coordinates from IMAGE_TSP and puts them into an array list_of_nodes = [] with open(IMAGE_TSP) as f: for _ in range(6): next(f) for line in f: i,x,y = line.split() list_of_nodes.append((int(float(x)),int(float(y)))) data = {} # Locations in block units data['locations'] = list_of_nodes # yapf: disable data['num_vehicles'] = 1 data['depot'] = 0 return data def compute_euclidean_distance_matrix(locations): """Creates callback to return distance between points.""" distances = {} for from_counter, from_node in enumerate(locations): distances[from_counter] = {} for to_counter, to_node in enumerate(locations): if from_counter == to_counter: distances[from_counter][to_counter] = 0 else: # Euclidean distance distances[from_counter][to_counter] = (int( math.hypot((from_node[0] - to_node[0]), (from_node[1] - to_node[1])))) return distances def print_solution(manager, routing, solution): """Prints solution on console.""" print('Objective: {}'.format(solution.ObjectiveValue())) index = routing.Start(0) plan_output = 'Route:\n' route_distance = 0 while not routing.IsEnd(index): plan_output += ' {} ->'.format(manager.IndexToNode(index)) previous_index = index index = solution.Value(routing.NextVar(index)) route_distance += routing.GetArcCostForVehicle(previous_index, index, 0) plan_output += ' {}\n'.format(manager.IndexToNode(index)) print(plan_output) plan_output += 'Objective: {}m\n'.format(route_distance) def get_routes(solution, routing, manager): """Get vehicle routes from a solution and store them in an array.""" # Get vehicle routes and store them in a two dimensional array whose # i,j entry is the jth location visited by vehicle i along its route. routes = [] for route_nbr in range(routing.vehicles()): index = routing.Start(route_nbr) route = [manager.IndexToNode(index)] #while not routing.IsEnd(index): # index = solution.Value(routing.NextVar(index)) counter = 0 while counter < len(solution): counter += 1 index = solution[index] route.append(manager.IndexToNode(index)) routes.append(route) return routes[0] def draw_routes(nodes, path): """Takes a set of nodes and a path, and outputs an image of the drawn TSP path""" tsp_path = [] for location in path: tsp_path.append(nodes[int(location)]) original_image = Image.open(ORIGINAL_IMAGE) width, height = original_image.size tsp_image = Image.new("RGBA",(width,height),color='white') tsp_image_draw = ImageDraw.Draw(tsp_image) #tsp_image_draw.point(tsp_path,fill='black') tsp_image_draw.line(tsp_path,fill='black',width=1) tsp_image = tsp_image.transpose(Image.FLIP_TOP_BOTTOM) FINAL_IMAGE = IMAGE_TSP.replace("-stipple.tsp","-tsp.png") tsp_image.save(FINAL_IMAGE) print("TSP solution has been drawn and can be viewed at", FINAL_IMAGE) def nearest_neighbors_solution(distance_matrix): visited = {i: False for i in range(NUMBER_OF_POINTS)} nearest_neighbors = {i: -1 for i in range(NUMBER_OF_POINTS)} last_vertex = INITIAL_VERTEX should_continue = True while should_continue: should_continue = False visited[last_vertex] = True shortest_distance = float("inf") closest_neighbor = -1 for i in distance_matrix[last_vertex]: if distance_matrix[last_vertex][i] < shortest_distance and not (visited[i]): shortest_distance = distance_matrix[last_vertex][i] closest_neighbor = i should_continue = True if should_continue: nearest_neighbors[last_vertex] = closest_neighbor last_vertex = closest_neighbor else: nearest_neighbors[last_vertex] = INITIAL_VERTEX return nearest_neighbors def two_opt_solution(distance_matrix): solution = nearest_neighbors_solution(distance_matrix) original_group = convert_solution_to_group(solution) partitions = NUMBER_OF_PARTITIONS while(partitions > 0): two_opt(distance_matrix, original_group, partitions) partitions = int(partitions / 2) new_solution = convert_group_to_solution(original_group) return new_solution def two_opt(distance_matrix, group, partitions): partition_size = int(len(group)/partitions) for k in range(partitions): while True: min_change = 0 min_i = -1 min_j = -1 for i in range(1 + (k*partition_size), ((k+1)*partition_size)-2): for j in range(i+1, ((k+1)*partition_size)): u = group[i-1] v = group[i] w = group[j] x = group[(j+1) % ((k+1)*partition_size)] current_distance = (distance_matrix[u][v] + distance_matrix[w][x]) new_distance = (distance_matrix[u][w] + distance_matrix[v][x]) change = new_distance - current_distance if change < min_change: min_change = change min_i = i min_j = j swap_edges(group, min_i, min_j) if min_change == 0: break print(min_change) def swap_edges(group, v, w): #Reverses the entire slice, from vertex v to vertex w (including v and w) group[v:w+1] = group[v:w+1][::-1] def convert_group_to_solution(group): solution = {} for i in range(len(group)-1): solution[group[i]] = group[i+1] solution[group[-1]] = NUMBER_OF_POINTS print(solution) return solution def convert_solution_to_group(solution): head = INITIAL_VERTEX group = [] for i in range(NUMBER_OF_POINTS): group.append(head) head = solution[head] return group def calculate_group_cost(distance_matrix, group): cost = 0 for i in range(len(group)): cost += distance_matrix[group[i]][group[(i+1) % len(group)]] return cost def main(): """Entry point of the program.""" starting_moment = time.time() # Instantiate the data problem. print("Step 1/5: Initialising variables") data = create_data_model() # Create the routing index manager. manager = pywrapcp.RoutingIndexManager(len(data['locations']), data['num_vehicles'], data['depot']) # Create Routing Model. routing = pywrapcp.RoutingModel(manager) print("Step 2/5: Computing distance matrix") distance_matrix = compute_euclidean_distance_matrix(data['locations']) def distance_callback(from_index, to_index): """Returns the distance between the two nodes.""" # Convert from routing variable Index to distance matrix NodeIndex. from_node = manager.IndexToNode(from_index) to_node = manager.IndexToNode(to_index) return distance_matrix[from_node][to_node] transit_callback_index = routing.RegisterTransitCallback(distance_callback) # Define cost of each arc. routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index) # Setting first solution heuristic. print("Step 3/5: Setting an initial solution") search_parameters = pywrapcp.DefaultRoutingSearchParameters() search_parameters.first_solution_strategy = ( routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # Solve the problem. print("Step 4/5: Solving") #solution = routing.SolveWithParameters(search_parameters) #solution = nearest_neighbors_solution(distance_matrix) solution = two_opt_solution(distance_matrix) # Print solution on console. if solution: #print_solution(manager, routing, solution) print("Step 5/5: Drawing the solution") routes = get_routes(solution, routing, manager) draw_routes(data['locations'], routes) else: print("A solution couldn't be found :(") finishing_moment = time.time() print("Total time elapsed during execution: " + str(finishing_moment - starting_moment) + " seconds") print("Total distance: " + str(calculate_group_cost(distance_matrix, convert_solution_to_group(solution)))) if __name__ == '__main__': main()

36.402344

111

0.65962

1,172

9,319

5.037543

0.234642

0.054539

0.007114

0.011179

0.166328

0.04895

0.026931

0

0.010317

0.240691

9,319

256

112

36.402344

0.824053

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0

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0

0.066401

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0

1

0.076087

false

0

0.048913

0

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0.081522

0

null

0

null

0

1

0

0357185cdbc8f0ba6e573ad391b048407dabd43e

1,027

py

Python

script/shaderutil.py

xzfn/toy

5d4f6e631c662634a059a4a178174032b01cc81a

[ "MIT" ]

null

script/shaderutil.py

xzfn/toy

5d4f6e631c662634a059a4a178174032b01cc81a

[ "MIT" ]

null

script/shaderutil.py

xzfn/toy

5d4f6e631c662634a059a4a178174032b01cc81a

[ "MIT" ]

null

import os import shadercompiler def spv_folder_to_glsl_folder(spv_folder): return os.path.join(spv_folder, '../../toy/shader') def glsl_from_spv(spv): spv_folder, spv_name = os.path.split(spv) glsl_folder = spv_folder_to_glsl_folder(spv_folder) glsl_name = spv_name[:-4] + '.glsl' glsl = os.path.join(glsl_folder, glsl_name) return glsl def reload_pipelines(pipelines): spv_pipeline_map = {} for pipeline in pipelines: spvs = pipeline.get_shader_spvs() for spv in spvs: spv_pipeline_map.setdefault(spv, set()).add(pipeline) outdated_pipelines = set() for spv in spv_pipeline_map: glsl = glsl_from_spv(spv) if shadercompiler.is_shader_outdated(glsl, spv): res = shadercompiler.compile_glsl(glsl, spv) if not res: print('ERROR reload failed') return outdated_pipelines.update(spv_pipeline_map[spv]) for pipeline in outdated_pipelines: pipeline.reload_shader()

31.121212

65

0.666991

136

1,027

4.742647

0.279412

0.083721

0.086822

0.088372

0.093023

0

0.00128

0.239533

1,027

32

66

32.09375

0.824584

0

0.038986

0

1

0.111111

false

0

0.074074

0.037037

0.296296

0.037037

0

null

0

null

0

1

0

035a72cf11a14ddfaed1b39b4cd2c2f14c4da51e

2,046

py

Python

GeneralizeDEMConsole.py

tsamsonov/generalize-dem

4e180944cd3488654240d47464cf8a0b8a7bc640

[ "Python-2.0", "OLDAP-2.7" ]

16

2017-07-10T15:28:41.000Z

2021-12-30T16:25:06.000Z

GeneralizeDEMConsole.py

tsamsonov/Small-Scale-Terrain-Generalization

4e180944cd3488654240d47464cf8a0b8a7bc640

[ "Python-2.0", "OLDAP-2.7" ]

4

2017-07-17T13:35:25.000Z

2019-12-02T20:15:28.000Z

GeneralizeDEMConsole.py

tsamsonov/generalize-dem

4e180944cd3488654240d47464cf8a0b8a7bc640

[ "Python-2.0", "OLDAP-2.7" ]

null

import sys import time import arcpy import traceback import GeneralizeDEM if __name__ == '__main__': # SET PARAMETERS HERE # -------------------------------------------------------------------- demdataset = 'X:/Work/Scripts & Tools/MY/DEMGEN/mistral' marine = 'X:/Work/Scripts & Tools/MY/DEMGEN/DEMGENEW.gdb/ne_10m_ocean_P' output = 'X:/Work/DEMGEN/DEMGENEW.gdb/mistral_gen2' outputcellsize = 2000 minacc1 = 40 minlen1 = 10 minacc2 = 20 minlen2 = 5 is_widen = True widentype = 'Min/Max' widendist = 4000 filtersize = 5 is_smooth = True is_tiled = True is_parallel = True num_processes = 6 tilesize = 256 is_continued = False continued_folder = 'X:/Work/DEMGEN/scratch1' # -------------------------------------------------------------------- print('> Initializing GeneralizeDEM script...') print('') start = int(time.time()) try: if arcpy.CheckProduct("ArcInfo") == "Available": GeneralizeDEM.execute(demdataset, marine, output, outputcellsize, minacc1, minlen1, minacc2, minlen2, is_widen, widentype, widendist, filtersize, is_smooth, is_tiled, tilesize, num_processes, is_parallel, is_continued, continued_folder) else: msg = 'ArcGIS for Desktop Advanced license not available' arcpy.AddError(msg) except Exception: tb = sys.exc_info()[2] tbinfo = traceback.format_tb(tb)[0] pymsg = "Traceback Info:\n" + tbinfo + "\nError Info:\n " + \ str(sys.exc_type) + ": " + str(sys.exc_value) + "\n" arcpy.AddError(pymsg) print("Processing failed") finish = int(time.time()) seconds = finish - start m, s = divmod(seconds, 60) h, m = divmod(m, 60) print('') print("> Finished in %02d h %02d m %02d s" % (h, m, s)) print('') input("Press Enter to continue...")

31.476923

79

0.540078

215

2,046

5

0.516279

0.018605

0.022326

0.031628

0.046512

0

0.030199

0.287879

2,046

64

80

31.96875

0.707618

0.076735

0

0.056604

0

0.212202

0.068435

0

1

0

false

0

0.09434

0

0.09434

0.113208

0

null

0

null

0

1

0

035c76dc73552701e77d5f647636dafc183b09c4

534

py

Python

reports/migrations/0097_auto_20171006_0951.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

3

2020-04-26T06:28:50.000Z

2021-04-05T08:02:26.000Z

reports/migrations/0097_auto_20171006_0951.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

10

2020-06-05T17:36:10.000Z

2022-03-11T23:16:42.000Z

reports/migrations/0097_auto_20171006_0951.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

5

2021-04-08T08:43:49.000Z

2021-11-27T06:36:46.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('reports', '0096_auto_20170920_1521'), ] operations = [ migrations.AlterField( model_name='reporttype', name='notification_buffer', field=models.FloatField(help_text=b'Radius of buffer that use to find intersects authorities', null=True, blank=True), preserve_default=True, ), ]

25.428571

130

0.644195

55

534

6.036364

0.818182

0

0.042607

0.252809

534

20

131

26.7

0.789474

0.039326

0

0.225049

0.04501

0

1

0

false

0

0.142857

0

0.357143

0

null

0

null

0

1

0

035ca68338c7107344756496893369eeae016a6c

513

py

Python

server/functions/calcdistance.py

yrpmsg/SCSV15

bbe953d676c082f4a30c4d5e0e0cbfcc624d969c

[ "Apache-2.0" ]

null

server/functions/calcdistance.py

yrpmsg/SCSV15

bbe953d676c082f4a30c4d5e0e0cbfcc624d969c

[ "Apache-2.0" ]

null

server/functions/calcdistance.py

yrpmsg/SCSV15

bbe953d676c082f4a30c4d5e0e0cbfcc624d969c

[ "Apache-2.0" ]

null

from math import radians, cos, sin, asin, sqrt, floor, pow import math lat1 = 11.00461011 lon1 = 76.95691543 lat2 = 11.0070471 lon2 = 76.96110704 lon1 = radians(lon1) lon2 = radians(lon2) lat1 = radians(lat1) lat2 = radians(lat2) # Haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2 c = 2 * asin(sqrt(a)) # Radius of earth in kilometers. Use 3956 for miles r = 6371 # calculate the result print(c * r)

19

63

0.62768

79

513

4.075949

0.531646

0.049689

0

0.182292

0.251462

513

26

64

19.730769

0.65625

0.177388

0

1

0

false

0

0.125

0

0.125

0.0625

0

null

0

null

0

1

0

035cf0e95ad473da5f8cd509f8390672c271dc26

3,106

py

Python

xmppserver/xmpp/mechanisms.py

ovekaaven/django-xmpp-server

aa391173b4cdfc98e2f6de29d24aa4273b3620c3

[ "MIT" ]

null

xmppserver/xmpp/mechanisms.py

ovekaaven/django-xmpp-server

aa391173b4cdfc98e2f6de29d24aa4273b3620c3

[ "MIT" ]

null

xmppserver/xmpp/mechanisms.py

ovekaaven/django-xmpp-server

aa391173b4cdfc98e2f6de29d24aa4273b3620c3

[ "MIT" ]

null

from slixmpp.exceptions import XMPPError from ..conf import settings mechanisms = {} def sasl_mech(): def register(mech): mechanisms[mech.name] = mech return mech return register class Mechanism(object): name = None def __init__(self, auth): self.auth = auth @staticmethod async def available(auth): return True @property def stream(self): return self.auth.stream @property def boundjid(self): return self.auth.stream.boundjid async def challenge(self, data=None): return await self.auth._async_challenge(data) def process(self, request): raise NotImplementedError() class LegacyAuth(Mechanism): name = 'xep_0078' @staticmethod async def available(auth): return settings.ALLOW_LEGACY_AUTH async def process(self, request): if 'username' not in request or \ 'resource' not in request: raise XMPPError('not-acceptable') username = request['username'] if not await self.auth.check_password(username, request.get('password', '')): raise XMPPError('not-authorized') self.boundjid.user = username self.boundjid.resource = request['resource'] @sasl_mech() class Anonymous(Mechanism): name = 'ANONYMOUS' @staticmethod async def available(auth): if settings.ALLOW_ANONYMOUS_LOGIN: return True else: return False async def process(self, request): if settings.ALLOW_ANONYMOUS_LOGIN: username = self.auth.generate_anonymous_user() else: raise XMPPError('not-authorized') self.boundjid.user = username @sasl_mech() class External(Mechanism): name = 'EXTERNAL' @staticmethod async def available(auth): # check client certificate, if available cert = auth.stream.get_client_cert() if not cert: return False # TODO: handle client certificates return False async def process(self, request): pass @sasl_mech() class Plain(Mechanism): name = 'PLAIN' async def process(self, request): if request.xml.text: value = request['value'] else: value = await self.challenge() toks = value.split(b'\0') if len(toks) != 3: raise XMPPError('malformed-request') toks = [x.decode('utf8') for x in toks] username = toks[1] if not await self.auth.check_password(username, toks[2]): raise XMPPError('not-authorized') authcid = "%s@%s" % (username, self.stream.host) if toks[0] != '' and toks[0] != authcid: # authzid not supported yet raise XMPPError('invalid-authzid') self.boundjid.user = username def get_sasl_by_name(name): return mechanisms.get(name, None) async def get_sasl_available(stream): return [m for m in mechanisms.values() if await m.available(stream)]

26.775862

75

0.603348

345

3,106

5.350725

0.269565

0.043337

0.03792

0.05688

0.303359

0.211268

0.137595

0.097508

0

0.005064

0.300708

3,106

115

76

27.008696

0.844843

0.03123

0

0.388889

0

0.057903

0

0.008696

0

1

0.077778

false

0.044444

0.022222

0.033333

0.355556

0

null

0

null

0

1

0

0362625fd6644541d017afd28f886693da68a5e3

7,625

py

Python

arcade/examples/sprite_rotate_tank.py

DragonMoffon/arcade

98fb1809363ccc537d6852be487aeae0b5fb7fb8

[ "MIT" ]

null

arcade/examples/sprite_rotate_tank.py

DragonMoffon/arcade

98fb1809363ccc537d6852be487aeae0b5fb7fb8

[ "MIT" ]

null

arcade/examples/sprite_rotate_tank.py

DragonMoffon/arcade

98fb1809363ccc537d6852be487aeae0b5fb7fb8

[ "MIT" ]

null

""" Sprite Rotation With A Tank. Vehicles or tower defense turrets can have parts that can rotate toward targets. These parts are usually represented with separate sprites drawn relative to attachment points on the main body. Because these sprites are usually asymmetrical, we have to rotate them around their attachment points on the main body. They will look wrong otherwise! This example allows the player to switch between two ways of rotating a tank's turret and barrel: 1. correctly, around a point on the tank's body 2. incorrectly, around the center of the barrel. Artwork from https://kenney.nl If Python and Arcade are installed, this example can be run from the command line with: python -m arcade.examples.sprite_rotate_tank """ import arcade import math TANK_SPEED = 64 # How many pixels per second the tank travels TANK_TURNING_SPEED = 60 # how many degrees per second the tank spins by. # This is half the length of the barrel sprite. # We use this value to ensure the end of the barrel sit in the middle of the tank. TANK_BARREL_LENGTH_HALF = 15 SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 SCREEN_TITLE = "Rotating Tank Example" class RotatingSprite(arcade.Sprite): """ Sprite subclass which can be rotated around a point. """ def rotate_around_point(self, point, degrees): """ Rotates the sprite around a point by the set amount of degrees :param point: The point that the sprite will rotate about :param degrees: How many degrees to rotate the sprite """ # This is so the direction the sprite faces changes when rotating. # It isn't necessary to have this. # For example, you would want a rotating platform to always face upwards. self.angle += degrees # rotate the sprite around. self.position = arcade.rotate_point(self.center_x, self.center_y, point[0], point[1], degrees) class ExampleWindow(arcade.Window): def __init__(self): super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE) # Set Background to be green. self.background_color = arcade.color.GREEN # The tank and barrel sprite. self.tank = arcade.Sprite(":resources:images/topdown_tanks/tankBody_dark_outline.png") self.tank.position = SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 self.barrel = RotatingSprite(":resources:images/topdown_tanks/tankDark_barrel3_outline.png") self.barrel.position = SCREEN_WIDTH // 2, SCREEN_HEIGHT // 2 - TANK_BARREL_LENGTH_HALF self.tank_direction = 0.0 # If the tank is moving forward or backwards. self.tank_turning = 0.0 # If the tank is turning left or right. self.mouse_pos = [0, 0] self.tank_sprite_list = arcade.SpriteList() self.tank_sprite_list.extend([self.tank, self.barrel]) self._correct = True self.correct_text = arcade.Text("Turret Rotation is Correct, Press P to Switch", SCREEN_WIDTH // 2, SCREEN_HEIGHT - 25, anchor_x='center') self.control_text = arcade.Text("WASD to move tank, Mouse to aim", SCREEN_WIDTH // 2, 15, anchor_x='center') def on_draw(self): self.clear() self.background.draw() self.tank_sprite_list.draw() self.control_text.draw() self.correct_text.draw() def on_update(self, delta_time: float): self.move_tank(delta_time) def move_tank(self, delta_time): """ Perform all calculations about how to move the tank. This includes both the body and the barrel """ # update the angle of the tank's body alone. # The barrel will be updated after the body is moved self.tank.angle += TANK_SPEED * self.tank_turning * delta_time # find how much the tank's x and y should change to move forward or back. x_dir = (math.cos(self.tank.radians - math.pi / 2) * self.tank_direction * TANK_SPEED * delta_time) y_dir = (math.sin(self.tank.radians - math.pi / 2) * self.tank_direction * TANK_SPEED * delta_time) # we then move the tank and the barrel since they are connected together. self.tank.center_x += x_dir self.tank.center_y += y_dir self.barrel.center_x += x_dir self.barrel.center_y += y_dir if self.correct: # Rotate the barrel sprite around the center of the tank, # not the center of the barrel sprite # we need to add 90 to the angle due to orientation of the barrel texture. # we need to remove the barrels angle as we only want the change in angle. angle_change = (arcade.get_angle_degrees(self.tank.center_y, self.tank.center_x, self.mouse_pos[1], self.mouse_pos[0]) - self.barrel.angle + 90) self.barrel.rotate_around_point((self.tank.center_x, self.tank.center_y), angle_change) else: # In this situation we only change the angle without changing the position which is incorrect. # we need to add 90 to the angle due to orientation of the barrel texture. angle = arcade.get_angle_degrees(self.tank.center_y, self.tank.center_x, self.mouse_pos[1], self.mouse_pos[0]) + 90 self.barrel.angle = angle def on_key_press(self, symbol: int, modifiers: int): if symbol == arcade.key.W: self.tank_direction += 1 elif symbol == arcade.key.S: self.tank_direction -= 1 elif symbol == arcade.key.A: self.tank_turning += 1 elif symbol == arcade.key.D: self.tank_turning -= 1 elif symbol == arcade.key.P: self.correct = bool(1 - self.correct) self.correct_text.text = f"Turret Rotation is" \ f" {'Correct' if self.correct else 'Incorrect'}," \ f" Press P to Switch" def on_key_release(self, symbol: int, modifiers: int): if symbol == arcade.key.W: self.tank_direction -= 1 elif symbol == arcade.key.S: self.tank_direction += 1 elif symbol == arcade.key.A: self.tank_turning -= 1 elif symbol == arcade.key.D: self.tank_turning += 1 def on_mouse_motion(self, x: int, y: int, dx: int, dy: int): self.mouse_pos = x, y @property def correct(self): return self._correct @correct.setter def correct(self, value): if value: self._correct = True angle = math.radians(arcade.get_angle_degrees(self.tank.center_y, self.tank.center_x, self.mouse_pos[1], self.mouse_pos[0])) self.barrel.center_x = (self.tank.center_x + math.cos(angle) * TANK_BARREL_LENGTH_HALF) self.barrel.center_y = (self.tank.center_y + math.sin(angle) * TANK_BARREL_LENGTH_HALF) else: self._correct = False self.barrel.center_x = self.tank.center_x self.barrel.center_y = self.tank.center_y def main(): window = ExampleWindow() window.run() if __name__ == '__main__': main()

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0.610098

1,025

7,625

4.39122

0.24

0.063986

0.043546

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null

0

null

0

1

0

03636efc34bffdf5e5bc02cd6599c5ce0ac214e9

4,913

py

Python

api/modules/github/views.py

prabhakar267/travel-mate-server

2e0aa7c6ac9963c1ee95bda5966be01293935ded

[ "MIT" ]

43

2018-05-23T10:03:40.000Z

2021-09-02T15:55:52.000Z

api/modules/github/views.py

prabhakar267/travel-mate-server

2e0aa7c6ac9963c1ee95bda5966be01293935ded

[ "MIT" ]

141

2018-05-24T16:03:12.000Z

2021-04-30T23:47:59.000Z

api/modules/github/views.py

prabhakar267/travel-mate-server

2e0aa7c6ac9963c1ee95bda5966be01293935ded

[ "MIT" ]

77

2018-06-13T13:51:31.000Z

2021-06-16T16:10:18.000Z

import datetime import requests import requests_cache from rest_framework import status from rest_framework.decorators import api_view from rest_framework.response import Response from api.commonresponses import DOWNSTREAM_ERROR_RESPONSE from api.modules.github import constants from api.modules.github.github_response import ContributorResponse, IssueResponse requests_cache.install_cache(expire_after=datetime.timedelta(days=7)) @api_view(['GET']) def get_contributors(request, project): """ Return list of people contributed :param request: :param project: :return: 503 if github api fails :return: 200 successful """ try: api_response = requests.get( constants.GITHUB_API_GET_CONTRIBUTORS_URL.format(project_name=project) ) api_response_json = api_response.json() # if authentication fails if api_response.status_code == 401: raise Exception("Authentication fails. Invalid github access token.") response = [] for contributor in api_response_json: if contributor['type'] != 'User': continue result = ContributorResponse( username=contributor['login'], url=contributor['html_url'], avatar_url=contributor['avatar_url'], contributions=contributor['contributions'], repository_name=project, ) result_as_json = result.to_json() response.append(result_as_json) except Exception: return DOWNSTREAM_ERROR_RESPONSE return Response(response) @api_view(['GET']) def get_all_contributors(request): """ Return list of people contributed :param request: :return: 503 if github api fails :return: 200 successful """ response_dict = {} for project in constants.ACTIVE_REPOSITORIES: try: api_response = requests.get( constants.GITHUB_API_GET_CONTRIBUTORS_URL.format(project_name=project) ) api_response_json = api_response.json() # if authentication fails if api_response.status_code == 401: raise Exception("Authentication fails. Invalid github access token.") for contributor in api_response_json: if contributor['type'] != 'User': continue result = ContributorResponse( username=contributor['login'], url=contributor['html_url'], avatar_url=contributor['avatar_url'], contributions=contributor['contributions'], repository_name=[project], ) if result.username in response_dict.keys(): response_dict[result.username]['contributions'] += result.contributions response_dict[result.username]['repository_name'].append(project) else: response_dict[result.username] = result.to_json() except Exception: return DOWNSTREAM_ERROR_RESPONSE response = sorted(response_dict.values(), key=lambda x: x['contributions'], reverse=True) return Response(response) @api_view(['GET']) def get_issues(request, project): """ Return list of issues :param request: :param project: :return: 503 if github api fails :return: 200 successful """ try: api_response = requests.get(constants.GITHUB_API_GET_ISSUES_URL.format(project_name=project)) api_response_json = api_response.json() if api_response.status_code == 404: error_message = "Repository does not exist" return Response(error_message, status=status.HTTP_404_NOT_FOUND) if api_response.status_code == 401: raise Exception("Authentication fails. Invalid github access token.") response = [] for issue in api_response_json: labels_length = len(issue['labels']) tags = [] # Making custom dictionary for tags for i in range(0, labels_length): # Searching inside "labels" key for tag_name for tag, tag_name in issue["labels"][i].items(): if tag in ["name"]: label = tag_name tags.append(label) result = IssueResponse( title=issue['title'], created_at=issue['created_at'], comments=issue['comments'], issue_number=issue['number'], repository_url=issue['repository_url'], labels=tags ) result_as_json = result.to_json() response.append(result_as_json) except Exception: return DOWNSTREAM_ERROR_RESPONSE return Response(response)

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0.612864

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5.784861

0.227092

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0

0.195876

0

null

0

null

0

1

0

036543a1fbcdcc35bf430e0b5d4150196450f6d6

4,910

py

Python

mkpy3/mkpy3_finder_chart_survey_fits_image_get_v1.py

KenMighell/mkpy3

598126136b43fa93bc4aded5db65a1251d60a9ba

[ "MIT" ]

null

mkpy3/mkpy3_finder_chart_survey_fits_image_get_v1.py

KenMighell/mkpy3

598126136b43fa93bc4aded5db65a1251d60a9ba

[ "MIT" ]

null

mkpy3/mkpy3_finder_chart_survey_fits_image_get_v1.py

KenMighell/mkpy3

598126136b43fa93bc4aded5db65a1251d60a9ba

[ "MIT" ]

1

2020-11-01T18:37:53.000Z

#!/usr/bin/env python3 # file://mkpy3_finder_chart_survey_fits_image_get_v1.py # Kenneth Mighell # SETI Institute # ============================================================================= def mkpy3_finder_chart_survey_fits_image_get_v1( ra_deg=None, dec_deg=None, radius_arcmin=None, survey=None, cframe=None, verbose=None, ): """ Function: mkpy3_finder_chart_survey_fits_image_get_v1() Purpose: Gets sky survey image data around a position on the sky. Parameters ---------- ra_deg : float (optional) right ascencsion [deg] dec_deg : float (optional) declination [deg] radius_arcmin : float (optional) radius (halfwidth and halfheight of image) [arcmin] survey : string (optional) [e.g., '2MASS-J', 'DSS2 Red', etc.] survey string name cframe : str (optional) coordinate frame name [e.g., 'fk5', 'icrs', etc.] verbose : bool (optional) if True, print extra information Returns ------- hdu : Header/Data Unit (HDU) of the survey FITS file hdr : header associated with hdu data : data associated with hdu wcs : World Coordinate System from hdu cframe : coordinate frame of the survey data Kenneth Mighell SETI Institute """ import astropy.units as u from astropy.coordinates import SkyCoord from astroquery.skyview import SkyView from astropy.wcs import WCS # if ra_deg is None: ra_deg = 291.41829 # Kepler-93b if dec_deg is None: dec_deg = 38.67236 # Kepler-93b if radius_arcmin is None: radius_arcmin = 1.99 if survey is None: survey = "2MASS-J" # alternate: 'DSS2 Red' # ^--- to see all surveys: astroquery.skyview.SkyView.list_surveys() if cframe is None: cframe = "fk5" # N.B.: '2MASS-J' uses 'fk5' if verbose is None: verbose = False if verbose: print(ra_deg, "=ra_deg") print(dec_deg, "=dec_deg") print(radius_arcmin, "=radius_arcmin") print("'%s' =survey" % (survey)) print("'%s' =cframe" % (cframe)) print(verbose, "=verbose") print() # # sc <--- astropy sky coordinates sc = SkyCoord(ra=ra_deg * u.degree, dec=dec_deg * u.degree, frame=cframe) # image list # assume that the list contains a single image imgl = SkyView.get_images( position=sc, survey=survey, radius=radius_arcmin * u.arcmin ) # # outputs: hdu = imgl[0] # Header/Data Unit of the FITS image hdr = hdu[0].header # header associated with the HDU data = hdu[0].data # data associated with the HDU wcs = WCS(hdr) # World Coordinate System from the FITS header of the survey image # return hdu, hdr, data, wcs, cframe # fed def xmkpy3_finder_chart_survey_fits_image_get_v1(): import lightkurve as lk lk.log.setLevel("INFO") import matplotlib.pyplot as plt import astropy.units as u from astropy.visualization import ImageNormalize, PercentileInterval, SqrtStretch import os import ntpath # Exoplanet Kelper-138b is "KIC 7603200": tpf = lk.search_targetpixelfile( target="kepler-138b", mission="kepler", cadence="long", quarter=10 ).download(quality_bitmask=0) print("TPF filename:", ntpath.basename(tpf.path)) print("TPF dirname: ", os.path.dirname(tpf.path)) target = "Kepler-138b" ra_deg = tpf.ra dec_deg = tpf.dec # get survey image data width_height_arcmin = 3.00 survey = "2MASS-J" ( survey_hdu, survey_hdr, survey_data, survey_wcs, survey_cframe, ) = mkpy3_finder_chart_survey_fits_image_get_v1( ra_deg, dec_deg, radius_arcmin=width_height_arcmin, survey=survey, verbose=True ) # create a matplotlib figure object fig = plt.figure(figsize=(12, 12)) # create a matplotlib axis object with right ascension and declination axes ax = plt.subplot(projection=survey_wcs) norm = ImageNormalize( survey_data, interval=PercentileInterval(99.0), stretch=SqrtStretch() ) ax.imshow(survey_data, origin="lower", norm=norm, cmap="gray_r") ax.set_xlabel("Right Ascension (J2000)") ax.set_ylabel("Declination (J2000)") ax.set_title("") plt.suptitle(target) # put a yellow circle at the target position ax.scatter( ra_deg * u.deg, dec_deg * u.deg, transform=ax.get_transform(survey_cframe), s=600, edgecolor="yellow", facecolor="None", lw=3, zorder=100, ) pname = "mkpy3_plot.png" if pname != "": plt.savefig(pname, bbox_inches="tight") print(pname, " <--- plot filename has been written! :-)\n") # fi return None # fed # ============================================================================= if __name__ == "__main__": xmkpy3_finder_chart_survey_fits_image_get_v1() # fi # EOF

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87

0.627495

633

4,910

4.706161

0.333333

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0.03424

0.042296

0.098019

0.076536

0.027526

0

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0.238493

4,910

183

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0

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0

1

0.021053

false

0

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0

0.147368

0.105263

0

null

0

null

0

1

0

0366c6b949b300f8072c9d5d7dfdc2a101c2a39c

1,737

py

Python

marathontcp.py

StevenPG/JMXMarathonDataAggregator

a976edc2ea27255dca36f584923e3a06dbdec8c6

[ "MIT" ]

null

marathontcp.py

StevenPG/JMXMarathonDataAggregator

a976edc2ea27255dca36f584923e3a06dbdec8c6

[ "MIT" ]

null

marathontcp.py

StevenPG/JMXMarathonDataAggregator

a976edc2ea27255dca36f584923e3a06dbdec8c6

[ "MIT" ]

null

""" marathontcp.py Author: Steven Gantz Date: 11/22/2016 These two classes are used as custom TCP Servers and its accompanying handler that defines each request. These class are what forward the data from the preset /metrics endpoints in the scaled marathon instances directly to the TCP servers running from this application. """ # Official Imports import socketserver import urllib.request class MarathonRedirectTCPServer(socketserver.TCPServer): """ TCP Server that takes special extra arguments if needed """ def __init__(self, server_address, RequestHandlerClass, bind_and_activate=True, api_url="Empty Request"): # As per http://stackoverflow.com/questions/15889241/send-a-variable-to-a-tcphandler-in-python self.api_url = api_url socketserver.TCPServer.__init__(self, server_address, RequestHandlerClass, bind_and_activate=True) class MarathonRedirectTCPHandler(socketserver.BaseRequestHandler): """ Makes a metrics request and forwards to preset ports through the application""" def handle(self): print("Retrieving metrics from http://" + self.server.api_url + "/metrics") # Make a request to the api_url metrics and fwd to page encoded_response = urllib.request.urlopen("http://" + self.server.api_url + "/metrics") # Change encoded response in to simple string header = "HTTP/1.0 200 OK \r\n" content_type = "Content-Type: text/plain\r\n\r\n" text_response = header + content_type + encoded_response.read().decode() # self.request is the TCP socket connected to the client self.request.sendall(text_response.encode()) # Read Response to close request res = self.request.recv(1024)

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0.029079

0.031502

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0.138934

0.095315

0

0.017693

0.186529

1,737

42

111

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0.43293

0

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0

1

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false

0

0.133333

0

0.4

0.066667

0

null

0

null

0

1

0

03672787b107ccc21fb75165c7801c0b958f1461

4,600

py

Python

tests/test_io.py

Laharah/horcrux

68f7c6aad0678b39bae888f0dfeb9d1926501a53

[ "MIT" ]

null

tests/test_io.py

Laharah/horcrux

68f7c6aad0678b39bae888f0dfeb9d1926501a53

[ "MIT" ]

null

tests/test_io.py

Laharah/horcrux

68f7c6aad0678b39bae888f0dfeb9d1926501a53

[ "MIT" ]

null

import pytest import io import random from copy import deepcopy from horcrux import io as hio from horcrux.hrcx_pb2 import StreamBlock from horcrux.sss import Share, Point @pytest.fixture() def hx(): return hio.Horcrux(io.BytesIO()) @pytest.fixture() def share(): return Share(b'0123456789abcdef', 2, Point(0, b'123')) @pytest.fixture() def two_block_hrcx(): return io.BytesIO(b'\x1b\n\x100123456789ABCDEF\x10\x04\x1a\x05\x12\x03123\x08\n\x06' b'566784\x00\x08\x12\x06abcdef\x02\x08\x01\n\x12\x08ghijklmn') def test_init_horcrux(): h = hio.Horcrux(io.BytesIO()) def test_horcrux__write_bytes(hx): hx._write_bytes(b'123') assert hx.stream.getvalue() == b'\x03123' def test_horcurx__read_message_bytes_small(hx): hx._write_bytes(b'123') hx._write_bytes(b'4567890') stream = hx.stream del hx stream.seek(0) hx = hio.Horcrux(stream) m1 = hx._read_message_bytes() assert m1 == b'123' m2 = hx._read_message_bytes() assert m2 == b'4567890' def test_horcrux__read_message_bytes_large(hx): m1 = bytes(255 for _ in range(500)) m2 = bytes(random.getrandbits(8) for _ in range(4)) m3 = bytes(random.getrandbits(8) for _ in range(4096)) for m in (m1, m2, m3): hx._write_bytes(m) stream = hx.stream del hx stream.seek(0) hx = hio.Horcrux(stream) assert hx._read_message_bytes() == m1 assert hx._read_message_bytes() == m2 assert hx._read_message_bytes() == m3 def test_horcrux_write_data_block(hx): _id = 1 data = b'my data' hx.write_data_block(_id, data) out = hx.stream.getvalue() print(out) assert out == b'\x02\x08\x01\t\x12\x07my data' def test_horcrux_write_share_header(hx, share): hx._write_share_header(share) stream = hx.stream del hx stream.seek(0) print(stream.getvalue()) assert stream.getvalue() == b'\x1b\n\x100123456789abcdef\x10\x02\x1a\x05\x12\x03123' def test_horcrux_write_stream_header(hx): header = b'u\x14Op\xa3\x13\x01Jt\xa8' hx._write_stream_header(header) hx._write_stream_header(header, encrypted_filename=b'testname') stream = hx.stream del hx stream.seek(0) hx = hio.Horcrux(stream) h1 = hx._read_message_bytes() assert h1 == b'\n\nu\x14Op\xa3\x13\x01Jt\xa8' h2 = hx._read_message_bytes() assert h2 == b'\n\nu\x14Op\xa3\x13\x01Jt\xa8\x1a\x08testname' def test_horcrux_init_write(hx, share): cryptoheader = b'u\x14Op\xa3\x13\x01Jt\xa8' hx.init_write(share, cryptoheader, encrypted_filename=b'slkfjwnfa;') assert hx.hrcx_id == 0 stream = hx.stream del hx stream.seek(0) headers = stream.getvalue() print(headers) assert headers == ( b'\x1b\n\x100123456789abcdef\x10\x02\x1a' b'\x05\x12\x03123\x18\n\nu\x14Op\xa3\x13\x01Jt\xa8\x1a\nslkfjwnfa;') def test_horcrux_init_read(share): stream = io.BytesIO( b'\x1b\n\x100123456789abcdef\x10\x02\x1a' b'\x05\x12\x03123\x18\n\nu\x14Op\xa3\x13\x01Jt\xa8\x1a\nslkfjwnfa;') stream.seek(0) hx = hio.Horcrux(stream) hx.init_read() assert hx.share == share assert hx.hrcx_id == 0 assert hx.encrypted_filename == b'slkfjwnfa;' assert hx.next_block_id == None def test_horcrux_read_block(hx): data1 = bytes(random.getrandbits(8) for _ in range(30)) data2 = bytes(random.getrandbits(8) for _ in range(30)) hx.write_data_block(33, data1) hx.write_data_block(45, data2) stream = hx.stream stream.seek(0) del hx hx = hio.Horcrux(stream) hx._read_next_block_id() _id, d = hx.read_block() assert d == data1 assert _id == 33 _id, d = hx.read_block() assert d == data2 assert _id == 45 def test_horcrux_skip_block(hx): data1 = bytes(255 for _ in range(30)) data2 = bytes(255 for _ in range(30)) hx.write_data_block(33, data1) hx.write_data_block(45, data2) stream = hx.stream stream.seek(0) del hx hx = hio.Horcrux(stream) hx._read_next_block_id() hx.skip_block() _id, d = hx.read_block() assert d == data2 assert _id == 45 def test_get_horcrux_files(tmpdir, share): fn = 'test_horcrux' shares = [deepcopy(share) for _ in range(4)] crypto_header = b'1234567' expected = b'\x1b\n\x100123456789abcdef\x10\x02\x1a\x05\x12\x03123\t\n\x071234567' hxs = hio.get_horcrux_files(fn, shares, crypto_header, outdir=tmpdir) assert len(hxs) == 4 for h in hxs: h.stream.close() with open(h.stream.name, 'rb') as fin: assert fin.read() == expected

27.218935

88

0.668696

709

4,600

4.143865

0.180536

0.038121

0.042886

0.513955

0.42614

0.377127

0.322328

0.24983

0

0.101824

0.201522

4,600

168

89

27.380952

0.698067

0

0.382353

0

0.036765

0.153043

0.12913

0

0.176471

1

0.110294

false

0

0.051471

0.022059

0.183824

0.022059

0

null

0

null

0

1

0

0369ea60607087cd24210a21d5453a467593c1f0

1,817

py

Python

template/diff.py

Nauja/Entropy

e418a7db68a55f17fb3e6c0c3b5018aed7002d4d

[ "MIT" ]

null

template/diff.py

Nauja/Entropy

e418a7db68a55f17fb3e6c0c3b5018aed7002d4d

[ "MIT" ]

null

template/diff.py

Nauja/Entropy

e418a7db68a55f17fb3e6c0c3b5018aed7002d4d

[ "MIT" ]

null

#!/usr/bin/env python3 """ A Pandoc filter to create non-code diffs. `add` and `rm` are the classes that can be added to a `Div` or a `Span`. `add` colors the text green, and `rm` colors the text red. For HTML, `add` also underlines the text, and `rm` also strikes out the text. # Example ## `Div` Unchanged portion ::: add New paragraph > Quotes More new paragraphs ::: ## `Span` > The return type is `decltype(`_e_(`m`)`)` [for the first form]{.add}. """ import panflute as pf def action(elem, doc): if not isinstance(elem, pf.Div) and not isinstance(elem, pf.Span): return None color_name = None tag_name = None for cls in elem.classes: color_name = cls + 'color' if cls == 'add': tag_name = 'ins' elif cls == 'rm': tag_name = 'del' if tag_name is None: return None open_tag = pf.RawInline('<{}>'.format(tag_name), 'html') open_color = pf.RawInline('{{\\color{{{}}}'.format(color_name), 'tex') close_color = pf.RawInline('}', 'tex') close_tag = pf.RawInline('</{}>'.format(tag_name), 'html') color = doc.get_metadata(color_name) attributes = {} if color is None else {'style': 'color: #{}'.format(color)} if isinstance(elem, pf.Div): return pf.Div(pf.Plain(open_tag), pf.Plain(open_color), elem, pf.Plain(close_color), pf.Plain(close_tag), attributes=attributes) elif isinstance(elem, pf.Span): return pf.Span(open_tag, open_color, elem, close_color, close_tag, attributes=attributes) if __name__ == '__main__': pf.run_filter(action)

25.236111

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0.555861

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1,817

4.206897

0.362069

0.043033

0.065574

0.038934

0.116803

0.063525

0

0.0008

0.312053

1,817

71

80

25.591549

0.78

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0

null

0

null

0

1

0

036a6e6b57ea2d5221b7c56f2e175c6cb9c0ca3b

1,304

py

Python

paxful/exceptions.py

tholness/Paxful-API-Wrapper

c66620aa2ef40b97f2794998c63a6bd7504cea3c

[ "MIT" ]

null

paxful/exceptions.py

tholness/Paxful-API-Wrapper

c66620aa2ef40b97f2794998c63a6bd7504cea3c

[ "MIT" ]

null

paxful/exceptions.py

tholness/Paxful-API-Wrapper

c66620aa2ef40b97f2794998c63a6bd7504cea3c

[ "MIT" ]

3

2020-08-09T17:02:06.000Z

2021-04-13T17:45:39.000Z

from __future__ import absolute_import, unicode_literals class PaxfulError(Exception): """Base (catch-all) client exception.""" class RequestError(PaxfulError): """Raised when an API request to fails. :ivar message: Error message. :vartype message: str | unicode :ivar url: API endpoint. :vartype url: str | unicode :ivar body: Raw response body from Pax. :vartype body: str | unicode :ivar headers: Response headers. :vartype headers: requests.structures.CaseInsensitiveDict :ivar http_code: HTTP status code. :vartype http_code: int :ivar error_code: Error code from Pax. :vartype error_code: int :ivar response: Response object. :vartype response: requests.Response """ def __init__(self, response, message, error_code=None): self.message = message self.url = response.url self.body = response.text self.headers = response.headers self.http_code = response.status_code self.error_code = error_code self.response = response Exception.__init__(self, message) class InvalidCurrencyError(PaxfulError): """Raised when an invalid major currency is given.""" class InvalidOrderBookError(PaxfulError): """Raised when an invalid order book is given."""

28.977778

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0.692485

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0.359477

0.061364

0.071591

0.078409

0.068182

0

0.220092

1,304

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false

0

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0

0.428571

0

null

0

null

0

1

0

036bed92a5a2372689c9a48c62d3c2e337ec2c9b

1,459

py

Python

cogs/logs.py

CoffeeOrg/Coffee

73bf194c3811bb9cf776a0a4db4c6234e471d5ce

[ "MIT" ]

6

2021-02-06T05:43:40.000Z

2021-08-01T22:55:33.000Z

cogs/logs.py

elfw/Coffee

e83868a323084b96b0df3f916090dd17ce34de93

[ "MIT" ]

2

2021-02-06T07:18:10.000Z

2021-02-06T18:42:07.000Z

cogs/logs.py

elfw/Coffee

e83868a323084b96b0df3f916090dd17ce34de93

[ "MIT" ]

10

2021-02-06T03:31:26.000Z

2021-09-22T04:00:23.000Z

import discord from discord.ext import commands from utils.database import sqlite, create_tables class Events(commands.Cog): def __init__(self, bot): self.bot = bot self.db = sqlite.Database() def logs(self, guild_id): data = self.db.fetchrow("SELECT * FROM Logging WHERE guild_id=?", (guild_id,)) if data: return data["logs_id"] else: return None @commands.Cog.listener() async def on_message_delete(self, message): log_channel = self.bot.get_channel(self.logs(message.guild.id)) if log_channel: embed = discord.Embed( title="Message Deleted 📝", description=f"**Deleted in:** `#{message.channel}`\n**Author:** `{message.author}`\n**Message:** ```{message.content}```", color=0x2F3136 ) embed.timestamp = message.created_at await log_channel.send(embed=embed) @commands.Cog.listener() async def on_message_edit(self, before, after): log_channel = self.bot.get_channel(self.logs(before.guild.id)) if before.author.bot is True: return None if log_channel: embed = discord.Embed( title="Message Edited 📝", description=f"**Edited in:** `#{before.channel}`\n**Author:** `{before.author}`\n**Before:** ```{before.content}```\n**Now:** ```{after.content}```", color=0x2F3136 ) embed.timestamp = before.created_at await log_channel.send(embed=embed) def setup(bot): bot.add_cog(Events(bot))

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0.651131

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1,459

4.833333

0.333333

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0.029095

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0.396552

0.323276

0.24569

0

0.010213

0.194654

1,459

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155

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false

0

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0

0.25641

0

null

0

null

0

1

0

036e3669e539c1ee359752125217465762d9b017

4,151

py

Python

scripts/text/text_particles.py

mou3adb/spread_the_particle

6cc666fded62f07380ed1e3ed52969c436295906

[ "MIT" ]

4

2020-08-18T18:33:05.000Z

2021-05-18T23:55:56.000Z

scripts/text/text_particles.py

mou3adb/spread_the_particle

6cc666fded62f07380ed1e3ed52969c436295906

[ "MIT" ]

null

scripts/text/text_particles.py

mou3adb/spread_the_particle

6cc666fded62f07380ed1e3ed52969c436295906

[ "MIT" ]

2

2021-03-03T18:57:06.000Z

2021-05-18T20:43:44.000Z

""" The outfile structure is the following: diameter density birth lifetime is_captured stuck_to_geometry theta (blank line) Re Ur (blank line) n_trajectory x1 y1 up1 vp1 Uf1 Vf1 gradpx1 gradpy1 ap_x1 ap_y1 af_x1 af_y1 x2 y2 up2 vp2 Uf2 Vf2 gradpx2 gradpy2 ap_x2 ap_y2 af_x2 af_y2 ... xNt yNt upNt vpNt UfNt VfNt gradpxNt gradpyNt ap_xN ap_yN af_xN af_yN """ import sys sys.path.append('..') import numpy as np from particle import Particle #============================================================================== def floatIt(l): return np.array([float(e) for e in l]) def intIt(l): return np.array([int(e) for e in l]) def write_particle(p, f): f.write('%2.3f %1.3f\n' % (p.diameter, p.density)) f.write('%d %d\n' % (p.birth, p.lifetime)) f.write('%s %s %s\n' % (p.captured, p.stuck_to_geometry, p.theta)) f.write('\n') # blank line f.write('%d %.1f\n' % (p.Re, p.Ur)) f.write('\n') Nt = len(p.trajectory) f.write('%d\n' % Nt) for n in range(Nt): f.write('%e '*12 % \ (p.trajectory[n,0], p.trajectory[n,1], p.velocities[n,0], p.velocities[n,1], p.fluid_velocities[n,0], p.fluid_velocities[n,1], p.pressure_gradients[n,0], p.pressure_gradients[n,1], p.accelerations[n,0], p.accelerations[n,1], p.fluid_accelerations[n,0], p.fluid_accelerations[n,1])) f.write('\n') def write_particles(particles, outfile): f = open(outfile, 'w') Np = len(particles) f.write('%d\n' % Np) f.write('\n') # blank line for p in particles: write_particle(p, f) f.write('\n') f.close() def read_particle(f, old_version=False): # I kept old_version because I had many particles saved before the final # update of this function. diameter, density = floatIt(f.readline().strip().split()) birth, lifetime = intIt(f.readline().strip().split()) if not(old_version): str_captured, str_stuck, str_theta = f.readline().strip().split() theta = float(str_theta) else: str_captured, str_stuck = f.readline().strip().split() captured = False if str_captured == 'False' else True stuck = None if str_stuck == 'None' else int(str_stuck) f.readline() # read the blank line Re, Ur = floatIt(f.readline().strip().split()) f.readline() Nt = int(f.readline().strip()) trajectory = [] velocities = [] fluid_velocities = [] pressure_gradients = [] accelerations = [] fluid_accelerations = [] for n in range(Nt): if old_version: x, y, u, v, U, V, gradpx, gradpy \ = floatIt(f.readline().strip().split()) else: x, y, u, v, U, V, gradpx, gradpy, ap_x, ap_y, af_x, af_y \ = floatIt(f.readline().strip().split()) trajectory.append([x, y]) velocities.append([u, v]) fluid_velocities.append([U, V]) pressure_gradients.append([gradpx, gradpy]) if not(old_version): accelerations.append([ap_x, ap_y]) fluid_accelerations.append([af_x, af_y]) pos0 = trajectory[0] u0 = velocities[0] p = Particle(diameter, density, birth, lifetime, pos0, u0) p.captured, p.stuck_to_geometry = captured, stuck p.Re, p.Ur = Re, Ur p.trajectory = np.array(trajectory) p.velocities = np.array(velocities) p.fluid_velocities = np.array(fluid_velocities) p.pressure_gradients = np.array(pressure_gradients) if not(old_version): p.accelerations = np.array(accelerations) p.fluid_accelerations = np.array(fluid_accelerations) p.theta = theta return p def read_particles(infile, old_version=False): f = open(infile, 'r') Np = int(f.readline()) f.readline() # read a blank line particles = [] for i in range(Np): particles.append(read_particle(f, old_version)) f.readline() f.close() return np.array(particles)

25.466258

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0.578897

580

4,151

4.024138

0.218966

0.050129

0.047986

0.056984

0.153385

0.064267

0.015424

0

0.016081

0.26596

4,151

162

80

25.623457

0.749918

0.141171

0

0.20202

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0

1

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false

0

0.030303

0.020202

0.131313

0

null

0

null

0

1

0

03740eb7b2e0fb107f339fc022308a8b7f886123

3,195

py

Python

csrv/model/deck.py

mrroach/CentralServer

e377c65d8f3adf5a2d3273acd4f459be697aea56

[ "Apache-2.0" ]

null

csrv/model/deck.py

mrroach/CentralServer

e377c65d8f3adf5a2d3273acd4f459be697aea56

[ "Apache-2.0" ]

null

csrv/model/deck.py

mrroach/CentralServer

e377c65d8f3adf5a2d3273acd4f459be697aea56

[ "Apache-2.0" ]

1

2020-09-20T11:26:20.000Z

"""A collection of cards.""" import random from csrv.model import cards from csrv.model.cards import card_info # This import is just to pull in all the card definitions import csrv.model.cards.corp import csrv.model.cards.runner class Deck(object): def __init__(self, identity_name, card_names): self.identity = cards.Registry.get(identity_name) self.cards = [] self.is_valid = True for name in card_names: c = cards.Registry.get(name) if c: self.cards.append(c) def _verify_less_than_three_copies(self): """Make sure we have no more than 3 copies of a single cards""" card_list = {} for c in self.cards: card_list[c.NAME] = card_list.setdefault(c.NAME, 0) + 1 invalid_cards = filter(lambda x: card_list[x] > 3, card_list) if len(invalid_cards): return "Deck contains more than 3 copies of the following cards: {}".format(', '.join(invalid_cards)) def _verify_min_deck_size(self): """Make sure deck meets minimum deck size limit""" if len(self.cards) < self.identity.MIN_DECK_SIZE: self.is_valid = False return "Deck does not meet minimum deck size requirement" def _verify_influence_points(self): """Make sure deck doesnt exceed maximum influence points""" influence_spent = reduce(lambda x,y: x+y.influence_cost(self.identity.FACTION), self.cards, 0) if influence_spent > self.identity.MAX_INFLUENCE: return "Deck contains {} influence but only {} allowed".format(influence_spent, self.identity.MAX_INFLUENCE) def _verify_side_only(self, side): """Make sure we only have cards belonging to the correct side""" if len(filter(lambda c: c.SIDE != side, self.cards)): return "Deck contains cards from the other side (corp/runner)" class CorpDeck(Deck): """A deck for a corp.""" def validate(self): """Return a list of errors with the deck.""" return filter(None, [ self._verify_min_deck_size(), self._verify_influence_points(), self._verify_less_than_three_copies(), self._verify_in_faction_agendas(), self._verify_agenda_points(), self._verify_side_only(card_info.CORP) ]) def _verify_agenda_points(self): """Make sure deck has required agenda points based on deck size""" agenda_points = reduce(lambda x,y: x+y.AGENDA_POINTS, self.cards, 0) deck_size = len(self.cards) if agenda_points/float(deck_size) < 2.0/5.0: self.is_valid = False return "Only {} Agenda Points in deck of {} cards".format(agenda_points, deck_size) def _verify_in_faction_agendas(self): """Make sure deck only contains in faction agendas""" agendas = filter(lambda c: c.TYPE == card_info.AGENDA, self.cards) if len(filter(lambda a: not a.FACTION in [card_info.NEUTRAL, self.identity.FACTION], agendas)): return "Deck contains out-of-faction Agendas" class RunnerDeck(Deck): """A deck for a runner.""" def validate(self): """Return a list of errors with the deck.""" return filter(None, [ self._verify_min_deck_size(), self._verify_influence_points(), self._verify_less_than_three_copies(), self._verify_side_only(card_info.RUNNER) ])

35.10989

114

0.699531

475

3,195

4.498947

0.235789

0.037436

0.028077

0.321011

0.220402

0.13664

0

0.004252

0.190297

3,195

90

115

35.5

0.821801

0.16338

0

0.233333

0

0.108945

0

1

0.15

false

0

0.083333

0

0.416667

0

null

0

null

0

1

0

03766bbc82ca9d0b806101dbc0e7af7f9c47c209

476

py

Python

data_structures_and_algorithms/04_menu.py

dileepabandara/return-python

fc269d577eade231bc9e3813654ce9c5848837ca

[ "MIT" ]

1

2022-01-12T17:44:51.000Z

data_structures_and_algorithms/04_menu.py

dileepabandara/return-python

fc269d577eade231bc9e3813654ce9c5848837ca

[ "MIT" ]

null

data_structures_and_algorithms/04_menu.py

dileepabandara/return-python

fc269d577eade231bc9e3813654ce9c5848837ca

[ "MIT" ]

null

ans = True while ans: print(""" 1.Add a Student 2.Delete a Student 3.Look Up Student Record 4.Exit/Quit """) ans = input("What would you like to do? ") if ans == "1": print("\nStudent Added") elif ans == "2": print("\n Student Deleted") elif ans == "3": print("\n Student Record Found") elif ans == "4": print("\n Goodbye") ans = None else: print("\n Not Valid Choice Try again")

22.666667

46

0.521008

66

476

3.757576

0.590909

0.096774

0.104839

0

0.025316

0.336134

476

20

47

23.8

0.759494

0

0.460084

0

1

0

false

0

0.3

0

null

0

null

0

1

0

0376ebb01bd1aa62d9b4075181468b5d09068e7f

525

py

Python

libok.py

txt/se4dm

c38c742039eaa7a15730eb655c4eed067c8a5409

[ "Unlicense" ]

null

libok.py

txt/se4dm

c38c742039eaa7a15730eb655c4eed067c8a5409

[ "Unlicense" ]

9

2015-10-30T12:46:53.000Z

2015-11-25T03:27:49.000Z

libok.py

txt/se4dm

c38c742039eaa7a15730eb655c4eed067c8a5409

[ "Unlicense" ]

2

2018-06-22T15:23:44.000Z

2020-11-05T01:47:54.000Z

from __future__ import print_function, division import sys sys.dont_write_bytecode = True from lib import * @ok def _rseed(): rseed(1) one = list('abcdefghijklm') assert shuffle(one) == ['m', 'h', 'j', 'f', 'a', 'g', 'l', 'd', 'e', 'c', 'i', 'k', 'b'] @ok def _defDict(): d = DefaultDict(lambda: []) for n,c in enumerate(list('tobeornottobe')): d[c].append(n) assert d == {'b': [2, 11], 'e': [3, 12], 'o': [1, 4, 7, 10], 'n': [6], 'r': [5], 't': [0, 8, 9]}

22.826087

50

0.491429

78

525

3.192308

0.74359

0.040161

0

0.044041

0.264762

525

22

51

23.863636

0.601036

0

0.111111

0

0.085714

0

0.111111

1

0.111111

false

0

0.166667

0

0.277778

0.055556

0

null

0

null

0

1

0

037803daf8f26a1fd6b807cb352e059357d3aa0d

734

py

Python

setup.py

ignalex/HAP-python

855577cfcde1bf2f8562caf9fbefda3e4fa8b497

[ "Apache-2.0" ]

1

2018-09-23T20:44:46.000Z

setup.py

ignalex/HAP-python

855577cfcde1bf2f8562caf9fbefda3e4fa8b497

[ "Apache-2.0" ]

1

2019-10-02T11:12:13.000Z

setup.py

ilyamordasov/HAP-python

698eb612c35b5672c4aab9d7896093924cbd358c

[ "Apache-2.0" ]

null

from setuptools import setup import pyhap.const as pyhap_const PROJECT_NAME = 'HAP-python' URL = 'https://github.com/ikalchev/{}'.format(PROJECT_NAME) PROJECT_URLS = { 'Bug Reports': '{}/issues'.format(URL), 'Documentation': 'http://hap-python.readthedocs.io/en/latest/', 'Source': '{}/tree/master'.format(URL), } PYPI_URL = 'https://pypi.python.org/pypi/{}'.format(PROJECT_NAME) DOWNLOAD_URL = '{}/archive/{}.zip'.format(URL, pyhap_const.__version__) MIN_PY_VERSION = '.'.join(map(str, pyhap_const.REQUIRED_PYTHON_VER)) setup( name=PROJECT_NAME, version=pyhap_const.__version__, url=URL, project_urls=PROJECT_URLS, download_url=DOWNLOAD_URL, python_requires='>={}'.format(MIN_PY_VERSION), )

27.185185

71

0.709809

96

734

5.114583

0.458333

0.101833

0.069246

0

0.117166

734

26

72

28.230769

0.757716

0

0.257493

0

1

0

false

0

0.1

0

0.1

0

null

0

null

0

1

0

037a4b8c8dc9b844a65be270c4263033b7498224

1,291

py

Python

experiments/2014_PLOS-Comp-Bio_Wikidemics-feasibility/scrape_mmwr.py

casmlab/quac

f7b037b15f5ff0db1b9669159f645040abce1766

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

34

2015-01-10T05:44:02.000Z

2021-05-18T02:57:19.000Z

experiments/2014_PLOS-Comp-Bio_Wikidemics-feasibility/scrape_mmwr.py

casmlab/quac

f7b037b15f5ff0db1b9669159f645040abce1766

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

14

2015-02-15T21:58:09.000Z

2020-06-05T18:31:47.000Z

experiments/2014_PLOS-Comp-Bio_Wikidemics-feasibility/scrape_mmwr.py

casmlab/quac

f7b037b15f5ff0db1b9669159f645040abce1766

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

19

2015-02-08T02:24:15.000Z

2020-11-07T13:39:55.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Scrape the MMWR morbidity tables at http://wonder.cdc.gov/mmwr/mmwrmorb.asp. No processing is done; we simply save the files for potential offline processing. """ # Copyright (c) Los Alamos National Security, LLC and others. from __future__ import print_function, division import requests import codecs import os mmwr_table_url = 'http://wonder.cdc.gov/mmwr/mmwr_reps.asp?mmwr_year=%d&mmwr_week=%02d&mmwr_table=%s&request=Submit' mmwr_file = '../data/mmwr/%d-%02d-%s.html' tables = {'1', '2A', '2B', '2C', '2D', '2E', '2F', '2G', '2H', '2I', '2J', '2K', '3A', '3B', '4'} error_messages = {'Data are not available for the week requested.', 'No records found.', 'does not exist before the week ending'} for year in range(1996, 2015): for week in range(1, 54): for table in tables: if not os.path.exists(mmwr_file % (year, week, table)): response = requests.get(mmwr_table_url % (year, week, table)) error = False for error_message in error_messages: if error_message in response.text: error = True break if not error: with codecs.open(mmwr_file % (year, week, table), 'w', 'utf-8') as output: output.write(response.text) print('saved %s' % (mmwr_file % (year, week, table)))

32.275

130

0.670023

200

1,291

4.22

0.55

0.037915

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0.056872

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null

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0

037b522742c7eb6098ac17119575246e7a1d22e3

6,970

py

Python

cluster_status.py

jtimberlake/hyper-kube-config

d624f81e04d1560b584bb7b748451dd5181e15bf

[ "MIT" ]

29

2018-10-11T17:34:33.000Z

2019-10-09T04:24:22.000Z

cluster_status.py

silvermullet/kube-auth-store

a9c6966fe7b29e0bf80f9e40027310fd4a07dbc3

[ "MIT" ]

14

2018-12-18T18:14:19.000Z

2019-10-19T18:38:12.000Z

cluster_status.py

silvermullet/kube-auth-store

a9c6966fe7b29e0bf80f9e40027310fd4a07dbc3

[ "MIT" ]

6

2018-11-06T09:32:40.000Z

2019-10-17T18:18:08.000Z

import json import logging import os import traceback from boto3.dynamodb.conditions import Attr, Key import storage from util import lambda_result logger = logging.getLogger('cluster_status') if os.environ.get('DEBUG'): logger.setLevel(logging.DEBUG) def set_cluster_status(event, context): """Set the status of a cluster, ie active, inactive, maintainance_mode, etc""" CLUSTER_TABLE = storage.get_cluster_table() query_string_params = event.get('queryStringParameters', {}) cluster_status = query_string_params.get('cluster_status') if cluster_status is None: return lambda_result( {"message": f'Must provide a status variable in uri query string'}, status_code=500) cluster_name = query_string_params.get('cluster_name') if cluster_name is None: return lambda_result( {"message": (f'Must provide a cluster_name ' f'variable in uri query string')}, status_code=500) try: CLUSTER_TABLE.update_item( Key={ 'id': cluster_name, }, UpdateExpression="SET cluster_status = :r", ExpressionAttributeValues={ ':r': cluster_status }, ReturnValues="UPDATED_NEW" ) return lambda_result( {"message": (f'Updated cluster status for {cluster_name} ' f'to {cluster_status}')}) except Exception: failed_txt = f'Failed to update cluster status for {cluster_name}' logger.exception(failed_txt) return lambda_result({"message": failed_txt}, status_code=500) def set_cluster_environment(event, context): """Set the environment of a cluster, ie dev, stage, prod""" CLUSTER_TABLE = storage.get_cluster_table() query_string_params = event.get('queryStringParameters', {}) environment = query_string_params.get('environment') if environment is None: return lambda_result( {"message": f'Must provide an environment param in uri query string'}, status_code=500) cluster_name = query_string_params.get('cluster_name') if cluster_name is None: return lambda_result( {"message": (f'Must provide a cluster_name ' f'variable in uri query string')}, status_code=500) try: CLUSTER_TABLE.update_item( Key={ 'id': cluster_name, }, UpdateExpression="ADD environment :e", ExpressionAttributeValues={ ':e': set([environment]) }, ReturnValues="UPDATED_NEW" ) msg = (f'Updated cluster environment for {cluster_name} ' f'to {environment}') return lambda_result(msg) except Exception as e: failed_txt = f'Failed to update cluster environment for {cluster_name}' failed_txt += "\n{} \n{}".format( str(e), repr(traceback.format_stack())) print(failed_txt) return lambda_result({"message": failed_txt}, status_code=500) def clusters_per_environment(event, context): """Query cluster status attribute for given environment, requires 'environment' query param, or defaults to all clusters""" clusters = [] environment = event.get('queryStringParameters', {}).get('environment') items = _query_dynamodb(environment) for cluster in items: clusters.append(cluster['id']) return lambda_result(clusters) def cluster_status(event, context): """Query cluster status attribute for given environment, requires 'environment' query param, or defaults to all clusters""" clusters = [] query_string_params = event.get('queryStringParameters', {}) environment = query_string_params.get('environment') cluster_status = query_string_params.get('cluster_status') items = _query_dynamodb(environment, cluster_status) for cluster in items: clusters.append(cluster['id']) return lambda_result(clusters) def set_cluster_metadata(event, context): """Set the metadata of a cluster. metadata is a json blob use for describing extra details about a cluster. """ CLUSTER_TABLE = storage.get_cluster_table() query_string_params = event.get('queryStringParameters', {}) metadata = event.get('body', {}) cluster_name = query_string_params.get('cluster_name') if cluster_name is None: return lambda_result( {"message": (f'Must provide a cluster_name ' f'variable in uri query string')}, status_code=500) try: if isinstance(metadata, str): metadata = json.loads(metadata) CLUSTER_TABLE.update_item( Key={ 'id': cluster_name, }, UpdateExpression="set metadata = :md", ExpressionAttributeValues={ ':md': metadata }, ReturnValues="UPDATED_NEW" ) return lambda_result( {"message": f'Updated cluster metadata for {cluster_name}'} ) except Exception: failed_txt = f'Failed to update cluster metadata for {cluster_name}' logger.exception(failed_txt) logger.error(json.dumps(event)) return lambda_result({"message": failed_txt}, status_code=500) def get_cluster_metadata(event, context): """Get the metadata of a cluster. metadata is a json blob use for describing extra details about a cluster. """ CLUSTER_TABLE = storage.get_cluster_table() query_string_params = event.get('queryStringParameters', {}) cluster_name = query_string_params.get('cluster_name') if cluster_name is None: return { "statusCode": 500, "body": json.dumps( {"message": (f'Must provide a cluster_name ' f'variable in uri query string')}) } status_code = 404 db_response = CLUSTER_TABLE.get_item( Key={ 'id': cluster_name, } ) metadata = {} if 'Item' in db_response: status_code = 200 metadata = db_response['Item'].get('metadata', {}) if isinstance(metadata, str): metadata = json.loads(metadata) metadata['environment'] = db_response['Item'].get('environment') metadata['status'] = db_response['Item'].get('status') metadata['id'] = cluster_name return lambda_result(metadata, status_code=status_code) def _query_dynamodb(environment, status=None, metadata=False): CLUSTER_TABLE = storage.get_cluster_table() fkey = Attr('environment').contains(environment) if status is not None: fkey = fkey & Key('cluster_status').eq(status) response = CLUSTER_TABLE.scan( ProjectionExpression="id", FilterExpression=fkey ) return response.get('Items', [])

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6,970

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0

null

0

null

0

1

0

037cb54aac999a27c21c13f841feb80028eba68f

1,366

py

Python

ote_sdk/ote_sdk/utils/labels_utils.py

ntyukaev/training_extensions

c897d42e50828fea853ceda0795e1f0e7d6e9909

[ "Apache-2.0" ]

null

ote_sdk/ote_sdk/utils/labels_utils.py

ntyukaev/training_extensions

c897d42e50828fea853ceda0795e1f0e7d6e9909

[ "Apache-2.0" ]

null

ote_sdk/ote_sdk/utils/labels_utils.py

ntyukaev/training_extensions

c897d42e50828fea853ceda0795e1f0e7d6e9909

[ "Apache-2.0" ]

1

2020-12-13T22:13:51.000Z

""" This module implements utilities for labels """ # Copyright (C) 2021-2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # from typing import List, Optional from ote_sdk.entities.label import LabelEntity from ote_sdk.entities.label_schema import LabelSchemaEntity from ote_sdk.entities.scored_label import ScoredLabel def get_empty_label(label_schema: LabelSchemaEntity) -> Optional[LabelEntity]: """ Get first empty label from label_schema """ empty_candidates = list( set(label_schema.get_labels(include_empty=True)) - set(label_schema.get_labels(include_empty=False)) ) if empty_candidates: return empty_candidates[0] return None def get_leaf_labels(label_schema: LabelSchemaEntity) -> List[LabelEntity]: """ Get leafs from label tree """ leaf_labels = [] all_labels = label_schema.get_labels(False) for lbl in all_labels: if not label_schema.get_children(lbl): leaf_labels.append(lbl) return leaf_labels def get_ancestors_by_prediction( label_schema: LabelSchemaEntity, prediction: ScoredLabel ) -> List[ScoredLabel]: """ Get all the ancestors for a given label node """ ancestor_labels = label_schema.get_ancestors(prediction.get_label()) return [ScoredLabel(al, prediction.probability) for al in ancestor_labels]

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0.458333

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null

0

null

0

1

0

cee3692c9f60cfa65662bb7421bd3d405f7b7920

4,329

py

Python

meggie/actions/spectrum_plot/controller/spectrum.py

Teekuningas/meggie

0790559febb990a5487d4f0c92987066632e1d99

[ "BSD-2-Clause-FreeBSD" ]

4

2020-04-29T08:57:11.000Z

2021-01-15T21:21:51.000Z

meggie/actions/spectrum_plot/controller/spectrum.py

Teekuningas/meggie

0790559febb990a5487d4f0c92987066632e1d99

[ "BSD-2-Clause-FreeBSD" ]

16

2019-05-03T10:31:16.000Z

2021-05-06T14:59:55.000Z

meggie/actions/spectrum_plot/controller/spectrum.py

cibr-jyu/meggie

0790559febb990a5487d4f0c92987066632e1d99

[ "BSD-2-Clause-FreeBSD" ]

3

2020-12-12T09:57:00.000Z

2020-12-20T17:12:05.000Z

""" Contains functions for plot spectrum action """ import mne import numpy as np import matplotlib.pyplot as plt from meggie.utilities.plotting import color_cycle from meggie.utilities.plotting import create_channel_average_plot from meggie.utilities.channels import average_to_channel_groups from meggie.utilities.channels import iterate_topography from meggie.utilities.units import get_power_unit def plot_spectrum_averages(subject, channel_groups, name, log_transformed=True): """ Plots spectrum averages. """ subject_name = subject.name spectrum = subject.spectrum.get(name) data = spectrum.content freqs = spectrum.freqs ch_names = spectrum.ch_names info = spectrum.info colors = color_cycle(len(data)) conditions = spectrum.content.keys() averages = {} for key, psd in sorted(data.items()): data_labels, averaged_data = average_to_channel_groups( psd, info, ch_names, channel_groups) for label_idx, label in enumerate(data_labels): if not label in averages: averages[label] = [] averages[label].append((key, averaged_data[label_idx])) ch_types = sorted(set([label[0] for label in averages.keys()])) for ch_type in ch_types: ch_groups = sorted([label[1] for label in averages.keys() if label[0] == ch_type]) def plot_fun(ax_idx, ax): ch_group = ch_groups[ax_idx] ax.set_title(ch_group) ax.set_xlabel('Frequency (Hz)') ax.set_ylabel('Power ({})'.format( get_power_unit(ch_type, log_transformed))) for color_idx, (key, curve) in enumerate(averages[(ch_type, ch_group)]): if log_transformed: curve = 10 * np.log10(curve) ax.plot(freqs, curve, color=colors[color_idx]) title = ' '.join([name, ch_type]) legend = list(zip(conditions, colors)) create_channel_average_plot(len(ch_groups), plot_fun, title, legend) plt.show() def plot_spectrum_topo(subject, name, log_transformed=True, ch_type='meg'): """ Plots spectrum topography. """ subject_name = subject.name spectrum = subject.spectrum.get(name) data = spectrum.content freqs = spectrum.freqs ch_names = spectrum.ch_names info = spectrum.info if ch_type == 'meg': picked_channels = [ch_name for ch_idx, ch_name in enumerate(info['ch_names']) if ch_idx in mne.pick_types(info, meg=True, eeg=False)] else: picked_channels = [ch_name for ch_idx, ch_name in enumerate(info['ch_names']) if ch_idx in mne.pick_types(info, eeg=True, meg=False)] info = info.copy().pick_channels(picked_channels) colors = color_cycle(len(data)) def individual_plot(ax, info_idx, names_idx): """ """ ch_name = ch_names[names_idx] for color_idx, (key, psd) in enumerate(sorted(data.items())): if log_transformed: curve = 10 * np.log10(psd[names_idx]) else: curve = psd[names_idx] ax.plot(freqs, curve, color=colors[color_idx], label=key) title = ' '.join([name, ch_name]) ax.figure.canvas.set_window_title(title.replace(' ', '_')) ax.figure.suptitle(title) ax.set_title('') ax.legend() ax.set_xlabel('Frequency (Hz)') ax.set_ylabel('Power ({})'.format(get_power_unit( mne.io.pick.channel_type(info, info_idx), log_transformed ))) plt.show() fig = plt.figure() for ax, info_idx, names_idx in iterate_topography( fig, info, ch_names, individual_plot): handles = [] for color_idx, (key, psd) in enumerate(sorted(data.items())): if log_transformed: curve = 10 * np.log10(psd[names_idx]) else: curve = psd[names_idx] handles.append(ax.plot(curve, color=colors[color_idx], linewidth=0.5, label=key)[0]) if not handles: return fig.legend(handles=handles) title = '{0}_{1}'.format(name, ch_type) fig.canvas.set_window_title(title) plt.show()

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550

4,329

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0.136842

0

null

0

null

0

1

0

cee59d1b21ebd4c01ff98f1b398ff22e296663a6

7,733

py

Python

data/cyclesps.py

DawyD/UNet-PS-4D

bdd31308854dbd5f309aec9bcc1f7a35f267481d

[ "MIT" ]

1

2021-12-06T17:20:36.000Z

data/cyclesps.py

DawyD/UNet-PS-4D

bdd31308854dbd5f309aec9bcc1f7a35f267481d

[ "MIT" ]

null

data/cyclesps.py

DawyD/UNet-PS-4D

bdd31308854dbd5f309aec9bcc1f7a35f267481d

[ "MIT" ]

3

2021-12-06T08:09:42.000Z

2022-03-12T08:09:34.000Z

""" DataGenerator for CyclesPS Dataset This file use substantial portion of code from the original CNN-PS repository https://github.com/satoshi-ikehata/CNN-PS/ """ import numpy as np import cv2 import os import gc from data.datagenerator import DataGenerator from data.utils import rotate_images from misc.projections import standard_proj from tensorflow.keras.models import Model class CyclesDataGenerator(DataGenerator): def __init__(self, datapath, objlist=None, batch_size=256, spatial_patch_size=5, obs_map_size=32, shuffle=False, random_illums=False, keep_axis=True, validation_split=None, nr_rotations=1, rotation_start=0, rotation_end=2 * np.pi, projection=standard_proj, add_raw=False, images=None, normals=None, masks=None, illum_dirs=None, order=2, divide_maps=False, round_nearest=True, rot_2D=False, verbose=False): self.datapath = datapath self.objlist = objlist if objlist is not None else sorted(os.listdir(datapath + '/PRPS')) self.verbose = verbose super(CyclesDataGenerator, self).__init__( batch_size=batch_size, spatial_patch_size=spatial_patch_size, obs_map_size=obs_map_size, shuffle=shuffle, random_illums=random_illums, keep_axis=keep_axis, validation_split=validation_split, nr_rotations=nr_rotations, rotation_start=rotation_start, rotation_end=rotation_end, projection=projection, add_raw=add_raw, images=images, normals=normals, masks=masks, illum_dirs=illum_dirs, order=order, divide_maps=divide_maps, round_nearest=round_nearest, rot_2D=rot_2D) def load_data(self): objid = 0 for obj in self.objlist: for dirb, dirn, scale in zip(['PRPS_Diffuse/' + '%s' % obj, 'PRPS/' + '%s' % obj, 'PRPS/' + '%s' % obj], ['images_diffuse', 'images_specular', 'images_metallic'], [1, 0.5, 0.5]): if self.verbose: print("\rPre-loading image ({:}/{:}) {:} ".format(objid + 1, self.nr_objects, dirb), end="") nr_ch = 3 if self.add_raw else 1 sample_path = os.path.join(self.datapath, dirb, dirn) imgs, nmls, msks, light_dirs = self.load_sample(sample_path, scale, -1, nr_ch) self.fill_data(imgs, nmls, msks, light_dirs, objid) if self.verbose: print("", end="\x1b[1K\r") objid += 1 if self.verbose: print() def get_max_shape(self, rotations=None): """ Returns a shape of an array (height, width, channels) which all images of various sizes under all rotations fit :param rotations: List of rotation angles (in radians) :return: max_shape [nr_objects, height, width, channels] """ max_shape = [0, 0, 0, 0] for obj in self.objlist: for p, scale in zip(['PRPS_Diffuse/' + '%s' % obj, 'PRPS/' + '%s' % obj, 'PRPS/' + '%s' % obj], [1, 0.5, 0.5]): max_shape[0] += 1 normal_path = os.path.join(self.datapath, p, 'gt_normal.tif') if not os.path.exists(normal_path): raise ValueError("Path\"{:}\"does not exists.".format(normal_path)) normals = cv2.imread(normal_path, -1) normals = cv2.resize(normals, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) f = open(os.path.join(self.datapath, p, 'light.txt')) data = f.read() f.close() lines = data.split('\n') nr_illums = len(lines) - 1 # the last line is empty (how to fix it?) if nr_illums > max_shape[3]: max_shape[3] = nr_illums if rotations is not None: # In case of rotations, the width and height might be larger for angle in rotations: img_shape = rotate_images(2 * np.pi - angle, normals[..., 0], axes=(0, 1), order=0).shape for k in range(2): if img_shape[k] > max_shape[k+1]: max_shape[k+1] = img_shape[k] else: for k in range(2): if normals.shape[k] > max_shape[k+1]: max_shape[k+1] = normals.shape[k] gc.collect() return max_shape @staticmethod def load_sample(dirpath, scale, illum_ids=-1, nr_channels=1): assert illum_ids == -1 normal_path = os.path.join(dirpath, '../gt_normal.tif') inboundary_path = os.path.join(dirpath, '../inboundary.png') onboundary_path = os.path.join(dirpath, '../onboundary.png') if not os.path.exists(normal_path): raise ValueError("Path\"{:}\"does not exists.".format(normal_path)) # read ground truth surface normal normals = np.float32(cv2.imread(normal_path, -1)) / 65535.0 # [-1,1] normals = normals[:, :, ::-1] normals = 2 * normals - 1 normals = cv2.resize(normals, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) normals = normals / np.sqrt(np.sum(normals**2, axis=-1, keepdims=True)) height, width = np.shape(normals)[:2] # read mask images_metallic if os.path.exists(inboundary_path) and os.path.exists(onboundary_path): inboundary = cv2.imread(inboundary_path, -1) inboundary = cv2.resize(inboundary, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) inboundary = inboundary > 0 onboundary = cv2.imread(onboundary_path, -1) onboundary = cv2.resize(onboundary, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) onboundary = onboundary > 0 masks = inboundary | onboundary else: masks = normals[..., 2] > 0 masks = masks[..., None] # read light filenames f = open(os.path.join(dirpath, '../light.txt')) data = f.read() f.close() lines = data.split('\n') nr_illums = len(lines) - 1 # the last line is empty (how to fix it?) light_directions = np.zeros((nr_illums, 3), np.float32) for i, l in enumerate(lines): s = l.split(' ') if len(s) == 3: light_directions[i, 0] = float(s[0]) light_directions[i, 1] = float(s[1]) light_directions[i, 2] = float(s[2]) # read images images = np.zeros((height, width, nr_illums, nr_channels), np.float32) for i in range(nr_illums): if i % np.floor(nr_illums / 10) == 0: print('.', end='') image_path = os.path.join(dirpath, '%05d.tif' % i) cv2_im = cv2.imread(image_path, -1) / 65535.0 cv2_im = cv2.resize(cv2_im, (height, width), interpolation=cv2.INTER_NEAREST) if nr_channels == 1: cv2_im = (cv2_im[:, :, 0:1] + cv2_im[:, :, 1:2] + cv2_im[:, :, 2:3]) / 3 images[:, :, i] = cv2_im return images, normals, masks, light_directions @staticmethod def load_sample_test(dir_path, obj_path, scale, index=-1): assert index == -1 obj, dirn = obj_path.split("/") return CyclesDataGenerator.load_sample(dir_path + obj, dirn, scale)

40.915344

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7,733

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0

0.014388

1

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0

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0.122302

0.028777

0

null

0

null

0

1

0

ceea1cba85bb3d624953e8ecf28fb6d54fd02614

4,429

py

Python

code/vocabulary.py

TimothyBenger/knausj_talon

10c2440fb3646abda1adc84ca9fd230f752eb353

[ "MIT" ]

null

code/vocabulary.py

TimothyBenger/knausj_talon

10c2440fb3646abda1adc84ca9fd230f752eb353

[ "MIT" ]

null

code/vocabulary.py

TimothyBenger/knausj_talon

10c2440fb3646abda1adc84ca9fd230f752eb353

[ "MIT" ]

null

from talon import Context, Module from .user_settings import get_list_from_csv mod = Module() ctx = Context() mod.list("vocabulary", desc="additional vocabulary words") # Default words that will need to be capitalized (particularly under w2l). # NB. These defaults and those later in this file are ONLY used when # auto-creating the corresponding settings/*.csv files. Those csv files # determine the contents of user.vocabulary and dictate.word_map. Once they # exist, the contents of the lists/dictionaries below are irrelevant. _capitalize_defaults = [ "I", "I'm", "I've", "I'll", "I'd", "Monday", "Mondays", "Tuesday", "Tuesdays", "Wednesday", "Wednesdays", "Thursday", "Thursdays", "Friday", "Fridays", "Saturday", "Saturdays", "Sunday", "Sundays", "January", "February", # March omitted because it's a regular word too "April", # May omitted because it's a regular word too "June", "July", "August", "September", "October", "November", "December", ] # Default words that need to be remapped. _word_map_defaults = { # E.g: # "cash": "cache", # This is the opposite ordering to words_to_replace.csv (the latter has the target word first) } _word_map_defaults.update({word.lower(): word for word in _capitalize_defaults}) # "dictate.word_map" is used by `actions.dictate.replace_words` to rewrite words # Talon recognized. Entries in word_map don't change the priority with which # Talon recognizes some words over others. ctx.settings["dictate.word_map"] = get_list_from_csv( "words_to_replace.csv", headers=("Replacement", "Original"), default=_word_map_defaults, ) # Default words that should be added to Talon's vocabulary. _simple_vocab_default = ["nmap", "admin", "Cisco", "Citrix", "VPN", "DNS", "Minecraft", "Ferran", "Angelos", "storageos"] # Defaults for different pronounciations of words that need to be added to # Talon's vocabulary. _default_vocabulary = { "N map": "nmap", "under documented": "under-documented", "koob control": "kubectl", "cube control": "kubectl", "keep control": "kubectl", "chang pod": "pod", "chang pods": "pods", "chang node": "node", "chang nodes": "nodes", "chang kubernetes": "kubernetes", "chang git": "git", "chang pull": "pull", "chang com": "com", "chang delete": "delete", "trying to lead": "delete", "replica set": "replicaset", "change delete": "delete", "name space": "namespace", "at it": "edit", "chang sudo": "sudo", "diagnostic yew till": "diagnosticutil", "stateful set": "statefulset", "in flux": "influx", "you control": "kubectl", "check out": "checkout", "make directory": "mkdir", "demon set": "daemonset", "demon sets": "daemonsets", "chang log": "log", "chang logs": "log", "koob control create from file": "kubectl create -f", "cube control create from file": "kubectl create -f", "keep control create from file": "kubectl create -f", "chang seff": "ceph", "ray doss": "RADOS", "raydos": "RADOS", "open sauce": "open-source", "all namespaces": "--all-namespaces", "output wide": "-o wide", "etsy dee": "etcd", "at city": "etcd", "at cd": "etcd", "cube system": "kube-system", "from file": " - f ", "with namespace": " - n ", "chang log": "log", "chang logs": "logs", "change directory": "cd", "storage class": "storageclass", "my sequel": "mysql", "dee bench": "dbench", "chang hay": "hey", "elastic search": "elasticsearch", "elastic such": "elasticsearch", "storage oh ess": "storageos", "store to us": "storageos", "store ous": "storageos", "store joes": "store joes" } _default_vocabulary.update({word: word for word in _simple_vocab_default}) # "user.vocabulary" is used to explicitly add words/phrases that Talon doesn't # recognize. Words in user.vocabulary (or other lists and captures) are # "command-like" and their recognition is prioritized over ordinary words. ctx.lists["user.vocabulary"] = get_list_from_csv( "additional_words.csv", headers=("Word(s)", "Spoken Form (If Different)"), default=_default_vocabulary, ) # for quick verification of the reload # print(str(ctx.settings["dictate.word_map"])) # print(str(ctx.lists["user.vocabulary"]))

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121

0.64326

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null

0

null

0

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ceef88b42a5304577b2b39be8918b4680ae52465

9,605

py

Python

upetem_service.py

myroslav/robot_tests.broker.upetem

323314259faa60618113fbc37b5e1f1d79c2192b

[ "Apache-2.0" ]

null

upetem_service.py

myroslav/robot_tests.broker.upetem

323314259faa60618113fbc37b5e1f1d79c2192b

[ "Apache-2.0" ]

null

upetem_service.py

myroslav/robot_tests.broker.upetem

323314259faa60618113fbc37b5e1f1d79c2192b

[ "Apache-2.0" ]

null

# coding=utf-8 from datetime import datetime, timedelta import dateutil.parser import pytz import urllib TZ = pytz.timezone('Europe/Kiev') def adapt_data(data): data['data']['procuringEntity']['name'] = 'testuser_tender_owner' for x in data['data']['items']: x['unit']['name'] = get_unit_name(x['unit']['name']) x['deliveryAddress']['region'] = get_delivery_region(x['deliveryAddress']['region']) x['deliveryAddress']['locality'] = convert_locality(x['deliveryAddress']['locality']) x['deliveryDate']['startDate'] = adapt_delivery_date(x['deliveryDate']['startDate']) x['deliveryDate']['endDate'] = adapt_delivery_date(x['deliveryDate']['endDate']) data['data']['procuringEntity']['address']['region'] = get_delivery_region(data['data']['procuringEntity']['address']['region']) data['data']['procuringEntity']['address']['locality'] = convert_locality(data['data']['procuringEntity']['address']['locality']) data['data']['procuringEntity']['contactPoint']['telephone'] = data['data']['procuringEntity']['contactPoint']['telephone'][:13] return data def adapt_step(data, new_step): data['data']['minimalStep']['amount'] = round(new_step, 2) data['data']['lots'][0]['minimalStep']['amount'] = round(new_step, 2) def adapt_unit_name(data): return { u"наб.": u"набір", u"шт.": u"штуки", u"упак.": u"упаковка" }.get(data, data) def adapt_data_view(data): for x in data['data']['items']: x['deliveryDate']['startDate'] = adapt_delivery_date(x['deliveryDate']['startDate']) x['deliveryDate']['endDate'] = adapt_delivery_date(x['deliveryDate']['endDate']) return data def download_file(url, file_name, output_dir): urllib.urlretrieve(url, ('{}/{}'.format(output_dir, file_name))) def get_type_field(field): value = ['deliveryDate.startDate', 'deliveryDate.endDate', 'deliveryAddress.postalCode', 'deliveryAddress.region', 'deliveryAddress.streetAddress', 'additionalClassifications.id', 'classification.id', 'unit.name', 'unit.code', 'deliveryLocation.latitude', 'deliveryLocation.longitude', 'quantity', 'deliveryAddress.locality', 'title', 'value.amount', 'value.valueAddedTaxIncluded', 'minimalStep.amount', 'minimalStep.valueAddedTaxIncluded'] text = ['description', 'deliveryAddress.countryName', 'classification.scheme', 'classification.description', 'additionalClassifications.scheme', 'additionalClassifications.description', 'value.currency', 'minimalStep.currency', 'featureOf', 'status', 'resolutionType', 'resolution', 'satisfied', 'complaintID', 'cancellationReason'] if field in value: type_fields = 'value' elif field in text: type_fields = 'text' return type_fields def get_delivery_region(region): if region == u"місто Київ": delivery_region = u"м.Київ" elif region == u"Дніпропетровська область": delivery_region = u"Днiпропетровська область" elif region == u"Рівненська область": delivery_region = u"Рiвненська область" elif region == u"Чернігівська область": delivery_region = u"Чернiгiвська область" else: delivery_region = region return delivery_region def convert_float_to_string(number): return format(number, '.2f') def convert_coordinates_to_string(number): return format(number) def adapt_delivery_date(date): adapt_date = ''.join([date[:date.index('T') + 1], '00:00:00', date[date.index('+'):]]) return adapt_date def parse_date(date_str): date_str = datetime.strptime(date_str, "%d.%m.%Y %H:%M") date = datetime(date_str.year, date_str.month, date_str.day, date_str.hour, date_str.minute, date_str.second, date_str.microsecond) date = TZ.localize(date).isoformat() return date def parse_item_date(date_str): date_str = datetime.strptime(date_str, "%d.%m.%Y") date = datetime(date_str.year, date_str.month, date_str.day) date = TZ.localize(date).isoformat() return date def convert_date_to_string(date): date = dateutil.parser.parse(date) date = date.strftime("%d.%m.%Y %H:%M") return date def convert_item_date_to_string(date): date = dateutil.parser.parse(date) date = date.strftime("%d.%m.%Y") return date def parse_complaintPeriod_date(date_string): date_str = datetime.strptime(date_string, "%d.%m.%Y %H:%M") date_str -= timedelta(minutes=5) date = datetime(date_str.year, date_str.month, date_str.day, date_str.hour, date_str.minute, date_str.second, date_str.microsecond) date = TZ.localize(date).isoformat() return date def parse_complaintPeriod_endDate(date_str): if '-' in date_str: date_str = datetime.strptime(date_str, "%Y-%m-%d %H:%M:%S") else: date_str = datetime.strptime(date_str, "%d.%m.%Y %H:%M") date = datetime(date_str.year, date_str.month, date_str.day, date_str.hour, date_str.minute, date_str.second, date_str.microsecond) date = TZ.localize(date).isoformat() return date def capitalize_first_letter(string): string = string.capitalize() return string def get_unit_name(name): return { u'штуки': u'шт.', u'упаковка': u'упак.', u'набір': u'наб.', u'кілограми': u'кг.', u'лот': u'лот', u'флакон': u'флак.', u'Флакон': u'флак.' }.get(name, name) def convert_locality(name): if name == u"Київ": adapted_name = u"М.КИЇВ" elif name == u"Дніпропетровськ": adapted_name = u"ДНІПРОПЕТРОВСЬКА ОБЛАСТЬ/М.ДНІПРО" else: adapted_name = name return adapted_name.upper() def convert_status(tender_status): status = { u'Очікування пропозицій': u'active.tendering', u'Період аукціону': u'active.auction', u'Період уточнень': u'active.enquiries', u'Перед-кваліфікаційний період': u'active.pre-qualification', u'Період оскарження': u'active.pre-qualification.stand-still' } return status[tender_status] def get_claim_status(claim_status, test_name): status = { u'Вимога': 'claim', u'Розглянуто': 'answered', u'Вирішена': 'resolved', u'Відхилено': 'cancelled', u'Відхилена': 'declined', u'Обробляється': 'pending', u'Недійсна': 'invalid', u'Проігнорована': 'ignored' } return status[claim_status] def get_resolution_type(resolution): types = { u'Вирішено': 'resolved', u'Задоволено': 'resolved', u'Відхилено': 'declined', u'Недійсно': 'invalid' } return types[resolution] def convert_satisfied(value): if value == u'Так': satisfied = True else: satisfied = False return satisfied def get_unit(field,unit_data): unit = unit_data.split() unit[1] = adapt_unit_name(unit[1]) unit_value = { 'unit.code': unit[0], 'unit.name': unit[1] } return unit_value[field] def convert_type_tender(key): type_tender = { u'Відкриті торги': 'aboveThresholdUA', u'Відкриті торги з публікацією англ.мовою': 'aboveThresholdEU', u'Переговорна процедура': 'reporting' } return type_tender[key] def convert_data_lot(key): data_lot = { u'грн.': 'UAH' } return data_lot[key] def convert_data_feature(key): data_feature = { u'Закупівлі': 'tenderer', u'Лоту': 'lot', u'Предмету лоту': 'item' } return data_feature[key] def convert_complaintID(tender_uaid, type_complaint): if 'complaint_number' not in globals(): complaint_number = 1 value = '%s.a%s' % (tender_uaid, complaint_number) global complaint_number complaint_number += 1 return value def get_pos(featureOf): if featureOf == u'Закупівлі': position = 1 elif featureOf == u'Лоту': position = 2 elif featureOf == u'Предмету лоту': position = 1 return position def get_value_feature(value): value = value * 100 value = str(int(value)) + '%' return value def get_feature_xpath(field_name, feature_id): xpath = { 'title': "//*[contains(@value, '" +feature_id+ "')]", 'description': "//*[contains(@value, '" +feature_id+ "')]/ancestor::tbody/tr[2]/td[2]/textarea", 'featureOf': "//*[contains(@value, '" +feature_id+ "')]/ancestor::tbody/tr[3]/td[2]//td[2]/div[1]/label" } return xpath[field_name] def convert_bid_status(value): status = { u'Недійсна пропозиція': 'invalid' } return status[value] def get_all_dates(initial_tender_data, key): tender_period = initial_tender_data.data.tenderPeriod start_dt = dateutil.parser.parse(tender_period['startDate']) end_dt = dateutil.parser.parse(tender_period['endDate']) data = { 'EndPeriod': start_dt.strftime("%d.%m.%Y %H:%M"), 'StartDate': start_dt.strftime("%d.%m.%Y %H:%M"), 'EndDate': end_dt.strftime("%d.%m.%Y %H:%M"), } return data.get(key, '') def increment_identifier(data): data['data']['procuringEntity']['identifier']['id'] = str(int(data['data']['procuringEntity']['identifier']['id']) + 1) def convert_cause_type(key): cause_type = { '1': 'artContestIP', '2': 'noCompetition', '4': 'twiceUnsuccessful', '5': 'additionalPurchase', '6': 'additionalConstruction', '7': 'stateLegalServices', } return cause_type[key]

30.785256

158

0.639771

1,143

9,605

5.210849

0.223972

0.044661

0.034755

0.004701

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0.180826

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0

0.005233

0.204165

9,605

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30.884244

0.77394

0.001249

0

0.168776

0

0.004219

0.28485

0.064644

0

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false

0

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0.299578

0

null

0

null

0

1

0

ceefdd4f273021acf57a211bf9db5e4727c86333

1,638

py

Python

sway/tiling-indicator.py

iziGor/scripts

0076711ab6c423d97c2dad72119fbd57e27fb250

[ "BSD-2-Clause" ]

null

sway/tiling-indicator.py

iziGor/scripts

0076711ab6c423d97c2dad72119fbd57e27fb250

[ "BSD-2-Clause" ]

null

sway/tiling-indicator.py

iziGor/scripts

0076711ab6c423d97c2dad72119fbd57e27fb250

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 """ Show split layout indicator Usage: ./tiling-indicator.py Suppoused to be used inside waybar or polybar. Config example: Waybar: "custom/ws": { "exec": "python -u $HOME/.config/sway/scripts/tiling-indicator-2.py 2> /dev/null } Polybar: [module/layout] type = custom/script exec = PYTHONPATH=${XDG_CONFIG_HOME}/i3 python -u -m scripts.tiling-indicator.py 2> /dev/null interval = 0 format = "<label>" tail = true label-font = 6 github :: https://github.com/iziGor year :: 2021 """ import i3ipc i3 = i3ipc.Connection() last = '' # Font Awesome 5 Free:style=Solid # layouts = { "tabbed": ("61bbf6", "\uf24d") # , "stacked": ("00AA00", "\uf5fd") # , "splitv": ("82B8DF", "\uf103") # , "splith": ("CF4F88", "\uf101") # } layouts = { "tabbed": ("61bbf6", "\uf24d") , "stacked": ("00AA00", "\uf5fd") , "splitv": ("82B8DF", "\u2b9f") , "splith": ("CF4F88", "\u2b9e") } # Material Icons # layouts = {"tabbed":"\ue8d8", "stacked":"\ue3c7", "splitv":"\ue947", "splith":"\ue949"} def on_event(sway, _): global last layout = sway.get_tree().find_focused().parent.layout if not layout == last: ## polybar format output # print("%{{F#{}}}{}%{{F-}}".format(*layouts.get(layout, ("888800", "?")))) ## waybar format output print("<span color='#{}'>{}</span>".format(*layouts.get(layout, ("888800", "?")))) last = layout # Subscribe to events i3.on("window::focus", on_event) i3.on("binding", on_event) # Start the main loop and wait for events to come in. i3.main()

22.135135

93

0.582418

196

1,638

4.826531

0.581633

0.047569

0.035941

0.021142

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0

0.063467

0.211233

1,638

73

94

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0.668731

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0

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0

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false

0

0.058824

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0.058824

0

null

0

null

0

1

0

cef07786e7e7ac506670e6c1114e7cf83e1eb3a0

5,822

py

Python

cogs/tags.py

Chrovo/Productivity

4bdb7eecfb8ae16b013ce58a1b0421f8f791499e

[ "MIT" ]

null

cogs/tags.py

Chrovo/Productivity

4bdb7eecfb8ae16b013ce58a1b0421f8f791499e

[ "MIT" ]

null

cogs/tags.py

Chrovo/Productivity

4bdb7eecfb8ae16b013ce58a1b0421f8f791499e

[ "MIT" ]

null

from typing import Optional import discord import asyncpg from discord.ext import commands from .utils.pagination import create_paginated_embed class Tags(commands.Cog): """Productivity's tag system.""" def __init__(self, bot:commands.Bot) -> None: self.bot = bot self.emoji = "🏷️ " async def delete_check(self, ctx:commands.Context, tag_name) -> bool: query = """ SELECT * FROM tags WHERE tag_name = $1 AND guild_id = $2; """ async with self.bot.db.acquire() as connection: async with connection.transaction(): fetched = await connection.fetchrow(query, tag_name, ctx.guild.id) return fetched['user_id'] == ctx.author or ctx.author.guild_permissions.manage_messages @commands.group(invoke_without_command=True) @commands.cooldown(1, 5, commands.BucketType.user) async def tag(self, ctx, *, tag:str): """A tag system!""" async with self.bot.db.acquire() as connection: async with connection.transaction(): try: query = """ SELECT * FROM tags WHERE tag_name = $1 AND guild_id = $2; """ tag = await connection.fetchrow(query, tag, ctx.guild.id) return await ctx.send(tag['tag_content']) except TypeError: return await ctx.send("Tag not found.") @tag.command(description="Create a tag!", aliases=['add']) @commands.cooldown(1, 5, commands.BucketType.user) async def create(self, ctx, name, *, content): try: query = """ INSERT INTO tags (user_id, guild_id, tag_name, tag_content) VALUES ($1, $2, $3, $4); """ await self.bot.db.execute(query, ctx.author.id, ctx.guild.id, name, content) await ctx.send("Succesfully created the tag!") except Exception as e: await ctx.send(e) await ctx.send("An error has occurred whilst creating the tag") @tag.command(description="Start your use of creating tags") @commands.cooldown(1, 5, commands.BucketType.user) async def start(self, ctx): try: query = """ INSERT INTO tag_users (user_id, username) VALUES ($1, $2); """ await self.bot.db.execute(query, ctx.author.id, ctx.author.name) await ctx.send("Successfully started your use of our tag system!") except Exception: await ctx.send("You are already in our database!") @tag.command(description="Delete a tag!") @commands.cooldown(1, 5, commands.BucketType.user) async def delete(self, ctx, *, tag:str): check = await self.delete_check(ctx, tag) if check: try: query = """ DELETE FROM tags WHERE tag_name = $1 AND guild_id = $2; """ await self.bot.db.execute(query, tag, ctx.guild.id) await ctx.send("Successfully deleted tag!") except: await ctx.send("An error has occurred while attempting to delete the tag.") else: await ctx.send("You do not have permission to delete this tag!") @commands.command(description="Look at all of the tags a member has!") @commands.cooldown(1, 5, commands.BucketType.user) async def tags(self, ctx, member:Optional[discord.Member]=None): member = member or ctx.author async with self.bot.db.acquire() as connection: async with connection.transaction(): query = """ SELECT * FROM tags WHERE user_id = $1 AND guild_id = $2; """ tags = await connection.fetch(query, member.id, ctx.guild.id) paginate = create_paginated_embed(ctx, tags, 'tag_name', f"{member}'s tags", member.avatar_url, member.name) await paginate.start(ctx) @tag.command(description="Edit a tag!") @commands.cooldown(1, 5, commands.BucketType.user) async def edit(self, ctx, old_tag, new_name, *, new_content): query = """ UPDATE tags SET tag_name = $1, tag_content = $2 WHERE user_id = $3 AND tag_name = $4 AND guild_id = $5; """ try: await self.bot.db.execute(query, new_name, new_content, ctx.author.id, old_tag, ctx.guild.id) return await ctx.send("Successfully edited tag!") except Exception: return await ctx.send( """ An error occurred while editing the tag, this is likely because u dont own this tag or it doesnt exist. """ ) @tag.command(description="View information about a tag!") @commands.cooldown(1, 5, commands.BucketType.user) async def info(self, ctx, *, tag:str): async with self.bot.db.acquire() as connection: async with connection.transaction(): query = """ SELECT * FROM tags WHERE guild_id = $1 AND tag_name = $2; """ try: tag_info = await connection.fetchrow(query, ctx.guild.id, tag) owner = ctx.guild.get_member(tag_info['user_id']) embed = discord.Embed(title=tag_info['tag_name']) embed.add_field(name="Owner", value=owner.mention) embed.set_author(name=owner, icon_url=owner.avatar_url) return await ctx.send(embed=embed) except TypeError: return await ctx.send("Tag not found.") def setup(bot:commands.Bot): bot.add_cog(Tags(bot))

39.605442

124

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0.220339

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0.293119

0.206726

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5,822

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39.605442

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0.125

0

null

0

null

0

1

0

cef53f21d6ccfd1533b28e30e6717c9396761c37

4,027

py

Python

terminalgame/world.py

naslundx/terminalgame

d855ec33ff8a057b1308ad30c54f138343baf56f

[ "MIT" ]

null

terminalgame/world.py

naslundx/terminalgame

d855ec33ff8a057b1308ad30c54f138343baf56f

[ "MIT" ]

null

terminalgame/world.py

naslundx/terminalgame

d855ec33ff8a057b1308ad30c54f138343baf56f

[ "MIT" ]

null

import curses from contextlib import contextmanager from time import sleep from typing import List, Tuple, TYPE_CHECKING from .actions import Action from .properties import Property if TYPE_CHECKING: from .object import Object class World: class __World: def __init__(self, fps: int, render: bool = True): self.fps = fps self._objects: List["Object"] = [] self._draw_queue: List[Tuple[int, int, str]] = [] self.running = True self._window = None self._height, self._width = 50, 80 # s.getmaxyx() self._key = None if render: _ = curses.initscr() curses.curs_set(0) self._window = curses.newwin(self._height, self._width, 0, 0) self._window.keypad(True) self._window.timeout(1000 // self.fps) def register(self, obj: "Object"): assert obj not in self._objects self._objects.append(obj) self._draw_queue.append((obj.x, obj.y, obj.sign)) def get_properties(self, x: int, y: int) -> List[Property]: for o in self._objects: if o.xy == (x, y) and not o.is_destroyed: return o.properties[:] return [] def draw(self): while self._draw_queue: x, y, s = self._draw_queue.pop() if self._window: if x in range(0, self.width) and y in range(0, self.height): self._window.addch(y, x, s) else: print(x, y, s) def tick(self): # Handle keypress mapping key = self.keypress if key: for obj in (o for o in self._objects if o.mapping): if key in obj.mapping: new_x, new_y = obj.x, obj.y action = obj.mapping[key] if action == Action.MOVE_UP: new_y -= 1 if action == Action.MOVE_DOWN: new_y += 1 if action == Action.MOVE_LEFT: new_x -= 1 if action == Action.MOVE_RIGHT: new_x += 1 if Property.SOLID not in self.get_properties(new_x, new_y): obj.x, obj.y = new_x, new_y # Update draw queue for obj in self._objects: if obj.is_destroyed: self._draw_queue.append((obj._oldx, obj._oldy, " ")) elif obj.has_moved: self._draw_queue.append((obj._oldx, obj._oldy, " ")) self._draw_queue.append((obj.x, obj.y, obj.sign)) obj.tick() # Render self.draw() # Remove destroyed objects self._objects = [o for o in self._objects if not o.is_destroyed] # Get keypress if self._window: self._key = self._window.getch() else: sleep(1.0 / self.fps) self._key = None return self.running def quit(self): self.running = False if self._window: curses.endwin() self._window = False @property def width(self): return self._width @property def height(self): return self._height @property def keypress(self): return self._key if self._key != -1 else None instance = None def __init__(self, *args, **kwargs): assert not World.instance World.instance = World.__World(*args, **kwargs) def __getattr__(self, name): return getattr(self.instance, name) @contextmanager def renderer(self): try: yield self finally: self.quit()

31.217054

83

0.48746

454

4,027

4.132159

0.231278

0.053305

0.048507

0.040512

0.15565

0.145522

0.088486

0.036247

0

0.008591

0.421902

4,027

128

84

31.460938

0.797251

0.024584

0

0.141414

0

0.003571

0

0.020202

1

0.121212

false

0

0.070707

0.040404

0.292929

0.010101

0

null

0

null

0

1

0

cef7c80ce2c92bb1a2333400473042e3222f6983

1,012

py

Python

data/data-pipeline/data_pipeline/etl/constants.py

vim-usds/justice40-tool

6691df3e318b531b0e05454a79b8560b7d307b36

[ "CC0-1.0" ]

null

data/data-pipeline/data_pipeline/etl/constants.py

vim-usds/justice40-tool

6691df3e318b531b0e05454a79b8560b7d307b36

[ "CC0-1.0" ]

null

data/data-pipeline/data_pipeline/etl/constants.py

vim-usds/justice40-tool

6691df3e318b531b0e05454a79b8560b7d307b36

[ "CC0-1.0" ]

null

DATASET_LIST = [ { "name": "tree_equity_score", "module_dir": "tree_equity_score", "class_name": "TreeEquityScoreETL", }, { "name": "census_acs", "module_dir": "census_acs", "class_name": "CensusACSETL", }, { "name": "ejscreen", "module_dir": "ejscreen", "class_name": "EJScreenETL", }, { "name": "housing_and_transportation", "module_dir": "housing_and_transportation", "class_name": "HousingTransportationETL", }, { "name": "hud_housing", "module_dir": "hud_housing", "class_name": "HudHousingETL", }, { "name": "calenviroscreen", "module_dir": "calenviroscreen", "class_name": "CalEnviroScreenETL", }, { "name": "hud_recap", "module_dir": "hud_recap", "class_name": "HudRecapETL", }, ] CENSUS_INFO = { "name": "census", "module_dir": "census", "class_name": "CensusETL", }

23.534884

51

0.528656

82

1,012

6.134146

0.329268

0.143141

0.059642

0

0.302372

1,012

42

52

24.095238

0.712465

0

0.505929

0.075099

0

1

0

false

0

null

0

null

0

1

0

cefa905aac2153e51d910363e7adb666340184b1

1,955

py

Python

e2e/test_get.py

sturzl/guet

b8c453f07968b689b303e20e7a31b405c02c54ef

[ "Apache-2.0" ]

null

e2e/test_get.py

sturzl/guet

b8c453f07968b689b303e20e7a31b405c02c54ef

[ "Apache-2.0" ]

null

e2e/test_get.py

sturzl/guet

b8c453f07968b689b303e20e7a31b405c02c54ef

[ "Apache-2.0" ]

null

from e2e import DockerTest class TestGet(DockerTest): def test_get_current_prints_currently_set_committers(self): self.guet_init() self.git_init() self.guet_add('initials1', 'name1', 'email1') self.guet_add('initials2', 'name2', 'email2') self.guet_start() self.guet_set(['initials1', 'initials2']) self.guet_get_current() self.save_file_content('.guet/errors') self.execute() self.assert_text_in_logs(5, 'Currently set committers') self.assert_text_in_logs(6, 'initials1 - name1 <email1>') self.assert_text_in_logs(7, 'initials2 - name2 <email2>') def test_get_committers_prints_all_committers_on_the_system(self): self.guet_init() self.guet_add('initials1', 'name1', 'email1') self.guet_add('initials2', 'name2', 'email2') self.guet_get_committers() self.save_file_content('.guet/errors') self.execute() self.assert_text_in_logs(0, 'All committers') self.assert_text_in_logs(1, 'initials1 - name1 <email1>') self.assert_text_in_logs(2, 'initials2 - name2 <email2>') def test_get_prints_error_message_if_trying_to_run_before_guet_init(self): self.guet_get_committers() self.execute() self.assert_text_in_logs(0, ('guet has not been initialized yet! ' + 'Please do so by running the command "guet init".')) def test_prints_help_message(self): self.guet_init() self.guet_get_committers(help=True) self.execute() self.assert_text_in_logs(0, 'usage: guet get <identifier> [-flag, ...]') self.assert_text_in_logs(2, 'Get currently set information.') self.assert_text_in_logs(4, 'Valid Identifier') self.assert_text_in_logs(6, '\tcurrent - lists currently set committers') self.assert_text_in_logs(7, '\tcommitters - lists all committers')

38.333333

89

0.658824

253

1,955

4.750988

0.29249

0.086522

0.139767

0.159734

0.618136

0.556572

0.413478

0.222962

0

0.025675

0.223018

1,955

50

90

39.1

0.765635

0

0.384615

0

0.261381

0

0.307692

1

0.102564

false

0

0.025641

0

0.153846

0.102564

0

null

0

null

0

1

0

cefff0cd7583924688b44d3f4da180ddf1bf3140

1,813

py

Python

lesion_tool/waimea.py

alaurent4/nighres

ffb4a478a224190ffe0112f7e4d214ad6825716e

[ "Apache-2.0" ]

null

lesion_tool/waimea.py

alaurent4/nighres

ffb4a478a224190ffe0112f7e4d214ad6825716e

[ "Apache-2.0" ]

null

lesion_tool/waimea.py

alaurent4/nighres

ffb4a478a224190ffe0112f7e4d214ad6825716e

[ "Apache-2.0" ]

1

2019-01-21T10:53:38.000Z

#!/usr/bin/env python """ """ from xml.etree.ElementTree import Element import xml.etree.ElementTree as etree import xml.dom.minidom import re import sys import getopt import os from time import gmtime, strftime from nipype import config, logging from nighres.lesion_tool.lesion_pipeline import Lesion_extractor def main(): try: o, a = getopt.getopt(sys.argv[1:], "n:d:s:f:a:l:") except getopt.GetoptError as err: print(err) print('waimea.py -n <directory> -d <base_directory> -s <subject> -f <freesurfer dir> -a <atlas> -l <labels>') sys.exit(2) if len(o) < 4: print('waimea.py -n <directory> -d <base_directory> -s <subject> -f <freesurfer dir> -a <atlas> -l <labels>') sys.exit(2) for opt, arg in o: if opt == '-n': wf_name = arg elif opt == '-d': base_dir = arg elif opt == '-s': sub = arg elif opt == '-f': fsdir = arg elif opt == '-a': atlas = arg elif opt == '-l': labels = arg wf = Lesion_extractor(wf_name=wf_name, base_dir=base_dir, subjects=[sub], #main=main, #acc=acc, atlas=atlas, fs_subjects_dir=fsdir, labels=labels) config.update_config({'logging': {'log_directory': wf.base_dir,'log_to_file': True}}) logging.update_logging(config) config.set('execution','job_finished_timeout','20.0') wf.config['execution'] = {'job_finished_timeout': '10.0'} try: wf.run() except: print('Error! Pipeline exited ') raise if __name__ == "__main__": main()

29.241935

117

0.529509

222

1,813

4.18018

0.391892

0.037716

0.053879

0.030172

0.172414

0

0.008382

0.341975

1,813

62

118

29.241935

0.769489

0.02096

0

0.12

0

0.04

0.199321

0

1

0.02

false

0

0.2

0

0.22

0.08

0

null

0

null

0

1

0

3002f09ad0b6466dc363ed3ed13747fd93bb53e6

2,208

py

Python

system/__init__.py

JHUAPL/meta-system

d3e80e50d64e1a9e83d81efbcb8de1ec9cc34e03

[ "Apache-2.0" ]

5

2021-07-30T00:59:59.000Z

2022-03-23T16:52:46.000Z

system/__init__.py

JHUAPL/meta-system

d3e80e50d64e1a9e83d81efbcb8de1ec9cc34e03

[ "Apache-2.0" ]

null

system/__init__.py

JHUAPL/meta-system

d3e80e50d64e1a9e83d81efbcb8de1ec9cc34e03

[ "Apache-2.0" ]

null

# ********************************************************************** # Copyright (C) 2020 Johns Hopkins University Applied Physics Laboratory # # All Rights Reserved. # For any other permission, please contact the Legal Office at JHU/APL. # # 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 flask import Flask from shared.config import config from shared.log import logger from system.extensions import FlaskExtensions, JobManagerClient, DockerClient from system.job_queue_manager import job_queue_watchdog cors = FlaskExtensions.cors mail = FlaskExtensions.mail mongodb = FlaskExtensions.mongodb jwt = FlaskExtensions.jwt bcrypt = FlaskExtensions.bcrypt class FlaskApp(object): def __init__(self): self.app = Flask(__name__, static_folder=config.STATIC_DIR, static_url_path="") self.app.config.update(config.dict()) bcrypt.init_app(self.app) jwt.init_app(self.app) mongodb.init_app(self.app) mail.init_app(self.app) cors.init_app(self.app) DockerClient() JobManagerClient() job_queue_watchdog() self.register_routes() def register_routes(self): from system.api.web import web_bp self.app.register_blueprint(web_bp) from system.api.info import info_bp self.app.register_blueprint(info_bp) from system.api.database import database_bp self.app.register_blueprint(database_bp) from system.api.jobs import jobs_bp self.app.register_blueprint(jobs_bp) from system.api.results import results_bp self.app.register_blueprint(results_bp)

32.955224

87

0.682065

281

2,208

5.217082

0.44484

0.057299

0.037517

0.047749

0.088677

0

0.004452

0.186141

2,208

66

88

33.454545

0.811352

0.379076

0

1

0.058824

false

0

0.294118

0

0.382353

0.147059

0

null

0

null

0

1

0

30050dc99f412b59d4c595ff2aeb87a711b709c5

1,262

py

Python

main.py

hakierspejs/corononews

f1103da57d5c39694649bf7d7ba7748541dcfbe0

[ "WTFPL" ]

null

main.py

hakierspejs/corononews

f1103da57d5c39694649bf7d7ba7748541dcfbe0

[ "WTFPL" ]

null

main.py

hakierspejs/corononews

f1103da57d5c39694649bf7d7ba7748541dcfbe0

[ "WTFPL" ]

null

#!/usr/bin/env python import flask import requests import lxml.html import logging app = flask.Flask(__name__) LOGGER = logging.getLogger(__name__) HN_BASE_URL = 'https://news.ycombinator.com/' def has_virus(url): if not url.startswith('http://') and not url.startswith('https://'): return True s = requests.get(url).text.lower() for w in ['covid', 'virus']: if w in s: return True return False @app.route('/') def main(): h = lxml.html.fromstring(requests.get(HN_BASE_URL).text) ret = '<ol>' for n, row in enumerate(h.xpath('//tr [@id]')[1:]): story = row.xpath('.//a [@class="storylink"]').pop() LOGGER.info('%d: %s', n, story.get('href')) c_row = row.getnext() comments = c_row.xpath('.//a [contains(@href, "item?id=")]')[-1] comments_url = HN_BASE_URL + comments.get('href') if has_virus(story.get('href')) or has_virus(comments_url): continue ret += f''' <li> <a href="{story.get("href")}">{story.text}</a> (<a href="{comments_url}">{comments.text}</a>) </li>''' return ret if __name__ == '__main__': logging.basicConfig(level='INFO') app.run(host='0.0.0.0')

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72

0.568938

173

1,262

3.976879

0.433526

0.040698

0.039244

0

0.006244

0.23851

1,262

43

73

29.348837

0.709677

0.015848

0

0.055556

0

0.262691

0.068493

0

1

0.055556

false

0

0.111111

0

0.277778

0

null

0

null

0

1

0

3007056835a3f4cdbc36d9cf5d7aec07fbd6a6ae

7,134

py

Python

code/func/func.py

lindenmp/neurodev_long

d6efc6b2e212bc6fc0669c80efcfa0b67d1e4b06

[ "MIT" ]

null

code/func/func.py

lindenmp/neurodev_long

d6efc6b2e212bc6fc0669c80efcfa0b67d1e4b06

[ "MIT" ]

5

2020-03-24T17:56:29.000Z

2021-12-13T20:35:48.000Z

code/func/func.py

lindenmp/neurodev_long

d6efc6b2e212bc6fc0669c80efcfa0b67d1e4b06

[ "MIT" ]

null

# Functions for project: NormativeNeuroDev_Longitudinal # Linden Parkes, 2019 # lindenmp@seas.upenn.edu from IPython.display import clear_output import numpy as np import scipy as sp from scipy import stats import pandas as pd from statsmodels.stats import multitest def get_cmap(which_type = 'qual1', num_classes = 8): # Returns a nice set of colors to make a nice colormap using the color schemes # from http://colorbrewer2.org/ # # The online tool, colorbrewer2, is copyright Cynthia Brewer, Mark Harrower and # The Pennsylvania State University. if which_type == 'linden': cmap_base = np.array([[255,105,97],[97,168,255],[178,223,138],[117,112,179],[255,179,71]]) elif which_type == 'pair': cmap_base = np.array([[124,230,199],[255,169,132]]) elif which_type == 'qual1': cmap_base = np.array([[166,206,227],[31,120,180],[178,223,138],[51,160,44],[251,154,153],[227,26,28], [253,191,111],[255,127,0],[202,178,214],[106,61,154],[255,255,153],[177,89,40]]) elif which_type == 'qual2': cmap_base = np.array([[141,211,199],[255,255,179],[190,186,218],[251,128,114],[128,177,211],[253,180,98], [179,222,105],[252,205,229],[217,217,217],[188,128,189],[204,235,197],[255,237,111]]) elif which_type == 'seq_red': cmap_base = np.array([[255,245,240],[254,224,210],[252,187,161],[252,146,114],[251,106,74], [239,59,44],[203,24,29],[165,15,21],[103,0,13]]) elif which_type == 'seq_blu': cmap_base = np.array([[247,251,255],[222,235,247],[198,219,239],[158,202,225],[107,174,214], [66,146,198],[33,113,181],[8,81,156],[8,48,107]]) elif which_type == 'redblu_pair': cmap_base = np.array([[222,45,38],[49,130,189]]) elif which_type == 'yeo17': cmap_base = np.array([[97,38,107], # VisCent [194,33,39], # VisPeri [79,130,165], # SomMotA [44,181,140], # SomMotB [75,148,72], # DorsAttnA [23,116,62], # DorsAttnB [149,77,158], # SalVentAttnA [222,130,177], # SalVentAttnB [75,87,61], # LimbicA [149,166,110], # LimbicB [210,135,47], # ContA [132,48,73], # ContB [92,107,131], # ContC [218,221,50], # DefaultA [175,49,69], # DefaultB [41,38,99], # DefaultC [53,75,158] # TempPar ]) elif which_type == 'yeo17_downsampled': cmap_base = np.array([[97,38,107], # VisCent [79,130,165], # SomMotA [75,148,72], # DorsAttnA [149,77,158], # SalVentAttnA [75,87,61], # LimbicA [210,135,47], # ContA [218,221,50], # DefaultA [53,75,158] # TempPar ]) if cmap_base.shape[0] > num_classes: cmap = cmap_base[0:num_classes] else: cmap = cmap_base cmap = cmap / 255 return cmap def update_progress(progress, my_str = ''): bar_length = 20 if isinstance(progress, int): progress = float(progress) if not isinstance(progress, float): progress = 0 if progress < 0: progress = 0 if progress >= 1: progress = 1 block = int(round(bar_length * progress)) clear_output(wait = True) text = my_str + " Progress: [{0}] {1:.1f}%".format( "#" * block + "-" * (bar_length - block), progress * 100) print(text) def get_synth_cov(df, cov = 'scanageYears', stp = 1): # Synthetic cov data X_range = [np.min(df[cov]), np.max(df[cov])] X = np.arange(X_range[0],X_range[1],stp) X = X.reshape(-1,1) return X def run_corr(df_X, df_y, typ = 'spearmanr'): df_corr = pd.DataFrame(index = df_y.columns, columns = ['coef', 'p']) for i, row in df_corr.iterrows(): if typ == 'spearmanr': df_corr.loc[i] = sp.stats.spearmanr(df_X, df_y[i]) elif typ == 'pearsonr': df_corr.loc[i] = sp.stats.pearsonr(df_X, df_y[i]) return df_corr def get_fdr_p(p_vals): out = multitest.multipletests(p_vals, alpha = 0.05, method = 'fdr_bh') p_fdr = out[1] return p_fdr def get_fdr_p_df(p_vals): p_fdr = pd.DataFrame(index = p_vals.index, columns = p_vals.columns, data = np.reshape(get_fdr_p(p_vals.values.flatten()), p_vals.shape)) return p_fdr def mark_outliers(x, thresh = 3, c = 1.4826): my_med = np.median(x) mad = np.median(abs(x - my_med))/c cut_off = mad * thresh upper = my_med + cut_off lower = my_med - cut_off outliers = np.logical_or(x > upper, x < lower) return outliers def perc_dev(Z, thr = 2.6, sign = 'abs'): if sign == 'abs': bol = np.abs(Z) > thr; elif sign == 'pos': bol = Z > thr; elif sign == 'neg': bol = Z < -thr; # count the number that have supra-threshold z-stats and store as percentage Z_perc = np.sum(bol, axis = 1) / Z.shape[1] * 100 return Z_perc def evd(Z, thr = 0.01, sign = 'abs'): m = Z.shape l = np.int(m[1] * thr) # assumes features are on dim 1, subjs on dim 0 if sign == 'abs': T = np.sort(np.abs(Z), axis = 1)[:,m[1] - l:m[1]] elif sign == 'pos': T = np.sort(Z, axis = 1)[:,m[1] - l:m[1]] elif sign == 'neg': T = np.sort(Z, axis = 1)[:,:l] E = sp.stats.trim_mean(T, 0.1, axis = 1) return E def summarise_network(df, roi_loc, network_idx, metrics = ('ct',), method = 'mean'): df_out = pd.DataFrame() for metric in metrics: if metric == 'ct': if method == 'median': df_tmp = df.filter(regex = metric).groupby(network_idx[roi_loc == 1], axis = 1).median() if method == 'mean': df_tmp = df.filter(regex = metric).groupby(network_idx[roi_loc == 1], axis = 1).mean() if method == 'max': df_tmp = df.filter(regex = metric).groupby(network_idx[roi_loc == 1], axis = 1).max() my_list = [metric + '_' + str(i) for i in np.unique(network_idx[roi_loc == 1]).astype(int)] df_tmp.columns = my_list else: if method == 'median': df_tmp = df.filter(regex = metric).groupby(network_idx, axis = 1).median() if method == 'mean': df_tmp = df.filter(regex = metric).groupby(network_idx, axis = 1).mean() if method == 'max': df_tmp = df.filter(regex = metric).groupby(network_idx, axis = 1).max() my_list = [metric + '_' + str(i) for i in np.unique(network_idx).astype(int)] df_tmp.columns = my_list df_out = pd.concat((df_out, df_tmp), axis = 1) return df_out

36.963731

123

0.528595

1,008

7,134

3.619048

0.343254

0.026316

0.024671

0.037007

0.219298

0.186678

0.171875

0.157072

0.141173

0

0.141251

0.31427

7,134

192

124

37.15625

0.604456

0.094477

0

0.215827

0

0.032389

0

1

0.071942

false

0

0.043165

0

0.179856

0.007194

0

null

0

null

0

1

0

3007a5e506648223a9acc7a03be0c3a03d473f6f

15,808

py

Python

sympy/simplify/sqrtdenest.py

goodok/sympy

de84ed2139125a755ea7b6ba91d945d9fbbe5ed9

[ "BSD-3-Clause" ]

2

2015-05-11T12:26:38.000Z

2016-08-19T00:11:03.000Z

sympy/simplify/sqrtdenest.py

goodok/sympy

de84ed2139125a755ea7b6ba91d945d9fbbe5ed9

[ "BSD-3-Clause" ]

null

sympy/simplify/sqrtdenest.py

goodok/sympy

de84ed2139125a755ea7b6ba91d945d9fbbe5ed9

[ "BSD-3-Clause" ]

null

from sympy.functions import sqrt, sign, root from sympy.core import S, Wild, sympify, Mul, Add, Expr from sympy.core.function import expand_multinomial, expand_mul from sympy.core.symbol import Dummy from sympy.polys import Poly, PolynomialError from sympy.core.function import count_ops def _mexpand(expr): return expand_mul(expand_multinomial(expr)) def is_sqrt(expr): """Return True if expr is a sqrt, otherwise False.""" return expr.is_Pow and expr.exp.is_Rational and abs(expr.exp) is S.Half def sqrt_depth(p): """Return the maximum depth of any square root argument of p. >>> from sympy.functions.elementary.miscellaneous import sqrt >>> from sympy.simplify.sqrtdenest import sqrt_depth Neither of these square roots contains any other square roots so the depth is 1: >>> sqrt_depth(1 + sqrt(2)*(1 + sqrt(3))) 1 The sqrt(3) is contained within a square root so the depth is 2: >>> sqrt_depth(1 + sqrt(2)*sqrt(1 + sqrt(3))) 2 """ if p.is_Atom: return 0 elif p.is_Add or p.is_Mul: return max([sqrt_depth(x) for x in p.args]) elif is_sqrt(p): return sqrt_depth(p.base) + 1 else: return 0 def is_algebraic(p): """Return True if p is comprised of only Rationals or square roots of Rationals and algebraic operations. Examples ======== >>> from sympy.functions.elementary.miscellaneous import sqrt >>> from sympy.simplify.sqrtdenest import is_algebraic >>> from sympy import cos >>> is_algebraic(sqrt(2)*(3/(sqrt(7) + sqrt(5)*sqrt(2)))) True >>> is_algebraic(sqrt(2)*(3/(sqrt(7) + sqrt(5)*cos(2)))) False """ if p.is_Rational: return True elif p.is_Atom: return False elif is_sqrt(p) or p.is_Pow and p.exp.is_Integer: return is_algebraic(p.base) elif p.is_Add or p.is_Mul: return all(is_algebraic(x) for x in p.args) else: return False def subsets(n): """ Returns all possible subsets of the set (0, 1, ..., n-1) except the empty set, listed in reversed lexicographical order according to binary representation, so that the case of the fourth root is treated last. Examples ======== >>> from sympy.simplify.sqrtdenest import subsets >>> subsets(2) [[1, 0], [0, 1], [1, 1]] """ if n == 1: a = [[1]] elif n == 2: a = [[1, 0], [0, 1], [1, 1]] elif n == 3: a = [[1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]] else: b = subsets(n-1) a0 = [x+[0] for x in b] a1 = [x+[1] for x in b] a = a0 + [[0]*(n-1) + [1]] + a1 return a def sqrtdenest(expr, max_iter=3): """Denests sqrts in an expression that contain other square roots if possible, otherwise returns the expr unchanged. This is based on the algorithms of [1]. Examples ======== >>> from sympy.simplify.sqrtdenest import sqrtdenest >>> from sympy import sqrt >>> sqrtdenest(sqrt(5 + 2 * sqrt(6))) sqrt(2) + sqrt(3) See Also ======== sympy.solvers.solvers.unrad References ========== [1] http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf [2] D. J. Jeffrey and A. D. Rich, 'Symplifying Square Roots of Square Roots by Denesting' (available at http://www.cybertester.com/data/denest.pdf) """ expr = expand_mul(sympify(expr)) for i in range(max_iter): z = _sqrtdenest0(expr) if expr == z: return expr expr = z return expr def _sqrt_match(p): """Return [a, b, r] for p.match(a + b*sqrt(r)) where, in addition to matching, sqrt(r) also has then maximal sqrt_depth among addends of p. Examples ======== >>> from sympy.functions.elementary.miscellaneous import sqrt >>> from sympy.simplify.sqrtdenest import _sqrt_match >>> _sqrt_match(1 + sqrt(2) + sqrt(2)*sqrt(3) + 2*sqrt(1+sqrt(5))) [1 + sqrt(2) + sqrt(6), 2, 1 + sqrt(5)] """ p = _mexpand(p) if p.is_Number: res = (p, S.Zero, S.Zero) elif p.is_Add: pargs = list(p.args) # to make the process canonical, the argument is included in the tuple # so when the max is selected, it will be the largest arg having a # given depth v = [(sqrt_depth(x), x, i) for i, x in enumerate(pargs)] nmax = max(v) if nmax[0] == 0: res = [] else: depth, _, i = nmax r = pargs.pop(i) a = Add._from_args(pargs) b = S.One if r.is_Mul: bv = [] rv = [] for x in r.args: if sqrt_depth(x) < depth: bv.append(x) else: rv.append(x) b = Mul._from_args(bv) r = Mul._from_args(rv) res = (a, b, r**2) else: b, r = p.as_coeff_Mul() if is_sqrt(r): res = (S.Zero, b, r**2) else: res = [] return list(res) class SqrtdenestStopIteration(StopIteration): pass def _sqrtdenest0(expr): """Returns expr after denesting its arguments.""" if is_sqrt(expr): n, d = expr.as_numer_denom() if d is S.One: # n is a square root if n.base.is_Add: args = n.base.args if len(args) > 2 and all((x**2).is_Integer for x in args): try: return _sqrtdenest_rec(n) except SqrtdenestStopIteration: pass expr = sqrt(_mexpand(Add(*[_sqrtdenest0(x) for x in args]))) return _sqrtdenest1(expr) else: n, d = [_sqrtdenest0(i) for i in (n, d)] return n/d if isinstance(expr, Expr): args = expr.args if args: return expr.func(*[_sqrtdenest0(a) for a in args]) return expr def _sqrtdenest_rec(expr): """Helper that denests the square root of three or more surds. It returns the denested expression; if it cannot be denested it throws SqrtdenestStopIteration Algorithm: expr.base is in the extension Q_m = Q(sqrt(r_1),..,sqrt(r_k)); split expr.base = a + b*sqrt(r_k), where `a` and `b` are on Q_(m-1) = Q(sqrt(r_1),..,sqrt(r_(k-1))); then a**2 - b**2*r_k is on Q_(m-1); denest sqrt(a**2 - b**2*r_k) and so on. See [1], section 6. Examples ======== >>> from sympy import sqrt >>> from sympy.simplify.sqrtdenest import _sqrtdenest_rec >>> _sqrtdenest_rec(sqrt(-72*sqrt(2) + 158*sqrt(5) + 498)) -sqrt(10) + sqrt(2) + 9 + 9*sqrt(5) >>> w=-6*sqrt(55)-6*sqrt(35)-2*sqrt(22)-2*sqrt(14)+2*sqrt(77)+6*sqrt(10)+65 >>> _sqrtdenest_rec(sqrt(w)) -sqrt(11) - sqrt(7) + sqrt(2) + 3*sqrt(5) """ from sympy.simplify.simplify import radsimp, split_surds, rad_rationalize if expr.base < 0: return sqrt(-1)*_sqrtdenest_rec(sqrt(-expr.base)) a, b = split_surds(expr.base) if a < b: a, b = b, a c2 = _mexpand(a**2 - b**2) if len(c2.args) > 2: a1, b1 = split_surds(c2) if a1 < b1: a1, b1 = b1, a1 c2_1 = _mexpand(a1**2 - b1**2) c_1 = _sqrtdenest_rec(sqrt(c2_1)) d_1 = _sqrtdenest_rec(sqrt(a1 + c_1)) num, den = rad_rationalize(b1, d_1) c = _mexpand(d_1/sqrt(2) + num/(den*sqrt(2))) else: c = _sqrtdenest1(sqrt(c2)) if sqrt_depth(c) > 1: raise SqrtdenestStopIteration ac = a + c if len(ac.args) >= len(expr.args): if count_ops(ac) >= count_ops(expr.base): raise SqrtdenestStopIteration d = sqrtdenest(sqrt(ac)) if sqrt_depth(d) > 1: raise SqrtdenestStopIteration num, den = rad_rationalize(b, d) r = d/sqrt(2) + num/(den*sqrt(2)) r = radsimp(r) return _mexpand(r) def _sqrtdenest1(expr): """Return denested expr after denesting with simpler methods or, that failing, using the denester.""" from sympy.simplify.simplify import radsimp if not is_sqrt(expr): return expr a = expr.base if a.is_Atom: return expr val = _sqrt_match(a) if not val: return expr a, b, r = val # try a quick numeric denesting d2 = _mexpand(a**2 - b**2*r) if d2.is_Rational: if d2.is_positive: z = _sqrt_numeric_denest(a, b, r, d2) if z is not None: return z else: # fourth root case # sqrtdenest(sqrt(3 + 2*sqrt(3))) = # sqrt(2)*3**(1/4)/2 + sqrt(2)*3**(3/4)/2 dr2 = _mexpand(-d2*r) dr = sqrt(dr2) if dr.is_Rational: z = _sqrt_numeric_denest(_mexpand(b*r), a, r, dr2) if z is not None: return z/root(r, 4) else: z = _sqrt_symbolic_denest(a, b, r) if z is not None: return z if not is_algebraic(expr): return expr # now call to the denester av0 = [a, b, r, d2] z = _denester([radsimp(expr**2)], av0, 0, sqrt_depth(expr) - 1)[0] if av0[1] is None: return expr if z is not None: return z return expr def _sqrt_symbolic_denest(a, b, r): """Given an expression, sqrt(a + b*sqrt(b)), return the denested expression or None. Algorithm: If r = ra + rb*sqrt(rr), try replacing sqrt(rr) in ``a`` with (y**2 - ra)/rb, and if the result is a quadratic, ca*y**2 + cb*y + cc, and (cb + b)**2 - 4*ca*cc is 0, then sqrt(a + b*sqrt(r)) can be rewritten as sqrt(ca*(sqrt(r) + (cb + b)/(2*ca))**2). Examples ======== >>> from sympy.simplify.sqrtdenest import _sqrt_symbolic_denest, sqrtdenest >>> from sympy import sqrt, Symbol, Poly >>> from sympy.abc import x >>> a, b, r = 16 - 2*sqrt(29), 2, -10*sqrt(29) + 55 >>> _sqrt_symbolic_denest(a, b, r) sqrt(-2*sqrt(29) + 11) + sqrt(5) If the expression is numeric, it will be simplified: >>> w = sqrt(sqrt(sqrt(3) + 1) + 1) + 1 + sqrt(2) >>> sqrtdenest(sqrt((w**2).expand())) 1 + sqrt(2) + sqrt(1 + sqrt(1 + sqrt(3))) Otherwise, it will only be simplified if assumptions allow: >>> w = w.subs(sqrt(3), sqrt(x + 3)) >>> sqrtdenest(sqrt((w**2).expand())) sqrt((sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2))**2) Notice that the argument of the sqrt is a square. If x is made positive then the sqrt of the square is resolved: >>> _.subs(x, Symbol('x', positive=True)) sqrt(sqrt(sqrt(x + 3) + 1) + 1) + 1 + sqrt(2) """ a, b, r = sympify([a, b, r]) rval = _sqrt_match(r) if not rval: return None ra, rb, rr = rval if rb: y = Dummy('y', positive=True) try: newa = Poly(a.subs(sqrt(rr), (y**2 - ra)/rb), y) except PolynomialError: return None if newa.degree() == 2: ca, cb, cc = newa.all_coeffs() cb += b if _mexpand(cb**2 - 4*ca*cc).equals(0): z = sqrt(ca*(sqrt(r) + cb/(2*ca))**2) if z.is_number: z = _mexpand(Mul._from_args(z.as_content_primitive())) return z def _sqrt_numeric_denest(a, b, r, d2): """Helper that denest expr = a + b*sqrt(r), with d2 = a**2 - b**2*r > 0 or returns None if not denested. """ from sympy.simplify.simplify import radsimp depthr = sqrt_depth(r) d = sqrt(d2) vad = a + d # sqrt_depth(res) <= sqrt_depth(vad) + 1 # sqrt_depth(expr) = depthr + 2 # there is denesting if sqrt_depth(vad)+1 < depthr + 2 # if vad**2 is Number there is a fourth root if sqrt_depth(vad) < depthr + 1 or (vad**2).is_Rational: vad1 = radsimp(1/vad) return (sqrt(vad/2) + sign(b)*sqrt((b**2*r*vad1/2).expand())).expand() def _denester(nested, av0, h, max_depth_level): """Denests a list of expressions that contain nested square roots. Algorithm based on <http://www.almaden.ibm.com/cs/people/fagin/symb85.pdf>. It is assumed that all of the elements of 'nested' share the same bottom-level radicand. (This is stated in the paper, on page 177, in the paragraph immediately preceding the algorithm.) When evaluating all of the arguments in parallel, the bottom-level radicand only needs to be denested once. This means that calling _denester with x arguments results in a recursive invocation with x+1 arguments; hence _denester has polynomial complexity. However, if the arguments were evaluated separately, each call would result in two recursive invocations, and the algorithm would have exponential complexity. This is discussed in the paper in the middle paragraph of page 179. """ from sympy.simplify.simplify import radsimp if h > max_depth_level: return None, None if av0[1] is None: return None, None if (av0[0] is None and all(n.is_Number for n in nested)): # no arguments are nested for f in subsets(len(nested)): # test subset 'f' of nested p = _mexpand(Mul(*[nested[i] for i in range(len(f)) if f[i]])) if f.count(1) > 1 and f[-1]: p = -p sqp = sqrt(p) if sqp.is_Rational: return sqp, f # got a perfect square so return its square root. # Otherwise, return the radicand from the previous invocation. return sqrt(nested[-1]), [0]*len(nested) else: R = None if av0[0] is not None: values = [av0[:2]] R = av0[2] nested2 = [av0[3], R] av0[0] = None else: values = filter(None, [_sqrt_match(expr) for expr in nested]) for v in values: if v[2]: #Since if b=0, r is not defined if R is not None: if R != v[2]: av0[1] = None return None, None else: R = v[2] if R is None: # return the radicand from the previous invocation return sqrt(nested[-1]), [0]*len(nested) nested2 = [_mexpand(v[0]**2) - _mexpand(R*v[1]**2) for v in values] + [R] d, f = _denester(nested2, av0, h + 1, max_depth_level) if not f: return None, None if not any(f[i] for i in range(len(nested))): v = values[-1] return sqrt(v[0] + v[1]*d), f else: p = Mul(*[nested[i] for i in range(len(nested)) if f[i]]) v = _sqrt_match(p) if 1 in f and f.index(1) < len(nested) - 1 and f[len(nested) - 1]: v[0] = -v[0] v[1] = -v[1] if not f[len(nested)]: #Solution denests with square roots vad = _mexpand(v[0] + d) if vad <= 0: # return the radicand from the previous invocation. return sqrt(nested[-1]), [0]*len(nested) if not(sqrt_depth(vad) < sqrt_depth(R) + 1 or (vad**2).is_Number): av0[1] = None return None, None vad1 = radsimp(1/vad) return _mexpand(sqrt(vad/2) + sign(v[1])*sqrt(_mexpand(v[1]**2*R*vad1/2))), f else: #Solution requires a fourth root s2 = _mexpand(v[1]*R) + d if s2 <= 0: return sqrt(nested[-1]), [0]*len(nested) FR, s = root(_mexpand(R), 4), sqrt(s2) return _mexpand(s/(sqrt(2)*FR) + v[0]*FR/(sqrt(2)*s)), f

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3007da11fc7ae07380226f4b542b097442751381

17,074

py

Python

backend/modules/doc/views/doc.py

YouFacai/iWiki

7a2cbb514f25b72932b0212f6165cdb426243243

[ "MIT" ]

null

backend/modules/doc/views/doc.py

YouFacai/iWiki

7a2cbb514f25b72932b0212f6165cdb426243243

[ "MIT" ]

null

backend/modules/doc/views/doc.py

YouFacai/iWiki

7a2cbb514f25b72932b0212f6165cdb426243243

[ "MIT" ]

null

import datetime import os import shutil from django.conf import settings from django.contrib.auth import get_user_model from django.core.cache import cache from django.db import transaction, IntegrityError from django.db.models import Q, F from django.http import FileResponse from django.utils.encoding import escape_uri_path from django.utils.translation import gettext as _ from rest_framework.decorators import action from rest_framework.response import Response from rest_framework.viewsets import ModelViewSet, GenericViewSet from constents import DocAvailableChoices, RepoTypeChoices, UserTypeChoices from modules.account.serializers import UserInfoSerializer from modules.doc.models import Doc, DocVersion, DocCollaborator, Comment from modules.doc.permissions import DocManagePermission, DocCommonPermission from modules.doc.serializers import ( DocCommonSerializer, DocListSerializer, DocUpdateSerializer, DocVersionSerializer, DocPublishChartSerializer, ) from modules.repo.models import Repo, RepoUser from modules.repo.serializers import RepoSerializer from utils.authenticators import SessionAuthenticate from utils.exceptions import Error404, ParamsNotFound, UserNotExist, OperationError from utils.paginations import NumPagination from utils.throttlers import DocSearchThrottle from utils.viewsets import ThrottleAPIView USER_MODEL = get_user_model() class DocManageView(ModelViewSet): """文章管理入口""" queryset = Doc.objects.filter(is_deleted=False) serializer_class = DocCommonSerializer permission_classes = [ DocManagePermission, ] def perform_create(self, serializer): return serializer.save() def list(self, request, *args, **kwargs): """个人文章""" self.serializer_class = DocListSerializer # 获取个人的所有文章 sql = ( "SELECT d.*, r.name 'repo_name' FROM `doc_doc` d " "JOIN `repo_repo` r ON d.repo_id=r.id " "JOIN `auth_user` au ON au.uid=d.creator " "WHERE d.creator=%s AND NOT d.is_deleted " "{} " "ORDER BY d.id DESC;" ) # 标题关键字搜索 search_key = request.GET.get("searchKey", "") if search_key: sql = sql.format("AND d.title like %s") search_key = f"%%{search_key}%%" self.queryset = self.queryset.raw(sql, [request.user.uid, search_key]) else: sql = sql.format("") self.queryset = self.queryset.raw(sql, [request.user.uid]) return super().list(request, *args, **kwargs) def create(self, request, *args, **kwargs): """新建文章""" request.data["creator"] = request.user.uid serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) with transaction.atomic(): instance = self.perform_create(serializer) DocVersion.objects.create(**DocVersionSerializer(instance).data) return Response({"id": instance.id}) def update(self, request, *args, **kwargs): """更新文章""" partial = kwargs.pop("partial", False) instance = self.get_object() serializer = DocUpdateSerializer(instance, data=request.data, partial=partial) serializer.is_valid(raise_exception=True) with transaction.atomic(): serializer.save(update_by=request.user.uid) DocVersion.objects.create(**DocVersionSerializer(instance).data) return Response({"id": instance.id}) def destroy(self, request, *args, **kwargs): instance = self.get_object() self.perform_destroy(instance) return Response() @action(detail=True, methods=["GET"]) def list_collaborator(self, request, *args, **kwargs): """获取协作者""" instance = self.get_object() sql = ( "SELECT au.* " "FROM `doc_collaborator` dc " "JOIN `doc_doc` dd ON dd.id = dc.doc_id AND dd.id = %s " "JOIN `auth_user` au on dc.uid = au.uid;" ) collaborators = USER_MODEL.objects.raw(sql, [instance.id]) serializer = UserInfoSerializer(collaborators, many=True) return Response(serializer.data) @action(detail=True, methods=["POST"]) def add_collaborator(self, request, *args, **kwargs): """增加协作者""" instance = self.get_object() uid = request.data.get("uid") if not uid or uid == request.user.uid: raise OperationError() try: DocCollaborator.objects.create(doc_id=instance.id, uid=uid) except IntegrityError: raise OperationError(_("已添加该用户为协作者，请勿重复添加")) return Response() @action(detail=True, methods=["POST"]) def remove_collaborator(self, request, *args, **kwargs): """删除协作者""" instance = self.get_object() uid = request.data.get("uid") if not uid or uid == request.user.uid: raise OperationError() DocCollaborator.objects.filter(doc_id=instance.id, uid=uid).delete() return Response() @action(detail=True, methods=["GET"]) def edit_status(self, request, *args, **kwargs): """为文章添加编辑中状态""" instance = self.get_object() cache_key = f"{self.__class__.__name__}:{self.action}:{instance.id}" uid = cache.get(cache_key) if uid is None or uid == request.user.uid: cache.set(cache_key, request.user.uid, 60) return Response(True) else: return Response(False) @action(detail=True, methods=["GET"]) def export(self, request, *args, **kwargs): """导出文章""" instance = self.get_object() sql = ( "SELECT dc.*, au.username FROM `doc_comment` dc " "JOIN `auth_user` au ON au.uid=dc.creator " "WHERE dc.doc_id=%s AND NOT dc.is_deleted " "ORDER BY dc.id DESC;" ) comments = Comment.objects.raw(sql, [instance.id]) file_dir = os.path.join( settings.BASE_DIR, "tmp", "doc", request.user.uid, str(instance.id) ) if os.path.exists(file_dir): shutil.rmtree(file_dir) os.makedirs(file_dir) filename = "{}.md".format(instance.title.replace(" ", "").replace("/", "")) file_path = os.path.join(file_dir, filename) with open(file_path, "w", encoding="utf-8") as file: file.write(instance.content) for comment in comments: file.write("\n\n---\n\n") file.write(comment.content) file = open(file_path, "rb") response = FileResponse(file) response["Content-Type"] = "application/octet-stream" response[ "Content-Disposition" ] = f"attachment; filename={escape_uri_path(filename)}" return response class DocCommonView(GenericViewSet): """文章常规入口""" queryset = Doc.objects.filter(is_deleted=False, is_publish=True) serializer_class = DocListSerializer permission_classes = [DocCommonPermission] authentication_classes = [SessionAuthenticate] def list(self, request, *args, **kwargs): """获取仓库文章""" repo_id = request.GET.get("repo_id", None) # 没有传参直接返回 if repo_id is None: raise Error404() # 传入参数获取对应仓库的文章 try: Repo.objects.get(id=repo_id, is_deleted=False) except Repo.DoesNotExist: raise Error404() # 获取仓库的公开或自己的文章 sql = ( "SELECT d.*, au.username creator_name, r.name repo_name " "FROM `doc_doc` d " "JOIN `repo_repo` r ON r.id=d.repo_id " "LEFT JOIN `doc_pin` dp ON dp.doc_id=d.id AND dp.in_use " "LEFT JOIN `auth_user` au ON au.uid=d.creator " "WHERE NOT d.`is_deleted` AND d.`is_publish` " "AND dp.in_use IS NULL " "AND d.repo_id = %s " "AND (d.available = %s OR d.creator = %s) " "AND d.title like %s " "ORDER BY d.id DESC" ) search_key = request.GET.get("searchKey") search_key = f"%%{search_key}%%" if search_key else "%%" queryset = self.queryset.raw( sql, [repo_id, DocAvailableChoices.PUBLIC, request.user.uid, search_key] ) page = self.paginate_queryset(queryset) serializer = self.get_serializer(page, many=True) return self.get_paginated_response(serializer.data) def retrieve(self, request, *args, **kwargs): """获取文章详情""" instance = self.get_object() Doc.objects.filter(id=instance.id).update(pv=F("pv") + 1) instance.pv += 1 serializer = DocCommonSerializer(instance) return Response(serializer.data) @action(detail=True, methods=["GET"]) def is_collaborator(self, request, *args, **kwargs): """判断是否是协作者""" instance = self.get_object() try: DocCollaborator.objects.get(doc_id=instance.id, uid=request.user.uid) return Response() except DocCollaborator.DoesNotExist: return Response({"result": False}) @action(detail=False, methods=["GET"]) def load_pin_doc(self, request, *args, **kwargs): """获取置顶文章""" repo_id = request.GET.get("repo_id", None) # 没有传参直接返回 if repo_id is None: raise Error404() # 传入参数获取对应仓库的文章 try: Repo.objects.get(id=repo_id, is_deleted=False) except Repo.DoesNotExist: raise Error404() sql = ( "SELECT distinct dd.*, au.username creator_name, rr.name repo_name " "FROM `doc_doc` dd " "JOIN `auth_user` au ON dd.creator=au.uid " "JOIN `repo_repo` rr ON rr.id=dd.repo_id " "JOIN `doc_pin` dp ON dp.doc_id=dd.id AND dp.in_use " "WHERE rr.id=%s AND dd.available=%s " "AND dd.is_publish AND NOT dd.is_deleted; " ) queryset = Doc.objects.raw(sql, [repo_id, DocAvailableChoices.PUBLIC]) serializer = self.get_serializer(queryset, many=True) return Response(serializer.data) class DocPublicView(GenericViewSet): """公共入口""" queryset = Doc.objects.filter( is_deleted=False, is_publish=True, available=DocAvailableChoices.PUBLIC ) authentication_classes = [SessionAuthenticate] def list(self, request, *args, **kwargs): # 获取公开或成员仓库的公开或自己的文章 sql = ( "SELECT d.*, au.username creator_name, r.name repo_name " "FROM `repo_repo` r " "JOIN `repo_user` ru ON r.id=ru.repo_id AND ru.u_type!=%s " "JOIN `doc_doc` d ON r.id=d.repo_id " "JOIN `auth_user` au ON au.uid=d.creator " "WHERE NOT r.is_deleted AND (ru.uid=%s OR r.r_type=%s) " "AND (d.available = %s OR d.creator = %s) AND NOT d.`is_deleted` AND d.`is_publish` " "GROUP BY d.id " "ORDER BY d.id DESC;" ) docs = Doc.objects.raw( sql, [ UserTypeChoices.VISITOR, request.user.uid, RepoTypeChoices.PUBLIC, DocAvailableChoices.PUBLIC, request.user.uid, ], ) page = NumPagination() queryset = page.paginate_queryset(docs, request, self) serializer = DocListSerializer(queryset, many=True) return page.get_paginated_response(serializer.data) @action(detail=False, methods=["GET"]) def recent(self, request, *args, **kwargs): """热门文章""" cache_key = f"{self.__class__.__name__}:{self.action}" cache_data = cache.get(cache_key) if cache_data is not None: return Response(cache_data) # 公开库的近期文章 public_repo_ids = Repo.objects.filter( r_type=RepoTypeChoices.PUBLIC, is_deleted=False ).values("id") queryset = self.queryset.filter(repo_id__in=public_repo_ids, pv__gt=0).order_by( "-pv" )[:10] serializer = DocListSerializer(queryset, many=True) cache.set(cache_key, serializer.data, 1800) return Response(serializer.data) @action(detail=False, methods=["GET"]) def hot_repo(self, request, *args, **kwargs): """热门库""" cache_key = f"{self.__class__.__name__}:{self.action}" cache_data = cache.get(cache_key) if cache_data is not None: return Response(cache_data) sql = ( "SELECT rr.*, dd.repo_id, COUNT(1) 'count' " "FROM `doc_doc` dd " "JOIN (SELECT MIN(dd2.id) 'min_id' from `doc_doc` dd2 ORDER BY dd2.id DESC LIMIT 100) dd3 " "JOIN `repo_repo` rr ON rr.id=dd.repo_id " "WHERE dd.id>=dd3.min_id " "GROUP BY dd.repo_id " "ORDER BY count DESC " "LIMIT 10" ) repos = Repo.objects.raw(sql) serializer = RepoSerializer(repos, many=True) cache.set(cache_key, serializer.data, 1800) return Response(serializer.data) @action(detail=False, methods=["GET"]) def user_doc(self, request, *args, **kwargs): """用户发布文章""" username = request.GET.get("username") if not username: raise ParamsNotFound(_("用户名不能为空")) try: user = USER_MODEL.objects.get(username=username) except USER_MODEL.DoesNotExist: raise UserNotExist() # 共同或公开仓库的公开文章 union_repo_ids = RepoUser.objects.filter( Q(uid=request.user.uid) & ~Q(u_type=UserTypeChoices.VISITOR) ).values("repo_id") allowed_repo_ids = Repo.objects.filter( Q(r_type=RepoTypeChoices.PUBLIC) | Q(id__in=union_repo_ids) ).values("id") docs = self.queryset.filter( creator=user.uid, repo_id__in=allowed_repo_ids ).order_by("-id") page = NumPagination() queryset = page.paginate_queryset(docs, request, self) serializer = DocListSerializer(queryset, many=True) return page.get_paginated_response(serializer.data) @action(detail=False, methods=["GET"]) def recent_chart(self, request, *args, **kwargs): """文章发布图表数据""" cache_key = f"{self.__class__.__name__}:{self.action}" cache_data = cache.get(cache_key) if cache_data is not None: return Response(cache_data) today = datetime.datetime.today() last_day = today - datetime.timedelta(days=30) sql = ( "SELECT dd.id, DATE_FORMAT(dd.update_at, \"%%m-%%d\") 'date', COUNT(1) 'count' " "FROM `doc_doc` dd " "WHERE dd.update_at>='{}' AND NOT dd.is_deleted AND dd.available = '{}' " "GROUP BY DATE(dd.update_at); " ).format(last_day, DocAvailableChoices.PUBLIC) docs_count = Doc.objects.raw(sql) serializer = DocPublishChartSerializer(docs_count, many=True) data = {item["date"]: item["count"] for item in serializer.data} cache.set(cache_key, data, 1800) return Response(data) class SearchDocView(ThrottleAPIView): """搜索入口""" throttle_classes = [ DocSearchThrottle, ] def post(self, request, *args, **kwargs): search_key = request.data.get("searchKey") if not search_key: raise ParamsNotFound(_("搜索关键字不能为空")) # 公开或成员仓库的公开或个人文章 sql = ( "SELECT dd.*, au.username creator_name, rr.name repo_name " "FROM `repo_repo` rr " "JOIN `repo_user` ru ON ru.repo_id=rr.id AND ru.u_type!=%s " "JOIN `doc_doc` dd ON rr.id = dd.repo_id " "JOIN `auth_user` au ON au.uid = dd.creator " "WHERE NOT rr.is_deleted AND (ru.uid = %s OR rr.r_type = %s) " "AND NOT dd.is_deleted AND dd.is_publish AND (dd.available = %s OR dd.creator = %s) " "AND (({}) OR ({})) " "GROUP BY dd.id " "ORDER BY dd.id DESC;" ) # 处理 key extend_title_sqls = [] extend_content_sqls = [] params_keys = [] for key in search_key: if key: extend_title_sqls.append(" dd.title like %s ") extend_content_sqls.append(" dd.content like %s ") params_keys.append(f"%{key}%") extend_title_sql = "AND".join(extend_title_sqls) extend_content_sql = "AND".join(extend_content_sqls) sql = sql.format(extend_title_sql, extend_content_sql) docs = Doc.objects.raw( sql, [ UserTypeChoices.VISITOR, request.user.uid, RepoTypeChoices.PUBLIC, DocAvailableChoices.PUBLIC, request.user.uid, *params_keys, *params_keys, ], ) page = NumPagination() queryset = page.paginate_queryset(docs, request, self) serializer = DocListSerializer(queryset, many=True) return page.get_paginated_response(serializer.data)

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0.278786

17,074

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38.628959

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null

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null

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3007e107fbb9661b8e7f7e4c1d4a01f5c735b272

21,175

py

Python

python/cli/report_study.py

mediumroast/mr_sdk

55c7a13c5cef73e677297026b41b7ec23855391f

[ "Apache-2.0" ]

1

2021-10-06T02:46:48.000Z

python/cli/report_study.py

mediumroast/mr_sdk

55c7a13c5cef73e677297026b41b7ec23855391f

[ "Apache-2.0" ]

3

2021-10-16T03:34:07.000Z

2022-02-23T05:10:12.000Z

python/cli/report_study.py

mediumroast/mr_sdk

55c7a13c5cef73e677297026b41b7ec23855391f

[ "Apache-2.0" ]

null

#!/bin/env python3 import sys import argparse import configparser import docx from docx import Document from docx.shared import Pt, Inches from docx.enum.dml import MSO_THEME_COLOR_INDEX from docx.enum.section import WD_ORIENT, WD_SECTION from datetime import datetime from mediumroast.api.high_level import Auth as authenticate from mediumroast.api.high_level import Studies as study from mediumroast.api.high_level import Interactions as interaction ### General utilities def parse_cli_args(program_name='report_study', desc='A mediumroast.io utility that generates a Microsoft Word formatted report for a study.'): parser = argparse.ArgumentParser(prog=program_name, description=desc) parser.add_argument('--exclude_substudies', help="The names for the substudies to exclude in a comma separated list", type=str, dest='exclude_substudies', default=None) parser.add_argument('--rest_url', help="The URL of the target REST server", type=str, dest='rest_url', default='http://mr-01:3000') parser.add_argument('--guid', help="The GUID for the study to be reported on.", type=str, dest='guid', required=True) parser.add_argument('--org', help="The organization name for the report.", type=str, dest='org', required=True) parser.add_argument('--user', help="User name", type=str, dest='user', default='foo') parser.add_argument('--secret', help="Secret or password", type=str, dest='secret', default='bar') parser.add_argument('--config_file', help="The location to the configuration files", type=str, dest='config_file', default='./reports.ini') cli_args = parser.parse_args() return cli_args def read_config(conf_file='./reports.ini'): c = configparser.ConfigParser() c.read(conf_file) return c def get_interaction_name(guid): """Get the interaction name by the GUID """ interaction_ctl = interaction(credential) return interaction_ctl.get_name_by_guid(guid)[1]['interactionName'] def _create_header(doc_obj, conf, font_size=7): date_string = f'{datetime.now():%Y-%m-%d %H:%M}' s = doc_obj.sections[0] header = s.header header_p = header.paragraphs[0] header_p.text = conf['org'] + "\t | \t Created on: " + date_string style = doc_obj.styles['Header'] font = style.font font.name = conf['font'] font.size = Pt(font_size) header_p.style = doc_obj.styles['Header'] def _create_footer(doc_obj, conf, font_size=7): date_string = f'{datetime.now():%Y-%m-%d %H:%M}' s = doc_obj.sections[0] footer = s.footer footer_p = footer.paragraphs[0] footer_p.text = conf['confidentiality'] + "\t | \t" + conf['copyright'] style = doc_obj.styles['Footer'] font = style.font font.name = conf['font'] font.size = Pt(font_size) footer_p.style = doc_obj.styles['Footer'] def _create_cover_page(doc_obj, study, conf, logo_size=60, font_size=30): # Generics title_font_size = Pt(font_size) # Title Font Size logo_size = Pt(font_size*2.5) # Organization name and logo logo = conf['logo'] logo_title = doc_obj.add_paragraph().add_run() logo_title.add_picture(logo, height=logo_size) # Define the Cover Title Style org = conf['org'] # Organization title = "\n\nTitle: " + study['studyName'] cover_title = doc_obj.add_paragraph(title) style = doc_obj.styles['Title'] font = style.font font.name = conf['font'] font.size = title_font_size cover_title.style = doc_obj.styles['Title'] # Define the Subtitle content subtitle = "A " + org + " study report enabling attributable market insights." cover_subtitle = doc_obj.add_paragraph("") s = cover_subtitle.add_run(subtitle) subtitle_font = s.font subtitle_font.bold = True # Define the Author content author = "Mediumroast Barrista Robot" cover_author = doc_obj.add_paragraph("\nAuthor: ") a = cover_author.add_run(author) author_font = a.font author_font.bold = True # Define the Creation date content creation_date = f'{datetime.now():%Y-%m-%d %H:%M}' cover_date = doc_obj.add_paragraph("Creation Date: ") d = cover_date.add_run(creation_date) date_font = d.font date_font.bold = True # Add a page break doc_obj.add_page_break() def _create_summary(doc_obj, study_doc, conf): # Create the Introduction section section_title = doc_obj.add_paragraph( 'Findings') # Create the Findings section section_title.style = doc_obj.styles['Title'] doc_obj.add_heading('Introduction') clean_intro = " ".join(study_doc['Introduction'].split("\n")) doc_obj.add_paragraph(clean_intro) # Create the Opportunity section doc_obj.add_heading('Opportunity') clean_opportunity = " ".join(study_doc['Opportunity']['text'].split("\n")) doc_obj.add_paragraph(clean_opportunity) # Remove the text section before we process the numbered bullets del(study_doc['Opportunity']['text']) for opp in study_doc['Opportunity']: clean_opp = " ".join(study_doc['Opportunity'][opp].split("\n")) doc_obj.add_paragraph(clean_opp, style='List Bullet') # Create the Action section doc_obj.add_heading('Actions') clean_action = " ".join(study_doc['Action']['text'].split("\n")) doc_obj.add_paragraph(clean_action) # Remove the text section before we process the numbered bullets del(study_doc['Action']['text']) for action in study_doc['Action']: clean_act = " ".join(study_doc['Action'][action].split("\n")) doc_obj.add_paragraph(clean_act, style='List Number') # Add a page break doc_obj.add_page_break() def _add_hyperlink(paragraph, text, url): """Taken from https://stackoverflow.com/questions/47666642/adding-an-hyperlink-in-msword-by-using-python-docx """ # This gets access to the document.xml.rels file and gets a new relation id value part = paragraph.part r_id = part.relate_to( url, docx.opc.constants.RELATIONSHIP_TYPE.HYPERLINK, is_external=True) # Create the w:hyperlink tag and add needed values hyperlink = docx.oxml.shared.OxmlElement('w:hyperlink') hyperlink.set(docx.oxml.shared.qn('r:id'), r_id, ) # Create a w:r element and a new w:rPr element new_run = docx.oxml.shared.OxmlElement('w:r') rPr = docx.oxml.shared.OxmlElement('w:rPr') # Join all the xml elements together add add the required text to the w:r element new_run.append(rPr) new_run.text = text hyperlink.append(new_run) # Create a new Run object and add the hyperlink into it r = paragraph.add_run() r._r.append(hyperlink) # A workaround for the lack of a hyperlink style (doesn't go purple after using the link) # Delete this if using a template that has the hyperlink style in it r.font.color.theme_color = MSO_THEME_COLOR_INDEX.HYPERLINK r.font.underline = True return hyperlink def _create_reference(interaction_guid, substudy, doc_obj, conf, char_limit=500): interaction_ctl = interaction(credential) success, interaction_data = interaction_ctl.get_by_guid(interaction_guid) if success: doc_obj.add_heading(interaction_data['interactionName'], 2) my_time = str(interaction_data['time'][0:2]) + \ ':' + str(interaction_data['time'][2:4]) my_date = str(interaction_data['date'][0:4]) + '-' + str(interaction_data['date'][4:6]) + '-' \ + str(interaction_data['date'][6:8]) interaction_meta = "\t\t|\t".join(['Date: ' + my_date + "\t" + my_time, 'Sub-Study Identifier: ' + substudy]) doc_obj.add_paragraph(interaction_meta) doc_obj.add_paragraph( interaction_data['abstract'][0:char_limit] + '...') resource = doc_obj.add_paragraph('Interaction Resource: ') _add_hyperlink( resource, interaction_data['interactionName'], interaction_data['url'].replace('s3', 'http')) else: print( 'Something went wrong obtaining the interaction data for [' + interaction_guid + ']') def _create_references(doc_obj, substudy_list, conf): section_title = doc_obj.add_paragraph( 'References') # Create the References section section_title.style = doc_obj.styles['Title'] for substudy in substudy_list: for interaction in substudy_list[substudy]['interactions']: interaction_guid = substudy_list[substudy]['interactions'][interaction]['GUID'] _create_reference(interaction_guid, substudy, doc_obj, conf) def _create_quote(doc_obj, quote, indent, font_size): my_quote = quote my_para = doc_obj.add_paragraph(style='List Bullet') my_para.paragraph_format.left_indent = Pt(1.5 * indent) my_bullet = my_para.add_run(my_quote) my_bullet.font.size = Pt(font_size) my_para.paragraph_format.space_after = Pt(3) def _create_quotes(doc_obj, quotes, indent, font_size, location='quotes'): for quote in quotes: my_quote = quotes[quote][location] my_para = doc_obj.add_paragraph(style='List Bullet') my_para.paragraph_format.left_indent = Pt(1.5 * indent) my_bullet = my_para.add_run(my_quote) my_bullet.font.size = Pt(font_size) my_para.paragraph_format.space_after = Pt(3) def _create_subsection(doc_obj, start_text, body_text, indent, font_size, to_bold=False, to_italics=False): para = doc_obj.add_paragraph() para.paragraph_format.left_indent = Pt(indent) start_run = para.add_run(start_text) start_run.font.bold = to_bold start_run.font.size = Pt(font_size) body_run=para.add_run(body_text) body_run.font.size = Pt(font_size) if to_italics: body_run.font.italic = to_italics def _create_intro(doc_obj, intro_name, intro_body, heading_level=2): doc_obj.add_heading(intro_name, level=heading_level) doc_obj.add_paragraph(intro_body) def _create_key_theme(doc_obj, themes, quotes, conf, include_fortune=True): ### Define the summary theme _create_intro(doc_obj, 'Summary Theme', conf['themes']['summary_intro'].replace("\n", " ")) ## Create the definition theme = 'summary_theme' _create_subsection(doc_obj, 'Definition: ', themes[theme]['description'], int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True) ## Determine if we should include the theme fortune or not if include_fortune: _create_subsection(doc_obj, 'Fortune: ', themes[theme]['fortune'][0].upper() + themes[theme]['fortune'][1:] + ' [system generated]', int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True) ## Create the tags _create_subsection(doc_obj, 'Tags: ', " | ".join(themes[theme]['tags'].keys()), int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True, to_italics = True) ## Create the quotes subsection_name = 'Theme Quotes' doc_obj.add_heading(subsection_name, level=3) _create_quotes(doc_obj, quotes['summary'], int(conf['themes']['indent']), font_size = int(conf['themes']['font_size'])) ### Add the discrete/detailed themes theme_loc = 'discrete_themes' quotes_loc = 'discrete' ## Create the starting paragraph _create_intro(doc_obj, 'Detailed Themes', conf['themes']['discrete_intro'].replace("\n", " ")) ## Add in the individual themes and their quotes my_themes = themes[theme_loc] for my_theme in my_themes: # Put in the theme identifier _create_intro(doc_obj, 'Detailed Theme Identifier: ' + my_theme, conf['themes']['discrete_theme_intro'].replace("\n", " "), heading_level=3) # Add the description _create_subsection(doc_obj, 'Definition: ', my_themes[my_theme]['description'], int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True) # Include the fortune if the setting is true if include_fortune: _create_subsection(doc_obj, 'Fortune: ', my_themes[my_theme]['fortune'][0].upper() + my_themes[my_theme]['fortune'][1:] + ' [system generated]', int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True) # Add the tags _create_subsection(doc_obj, 'Tags: ', " | ".join(my_themes[my_theme]['tags'].keys()), int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True, to_italics = True) # Pull in the quotes subsection_name = 'Theme Quotes by Interaction' doc_obj.add_heading(subsection_name, level=4) if my_theme in quotes[quotes_loc]: for interaction in quotes[quotes_loc][my_theme]: doc_obj.add_heading(get_interaction_name(interaction), level=5) the_quotes = quotes[quotes_loc][my_theme][interaction]['quotes'] # Explain that the system was not able to find a relevant quote if not the_quotes: the_quotes=[['mediumroast.io was unable to find a relevant quote or text snippet for this theme.']] for my_quote in the_quotes: _create_quote(doc_obj, my_quote[0], int(conf['themes']['indent']), font_size = int(conf['themes']['font_size'])) _create_subsection(doc_obj, 'Frequency: ', str(quotes[quotes_loc][my_theme][interaction]['frequency']), int(conf['themes']['indent']), font_size = int(conf['themes']['font_size']), to_bold = True, to_italics = True) doc_obj.add_page_break() def _create_key_themes(doc_obj, substudies, conf, substudy_excludes=list()): section_title = doc_obj.add_paragraph( 'Key Themes by Sub-Study') # Create the Themes section section_title.style = doc_obj.styles['Title'] doc_obj.add_paragraph(conf['themes']['intro'].replace("\n", " ")) for substudy in substudies: if substudy in substudy_excludes: continue doc_obj.add_heading('Sub-Study Identifier: ' + substudy + ' — ' + substudies[substudy]['description'], 1) _create_key_theme( doc_obj, substudies[substudy]['keyThemes'], substudies[substudy]['keyThemeQuotes'], conf) def change_orientation(doc_obj): current_section = doc_obj.sections[-1] new_width, new_height = current_section.page_height, current_section.page_width new_section = doc_obj.add_section(WD_SECTION.NEW_PAGE) new_section.orientation = WD_ORIENT.LANDSCAPE new_section.page_width = new_width new_section.page_height = new_height return new_section def _create_row(the_row, id, type,freq, src, snip): ID = 0 TYPE = 1 FREQ = 2 SNIP = 4 SRC = 3 the_row[ID].text = str(id) the_row[TYPE].text = str(type) the_row[FREQ].text = str(freq) the_row[SNIP].text = str(snip) the_row[SRC].text = str(src) def _create_rows(): """ For summary create single row For discrete foreach theme create single row """ pass def _create_summary_theme_tables(doc_obj, substudies, conf, substudy_excludes=list()): change_orientation(doc_obj) # Flip to landscape mode my_widths = [Inches(1.5), Inches(0.75), Inches(0.75), Inches(1.5), Inches(3.5)] section_title = doc_obj.add_paragraph( 'Key Theme Summary Tables') # Create the References section section_title.style = doc_obj.styles['Title'] for substudy in substudies: if substudy in substudy_excludes: continue doc_obj.add_heading('Sub-Study Identifier: ' + substudy + ' — ' + substudies[substudy]['description'], 1) my_table = doc_obj.add_table(rows=1, cols=5) my_table.style = 'Colorful Grid' header_row = my_table.rows[0].cells header_row[0].text = 'Identifier' header_row[1].text = 'Type' header_row[2].text = 'Frequency' header_row[3].text = 'Source' header_row[4].text = 'Snippet' my_row = my_table.add_row().cells ## Process the summary theme my_theme = 'Summary Theme' my_type = 'Summary' my_frequency = 'N/A' my_interaction = list(substudies[substudy]['keyThemeQuotes']['summary'].keys())[0] my_snippet = substudies[substudy]['keyThemeQuotes']['summary'][my_interaction]['quotes'][0] my_source = get_interaction_name(my_interaction) _create_row(my_row, my_theme, my_type, my_frequency, my_source, my_snippet) ## Process the discrete themes theme_loc = 'discrete_themes' quotes_loc = 'discrete' ## Add in the individual themes and their quotes my_themes = substudies[substudy]['keyThemes'][theme_loc] my_quotes = substudies[substudy]['keyThemeQuotes'][quotes_loc] my_type = 'Detailed' for my_theme in my_themes: if my_theme in my_quotes: my_row = my_table.add_row().cells my_interaction = list(my_quotes[my_theme].keys())[0] my_source = get_interaction_name(my_interaction) the_quotes = my_quotes[my_theme][my_interaction]['quotes'] # Explain that the system was not able to find a relevant quote if not the_quotes: the_quotes=[['mediumroast.io was unable to find a relevant quote or text snippet for this theme.']] my_snippet = the_quotes[0][0] my_frequency = my_themes[my_theme]['frequency'] _create_row(my_row, my_theme, my_type, my_frequency, my_source, my_snippet) doc_obj.add_page_break() change_orientation(doc_obj) # Flip to portrait mode def report(study, conf, substudy_excludes): # Document generics d = Document() # Create doc object style = d.styles['Normal'] font = style.font font.name = conf['font'] font.size = Pt(int(conf['font_size'])) _create_cover_page(d, study, conf) # Create the cover page _create_header(d, conf) # Create the doc header _create_footer(d, conf) # Create the doc footer ### Intro, opportunity and actions sections _create_summary(d, study['document'], conf) ### Key Themes ## Key Themes Summary Table _create_summary_theme_tables(d, study['substudies'], conf, substudy_excludes) ## Detailed Key Themes _create_key_themes(d, study['substudies'], conf, substudy_excludes) ### References _create_references(d, study['substudies'], conf) return d if __name__ == "__main__": my_args = parse_cli_args() configurator = read_config(conf_file=my_args.config_file) my_org = my_args.org.upper() # Set default items from the configuration file for the report report_conf = { 'org': configurator[my_org]['organization_name'], 'logo': configurator[my_org]['logo_image'], 'font': configurator[my_org]['font_type'], 'font_size': configurator[my_org]['font_size'], 'font_measure': configurator[my_org]['font_measure'], 'copyright': configurator[my_org]['copyright_notice'], 'confidentiality': configurator[my_org]['confidential_notice'], 'themes': { 'font_size': configurator['THEME_FORMAT']['font_size'], 'intro': configurator['THEME_FORMAT']['key_theme_intro'], 'summary_intro': configurator['THEME_FORMAT']['summary_theme_intro'], 'discrete_intro': configurator['THEME_FORMAT']['discrete_themes_intro'], 'discrete_theme_intro': configurator['THEME_FORMAT']['discrete_theme_intro'], 'indent': configurator['THEME_FORMAT']['indent'], } } auth_ctl = authenticate( user_name=my_args.user, secret=my_args.secret, rest_server_url=my_args.rest_url) credential = auth_ctl.login() substudy_excludes = my_args.exclude_substudies.split(',') if my_args.exclude_substudies else list() study_ctl = study(credential) success, study_obj = study_ctl.get_by_guid(my_args.guid) if success: doc_name = study_obj['studyName'].replace( ' ', '_') + "_study_report.docx" document = report(study_obj, report_conf, substudy_excludes) document.save(doc_name) else: print('CLI ERROR: This is a generic error message, as something went wrong.') sys.exit(-1)

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null

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null

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0

300d26a47173ec68c9cbc913d71ba1fd8d873df4

17,388

py

Python

hubcheck/browser/browser.py

codedsk/hubcheck

2ff506eb56ba00f035300862f8848e4168452a17

[ "MIT" ]

1

2016-02-13T13:42:23.000Z

hubcheck/browser/browser.py

codedsk/hubcheck

2ff506eb56ba00f035300862f8848e4168452a17

[ "MIT" ]

null

hubcheck/browser/browser.py

codedsk/hubcheck

2ff506eb56ba00f035300862f8848e4168452a17

[ "MIT" ]

null

import pprint import logging import datetime from selenium import webdriver import hubcheck.conf # block websites that make linkcheck slow # these are usually blocked by the workspace firewall # mozillalabs comes from using a nightly version of firefox browser # many of the others are from login authentication sites PROXY_BLACKLIST = [ "http(s)?://.*mozillalabs\\.com/?.*", # testpilot.mozillalabs.com "http(s)?://.*google-analytics\\.com/.*", # ssl.google-analytics.com 'http(s)?://.*facebook\\.com/?.*', # www.facebook.com/login.php 'http(s)?://.*fbcdn\\.com/?.*', # www.facebook.com/login.php 'http(s)?://.*accounts\\.google\\.com/?.*', # accounts.google.com 'http(s)?://.*linkedin\\.com/?.*', # linkedin.com 'http(s)?://.*twitter\\.com/?.*', # api.twitter.com # 'http(s)?://.*purdue\\.edu/apps/account/cas/?.*', # purdue cas ] MIMETYPES = [ "appl/text", # .doc \ "application/acad", # .dwg \ "application/acrobat", # .pdf \ "application/autocad_dwg", # .dwg \ "application/doc", # .doc, .rtf \ "application/dwg", # .dwg \ "application/eps", # .eps \ "application/futuresplash", # .swf \ "application/gzip", # .gz \ "application/gzipped", # .gz \ "application/gzip-compressed", # .gz \ "application/jpg", # .jpg \ "application/ms-powerpoint", # .ppt \ "application/msexcel", # .xls \ "application/mspowerpnt", # .ppt \ "application/mspowerpoint", # .ppt \ "application/msword", # .doc, .rtf \ "application/octet-stream", # .gz, .zip \ "application/pdf", # .pdf \ "application/photoshop", # .psd \ "application/postscript", # .ps, .avi, .eps \ "application/powerpoint", # .ppt \ "application/psd", # .psd \ "application/rss+xml", # .rss \ "application/rtf", # .rtf \ "application/tar", # .tar \ "application/vnd.ms-excel", # .xls, .xlt, .xla \ "application/vnd.ms-excel.addin.macroEnabled.12", # .xlam \ "application/vnd.ms-excel.sheet.binary.macroEnabled.12", # .xlsb \ "application/vnd.ms-excel.sheet.macroEnabled.12", # .xlsm \ "application/vnd.ms-excel.template.macroEnabled.12", # .xltm \ "application/vnd.ms-powerpoint", # .pps, .ppt, .pot, .ppa \ "application/vnd.ms-powerpoint.addin.macroEnabled.12", # .ppam \ "application/vnd.ms-powerpoint.presentation.macroEnabled.12", # .pptm \ "application/vnd.ms-powerpoint.slideshow.macroEnabled.12", # .ppsm \ "application/vnd.ms-powerpoint.template.macroEnabled.12", # .potm \ "application/vnd.ms-word", # .doc \ "application/vnd.ms-word.document.macroEnabled.12", # .docm \ "application/vnd.ms-word.template.macroEnabled.12", # .dotm \ "application/vnd.msexcel", # .xls \ "application/vnd.mspowerpoint", # .ppt \ "application/vnd.msword", # .doc \ "application/vnd.openxmlformats-officedocument.presentationml.presentation", # .pptx \ "application/vnd.openxmlformats-officedocument.presentationml.template", # .potx \ "application/vnd.openxmlformats-officedocument.presentationml.slideshow", # .ppsx \ "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", # .xlsx \ "application/vnd.openxmlformats-officedocument.spreadsheetml.template", # .xltx \ "application/vnd.openxmlformats-officedocument.wordprocessingml.document", # .docx \ "application/vnd.openxmlformats-officedocument.wordprocessingml.template", # .dotx \ "application/vnd.pdf", # .pdf \ "application/vnd-mspowerpoint", # .ppt \ "application/winword", # .doc \ "application/word", # .doc \ "application/x-acad", # .dwg \ "application/x-apple-diskimage", # .dmg \ "application/x-autocad", # .dwg \ "application/x-bibtex", # .bib \ "application/x-compress", # .gz, .tar, .zip \ "application/x-compressed", # .gz, .tar, .zip \ "application/x-dos_ms_excel", # .xls \ "application/x-dwg", # .dwg \ "application/x-endnote-refer", # .enw \ "application/x-eps", # .eps \ "application/x-excel", # .xls \ "application/x-gtar", # .tar \ "application/x-gunzip", # .gz \ "application/x-gzip", # .gz \ "application/x-jpg", # .jpg \ "application/x-m", # .ppt \ "application/x-ms-excel", # .xls \ "application/x-msexcel", # .xls \ "application/x-mspublisher", # .pub \ "application/x-msw6", # .doc \ "application/x-msword", # .doc \ "application/x-ole-storage", # .msi \ "application/x-pdf", # .pdf \ "application/x-powerpoint", # .ppt \ "application/x-rtf", # .rtf \ "application/x-shockwave-flash", # .swf \ "application/x-shockwave-flash2-preview", # .swf \ "application/x-tar", # .tar \ "application/x-troff-msvideo", # .avi \ "application/x-soffice", # .rtf \ "application/x-xml", # .xml, .pub \ "application/x-zip", # .zip \ "application/x-zip-compressed", # .zip \ "application/xls", # .xls \ "application/xml", # .xml, .pub \ "application/zip", # .zip \ "audio/aiff", # .avi, .mov \ "audio/avi", # .avi \ "audio/mp3", # .mp3 \ "audio/mp4", # .mp4 \ "audio/mpg", # .mp3 \ "audio/mpeg", # .mp3 \ "audio/mpeg3", # .mp3 \ "audio/x-midi", # .mov \ "audio/x-mp3", # .mp3 \ "audio/x-mpg", # .mp3 \ "audio/x-mpeg", # .mp3 \ "audio/x-mpeg3", # .mp3 \ "audio/x-mpegaudio", # .mp3 \ "audio/x-wav", # .mov \ "drawing/dwg", # .dwg \ "gzip/document", # .gz \ "image/avi", # .avi \ "image/eps", # .eps \ "image/gi_", # .gif \ "image/gif", # .eps, .gif \ "image/jpeg", # .jpg, .jpeg \ "image/jpg", # .jpg \ "image/jp_", # .jpg \ "image/mpeg", # .mpeg \ "image/mov", # .mov \ "image/photoshop", # .psd \ "image/pipeg", # .jpg \ "image/pjpeg", # .jpg \ "image/png", # .png \ "image/psd", # .psd \ "image/vnd.dwg", # .dwg \ "image/vnd.rn-realflash", # .swf \ "image/vnd.swiftview-jpeg", # .jpg \ "image/x-eps", # .eps \ "image/x-dwg", # .dwg \ "image/x-photoshop", # .psd \ "image/x-xbitmap", # .gif, .jpg \ "multipart/x-tar", # .tar \ "multipart/x-zip", # .zip \ "octet-stream", # possibly some .ppt files \ "text/csv", # .csv \ "text/mspg-legacyinfo", # .msi \ "text/pdf", # .pdf \ "text/richtext", # .rtf \ "text/rtf", # .rtf \ "text/x-pdf", # .pdf \ "text/xml", # .xml, .rss \ "video/avi", # .avi, .mov \ "video/mp4v-es", # .mp4 \ "video/msvideo", # .avi \ "video/quicktime", # .mov \ "video/x-flv", # .flv \ "video/x-m4v", # .m4v \ "video/x-msvideo", # .avi \ "video/x-quicktime", # .mov \ "video/xmpg2", # .avi \ "zz-application/zz-winassoc-psd", # .psd \ ] class Browser(object): """hubcheck webdriver interface""" def __init__(self, mimetypes=[], downloaddir='/tmp'): self.logger = logging.getLogger(__name__) self.logger.info("setting up a web browser") self._browser = None self.wait_time = 2 self.marker = 0 self.proxy_client = None self.proxy_blacklist = PROXY_BLACKLIST self.profile = None self.downloaddir = downloaddir self.mimetypes = mimetypes def __del__(self): self.close() def setup_browser_preferences(self): """browser preferences should be setup by subclasses """ pass def start_proxy_client(self): # setup proxy if needed if hubcheck.conf.settings.proxy is None: self.logger.info("proxy not started, not starting client") return # start the client self.proxy_client = hubcheck.conf.settings.proxy.create_client() # setup the proxy website blacklist if self.proxy_client is not None: self.logger.info("setting up proxy blacklist") for url_re in self.proxy_blacklist: self.logger.debug("blacklisting %s" % url_re) self.proxy_client.blacklist(url_re,200) def stop_proxy_client(self): if self.proxy_client is not None: self.logger.info("stopping proxy client") self.proxy_client.close() self.proxy_client = None def setup_browser_size_and_position(self): # set the amount of time to wait for an element to appear on the page self._browser.implicitly_wait(self.wait_time) # place the browser window in the upper left corner of the screen self._browser.set_window_position(0, 0) # resize the window to just shy of our 1024x768 screen self._browser.set_window_size(1070,700) def launch(self): """subclass should add code required to launch the browser """ pass def get(self,url): if self._browser is None: self.launch() self.logger.debug("retrieving url: %s" % (url)) self._browser.get(url) def close(self): if self._browser is None: return self.logger.info("closing browser") self._browser.quit() self._browser = None self.profile self.stop_proxy_client() def error_loading_page(self,har_entry): """ check if there was an error loading the web page returns True or False """ harurl = har_entry['request']['url'] harstatus = har_entry['response']['status'] self.logger.debug("%s returned status %s" % (harurl,harstatus)) result = None if (harstatus >= 100) and (harstatus <= 199): # information codes result = False elif (harstatus >= 200) and (harstatus <= 299): # success codes result = False elif (harstatus >= 300) and (harstatus <= 399): # redirect codes result = False elif (harstatus >= 400) and (harstatus <= 499): # client error codes # client made an invalid request (bad links) # page does not exist result = True elif (harstatus >= 500) and (harstatus <= 599): # server error codes # client made a valid request, # but server failed while responsing. result = True else: result = True return result def page_load_details(self,url=None,follow_redirects=True): """ return the har entry for the last page loaded follow redirects to make sure you get the har entry for the page that was eventually loaded. A return value of None means no page was ever loaded. """ if not self.proxy_client: return None if url is None: url = self._browser.current_url self.logger.debug("processing har for %s" % (url)) har = self.proxy_client.har self.logger.debug("har entry = %s" % (pprint.pformat(har))) return_entry = None for entry in har['log']['entries']: harurl = entry['request']['url'] harstatus = entry['response']['status'] if url == None: # we are following a redirect from below return_entry = entry elif url == harurl: # the original url matches the url for this har entry exactly return_entry = entry elif (not url.endswith('/')) and (url+'/' == harurl): # the original url almost matches the url for this har entry return_entry = entry if return_entry is not None: if follow_redirects and (harstatus >= 300) and (harstatus <= 399): # follow the redirect (should be the next har entry) url = None continue else: # found our match break self.logger.debug("har for url = %s" % (pprint.pformat(return_entry))) return return_entry def take_screenshot(self,filename=None): """ Take a screen shot of the browser, store it in filename. """ if self._browser is None: return if filename is None: dts = datetime.datetime.today().strftime("%Y%m%d%H%M%S") filename = 'hcss_%s.png' % dts self.logger.debug("screenshot filename: %s" % (filename)) self._browser.save_screenshot(filename) def next_marker(self): self.marker += 1 return self.marker

45.046632

93

0.41868

1,428

17,388

5.037815

0.267507

0.055046

0.028913

0.040867

0.162775

0.034195

0.027245

0.019461

0.01112

0

0.010867

0.470784

17,388

385

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0.770919

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0

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0

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0

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false

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null

0

null

0

1

0

300f199f66802964a7132356800aed7b1a0be7f9

7,057

py

Python

appengine/gae_defer_manager/deferred_manager/tests.py

meedan/montage

4da0116931edc9af91f226876330645837dc9bcc

[ "Apache-2.0" ]

6

2018-07-31T16:48:07.000Z

2020-02-01T03:17:51.000Z

appengine/gae_defer_manager/deferred_manager/tests.py

meedan/montage

4da0116931edc9af91f226876330645837dc9bcc

[ "Apache-2.0" ]

41

2018-08-07T16:43:07.000Z

2020-06-05T18:54:50.000Z

appengine/gae_defer_manager/deferred_manager/tests.py

meedan/montage

4da0116931edc9af91f226876330645837dc9bcc

[ "Apache-2.0" ]

1

2018-08-07T16:40:18.000Z

# -*- coding: utf8 -*- import datetime import mock import os import unittest import webapp2 from google.appengine.ext import testbed, deferred from google.appengine.api import queueinfo from . import models from .handler import application from .wrapper import defer TESTCONFIG_DIR = os.path.join( os.path.dirname(os.path.realpath(__file__)), "testconfig") def noop(*args, **kwargs): pass def noop_fail(*args, **kwargs): raise Exception def noop_permanent_fail(*args, **kwargs): raise deferred.PermanentTaskFailure class Foo(object): def bar(self): pass def __call__(self): pass class BaseTest(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() self.testbed.init_datastore_v3_stub() self.testbed.init_taskqueue_stub(root_path=TESTCONFIG_DIR) self.taskqueue_stub = self.testbed.get_stub(testbed.TASKQUEUE_SERVICE_NAME) super(BaseTest, self).setUp() def reload(self, obj): return obj.get(obj.key()) class DeferTaskTests(BaseTest): def test_creates_state(self): task_state = defer(noop) queue_state = models.QueueState.get_by_key_name("default") self.assertTrue(queue_state) self.assertEqual(task_state.parent().key(), queue_state.key()) def test_unique_task_ref(self): unique_until = datetime.datetime.utcnow() + datetime.timedelta(days=1) self.assertRaises(AssertionError, defer, noop, unique_until=unique_until) self.assertTrue(defer(noop, task_reference="project1", unique_until=unique_until)) self.assertFalse(defer(noop, task_reference="project1", unique_until=unique_until)) def test_args_repr(self): task_state = defer(noop, 2, u"bår") self.assertEqual(task_state.deferred_args, u"(2, u'b\\xe5r')") def test_kwargs_repr(self): task_state = defer(noop, foo="bår", _bar="foo") self.assertEqual(task_state.deferred_kwargs, u"{'foo': 'b\\xc3\\xa5r'}") def test_class_method_repr(self): task_state = defer(Foo().bar) self.assertEqual(task_state.deferred_function, u"<class 'deferred_manager.tests.Foo'>.bar") def test_module_func_repr(self): task_state = defer(noop) self.assertEqual(task_state.deferred_function, u"deferred_manager.tests.noop") def test_builtin_func_repr(self): task_state = defer(map) self.assertEqual(task_state.deferred_function, u"map") def test_callable_obj_func_repr(self): task_state = defer(Foo) self.assertEqual(task_state.deferred_function, u"deferred_manager.tests.Foo") def test_builtin_method_repr(self): task_state = defer(datetime.datetime.utcnow) self.assertEqual(task_state.deferred_function, u"<type 'datetime.datetime'>.utcnow") class ModelTaskTests(unittest.TestCase): def test_queue_state(self): queue_state = models.QueueState(name="default") self.assertEqual(queue_state.retry_limit, 7) self.assertEqual(queue_state.age_limit, 2*24*3600) # 2 days class HandlerTests(BaseTest): def make_request(self, path, task_name, queue_name, headers=None, environ=None, **kwargs): request_headers = { "X-AppEngine-TaskName": task_name, "X-AppEngine-QueueName": queue_name, 'X-AppEngine-TaskExecutionCount': kwargs.pop('retries', 0) } if headers: request_headers.update(headers) request_environ = { "SERVER_SOFTWARE": "Development" } if environ: request_environ.update(environ) return webapp2.Request.blank('/', environ=request_environ, headers=request_headers, **kwargs) def test_success(self): task_state = defer(noop) noop_pickle = deferred.serialize(noop) request = self.make_request("/", task_state.task_name, 'default', POST=noop_pickle) response = request.get_response(application) self.assertEqual(response.status_int, 200) task_state = self.reload(task_state) self.assertTrue(task_state.task_name) self.assertTrue(task_state.is_complete) self.assertFalse(task_state.is_running) self.assertFalse(task_state.is_permanently_failed) def test_failure(self): task_state = defer(noop_fail) noop_pickle = deferred.serialize(noop_fail) request = self.make_request("/", task_state.task_name, 'default', POST=noop_pickle) response = request.get_response(application) self.assertEqual(response.status_int, 500) task_state = self.reload(task_state) self.assertFalse(task_state.is_complete) self.assertFalse(task_state.is_running) self.assertFalse(task_state.is_permanently_failed) def test_retry_success(self): task_state = defer(noop) noop_pickle = deferred.serialize(noop) request = self.make_request("/", task_state.task_name, 'default', POST=noop_pickle, retries=2) response = request.get_response(application) self.assertEqual(response.status_int, 200) task_state = self.reload(task_state) self.assertEqual(task_state.retry_count, 2) self.assertTrue(task_state.is_complete) self.assertFalse(task_state.is_running) self.assertFalse(task_state.is_permanently_failed) def test_retry_max_retries(self): task_state = defer(noop_fail) # give the task an old age. tasks must fail both the retry and age conditions (if specified) task_state.first_run = datetime.datetime.utcnow() - datetime.timedelta(days=2) task_state.put() noop_pickle = deferred.serialize(noop_fail) request = self.make_request("/", task_state.task_name, 'default', POST=noop_pickle, retries=8) response = request.get_response(application) self.assertEqual(response.status_int, 500) task_state = self.reload(task_state) self.assertEqual(task_state.retry_count, 8) self.assertTrue(task_state.is_complete) self.assertFalse(task_state.is_running) self.assertTrue(task_state.is_permanently_failed) def test_permanent_failure(self): task_state = defer(noop_permanent_fail) noop_pickle = deferred.serialize(noop_permanent_fail) request = self.make_request("/", task_state.task_name, 'default', POST=noop_pickle) response = request.get_response(application) self.assertEqual(response.status_int, 200) task_state = self.reload(task_state) self.assertEqual(task_state.retry_count, 0) self.assertTrue(task_state.is_complete) self.assertFalse(task_state.is_running) self.assertTrue(task_state.is_permanently_failed) def test_no_task_state(self): noop_pickle = deferred.serialize(noop) request = self.make_request("/", 'task1', 'default', POST=noop_pickle) response = request.get_response(application) self.assertEqual(response.status_int, 200)

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876

7,057

5.360731

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0.111158

0.035136

0.04983

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false

0.020833

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0.006944

0.284722

0

null

0

null

0

1

0

30113171dc48ed74cadebf84f1f1fd11cb8f6566

4,023

py

Python

BdBG.py

rongjiewang/BdBG

b4a8fab0fa083aecab10f15431e37b0445722007

[ "MIT" ]

2

2018-11-21T06:39:34.000Z

2018-11-21T06:43:53.000Z

BdBG.py

rongjiewang/BdBG

b4a8fab0fa083aecab10f15431e37b0445722007

[ "MIT" ]

null

BdBG.py

rongjiewang/BdBG

b4a8fab0fa083aecab10f15431e37b0445722007

[ "MIT" ]

null

"""MIT License Copyright (c) 2018 rongjiewang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" import sys import argparse from bucket import encodeBucketClass, decodeBucketClass from deBruijnGraph import encodeGraphClass, decodeGraphClass def args_check(args): if not args.encode and not args.decode: sys.exit("you must give a -e or -d for encode/decode") if not args.input and not args.paired: sys.exit("you must give a file input with -i input for single end data or -p -1 input1 -2 input2 for paired-end data") if not args.output: sys.exit("you must give a file output with -o output") return def main(args): args_check(args) #encode if args.encode: en_bucket = encodeBucketClass(args.input, args.output, args.paired, \ args.input1, args.input2, args.kmer, args.lossless, args.verbose) en_bucket.encode() en_graph = encodeGraphClass(args.output, args.paired, args.kmer, \ args.verbose, en_bucket.sequenceTableSave) del en_bucket en_graph.encode() del en_graph sys.exit() #decode else: de_bucket = decodeBucketClass(args.input, args.output, args.verbose) de_bucket.decode() de_graph = decodeGraphClass(args.input, args.output, de_bucket.paired, de_bucket.readNum,\ de_bucket.bucketIndexLen, de_bucket.lossless, de_bucket.verbose) de_graph.loadBucktData(de_bucket.bucketIndex, de_bucket.bucketCov, de_bucket.readIndexPos,\ de_bucket.readrc, de_bucket.readN, de_bucket.readLen, de_bucket.readOrder) del de_bucket de_graph.decode() del de_graph sys.exit() if __name__ == '__main__': parser = argparse.ArgumentParser(description = 'BdBG') parser.add_argument("-e", "--encode", help="encoding",action="store_true") parser.add_argument("-d", "--decode", help="decoding",action="store_true") parser.add_argument("-i", "--input",type=str, help="inputFile") parser.add_argument("-o", "--output", help="outputFile") parser.add_argument("-p", "--paired", help="paired-end flag",action="store_true") parser.add_argument("-1", "--input1", help="paired-end file1") parser.add_argument("-2", "--input2", help="paired-end file2") parser.add_argument("-l", "--lossless", help="keep the reads orders, default:false, \ if encode paired-end files, default:ture ",action="store_true") parser.add_argument("-k", "--kmer",type=int, default=15, help="kmer size for bucket and de Bruijn graph, default=15") parser.add_argument("-v","--verbose", action="store_true", help="verbose information") args = parser.parse_args() main(args)

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null

0

null

0

1

0

30119e15a78b5e7aea8cf1c27d45b2140994ce7e

10,820

py

Python

web/lib/console.py

jonathanverner/brython-jinja2

cec6e16de1750203a858d0acf590f230fc3bf848

[ "BSD-3-Clause" ]

2

2020-09-13T17:51:55.000Z

2020-11-25T18:47:12.000Z

web/lib/console.py

jonathanverner/brython-jinja2

cec6e16de1750203a858d0acf590f230fc3bf848

[ "BSD-3-Clause" ]

2

2020-11-25T19:18:15.000Z

2021-06-01T21:48:12.000Z

web/lib/console.py

jonathanverner/brython-jinja2

cec6e16de1750203a858d0acf590f230fc3bf848

[ "BSD-3-Clause" ]

null

""" This module provides the interactive Python console. """ import sys import traceback from browser import window class Console: """ A class providing a console widget. The constructor accepts a domnode which should be a textarea and it takes it over and turns it into a python interactive console. """ _credits = """ Thanks to CWI, CNRI, BeOpen.com, Zope Corporation and a cast of thousands for supporting Python development. See www.python.org for more information. """ _copyright = """Copyright (c) 2012, Pierre Quentel pierre.quentel@gmail.com All Rights Reserved. Copyright (c) 2001-2013 Python Software Foundation. All Rights Reserved. Copyright (c) 2000 BeOpen.com. All Rights Reserved. Copyright (c) 1995-2001 Corporation for National Research Initiatives. All Rights Reserved. Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam. All Rights Reserved. """ _license = """Copyright (c) 2012, Pierre Quentel pierre.quentel@gmail.com All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the <ORGANIZATION> nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ def __init__(self, elem): self._elem = elem self.credits.__repr__ = lambda: Console._credits self.copyright.__repr__ = lambda: Console._copyright self.license.__repr__ = lambda: Console._license self._redirected = False self._oldstdout = None self._oldstderr = None self.history = [] self.current = 0 self._status = "main" # or "block" if typing inside a block self.current_line = "" # execution namespace self.editor_ns = { 'credits': self.credits, 'copyright': self.copyright, 'license': self.license, '__name__': '__console__', } self._elem.bind('keypress', self.my_key_press) self._elem.bind('keydown', self.my_key_down) self._elem.bind('click', self.cursor_to_end) version = sys.implementation.version self._elem.value = "Brython %s.%s.%s on %s %s\n%s\n>>> " % (version[0], version[1], version[2], window.navigator.appName, window.navigator.appVersion, 'Type "copyright()", "credits()" or "license()" for more information.') self._elem.focus() self.cursor_to_end() def add_to_ns(self, key, value): """ Adds key to the console's local scope. Think: ``` key=value ``` """ self.editor_ns[key] = value def _redirect_out(self): if self._redirected: sys.__console__ = False sys.stdout = self._oldstdout sys.stderr = self._oldstderr self._redirected = False else: sys.__console__ = True self._oldstdout = sys.stdout self._oldstderr = sys.stderr sys.stdout = self sys.stderr = self self._redirected = True def credits(self): self.write(self._credits) def copyright(self): self.write(self._copyright) def license(self): self.write(self._license) def write(self, data): self._elem.value += str(data) def cursor_to_end(self, *_args): pos = len(self._elem.value) self._elem.setSelectionRange(pos, pos) self._elem.scrollTop = self._elem.scrollHeight def get_col(self, _area): """ returns the column position of the cursor """ sel = self._elem.selectionStart lines = self._elem.value.split('\n') for line in lines[:-1]: sel -= len(line) + 1 return sel def my_key_press(self, event): if event.keyCode == 9: # tab key event.preventDefault() self._elem.value += " " elif event.keyCode == 13: # return src = self._elem.value if self._status == "main": self.current_line = src[src.rfind('>>>') + 4:] elif self._status == "3string": self.current_line = src[src.rfind('>>>') + 4:] self.current_line = self.current_line.replace('\n... ', '\n') else: self.current_line = src[src.rfind('...') + 4:] if self._status == 'main' and not self.current_line.strip(): self._elem.value += '\n>>> ' event.preventDefault() return self._elem.value += '\n' self.history.append(self.current_line) self.current = len(self.history) if self._status == "main" or self._status == "3string": try: self._redirect_out() _ = self.editor_ns['_'] = eval(self.current_line, self.editor_ns) if _ is not None: self.write(repr(_) + '\n') self._elem.value += '>>> ' self._status = "main" except IndentationError: self._elem.value += '... ' self._status = "block" except SyntaxError as msg: if str(msg) == 'invalid syntax : triple string end not found' or \ str(msg).startswith('Unbalanced bracket'): self._elem.value += '... ' self._status = "3string" elif str(msg) == 'eval() argument must be an expression': try: self._redirect_out() exec(self.current_line, self.editor_ns) except: # pylint: disable=bare-except; any exception can happen here traceback.print_exc(self) finally: self._redirect_out() self._elem.value += '>>> ' self._status = "main" elif str(msg) == 'decorator expects function': self._elem.value += '... ' self._status = "block" else: traceback.print_exc(self) self._elem.value += '>>> ' self._status = "main" # pylint: disable=bare-except; any exception can happen here except: traceback.print_exc(self) self._elem.value += '>>> ' self._status = "main" finally: self._redirect_out() elif self.current_line == "": # end of block block = src[src.rfind('>>>') + 4:].splitlines() block = [block[0]] + [b[4:] for b in block[1:]] block_src = '\n'.join(block) # status must be set before executing code in globals() self._status = "main" try: self._redirect_out() _ = exec(block_src, self.editor_ns) if _ is not None: print(repr(_)) # pylint: disable=bare-except; any exception can happen here except: traceback.print_exc(self) finally: self._redirect_out() self._elem.value += '>>> ' else: self._elem.value += '... ' self.cursor_to_end() event.preventDefault() def my_key_down(self, event): if event.keyCode == 37: # left arrow sel = self.get_col(self._elem) if sel < 5: event.preventDefault() event.stopPropagation() elif event.keyCode == 36: # line start pos = self._elem.selectionStart col = self.get_col(self._elem) self._elem.setSelectionRange(pos - col + 4, pos - col + 4) event.preventDefault() elif event.keyCode == 38: # up if self.current > 0: pos = self._elem.selectionStart col = self.get_col(self._elem) # remove self.current line self._elem.value = self._elem.value[:pos - col + 4] self.current -= 1 self._elem.value += self.history[self.current] event.preventDefault() elif event.keyCode == 40: # down if self.current < len(self.history) - 1: pos = self._elem.selectionStart col = self.get_col(self._elem) # remove self.current line self._elem.value = self._elem.value[:pos - col + 4] self.current += 1 self._elem.value += self.history[self.current] event.preventDefault() elif event.keyCode == 8: # backspace src = self._elem.value lstart = src.rfind('\n') if (lstart == -1 and len(src) < 5) or (len(src) - lstart < 6): event.preventDefault() event.stopPropagation()

39.926199

139

0.539464

1,154

10,820

4.906412

0.279029

0.059343

0.055104

0.039032

0.304839

0.237725

0.206111

0.183681

0.175203

0

0.011079

0.365989

10,820

270

140

40.074074

0.814286

0.067837

0

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0

0.249673

0.004832

0

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false

0

0.013889

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0.023148

0

null

0

null

0

1

0

30124f8335d73ee7802841f7737a00cbfad26c9f

1,333

py

Python

lldb/test/API/functionalities/breakpoint/breakpoint_on_overload/TestBreakOnOverload.py

LaudateCorpus1/llvm-project

ff2e0f0c1112558b3f30d8afec7c9882c33c79e3

[ "Apache-2.0" ]

null

lldb/test/API/functionalities/breakpoint/breakpoint_on_overload/TestBreakOnOverload.py

LaudateCorpus1/llvm-project

ff2e0f0c1112558b3f30d8afec7c9882c33c79e3

[ "Apache-2.0" ]

null

lldb/test/API/functionalities/breakpoint/breakpoint_on_overload/TestBreakOnOverload.py

LaudateCorpus1/llvm-project

ff2e0f0c1112558b3f30d8afec7c9882c33c79e3

[ "Apache-2.0" ]

null

""" Test setting a breakpoint on an overloaded function by name. """ import re import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test import lldbutil class TestBreakpointOnOverload(TestBase): mydir = TestBase.compute_mydir(__file__) def check_breakpoint(self, name): bkpt = self.target.BreakpointCreateByName(name) self.assertEqual(bkpt.num_locations, 1, "Got one location") addr = bkpt.locations[0].GetAddress() self.assertTrue(addr.function.IsValid(), "Got a real function") # On Window, the name of the function includes the return value. # We still succeed in setting the breakpoint, but the resultant # name is not the same. # So just look for the name we used for the breakpoint in the # function name, rather than doing an equality check. self.assertIn(name, addr.function.name, "Got the right name") def test_break_on_overload(self): self.build() self.target = lldbutil.run_to_breakpoint_make_target(self) self.check_breakpoint("a_function(int)") self.check_breakpoint("a_function(double)") self.check_breakpoint("a_function(int, double)") self.check_breakpoint("a_function(double, int)")

35.078947

72

0.685671

170

1,333

5.247059

0.452941

0.084081

0.085202

0.089686

0.152466

0

0.001942

0.227307

1,333

37

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false

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0

0.45

0

null

0

null

0

1

0

30142c5188e376313f7d79178393a7007c7faa25

2,611

py

Python

Intermedio/28 Pomodoro/main.py

YosafatM/100-days-of-Python

e81ab663b7aacb7a904f27a4e6774837cf3594a1

[ "MIT" ]

null

Intermedio/28 Pomodoro/main.py

YosafatM/100-days-of-Python

e81ab663b7aacb7a904f27a4e6774837cf3594a1

[ "MIT" ]

null

Intermedio/28 Pomodoro/main.py

YosafatM/100-days-of-Python

e81ab663b7aacb7a904f27a4e6774837cf3594a1

[ "MIT" ]

null

from tkinter import * # ---------------------------- CONSTANTS ------------------------------- # PINK = "#e2979c" RED = "#e7305b" GREEN = "#9bdeac" YELLOW = "#f7f5dd" FONT_NAME = "Courier" WORK_MIN = 25 SHORT_BREAK_MIN = 5 LONG_BREAK_MIN = 20 is_counting = False reps = 0 timer = None # ---------------------------- TIMER RESET ------------------------------- # def reset_timer(): global timer, reps, is_counting if timer is not None: window.after_cancel(timer) lb_title.config(text="Timer", fg=GREEN) canvas.itemconfig(count_text, text="00:00") lb_checks["text"] = "" timer = None reps = 0 is_counting = False # ---------------------------- TIMER MECHANISM ------------------------------- # def start_timer(): global is_counting, reps if is_counting: pass reps += 1 if reps % 8 == 0: lb_title.config(text="Break", fg=RED) minutes = LONG_BREAK_MIN elif reps % 2 == 0: lb_title.config(text="Break", fg=PINK) minutes = SHORT_BREAK_MIN else: lb_title.config(text="Work", fg=GREEN) minutes = WORK_MIN is_counting = True count_down(minutes * 60) # ---------------------------- COUNTDOWN MECHANISM ------------------------------- # def count_down(count): global reps minutes = count // 60 seconds = count % 60 seconds = f"0{seconds}" if seconds < 10 else seconds canvas.itemconfig(count_text, text=f"{minutes}:{seconds}") if count > 0: global timer timer = window.after(1000, count_down, count - 1) elif reps % 2 == 1: global is_counting is_counting = False lb_checks["text"] += "✅" start_timer() # Break # ---------------------------- UI SETUP ------------------------------- # window = Tk() window.title("Pomodoro") window.config(padx=100, pady=50, bg=YELLOW) canvas = Canvas(width=200, height=224, bg=YELLOW, highlightthickness=0) image = PhotoImage(file="tomato.png") canvas.create_image(100, 112, image=image) count_text = canvas.create_text(100, 130, text="00:00", fill="white", font=(FONT_NAME, 35, "bold")) bt_start = Button(text="Start", highlightthickness=0, command=start_timer) bt_reset = Button(text="Reset", highlightthickness=0, command=reset_timer) lb_checks = Label(text="", fg=GREEN, bg=YELLOW) lb_title = Label(text="Timer", fg=GREEN, bg=YELLOW, font=(FONT_NAME, 30, "bold")) lb_title.grid(column=1, row=0) canvas.grid(column=1, row=1) bt_start.grid(column=0, row=2) bt_reset.grid(column=2, row=2) lb_checks.grid(column=1, row=3) window.mainloop()

27.484211

99

0.573727

332

2,611

4.36747

0.307229

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0.035862

0.046897

0.074483

0.034483

0

0.041805

0.193795

2,611

94

100

27.776596

0.646556

0.146304

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null

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1

0

3017c55b6cc7146b6404407438f7cdac4217ef3c

2,529

py

Python

dtcwt/plotting.py

santosh653/dtcwt

01d9e87dc9abfa244a89c1f05aebf3dec6999f3a

[ "BSD-2-Clause" ]

61

2015-01-04T09:21:29.000Z

2022-03-07T16:25:02.000Z

dtcwt/plotting.py

santosh653/dtcwt

01d9e87dc9abfa244a89c1f05aebf3dec6999f3a

[ "BSD-2-Clause" ]

17

2015-04-02T13:37:07.000Z

2018-03-07T09:57:57.000Z

dtcwt/plotting.py

santosh653/dtcwt

01d9e87dc9abfa244a89c1f05aebf3dec6999f3a

[ "BSD-2-Clause" ]

26

2015-04-16T06:22:16.000Z

2021-12-07T09:17:44.000Z

""" Convenience functions for plotting DTCWT-related objects. """ from __future__ import absolute_import import numpy as np from matplotlib.pyplot import * __all__ = ( 'overlay_quiver', ) def overlay_quiver(image, vectorField, level, offset): """Overlays nicely coloured quiver plot of complex coefficients over original full-size image, providing a useful phase visualisation. :param image: array holding grayscale values on the interval [0, 255] to display :param vectorField: a single [MxNx6] numpy array of DTCWT coefficients :param level: the transform level (1-indexed) of *vectorField*. :param offset: Offset for DTCWT coefficients (typically 0.5) .. note:: The *level* parameter is 1-indexed meaning that the third level has index "3". This is unusual in Python but is kept for compatibility with similar MATLAB routines. Should also work with other types of complex arrays (e.g., SLP coefficients), as long as the format is the same. Usage example: .. plot:: :include-source: true import dtcwt import dtcwt.plotting as plotting mandrill = datasets.mandrill() transform2d = dtcwt.Transform2d() mandrill_t = transform2d.forward(mandrill, nlevels=5) plotting.overlay_quiver(mandrill*255, mandrill_t.highpasses[-1], 5, 0.5) .. codeauthor:: R. Anderson, 2005 (MATLAB) .. codeauthor:: S. C. Forshaw, 2014 (Python) """ # Make sure imshow() uses the full range of greyscale values imshow(image, cmap=cm.gray, clim=(0,255)) hold(True) # Set up the grid for the quiver plot g1 = np.kron(np.arange(0, vectorField[:,:,0].shape[0]).T, np.ones((1,vectorField[:,:,0].shape[1]))) g2 = np.kron(np.ones((vectorField[:,:,0].shape[0], 1)), np.arange(0, vectorField[:,:,0].shape[1])) # Choose a coloUrmap cmap = cm.spectral scalefactor = np.max(np.max(np.max(np.max(np.abs(vectorField))))) vectorField[-1,-1,:] = scalefactor for sb in range(0, vectorField.shape[2]): hold(True) thiscolour = cmap(sb / float(vectorField.shape[2])) # Select colour for this subband hq = quiver(g2*(2**level) + offset*(2**level), g1*(2**level) + offset*(2**level), np.real(vectorField[:,:,sb]), \ np.imag(vectorField[:,:,sb]), color=thiscolour, scale=scalefactor*2**level) quiverkey(hq, 1.05, 1.00-0.035*sb, 0, "subband " + np.str(sb), coordinates='axes', color=thiscolour, labelcolor=thiscolour, labelpos='E') hold(False) return hq

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null

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null

0

1

0

301a3402ce430cb702ae2f205a2ed74937b55dc4

2,481

py

Python

graphdata/loglog.py

whalenpt/graphdata

d169150f860551d2049342ecf310dc1783987266

[ "MIT" ]

null

graphdata/loglog.py

whalenpt/graphdata

d169150f860551d2049342ecf310dc1783987266

[ "MIT" ]

null

graphdata/loglog.py

whalenpt/graphdata

d169150f860551d2049342ecf310dc1783987266

[ "MIT" ]

null

from graphdata.shared.shared1D import AuxPlotLabelLL1D from graphdata.shared.shared1D import ProcessData1D from graphdata.shared.shared1D import LoadData1D from graphdata.shared.figsizes import LogLogSize from graphdata.shared.shared import ExtendDictionary from graphdata.shared.shared import ProcessComplex from graphdata import plt from graphdata import np from graphdata import configs def loglog(filename,figsize=None,decades=None,xlim=None,ylim=None,\ complex_op=None,overwrite=False,**kwargs): """ Loglog graph of 1D data file using Matplotlib plt.loglog INPUTS: filename: string name of file containing 1D data to be plotted figsize: tuple (width,height) size of figure to be displayed xlim: np.array x-axis limits of graph ylim: np.array x-axis limits of graph decades: int number of decades of data below maximum to plot overwrite: bool add lines to an existing plt.semilogy graph if it exists (default is False which will create graph on a new figure) **kwargs: dictionary (optional) arguments to be passed onto plt.loglog plot OUTPUTS: ax : matplotlib.axes.Axes Matplotlib axes object, allows for setting limits and other manipulation of the axes (e.g. ax.set_xlim([0,1]) would set the graph x-limits to be between 0 and 1) """ x,y,auxDict = LoadData1D(filename) if complex_op is not None: y = ProcessComplex(complex_op,y) if decades is None: decades = configs._G['decades'] if xlim is None: xlim = [x[0],x[-1]] if ylim is None: ylim = [np.min(y),np.max(y)] figsize = LogLogSize(figsize) ExtendDictionary(auxDict,figsize=figsize,decades=decades,\ xlim=xlim,ylim=ylim,overwrite=overwrite) x,y,auxDict = ProcessData1D(x,y,auxDict) figsize = LogLogSize(figsize) if overwrite: labs = plt.get_figlabels() if "LogLog" not in labs: configs.defaultLS() else: configs.toggleLS() plt.figure("LogLog",figsize=figsize) else: configs.defaultLS() plt.figure(figsize=figsize) fig = plt.loglog(x,y,configs.LS,**kwargs) plt.grid(True) AuxPlotLabelLL1D(auxDict) if xlim: plt.xlim(xlim) if ylim: plt.ylim(ylim) plt.ion() plt.show() return fig

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null

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null

0

1

0

301d726bb49a99005fdbccd8050406dd9847256c

5,387

py

Python

integration-testing/tests/suites/test_premium_account.py

pwei1018/bcrs-testing

318845dede6ce5994b74b976d01f36a503036551

[ "Apache-2.0" ]

2

2020-10-23T22:08:34.000Z

2021-10-19T19:37:21.000Z

integration-testing/tests/suites/test_premium_account.py

pwei1018/bcrs-testing

318845dede6ce5994b74b976d01f36a503036551

[ "Apache-2.0" ]

6

2020-09-29T23:05:34.000Z

2022-01-29T20:59:08.000Z

integration-testing/tests/suites/test_premium_account.py

pwei1018/bcrs-testing

318845dede6ce5994b74b976d01f36a503036551

[ "Apache-2.0" ]

10

2020-09-29T23:05:46.000Z

2021-11-29T23:07:10.000Z

import datetime import json import requests import pytest import random from tests.suites.test_payment import TestPayment from tests.utilities.settings import get_settings, get_test_data, setup_access_data @pytest.mark.incremental @pytest.mark.parametrize('login_session', setup_access_data('PREMIUM', ['BCSC']), indirect=True, scope='class') @pytest.mark.usefixtures('setup_data') class TestPremiumAccount: __test__ = True def test_get_user_profile(self, testing_config, logger): """Test get user profile. After login, the user should be created in db.""" response = requests.get(f'{testing_config.auth_api_url}/users/@me', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200 response_json = response.json() testing_config.user_id = response_json.get('keycloakGuid') def test_get_last_terms(self, testing_config, logger): """Get last version of termofuse.""" response = requests.get(f'{testing_config.auth_api_url}/documents/termsofuse', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200 response_json = response.json() testing_config.terms_version = response_json.get('versionId') def test_accept_terms(self, testing_config, logger): """Test accept termofuser.""" input_data = json.dumps({'termsversion': testing_config.terms_version, 'istermsaccepted': True}) response = requests.patch(f'{testing_config.auth_api_url}/users/@me', headers={'Authorization': f'Bearer {testing_config.keycloak_token}', 'Content-Type': 'application/json'}, data=input_data) assert response.status_code == 200 def test_get_user_profile(self, testing_config, logger): """Test get user profile.""" response = requests.get(f'{testing_config.auth_api_url}/users/@me', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200 response_json = response.json() testing_config.user_id = response_json.get('keycloakGuid') @pytest.mark.skip_login_as('bcsc_member') def test_link_bcol_account(self, testing_config, logger): """Test link bcol account.""" load_data = random.sample(get_settings().BCOL_USERS, 1)[0] input_data = json.dumps({ 'userId': load_data.username, 'password': load_data.password }) response = requests.post(f'{testing_config.auth_api_url}/bcol-profiles', headers={'Authorization': f'Bearer {testing_config.keycloak_token}', 'Content-Type': 'application/json'}, data=input_data) assert response.status_code == 200 response_json = response.json() @pytest.mark.skip_login_as('bcsc_member') def test_create_account(self, testing_config, logger): """Test create account.""" input_data = json.dumps(get_test_data(testing_config.test_data['org'])) response = requests.post(f'{testing_config.auth_api_url}/orgs', headers={'Authorization': f'Bearer {testing_config.keycloak_token}', 'Content-Type': 'application/json'}, data=input_data) assert response.status_code == 201 response_json = response.json() testing_config.org_id = response_json.get('id') def test_create_user_profile(self, testing_config, logger): """Test create user profile (contact information).""" input_data = json.dumps(get_test_data(testing_config.test_data['user_profile'])) response = requests.post(f'{testing_config.auth_api_url}/users/contacts', headers={'Authorization': f'Bearer {testing_config.keycloak_token}', 'Content-Type': 'application/json'}, data=input_data) assert response.status_code == 201 def test_get_account(self, testing_config, logger): """Test get account.""" response = requests.get(f'{testing_config.auth_api_url}/orgs/{testing_config.org_id}', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200 def test_get_user_settings(self, testing_config, logger): """Test get user settings.""" response = requests.get(f'{testing_config.auth_api_url}/users/{testing_config.user_id}/settings', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200 def test_get_user_notifications(self, testing_config, logger): """Test get user notifications.""" response = requests.get(f'{testing_config.auth_api_url}/users/{testing_config.user_id}/org/{testing_config.org_id}/notifications', headers={'Authorization': f'Bearer {testing_config.keycloak_token}'}) assert response.status_code == 200

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0.240506

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null

0

null

0

1

0

30220c4e2730501b7f29fa25506bdd0fdf76716c

16,677

py

Python

ehr_ml/clmbr/__init__.py

som-shahlab/ehr_ml

4f83ac5b882916a175f0d242b38d914d00bf8a7c

[ "MIT" ]

4

2021-03-12T21:41:37.000Z

2021-06-25T16:49:52.000Z

ehr_ml/clmbr/__init__.py

som-shahlab/ehr_ml

4f83ac5b882916a175f0d242b38d914d00bf8a7c

[ "MIT" ]

22

2020-11-19T00:04:27.000Z

2022-03-02T18:16:08.000Z

ehr_ml/clmbr/__init__.py

som-shahlab/ehr_ml

4f83ac5b882916a175f0d242b38d914d00bf8a7c

[ "MIT" ]

2

2021-05-12T13:11:46.000Z

2021-10-15T18:30:14.000Z

from __future__ import annotations import argparse import pickle import numpy as np import json import logging import math import glob import random import os import sys import datetime import time from functools import partial from pathlib import Path from collections import defaultdict from shutil import copyfile from tqdm import tqdm import sklearn.model_selection import sklearn.metrics import torch from ..extension.clmbr import * from .. import timeline from .. import ontology from .. import labeler from .dataset import DataLoader, convert_patient_data from .prediction_model import CLMBR from .trainer import Trainer from .utils import read_config, read_info, device_from_config from ..featurizer import ColumnValue, Featurizer from ..splits import read_time_split from ..utils import OnlineStatistics, set_up_logging from .opt import OpenAIAdam from typing import Mapping, Any, Dict, Optional, Tuple def check_dir_for_overwrite(dirname: str) -> bool: return bool( glob.glob(os.path.join(dirname, "*.json")) or glob.glob(os.path.join(dirname, "checkpoints")) ) def create_info_program() -> None: parser = argparse.ArgumentParser( description="Precompute training data summary statistics etc for CLMBR experiments" ) parser.add_argument( "input_data_dir", type=str, help="Location of the dataset extract to be used for CLMBR training", ) parser.add_argument( "save_dir", type=str, help="Location where model info is to be saved", ) parser.add_argument( "train_end_date", type=str, help="The end date for training" ) parser.add_argument( "val_end_date", type=str, help="The end date for validation. Should be later than the end date for training", ) parser.add_argument( "--min_patient_count", type=int, default=100, help="Only keep statistics on codes/terms that appear for this many patients (default 100)", ) parser.add_argument( "--excluded_patient_file", type=str, help="A file containing a list of patients to exclude from training. " "Any patient ID you plan to use for finetuning / evaluation should be " "listed in this file. If not provided, exclude_patient_ratio must be specified.", default=None, ) parser.add_argument( "--exclude_patient_ratio", type=float, default=None, help="Ratio of patients to exclude from pre-training between 0 and 1." " If provided, excluded patient IDs will " "be randomly selected and written out to a file " '"excluded_patient_ids.txt" in the save directory. If not ' "provided, excluded_patient_file must be specified.", ) parser.add_argument( "--seed", type=int, default=3451235, help="Random seed (default 3451235)", ) args = parser.parse_args() if args.save_dir is None: print("Error - must specify save_dir", file=sys.stderr) exit(1) else: save_dir = args.save_dir os.makedirs(save_dir, exist_ok=True) set_up_logging(os.path.join(save_dir, "create_info.log")) logging.info("Args: %s", str(args)) if check_dir_for_overwrite(save_dir): print( "Fatal error - model dir {} is not empty".format(save_dir), file=sys.stderr, ) logging.info("Fatal error - model dir {} is not empty".format(save_dir)) exit(1) ontologies_path = os.path.join(args.input_data_dir, "ontology.db") timelines_path = os.path.join(args.input_data_dir, "extract.db") train_end_date = datetime.datetime.fromisoformat(args.train_end_date) val_end_date = datetime.datetime.fromisoformat(args.val_end_date) if train_end_date == val_end_date: logging.info("Could not creat info with the same train and validation end date") exit(1) result = json.loads( create_info( timelines_path, ontologies_path, train_end_date, val_end_date, args.min_patient_count, ) ) result["extract_dir"] = args.input_data_dir result["extract_file"] = "extract.db" result["train_start_date"] = "1900-01-01" result["train_end_date"] = args.train_end_date result["val_start_date"] = args.train_end_date result["val_end_date"] = args.val_end_date result["seed"] = args.seed result["min_patient_count"] = args.min_patient_count def remove_pids(a, x): return [(p, c) for p, c in a if p not in x] if args.excluded_patient_file is not None: with open(args.excluded_patient_file) as f: pids = {int(a) for a in f} result["train_patient_ids_with_length"] = remove_pids( result["train_patient_ids_with_length"], pids ) result["val_patient_ids_with_length"] = remove_pids( result["val_patient_ids_with_length"], pids ) logging.info( "Removed %d patient IDs from file %s" % (len(pids), args.excluded_patient_file) ) elif args.exclude_patient_ratio is not None: assert 0 < args.exclude_patient_ratio and args.exclude_patient_ratio < 1 train_pids = set([x[0] for x in result["train_patient_ids_with_length"]]) val_pids = set([x[0] for x in result["val_patient_ids_with_length"]]) all_pids = train_pids.union(val_pids) excluded_pids = set( random.sample( list(all_pids), int(round(len(all_pids) * args.exclude_patient_ratio)), ) ) result["train_patient_ids_with_length"] = remove_pids( result["train_patient_ids_with_length"], excluded_pids ) result["val_patient_ids_with_length"] = remove_pids( result["val_patient_ids_with_length"], excluded_pids ) with open( os.path.join(args.save_dir, "excluded_patient_ids.txt"), "w" ) as f: for pid in excluded_pids: f.write("%d\n" % pid) logging.info( "Removed %d patient IDs using ratio %f" % (len(excluded_pids), args.exclude_patient_ratio) ) def count_frequent_items(counts: Mapping[Any, int], threshold: int) -> int: return len( {item for item, count in counts.items() if count >= threshold} ) logging.info( "Codes with >= 10 {}".format( count_frequent_items(result["code_counts"], 10) ) ) logging.info( "Codes with >= 25 {}".format( count_frequent_items(result["code_counts"], 25) ) ) logging.info( "Codes with >= 50 {}".format( count_frequent_items(result["code_counts"], 50) ) ) logging.info( "Codes with >= 100 {}".format( count_frequent_items(result["code_counts"], 100) ) ) logging.info( "Codes with >= 1000 {}".format( count_frequent_items(result["code_counts"], 1000) ) ) logging.info("Number codes: {}".format(len(result["code_counts"]))) logging.info("Number valid codes: {}".format(len(result["valid_code_map"]))) with open(os.path.join(args.save_dir, "info.json"), "w") as fp: json.dump(result, fp) def train_model() -> None: parser = argparse.ArgumentParser( description="Representation Learning Experiments" ) # paths parser.add_argument( "model_dir", type=str, help="Location where model logs and weights should be saved", ) parser.add_argument( "info_dir", type=str, help="Location where `clmbr_create_info` results were saved", ) parser.add_argument( "--extract_dir", action="store_true", help="Use the doctorai task definition", ) # model specification parser.add_argument( "--size", default=768, type=int, help="Dimensionality of the output embeddings", ) parser.add_argument( "--encoder_type", default="gru", choices=["gru", "lstm", "transformer"], help='the sequence encoder module type (default "gru")', ) parser.add_argument("--no_tied_weights", default=False, action="store_true") parser.add_argument( "--rnn_layers", default=1, type=int, help='number of recurrent layers to use if encoder_type is "gru" or ' '"lstm" (default 1), not used if encoder_type is "transformer"', ) parser.add_argument( "--dropout", default=0, type=float, help="dropout percentage (default 0)", ) # optimization specification parser.add_argument( "--batch_size", type=int, default=500, help="Batch size (default 500)" ) parser.add_argument( "--eval_batch_size", type=int, default=2000, help="Batch size during evaluation (default 2000)", ) parser.add_argument( "--epochs", type=int, default=50, help="Number of training epochs (default 50)", ) parser.add_argument( "--warmup_epochs", type=int, default=2, help="Number of warmup epochs (default 2)", ) parser.add_argument( "--lr", type=float, default=0.01, help="learning rate (default 0.01)" ) parser.add_argument( "--l2", default=0.01, type=float, help="l2 regularization strength (default 0.01)", ) parser.add_argument( "--device", default="cpu", help='Specify whether the model should be run on CPU or GPU. Can specify a specific GPU, e.g. "cuda:0" (default "cpu")', ) parser.add_argument("--code_dropout", type=float, default=0.2) # Day dropout added in reference to Lawrence's comment, # although Ethan mentioned it should be removed from the API parser.add_argument("--day_dropout", type=float, default=0.2) args = parser.parse_args() model_dir = args.model_dir os.makedirs(model_dir, exist_ok=True) if check_dir_for_overwrite(model_dir): print( "Fatal error - model dir {} is not empty".format(model_dir), file=sys.stderr, ) logging.info( "Fatal error - model dir {} is not empty".format(model_dir) ) exit(1) # Try to load info.json file; see create_info above for details. info = read_info(os.path.join(args.info_dir, "info.json")) copyfile( os.path.join(args.info_dir, "info.json"), os.path.join(model_dir, "info.json"), ) first_too_small_index = float("inf") for code, index in info["valid_code_map"].items(): if info["code_counts"][code] < 10 * info["min_patient_count"]: first_too_small_index = min(first_too_small_index, index) print(len(info["valid_code_map"]), flush=True) # Create and save config dictionary config = { "batch_size": args.batch_size, "eval_batch_size": args.eval_batch_size, "num_first": first_too_small_index, "num_second": len(info["valid_code_map"]) - first_too_small_index, "size": args.size, "lr": args.lr, "dropout": args.dropout, "encoder_type": args.encoder_type, "rnn_layers": args.rnn_layers, "tied_weights": not args.no_tied_weights, "l2": args.l2, "b1": 0.9, "b2": 0.999, "e": 1e-8, "epochs_per_cycle": args.epochs, "warmup_epochs": args.warmup_epochs, "code_dropout": args.code_dropout, "day_dropout": args.day_dropout, "model_dir": os.path.abspath(model_dir), } with open(os.path.join(model_dir, "config.json"), "w") as outfile: json.dump(config, outfile) set_up_logging(os.path.join(model_dir, "train.log")) logging.info("Args: %s", str(args)) dataset = PatientTimelineDataset( os.path.join(info["extract_dir"], "extract.db"), os.path.join(info["extract_dir"], "ontology.db"), os.path.join(args.info_dir, "info.json"), ) random.seed(info["seed"]) model = CLMBR(config, info).to(torch.device(args.device)) trainer = Trainer(model) trainer.train(dataset, use_pbar=False) def debug_model() -> None: parser = argparse.ArgumentParser( description="Representation Learning Experiments" ) parser.add_argument( "--model_dir", type=str, help="Override where model is saved" ) args = parser.parse_args() model_dir = args.model_dir config = read_config(os.path.join(model_dir, "config.json")) info = read_info(os.path.join(model_dir, "info.json")) use_cuda = torch.cuda.is_available() model = CLMBR(config, info).to(device_from_config(use_cuda=use_cuda)) model_data = torch.load(os.path.join(model_dir, "best"), map_location="cpu") model.load_state_dict(model_data) loaded_data = PatientTimelineDataset( os.path.join(info["extract_dir"], "extract.db"), os.path.join(info["extract_dir"], "ontology.db"), os.path.join(model_dir, "info.json"), ) ontologies = ontology.OntologyReader( os.path.join(info["extract_dir"], "ontology.db") ) timelines = timeline.TimelineReader( os.path.join(info["extract_dir"], "extract.db") ) reverse_map = {} for b, a in info["valid_code_map"].items(): word = ontologies.get_dictionary().get_word(b) reverse_map[a] = word reverse_map[len(info["valid_code_map"])] = "None" with DataLoader( loaded_data, threshold=config["num_first"], is_val=True, batch_size=1, seed=info["seed"], day_dropout=0, code_dropout=0, ) as batches: for batch in batches: if batch["task"][0].size()[0] == 0: continue values, non_text_loss = model(batch) values = torch.sigmoid(values) patient_id = int(batch["pid"][0]) patient = timelines.get_patient(patient_id) original_day_indices = batch["day_index"][0] indices, targets, seen_before, _, _, _ = batch["task"] day_indices = indices[:, 0] word_indices = indices[:, 1] ( all_non_text_codes, all_non_text_offsets, all_non_text_codes1, all_non_text_offsets1, all_day_information, all_positional_encoding, all_lengths, ) = batch["rnn"] all_non_text_codes = list(all_non_text_codes) all_non_text_offsets = list(all_non_text_offsets) + [ len(all_non_text_codes) ] print(patient_id, batch["pid"], original_day_indices) all_seen = set() for i, index in enumerate(original_day_indices): day = patient.days[index] print("------------------") print(patient_id, i, index, day.age / 365, day.date) words = set() for code in day.observations: for subword in ontologies.get_subwords(code): words.add(ontologies.get_dictionary().get_word(subword)) all_seen.add( ontologies.get_dictionary().get_word(subword) ) print("Source", words) wordsA = set() if (i + 1) < len(all_non_text_offsets): for code in all_non_text_codes[ all_non_text_offsets[i] : all_non_text_offsets[i + 1] ]: wordsA.add(reverse_map[code.item()]) print("Given", wordsA) day_mask = day_indices == i w = word_indices[day_mask] p = values[day_mask] t = targets[day_mask] f = seen_before[day_mask] items = [ ( t_i.item(), reverse_map[w_i.item()], p_i.item(), reverse_map[w_i.item()] in all_seen, w_i.item(), f_i.item(), ) for p_i, t_i, w_i, f_i in zip(p, t, w, f) ] items.sort(key=lambda x: (-x[0], x[1])) for a in items: print(a)

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128

0.59651

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0

null

0

null

0

1

0

302482d09ddf1f774c52862c7051a379cb2cfac1

8,215

py

Python

scenarios.py

Sanghyun-Hong/DeepSloth

92b3d0d3ef3f974d8bce7b4b4a1828776227e3c6

[ "MIT" ]

9

2020-12-16T04:55:57.000Z

2022-01-13T08:28:11.000Z

scenarios.py

Sanghyun-Hong/DeepSloth

92b3d0d3ef3f974d8bce7b4b4a1828776227e3c6

[ "MIT" ]

null

scenarios.py

Sanghyun-Hong/DeepSloth

92b3d0d3ef3f974d8bce7b4b4a1828776227e3c6

[ "MIT" ]

1

2021-10-11T06:21:04.000Z

""" A script that partitions the dataset for transferability scenarios """ # basics import numpy as np from PIL import Image # torch... import torch # custom libs import utils # ------------------------------------------------------------------------------ # Misc. functions # ------------------------------------------------------------------------------ def update_numpy(acc, term, func): if acc is None: acc = term else: acc = func((acc, term)) return acc def get_class_wise_lists(n_classes_cifar10, return_test=False): if not return_test: class_wise_dataset = [] for n_class in range(n_classes_cifar10): train_data, train_labels, _, _ = af.get_cifar10_class_data(n_class) # don't use class_wise_dataset.append((train_data, train_labels)) return class_wise_dataset else: class_wise_dataset = [] test_class_wise_dataset = [] for n_class in range(n_classes_cifar10): train_data, train_labels, test_data, test_labels = af.get_cifar10_class_data(n_class) # don't use class_wise_dataset.append((train_data, train_labels)) test_class_wise_dataset.append((test_data, test_labels)) return class_wise_dataset, test_class_wise_dataset # ------------------------------------------------------------------------------ # Scenario related... # ------------------------------------------------------------------------------ def scenario_1_split(int_percentages=None): np.random.seed(0) """ Scenario 1) Train CIFAR10 models that use 10%, 25%, 50% of the full training set. Chooses p% of data in each class (and corresponding labels) Parameter int_percentages contains percentages as integers, NOT FLOATS! Returns: - percent_loaders (dict): each key p% contains an af.ManualData object containing p% of dataset (p% from each label) * Loader data contains p% of images (p% of class 0, ..., p% of class 9) - consecutive * Loader labels (np.ndarray): contains p% of labels (p% 0s, ..., p% 9s) - consecutive """ if int_percentages is None: int_percentages = [10, 25, 50, 100] print('Running scenario_1_split\n') n_classes_cifar10 = 10 # get a list containing CIFAR10 data class by class (class k at index k) class_wise_dataset = get_class_wise_lists(n_classes_cifar10) percent_loaders = {} for p in int_percentages: subset_data = None subset_labels = None for n_class in range(n_classes_cifar10): crt_train_data, crt_train_labels = class_wise_dataset[n_class] count = crt_train_data.shape[0] how_many_2_choose = int(count * p / 100.0) indexes = np.random.choice(np.arange(count), how_many_2_choose, replace=False) subset_data = update_numpy(acc=subset_data, term=np.copy(crt_train_data[indexes]), func=np.vstack) subset_labels = update_numpy(acc=subset_labels, term=np.copy(crt_train_labels[indexes]), func=np.hstack) # end for n_class print(f'p={p}, data: {subset_data.shape}, labels: {subset_labels.shape}\n') percent_loaders[p] = af.ManualData(data=subset_data, labels=subset_labels) # end for p np.random.seed(af.get_random_seed()) return percent_loaders def scenario_2_split(int_classes=None): np.random.seed(0) """ Scenario 2) Split CIFAR10 training set into non-overlapping 5 classes - 5 classes, 6 - 6 and 7 - 7. Parameter int_classes_left: - each value c is used to generate the two datasets that contain c classes Returns: - percent_loaders (dict): each key c contains a pair of af.ManualData meaning ( Dataset w c classes, another dataset c classes) * Loader data contains p% of images (p% of class 0, ..., p% of class 9) - consecutive * Loader labels (np.ndarray): contains p% of labels (p% 0s, ..., p% 9s) - consecutive """ if int_classes is None: int_classes = [5, 6, 7] print('Running scenario_2_split\n') n_classes_cifar10 = 10 # get a list containing CIFAR10 data class by class (class k at index k) class_wise_dataset, test_class_wise_dataset = get_class_wise_lists(n_classes_cifar10, return_test=True) all_classes = np.arange(n_classes_cifar10) class_loaders = {} for classes in int_classes: num_class_overlap = 2*(classes - 5) class_indexes_overlap = np.random.choice(all_classes, num_class_overlap, replace=False) left_unique_classes = np.random.choice([x for x in all_classes if x not in class_indexes_overlap], classes-num_class_overlap, replace=False) right_unique_classes = [x for x in all_classes if (x not in class_indexes_overlap) and (x not in left_unique_classes)] class_indexes_left = np.array(list(left_unique_classes) + list(class_indexes_overlap)) class_indexes_right = np.array(list(right_unique_classes) + list(class_indexes_overlap)) print(class_indexes_left) print(class_indexes_right) subset_data_left, subset_labels_left = None, None subset_data_right, subset_labels_right = None, None subset_test_data_left, subset_test_labels_left = None, None subset_test_data_right, subset_test_labels_right = None, None label_left = 0 label_right = 0 for n_class in all_classes: crt_train_data, crt_train_labels = class_wise_dataset[n_class] crt_test_data, crt_test_labels = test_class_wise_dataset[n_class] if n_class in class_indexes_left: new_train_labels = np.ones(crt_train_labels.shape) * label_left # we have to relabel the dataset because pytorch expects labels as 0,1,2,3,... subset_data_left = update_numpy(acc=subset_data_left, term=np.copy(crt_train_data), func=np.vstack) subset_labels_left = update_numpy(acc=subset_labels_left, term=np.copy(new_train_labels), func=np.hstack) new_test_labels = np.ones(crt_test_labels.shape) * label_left subset_test_data_left = update_numpy(acc=subset_test_data_left, term=np.copy(crt_test_data), func=np.vstack) subset_test_labels_left = update_numpy(acc=subset_test_labels_left, term=np.copy(new_test_labels), func=np.hstack) label_left += 1 if n_class in class_indexes_right: new_train_labels = np.ones(crt_train_labels.shape) * label_right # we have to relabel the dataset because pytorch expects labels as 0,1,2,3,... subset_data_right = update_numpy(acc=subset_data_right, term=np.copy(crt_train_data), func=np.vstack) subset_labels_right = update_numpy(acc=subset_labels_right, term=np.copy(new_train_labels), func=np.hstack) new_test_labels = np.ones(crt_test_labels.shape) * label_right subset_test_data_right = update_numpy(acc=subset_test_data_right, term=np.copy(crt_test_data), func=np.vstack) subset_test_labels_right = update_numpy(acc=subset_test_labels_right, term=np.copy(new_test_labels), func=np.hstack) label_right += 1 # end for n_class print(f'{classes}: train - data-left: {subset_data_left.shape}, labels-left: {subset_labels_left.shape}, data-right: {subset_data_right.shape}, labels-right: {subset_labels_right.shape}\n') print(f'{classes}: test - data-left: {subset_test_data_left.shape}, labels-left: {subset_test_labels_left.shape}, data-right: {subset_test_data_right.shape}, labels-right: {subset_test_labels_right.shape}\n') loaders_left = (af.ManualData(data=subset_data_left, labels=subset_labels_left), af.ManualData(data=subset_test_data_left, labels=subset_test_labels_left)) loaders_right = (af.ManualData(data=subset_data_right, labels=subset_labels_right), af.ManualData(data=subset_test_data_right, labels=subset_test_labels_right)) class_loaders[classes] = (loaders_left, loaders_right) np.random.seed(af.get_random_seed()) # end for class_left, class_right return class_loaders

45.893855

216

0.667925

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8,215

4.45953

0.136641

0.039032

0.046838

0.039032

0.622365

0.53064

0.356557

0.33509

0.32904

0.318891

0

0.014627

0.209373

8,215

179

217

45.893855

0.774288

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0

0.175258

0

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0.080312

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0

1

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false

0

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0

0.134021

0.072165

0

null

0

null

0

1

0

3026fa5fec5a07d66102ae71523732602b37bf87

6,637

py

Python

Multi-agent Transfer RL/Transfer across tasks/Bayes-ToMoP/games.py

TJU-DRL-LAB/transfer-and-multi-task-reinforcement-learning

2d8c12c2b5a4865c02934b63091945d3e2c92e90

[ "MIT" ]

null

Multi-agent Transfer RL/Transfer across tasks/Bayes-ToMoP/games.py

TJU-DRL-LAB/transfer-and-multi-task-reinforcement-learning

2d8c12c2b5a4865c02934b63091945d3e2c92e90

[ "MIT" ]

null

Multi-agent Transfer RL/Transfer across tasks/Bayes-ToMoP/games.py

TJU-DRL-LAB/transfer-and-multi-task-reinforcement-learning

2d8c12c2b5a4865c02934b63091945d3e2c92e90

[ "MIT" ]

null

# coding=utf-8 import numpy as np import imageio from gym import spaces import tkinter as tk from PIL import Image, ImageTk import matplotlib.pyplot as plt import time CELL, BLOCK, AGENT_GOAL, OPPONENT_GOAL, AGENT, OPPONENT = range(6) WIN, LOSE = 5, -5 UP, RIGHT, DOWN, LEFT, HOLD = range(5) UNIT = 40 class Soccer(tk.Tk, object): playground = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1] action_map = { UP: np.array([-1, 0]), RIGHT: np.array([0, 1]), DOWN: np.array([1, 0]), LEFT: np.array([0, -1]), HOLD: np.array([0, 0])} def __init__(self): super(Soccer, self).__init__() self.size = 7 self.agent = np.array([3, 1]) self.opponent = np.array([3, 5]) self.grids = np.array(self.playground).reshape(self.size, self.size) self.agent_keep_ball = False self.action_space = [UP, RIGHT, DOWN, LEFT, HOLD] self.n_actions = len(self.action_space) self.n_features = 5 self.visualize() # low high to observe #self.observation_space = spaces.Discrete(7 * 7 * 2) def step(self, act_a, act_o): new_pos_a = self.agent + self.action_map[act_a] new_pos_o = self.opponent + self.action_map[act_o] reward, done, s_ = 0, False, [] # opponent win if self.grids[tuple(new_pos_o)] == 3 and not self.agent_keep_ball: reward = LOSE done = True # agent win if self.grids[tuple(new_pos_a)] == 2 and self.agent_keep_ball: reward = WIN done = True # valid check for opponent and agent if self.grids[tuple(new_pos_a)] in (1, 2, 3): new_pos_a = self.agent if self.grids[tuple(new_pos_o)] in (1, 2, 3): new_pos_o = self.opponent # collision if np.array_equal(new_pos_a, new_pos_o) and self.grids[tuple(new_pos_a)] != 1: self.agent_keep_ball = not self.agent_keep_ball #print(self.canvas.coords(self.agent_rect)) self.agent = new_pos_a self.opponent = new_pos_o self.canvas.delete(self.agent_rect) self.canvas.delete(self.opp_rect) self.agent_rect = self.canvas.create_rectangle(self.agent[1] * UNIT, self.agent[0] * UNIT, (self.agent[1] + 1) * UNIT, (self.agent[0] + 1) * UNIT, fill='red') self.opp_rect = self.canvas.create_rectangle(self.opponent[1] * UNIT, self.opponent[0] * UNIT, (self.opponent[1] + 1) * UNIT, (self.opponent[0] + 1) * UNIT, fill='blue') self.canvas.delete(self.ball_rect) if self.agent_keep_ball: self.ball_rect = self.canvas.create_oval((self.agent[1] * UNIT, self.agent[0] * UNIT, (self.agent[1] + 1) * UNIT, (self.agent[0] + 1) * UNIT), fill='white') else: self.ball_rect = self.canvas.create_oval(self.opponent[1] * UNIT, self.opponent[0] * UNIT, (self.opponent[1] + 1) * UNIT, (self.opponent[0] + 1) * UNIT, fill='white') s_ = [self.agent[0], self.agent[1], self.opponent[0], self.opponent[1]] if self.agent_keep_ball: s_.append(0) else: s_.append(1) s_ = np.array(s_[:5])/ 10 return s_, reward, done # reset position and ball def reset(self): self.agent = np.array([3, 1]) self.opponent = np.array([3, 5]) self.agent_keep_ball = False self.update() s_ = [self.agent[0], self.agent[1], self.opponent[0], self.opponent[1]] if self.agent_keep_ball: s_.append(0) else: s_.append(1) s_ = np.array(s_[:5])/ 10 return s_ # render array def render(self): m = np.copy(self.grids) m[tuple(self.agent)] = 4 m[tuple(self.opponent)] = 5 if self.agent_keep_ball: m[tuple(self.agent)] += 2 else: m[tuple(self.opponent)] += 2 #print(m, end='\n\n') self.update() return m.reshape(49) # render img def visualize(self): self.canvas = tk.Canvas(self, bg='white', height=self.size * UNIT, width=self.size * UNIT) # create grids for c in range(0, self.size * UNIT, UNIT): x0, y0, x1, y1 = c, 0, c, self.size * UNIT self.canvas.create_line(x0, y0, x1, y1) for r in range(0, self.size * UNIT, UNIT): x0, y0, x1, y1 = 0, r, self.size * UNIT, r self.canvas.create_line(x0, y0, x1, y1) m = np.copy(self.grids) m[tuple(self.agent)] = 4 m[tuple(self.opponent)] = 5 #print(m) for j in range(self.size): for i in range(self.size): if m[j, i] == 1: self.canvas.create_rectangle(i * UNIT, j * UNIT, (i + 1) * UNIT, (j + 1) * UNIT, fill='black') elif m[j, i] == 2 or m[j, i] == 3: self.canvas.create_rectangle(i * UNIT, j * UNIT, (i + 1) * UNIT, (j + 1) * UNIT, fill='white') elif m[j, i] == 0 or m[j, i] == 4 or m[j, i] == 5: self.canvas.create_rectangle(i * UNIT, j * UNIT, (i + 1) * UNIT, (j + 1) * UNIT, fill='green') self.agent_rect = self.canvas.create_rectangle(self.agent[1] * UNIT, self.agent[0] * UNIT, (self.agent[1] + 1) * UNIT, (self.agent[0] + 1) * UNIT, fill='red') self.opp_rect = self.canvas.create_rectangle(self.opponent[1] * UNIT, self.opponent[0] * UNIT, (self.opponent[1] + 1) * UNIT, (self.opponent[0] + 1) * UNIT, fill='blue') if self.agent_keep_ball: self.ball_rect = self.canvas.create_oval((self.agent[0] * UNIT, self.agent[0] * UNIT, (self.agent[1] + 1) * UNIT, (self.agent[1] + 1) * UNIT), fill='white') else: self.ball_rect = self.canvas.create_oval(self.opponent[1] * UNIT, self.opponent[0] * UNIT, (self.opponent[1] + 1) * UNIT, (self.opponent[0] + 1) * UNIT, fill='white') # pack all self.canvas.pack() if __name__ == '__main__': env = Soccer() env.reset() # agent strategy agent_actions = [RIGHT, RIGHT, UP, RIGHT, RIGHT, RIGHT] # opponent strategy, you can initialize it randomly opponent_actions = [UP, LEFT, LEFT, LEFT, LEFT, LEFT, LEFT] for a_a, a_o in zip(agent_actions, opponent_actions): env.render() env.step(a_a, a_o) time.sleep(1) #env.after(100, run_maze) #env.mainloop() # env.render()

39.041176

178

0.552509

998

6,637

3.548096

0.152305

0.109291

0.012708

0.05281

0.57752

0.513697

0.487433

0.453544

0.433211

0

0.042594

0.296068

6,637

169

179

39.272189

0.715325

0.061323

0

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0

0.009986

0

1

0.040323

false

0

0.056452

0

0.145161

0

null

0

null

0

1

0

302e8f1be7a7ffb783af9f6bd1bdc7f3405e6a18

745

py

Python

brainutils/context.py

jimbuho/django-brain

201237266a64e49b5c37f3d373ff6913dfbd099e

[ "BSD-2-Clause", "Apache-2.0" ]

null

brainutils/context.py

jimbuho/django-brain

201237266a64e49b5c37f3d373ff6913dfbd099e

[ "BSD-2-Clause", "Apache-2.0" ]

null

brainutils/context.py

jimbuho/django-brain

201237266a64e49b5c37f3d373ff6913dfbd099e

[ "BSD-2-Clause", "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ .. module:: dbu - context :platform: Unix, Windows :synopsis: Contexto Principal por defecto .. moduleauthor:: Diego Gonzalez <dgonzalez.jim@gmail.com> """ from . import configuration from . import models def load_context(request): """ Load Context Description Carga las variables de contexto principales :param request: :return: """ IS_TEST_MODE = configuration.isTESTMode() IS_MAINTENANCE = configuration.isMaintenanceMode() try: LANGUAGES = models.Language.objects.get_active() except: LANGUAGES = [] return { 'IS_TEST_MODE' : IS_TEST_MODE, 'IS_MAINTENANCE' : IS_MAINTENANCE, 'LANGUAGES' : LANGUAGES }

19.102564

58

0.640268

74

745

6.297297

0.662162

0.038627

0.064378

0.06867

0

0.001792

0.251007

745

39

59

19.102564

0.833333

0.377181

0

0.082547

0

1

0.071429

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

302ff0768eb4d4a5356d5db0b329d974eeccb455

689

py

Python

tests/trainer/test_multi_trainer.py

michael-aloys/knodle

393e7ba0558036828fb228875511977c40000ed5

[ "Apache-2.0" ]

71

2021-04-26T10:39:56.000Z

2022-03-28T14:36:16.000Z

tests/trainer/test_multi_trainer.py

michael-aloys/knodle

393e7ba0558036828fb228875511977c40000ed5

[ "Apache-2.0" ]

92

2021-04-08T12:49:38.000Z

2022-02-03T14:24:05.000Z

tests/trainer/test_multi_trainer.py

michael-aloys/knodle

393e7ba0558036828fb228875511977c40000ed5

[ "Apache-2.0" ]

10

2021-07-08T06:49:28.000Z

2022-01-15T23:28:13.000Z

from tests.trainer.generic import std_trainer_input_1 from knodle.trainer.multi_trainer import MultiTrainer def test_auto_train(std_trainer_input_1): ( model, model_input_x, rule_matches_z, mapping_rules_labels_t, y_labels ) = std_trainer_input_1 trainers = ["majority", "snorkel", "knn", "snorkel_knn"] trainer = MultiTrainer( name=trainers, model=model, mapping_rules_labels_t=mapping_rules_labels_t, model_input_x=model_input_x, rule_matches_z=rule_matches_z, ) trainer.train() metrics = trainer.test(model_input_x, y_labels) # Check whether the code ran up to here assert 2 == 2

25.518519

62

0.69521

94

689

4.691489

0.43617

0.090703

0.099773

0.108844

0.104308

0

0.009416

0.229318

689

26

63

26.5

0.821092

0.053701

0

0.044615

0

0.052632

1

0.052632

false

0

0.105263

0

0.157895

0

null

0

null

0

1

0

3033df96925128ff7539e6a8f86e4620e486d85d

18,447

py

Python

iogt_content_migration/management/commands/load_v1_db.py

Albert-Jokelin/iogt

79b1b86c11df7d61ddbbd4ce16303dfe4a1b8465

[ "BSD-2-Clause" ]

null

iogt_content_migration/management/commands/load_v1_db.py

Albert-Jokelin/iogt

79b1b86c11df7d61ddbbd4ce16303dfe4a1b8465

[ "BSD-2-Clause" ]

null

iogt_content_migration/management/commands/load_v1_db.py

Albert-Jokelin/iogt

79b1b86c11df7d61ddbbd4ce16303dfe4a1b8465

[ "BSD-2-Clause" ]

null

from pathlib import Path from django.core.management.base import BaseCommand from wagtail.core.models import Page, Site, Locale from django.core.files.images import ImageFile from wagtail.images.models import Image from wagtail_localize.models import Translation from wagtail_localize.views.submit_translations import TranslationCreator import home.models as models import psycopg2 import psycopg2.extras import json class Command(BaseCommand): def add_arguments(self, parser): parser.add_argument( '--host', default='0.0.0.0', help='IoGT V1 database host' ) parser.add_argument( '--port', default='5432', help='IoGT V1 database port' ) parser.add_argument( '--name', default='postgres', help='IoGT V1 database name' ) parser.add_argument( '--user', default='postgres', help='IoGT V1 database user' ) parser.add_argument( '--password', default='', help='IoGT V1 database password' ) parser.add_argument( '--media-dir', required=True, help='Path to IoGT v1 media directory' ) parser.add_argument( '--skip-locales', action='store_true', help='Skip data of locales other than default language' ) def handle(self, *args, **options): self.db_connect(options) self.media_dir = options.get('media_dir') self.skip_locales = options.get('skip_locales') self.image_map = {} self.page_translation_map = {} self.v1_to_v2_page_map = {} self.clear() self.stdout.write('Existing site structure cleared') root = Page.get_first_root_node() self.migrate(root) def clear(self): models.FooterPage.objects.all().delete() models.FooterIndexPage.objects.all().delete() models.BannerPage.objects.all().delete() models.BannerIndexPage.objects.all().delete() models.Article.objects.all().delete() models.Section.objects.all().delete() models.SectionIndexPage.objects.all().delete() models.HomePage.objects.all().delete() Site.objects.all().delete() Image.objects.all().delete() def db_connect(self, options): connection_string = self.create_connection_string(options) self.stdout.write(f'DB connection string created, string={connection_string}') self.v1_conn = psycopg2.connect(connection_string) self.stdout.write('Connected to v1 DB') def __del__(self): try: self.v1_conn.close() self.stdout.write('Closed connection to v1 DB') except AttributeError: pass def create_connection_string(self, options): host = options.get('host', '0.0.0.0') port = options.get('port', '5432') name = options.get('name', 'postgres') user = options.get('user', 'postgres') password = options.get('password', '') return f"host={host} port={port} dbname={name} user={user} password={password}" def db_query(self, q): cur = self.v1_conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) cur.execute(q) return cur def migrate(self, root): self.migrate_images() self.load_page_translation_map() home = self.create_home_page(root) section_index_page, banner_index_page, footer_index_page = self.create_index_pages(home) self.migrate_sections(section_index_page) self.migrate_articles(section_index_page) self.migrate_banners(banner_index_page) self.migrate_footers(footer_index_page) self.stop_translations() Page.fix_tree() def create_home_page(self, root): sql = 'select * from core_main main join wagtailcore_page page on main.page_ptr_id = page.id' cur = self.db_query(sql) main = cur.fetchone() cur.close() home = None if main: home = models.HomePage( title=main['title'], draft_title=main['draft_title'], seo_title=main['seo_title'], slug=main['slug'], live=main['live'], latest_revision_created_at=main['latest_revision_created_at'], first_published_at=main['first_published_at'], last_published_at=main['last_published_at'], ) root.add_child(instance=home) else: raise Exception('Could not find a main page in v1 DB') cur.close() cur = self.db_query('select * from wagtailcore_site') v1_site = cur.fetchone() cur.close() if v1_site: Site.objects.create( hostname=v1_site['hostname'], port=v1_site['port'], root_page=home, is_default_site=True, site_name=v1_site['site_name'] if v1_site['site_name'] else 'Internet of Good Things', ) else: raise Exception('Could not find site in v1 DB') return home def create_index_pages(self, homepage): section_index_page = models.SectionIndexPage(title='Sections') homepage.add_child(instance=section_index_page) banner_index_page = models.BannerIndexPage(title='Banners') homepage.add_child(instance=banner_index_page) footer_footer_page = models.FooterIndexPage(title='Footers') homepage.add_child(instance=footer_footer_page) return section_index_page, banner_index_page, footer_footer_page def migrate_images(self): cur = self.db_query('select * from wagtailimages_image') content_type = self.find_content_type_id('wagtailimages', 'image') for row in cur: image_file = self.open_image_file(row['file']) if image_file: image = Image.objects.create( title=row['title'], file=ImageFile(image_file, name=row['file'].split('/')[-1]), focal_point_x=row['focal_point_x'], focal_point_y=row['focal_point_y'], focal_point_width=row['focal_point_width'], focal_point_height=row['focal_point_height'], # uploaded_by_user='', ) image.get_file_size() image.get_file_hash() tags = self.find_tags(content_type, row['id']) if tags: image.tags.add(*tags) self.image_map.update({ row['id']: image }) cur.close() self.stdout.write('Images migrated') def find_content_type_id(self, app_label, model): cur = self.db_query(f"select id from django_content_type where app_label = '{app_label}' and model = '{model}'") content_type = cur.fetchone() cur.close() return content_type.get('id') def open_image_file(self, file): file_path = Path(self.media_dir) / file try: return open(file_path, 'rb') except: self.stdout.write(f"Image file not found: {file_path}") def find_tags(self, content_type, object_id): tags_query = 'select t.name from taggit_tag t join taggit_taggeditem ti on t.id = ti.tag_id where ti.content_type_id = {} and ti.object_id = {}' cur = self.db_query(tags_query.format(content_type, object_id)) tags = [tag['name'] for tag in cur] cur.close() return tags def migrate_sections(self, section_index_page): sql = "select * " \ "from core_sectionpage csp, wagtailcore_page wcp, core_languagerelation clr, core_sitelanguage csl " \ "where csp.page_ptr_id = wcp.id " \ "and wcp.id = clr.page_id " \ "and clr.language_id = csl.id " if self.skip_locales: sql += " and locale = 'en' " sql += 'order by wcp.path' cur = self.db_query(sql) section_page_translations = [] for row in cur: if row['page_ptr_id'] in self.page_translation_map: section_page_translations.append(row) else: self.create_section(section_index_page, row) else: for row in section_page_translations: section = self.v1_to_v2_page_map.get(self.page_translation_map[row['page_ptr_id']]) locale, __ = Locale.objects.get_or_create(language_code=row['locale']) self.translate_page(locale=locale, page=section) translated_section = section.get_translation_or_none(locale) if translated_section: translated_section.title = row['title'] translated_section.draft_title = row['draft_title'] translated_section.live = row['live'] translated_section.save(update_fields=['title', 'draft_title', 'slug', 'live']) self.stdout.write(f"Translated section, title={row['title']}") cur.close() def create_section(self, section_index_page, row): section = models.Section( title=row['title'], draft_title=row['draft_title'], show_in_menus=True, font_color='1CABE2', slug=row['slug'], path=section_index_page.path + row['path'][12:], depth=row['depth'], numchild=row['numchild'], live=row['live'], ) section.save() self.v1_to_v2_page_map.update({ row['page_ptr_id']: section }) self.stdout.write(f"saved section, title={section.title}") def migrate_articles(self, section_index_page): sql = "select * " \ "from core_articlepage cap, wagtailcore_page wcp, core_languagerelation clr, core_sitelanguage csl " \ "where cap.page_ptr_id = wcp.id " \ "and wcp.id = clr.page_id " \ "and clr.language_id = csl.id " if self.skip_locales: sql += "and locale = 'en' " sql += " and wcp.path like '000100010002%'order by wcp.path" cur = self.db_query(sql) article_page_translations = [] for row in cur: if row['page_ptr_id'] in self.page_translation_map: article_page_translations.append(row) else: self.create_article(section_index_page, row) else: for row in article_page_translations: article = self.v1_to_v2_page_map.get(self.page_translation_map[row['page_ptr_id']]) locale, __ = Locale.objects.get_or_create(language_code=row['locale']) self.translate_page(locale=locale, page=article) translated_article = article.get_translation_or_none(locale) if translated_article: translated_article.lead_image = self.image_map.get(row['image_id']) translated_article.title = row['title'] translated_article.draft_title = row['draft_title'] translated_article.live = row['live'] translated_article.body = self.map_article_body(row['body']) translated_article.save(update_fields=['lead_image', 'title', 'draft_title', 'slug', 'live', 'body']) self.stdout.write(f"Translated article, title={row['title']}") cur.close() def create_article(self, section_index_page, row): article = models.Article( lead_image=self.image_map.get(row['image_id']), title=row['title'], draft_title=row['draft_title'], slug=row['slug'], path=section_index_page.path + row['path'][12:], depth=row['depth'], numchild=row['numchild'], live=row['live'], body=self.map_article_body(row['body']), ) try: article.save() self.v1_to_v2_page_map.update({ row['page_ptr_id']: article }) except Page.DoesNotExist: self.stdout.write(f"Skipping page with missing parent: title={row['title']}") return self.stdout.write(f"saved article, title={article.title}") def map_article_body(self, v1_body): v2_body = json.loads(v1_body) for block in v2_body: if block['type'] == 'paragraph': block['type'] = 'markdown' return json.dumps(v2_body) def migrate_banners(self, banner_index_page): sql = "select * " \ "from core_bannerpage cbp, wagtailcore_page wcp, core_languagerelation clr, core_sitelanguage csl " \ "where cbp.page_ptr_id = wcp.id " \ "and wcp.id = clr.page_id " \ "and clr.language_id = csl.id " if self.skip_locales: sql += " and locale = 'en' " sql += ' order by wcp.path' cur = self.db_query(sql) banner_page_translations = [] for row in cur: if row['page_ptr_id'] in self.page_translation_map: banner_page_translations.append(row) else: self.create_banner(banner_index_page, row) else: for row in banner_page_translations: banner = self.v1_to_v2_page_map.get(self.page_translation_map[row['page_ptr_id']]) locale, __ = Locale.objects.get_or_create(language_code=row['locale']) try: self.translate_page(locale=locale, page=banner) except: continue translated_banner = banner.get_translation_or_none(locale) if translated_banner: translated_banner.banner_image = self.image_map.get(row['banner_id']) translated_banner.banner_link_page = self.v1_to_v2_page_map.get(row['banner_link_page_id']) translated_banner.title = row['title'] translated_banner.draft_title = row['draft_title'] translated_banner.live = row['live'] translated_banner.save(update_fields=['banner_image', 'title', 'draft_title', 'slug', 'live']) self.stdout.write(f"Translated banner, title={row['title']}") cur.close() def create_banner(self, banner_index_page, row): banner = models.BannerPage( banner_image=self.image_map.get(row['banner_id']), banner_link_page=self.v1_to_v2_page_map.get(row['banner_link_page_id']), title=row['title'], draft_title=row['draft_title'], slug=row['slug'], path=banner_index_page.path + row['path'][12:], depth=row['depth'], numchild=row['numchild'], live=row['live'], banner_description='' ) banner.save() self.v1_to_v2_page_map.update({ row['page_ptr_id']: banner }) self.stdout.write(f"saved banner, title={banner.title}") def migrate_footers(self, footer_index_page): sql = "select * " \ "from core_footerpage cfp, core_articlepage cap, wagtailcore_page wcp, core_languagerelation clr, core_sitelanguage csl " \ "where cfp.articlepage_ptr_id = cap.page_ptr_id " \ "and cap.page_ptr_id = wcp.id " \ "and wcp.id = clr.page_id " \ "and clr.language_id = csl.id " if self.skip_locales: sql += " and locale = 'en' " sql += ' order by wcp.path' cur = self.db_query(sql) footer_page_translations = [] for row in cur: if row['page_ptr_id'] in self.page_translation_map: footer_page_translations.append(row) else: self.create_footer(footer_index_page, row) else: for row in footer_page_translations: footer = self.v1_to_v2_page_map.get(self.page_translation_map[row['page_ptr_id']]) locale, __ = Locale.objects.get_or_create(language_code=row['locale']) self.translate_page(locale=locale, page=footer) translated_footer = footer.get_translation_or_none(locale) if translated_footer: translated_footer.lead_image = self.image_map.get(row['image_id']) translated_footer.title = row['title'] translated_footer.draft_title = row['draft_title'] translated_footer.live = row['live'] translated_footer.body = self.map_article_body(row['body']) translated_footer.save(update_fields=['lead_image', 'title', 'draft_title', 'slug', 'live', 'body']) self.stdout.write(f"Translated footer, title={row['title']}") cur.close() def create_footer(self, footer_index_page, row): footer = models.FooterPage( lead_image=self.image_map.get(row['image_id']), title=row['title'], draft_title=row['draft_title'], slug=row['slug'], path=footer_index_page.path + row['path'][12:], depth=row['depth'], numchild=row['numchild'], live=row['live'], body=self.map_article_body(row['body']), ) footer.save() self.v1_to_v2_page_map.update({ row['page_ptr_id']: footer }) self.stdout.write(f"saved footer, title={footer.title}") def load_page_translation_map(self): sql = "select * " \ "from core_pagetranslation" cur = self.db_query(sql) for row in cur: self.page_translation_map.update({ row['translated_page_id']: row['page_id'], }) cur.close() self.stdout.write('Page translation map loaded.') def translate_page(self, locale, page): translator = TranslationCreator(user=None, target_locales=[locale]) translator.create_translations(page) def stop_translations(self): Translation.objects.update(enabled=False) self.stdout.write('Translations stopped.')

40.277293

152

0.58725

2,150

18,447

4.782791

0.11814

0.025382

0.015754

0.014004

0.4417

0.397744

0.350579

0.297092

0.273169

0.262958

0

0.006906

0.301404

18,447

457

153

40.365427

0.79103

0.001084

0

0.295567

0

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0.187246

0.010855

0

1

0.064039

false

0.012315

0.027094

0

0.115764

0

null

0

null

0

1

0

3033fd8167635b4a38bf68dbe82419686667a557

2,968

py

Python

run_files/cip_area_threshold/tissue_data/run_CIP_relaxation_times.py

jessiesrr/VTdyn

6f71ef94525d95221f5bd5e5290f4df10648cd18

[ "MIT" ]

null

run_files/cip_area_threshold/tissue_data/run_CIP_relaxation_times.py

jessiesrr/VTdyn

6f71ef94525d95221f5bd5e5290f4df10648cd18

[ "MIT" ]

null

run_files/cip_area_threshold/tissue_data/run_CIP_relaxation_times.py

jessiesrr/VTdyn

6f71ef94525d95221f5bd5e5290f4df10648cd18

[ "MIT" ]

null

import numpy as np import libs.contact_inhibition_lib as lib #library for simulation routines import libs.data as data import libs.plot as vplt #plotting library from structure.global_constants import * import structure.initialisation as init from structure.cell import Tissue, BasicSpringForceNoGrowth import matplotlib.pyplot as plt import os """run a single voronoi tessellation model simulation""" OUTDIR = "CIP_cell_division_relaxation_time2/" l = 10 # population size N=l*l timend = 30. # simulation time (hours) timestep = 1.0 # time intervals to save simulation history rand = np.random.RandomState() simulation = lib.simulation_contact_inhibition_area_dependent #simulation routine imported from lib threshold_area_fraction=1.0 DEATH_RATE = 1./12 rates = (DEATH_RATE,DEATH_RATE/0.4) #death_rate,division_rate domain_size_multiplier=0.980940 eta,mu,dt=1.,-250,0.001 T_m_init=0.1 def get_relaxation_data(T_m_vals,T_m_init,eta,mu,dt,relaxtime): history = lib.run_simulation(simulation,l,timestep,timend,rand,progress_on=True, init_time=None,til_fix=False,save_areas=True,cycle_phase=None,eta=eta,mu=mu,dt=dt,T_m=T_m_init, return_events=False,save_cell_histories=True,domain_size_multiplier=domain_size_multiplier, rates=rates,threshold_area_fraction=threshold_area_fraction) tissue = lib.run_return_final_tissue(lib.simulation_no_division(history[-1],dt,200,rand,eta),200) division_ready = lib.check_area_threshold(tissue.mesh,threshold_area_fraction) mother = rand.choice(division_ready) tissue.add_daughter_cells(mother,rand) tissue.remove(mother,True) tissue.update(dt) init_tissues = [tissue.copy() for T_m in T_m_vals] for T_m,tissue in zip(T_m_vals,init_tissues): tissue.Force = BasicSpringForceNoGrowth(mu,T_m) histories = [lib.run(lib.simulation_no_division(tissue,dt,int(relaxtime/dt),rand,eta),int(relaxtime/dt),1) for tissue in init_tissues] for T_m,history in zip(T_m_vals,histories): cell1,cell2 = len(history[0])-2,len(history[0])-1 sibling_distance = get_sibling_distance(history,cell1,cell2) mean_area = np.array([np.mean(tissue.mesh.areas[-2:]) for tissue in history]) time = np.arange(0,relaxtime,dt) data = np.vstack((time,sibling_distance,mean_area)) try: np.savetxt(OUTDIR+"T_m=%.3f.txt"%T_m,data) except IOError: os.makedirs(OUTDIR) np.savetxt(OUTDIR+"T_m=%.3f.txt"%T_m,data) def narg(tissue,i,j): try: return np.where(tissue.mesh.neighbours[i]==j)[0][0] except IndexError: return np.nan def get_sibling_distance(history,cell1,cell2): return np.array([tissue.mesh.distances[cell1][narg(tissue,cell1,cell2)] if narg(tissue,cell1,cell2)<100 else np.nan for tissue in history]) relaxtime = 2.0 T_m_vals=[0.001,0.01,0.1,0.25,0.5,1.0,2.0] get_relaxation_data(T_m_vals,T_m_init,eta,mu,dt,relaxtime)

44.298507

143

0.738544

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2,968

4.450106

0.312102

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0

0.032832

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2,968

67

144

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0.021763

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1

0.055556

false

0

0.166667

0.018519

0.240741

0

null

0

null

0

1

0

3035188b0c9611cb3581b6c9c987990a72ba1ab9

17,920

py

Python

TAPI_RI/flask_server/tapi_server/models/connection_end_point.py

bartoszm/Snowmass-ONFOpenTransport

874e7a3f311d915d692b27fcbd24032c89064f00

[ "Apache-2.0" ]

null

TAPI_RI/flask_server/tapi_server/models/connection_end_point.py

bartoszm/Snowmass-ONFOpenTransport

874e7a3f311d915d692b27fcbd24032c89064f00

[ "Apache-2.0" ]

null

TAPI_RI/flask_server/tapi_server/models/connection_end_point.py

bartoszm/Snowmass-ONFOpenTransport

874e7a3f311d915d692b27fcbd24032c89064f00

[ "Apache-2.0" ]

null

# coding: utf-8 from __future__ import absolute_import from datetime import date, datetime # noqa: F401 from typing import List, Dict # noqa: F401 from tapi_server.models.base_model_ import Model from tapi_server.models.name_and_value import NameAndValue # noqa: F401,E501 from tapi_server.models.operational_state_pac import OperationalStatePac # noqa: F401,E501 from tapi_server.models.resource_spec import ResourceSpec # noqa: F401,E501 from tapi_server.models.termination_pac import TerminationPac # noqa: F401,E501 from tapi_server import util class ConnectionEndPoint(Model): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, uuid: str=None, name: List[NameAndValue]=None, operational_state: str=None, lifecycle_state: str=None, termination_direction: str=None, termination_state: str=None, layer_protocol_name: str=None, connectivity_service_end_point: str=None, parent_node_edge_point: List[str]=None, client_node_edge_point: List[str]=None, connection_port_direction: str=None, connection_port_role: str=None): # noqa: E501 """ConnectionEndPoint - a model defined in Swagger :param uuid: The uuid of this ConnectionEndPoint. # noqa: E501 :type uuid: str :param name: The name of this ConnectionEndPoint. # noqa: E501 :type name: List[NameAndValue] :param operational_state: The operational_state of this ConnectionEndPoint. # noqa: E501 :type operational_state: str :param lifecycle_state: The lifecycle_state of this ConnectionEndPoint. # noqa: E501 :type lifecycle_state: str :param termination_direction: The termination_direction of this ConnectionEndPoint. # noqa: E501 :type termination_direction: str :param termination_state: The termination_state of this ConnectionEndPoint. # noqa: E501 :type termination_state: str :param layer_protocol_name: The layer_protocol_name of this ConnectionEndPoint. # noqa: E501 :type layer_protocol_name: str :param connectivity_service_end_point: The connectivity_service_end_point of this ConnectionEndPoint. # noqa: E501 :type connectivity_service_end_point: str :param parent_node_edge_point: The parent_node_edge_point of this ConnectionEndPoint. # noqa: E501 :type parent_node_edge_point: List[str] :param client_node_edge_point: The client_node_edge_point of this ConnectionEndPoint. # noqa: E501 :type client_node_edge_point: List[str] :param connection_port_direction: The connection_port_direction of this ConnectionEndPoint. # noqa: E501 :type connection_port_direction: str :param connection_port_role: The connection_port_role of this ConnectionEndPoint. # noqa: E501 :type connection_port_role: str """ self.swagger_types = { 'uuid': str, 'name': List[NameAndValue], 'operational_state': str, 'lifecycle_state': str, 'termination_direction': str, 'termination_state': str, 'layer_protocol_name': str, 'connectivity_service_end_point': str, 'parent_node_edge_point': List[str], 'client_node_edge_point': List[str], 'connection_port_direction': str, 'connection_port_role': str } self.attribute_map = { 'uuid': 'uuid', 'name': 'name', 'operational_state': 'operational-state', 'lifecycle_state': 'lifecycle-state', 'termination_direction': 'termination-direction', 'termination_state': 'termination-state', 'layer_protocol_name': 'layer-protocol-name', 'connectivity_service_end_point': 'connectivity-service-end-point', 'parent_node_edge_point': 'parent-node-edge-point', 'client_node_edge_point': 'client-node-edge-point', 'connection_port_direction': 'connection-port-direction', 'connection_port_role': 'connection-port-role' } self._uuid = uuid self._name = name self._operational_state = operational_state self._lifecycle_state = lifecycle_state self._termination_direction = termination_direction self._termination_state = termination_state self._layer_protocol_name = layer_protocol_name self._connectivity_service_end_point = connectivity_service_end_point self._parent_node_edge_point = parent_node_edge_point self._client_node_edge_point = client_node_edge_point self._connection_port_direction = connection_port_direction self._connection_port_role = connection_port_role @classmethod def from_dict(cls, dikt) -> 'ConnectionEndPoint': """Returns the dict as a model :param dikt: A dict. :type: dict :return: The connection-end-point of this ConnectionEndPoint. # noqa: E501 :rtype: ConnectionEndPoint """ return util.deserialize_model(dikt, cls) @property def uuid(self) -> str: """Gets the uuid of this ConnectionEndPoint. UUID: An identifier that is universally unique within an identifier space, where the identifier space is itself globally unique, and immutable. An UUID carries no semantics with respect to the purpose or state of the entity. UUID here uses string representation as defined in RFC 4122. The canonical representation uses lowercase characters. Pattern: [0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-' + '[0-9a-fA-F]{4}-[0-9a-fA-F]{12} Example of a UUID in string representation: f81d4fae-7dec-11d0-a765-00a0c91e6bf6 # noqa: E501 :return: The uuid of this ConnectionEndPoint. :rtype: str """ return self._uuid @uuid.setter def uuid(self, uuid: str): """Sets the uuid of this ConnectionEndPoint. UUID: An identifier that is universally unique within an identifier space, where the identifier space is itself globally unique, and immutable. An UUID carries no semantics with respect to the purpose or state of the entity. UUID here uses string representation as defined in RFC 4122. The canonical representation uses lowercase characters. Pattern: [0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-' + '[0-9a-fA-F]{4}-[0-9a-fA-F]{12} Example of a UUID in string representation: f81d4fae-7dec-11d0-a765-00a0c91e6bf6 # noqa: E501 :param uuid: The uuid of this ConnectionEndPoint. :type uuid: str """ self._uuid = uuid @property def name(self) -> List[NameAndValue]: """Gets the name of this ConnectionEndPoint. List of names. A property of an entity with a value that is unique in some namespace but may change during the life of the entity. A name carries no semantics with respect to the purpose of the entity. # noqa: E501 :return: The name of this ConnectionEndPoint. :rtype: List[NameAndValue] """ return self._name @name.setter def name(self, name: List[NameAndValue]): """Sets the name of this ConnectionEndPoint. List of names. A property of an entity with a value that is unique in some namespace but may change during the life of the entity. A name carries no semantics with respect to the purpose of the entity. # noqa: E501 :param name: The name of this ConnectionEndPoint. :type name: List[NameAndValue] """ self._name = name @property def operational_state(self) -> str: """Gets the operational_state of this ConnectionEndPoint. :return: The operational_state of this ConnectionEndPoint. :rtype: str """ return self._operational_state @operational_state.setter def operational_state(self, operational_state: str): """Sets the operational_state of this ConnectionEndPoint. :param operational_state: The operational_state of this ConnectionEndPoint. :type operational_state: str """ allowed_values = ["DISABLED", "ENABLED"] # noqa: E501 if operational_state not in allowed_values: raise ValueError( "Invalid value for `operational_state` ({0}), must be one of {1}" .format(operational_state, allowed_values) ) self._operational_state = operational_state @property def lifecycle_state(self) -> str: """Gets the lifecycle_state of this ConnectionEndPoint. :return: The lifecycle_state of this ConnectionEndPoint. :rtype: str """ return self._lifecycle_state @lifecycle_state.setter def lifecycle_state(self, lifecycle_state: str): """Sets the lifecycle_state of this ConnectionEndPoint. :param lifecycle_state: The lifecycle_state of this ConnectionEndPoint. :type lifecycle_state: str """ allowed_values = ["PLANNED", "POTENTIAL_AVAILABLE", "POTENTIAL_BUSY", "INSTALLED", "PENDING_REMOVAL"] # noqa: E501 if lifecycle_state not in allowed_values: raise ValueError( "Invalid value for `lifecycle_state` ({0}), must be one of {1}" .format(lifecycle_state, allowed_values) ) self._lifecycle_state = lifecycle_state @property def termination_direction(self) -> str: """Gets the termination_direction of this ConnectionEndPoint. The overall directionality of the LP. - A BIDIRECTIONAL LP will have some SINK and/or SOURCE flowss. - A SINK LP can only contain elements with SINK flows or CONTRA_DIRECTION_SOURCE flows - A SOURCE LP can only contain SOURCE flows or CONTRA_DIRECTION_SINK flows # noqa: E501 :return: The termination_direction of this ConnectionEndPoint. :rtype: str """ return self._termination_direction @termination_direction.setter def termination_direction(self, termination_direction: str): """Sets the termination_direction of this ConnectionEndPoint. The overall directionality of the LP. - A BIDIRECTIONAL LP will have some SINK and/or SOURCE flowss. - A SINK LP can only contain elements with SINK flows or CONTRA_DIRECTION_SOURCE flows - A SOURCE LP can only contain SOURCE flows or CONTRA_DIRECTION_SINK flows # noqa: E501 :param termination_direction: The termination_direction of this ConnectionEndPoint. :type termination_direction: str """ allowed_values = ["BIDIRECTIONAL", "SINK", "SOURCE", "UNDEFINED_OR_UNKNOWN"] # noqa: E501 if termination_direction not in allowed_values: raise ValueError( "Invalid value for `termination_direction` ({0}), must be one of {1}" .format(termination_direction, allowed_values) ) self._termination_direction = termination_direction @property def termination_state(self) -> str: """Gets the termination_state of this ConnectionEndPoint. Indicates whether the layer is terminated and if so how. # noqa: E501 :return: The termination_state of this ConnectionEndPoint. :rtype: str """ return self._termination_state @termination_state.setter def termination_state(self, termination_state: str): """Sets the termination_state of this ConnectionEndPoint. Indicates whether the layer is terminated and if so how. # noqa: E501 :param termination_state: The termination_state of this ConnectionEndPoint. :type termination_state: str """ allowed_values = ["LP_CAN_NEVER_TERMINATE", "LT_NOT_TERMINATED", "TERMINATED_SERVER_TO_CLIENT_FLOW", "TERMINATED_CLIENT_TO_SERVER_FLOW", "TERMINATED_BIDIRECTIONAL", "LT_PERMENANTLY_TERMINATED", "TERMINATION_STATE_UNKNOWN"] # noqa: E501 if termination_state not in allowed_values: raise ValueError( "Invalid value for `termination_state` ({0}), must be one of {1}" .format(termination_state, allowed_values) ) self._termination_state = termination_state @property def layer_protocol_name(self) -> str: """Gets the layer_protocol_name of this ConnectionEndPoint. :return: The layer_protocol_name of this ConnectionEndPoint. :rtype: str """ return self._layer_protocol_name @layer_protocol_name.setter def layer_protocol_name(self, layer_protocol_name: str): """Sets the layer_protocol_name of this ConnectionEndPoint. :param layer_protocol_name: The layer_protocol_name of this ConnectionEndPoint. :type layer_protocol_name: str """ allowed_values = ["OTSiA", "OCH", "OTU", "ODU", "ETH", "ETY", "DSR"] # noqa: E501 if layer_protocol_name not in allowed_values: raise ValueError( "Invalid value for `layer_protocol_name` ({0}), must be one of {1}" .format(layer_protocol_name, allowed_values) ) self._layer_protocol_name = layer_protocol_name @property def connectivity_service_end_point(self) -> str: """Gets the connectivity_service_end_point of this ConnectionEndPoint. :return: The connectivity_service_end_point of this ConnectionEndPoint. :rtype: str """ return self._connectivity_service_end_point @connectivity_service_end_point.setter def connectivity_service_end_point(self, connectivity_service_end_point: str): """Sets the connectivity_service_end_point of this ConnectionEndPoint. :param connectivity_service_end_point: The connectivity_service_end_point of this ConnectionEndPoint. :type connectivity_service_end_point: str """ self._connectivity_service_end_point = connectivity_service_end_point @property def parent_node_edge_point(self) -> List[str]: """Gets the parent_node_edge_point of this ConnectionEndPoint. :return: The parent_node_edge_point of this ConnectionEndPoint. :rtype: List[str] """ return self._parent_node_edge_point @parent_node_edge_point.setter def parent_node_edge_point(self, parent_node_edge_point: List[str]): """Sets the parent_node_edge_point of this ConnectionEndPoint. :param parent_node_edge_point: The parent_node_edge_point of this ConnectionEndPoint. :type parent_node_edge_point: List[str] """ self._parent_node_edge_point = parent_node_edge_point @property def client_node_edge_point(self) -> List[str]: """Gets the client_node_edge_point of this ConnectionEndPoint. :return: The client_node_edge_point of this ConnectionEndPoint. :rtype: List[str] """ return self._client_node_edge_point @client_node_edge_point.setter def client_node_edge_point(self, client_node_edge_point: List[str]): """Sets the client_node_edge_point of this ConnectionEndPoint. :param client_node_edge_point: The client_node_edge_point of this ConnectionEndPoint. :type client_node_edge_point: List[str] """ self._client_node_edge_point = client_node_edge_point @property def connection_port_direction(self) -> str: """Gets the connection_port_direction of this ConnectionEndPoint. The orientation of defined flow at the EndPoint. # noqa: E501 :return: The connection_port_direction of this ConnectionEndPoint. :rtype: str """ return self._connection_port_direction @connection_port_direction.setter def connection_port_direction(self, connection_port_direction: str): """Sets the connection_port_direction of this ConnectionEndPoint. The orientation of defined flow at the EndPoint. # noqa: E501 :param connection_port_direction: The connection_port_direction of this ConnectionEndPoint. :type connection_port_direction: str """ allowed_values = ["BIDIRECTIONAL", "INPUT", "OUTPUT", "UNIDENTIFIED_OR_UNKNOWN"] # noqa: E501 if connection_port_direction not in allowed_values: raise ValueError( "Invalid value for `connection_port_direction` ({0}), must be one of {1}" .format(connection_port_direction, allowed_values) ) self._connection_port_direction = connection_port_direction @property def connection_port_role(self) -> str: """Gets the connection_port_role of this ConnectionEndPoint. Each EP of the FC has a role (e.g., working, protection, protected, symmetric, hub, spoke, leaf, root) in the context of the FC with respect to the FC function. # noqa: E501 :return: The connection_port_role of this ConnectionEndPoint. :rtype: str """ return self._connection_port_role @connection_port_role.setter def connection_port_role(self, connection_port_role: str): """Sets the connection_port_role of this ConnectionEndPoint. Each EP of the FC has a role (e.g., working, protection, protected, symmetric, hub, spoke, leaf, root) in the context of the FC with respect to the FC function. # noqa: E501 :param connection_port_role: The connection_port_role of this ConnectionEndPoint. :type connection_port_role: str """ allowed_values = ["SYMMETRIC", "ROOT", "LEAF", "TRUNK", "UNKNOWN"] # noqa: E501 if connection_port_role not in allowed_values: raise ValueError( "Invalid value for `connection_port_role` ({0}), must be one of {1}" .format(connection_port_role, allowed_values) ) self._connection_port_role = connection_port_role

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null

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null

0

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30357aaf0a514c1636520e15938cba9dc811ec7d

901

py

Python

idom_jupyter/jupyter_server_extension.py

idom-team/idom-jupyter

21037d41c51d4d9e23cca4486a850f2915f27d29

[ "MIT" ]

28

2020-09-12T19:59:27.000Z

2022-03-14T10:08:13.000Z

idom_jupyter/jupyter_server_extension.py

idom-team/idom-jupyter

21037d41c51d4d9e23cca4486a850f2915f27d29

[ "MIT" ]

11

2020-10-05T06:54:43.000Z

2022-02-19T21:16:31.000Z

idom_jupyter/jupyter_server_extension.py

idom-team/idom-jupyter

21037d41c51d4d9e23cca4486a850f2915f27d29

[ "MIT" ]

null

from urllib.parse import urljoin from appdirs import user_data_dir from notebook.notebookapp import NotebookApp from idom.config import IDOM_WED_MODULES_DIR from tornado.web import StaticFileHandler from tornado.web import Application IDOM_WED_MODULES_DIR.current = user_data_dir("idom-jupyter", "idom-team") def _load_jupyter_server_extension(notebook_app: NotebookApp): web_app: Application = notebook_app.web_app base_url = web_app.settings["base_url"] route_pattern = urljoin(base_url, rf"_idom_web_modules/(.*)") web_app.add_handlers( host_pattern=".*$", host_handlers=[ ( route_pattern, StaticFileHandler, {"path": str(IDOM_WED_MODULES_DIR.current.absolute())}, ), ], ) # compat for older versions of Jupyter load_jupyter_server_extension = _load_jupyter_server_extension

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0.318182

0

null

0

null

0

1

0

303647975ecbe068790f55c360ba681d772f2c4f

1,033

py

Python

dobot_gym/envs/real/dobot_env.py

sandipan1/dobot_gym

acea98da2506653d45d55e15a036da583415f31d

[ "MIT" ]

1

2020-11-22T11:07:01.000Z

dobot_gym/envs/real/dobot_env.py

sandipan1/dobot_gym

acea98da2506653d45d55e15a036da583415f31d

[ "MIT" ]

null

dobot_gym/envs/real/dobot_env.py

sandipan1/dobot_gym

acea98da2506653d45d55e15a036da583415f31d

[ "MIT" ]

1

2021-01-10T09:36:25.000Z

## common class for only dobot with cam import gym from gym import utils from glob import glob from dobot_gym.utils.dobot_controller import DobotController from gym.spaces import MultiDiscrete class DobotRealEnv(gym.Env, utils.EzPickle): def __init__(self): super().__init__() # Find the port on which dobot is connected available_ports = glob('/dev/tty*USB*') if len(available_ports) == 0: print('no port found for Dobot Magician') exit(1) def_port = available_ports[0] self.dobot = DobotController(port=def_port) self.observation_space = None self.action_space = MultiDiscrete([3, 3, 3]) def compute_reward(self): return 0 def step(self, action): real_action = action - 1 self.dobot.moveangleinc(*real_action, r=0, q=1) reward = self.compute_reward(image, centroid) poses = self.dobot.get_dobot_joint() done = False info =None return poses,reward, done, info

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0.038462

0

null

0

null

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1

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3037ce4b050caa1080564ccd27af84ba3f81c62a

1,609

py

Python

Calibration/LumiAlCaRecoProducers/test/crab3_raw_corrC.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

Calibration/LumiAlCaRecoProducers/test/crab3_raw_corrC.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

Calibration/LumiAlCaRecoProducers/test/crab3_raw_corrC.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

######################### #Author: Sam Higginbotham ######################## from WMCore.Configuration import Configuration config = Configuration() #name='Pt11to30' config.section_("General") config.General.requestName = 'PCC_Run2017E_Corrections' config.General.workArea = 'RawPCCZeroBias2017' config.section_("JobType") config.JobType.pluginName = 'Analysis' config.JobType.psetName = 'raw_corr_Random_cfg.py' config.JobType.allowUndistributedCMSSW = True config.JobType.outputFiles = ['rawPCC.csv'] config.JobType.inputFiles = ['c.db'] config.section_("Data") #config.Data.inputDataset = '/AlCaLumiPixels/Run2017E-AlCaPCCZeroBias-PromptReco-v1/ALCARECO' config.Data.userInputFiles=['/store/data/Run2017E/AlCaLumiPixels/ALCARECO/AlCaPCCRandom-PromptReco-v1/000/303/832/00000/E6B8ACA4-95A4-E711-9AA2-02163E014793.root'] #config.Data.lumiMask = '' #config.Data.runRange='303382'#,297283,297278,297280,297281,297271,297227,297230,297276,297261,297266' config.Data.ignoreLocality = True #useParent = True config.Data.inputDBS = 'global' #config.Data.splitting = 'LumiBased' config.Data.splitting = 'FileBased' config.Data.publication = False config.Data.unitsPerJob = 1000 #config.Data.totalUnits = -1 #config.Data.publishDbsUrl = 'test' config.Data.outputDatasetTag = 'PCC_AlCaLumiPixels_Run2017C_1kLS_NoZeroes' config.Data.outLFNDirBase = '/store/group/comm_luminosity/PCC/ForLumiComputations/2017/5Feb2018' config.section_("Site") config.Site.storageSite = 'T2_CH_CERN' config.Site.whitelist=['T2_FR_CCIN2P3','T2_IT_Pisa','T2_UK_London_IC','T2_HU_Budapest'] #config.Site.whitelist=['T2_FR_CCIN2P3']

36.568182

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0.114193

0.030995

0.034258

0.04894

0

0.100857

0.057178

1,609

43

164

37.418605

0.707317

0.252952

0

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0.371278

0.249562

0

1

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false

0

0.043478

0

0.043478

0

null

0

null

0

1

0

3037ecccd158f87250f163febc7c04a882e857b4

6,819

py

Python

active_frame.py

binhhoangtieu/C3D-tensorflow

d85ef6156abc7fcdb4ab91e5b47a50c5ef5123c6

[ "MIT" ]

1

2019-02-11T15:47:52.000Z

active_frame.py

binhhoangtieu/C3D-tensorflow

d85ef6156abc7fcdb4ab91e5b47a50c5ef5123c6

[ "MIT" ]

null

active_frame.py

binhhoangtieu/C3D-tensorflow

d85ef6156abc7fcdb4ab91e5b47a50c5ef5123c6

[ "MIT" ]

1

2018-12-04T04:55:19.000Z

import cv2 import os import glob import numpy as np from operator import itemgetter # import matplotlib.pyplot as plt import math import scipy.stats as stats def main(): video_dir = './UCF-101' #./testdata result_dir = './UCF101-OF' #test-image loaddata(video_dir = video_dir, depth = 24, dest_forder=result_dir) def save_image_to_file(frame_array, folder): for i in range(np.size(frame_array,axis=0)): cv2.imwrite(folder +"/" + format(i,'05d')+'.jpg', frame_array[i]) def loaddata(video_dir, depth, dest_forder): #video_dir can contain sub_directory dirs = os.listdir(video_dir) class_number = -1 #pbar = tqdm(total=len(files)) for dir in dirs: path = os.path.join(video_dir, dir, '*.avi') files = sorted(glob.glob(path),key=lambda name: path ) for filename in files: print('Extracting file:',filename) # frame_array = video3d_overlap(filename, depth) # frame_array = video3d_selected_active_frame(filename, depth) # frame_array = full_selected_active_frame(filename, depth) frame_array = video3d_opticalflow(filename, depth) newdir = dir + "/" + os.path.splitext(os.path.basename(filename))[0] directory = os.path.join(dest_forder,newdir) if not os.path.exists(directory): os.makedirs(directory) save_image_to_file(frame_array, directory) def active_frames(frame_array): d=[] #euclid distance frames =[] for i in range(np.size(frame_array,axis=0)-1): d.append((np.linalg.norm(frame_array[i+1]-frame_array[i]),i,0)) #Sort d[i] accending under first column of di d.sort(key=itemgetter(0)) #get the order of active frame d = normal_distribution(d) #assign each d one value based on normal distribution d.sort(key=itemgetter(1)) #re_order frames.append(frame_array[0]) for i in range(1,np.size(d,axis=0)): temp_frame = frame_array[i] * d[i][2] frames.append(temp_frame) temp_frame = np.sum(frames, axis = 0) temp_frame = cv2.normalize(temp_frame, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) return np.array(temp_frame) #This function select numbers of the most active frame in a segment def selected_active_frame(frame_array): max_euclidean_distance = 0 temp_frame = frame_array[0] #assign first frame max = 0 for i in range(np.size(frame_array,axis=0)-1): euclidean_distant = np.linalg.norm(frame_array[i+1]-frame_array[i]) if euclidean_distant > max_euclidean_distance: max_euclidean_distance = euclidean_distant temp_frame = frame_array[i+1] max = i+1 # print(max) return np.array(temp_frame) #this function get the most active frame def full_selected_active_frame(filename, depth): cap_images = read_video_from_file(filename) framearray = [] distance = [] for i in range(np.size(cap_images,axis=0)-1): distance.append((np.linalg.norm(cap_images[i+1]-cap_images[i]),i+1)) frames = [item[1] for item in sorted(distance,key = itemgetter(0))[-depth:]] frames.sort() for i in range(np.size(frames,axis=0)): framearray.append(cap_images[frames[i]]) # print(frames[i]) return framearray def video3d_selected_active_frame(filename, depth): cap_images = read_video_from_file(filename) framearray = [] flatten_framearray = [] nframe = np.size(cap_images,axis = 0) frames = [np.int(x * nframe / depth) for x in range(depth)] # print(nframe, frames) for i in range(np.size(frames,axis=0)): if i < np.size(frames,axis=0)-1: flatten_framearray = cap_images[frames[i]:frames[i+1]] # print(frames[i],frames[i+1]) else: #last frame flatten_framearray = cap_images[frames[i]:nframe] # print(frames[i], nframe) # newframe = selected_active_frame(flatten_framearray) # framearray.append(newframe) return np.array(framearray) def video3d_overlap(filename, depth = 16, overlap = 5, ): cap_images = read_video_from_file(filename) frame_array = [] flatten_framearray = [] nframe = np.size(cap_images,axis = 0) frames = [np.int(x * nframe / depth) for x in range(depth)] fromframe = 0 toframe = 0 for i in range(np.size(frames)): fromframe = frames[i] - overlap toframe = frames[i] + overlap if fromframe < 0: fromframe = 0 if toframe > nframe-1: toframe = nframe-1 flatten_framearray = cap_images[fromframe:toframe] frame = active_frames(flatten_framearray) frame_array.append(frame) return np.array(frame_array) def read_video_from_file(filename): video_cap = cv2.VideoCapture(filename) nframe = np.int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT)) frameWidth = np.int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frameHeight = np.int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) j = 0 ret = True cap_images = np.empty((nframe, frameHeight, frameWidth, 3)) while (j < nframe and ret): ret, cap_images[j] = video_cap.read() if ret != True: cap_images = cap_images[0:j-1] break else: j += 1 return cap_images def normal_distribution(d): dmax = max(l[0] for l in d) dmin = min(l[0] for l in d) mean = (dmax - dmin)/2 sd = (mean - dmin)/3 for i in range(np.size(d,axis=0)): temp = list(d[i]) if dmax == dmin: #2frame is definitely the same temp[2] = 1 else: # temp[2] = 5*i+1 temp[2] = alpha(16,i) # temp[2] = normpdf(i,mean,sd) # temp[2] = stats.norm(mean,sd).pdf(i) d[i] = tuple(temp) return d def video3d_opticalflow(filename, depth): framearray = [] cap_images = read_video_from_file(filename) nframe = np.size(cap_images,axis = 0) frames = [np.int(x * nframe / depth) for x in range(depth)] fromframe = 0 toframe = 0 cap_images = np.asarray(cap_images, dtype=np.float32) for i in range(np.size(frames)): fromframe = frames[i] toframe = frames[i] + 1 if toframe > nframe-1: fromframe = nframe-2 toframe = nframe-1 prevframe = cv2.cvtColor(cap_images[fromframe],cv2.COLOR_BGR2GRAY) nextframe = cv2.cvtColor(cap_images[toframe],cv2.COLOR_BGR2GRAY) hsvImg = np.zeros((np.size(cap_images[fromframe],axis=0), np.size(cap_images[fromframe],axis=1),3)) hsvImg[..., 1] = 0 flow = cv2.calcOpticalFlowFarneback(prevframe, nextframe, None, 0.5, 3, 15, 3, 5, 1.2, 0) mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1]) hsvImg[..., 0] = 0.5 * ang * 180 / np.pi hsvImg[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) hsvImg = np.asarray(hsvImg,dtype=np.float32) frame = cv2.cvtColor(hsvImg, cv2.COLOR_HSV2BGR) framearray.append(frame) return np.array(framearray) #https://en.wikipedia.org/wiki/Normal_distribution#Probability_density_function def normpdf(x, mean, sd): var = float(sd)**2 pi = 3.1415926 denom = (2*pi*var)**.5 num = math.exp(-(float(x)-float(mean))**2/(2*var)) return num/denom def alpha(T, t): return 2*(T-t+1)-(T+1)*(Harmonic_number(T)-Harmonic_number(t-1)) def Harmonic_number(n): if n==0: return 0 return sum(1.0/i for i in range(1,n+1)) if __name__ == '__main__': main()

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null

0

null

0

1

0

30393f74ae0c5c36f82aebb7e0b95f7f112a1231

1,133

py

Python

data_parser.py

JanoHorvath/k-means-clustering

c84e858c3e2bb417ffea11d441797a15c659a7ee

[ "MIT" ]

null

data_parser.py

JanoHorvath/k-means-clustering

c84e858c3e2bb417ffea11d441797a15c659a7ee

[ "MIT" ]

null

data_parser.py

JanoHorvath/k-means-clustering

c84e858c3e2bb417ffea11d441797a15c659a7ee

[ "MIT" ]

null

from random import randint class Dataset: def get_mock_scattered_dataset(self, numberOf, x_upper_bound, y_upper_bound): """ Mock 2D dataset with scattered data points. """ points = [] for i in range(numberOf): point = [randint(0,x_upper_bound), randint(0,y_upper_bound), 'black'] points.append(point) return points def get_mock_dataset(self, numberOf, x_upper_bound, y_upper_bound): """ Mock 2D dataset with clustered data points. """ points = [] clusters = [] """ Creates between 2 to 10 cluster areas with random x/y values """ for i in range(randint(2, 10)): cluster = [randint(0, x_upper_bound), randint(0, y_upper_bound)] clusters.append(cluster) """ Creates numberOf points each randomly assigned to one cluster area and random x/y values near that area """ for i in range(numberOf): j = randint(0, len(clusters)-1) point = [randint(0,30)+clusters[j][0], randint(0,30)+clusters[j][1], 'black'] points.append(point) return points

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33

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false

0

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0.315789

0

null

0

null

0

1

0

303a0f7118c7766ed025bf5dc2723fe60db8bf3e

623

py

Python

backend/colleges/utils.py

cesko-digital/zacni-uc

281c56aec5509d5dd8bbbd60f054ffcd9156609e

[ "MIT" ]

4

2021-02-26T09:28:14.000Z

2021-07-08T19:21:57.000Z

backend/colleges/utils.py

cesko-digital/zacni-uc

281c56aec5509d5dd8bbbd60f054ffcd9156609e

[ "MIT" ]

35

2021-01-27T08:38:59.000Z

2021-12-13T19:42:38.000Z

backend/colleges/utils.py

cesko-digital/zacni-uc

281c56aec5509d5dd8bbbd60f054ffcd9156609e

[ "MIT" ]

5

2021-01-21T21:35:42.000Z

2022-01-06T10:07:58.000Z

from openpyxl import load_workbook def import_msmt_college_registry_xlsx(path, sheet_name): """ Import XLSX from https://regvssp.msmt.cz/registrvssp/cvslist.aspx (list of colleges and faculties). Parameters: path -- path to XLSX file sheet_name -- "ExportVS" or "ExportFakulty" """ workbook = load_workbook(path) sheet = workbook[sheet_name] out = [] columns = [k.value.strip() for k in sheet[1]] for i in range(2, sheet.max_row + 1): values = [i.value for i in sheet[i]] item = dict(zip(columns, values)) out.append(item) return out

25.958333

69

0.638844

85

623

4.564706

0.576471

0.069588

0.030928

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623

23

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0.363636

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null

0

null

0

1

0

303b9455bfc79c1d55b5b456a3c9a97a10ddaa26

5,706

py

Python

act/qc/arm.py

jrobrien91/ACT

604b93d75366d23029f89d88df9053d52825c214

[ "BSD-3-Clause" ]

9

2019-03-11T19:41:34.000Z

2019-09-17T08:34:19.000Z

act/qc/arm.py

jrobrien91/ACT

604b93d75366d23029f89d88df9053d52825c214

[ "BSD-3-Clause" ]

127

2019-03-18T12:24:17.000Z

2020-01-06T20:53:06.000Z

act/qc/arm.py

jrobrien91/ACT

604b93d75366d23029f89d88df9053d52825c214

[ "BSD-3-Clause" ]

15

2019-03-11T15:30:56.000Z

2019-11-01T19:10:11.000Z

""" Functions specifically for working with QC/DQRs from the Atmospheric Radiation Measurement Program (ARM). """ import datetime as dt import numpy as np import requests from act.config import DEFAULT_DATASTREAM_NAME def add_dqr_to_qc( obj, variable=None, assessment='incorrect,suspect', exclude=None, include=None, normalize_assessment=True, cleanup_qc=True, ): """ Function to query the ARM DQR web service for reports and add as a new quality control test to ancillary quality control variable. If no anicllary quality control variable exist a new one will be created and lined to the data variable through ancillary_variables attribure. See online documentation from ARM Data Quality Office on the use of the DQR web service. https://code.arm.gov/docs/dqrws-examples/wikis/home Information about the DQR web-service avaible at https://adc.arm.gov/dqrws/ Parameters ---------- obj : xarray Dataset Data object variable : string, or list of str, or None Variables to check DQR web service. If set to None will attempt to update all variables. assessment : string assessment type to get DQRs. Current options include 'missing', 'suspect', 'incorrect' or any combination separated by a comma. exclude : list of strings DQR IDs to exclude from adding into QC include : list of strings List of DQR IDs to include in flagging of data. Any other DQR IDs will be ignored. normalize_assessment : boolean The DQR assessment term is different than the embedded QC term. Embedded QC uses "Bad" and "Indeterminate" while DQRs use "Incorrect" and "Suspect". Setting this will ensure the same terms are used for both. cleanup_qc : boolean Call clean.cleanup() method to convert to standardized ancillary quality control variables. Has a little bit of overhead so if the Dataset has already been cleaned up, no need to run. Returns ------- obj : xarray Dataset Data object Examples -------- .. code-block:: python from act.qc.arm import add_dqr_to_qc obj = add_dqr_to_qc(obj, variable=['temp_mean', 'atmos_pressure']) """ # DQR Webservice goes off datastreams, pull from object if 'datastream' in obj.attrs: datastream = obj.attrs['datastream'] elif '_datastream' in obj.attrs: datastream = obj.attrs['_datastream'] else: raise ValueError('Object does not have datastream attribute') if datastream == DEFAULT_DATASTREAM_NAME: raise ValueError("'datastream' name required for DQR service set to default value " f"{datastream}. Unable to perform DQR service query.") # Clean up QC to conform to CF conventions if cleanup_qc: obj.clean.cleanup() # In order to properly flag data, get all variables if None. Exclude QC variables. if variable is None: variable = list(set(obj.data_vars) - set(obj.clean.matched_qc_variables)) # Check to ensure variable is list if not isinstance(variable, (list, tuple)): variable = [variable] # Loop through each variable and call web service for that variable for var_name in variable: # Create URL url = 'http://www.archive.arm.gov/dqrws/ARMDQR?datastream=' url += datastream url += '&varname=' + var_name url += ''.join( [ '&searchmetric=', assessment, '&dqrfields=dqrid,starttime,endtime,metric,subject', ] ) # Call web service req = requests.get(url) # Check status values and raise error if not successful status = req.status_code if status == 400: raise ValueError('Check parameters') if status == 500: raise ValueError('DQR Webservice Temporarily Down') # Get data and run through each dqr dqrs = req.text.splitlines() time = obj['time'].values dqr_results = {} for line in dqrs: line = line.split('|') dqr_no = line[0] # Exclude DQRs if in list if exclude is not None and dqr_no in exclude: continue # Only include if in include list if include is not None and dqr_no not in include: continue starttime = np.datetime64(dt.datetime.utcfromtimestamp(int(line[1]))) endtime = np.datetime64(dt.datetime.utcfromtimestamp(int(line[2]))) ind = np.where((time >= starttime) & (time <= endtime)) if ind[0].size == 0: continue if dqr_no in dqr_results.keys(): dqr_results[dqr_no]['index'] = np.append(dqr_results[dqr_no]['index'], ind) else: dqr_results[dqr_no] = { 'index': ind, 'test_assessment': line[3], 'test_meaning': ': '.join([dqr_no, line[-1]]), } for key, value in dqr_results.items(): try: obj.qcfilter.add_test( var_name, index=value['index'], test_meaning=value['test_meaning'], test_assessment=value['test_assessment'], ) except IndexError: print(f"Skipping '{var_name}' DQR application because of IndexError") if normalize_assessment: obj.clean.normalize_assessment(variables=var_name) return obj

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0

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0.012346

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null

0

null

0

1

0

303d67c6ff0813b9d6fd68be88e83d8ab918ad04

9,018

py

Python

database/__init__.py

tclarkin/shread_dash

a45e2f2946c74526e69c087587676aaa4cb15fba

[ "CC0-1.0" ]

null

database/__init__.py

tclarkin/shread_dash

a45e2f2946c74526e69c087587676aaa4cb15fba

[ "CC0-1.0" ]

null

database/__init__.py

tclarkin/shread_dash

a45e2f2946c74526e69c087587676aaa4cb15fba

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Mar 21 11:55:27 2021 Snow-Hydrology Repo for Evaluation, Analysis, and Decision-making Dashboard (shread_dash.py) Database Initialization This is part of dashboard loading database and other data into memory. The data for the database relies on a series of retrieval scripts (/database/SUBS) that retrieve hydrometeorological data from online and store the data in local databases. Part of the retrieval process is dependent on the SHREAD repository (https://github.com/tclarkin/shread). The databases are built in SQLite. @author: tclarkin, buriona (2020-2022) """ import os import datetime as dt from pathlib import Path import pandas as pd from flask_sqlalchemy import SQLAlchemy import dash_bootstrap_components as dbc import dash ### Launch SQLite DB Server ### # Define directories and app this_dir = os.path.dirname(os.path.realpath(__file__)) #this_dir = Path('C:/Programs/shread_dash/database') app_dir = os.path.dirname(this_dir) # define functions def create_app(): """ This function launches the SALAlchemy db server """ assets_path = Path(app_dir, 'assets') app = dash.Dash( __name__, external_stylesheets=[dbc.themes.BOOTSTRAP], update_title="Updating...", # suppress_callback_exceptions=True, assets_folder=assets_path ) app.title="WCAO Dashboard" db_path = Path(app_dir, 'database') snodas_swe_db_path = Path(db_path, 'SHREAD', 'swe.db') snodas_sd_db_path = Path(db_path, 'SHREAD', 'sd.db') csas_iv_db_path = Path(db_path, 'CSAS', 'csas_iv.db') csas_dv_db_path = Path(db_path, 'CSAS', 'csas_dv.db') snotel_dv_db_path = Path(db_path, 'SNOTEL', 'snotel_dv.db') usgs_dv_db_path = Path(db_path, 'FLOW', 'usgs_dv.db') usgs_iv_db_path = Path(db_path, 'FLOW', 'usgs_iv.db') rfc_dv_db_path = Path(db_path, 'FLOW', 'rfc_dv.db') rfc_iv_db_path = Path(db_path, 'FLOW', 'rfc_iv.db') ndfd_mint_db_path = Path(db_path, 'SHREAD', 'mint.db') ndfd_maxt_db_path = Path(db_path, 'SHREAD', 'maxt.db') #ndfd_rhm_db_path = Path(db_path, 'SHREAD', 'rhm.db') ndfd_pop12_db_path = Path(db_path, 'SHREAD', 'pop12.db') ndfd_qpf_db_path = Path(db_path, 'SHREAD', 'qpf.db') ndfd_snow_db_path = Path(db_path, 'SHREAD', 'snow.db') ndfd_sky_db_path = Path(db_path, 'SHREAD', 'sky.db') snodas_swe_db_con_str = f'sqlite:///{snodas_swe_db_path.as_posix()}' snodas_sd_db_con_str = f'sqlite:///{snodas_sd_db_path.as_posix()}' csas_iv_db_con_str = f'sqlite:///{csas_iv_db_path.as_posix()}' csas_dv_db_con_str = f'sqlite:///{csas_dv_db_path.as_posix()}' snotel_dv_db_con_str = f'sqlite:///{snotel_dv_db_path.as_posix()}' usgs_dv_db_con_str = f'sqlite:///{usgs_dv_db_path.as_posix()}' usgs_iv_db_con_str = f'sqlite:///{usgs_iv_db_path.as_posix()}' rfc_dv_db_con_str = f'sqlite:///{rfc_dv_db_path.as_posix()}' rfc_iv_db_con_str = f'sqlite:///{rfc_iv_db_path.as_posix()}' ndfd_mint_db_con_str = f'sqlite:///{ndfd_mint_db_path}' ndfd_maxt_db_con_str = f'sqlite:///{ndfd_maxt_db_path}' #ndfd_rhm_db_con_str = f'sqlite:///{ndfd_rhm_db_path}' ndfd_pop12_db_con_str = f'sqlite:///{ndfd_pop12_db_path}' ndfd_qpf_db_con_str = f'sqlite:///{ndfd_qpf_db_path}' ndfd_snow_db_con_str = f'sqlite:///{ndfd_snow_db_path}' ndfd_sky_db_con_str = f'sqlite:///{ndfd_sky_db_path}' app.server.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False app.server.config['SQLALCHEMY_BINDS'] = { 'swe': snodas_swe_db_con_str, 'sd': snodas_sd_db_con_str, 'csas_iv':csas_iv_db_con_str, 'csas_dv':csas_dv_db_con_str, 'snotel_dv':snotel_dv_db_con_str, 'usgs_dv':usgs_dv_db_con_str, 'usgs_iv':usgs_iv_db_con_str, 'rfc_dv':rfc_dv_db_con_str, 'rfc_iv':rfc_iv_db_con_str, "mint": ndfd_mint_db_con_str, "maxt": ndfd_maxt_db_con_str, #"rhm": ndfd_rhm_db_con_str, "pop12": ndfd_pop12_db_con_str, "qpf": ndfd_qpf_db_con_str, "snow": ndfd_snow_db_con_str, "sky": ndfd_sky_db_con_str, } return app # Launch server app = create_app() db = SQLAlchemy(app.server) db.reflect() ### Load in other Data ### # Define working (data) directory os.chdir(os.path.join(app_dir, 'database')) # Identify files in database csas_dir = os.path.join(app_dir, 'database', 'CSAS') csas_files = os.listdir(csas_dir) res_dir = os.path.join(app_dir, 'resources') #switch working dir back to main dir so dash app can function correctly os.chdir(app_dir) print('Calculating bounds of SNODAS.db') # Create list of basins #TODO call from .csv for future user input basin_list = [ {'label': 'NONE', 'value': None}, {'label': 'SAN JUAN - NAVAJO RES NR ARCHULETA', 'value': 'NVRN5L_F'}, {'label': 'ANIMAS - DURANGO', 'value': 'DRGC2H_F'}, {'label': 'DOLORES - MCPHEE RESERVOIR', 'value': 'MPHC2L_F'}, {'label': 'FLORIDA - LEMON RES NR DURANGO', 'value': 'LEMC2H_F'}, {'label': 'LOS PINOS - NR BAYFIELD VALLECITO RES', 'value': 'VCRC2H_F'} ] # Set ranges of variables for use in dashboard elevrange =[5000, 15000] print(f' Elevations from {elevrange[0]} to {elevrange[-1]}') elevdict = dict() for e in range(1, 20): elevdict[str(e * 1000)] = f"{e * 1000:,}'" sloperange = [0.0, 100] print(f' Slopes from {sloperange[0]} to {sloperange[-1]}') slopedict = dict() for s in range(0, 11): slopedict[str(s * 10)] = f'{s * 10}°' aspectdict = {-90: "W", -45: "NW", 0: "N", 45: "NE", 90: "E", 135: "SE", 180: "S", 225: "SW", 270: "W", 315: "NW", 360: "N"} # Define colors: # https://colorbrewer2.org/?type=qualitative&scheme=Set1&n=9 color8 = ['#e41a1c','#377eb8','#4daf4a','#984ea3','#ff7f00','#a65628','#f781bf','#999999'] # Import FLOW gages and define list for dashboard drop down & add colors usgs_gages = pd.read_csv(os.path.join(this_dir,"FLOW", "usgs_gages.csv")) usgs_gages.index = usgs_gages.site_no colorg = color8 while len(colorg)<len(usgs_gages): colorg = colorg*2 usgs_gages["color"] = colorg[0:len(usgs_gages)] # Add list for dropdown menu usgs_list = list() for g in usgs_gages.index: usgs_list.append({"label": "0" + str(usgs_gages.site_no[g]) + " " + usgs_gages.name[g] + " (" + str( usgs_gages.elev_ft[g]) + " ft | " + str(usgs_gages.area[g]) + " sq.mi.)", "value": "0" + str(g)}) # Create list of SNOTEL sites & add colors snotel_sites = pd.read_csv(os.path.join(this_dir,"SNOTEL","snotel_sites.csv")) snotel_sites.index = snotel_sites.triplet colors = color8 while len(colors)<len(snotel_sites): colors = colors*2 snotel_sites["color"] = snotel_sites["prcp_color"] = colors[0:len(snotel_sites)] # Add list for dropdown menu snotel_list = list() for s in snotel_sites.index: snotel_list.append({"label": str(snotel_sites.site_no[s]) + " " + snotel_sites.name[s] + " (" + str( round(snotel_sites.elev_ft[s], 0)) + " ft)", "value": s}) # Create list of CSAS sites & add colors csas_gages = pd.DataFrame() csas_gages["site"] = ["SASP","SBSP","PTSP","SBSG"] csas_gages["name"] = ["Swamp Angel","Senator Beck","Putney [Meteo]","Senator Beck Gage [Flow]"] csas_gages["elev_ft"] = [11060,12186,12323,11030] colorc = color8 while len(colorc)<len(csas_gages): colorc = colorc*2 csas_gages["color"] = csas_gages["prcp_color"] = colorc[0:len(csas_gages)] csas_gages.index = csas_gages["site"] csas_list = list() for c in csas_gages.index: csas_list.append({"label": csas_gages.name[c] + " (" + str( round(csas_gages.elev_ft[c], 0)) + " ft)", "value": c}) # Generate NDFD list forecast_list = [{"label":"Flow (RFC)","value":"flow"}, {"label":"Min. Temp","value":"mint"}, {"label":"Max. Temp","value":"maxt"}, {"label":"Precip (QPF)","value":"qpf"}, {"label": "Precip Prob.", "value": "pop12"}, {"label":"Snow","value":"snow"}, #{"label":"Relative Humidity","value":"rhm"}, {"label":"Sky Coverage","value":"sky"} ] # Import CSAS dust on snow data try: dust = pd.read_csv(os.path.join(csas_dir, "csas_dust.csv")) except FileNotFoundError: dust = pd.DataFrame() if dust.empty: dust_disable = True else: dust_disable = False dust_ts = dust.loc[1:len(dust),] dust_ts = dust_ts.reset_index(drop=True) dust_ts["Date"] = pd.to_datetime(dust_ts["Date"],format="%d-%m-%y") dust_ts.index = dust_ts.Date dust_ts = dust_ts.drop("Date",axis=1) dust_ts = (dust_ts.apply(pd.to_numeric)/2.54) dust_layers = pd.DataFrame(index=dust_ts.columns) colord = color8 while len(colord) < len(dust_layers): colord = colord * 2 dust_layers["color"] = colord[0:len(dust_layers)] # set initial start and end date start_date = dt.datetime.now().date() - dt.timedelta(days=10) end_date = dt.datetime.now().date() + dt.timedelta(days=10)

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null

0

null

0

1

0

303f8a39fa8bc76e0faedd216816494e7f0cf74e

357

py

Python

scripts/test.py

germy/piprint

c4bf36ccf90cbce50ecae4e673b916f0b7a1b522

[ "MIT" ]

null

scripts/test.py

germy/piprint

c4bf36ccf90cbce50ecae4e673b916f0b7a1b522

[ "MIT" ]

null

scripts/test.py

germy/piprint

c4bf36ccf90cbce50ecae4e673b916f0b7a1b522

[ "MIT" ]

null

import sys def write(): print('Creating new text file') name = 'test.txt' # Name of text file coerced with +.txt try: file = open(name,'a') # Trying to create a new file or open one file.close() except: print('Something went wrong! Can\'t tell what?') sys.exit(0) # quit Python write()

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null

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null

0

1

0

304744cf40637da26f72f55057da5c765cb35850

27,025

py

Python

marketmodel/neuralsde.py

vicaws/neuralSDE-marketmodel

ffbc558fee273cbae81ffe9312fc878ba4d261d1

[ "MIT" ]

5

2021-08-19T15:24:08.000Z

2022-03-09T07:11:41.000Z

marketmodel/neuralsde.py

vicaws/neuralSDE-marketmodel

ffbc558fee273cbae81ffe9312fc878ba4d261d1

[ "MIT" ]

null

marketmodel/neuralsde.py

vicaws/neuralSDE-marketmodel

ffbc558fee273cbae81ffe9312fc878ba4d261d1

[ "MIT" ]

1

2021-11-10T07:55:06.000Z

""" Construct, train neural-SDE models and simulate trajectories from the learnt models. """ # Copyright 2021 Sheng Wang. # Affiliation: Mathematical Institute, University of Oxford # Email: sheng.wang@maths.ox.ac.uk import numpy as np import os import pandas as pd import tensorflow as tf import tensorflow_probability as tfp import tensorflow_model_optimization as tfmot import marketmodel.utils as utils from glob import glob from tqdm import tqdm from marketmodel.factors import PrepTrainData class Loss(object): """ Library of loss functions for neural SDE models. """ @staticmethod def loss_S(dt): """ Loss function for the neural SDE model of S. Parameters __________ dt: float Time increment. Returns _______ loss: method Loss function. """ def loss(y_true, y_pred): # extract data alpha = y_pred[:, 0] beta = y_pred[:, 1] dS = y_true[:, 0] S = y_true[:, 1] # compute drift mu = beta * S # drift term # compute log-likelihood l = tf.reduce_sum(2*tf.math.log(S)-alpha + tf.square(dS - mu*dt) * tf.exp(alpha) / dt / S**2) return l return loss @staticmethod def loss_xi(dt, n_dim, n_varcov, mask_diagonal, W, G, lbd_penalty_eq, lbd_penalty_sz): """ Loss function for the neural SDE model of xi. """ def loss(y_true, y_pred): # get diffusion terms in the predicted values; in particular, # diagonal terms of the diffusion matrix are taken exponentials sigma_term = tf.transpose( tf.where(tf.constant(mask_diagonal), tf.transpose(tf.exp(y_pred)), tf.transpose(y_pred)))[:, :n_varcov] # construct the transposed diffusion matrix sigma_tilde_T = tfp.math.fill_triangular(sigma_term, upper=True) # get diagonal terms of the diffusion matrix sigma_term_diagonal = tf.where(tf.constant(mask_diagonal), tf.transpose(y_pred), 0.) # get drift terms in the predicted values mu_residuals = y_pred[:, n_varcov:] # get pre-calculated terms from the inputs ## regarding diffusion scaling proj_dX = y_true[:, :n_dim] Omega = tf.reshape(y_true[:, n_dim:n_dim+n_dim**2], shape=[-1, n_dim, n_dim]) det_Omega = y_true[:, n_dim+n_dim**2:n_dim+n_dim**2+1] n1 = n_dim+n_dim**2+1 ## regarding drift correction n_bdy = W.shape[0] corr_dirs = tf.reshape(y_true[:, n1:n1+n_dim*n_bdy], shape=[-1, n_bdy, n_dim]) epsmu = y_true[:, n1+n_dim*n_bdy:n1+n_dim*n_bdy+n_bdy] n2 = n1+n_dim*n_bdy+n_bdy ## regarding baseline drift mu_base = y_true[:, n2:n2+n_dim] n3 = n2+n_dim ## regarding MPR penalty zed = tf.expand_dims(y_true[:, n3:], axis=-1) # compute corrected drifts ## compute drift mu_term = mu_base * mu_residuals ## compute weights assigned to each correction direction mu_tilde_inner_W = tf.matmul( mu_term, tf.constant(W.T, dtype=tf.float32)) corr_dir_inner_W = tf.reduce_sum( corr_dirs * tf.constant(W, dtype=tf.float32), axis=-1) gamma = tf.maximum(-mu_tilde_inner_W - epsmu, 0.) / corr_dir_inner_W ## compute corrected drift mu_tf = mu_term + tf.reduce_sum( tf.expand_dims(gamma, axis=-1) * corr_dirs, axis=1) mu_tf = tf.expand_dims(mu_tf, axis=-1) # compute log likelihood Omega_T = tf.transpose(Omega, perm=[0, 2, 1]) sigma_tilde = tf.transpose(sigma_tilde_T, perm=[0, 2, 1]) proj_mu = tf.linalg.solve(Omega_T, mu_tf) sol_mu = tf.linalg.triangular_solve( sigma_tilde, proj_mu, lower=True) sol_mu = tf.squeeze(sol_mu) proj_dX_tf = tf.expand_dims(proj_dX, axis=-1) sol_dX = tf.linalg.triangular_solve( sigma_tilde, proj_dX_tf, lower=True) sol_dX = tf.squeeze(sol_dX) l1 = 2 * tf.reduce_sum(tf.math.log(det_Omega)) + \ 2 * tf.reduce_sum(sigma_term_diagonal) l2 = 1./dt * tf.reduce_sum(tf.square(sol_dX)) l3 = dt * tf.reduce_sum(tf.square(sol_mu)) l4 = -2 * tf.reduce_sum(sol_mu * sol_dX) # compute the penalty term ## evaluate the X variable in the regression problem sigma = tf.matmul(Omega_T, sigma_tilde) G_tf = tf.expand_dims(tf.constant(G[1:], dtype=tf.float32), axis=0) reg_Xt = tf.matmul(sigma, G_tf, transpose_a=True) ## evaluate the Y variable in the regression problem reg_Y = tf.matmul(G_tf, mu_tf, transpose_a=True) - zed ## evaluate the OLS estimates of the regression problem reg_XtY = tf.matmul(reg_Xt, reg_Y) reg_XtX = tf.matmul(reg_Xt, reg_Xt, transpose_b=True) reg_psi = tf.linalg.solve(reg_XtX, reg_XtY) # reg_err = reg_Y - tf.matmul(reg_Xt, reg_psi, transpose_a=True) pnty = lbd_penalty_eq * tf.reduce_sum(tf.square(reg_err)) + \ lbd_penalty_sz * tf.reduce_sum(tf.square(reg_psi)) return l1 + l2 + l3 + l4 + pnty return loss class Model(object): """ Library of constructing neural network models. """ @staticmethod def construct_S(dim_input, n_obs, pruning_sparsity, validation_split, batch_size, epochs): # construct the fully connected model dim_output = 2 model_S = tf.keras.Sequential([ tf.keras.layers.Dense(128, input_shape=(dim_input,), activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(dim_output)]) # prune the model n_obs_train = n_obs * (1 - validation_split) end_step = np.ceil(n_obs_train / batch_size).astype(np.int32) * epochs pruning_schedule = tfmot.sparsity.keras.PolynomialDecay( initial_sparsity=0, final_sparsity=pruning_sparsity, begin_step=0, end_step=end_step ) model_S_pruning = tfmot.sparsity.keras.prune_low_magnitude( model_S, pruning_schedule ) return model_S_pruning @staticmethod def construct_mu(dim_input): # construct the fully connected model dim_output = 2 model_mu = tf.keras.Sequential([ tf.keras.layers.Dense(128, input_shape=(dim_input,), activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(dim_output)]) return model_mu @staticmethod def construct_xi(dim_input, dim_output, n_obs, pruning_sparsity, validation_split, batch_size, epochs): # construct the fully connected model model_xi = tf.keras.Sequential([ tf.keras.layers.Dense(256, input_shape=(dim_input,), activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(256, activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(256, activation=tf.nn.relu), tf.keras.layers.Dropout(rate=0.1), tf.keras.layers.Dense(dim_output)]) # prune the model n_obs_train = n_obs * (1 - validation_split) end_step = np.ceil(n_obs_train / batch_size).astype(np.int32) * epochs pruning_schedule = tfmot.sparsity.keras.PolynomialDecay( initial_sparsity=0.0, final_sparsity=pruning_sparsity, begin_step=0, end_step=end_step ) model_xi_pruning = tfmot.sparsity.keras.prune_low_magnitude( model_xi, pruning_schedule ) return model_xi_pruning class Train(object): """ Library of training methods for neural SDE models. """ @staticmethod def train_S(X_S, Y_S, pruning_sparsity=0.5, validation_split=0.1, batch_size=512, epochs=500, rand_seed=0, force_fit=False, model_name='model_S', out_dir='output/checkpoint/'): n_obs, dim_input = X_S.shape # construct the neural network model model_S = Model.construct_S( dim_input, n_obs, pruning_sparsity, validation_split, batch_size, epochs) # compile the neural network model model_S.compile( loss=Loss.loss_S(1e-3), optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4) ) # set up I/O tag = out_dir + model_name + '_' + str(rand_seed) checkpoint_filepath_model_S = tag checkpoint_filepath_model_S_all = tag + '*' csv_fname = tag + '_history.csv' pruning_dir = out_dir + 'pruning_summary/' if not os.path.exists(pruning_dir): os.mkdir(pruning_dir) # train the pruned model tf.random.set_seed(rand_seed) if glob(checkpoint_filepath_model_S_all) and not force_fit: model_S.load_weights(checkpoint_filepath_model_S) else: model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath_model_S, save_weights_only=True, monitor='loss', mode='min', save_best_only=True) csv_logger = tf.keras.callbacks.CSVLogger( filename=csv_fname, separator=',', append=False ) history = model_S.fit( X_S, Y_S, epochs=epochs, batch_size=batch_size, validation_split=validation_split, shuffle=True, verbose=True, callbacks=[ model_checkpoint_callback, csv_logger, tfmot.sparsity.keras.UpdatePruningStep(), tfmot.sparsity.keras.PruningSummaries(log_dir=pruning_dir)] ) # plot training loss history plot_fname = tag + '_history.png' utils.PlotLib.plot_loss_over_epochs(history, True, plot_fname) return model_S @staticmethod def train_mu(X_S, mu_base, validation_split=0.1, batch_size=512, epochs=200, rand_seed=0, force_fit=False, model_name='model_mu', out_dir='output/checkpoint/'): dim_input = X_S.shape[1] # construct the neural network model model_mu = Model.construct_mu(dim_input) model_mu.compile(loss='mean_absolute_error', optimizer='adam') # set up I/O tag = out_dir + model_name + '_' + str(rand_seed) checkpoint_filepath_model_mu = tag checkpoint_filepath_model_mu_all = tag + '*' csv_fname = tag + '_history.csv' # train the model if glob(checkpoint_filepath_model_mu_all) and not force_fit: model_mu.load_weights(checkpoint_filepath_model_mu) else: model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath_model_mu, save_weights_only=True, monitor='loss', mode='min', save_best_only=True) csv_logger = tf.keras.callbacks.CSVLogger( filename=csv_fname, separator=',', append=False ) history = model_mu.fit( X_S, mu_base, epochs=epochs, batch_size=batch_size, validation_split=validation_split, shuffle=True, verbose=True, callbacks=[model_checkpoint_callback, csv_logger] ) # plot training loss history plot_fname = tag + '_history.png' utils.PlotLib.plot_loss_over_epochs(history, True, plot_fname) return model_mu @staticmethod def train_xi(X_xi, Y_xi, W, G, lbd_penalty_eq, lbd_penalty_sz, pruning_sparsity=0.5, validation_split=0.1, batch_size=512, epochs=20000, rand_seed=0, force_fit=False, model_name='model_xi', out_dir='output/checkpoint/'): n_bdy, n_dim = W.shape n_varcov, mask_diagonal = Train._identify_diagonal_entries(n_dim) # construct the neural network model model_xi_pruning = Model.construct_xi( n_dim + 1, n_dim + n_varcov, X_xi.shape[0], pruning_sparsity, validation_split, batch_size, epochs) model_xi_pruning.compile( loss=Loss.loss_xi(1e-3, n_dim, n_varcov, mask_diagonal, W, G, lbd_penalty_eq, lbd_penalty_sz), optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4), ) # set up I/O tag = out_dir + model_name + '_' + str(rand_seed) checkpoint_filepath = tag checkpoint_filepath_all = tag + '*' csv_fname = tag + '_history.csv' pruning_dir = out_dir + 'pruning_summary/' if not os.path.exists(pruning_dir): os.mkdir(pruning_dir) # train the pruned model tf.random.set_seed(rand_seed) if glob(checkpoint_filepath_all) and not force_fit: model_xi_pruning.load_weights(checkpoint_filepath) else: model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath, save_weights_only=True, monitor='loss', mode='min', save_best_only=True) csv_logger = tf.keras.callbacks.CSVLogger( filename=csv_fname, separator=',', append=False ) history = model_xi_pruning.fit( X_xi, Y_xi, epochs=epochs, batch_size=batch_size, validation_split=validation_split, shuffle=True, verbose=True, callbacks=[ model_checkpoint_callback, csv_logger, tfmot.sparsity.keras.UpdatePruningStep(), tfmot.sparsity.keras.PruningSummaries(log_dir=pruning_dir)] ) # plot training loss history plot_fname = tag + '_history.png' utils.PlotLib.plot_loss_over_epochs(history, True, plot_fname) return model_xi_pruning @staticmethod def predict_in_sample_model_xi(model_xi, X_xi, Y_xi, W, G): n_dim = X_xi.shape[1] - 1 n_bdy = W.shape[0] n_varcov, mask_diagonal = Train._identify_diagonal_entries(n_dim) # predict underlying functions using the learnt NN y_pred_nn = model_xi.predict(X_xi) # get diffusion terms mask_diagonal_np = [m[0] for m in mask_diagonal] sigma_term = y_pred_nn.copy() sigma_term[:, mask_diagonal_np] = np.exp(sigma_term[:, mask_diagonal_np]) sigma_term = sigma_term[:, :n_varcov] sigma_tilde_T = Train._fill_triu(sigma_term, n_dim) # get drift terms mu_residuals = y_pred_nn[:, n_varcov:] # get inputs for scaling diffusions and correcting drifts ## regarding diffusions Omega = np.reshape(Y_xi[:, n_dim:n_dim+n_dim**2], newshape=[-1, n_dim, n_dim]) ## regarding drifts n1 = n_dim+n_dim**2+1 corr_dirs = np.reshape(Y_xi[:, n1:n1+n_dim*n_bdy], newshape=[-1, n_bdy, n_dim]) epsmu = Y_xi[:, n1+n_dim*n_bdy:n1+n_dim*n_bdy+n_bdy] n2 = n1+n_dim*n_bdy+n_bdy mu_base = Y_xi[:, n2:n2+n_dim] # compute drift term mu_tilde = mu_base * mu_residuals # scale diffusion sigma_T = np.matmul(sigma_tilde_T, Omega) # correct drift mu_tilde_inner_W = mu_tilde.dot(W.T) corr_dir_inner_W = np.sum(corr_dirs * W[None, :, :], axis=-1) gamma = np.maximum(- mu_tilde_inner_W - epsmu, 0.) / corr_dir_inner_W mu = mu_tilde + np.sum(gamma[:, :, None] * corr_dirs, axis=1) # LU deconposition of diffusion matrices mat_cov = np.matmul(np.transpose(sigma_T, axes=[0, 2, 1]), sigma_T) sigma_L = np.linalg.cholesky(mat_cov) return mu_tilde, sigma_tilde_T, mu, sigma_T, sigma_L @staticmethod def _identify_diagonal_entries(n_dim): """ Return the Boolean logical mask array that indicates diagonal terms in a diffusion matrix. """ # get the number of unknowns in the diffusion matrix n_varcov = int(n_dim*(n_dim+1)/2) # construct the diagonal entry mask x = np.arange(n_varcov) xc = np.concatenate([x, x[n_dim:][::-1]]) idxs_diagonal = [xc[i * (n_dim + 1)] for i in range(n_dim)] mask_diagonal = np.zeros(n_varcov + n_dim, dtype=bool) mask_diagonal[idxs_diagonal] = True mask_diagonal = [[m] for m in mask_diagonal] return n_varcov, mask_diagonal @staticmethod def _fill_triu(arrs_sigma, n_dim): """ Return a list of upper triangular diffusion matrices, given a list of flat arrays that contain non-zero elements of the diffusion matrices. """ n_obs = arrs_sigma.shape[0] mats_sigma = np.zeros((n_obs, n_dim, n_dim)) for i in range(n_obs): arr_sigma = arrs_sigma[i] xc = np.concatenate([arr_sigma, arr_sigma[n_dim:][::-1]]) g = np.reshape(xc, [n_dim, n_dim]) mats_sigma[i] = np.triu(g, k=0) return mats_sigma class Simulate(object): """ Library of forward-simulation methods. """ @staticmethod def simulate_S_xi_lite(dt, N, model_S, model_xi, model_mu, S0, X0, W, b, factor_multiplier, dist_multiplier, proj_scale, rho_star, epsmu_star, X_interior, reflect=False): # simulate innovations n_dim = X0.shape[0] dW = np.random.normal(0, np.sqrt(dt), (n_dim + 1, N + 1)) # initialise st = np.ones(N+1) * np.nan xit = np.ones((n_dim, N+1)) * np.nan st[0] = S0 xit[:, 0] = X0 mus_sim = [] vols_sim = [] n_varcov, mask_diagonal = Train._identify_diagonal_entries(n_dim) n_reflect = 0 for i in tqdm(range(1, N+1)): try: # get drift and diffusion of S xi = xit[:, i-1] x_S = np.hstack((st[i-1]/factor_multiplier, xi)) pred_S = model_S.predict(x_S.reshape(1, -1))[0] vol_S = np.sqrt(np.exp(-pred_S[0])) * st[i-1] mu_S = pred_S[1] * st[i-1] # simulate S S_ = st[i-1] + mu_S * dt + vol_S * dW[0, i] # get baseline drift x_mu = np.hstack((st[i-1]/factor_multiplier, xi)) pred_mu_base = model_mu.predict(x_mu.reshape(1, -1))[0] # get drift and diffusion of xi x_xi = np.hstack((st[i-1]/factor_multiplier, xi)) gamma_nn = model_xi.predict(x_xi.reshape(1,-1))[0] gamma_nn[np.array(mask_diagonal).ravel()] = np.exp( gamma_nn[np.array(mask_diagonal).ravel()]) sigma_term = gamma_nn[:n_varcov] xc = np.concatenate([sigma_term, sigma_term[n_dim:][::-1]]) g = np.reshape(xc, [n_dim, n_dim]) sigma_tilde = np.triu(g, k=0).T mu_residual = gamma_nn[n_varcov:] # scale diffusion and correct drift mu, mat_vol = Simulate.scale_drift_diffusion( xi, mu_residual, sigma_tilde, W, b, dist_multiplier, proj_scale, rho_star, epsmu_star, X_interior, pred_mu_base) # tame coefficients mu_norm = 1. + np.linalg.norm(mu) * np.sqrt(dt) vol_norm = 1. + np.linalg.norm(mat_vol) * np.sqrt(dt) # simulate xi using Euler-scheme xi_ = xi + mu / mu_norm * dt + \ mat_vol.dot(dW[1:, i].reshape((-1, 1))).flatten()/vol_norm if reflect: if np.any(W.dot(xi_) - b < 0): n_reflect += 1 print(f'Reflect simulated data point at index {i}.') xi_ = Simulate.reflect_data(xi, xi_, W, b) st[i] = S_ xit[:, i] = xi_ mus_sim.append(mu) vols_sim.append(mat_vol) except: break return st, xit, mus_sim, vols_sim, n_reflect @staticmethod def simulate_S_xi(dt, N, model_S, model_xi, model_mu, S, X, W, b, factor_multiplier, dist_multiplier, proj_scale, rho_star, epsmu_star, X_interior, train_rand_seed, sim_rand_seed, force_simulate=False, reflect=False, out_dir='output/checkpoint/'): print(f'Simulation number: {str(train_rand_seed)}_{str(sim_rand_seed)}') # set I/O plot_fname = f'{out_dir}simulation_{str(train_rand_seed)}' + \ f'_{str(sim_rand_seed)}.png' data_fname = f'{out_dir}simulation_{str(train_rand_seed)}' + \ f'_{str(sim_rand_seed)}.csv' if os.path.exists(data_fname) and not force_simulate: return # simulate np.random.seed(sim_rand_seed) S0 = S[0] X0 = X[0, :] st, xit, mus_sim, vols_sim, n_reflect = Simulate.simulate_S_xi_lite( dt, N, model_S, model_xi, model_mu, S0, X0, W, b, factor_multiplier, dist_multiplier, proj_scale, rho_star, epsmu_star, X_interior, reflect) if reflect: plot_fname = f'{out_dir}simulation_{str(train_rand_seed)}' + \ f'_{str(sim_rand_seed)}_reflect_{str(n_reflect)}.png' data_fname = f'{out_dir}simulation_{str(train_rand_seed)}' + \ f'_{str(sim_rand_seed)}_reflect_{str(n_reflect)}.csv' # save simulated data out_data = np.vstack((st, xit)) columns = ['S'] + ['xi' + str(i) for i in range(1, len(X0)+1)] out_data = pd.DataFrame(data=out_data.T, columns=columns) out_data.to_csv(data_fname, index=False) # plot utils.PlotLib.plot_simulated_xi(st, xit, X, plot_fname) return st, xit, mus_sim, vols_sim @staticmethod def scale_drift_diffusion(x, mu_residual, sigma_tilde, W, b, dist_multiplier, proj_scale, rho_star, epsmu_star, x_interior, mu_base): """ Scale the drift and diffusion functions. Parameters __________ Returns _______ """ n_dim = W.shape[1] # calculate the distance of the data point to each boundary dist_x = np.abs(W.dot(x) - b) / np.linalg.norm(W, axis=1) # calculate the normalised distance indicators epsilon_sigma = PrepTrainData.normalise_dist_diffusion( dist_x, dist_multiplier, proj_scale) # sort by distance and get first n_dim closest ones idxs_sorted_eps = np.argsort(epsilon_sigma) idxs_used_eps = idxs_sorted_eps[:n_dim] Wd = W[idxs_used_eps] epsilond_sigma = epsilon_sigma[idxs_used_eps] # scale the diffusions if np.max(epsilond_sigma) < 1e-8: # if the anchor point is on a corner Omega = np.zeros((n_dim, n_dim)) else: # if the anchor point is not on the corner # compute new bases V = np.linalg.qr(Wd.T)[0].T Omega = np.diag(np.sqrt(epsilond_sigma)).dot(V) mat_a = Omega.T.dot(sigma_tilde).dot(sigma_tilde.T).dot(Omega) mat_vol = np.linalg.cholesky(mat_a) # scale the drifts ## compute drift mu_tilde = mu_base * mu_residual ## compute correction directions corr_dirs_x = x_interior - x[None, :] epsmu_x = PrepTrainData.normalise_dist_drift( dist_x, rho_star, epsmu_star) mu_tilde_inner_W = W.dot(mu_tilde) corr_dir_inner_W = np.sum(corr_dirs_x * W, axis=-1) weights_corr_dir = np.maximum(-mu_tilde_inner_W-epsmu_x, 0.) /\ corr_dir_inner_W ## compute the corrected drift mu = mu_tilde + np.sum(corr_dirs_x * weights_corr_dir[:, None], axis=0) return mu, mat_vol @staticmethod def reflect_data(x0, x1, W, b): mask_arb = W.dot(x1) - b < 0 # reflect data if there is arbitrage if np.any(mask_arb): if np.sum(mask_arb) > 1: print('Break more than one boundaries, move to the closest ' 'boundary.') wi = W[mask_arb] bi = b[mask_arb] candidates = ((bi + 1e-6 - wi.dot(x0))/wi.dot((x1-x0))).\ reshape((-1, 1)) * (x1 - x0) + x0 idx_first_qualified = np.where( np.all(candidates.dot(W.T) - b[None,:] >= 0, axis=1))[0][0] x2 = candidates[idx_first_qualified] else: wi = W[mask_arb] bi = b[mask_arb] t = bi - wi.dot(x1) x2 = x1 + 2 * t * wi # if the reflected data point breaks any arbitrage bounds if np.any(x2.dot(W.T) - b < 0): print('Reflect failed, move back to boundary.') t = (bi - wi.dot(x1)) / (wi.dot(x1 - x0)) x2 = x0 + t * (x1-x0) return x2 else: return x1

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304748bc2e685abf6332deae76ea083ce94212c7

2,641

py

Python

Rock-paper-scissor/jokenpo.py

Thdahwache/python-learning-trail

357d5d1c6cfa966e347cb5a06cb1f90e3a9ed81d

[ "MIT" ]

null

Rock-paper-scissor/jokenpo.py

Thdahwache/python-learning-trail

357d5d1c6cfa966e347cb5a06cb1f90e3a9ed81d

[ "MIT" ]

null

Rock-paper-scissor/jokenpo.py

Thdahwache/python-learning-trail

357d5d1c6cfa966e347cb5a06cb1f90e3a9ed81d

[ "MIT" ]

null

import random pedra = ''' _______ ---' ____) (_____) (_____) (____) ---.__(___) ''' papel = ''' _______ ---' ____)____ ______) _______) _______) ---.__________) ''' tesoura = ''' _______ ---' ____)____ ______) __________) (____) ---.__(___) ''' # Write your code below this line 👇 #Escolha player e computador escolha = None v = 0 d = 0 e = 0 #Outcomes #pedra def pedra_empate(): global e print( f"Você escolheu:\n\n {pedra} \n\n O jogo escolheu: {pedra} \n\n Vocês empataram!") e += 1 def pedra_derrota(): global d print( f"Você escolheu:\n\n {pedra} \n\n O jogo escolheu: {papel} \n\n Você perdeu!") d += 1 def pedra_vitoria(): global v print( f"Você escolheu:\n\n {pedra} \n\n O jogo escolheu: {tesoura} \n\n Você ganhou!") v += 1 #papel def papel_empate(): global e print( f"Você escolheu:\n\n {papel} \n\n O jogo escolheu: {papel} \n\n Vocês empataram!") e += 1 def papel_derrota(): global d print( f"Você escolheu:\n\n {papel} \n\n O jogo escolheu: {tesoura} \n\n Você perdeu!") d += 1 def papel_vitoria(): global v print( f"Você escolheu:\n\n {papel} \n\n O jogo escolheu: {pedra} \n\n Você ganhou!") v += 1 #tesoura def tesoura_empate(): global e print( f"Você escolheu:\n\n {tesoura} \n\n O jogo escolheu: {tesoura} \n\n Vocês empataram!") e += 1 def tesoura_derrota(): global d print( f"Você escolheu:\n\n {tesoura} \n\n O jogo escolheu: {pedra} \n\n Você perdeu!") d += 1 def tesoura_vitoria(): global v print( f"Você escolheu:\n\n {tesoura} \n\n O jogo escolheu: {papel} \n\n Você ganhou!") v += 1 while escolha != "sair": escolha = input( "Pedra, papel ou tesoura? Digite sair para terminar ").lower() computador = random.randint(0, 2) if escolha == "pedra": escolha = 0 elif escolha == "papel": escolha = 1 elif escolha == "tesoura": escolha = 2 elif escolha == "sair": print( f"\nVocê ganhou {v} vezes, perdeu {d} vezes e empatou {e} com o computador, parabéns!") break #Lista e resultado final pedra_resultados = [pedra_empate, pedra_derrota, pedra_vitoria] papel_resultados = [papel_vitoria, papel_empate, papel_vitoria] tesoura_resultados = [tesoura_derrota, tesoura_vitoria, tesoura_empate] resultados = [pedra_resultados, papel_resultados, tesoura_resultados] fim = resultados[escolha][computador]() print("")

19.857143

99

0.584248

338

2,641

4.156805

0.180473

0.038434

0.064057

0.115302

0.495374

0.478292

0.414235

0.397865

0.260498

0

0.008915

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2,641

132

100

20.007576

0.72732

0.040894

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0

null

0

null

0

1

0

30508657d97ad92eed583819ab3856787d5dae52

1,061

py

Python

entity/messagerepo.py

LordOfNightmares/virtual-client-assistant

772282434472fc44caac0cd21f972d6a5cc5c5b8

[ "Apache-2.0" ]

null

entity/messagerepo.py

LordOfNightmares/virtual-client-assistant

772282434472fc44caac0cd21f972d6a5cc5c5b8

[ "Apache-2.0" ]

null

entity/messagerepo.py

LordOfNightmares/virtual-client-assistant

772282434472fc44caac0cd21f972d6a5cc5c5b8

[ "Apache-2.0" ]

null

from entity.message import Message from .databaserepo import DatabaseRepo class MessageDbRepo(DatabaseRepo): def __init__(self): super().__init__("Messages") def all(self, cid): query = "SELECT * FROM " + self.table + " WHERE conversation_id = '" + str(cid) + "'" m_results = self.db.select(query) if m_results: m = [Message(*m_res[1:-3], m_res[0]) for m_res in m_results] return m else: return None def get(self, id): current = super().get(id) m = Message(*current[:-3], id) m.created, m.modified, m.accessed = current[3], current[4], current[5] return m def last(self, cid): query = "SELECT * FROM " + self.table + " WHERE conversation_id = '" + str( cid) + "' ORDER BY created DESC LIMIT 0,1" m_results = self.db.select(query) if m_results: m_results = m_results[0] m = Message(*m_results[1:-3], m_results[0]) return m else: return None

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93

0.556079

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1,061

4.145985

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0.126761

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1,061

33

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0

0.428571

0

null

0

null

0

1

0

3052b87761414206a80d473cc44e97d3436908e3

5,043

py

Python

src/utils/interface_audio_io.py

waverDeep/WaveBYOL

ab062c26598e0fa6ab8426498f9920048988b5c1

[ "MIT" ]

1

2022-03-15T00:00:57.000Z

src/utils/interface_audio_io.py

waverDeep/WaveBYOL

ab062c26598e0fa6ab8426498f9920048988b5c1

[ "MIT" ]

null

src/utils/interface_audio_io.py

waverDeep/WaveBYOL

ab062c26598e0fa6ab8426498f9920048988b5c1

[ "MIT" ]

null

import soundfile as sf from tqdm import tqdm import src.utils.interface_file_io as io import librosa import wave import multiprocessing import src.utils.interface_multiprocessing as mi import torchaudio import numpy as np import torch.nn.functional as F import torch torchaudio.set_audio_backend("sox_io") def audio_loader(audio_file): return torchaudio.load(audio_file) def cutoff(waveform, sample_rate, start, end): cut = waveform[0][int(start*sample_rate): int(end*sample_rate+1)] return cut.unsqueeze(0) def random_cutoff(waveform, audio_window, index=None): audio_length = waveform.shape[1] if index is None: random_index = np.random.randint(audio_length - audio_window + 1) else: random_index = index cutoff_waveform = waveform[:, random_index: random_index + audio_window] return cutoff_waveform def audio_adjust_length(x, audio_window, fit=False): length_adj = audio_window - len(x[0]) if length_adj > 0: half_adj = length_adj // 2 x = F.pad(x, (half_adj, length_adj - half_adj)) audio_length = len(x[0]) if fit: random_index = np.random.randint(audio_length - audio_window + 1) x = x[:, random_index: random_index + audio_window] return x def audio_auto_trim(waveform, vad, audio_window=None): waveform = vad(waveform) waveform = torch.flip(waveform, [0, 1]) waveform = vad(waveform) waveform = torch.flip(waveform, [0, 1]) if audio_window is not None: while True: audio_length = waveform.shape[1] if audio_length < audio_window: waveform = torch.cat((waveform, waveform), 1) else: break return waveform def resampling_audio(file, original_sampling_rate=44100, resampling_rate=16000): waveform, sampling_rate = librosa(file, original_sampling_rate) resample_waveform = librosa.resample(waveform, original_sampling_rate, resampling_rate) return resample_waveform def resampling_audio_list(directory_list, new_file_path, file_extension, original_sampling_rate, resampling_rate): for dir_index, directory in directory_list: file_list = io.get_all_file_path(directory, file_extension=file_extension) for file_index, file in tqdm(file_list, desc=directory): resample_waveform = resampling_audio(file, original_sampling_rate=original_sampling_rate, resampling_rate=resampling_rate) filename = io.get_pure_filename(file) file_path = "{}/{}".format(new_file_path, filename) sf.write(file_path, resample_waveform, resampling_rate) # The parameters are prerequisite information. More specifically, # channels, bit_depth, sampling_rate must be known to use this function. def pcm2wav(pcm_file, wav_file=None, channels=1, bit_depth=16, sampling_rate=16000): # Check if the options are valid. if bit_depth % 8 != 0: raise ValueError("bit_depth " + str(bit_depth) + " must be a multiple of 8.") if wav_file is None: wav_file = pcm_file.replace("pcm", "wav") # Read the .pcm file as a binary file and store the data to pcm_data with open(pcm_file, 'rb') as opened_pcm_file: pcm_data = opened_pcm_file.read() obj2write = wave.open(wav_file, 'wb') obj2write.setnchannels(channels) obj2write.setsampwidth(bit_depth // 8) obj2write.setframerate(sampling_rate) obj2write.writeframes(pcm_data) obj2write.close() def distributed_pcm2wav(pcm_file): print("start data distribution...") for pcm_index, pcm in enumerate(pcm_file): pcm2wav(pcm) print("end data distribution...") class MelSpectrogramLibrosa: """Mel spectrogram using librosa.""" def __init__(self, fs=16000, n_fft=1024, shift=160, n_mels=64, fmin=60, fmax=7800): self.fs, self.n_fft, self.shift, self.n_mels, self.fmin, self.fmax = fs, n_fft, shift, n_mels, fmin, fmax self.mfb = librosa.filters.mel(sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) def __call__(self, audio): X = librosa.stft(np.array(audio), n_fft=self.n_fft, hop_length=self.shift) return torch.tensor(np.matmul(self.mfb, np.abs(X) ** 2 + np.finfo(float).eps)) if __name__ == '__main__': task = "" if task == "resampling": directory_path = ['../../dataset/UrbanSound8K/audio'] new_save_directory = '../../dataset/UrbanSound8K/audio_16k/' resampling_audio_list(directory_path, new_save_directory, 'wav', 44100, 16000) elif task == 'pcm2wav': input_dir = "../../dataset/KsponSpeech/train" file_extension = "pcm" divide_num = multiprocessing.cpu_count() - 1 file_list = io.get_all_file_path(input_dir, file_extension) file_list = io.list_divider(divide_num, file_list) print(len(file_list)) processes = mi.setup_multiproceesing(distributed_pcm2wav, data_list=file_list) mi.start_multiprocessing(processes)

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0.029703

0

null

0

null

0

1

0

3052bcfb77f4439da2dc55fb76a58f27be220c78

6,318

py

Python

diffusion_utils/utils.py

GallagherCommaJack/diffusion-utils

407e350ec62e204c10366afe66b793f1d1276c1e

[ "MIT" ]

2

2022-01-16T16:16:52.000Z

2022-03-01T11:51:35.000Z

diffusion_utils/utils.py

GallagherCommaJack/diffusion-utils

407e350ec62e204c10366afe66b793f1d1276c1e

[ "MIT" ]

null

diffusion_utils/utils.py

GallagherCommaJack/diffusion-utils

407e350ec62e204c10366afe66b793f1d1276c1e

[ "MIT" ]

null

import math from typing import MutableSequence, Optional, TypeVar, Union import torch from torch import nn from torch import Tensor from torch.types import Number from einops import repeat T = TypeVar("T") def exists(val: Optional[T]) -> bool: return val is not None def default(val: Optional[T], d: T) -> T: return d if val is None else val def cast_tuple(val, depth: int = 1): return val if isinstance(val, tuple) else (val,) * depth class DropKwargs(nn.Module): def __init__(self, inner: nn.Module): super().__init__() self.inner = inner def forward(self, *args, **kwargs): return self.inner(*args) class SequentialKwargs(nn.Module): def __init__(self, *modules: nn.Module): super().__init__() self.inner = nn.ModuleList(modules) def forward(self, x, **kwargs): out = x for module in self.inner: out = module(out, **kwargs) return out TensorSeq = MutableSequence[Tensor] class PushBack(nn.Module): def __init__(self, inner: nn.Module): super().__init__() self.inner = inner def forward( self, xtup: TensorSeq, ) -> TensorSeq: x = self.inner(*xtup) xtup.append(x) xtup[0] = x return xtup class PopBack(nn.Module): def __init__(self, inner: nn.Module, key: str): super().__init__() self.inner = inner self.key = key def forward(self, xtup: TensorSeq) -> TensorSeq: kwargs = {self.key: xtup.pop()} x = self.inner(*xtup, **kwargs) xtup[0] = x return xtup class ApplyMods(nn.Module): def __init__(self, *mods): super().__init__() self.inner = nn.ModuleList(mods) def forward(self, tup: TensorSeq) -> TensorSeq: for i, mod in enumerate(self.inner): tup[i] = mod(tup[i]) return tup class ApplyMod(nn.Module): def __init__(self, inner: nn.Module, ix: int = 0): super().__init__() self.inner = inner self.ix = ix def forward(self, tup: TensorSeq) -> TensorSeq: tup[self.ix] = self.inner(tup[self.ix]) return tup class RetIndex(nn.Module): def __init__(self, ix: int = 0): super().__init__() self.ix = ix def forward(self, tup: TensorSeq) -> Tensor: return tup[self.ix] class ClampWithGrad(torch.autograd.Function): @staticmethod def forward(ctx, input, min, max): ctx.min = min ctx.max = max ctx.save_for_backward(input) return input.clamp(min, max) @staticmethod def backward(ctx, grad_in): (input,) = ctx.saved_tensors return ( grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None, ) clamp_with_grad = ClampWithGrad.apply def clamp_exp( t: torch.Tensor, low: float = math.log(1e-2), high: float = math.log(100), ): return clamp_with_grad(t, low, high).exp() def mk_full(d: int, init: Union[torch.Tensor, Number]): if isinstance(init, torch.Tensor): return init else: return torch.full([d], init) @torch.no_grad() def ema_update(model, averaged_model, decay): """Incorporates updated model parameters into an exponential moving averaged version of a model. It should be called after each optimizer step.""" model_params = dict(model.named_parameters()) averaged_params = dict(averaged_model.named_parameters()) assert model_params.keys() == averaged_params.keys() for name, param in model_params.items(): averaged_params[name].lerp_(param, 1 - decay) model_buffers = dict(model.named_buffers()) averaged_buffers = dict(averaged_model.named_buffers()) assert model_buffers.keys() == averaged_buffers.keys() for name, buf in model_buffers.items(): averaged_buffers[name].copy_(buf) def get_ddpm_schedule(t): """Returns log SNRs for the noise schedule from the DDPM paper.""" return -torch.expm1(1e-4 + 10 * t ** 2).log() def get_alphas_sigmas(log_snrs): """Returns the scaling factors for the clean image and for the noise, given the log SNR for a timestep.""" alphas_squared = log_snrs.sigmoid() return alphas_squared.sqrt(), (1 - alphas_squared).sqrt() def calculate_stats(e): e_mean = e.mean() e_variance = (e - e_mean).pow(2).mean() e_variance_stable = max(e_variance, 1e-5) e_skewness = (e - e_mean).pow(3).mean() / e_variance_stable ** 1.5 e_kurtosis = (e - e_mean).pow(4).mean() / e_variance_stable ** 2 return e_mean, e_variance, e_skewness, e_kurtosis def measure_perf(f): start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() f() # Run some things here end_event.record() torch.cuda.synchronize() # Wait for the events to be recorded! elapsed_time_ms = start_event.elapsed_time(end_event) return elapsed_time_ms def calc_delta(t_in, t_out): return math.pi / 2 * (t_in - t_out) def diffusion_step(z, v, t_in, t_out): delta = calc_delta(t_in, t_out) z = torch.cos(delta) * z - torch.sin(delta) * v return z def calc_v_with_distillation_errors(net, z, t_in, t_out, *args, **kwargs): v = net(z, t_in, *args, **kwargs) with torch.no_grad(): delta = calc_delta(t_in, t_out) t_mid = (t_in + t_out) / 2 z_1 = diffusion_step(z, v, t_in, t_mid) v_2 = net(z_1, t_mid, *args, **kwargs) z_2 = diffusion_step(z_1 < v_2, t_mid, t_out) targets = z / torch.tan(delta) - z_2 / torch.sin(delta) e = v.sub(targets).pow(2).mean(dim=[1, 2, 3]) return v, e def factor_int(n): val = math.ceil(math.sqrt(n)) val2 = int(n / val) while val2 * val != float(n): val -= 1 val2 = int(n / val) return val, val2 def compute_channel_change_mat(io_ratio): base = torch.eye(1) if io_ratio < 1: # reduce channels c_in = int(1 / io_ratio) cmat = repeat(base * io_ratio, "i1 i2 -> i1 (i2 m)", m=c_in) else: c_out = int(io_ratio) cmat = repeat(base, "i1 i2 -> (i1 m) i2", m=c_out) return cmat def max_neg_value(tensor): return -torch.finfo(tensor.dtype).max

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915

6,318

4.102732

0.250273

0.038359

0.03463

0.02797

0.225892

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6,318

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0.426036

0

null

0

null

0

1

0

3053b63c3b7a677ef567ccc253454ec06d5b2791

828

py

Python

FB.py/blog.py

attia7/Test

c74f09816ba2e0798b0533e31ea8b72249dec598

[ "MIT" ]

null

FB.py/blog.py

attia7/Test

c74f09816ba2e0798b0533e31ea8b72249dec598

[ "MIT" ]

11

2020-03-24T17:40:26.000Z

2022-01-13T01:42:38.000Z

FB.py/blog.py

attia7/AttiaGit

c74f09816ba2e0798b0533e31ea8b72249dec598

[ "MIT" ]

null

class Blog: def __init__(self, title, photo, name,date,content): self.title = title self.photo = photo self.name = name self.date = date self.content = content blog1= Blog(title='python bisics', photo='https://images.pexels.com/photos/837140/pexels-photo-837140.jpeg', name='Yasser',date='10-06-2019',content='Hello How r u ?') blog2= Blog(title='python bisics', photo='https://images.pexels.com/photos/837140/pexels-photo-837140.jpeg', name='Mohammed',date='11-16-1979',content='Hello How r u ?') blog3= Blog(title='python bisics', photo='https://images.pexels.com/photos/837140/pexels-photo-837140.jpeg', name='Sara',date='10-06-2019',content='Hi ') print(blog1.name) print(blog2.date) print(blog3.content) blogs=[blog1,blog2,blog3] blogs[2].name='Ali' blogs.remove(blogs[0]) print(blog3.name)

29.571429

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127

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false

0

0.1

0.2

0

null

0

null

0

1

0

3057879143c17360d6498f89a5f3db75d2469ccb

4,075

py

Python

modules/RetrieveResource.py

opentargets/platform-input-support

555c3ed091a7a3a767dc0c37054dbcd369f02252

[ "Apache-2.0" ]

4

2019-03-26T15:54:35.000Z

2021-05-27T13:18:43.000Z

modules/RetrieveResource.py

opentargets/platform-input-support

555c3ed091a7a3a767dc0c37054dbcd369f02252

[ "Apache-2.0" ]

12

2019-04-23T14:45:04.000Z

2022-03-17T09:40:04.000Z

modules/RetrieveResource.py

opentargets/platform-input-support

555c3ed091a7a3a767dc0c37054dbcd369f02252

[ "Apache-2.0" ]

2

2019-06-15T17:21:14.000Z

2021-05-14T18:35:18.000Z

import logging from modules.common.GoogleBucketResource import GoogleBucketResource from modules.common.Utils import Utils from modules.common import create_output_dir, remove_output_dir from yapsy.PluginManager import PluginManager from definitions import PIS_OUTPUT_DIR logger = logging.getLogger(__name__) class RetrieveResource(object): def __init__(self, args, yaml): self.simplePluginManager = PluginManager() self.args = args self.output_dir = args.output_dir if args.output_dir is not None else PIS_OUTPUT_DIR self.yaml = yaml # Warning the user about the gc credential needs for access to GC itself def checks_gc_service_account(self): if self.args.google_credential_key is None: logger.info("Some of the steps might be not work properly due the lack of permissions to access to GCS. " "Eg. Evidence") else: GoogleBucketResource.has_valid_auth_key(self.args.google_credential_key) # Copy the local files to the Google Storage def copy_to_gs(self): if self.args.google_bucket is not None: Utils(self.yaml.config, self.yaml.outputs).gsutil_multi_copy_to(self.args.google_bucket) else: logger.error("Destination bucket info missing") # This function normalise the input inserted by the user. Lower and Upper cases can break the code if # not managed. Eg. SO/so/So -> SO Plugin def normalise_steps(self, steps, all_plugins_available): normalise_steps = [] lowercase_steps = [each_step.lower() for each_step in steps] for plugin in all_plugins_available: if plugin.lower() in lowercase_steps: normalise_steps.append(plugin) lowercase_steps.remove(plugin.lower()) logger.info("Steps not found:\n" + ','.join(lowercase_steps)) return normalise_steps # Extract and check the steps to run def steps(self): all_plugins_available = [] for plugin in self.simplePluginManager.getAllPlugins(): all_plugins_available.append(plugin.name) steps_requested = self.normalise_steps(self.args.steps, all_plugins_available) excluded_requested = self.normalise_steps(self.args.exclude, all_plugins_available) if len(self.args.steps) == 0: plugin_order = list(set(all_plugins_available) - set(excluded_requested)) else: plugin_order = list(set(steps_requested)) logger.info("Steps selected:\n" + ','.join(plugin_order)) return plugin_order # Init yapsy plugin manager def init_plugins(self): # Tell it the default place(s) where to find plugins self.simplePluginManager.setPluginPlaces(["plugins"]) # Load all plugins self.simplePluginManager.collectPlugins() # noinspection PyBroadException # Given a list of steps to run, this procedure executes the selected plugins/step def run_plugins(self): steps_to_execute = self.steps() for plugin_name in steps_to_execute: plugin = self.simplePluginManager.getPluginByName(plugin_name) try: plugin.plugin_object.process(self.yaml[plugin_name.lower()], self.yaml.outputs, self.yaml.config) except Exception as e: logger.info("WARNING Plugin not available {}".format(plugin_name)) logger.info(e) def create_output_structure(self, output_dir): """By default the directories prod and staging are created""" remove_output_dir(output_dir) if self.args.force_clean else logger.info("Warning: Output not deleted.") self.yaml.outputs.prod_dir = create_output_dir(output_dir + '/prod') self.yaml.outputs.staging_dir = create_output_dir(output_dir + '/staging') # Retrieve the resources requested. def run(self): self.create_output_structure(self.output_dir) self.init_plugins() self.checks_gc_service_account() self.run_plugins() self.copy_to_gs()

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null

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3062f03e110c5a790e2aa16881b269fc800f6f09

2,513

py

Python

baidu_API.py

spencerpomme/coconuts-on-fire

407d61b3583c472707a4e7b077a9a3ab12743996

[ "Apache-2.0" ]

1

2015-04-23T11:43:26.000Z

baidu_API.py

spencerpomme/coconuts-on-fire

407d61b3583c472707a4e7b077a9a3ab12743996

[ "Apache-2.0" ]

null

baidu_API.py

spencerpomme/coconuts-on-fire

407d61b3583c472707a4e7b077a9a3ab12743996

[ "Apache-2.0" ]

null

#!python3 """ A simple script that uses Baidu Place API to search certain kinds of place in a range of circular space. This API can be called maximum 2000 times per day. """ import requests, json # import psycopg2 class ConvertFailure(Exception): def __str__(self): return "Convertion Failed." mykey = "IniXfqhsWAyZQpkmh5FtEVv0" # my developer key city = "韶关" place = "公园" coor1 = (39.915, 116.404) coor2 = (39.975, 116.414) radius = 500 # meters city_params = { # parameters for place api 'ak': mykey, 'output': 'json', 'query': place, 'page_size': 10, 'page_num': 0, 'scope': 2, 'region': city } rect_params = { # parameters for place api 'ak': mykey, 'output': 'json', 'query': place, 'page_size': 10, 'page_num': 0, 'scope': 2, 'bounds': "%s, %s, %s, %s" % (*coor1, *coor2), 'location': coor1, 'radius': radius } circ_params = { # parameters for place api 'ak': mykey, 'output': 'json', 'query': place, 'page_size': 10, 'page_num': 0, 'scope': 2, } geocoder_params = { # parameters for geocoder api 'ak': mykey, 'output': 'json', 'address': None } placeAPI = "http://api.map.baidu.com/place/v2/search" geocoder = "http://api.map.baidu.com/geocoder/v2/" res_city = requests.get(placeAPI, params=city_params) res_rect = requests.get(placeAPI, params=rect_params) res_circ = requests.get(placeAPI, params=circ_params) # print(res_city.url) jsonobj = json.loads(res_city.text) print(type(jsonobj)) print(type(res_city.text)) # print(json.dumps(jsonobj, sort_keys=False, indent=4)) # Below this line defines a series of Baidu geo-data API calling functions def addr2coor(addresses: str)->tuple: ''' This function converts addresses to a (longitude, latitude) coordinate. ''' for address in addresses: geocoder_params['address'] = address res = requests.get(geocoder, params=geocoder_params) res.raise_for_status() coor = json.loads(requests.get(geocoder, params=geocoder_params).text) # print(coor) if coor['status'] == 0: location = coor['result']['location'] yield (address, location['lng'], location['lat']) else: raise ConvertFailure def rescounter(function)->tuple: """A addr2coor wraper""" pass if __name__ == '__main__': address_list = ["天安门", "故宫", "奥林匹克公园", "广州塔"] cor = addr2coor(address_list) for item in cor: print(item)

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0

3063873cf1d375e656a6b627f5a8d2ac1ba1cb4a

2,293

py

Python

src/models/evaluate_model.py

ThordurPall/MLOpsExercises-

8714d83477f6132893b74675e529bfeef13ece85

[ "MIT" ]

null

src/models/evaluate_model.py

ThordurPall/MLOpsExercises-

8714d83477f6132893b74675e529bfeef13ece85

[ "MIT" ]

null

src/models/evaluate_model.py

ThordurPall/MLOpsExercises-

8714d83477f6132893b74675e529bfeef13ece85

[ "MIT" ]

1

2021-06-11T12:38:38.000Z

# -*- coding: utf-8 -*- import logging from pathlib import Path import click import torch from classifier import Classifier from torchvision import datasets, transforms @click.command() @click.argument('data_filepath', type=click.Path(), default='data') @click.argument('trained_model_filepath', type=click.Path(), default='models/trained_model.pth') def main(data_filepath, trained_model_filepath): """ Evaluates the neural network using MNIST test data """ logger = logging.getLogger(__name__) logger.info('Evaluating a neural network using MNIST test data') # Load the trained model model = Classifier() project_dir = Path(__file__).resolve().parents[2] state_dict = torch.load(project_dir.joinpath(trained_model_filepath)) model.load_state_dict(state_dict) # Define a transform to normalize the data transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ]) # Load the test data test_set = datasets.MNIST(project_dir.joinpath(data_filepath), download=False, train=False, transform=transform) batch_size = 64 test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=True) # Evaluate test performance test_correct = 0 # Turn off gradients for validation, saves memory and computations with torch.no_grad(): model.eval() # Sets the model to evaluation mode # Run through all the test points for images, labels in test_loader: # Forward pass log_ps = model(images) ps = torch.exp(log_ps) # Keep track of how many are correctly classified top_p, top_class = ps.topk(1, dim=1) equals = top_class == labels.view(*top_class.shape) test_correct += equals.type(torch.FloatTensor).sum().item() test_accuracy = test_correct/len(test_set) logger.info(str("Test Accuracy: {:.3f}".format(test_accuracy))) if __name__ == '__main__': log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_fmt) main()

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null

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3068043469fb1de1fd2079110d1f76aaf77142ca

3,253

py

Python

adminwindow.py

gaurav0810-ga/Spot-Counselling-Management-System

34dffa34f1ffe016a912dc3cfcba6cd3f74eee18

[ "Apache-2.0" ]

null

adminwindow.py

gaurav0810-ga/Spot-Counselling-Management-System

34dffa34f1ffe016a912dc3cfcba6cd3f74eee18

[ "Apache-2.0" ]

null

adminwindow.py

gaurav0810-ga/Spot-Counselling-Management-System

34dffa34f1ffe016a912dc3cfcba6cd3f74eee18

[ "Apache-2.0" ]

null

from tkinter import* #=====importing self created module which will show the registartion form=======# import registrationform #=====importing self created module which will help in deleting student record from data base======# import deletestudent #=============importing selfcreated update student record ==============# import updatestudent #to import jpg image import allotment #it will import module #importing view database import tables from PIL import ImageTk #it will import Pillow librart import smtplib from email.message import EmailMessage import sqlite3 import allotedstudentrecords def student(): admin_window=Tk() admin_window.iconbitmap("student.ico") bg=ImageTk.PhotoImage(file="./images/login.jpg") bg_image=Label(admin_window,image=bg) bg_image.place(x=0,y=0,relwidth=1,relheight=1) width = admin_window.winfo_screenwidth() height = admin_window.winfo_screenheight() admin_window.geometry(f'{width}x{height-100}+0+0') admin_window.resizable(FALSE,FALSE) admin_window.title("Student Registration system") admin_text=Label(text="Spot Counslling Registration And Allotment System",font=("bold",30)).pack(side='top',pady=40) # admin_text.place(x=450,y=40) #======================registration window-=====================# Register_button=Button(admin_window,text="Register Student",relief=GROOVE,width=15,height=5,font=("bold", 10),command=registrationform.register,bg='#BB001B',fg='white') Register_button.place(x=70,y=150) #-=====================student record====================# Delete_student=Button(admin_window,text="Delete Student",relief=GROOVE,width=15,height=5,font=("bold", 10),command=deletestudent.delete_student,bg='#BB001B',fg='white') Delete_student.place(x=70,y=350) #============================view databasetable=========================# View_table_button=Button(admin_window,text="View Registerd \n\n Students Records",relief=GROOVE,width=15,height=5,font=("bold", 10),command=tables.viewdatabase,bg='#BB001B',fg='white') View_table_button.place(x=70,y=550) #=================================update student ==================# update_button=Button(admin_window,text="Update Student",relief=GROOVE,width=15,height=5,font=("bold", 10),command=updatestudent.updatefunc,bg='#BB001B',fg='white') update_button.place(x=1150,y=350) #===========================student selection table================# Student_selection_button=Button(admin_window,text="Seat Allotment",relief=GROOVE,width=15,height=5,font=("bold", 10),command=allotment.selection,bg='#BB001B',fg='white') Student_selection_button.place(x=1150,y=150) #========================view alloted student records======================# View_Alloted_button=Button(admin_window,text="View Alloted \n\n Students Records",relief=GROOVE,width=15,height=5,font=("bold", 10),command=allotedstudentrecords.viewdatabase,bg='#BB001B',fg='white') View_Alloted_button.place(x=1150,y=550) copy=Label(admin_window,text='Developed By Gaurav And Team ©',font=('bold',8),fg='white',bg='#01796F') copy.pack(side='bottom',fill='x') # admin_window.destroy() admin_window.mainloop() # student()

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null

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null

0

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3069d018acdd28b4231703721ede5dfa5cd4942f

4,665

py

Python

dnachisel/builtin_specifications/AvoidBlastMatches.py

simone-pignotti/DnaChisel

b7f0f925c9daefcc5fec903a13cfa74c3b726a7a

[ "MIT" ]

124

2017-11-14T14:42:25.000Z

2022-03-31T08:02:07.000Z

dnachisel/builtin_specifications/AvoidBlastMatches.py

simone-pignotti/DnaChisel

b7f0f925c9daefcc5fec903a13cfa74c3b726a7a

[ "MIT" ]

65

2017-11-15T07:25:38.000Z

2022-01-31T10:38:45.000Z

dnachisel/builtin_specifications/AvoidBlastMatches.py

simone-pignotti/DnaChisel

b7f0f925c9daefcc5fec903a13cfa74c3b726a7a

[ "MIT" ]

31

2018-10-18T12:59:47.000Z

2022-02-11T16:54:43.000Z

"""Implementation of AvoidBlastMatches.""" from ..Specification import Specification, SpecEvaluation # from .VoidSpecification import VoidSpecification from ..biotools import blast_sequence from ..Location import Location class AvoidBlastMatches(Specification): """Enforce that the sequence has no BLAST matches with a given database. WARNING: try using AvoidMatches instead, it is much better!! Uses NCBI Blast+. Only local BLAST is supported/tested as for now Parameters ---------- blast_db Path to a local BLAST database. These databases can be obtained with NCBI's `makeblastdb`. Omit the extension, e.g. `ecoli_db/ecoli_db`. word_size Word size used by the BLAST algorithm perc_identity Minimal percentage of identity for BLAST matches. 100 means that only perfect matches are considered. num_alignments Number alignments num_threads Number of threads/CPU cores to use for the BLAST algorithm. min_align_length Minimal length that an alignment should have to be considered. """ priority = -2 best_possible_score = 0 blasts_paths = {} def __init__( self, blast_db=None, sequences=None, word_size=4, perc_identity=100, num_alignments=100000, num_threads=3, min_align_length=20, ungapped=True, e_value=1e80, culling_limit=1, location=None, ): """Initialize.""" self.blast_db = blast_db self.sequences = sequences self.word_size = word_size self.perc_identity = perc_identity self.num_alignments = num_alignments self.num_threads = num_threads self.min_align_length = min_align_length self.location = Location.from_data(location) self.e_value = e_value self.ungapped = ungapped self.culling_limit = culling_limit def initialized_on_problem(self, problem, role=None): return self._copy_with_full_span_if_no_location(problem) def evaluate(self, problem): """Score as (-total number of blast identities in matches).""" location = self.location if location is None: location = Location(0, len(problem.sequence)) sequence = location.extract_sequence(problem.sequence) blast_record = blast_sequence( sequence, blast_db=self.blast_db, subject_sequences=self.sequences, word_size=self.word_size, perc_identity=self.perc_identity, num_alignments=self.num_alignments, num_threads=self.num_threads, ungapped=self.ungapped, e_value=self.e_value, culling_limit=self.culling_limit, task="megablast" ) if isinstance(blast_record, list): alignments = [ alignment for rec in blast_record for alignment in rec.alignments ] else: alignments = blast_record.alignments query_hits = [ ( min(hit.query_start, hit.query_end) + location.start - 1, max(hit.query_start, hit.query_end) + location.start, 1 - 2 * (hit.query_start > hit.query_end), hit.identities, ) for alignment in alignments for hit in alignment.hsps ] locations = sorted( [ (start, end, ids) for (start, end, strand, ids) in query_hits if (end - start) >= self.min_align_length ] ) score = -sum([ids for start, end, ids in locations]) locations = [Location(start, end) for start, end, ids in locations] if locations == []: return SpecEvaluation( self, problem, score=1, message="Passed: no BLAST match found" ) return SpecEvaluation( self, problem, score=score, locations=locations, message="Failed - %s matches at %s" % (len(locations), locations), ) def localized(self, location, problem=None, with_righthand=True): """Localize the evaluation.""" new_location = self.location.overlap_region(location) if new_location is None: return None new_location = location.extended( self.min_align_length - 1, right=with_righthand ) return self.copy_with_changes(location=new_location) def feature_label_parameters(self): return [self.blast_db]

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null

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null

0

1

0

3069d773ef41f354c54cc922824a8eac7764671a

8,769

py

Python

filesysdb/__init__.py

fictorial/filesysdb

bbf1e32218b71c7c15c33ada660433fffc6fa6ab

[ "MIT" ]

2

2016-06-25T16:07:09.000Z

2020-01-18T01:56:30.000Z

filesysdb/__init__.py

fictorial/filesysdb

bbf1e32218b71c7c15c33ada660433fffc6fa6ab

[ "MIT" ]

null

filesysdb/__init__.py

fictorial/filesysdb

bbf1e32218b71c7c15c33ada660433fffc6fa6ab

[ "MIT" ]

null

from aadict import aadict from cachetools import LRUCache import ujson as json import regex from shortuuid import uuid from functools import wraps from glob import glob from time import time import logging import os import shutil import unicodedata _logger = logging.getLogger(__name__) _basepath = None _serialize = None _deserialize = None _ext = None _db = aadict() class UniqueConstraintError(ValueError): pass def normalize_text(text, lcase=True): text = str(text).strip() if lcase: text = text.lower() text = unicodedata.normalize('NFKD', text) text = regex.subn(r'\p{P}+', '', text)[0] return text.encode('ascii', 'ignore').decode() def bench(fn): @wraps(fn) def wrapper(*args, **kwargs): start = time() ret = fn(*args, **kwargs) end = time() _logger.debug('function %s took %g secs', fn.__name__, end - start) return ret return wrapper def object_path(collection, id): """Returns path to the backing file of the object with the given ``id`` in the given ``collection``. Note that the ``id`` is made filesystem-safe by "normalizing" its string representation.""" _logger.debug(type(id)) _logger.debug(id) if isinstance(id, dict) and 'id' in id: id = id['id'] normalized_id = normalize_text(str(id), lcase=False) return os.path.join(_basepath, collection, '%s.%s' % (normalized_id, _ext)) def collection_path(collection): """Returns the base path to the ``collection``""" return os.path.join(_basepath, collection) def load_object_at_path(path): """Load an object from disk at explicit path""" with open(path, 'r') as f: data = _deserialize(f.read()) return aadict(data) def load_object(collection, id): """Load an object from disk at path based on its ``collection`` and ``id``.""" path = object_path(collection, id) return load_object_at_path(path) def get_object(collection, id): """Get an object by its ``collection``-unique ``id``""" return _db[collection].cache[id] def add_collection(collection, cache_size=1000, cache_cls=LRUCache, **cache_args): """Add a collection named ``collection``.""" assert collection not in _db cache = cache_cls(maxsize=cache_size, missing=lambda id: load_object(collection, id), **cache_args) _db[collection] = aadict(cache=cache, indexes={}) def _clear(): _db.clear() def prepare(base_path='data', serialize=json.dumps, deserialize=json.loads, file_ext='json'): """After you have added your collections, prepare the database for use.""" global _basepath, _deserialize, _serialize, _ext _basepath = base_path assert callable(serialize) assert callable(deserialize) _serialize = serialize _deserialize = deserialize _ext = file_ext _logger.debug('preparing with base path %s and file ext %s', _basepath, _ext) assert len(_db) for collection in _db.keys(): c_path = collection_path(collection) os.makedirs(c_path, exist_ok=True) _logger.info('collection "%s": %d objects', collection, object_count(collection)) def object_count(collection): """Returns the number of objects in the given ``collection``.""" return len(glob('%s/*.%s' % (collection_path(collection), _ext))) def each_object(collection): """Yields each object in the given ``collection``. The objects are loaded from cache and failing that, from disk.""" c_path = collection_path(collection) paths = glob('%s/*.%s' % (c_path, _ext)) for path in paths: yield load_object_at_path(path) def each_object_id(collection): """Yields each object ID in the given ``collection``. The objects are not loaded.""" c_path = collection_path(collection) paths = glob('%s/*.%s' % (c_path, _ext)) for path in paths: match = regex.match(r'.+/(.+)\.%s$' % _ext, path) yield match.groups()[0] @bench def save_object(collection, obj): """Save an object ``obj`` to the given ``collection``. ``obj.id`` must be unique across all other existing objects in the given collection. If ``id`` is not present in the object, a *UUID* is assigned as the object's ``id``. Indexes already defined on the ``collection`` are updated after the object is saved. Returns the object. """ if 'id' not in obj: obj.id = uuid() id = obj.id path = object_path(collection, id) temp_path = '%s.temp' % path with open(temp_path, 'w') as f: data = _serialize(obj) f.write(data) shutil.move(temp_path, path) if id in _db[collection].cache: _db[collection].cache[id] = obj _update_indexes_for_mutated_object(collection, obj) return obj @bench def delete_object(collection, obj): try: os.remove(object_path(collection, obj)) del _db[collection].cache[obj.id] except: pass _update_indexes_for_deleted_object(collection, obj) def indexed_value(index, obj): values = [obj.get(f) for f in index.fields] if callable(index.transformer): values = index.transformer(values) k = json.dumps(values) return k.lower() if index.case_insensitive else k @bench def add_index(collection, name, fields, transformer=None, unique=False, case_insensitive=False): """ Add a secondary index for a collection ``collection`` on one or more ``fields``. The values at each of the ``fields`` are loaded from existing objects and their object ids added to the index. You can later iterate the objects of an index via ``each_indexed_object``. If you update an object and call ``save_object``, the index will be updated with the latest values from the updated object. If you delete an object via ``delete_object``, the object will be removed from any indexes on the object's collection. If a function is provided for ``transformer``, the values extracted from each object in the collection will be passed to the ``transformer``. The ``transformer`` should return a list of values that will go into the index. If ``unique`` is true, then there may only be at most one object in the collection with a unique set of values for each the ``fields`` provided. If ``case_insensitive`` is true, then the value stored in the index will be lower-cased and comparisons thereto will be lower-cased as well. """ assert len(name) > 0 assert len(fields) > 0 indexes = _db[collection].indexes index = indexes.setdefault(name, aadict()) index.transformer = transformer index.value_map = {} # json([value]) => set(object_id) index.unique = unique index.case_insensitive = case_insensitive index.fields = fields for obj in each_object(collection): _add_to_index(index, obj) _logger.info('added %s, %s index to collection %s on fields: %s', 'unique' if unique else 'non-unique', 'case-insensitive' if case_insensitive else 'case-sensitive', collection, ', '.join(fields)) def _add_to_index(index, obj): """Adds the given object ``obj`` to the given ``index``""" id_set = index.value_map.setdefault(indexed_value(index, obj), set()) if index.unique: if len(id_set) > 0: raise UniqueConstraintError() id_set.add(obj.id) def _remove_from_index(index, obj): """Removes object ``obj`` from the ``index``.""" try: index.value_map[indexed_value(index, obj)].remove(obj.id) except KeyError: pass def each_indexed_object(collection, index_name, **where): """Yields each object indexed by the index with name ``name`` with ``values`` matching on indexed field values.""" index = _db[collection].indexes[index_name] for id in index.value_map.get(indexed_value(index, where), []): yield get_object(collection, id) def _update_indexes_for_mutated_object(collection, obj): """If an object is updated, this will simply remove it and re-add it to the indexes defined on the collection.""" for index in _db[collection].indexes.values(): _remove_from_index(index, obj) _add_to_index(index, obj) def _update_indexes_for_deleted_object(collection, obj): """If an object is deleted, it should no longer be indexed so this removes the object from all indexes on the given collection.""" for index in _db[collection].indexes.values(): _remove_from_index(index, obj)

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306d9087bb69542cb8af6dee2a42b1098927e7a0

1,192

py

Python

packages/postgres-database/src/simcore_postgres_database/migration/versions/39fa67f45cc0_adds_table_for_scicrunch_rrids.py

colinRawlings/osparc-simcore

bf2f18d5bc1e574d5f4c238d08ad15156184c310

[ "MIT" ]

25

2018-04-13T12:44:12.000Z

2022-03-12T15:01:17.000Z

packages/postgres-database/src/simcore_postgres_database/migration/versions/39fa67f45cc0_adds_table_for_scicrunch_rrids.py

colinRawlings/osparc-simcore

bf2f18d5bc1e574d5f4c238d08ad15156184c310

[ "MIT" ]

2,553

2018-01-18T17:11:55.000Z

2022-03-31T16:26:40.000Z

packages/postgres-database/src/simcore_postgres_database/migration/versions/39fa67f45cc0_adds_table_for_scicrunch_rrids.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

20

2018-01-18T19:45:33.000Z

2022-03-29T07:08:47.000Z

"""Adds table for scicrunch rrids Revision ID: 39fa67f45cc0 Revises: 3452ca7b13e9 Create Date: 2020-12-15 18:16:03.581479+00:00 """ import sqlalchemy as sa from alembic import op # revision identifiers, used by Alembic. revision = "39fa67f45cc0" down_revision = "3452ca7b13e9" branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table( "scicrunch_resources", sa.Column("rrid", sa.String(), nullable=False), sa.Column("name", sa.String(), nullable=False), sa.Column("description", sa.String(), nullable=True), sa.Column( "creation_date", sa.DateTime(), server_default=sa.text("now()"), nullable=False, ), sa.Column( "last_change_date", sa.DateTime(), server_default=sa.text("now()"), nullable=False, ), sa.PrimaryKeyConstraint("rrid"), ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table("scicrunch_resources") # ### end Alembic commands ###

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0

null

0

null

0

1

0

307114b26313aa3265e1bd2c48447893e64378ae

2,541

py

Python

conexionbasedato.py

feedesa/MyPythonScripts

66f06f9d44ea6c76cfadb1a620bb468176beefe0

[ "MIT" ]

null

conexionbasedato.py

feedesa/MyPythonScripts

66f06f9d44ea6c76cfadb1a620bb468176beefe0

[ "MIT" ]

null

conexionbasedato.py

feedesa/MyPythonScripts

66f06f9d44ea6c76cfadb1a620bb468176beefe0

[ "MIT" ]

null

import pymysql from tkinter import messagebox class Socios(): def abrir(self): bbdd= pymysql.connect( host= "localhost", user= "root", passwd="", db= "ejemplo1") return bbdd def alta(self,datos): ''' datos[0]: id datos[1]: nombre ''' bbdd=self.abrir() cursor=bbdd.cursor() sql = "INSERT INTO Socios (NOMBRE, CUOTAPAGA)\ values('{}','{}')".format(datos[0],datos[1]) print (sql) cursor.execute(sql) bbdd.commit() messagebox.showinfo(message = "registro exitoso", title = "Aviso") # except: # bbdd.rollback() # messagebox.showinfo(message= "No registrado", title = "Aviso" ) bbdd.close() def mostrarlistadosocio(self): bbdd= self.abrir() cursor=bbdd.cursor() sql="SELECT * FROM socios" cursor.execute(sql) datoslistadocompleto= cursor.fetchall() bbdd.commit() bbdd.close() # for lista in datoslistadocompleto: # print(lista) return datoslistadocompleto # def editarTabla(self, a_editar): # bbdd= pymysql.connect( host= "localhost", user= "root", passwd="", db= "ejemplo1") # cursor= bbdd.cursor() # sql="ALTER TABLE SOCIOS AUTO_INCREMENT = 1" # bbdd.commit() # cursor.execute(sql) # print(sql) # bbdd.close() # sql = "INSERT INTO SOCIOS (nombre, sexo )\ # values( '{}','{}')".format(datos[0],datos[1] ) # print (sql) # #sql="insert into articulos(descripcion, precio) values (%s,%s)" # try: # cursor.execute(sql) # bbdd.commit() # #messagebox.showinfo(message = "registro exitoso", title = "Aviso") # except: # bbdd.rollback() # #messagebox.showinfo(message= "No registrado", title = "Aviso" ) # bbdd.close() # bbdd= pymysql.connect( host= "localhost", user= "root", passwd="", db= "ejemplo1") # cursor= bbdd.cursor() # cursor.execute("DELETE FROM SOCIOS WHERE ID= 3") # bbdd.commit() # bbdd.close() # bbdd= pymysql.connect( host= "localhost", user= "root", passwd="", db= "ejemplo1") # cursor= bbdd.cursor() # cursor.execute("ALTER TABLE SOCIOS AUTO_INCREMENT = 1") # # "CREATE TABLE Socios (id INT PRIMARY KEY AUTO_INCREMENT, NOMBRE VARCHAR(50), CUOTAPAGA VARCHAR(2))") # bbdd.commit() # bbdd.close()

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0.04

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null

0

null

0

1

0

30713a8fbba0af913226f90bffb00ec0ccd49f74

1,420

py

Python

HLTrigger/Configuration/python/Tools/dasFileQuery.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

HLTrigger/Configuration/python/Tools/dasFileQuery.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

HLTrigger/Configuration/python/Tools/dasFileQuery.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import sys import json import das_client def dasFileQuery(dataset): query = 'dataset dataset=%s' % dataset host = 'https://cmsweb.cern.ch' # default idx = 0 # default limit = 0 # unlimited debug = 0 # default thr = 300 # default ckey = "" # default cert = "" # default jsondict = das_client.get_data(host, query, idx, limit, debug, thr, ckey, cert) # check if the pattern matches none, many, or one dataset if not jsondict['data'] or not jsondict['data'][0]['dataset']: sys.stderr.write('Error: the pattern "%s" does not match any dataset\n' % dataset) sys.exit(1) return [] elif len(jsondict['data']) > 1: sys.stderr.write('Error: the pattern "%s" matches multiple datasets\n' % dataset) for d in jsondict['data']: sys.stderr.write(' %s\n' % d['dataset'][0]['name']) sys.exit(1) return [] else: # expand the dataset name dataset = jsondict['data'][0]['dataset'][0]['name'] query = 'file dataset=%s' % dataset jsondict = das_client.get_data(host, query, idx, limit, debug, thr, ckey, cert) # parse the results in JSON format, and extract the list of files files = sorted( f['file'][0]['name'] for f in jsondict['data'] ) return files

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0.014418

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1,420

35

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40.571429

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0

0.241379

0

null

0

null

0

1

0

3073c042c76656cc6f035106fb8e66424b847e5f

477

py

Python

00-modules/builtin_modules/pickle_examples.py

cccaaannn/useful_functions

1570cda8c642a39f04ed9f22ebeeab2bfb9e9424

[ "MIT" ]

null

00-modules/builtin_modules/pickle_examples.py

cccaaannn/useful_functions

1570cda8c642a39f04ed9f22ebeeab2bfb9e9424

[ "MIT" ]

null

00-modules/builtin_modules/pickle_examples.py

cccaaannn/useful_functions

1570cda8c642a39f04ed9f22ebeeab2bfb9e9424

[ "MIT" ]

null

import pickle # pickle can serialize python objects data = {1:"hi", 2: "there"} # convert to byte byte_data = pickle.dumps(data) # convert back to python object data2 = pickle.loads(byte_data) # ----------using with files---------- filename = "" # write to a file pickle.dump(data, open(filename, "wb" )) with open(filename, "wb") as f: pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) # read from file unpickled_object = pickle.load(open(filename ,"rb"))

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0

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0

null

0

null

0

1

0

30750377e92de60cf64b84cb659e89e05ec986a3

4,676

py

Python

appsite/scripts/cactus/loaddb.py

inchiresolver/inchiresolver

6b3f080a4364ebe7499298e5a1b3cd4ed165322d

[ "BSD-3-Clause" ]

3

2020-10-22T06:18:17.000Z

2021-03-19T16:49:00.000Z

appsite/scripts/cactus/loaddb.py

inchiresolver/inchiresolver

6b3f080a4364ebe7499298e5a1b3cd4ed165322d

[ "BSD-3-Clause" ]

11

2019-11-01T23:04:31.000Z

2022-02-10T12:32:11.000Z

appsite/scripts/cactus/loaddb.py

inchiresolver/inchiresolver

6b3f080a4364ebe7499298e5a1b3cd4ed165322d

[ "BSD-3-Clause" ]

null

from os import sys, path from resolver.models import * sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) sys.path.append('/home/app') from client.lib.cactus_client import CactusClient def run(): Organization.objects.all().delete() Inchi.objects.all().delete() Publisher.objects.all().delete() EntryPoint.objects.all().delete() EndPoint.objects.all().delete() MediaType.objects.all().delete() client = CactusClient() m1 = MediaType.create( name="text/plain", description="plain text media type" ) m1.save() m2 = MediaType.create( name="image/gif", description="GIF image", ) m2.save() o1 = Organization.create( name="National Institutes of Health", abbreviation="NIH", href="https://www.nih.gov", category="government", parent=None ) o1.save() o2 = Organization.create( name="National Cancer Institute", abbreviation="NCI", href="https://www.cancer.gov", category="government", parent=o1 ) o2.save() p1 = Publisher.create( name="NCI Computer-Aided Drug Design (CADD) Group", category="group", organization=o2 ) p1.save() p2 = Publisher.create( name="Marc Nicklaus", category="person", email="marc.nicklaus@email.com", address="Frederick, MD 21702-1201, USA", href="https://ccr2.cancer.gov/resources/CBL/Scientists/Nicklaus.aspx", orcid="https://orcid.org/0000-0002-4775-7030", organization=o2, parent=p1 ) p2.save() e0 = EntryPoint.create( name="NCI/CADD InChI Resolver", description="Demonstration InChI Resolver of the NCI/CADD group", category="self", href="https://cactus.inchi-resolver.org", entrypoint_href="https://cactus.inchi-resolver.org/_self", publisher=p1 ) e0.save() e1 = EntryPoint.create( name="Chemical Identifier Resolver", description="This service works as a resolver for different chemical structure identifiers and allows " "the conversion of a given structure identifier into another representation or structure " "identifier. It can be used via a web form or a simple URL API.", category="api", href="http://cactus.nci.nih.gov/chemical/structure", publisher=p2, parent=e0 ) e1.save() e2 = EntryPoint.create( name="InChI Trust Root Resolver", description="Root InChI Resolver at InChI Trust", category="resolver", href="http://root.inchi-resolver.org" ) e2.save() x1 = EndPoint.create( entrypoint=e1, category="uritemplate", uri="{+stdinchi|+stdinchikey}/smiles", description="Standard InChI to SMILES conversion", request_methods=['GET'] ) x1.save() x1.accept_header_media_types.add(m1) x1.content_media_types.add(m1) x2 = EndPoint.create( entrypoint=e1, category="uritemplate", uri="{+stdinchi,+stdinchikey}/iupac_name", description="Standard InChI to IUPAC name conversion", request_methods=['GET'] ) x2.save() x2.accept_header_media_types.add(m1) x2.content_media_types.add(m1) x3 = EndPoint.create( entrypoint=e1, category="uritemplate", uri="{+stdinchi,+stdinchikey}/image", description="InChI to SMILES conversion", request_methods=['GET'] ) x3.save() x3.accept_header_media_types.add(m1) x3.content_media_types.add(m1) x4 = EndPoint.create( entrypoint=e1, category="uritemplate", uri="{+smiles}/stdinchi", description="SMILES to stdinchi conversion", ) x4.save() x4.accept_header_media_types.add(m1) x4.content_media_types.add(m1) # x5 = EndPoint.create( # entrypoint=e1, # category="uritemplate", # uri="{+smiles}/stdinchikey", # description="SMILES to stdinchikey conversion", # ) # x5.save() # x5.accept_header_media_types.add(m1) # x5.content_media_types.add(m1) for j in range(1, 10): ilist = client.fetch_inchi(range(j * 10, j * 10 + 10)) for cid, i in ilist: print("Loading: %s" % (i,)) try: inchi = Inchi.create( string=i ) print('{} {}'.format(inchi, inchi.added)) inchi.save() inchi.entrypoints.add(e1) except Exception as e: print(e)

27.833333

111

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5.24283

0.323136

0.03647

0.047411

0.054705

0.259664

0.213713

0.141867

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0.073304

0

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0.27716

4,676

167

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0.049829

0

0.096296

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false

0

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0

0.02963

0.022222

0

null

0

null

0

1

0

307b75907e2ef43110ad5b30dc9de4dad44b596b

1,454

py

Python

misago/acl/admin.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

1

2017-07-25T03:04:36.000Z

misago/acl/admin.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

null

misago/acl/admin.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

null

from django.conf.urls import url from django.utils.translation import ugettext_lazy as _ from .views import DeleteRole, EditRole, NewRole, RolesList, RoleUsers class MisagoAdminExtension(object): def register_urlpatterns(self, urlpatterns): # Permissions section urlpatterns.namespace(r'^permissions/', 'permissions') # Roles urlpatterns.namespace(r'^users/', 'users', 'permissions') urlpatterns.patterns( 'permissions:users', url(r'^$', RolesList.as_view(), name='index'), url(r'^new/$', NewRole.as_view(), name='new'), url(r'^edit/(?P<pk>\d+)/$', EditRole.as_view(), name='edit'), url(r'^users/(?P<pk>\d+)/$', RoleUsers.as_view(), name='users'), url(r'^delete/(?P<pk>\d+)/$', DeleteRole.as_view(), name='delete'), ) def register_navigation_nodes(self, site): site.add_node( name=_("Permissions"), icon='fa fa-adjust', parent='misago:admin', after='misago:admin:users:accounts:index', namespace='misago:admin:permissions', link='misago:admin:permissions:users:index', ) site.add_node( name=_("User roles"), icon='fa fa-th-large', parent='misago:admin:permissions', namespace='misago:admin:permissions:users', link='misago:admin:permissions:users:index', )

37.282051

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0

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38

80

38.263158

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0.129032

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0

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0

0.193548

0

null

0

null

0

1

0

307c7a8c88bf6ef5840be4d2b400a14178b101d4

5,138

py

Python

src/visualization.py

DianaTaukin/DSD-SATN

5a4ab5e3cfcb00e72ca27cf5ec10a8d8e29ef312

[ "Apache-2.0" ]

71

2020-04-06T08:23:30.000Z

2022-03-21T03:40:11.000Z

src/visualization.py

DianaTaukin/DSD-SATN

5a4ab5e3cfcb00e72ca27cf5ec10a8d8e29ef312

[ "Apache-2.0" ]

10

2020-04-11T14:45:52.000Z

2021-08-19T04:44:13.000Z

src/visualization.py

DianaTaukin/DSD-SATN

5a4ab5e3cfcb00e72ca27cf5ec10a8d8e29ef312

[ "Apache-2.0" ]

8

2020-05-19T12:18:49.000Z

2022-03-22T08:04:27.000Z

from base import * import utils.neuralrenderer_render as nr class Visualizer(object): def __init__(self,high_resolution=False): self.high_resolution = high_resolution self.renderer = nr.get_renderer(high_resolution=self.high_resolution).cuda() def visualize_renderer(self,verts,images): #verts = torch.from_numpy(verts).cuda() #verts = self.batch_orth_proj_verts(verts,cam) #verts = torch.cat((verts[:,:,1].unsqueeze(-1),\ # -verts[:,:,2].unsqueeze(-1),verts[:,:,0].unsqueeze(-1)),dim=-1) results = self.renderer.forward(verts) renders = (results.detach().cpu().numpy().transpose((0,2,3,1))*256).astype(np.uint8)[:,:,:,::-1] render_mask = ~(renders>100)#.astype(np.bool) 去除渲染结果（白底时）的黑色毛刺边 renders[render_mask] = images[render_mask] return renders def visulize_result(self,outputs,kps,data,name,vnum = 6, white_background=False,rtype='',nokp=False,org_name=True,org_img=False,keep_name=False): if not keep_name: if 'name' in data: img_names = data['name'] else: img_names = data['imgpath'] imgs = data['image_org'].contiguous().numpy().astype(np.uint8)[:vnum,:,:,::-1] vnum = imgs.shape[0] if self.high_resolution: kps = ((kps.detach().contiguous().cpu().numpy()+1)/2 * 500).reshape(-1,14,2)[:vnum] else: kps = ((kps.detach().contiguous().cpu().numpy()+1)/2 * imgs.shape[1]).reshape(-1,14,2)[:vnum] kp_imgs = [] #white_background=False for idx in range(vnum): if white_background: kp_imgs.append(draw_lsp_14kp__bone(np.ones_like(imgs[idx])*255, kps[idx])) else: kp_imgs.append(draw_lsp_14kp__bone(imgs[idx].copy(), kps[idx])) ((cam,pose,shape), predict_verts, predict_j2d, predict_j3d, predict_Rs,verts_camed,j3d_camed) = outputs if white_background: rendered_imgs = self.visualize_renderer(verts_camed[:vnum], np.ones_like(imgs)*255) else: rendered_imgs = self.visualize_renderer(verts_camed[:vnum], imgs) if org_img: offsets = data['offsets'].numpy() org_image_names = data['imgpath'] #image_org = data['org_image'].numpy() imgs = [] #imgs = data['orgimage'].numpy() org_image = [] for n in range(rendered_imgs.shape[0]): org_imge = cv2.imread(org_image_names[n])#image_org[n].numpy().astype(np.uint8) imgs.append(org_imge.copy()) resized_images = cv2.resize(rendered_imgs[n], (offsets[n,0]+1, offsets[n,1]+1), interpolation = cv2.INTER_CUBIC) #print(offsets[n,2],(offsets[n,3]-1),offsets[n,4],(offsets[n,5]-1)) org_imge[offsets[n,2]:(offsets[n,3]-1),offsets[n,4]:(offsets[n,5]-1),:] = resized_images[offsets[n,6]:(offsets[n,7]-1+offsets[n,6]),offsets[n,8]:(offsets[n,9]+offsets[n,8]-1),:] org_image.append(org_imge) #imgs = np.array(imgs) #org_image = np.array(org_image) for idx in range(vnum): if nokp: if org_img: if len(org_image[idx].shape)<3: print(org_image_names[idx],org_image[idx].shape) continue result_img = np.hstack((imgs[idx], org_image[idx])) else: result_img = np.hstack((imgs[idx], rendered_imgs[idx])) else: result_img = np.hstack((imgs[idx],kp_imgs[idx], rendered_imgs[idx])) #cv2.imwrite(name+'_{}_org_{}.jpg'.format(idx,rtype),imgs[idx]) if keep_name: #print(name[idx]) cv2.imwrite(name[idx],result_img) elif org_name: cv2.imwrite('{}{}-{}'.format(name.split(os.path.basename(name))[0],img_names[idx].split('/')[-2],os.path.basename(img_names[idx])),result_img) else: cv2.imwrite(name+'_{}_{}.jpg'.format(idx,rtype),result_img) def render_video(self,verts,params,images,org_image,offsets,name): rendered_images = self.visualize_renderer(verts,params[:,:3],images) for n in range(verts.shape[0]): resized_images = cv2.resize(rendered_images[n], (offsets[n,0]+1, offsets[n,1]+1), interpolation = cv2.INTER_CUBIC) org_image[n,offsets[n,2]:(offsets[n,3]-1),offsets[n,4]:(offsets[n,5]-1),:] = resized_images[offsets[n,6]:(offsets[n,7]-1+offsets[n,6]),offsets[n,8]:(offsets[n,9]+offsets[n,8]-1),:] self.make_mp4(org_image,name) def make_mp4(self,images,name): num = images.shape[0] fourcc = cv2.VideoWriter_fourcc(*'mp4v') output_movie = cv2.VideoWriter(name+'.mp4', fourcc, 50, (images.shape[2], images.shape[1])) for i in range(num): if i%100==0: print('Writing frame: ',i,'/',num) output_movie.write(images[i])

49.883495

194

0.571039

680

5,138

4.142647

0.214706

0.079517

0.022364

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0.242102

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0.122826

0

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0.263137

5,138

103

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49.883495

0.711041

0.107824

0

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0

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0

0.116883

0.025974

0

null

0

null

0

1

0

307f461b2850c76795bf48a0896004e467675d49

455

py

Python

proxy/test/test_proxy.py

SFFReganDowling/test

c141daca395585710e5ff335e96ffd23ce9c71bb

[ "MIT" ]

3

2015-11-26T11:44:57.000Z

2021-12-07T18:08:53.000Z

proxy/test/test_proxy.py

SFFReganDowling/test

c141daca395585710e5ff335e96ffd23ce9c71bb

[ "MIT" ]

5

2016-04-22T10:06:41.000Z

2022-02-27T02:53:10.000Z

proxy/test/test_proxy.py

SFFReganDowling/test

c141daca395585710e5ff335e96ffd23ce9c71bb

[ "MIT" ]

null

import wsgiref.util import flask from proxy import proxy # pylint: disable=W0212 def test_happy_path(): environ = { "REQUEST_METHOD": "GET", "PATH_INFO": "/locationforecast/1.9/", "QUERY_STRING": "lat=59.31895603;lon=18.0517762", "HTTP_REFERER": "https://walles.github.io/weatherclock", } wsgiref.util.setup_testing_defaults(environ) request = flask.Request(environ) proxy._proxy_request(request)

21.666667

64

0.674725

54

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5.5

0.722222

0.074074

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3080c9c3e2fa02a8d02d8a5c53a2bd5e45b99323

6,756

py

Python

exps/jets/top_quark_gbdt.py

ranigb/Set-Tree

fa3971f9a8ef98dbfd0f6de654efcde3006a197b

[ "MIT" ]

17

2021-07-26T01:03:59.000Z

2022-01-23T10:31:56.000Z

exps/jets/top_quark_gbdt.py

ranigb/Set-Tree

fa3971f9a8ef98dbfd0f6de654efcde3006a197b

[ "MIT" ]

2

2021-12-10T09:53:48.000Z

2022-01-25T17:08:41.000Z

exps/jets/top_quark_gbdt.py

ranigb/Set-Tree

fa3971f9a8ef98dbfd0f6de654efcde3006a197b

[ "MIT" ]

3

2021-09-14T11:39:35.000Z

2022-01-23T06:51:48.000Z

import os import numpy as np import argparse import logging import random import pickle from pprint import pformat from exps.data import ParticleNetDataset from settree.set_data import SetDataset, OPERATIONS, merge_init_datasets import exps.eval_utils as eval from exps.eval_utils import create_logger data_root = '/home/royhir/projects/data/physics/top_quark/proc' def pre_process(dataset, limit=None): x = dataset.X y = dataset.y if limit is None: limit = len(y) inds = random.sample(range(len(y)), limit) x_points = x['points'].take(inds, axis=0) x_features = x['features'].take(inds, axis=0) x_mask = x['mask'].take(inds, axis=0) y = y.take(inds, axis=0) y = y.argmax(1) records = [] ys = [] for p, f, m, y in zip(x_points, x_features, x_mask, y): try: m_row = np.where(p.any(axis=1))[0].max() records.append(np.concatenate((p[:m_row, :], f[:m_row, :], m[:m_row, :]),axis=1)) ys.append(y) except: pass return records, np.array(ys) def get_top_quark_datset(train=None, val=None, test=None): train_dataset = ParticleNetDataset(os.path.join(data_root, 'train_file_0.awkd'), data_format='channel_last') val_dataset = ParticleNetDataset(os.path.join(data_root, 'val_file_0.awkd'), data_format='channel_last') test_dataset = ParticleNetDataset(os.path.join(data_root, 'test_file_0.awkd'), data_format='channel_last') logging.info('Loaded raw data') train_records, train_y = pre_process(train_dataset, limit=train) val_records, val_y = pre_process(val_dataset, limit=val) test_records, test_y = pre_process(test_dataset, limit=test) logging.info('Finish pre-processing') logging.info('train: {} val: {} test: {}'.format(len(train_y), len(val_y), len(test_y))) return SetDataset(records=train_records, is_init=True), train_y, \ SetDataset(records=val_records, is_init=True), val_y, \ SetDataset(records=test_records, is_init=True), test_y if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--exp_name", type=str, default='test') parser.add_argument("--splits", type=int, nargs="+", default=[1200000, 400000, 400000]) parser.add_argument("--attention_set_limit", type=int, default=6) parser.add_argument("--use_attention_set", action='store_true') parser.add_argument('--save', action='store_true') parser.add_argument("--log", action='store_true') args = parser.parse_args() np.random.seed(42) random.seed(42) log_dir = os.path.join(os.path.abspath('__file__' + '/../'), 'outputs', 'top_quark') create_logger(log_dir=log_dir, log_name=args.exp_name, dump=args.log) logging.info(args) train, val, test = args.splits ds_train, y_train, ds_val, y_val, ds_test, y_test = get_top_quark_datset(train, val, test) shared_gbdt_params = {'n_estimators': 50, 'learning_rate': 0.1, 'max_depth': 8, 'max_features': None, 'subsample': 0.5, 'criterion': 'mse', 'early_stopping_rounds': 5, 'random_state': 42} logging.info('Shared params:\n' + pformat(shared_gbdt_params)) set_params = {'n_estimators': shared_gbdt_params['n_estimators'], 'operations': OPERATIONS, 'splitter': 'sklearn', 'use_attention_set': True, 'use_attention_set_comp': False, 'attention_set_limit': args.attention_set_limit, 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], 'subsample': shared_gbdt_params['subsample'], 'random_state': shared_gbdt_params['random_state'], 'save_path': None, 'validation_fraction': 0.25, 'tol': 1e-4, 'n_iter_no_change': shared_gbdt_params['early_stopping_rounds'], 'verbose': 3} sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'], 'criterion': 'mse', 'learning_rate': shared_gbdt_params['learning_rate'], 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], 'subsample': shared_gbdt_params['subsample'], 'validation_fraction': 0.25, 'tol': 1e-4, 'n_iter_no_change': shared_gbdt_params['early_stopping_rounds'], 'random_state': shared_gbdt_params['random_state']} xgboost_params = {#'tree_method': 'gpu_hist', #'gpu_id': 7, #'objective': 'binary:logistic', 'max_depth': shared_gbdt_params['max_depth'], 'n_jobs': 10, 'eval_metric': ['error'], 'learning_rate': shared_gbdt_params['learning_rate'], 'n_estimators': shared_gbdt_params['n_estimators'], 'colsample_bytree': shared_gbdt_params['max_features'], 'subsample': shared_gbdt_params['subsample'], 'reg_lambda': 0, 'verbosity': 0, 'random_state': shared_gbdt_params['random_state'], 'seed': shared_gbdt_params['random_state']} x_train, x_test, x_val = eval.flatten_datasets(ds_train, ds_test, operations_list=set_params['operations'], ds_val=ds_val) xgboost_gbtd = eval.train_and_predict_xgboost(xgboost_params, x_train, y_train, x_test, y_test, val_x=None, val_y=None, early_stopping_rounds=None) ds_train_val = merge_init_datasets(ds_train, ds_val) set_gbtd = eval.train_and_predict_set_gbdt(set_params, ds_train_val, np.concatenate([y_train, y_val]), ds_test, y_test, resume=None) if args.save: pkl_filename = os.path.join(log_dir, '{}_model.pkl'.format(args.exp_name)) with open(pkl_filename, 'wb') as file: pickle.dump(set_gbtd, file)

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0

null

0

null

0

1

0

061a89369cd09d094301833ef16484b3a48346f9

2,801

py

Python

wonk/config.py

cjduffett/wonk

7ee7d6e444497cb91901ed4bd6de53d5aa574963

[ "Apache-2.0" ]

103

2021-09-25T03:03:32.000Z

2022-03-20T19:13:48.000Z

wonk/config.py

cjduffett/wonk

7ee7d6e444497cb91901ed4bd6de53d5aa574963

[ "Apache-2.0" ]

null

wonk/config.py

cjduffett/wonk

7ee7d6e444497cb91901ed4bd6de53d5aa574963

[ "Apache-2.0" ]

6

2021-09-27T17:50:23.000Z

2022-02-15T22:44:12.000Z

"""Manage Wonk's configuration.""" import pathlib from typing import Any, Dict, List import yaml from pydantic import BaseModel from toposort import toposort_flatten # type: ignore from wonk.exceptions import UnknownParentError class PolicySet(BaseModel): """Describes a policy set.""" name: str managed: List[str] = [] local: List[str] = [] inherits: List[str] = [] def __ior__(self, other): """Append the values from another policy set onto this one's.""" # This is not an efficient algorithm, but it maintains ordering which lends stability to # the final output files. These lists are almost always going to be very short anyway, and # an easy to read algorithm is better than a more efficient but complex one for these # purposes. for value in other.managed: if value not in self.managed: self.managed.append(value) for value in other.local: if value not in self.local: self.local.append(value) return self class Config(BaseModel): """Describes a Wonk configuration file.""" policy_sets: Dict[str, PolicySet] def load_config(config_path: pathlib.Path = None) -> Config: """Load a configuration file and return its parsed contents.""" if config_path is None: config_path = pathlib.Path("wonk.yaml") data = yaml.load(config_path.read_text(), Loader=yaml.SafeLoader) return parse_config(data) def parse_config(block_all_config: Dict[str, Any]) -> Config: """Parse the dictionary containing all Wonk configuration into a Config object.""" try: block_policy_sets = block_all_config["policy_sets"] or {} except KeyError: policy_sets = {} else: policy_sets = parse_policy_sets(block_policy_sets) return Config(policy_sets=policy_sets) # type: ignore def parse_policy_sets(block_policy_sets: Dict[str, Any]) -> Dict[str, PolicySet]: """Parse the dictionary containing policy set definitions into a dict of PolicySets.""" policy_sets = {} deps = {} for name, definition in block_policy_sets.items(): with_name = {**definition, **{"name": name}} policy_set = PolicySet(**with_name) policy_sets[name] = policy_set for parent_name in policy_set.inherits: if parent_name not in block_policy_sets: raise UnknownParentError(name, parent_name) # Build a dependency graph from the set of inheritance definitions from the classes. deps[name] = set(policy_set.inherits) for name in toposort_flatten(deps): policy_set = policy_sets[name] for parent_name in policy_set.inherits: policy_set |= policy_sets[parent_name] return policy_sets

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null

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061be587da4bb712ec3ad5fa16ba9684df1988f9

5,325

py

Python

models/quantize_utils.py

ARM-software/sesr

26dd727996809fe13efb0c0f137f259c1b2d0f6e

[ "Apache-2.0" ]

25

2021-11-08T12:48:09.000Z

2022-03-29T02:56:18.000Z

models/quantize_utils.py

ARM-software/sesr

26dd727996809fe13efb0c0f137f259c1b2d0f6e

[ "Apache-2.0" ]

12

2021-10-04T05:59:56.000Z

2022-03-29T06:06:17.000Z

models/quantize_utils.py

ARM-software/sesr

26dd727996809fe13efb0c0f137f259c1b2d0f6e

[ "Apache-2.0" ]

6

2021-11-26T09:27:18.000Z

2022-02-24T14:52:01.000Z

# Copyright 2021 Arm 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. # ============================================================================== from typing import Callable, List, Tuple, Union import tensorflow as tf def compute_ranges(kernel: tf.Tensor, per_channel: bool, symmetric: bool) -> Tuple[tf.Tensor, tf.Tensor]: axes = tf.range(tf.rank(kernel) - 1) if per_channel else None if symmetric: quant_max = tf.stop_gradient(tf.math.reduce_max(tf.math.abs(kernel), axis=axes)) quant_min = -quant_max else: quant_max = tf.stop_gradient(tf.math.reduce_max(kernel, axis=axes)) quant_min = tf.stop_gradient(tf.math.reduce_min(kernel, axis=axes)) return quant_max, quant_min @tf.custom_gradient def floor_ste(x: tf.Tensor) -> Tuple[tf.Tensor, Callable[[tf.Tensor], List[tf.Tensor]]]: y = tf.floor(x) def grad(dy: tf.Tensor) -> List[tf.Tensor]: return [dy] return y, grad def get_nudged_ranges_scale( min: tf.Tensor, max: tf.Tensor, num_bits: int, narrow_range: bool = False) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: quant_max = tf.math.pow(2., tf.cast(num_bits, dtype=tf.dtypes.float32)) - 1. quant_min = tf.constant(1.) if narrow_range else tf.constant(0.) scale = (max - min) / (quant_max - quant_min) # Rounding the zero-point to ensure one of the quantized values snap to zero zero_point_from_min = quant_min - min / scale nudged_zero_point = tf.round(zero_point_from_min) nudged_zero_point = tf.where(zero_point_from_min < quant_min, quant_min * tf.ones(shape=tf.shape(nudged_zero_point)), nudged_zero_point) nudged_zero_point = tf.where(zero_point_from_min > quant_max, quant_max * tf.ones(shape=tf.shape(nudged_zero_point)), nudged_zero_point) # adjust/nudge the min/max to ensure zero-point snaps to real zero. nudged_min = (quant_min - nudged_zero_point) * scale nudged_max = (quant_max - nudged_zero_point) * scale return nudged_min, nudged_max, scale def fake_quant_with_min_max_vars( inputs: tf.Tensor, min: tf.Tensor, max: tf.Tensor, num_bits: int, narrow_range: bool = False) -> tf.Tensor: """ This is differentiable equivalent of the utility in tf.quantization. tf.quantization.fake_quant* utilities only allows the min/max ranges to increase through gradients, but we would have to rely on l2_loss to decrease the min/max ranges. This updated utility allows the gradients to both increase and decrease the min/max ranges. """ nudged_min, nudged_max, scale = get_nudged_ranges_scale(min, max, num_bits, narrow_range) clipped_data = tf.clip_by_value(inputs, nudged_min, nudged_max) shifted_data = clipped_data - nudged_min quant_data = floor_ste(shifted_data / scale + 0.5) quant_data = quant_data * scale + nudged_min return quant_data fake_quant_with_min_max_vars_per_channel = fake_quant_with_min_max_vars class ActivationQuantizationBlock(tf.keras.layers.Layer): def __init__(self, enabled: bool, mode: str): super().__init__() self.enabled = enabled self.mode = mode if self.mode == 'train': self.fake_quant_with_min_max_vars_fn = \ fake_quant_with_min_max_vars elif self.mode == 'infer': self.fake_quant_with_min_max_vars_fn = \ tf.quantization.fake_quant_with_min_max_vars def build(self, input_shape): if self.enabled: self.quant_min = self.add_weight( name='act_quant_min', trainable=True) self.quant_max = self.add_weight( name='act_quant_max', trainable=True) if self.mode == 'train': self.quant_initialized = tf.Variable(False, trainable=False) def init_quant_ranges(self, inputs: tf.Tensor) -> None: quant_max, quant_min = compute_ranges(inputs, per_channel=False, symmetric=False) self.quant_max.assign(quant_max) self.quant_min.assign(quant_min) self.quant_initialized.assign(True) def call(self, inputs): if self.enabled: if self.mode == "train": if not self.quant_initialized: self.init_quant_ranges(inputs) return self.fake_quant_with_min_max_vars_fn( inputs, min=self.quant_min, max=self.quant_max, num_bits=8, narrow_range=False) else: return inputs

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null

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null

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1

0

061ff9f824e772b63623e52f1ce6ebb062cf98da

3,192

py

Python

sorting/sorting_train.py

soumen-chakrabarti/gumbel_sinkhorn

aedf8adbc7f123821374da84a23e51d3a0cf54c5

[ "Apache-2.0" ]

65

2017-09-24T19:38:34.000Z

2022-01-18T16:07:05.000Z

sorting/sorting_train.py

soumen-chakrabarti/gumbel_sinkhorn

aedf8adbc7f123821374da84a23e51d3a0cf54c5

[ "Apache-2.0" ]

null

sorting/sorting_train.py

soumen-chakrabarti/gumbel_sinkhorn

aedf8adbc7f123821374da84a23e51d3a0cf54c5

[ "Apache-2.0" ]

22

2017-10-01T12:55:38.000Z

2022-01-13T19:33:15.000Z

# Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://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. """Trains a model that sorts numbers, keeping loss summaries in tensorboard. The flag hparam has to be passed as a string of comma separated statements of the form hparam=value, where the hparam's are any of the listed in the dictionary DEFAULT_HPARAMS. See the README.md file for further compilation and running instructions. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import sorting_model flags = tf.app.flags gfile = tf.gfile FLAGS = flags.FLAGS flags.DEFINE_string('hparams', '', 'Hyperparameters') flags.DEFINE_integer('num_iters', 500, 'Number of iterations') flags.DEFINE_integer( 'save_summaries_secs', 30, 'The frequency with which summaries are saved, in seconds.') flags.DEFINE_integer( 'save_interval_secs', 30, 'The frequency with which the model is saved, in seconds.') flags.DEFINE_string('exp_log_dir', '/tmp/sorting/', 'Directory where to write event logs.') flags.DEFINE_integer('max_to_keep', 1, 'Maximum number of checkpoints to keep') DEFAULT_HPARAMS = tf.contrib.training.HParams(n_numbers=50, lr=0.1, temperature=1.0, batch_size=10, prob_inc=1.0, samples_per_num=5, n_iter_sinkhorn=10, n_units=32, noise_factor=1.0, optimizer='adam', keep_prob=1.) def main(_): hparams = DEFAULT_HPARAMS hparams.parse(FLAGS.hparams) if not gfile.Exists(FLAGS.exp_log_dir): gfile.MakeDirs(FLAGS.exp_log_dir) tf.reset_default_graph() g = tf.Graph() model = sorting_model.SortingModel(g, hparams) with g.as_default(): model.set_input() model.build_network() model.build_l2s_loss() model.build_optimizer() model.add_summaries_train() with tf.Session(): tf.contrib.slim.learning.train( train_op=model.train_op, logdir=FLAGS.exp_log_dir, global_step=model.global_step, saver=tf.train.Saver(max_to_keep=FLAGS.max_to_keep), number_of_steps=FLAGS.num_iters, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs) if __name__ == '__main__': tf.app.run(main)

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null

0

null

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1

0

0624b48ea9d4dc4aaa2193d16b26d5a5663fbaf2

1,578

py

Python

tests/cli_test.py

chineseluo/opensourcetest

b0d222c8b29ff8f70a740ac2b1588a437d41b761

[ "Apache-2.0" ]

69

2020-10-20T14:25:49.000Z

2022-02-18T02:50:20.000Z

tests/cli_test.py

aoozoo/opensourcetest

6eaff706c9397847834ef3eef7ad57d5b7f5c5a3

[ "Apache-2.0" ]

6

2020-11-23T06:56:09.000Z

2022-03-16T04:33:53.000Z

tests/cli_test.py

aoozoo/opensourcetest

6eaff706c9397847834ef3eef7ad57d5b7f5c5a3

[ "Apache-2.0" ]

8

2021-02-01T03:23:20.000Z

2022-02-18T02:50:47.000Z

#!/user/bin/env python # -*- coding: utf-8 -*- """ ------------------------------------ @Project : opensourcetest @Time : 2020/11/12 15:01 @Auth : chineseluo @Email : 848257135@qq.com @File : cli_test.py @IDE : PyCharm ------------------------------------ """ import os import sys import unittest from opensourcetest.cli import main class TestCli(unittest.TestCase): def test_show_version(self): sys.argv = ["OST", "-V"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0) def test_show_help(self): sys.argv = ["OST", "-h"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0) def test_show_create_http_project(self): sys.argv = ["OST", "start_http_project"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0) def test_show_create_ui_project(self): sys.argv = ["OST", "start_ui_project"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0) def test_show_create_app_project(self): sys.argv = ["OST", "start_app_project"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0) def test_show_online_docs_address(self): sys.argv = ["OST", "onlinedocs"] with self.assertRaises(SystemExit) as cm: main() self.assertEqual(cm.exception.code, 0)

27.684211

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false

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0

0.314286

0

null

0

null

0

1

0

0625d68871943fd8a255cbe675fa3fb3bffccaf0

2,364

py

Python

multicast_client.py

mmuravytskyi/multiprotocol-chat

1a763c53c43d1d7e07ecf066bb0ed3d9dbc73af9

[ "MIT" ]

null

multicast_client.py

mmuravytskyi/multiprotocol-chat

1a763c53c43d1d7e07ecf066bb0ed3d9dbc73af9

[ "MIT" ]

2

2021-05-28T10:56:22.000Z

2021-05-28T10:56:35.000Z

multicast_client.py

mmuravytskyi/multiprotocol-chat

1a763c53c43d1d7e07ecf066bb0ed3d9dbc73af9

[ "MIT" ]

1

2021-12-31T15:08:27.000Z

import socket import struct import config import json import threading import random def multicast_handler(client_port: int): # create the datagram socket sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind(('', client_port)) # set a timeout so the socket does not block indefinitely when trying to receive data. sock.settimeout(0.2) # Set the time-to-live for messages to 1 so they do not go past the local network segment. ttl = struct.pack('b', 1) sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, ttl) try: # send request to the multicast group print(f'CLIENT: Sending multicast message to {config.MULTICAST_IP}') message = 'SERVER DISCOVERY' multicast_group = (config.MULTICAST_IP, config.MULTICAST_PORT) sock.sendto(bytes(message, encoding='utf-8'), multicast_group) finally: sock.close() def tcp_handler(port: int): sock_tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock_tcp.bind(('', port)) sock_tcp.listen(5) # empty buffer buff = b'' while True: print(f'CLIENT: Waiting for a TCP connection') connection, client_address = sock_tcp.accept() try: print(f'CLIENT: Connection from {client_address}') username = input('=== Provide Your Nickname === ') connection.sendall(bytes(username, encoding='utf8')) # receive the data in chunks and add to the buffer while True: print(f'CLIENT: Waiting for the server to send client base') data = connection.recv(512) buff += data if not data: break break finally: print(f'CLIENT: Client base received') res_dict = json.loads(buff.decode('utf-8')) # print(res_dict) print(f'CLIENT: Closing TCP connection') # clean up the connection connection.close() break if __name__ == '__main__': port = random.randint(50_000, 65_000) # pass selected port to the TCP thread, in order to listen on the same port # thread in the background as daemon th = threading.Thread(target=tcp_handler, args=(client_port,), daemon=True) th.start() multicast_handler(port) th.join()

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