paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
tools/prepare_macaque_data/split_trainval.py	MeowMeowLady/Towards-Neuro-Seg-Macaque-Brain	1	172353	<reponame>MeowMeowLady/Towards-Neuro-Seg-Macaque-Brain<gh_stars>1-10 # -- coding: utf-8 -- """ Created on 19-7-31 下午6:02 IDE PyCharm @author: <NAME> this script is used to split training and validation sets by generating list-txt. """ from os.path import join as opj import random from glob import glob import os txt_save_path = '' total_list = os.listdir('/') random.shuffle(total_list) random.shuffle(total_list) train_list = open(opj(txt_save_path, 'train.txt'), 'w+') valid_list = open(opj(txt_save_path, 'valid.txt'), 'w+') for i in range(3500): train_list.write('{}\n'.format(total_list[i][:-3])) for i in range(1000): valid_list.write('{}\n'.format(total_list[i+3500][:-3])) train_list.close() valid_list.close()	[ 1, 529, 276, 1112, 420, 29958, 6816, 340, 6816, 340, 29931, 3714, 29914, 29911, 340, 3163, 29899, 8139, 2192, 29899, 17669, 29899, 29924, 11989, 802, 29899, 22097, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 20399, 373, 29871, 29896, 29929, 29899, 29955, 29899, 29941, 29896, 29871, 30557, 232, 144, 139, 29953, 29901, 29900, 29906, 13, 22027, 10772, 1451, 2817, 29871, 13, 13, 29992, 8921, 29901, 529, 5813, 29958, 13, 1366, 2471, 338, 1304, 304, 6219, 6694, 322, 8845, 6166, 491, 14655, 1051, 29899, 3945, 29889, 13, 15945, 29908, 13, 13, 3166, 2897, 29889, 2084, 1053, 5988, 408, 1015, 29926, 13, 5215, 4036, 13, 3166, 13149, 1053, 13149, 13, 5215, 2897, 13, 13, 3945, 29918, 7620, 29918, 2084, 353, 6629, 13, 13, 7827, 29918, 1761, 353, 2897, 29889, 1761, 3972, 11219, 1495, 13, 8172, 29889, 845, 21897, 29898, 7827, 29918, 1761, 29897, 13, 8172, 29889, 845, 21897, 29898, 7827, 29918, 1761, 29897, 13, 13, 14968, 29918, 1761, 353, 1722, 29898, 459, 29926, 29898, 3945, 29918, 7620, 29918, 2084, 29892, 525, 14968, 29889, 3945, 5477, 525, 29893, 29974, 1495, 13, 3084, 29918, 1761, 353, 1722, 29898, 459, 29926, 29898, 3945, 29918, 7620, 29918, 2084, 29892, 525, 3084, 29889, 3945, 5477, 525, 29893, 29974, 1495, 13, 13, 1454, 474, 297, 3464, 29898, 29941, 29945, 29900, 29900, 1125, 13, 1678, 7945, 29918, 1761, 29889, 3539, 877, 29912, 1012, 29876, 4286, 4830, 29898, 7827, 29918, 1761, 29961, 29875, 3816, 13018, 29941, 12622, 13, 13, 1454, 474, 297, 3464, 29898, 29896, 29900, 29900, 29900, 1125, 13, 1678, 2854, 29918, 1761, 29889, 3539, 877, 29912, 1012, 29876, 4286, 4830, 29898, 7827, 29918, 1761, 29961, 29875, 29974, 29941, 29945, 29900, 29900, 3816, 13018, 29941, 12622, 13, 13, 14968, 29918, 1761, 29889, 5358, 580, 13, 3084, 29918, 1761, 29889, 5358, 580, 13, 13, 13, 2 ]
productdb/testing/unittests/products.py	tspycher/python-productdb	0	38248	from . import BasicTestCase class ProductsTestCase(BasicTestCase): def test_basic(self): rv = self.client.get('/') assert rv.status_code == 200 data = self.parseJsonResponse(rv) assert '_links' in data def test_get_all_products(self): rv = self.client.get('/product') assert rv.status_code == 200 data = self.parseJsonResponse(rv) assert '_items' in data # add further data tests here, load basic data by fixtures pass	[ 1, 515, 869, 1053, 19219, 3057, 8259, 13, 13, 1990, 10969, 29879, 3057, 8259, 29898, 16616, 3057, 8259, 1125, 13, 13, 1678, 822, 1243, 29918, 16121, 29898, 1311, 1125, 13, 4706, 364, 29894, 353, 1583, 29889, 4645, 29889, 657, 11219, 1495, 13, 4706, 4974, 364, 29894, 29889, 4882, 29918, 401, 1275, 29871, 29906, 29900, 29900, 13, 13, 4706, 848, 353, 1583, 29889, 5510, 8148, 5103, 29898, 15291, 29897, 13, 4706, 4974, 22868, 4965, 29915, 297, 848, 13, 13, 13, 1678, 822, 1243, 29918, 657, 29918, 497, 29918, 14456, 29898, 1311, 1125, 13, 4706, 364, 29894, 353, 1583, 29889, 4645, 29889, 657, 11219, 4704, 1495, 13, 4706, 4974, 364, 29894, 29889, 4882, 29918, 401, 1275, 29871, 29906, 29900, 29900, 13, 13, 4706, 848, 353, 1583, 29889, 5510, 8148, 5103, 29898, 15291, 29897, 13, 4706, 4974, 22868, 7076, 29915, 297, 848, 13, 4706, 396, 788, 4340, 848, 6987, 1244, 29892, 2254, 6996, 848, 491, 5713, 486, 1973, 13, 13, 4706, 1209, 13, 13, 13, 2 ]
core/cdag/node/sub.py	prorevizor/noc	84	16518	# ---------------------------------------------------------------------- # SubNode # ---------------------------------------------------------------------- # Copyright (C) 2007-2020 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # Python modules from typing import Optional # NOC modules from .base import BaseCDAGNode, ValueType, Category class SubNode(BaseCDAGNode): """ Subtract `y` from `y` """ name = "sub" categories = [Category.OPERATION] def get_value(self, x: ValueType, y: ValueType) -> Optional[ValueType]: return x - y	[ 1, 396, 448, 2683, 2683, 2683, 2683, 23648, 13, 29937, 3323, 4247, 13, 29937, 448, 2683, 2683, 2683, 2683, 23648, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29900, 29955, 29899, 29906, 29900, 29906, 29900, 450, 11698, 29907, 8010, 13, 29937, 2823, 365, 2965, 1430, 1660, 363, 4902, 13, 29937, 448, 2683, 2683, 2683, 2683, 23648, 13, 13, 29937, 5132, 10585, 13, 3166, 19229, 1053, 28379, 13, 13, 29937, 11698, 29907, 10585, 13, 3166, 869, 3188, 1053, 7399, 6530, 10051, 4247, 29892, 7865, 1542, 29892, 17943, 13, 13, 13, 1990, 3323, 4247, 29898, 5160, 6530, 10051, 4247, 1125, 13, 1678, 9995, 13, 1678, 3323, 29873, 1461, 421, 29891, 29952, 515, 421, 29891, 29952, 13, 1678, 9995, 13, 13, 1678, 1024, 353, 376, 1491, 29908, 13, 1678, 13997, 353, 518, 10900, 29889, 4590, 1001, 8098, 29962, 13, 13, 1678, 822, 679, 29918, 1767, 29898, 1311, 29892, 921, 29901, 7865, 1542, 29892, 343, 29901, 7865, 1542, 29897, 1599, 28379, 29961, 1917, 1542, 5387, 13, 4706, 736, 921, 448, 343, 13, 2 ]
kubeflow/utils/kaggle.py	GalaxyIT/kubeflow-utils	0	172939	<filename>kubeflow/utils/kaggle.py import json from pathlib import Path class Kaggle: def login(self, username, apikey): kaggle_json = Kaggle.__generate_kaggle_json_string(username, apikey) Kaggle.__save_kaggle_json(kaggle_json) @staticmethod def __generate_kaggle_json_string(username, apikey): data = { "username": username, "key": apikey } return json.dumps(data) @staticmethod def __save_kaggle_json(json_str): kaggle_path = Path.home().joinpath('.kaggle') if not kaggle_path.exists(): kaggle_path.mkdir(parents=True) with open(str(kaggle_path.joinpath('kaggle.json')), 'w+') as kaggle_json: print(json_str, file=kaggle_json)	[ 1, 529, 9507, 29958, 29895, 431, 1389, 677, 29914, 13239, 29914, 29895, 351, 6234, 29889, 2272, 13, 5215, 4390, 13, 3166, 2224, 1982, 1053, 10802, 13, 13, 13, 1990, 476, 351, 6234, 29901, 13, 1678, 822, 6464, 29898, 1311, 29892, 8952, 29892, 7882, 1989, 1125, 13, 4706, 413, 351, 6234, 29918, 3126, 353, 476, 351, 6234, 17255, 17158, 29918, 29895, 351, 6234, 29918, 3126, 29918, 1807, 29898, 6786, 29892, 7882, 1989, 29897, 13, 4706, 476, 351, 6234, 17255, 7620, 29918, 29895, 351, 6234, 29918, 3126, 29898, 29895, 351, 6234, 29918, 3126, 29897, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 4770, 17158, 29918, 29895, 351, 6234, 29918, 3126, 29918, 1807, 29898, 6786, 29892, 7882, 1989, 1125, 13, 4706, 848, 353, 426, 13, 9651, 376, 6786, 1115, 8952, 29892, 13, 9651, 376, 1989, 1115, 7882, 1989, 13, 4706, 500, 13, 13, 4706, 736, 4390, 29889, 29881, 17204, 29898, 1272, 29897, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 4770, 7620, 29918, 29895, 351, 6234, 29918, 3126, 29898, 3126, 29918, 710, 1125, 13, 4706, 413, 351, 6234, 29918, 2084, 353, 10802, 29889, 5184, 2141, 7122, 2084, 12839, 29895, 351, 6234, 1495, 13, 4706, 565, 451, 413, 351, 6234, 29918, 2084, 29889, 9933, 7295, 413, 351, 6234, 29918, 2084, 29889, 11256, 3972, 29898, 862, 1237, 29922, 5574, 29897, 13, 13, 4706, 411, 1722, 29898, 710, 29898, 29895, 351, 6234, 29918, 2084, 29889, 7122, 2084, 877, 29895, 351, 6234, 29889, 3126, 1495, 511, 525, 29893, 29974, 1495, 408, 413, 351, 6234, 29918, 3126, 29901, 13, 9651, 1596, 29898, 3126, 29918, 710, 29892, 934, 29922, 29895, 351, 6234, 29918, 3126, 29897, 13, 2 ]
all_raspi_code_backup/DriveTesting.py	lord-pradhan/SnowBot	2	15482	<reponame>lord-pradhan/SnowBot<gh_stars>1-10 """ Program: DriveTesting.py Revised On: 12/01/2019 """ ### Library Imports from DriveArduino import DriveArduino import numpy as np from time import sleep from sys import exit from signal import signal, SIGINT ### ### CTRL + C Signal Handler & Resource Cleanup def signal_handler(sig, frame): """Handler for CTRL + C clean exit.""" print('Quitting program.') cleanup() def cleanup(): """Resource cleanup.""" drive.close() print('Resource cleanup completed.') exit(0) signal(SIGINT, signal_handler) ### ### Arduino Configuration addr = 0x08 drive = DriveArduino(addr) ### ### Main Program print('Press CTRL + C to exit.') while True: setpoints = np.array([25, 25, -25, -25]) drive.set_rpm(setpoints) sleep(1) drive.update() print(drive.rpm) print(drive.pwm) print() ###	[ 1, 529, 276, 1112, 420, 29958, 29880, 536, 29899, 558, 328, 5403, 29914, 29903, 3707, 29933, 327, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 13, 9283, 29901, 268, 22850, 3057, 292, 29889, 2272, 13, 1123, 11292, 1551, 29901, 259, 29896, 29906, 29914, 29900, 29896, 29914, 29906, 29900, 29896, 29929, 13, 15945, 29908, 13, 13, 2277, 29937, 9538, 1954, 4011, 13, 3166, 22850, 1433, 28955, 1053, 22850, 1433, 28955, 13, 5215, 12655, 408, 7442, 13, 3166, 931, 1053, 8709, 13, 3166, 10876, 1053, 6876, 13, 3166, 7182, 1053, 7182, 29892, 317, 6259, 10192, 13, 2277, 29937, 13, 13, 13, 2277, 29937, 26637, 2241, 718, 315, 9954, 284, 5166, 1358, 669, 18981, 315, 14044, 786, 13, 1753, 7182, 29918, 13789, 29898, 18816, 29892, 3515, 1125, 13, 1678, 9995, 4598, 363, 26637, 2241, 718, 315, 5941, 6876, 1213, 15945, 13, 1678, 1596, 877, 2182, 5367, 1824, 29889, 1495, 13, 1678, 5941, 786, 580, 13, 13, 1753, 5941, 786, 7295, 13, 1678, 9995, 6848, 5941, 786, 1213, 15945, 13, 1678, 7899, 29889, 5358, 580, 13, 1678, 1596, 877, 6848, 5941, 786, 8676, 29889, 1495, 13, 1678, 6876, 29898, 29900, 29897, 13, 13, 25436, 29898, 5425, 29954, 10192, 29892, 7182, 29918, 13789, 29897, 13, 2277, 29937, 13, 13, 13, 2277, 29937, 826, 28955, 20999, 13, 10030, 353, 29871, 29900, 29916, 29900, 29947, 13, 21594, 353, 22850, 1433, 28955, 29898, 10030, 29897, 13, 2277, 29937, 13, 13, 13, 2277, 29937, 4241, 7835, 13, 2158, 877, 10923, 26637, 2241, 718, 315, 304, 6876, 29889, 1495, 13, 13, 8000, 5852, 29901, 13, 1678, 731, 9748, 353, 7442, 29889, 2378, 4197, 29906, 29945, 29892, 29871, 29906, 29945, 29892, 448, 29906, 29945, 29892, 448, 29906, 29945, 2314, 13, 1678, 7899, 29889, 842, 29918, 29878, 3358, 29898, 842, 9748, 29897, 13, 1678, 8709, 29898, 29896, 29897, 13, 268, 13, 1678, 7899, 29889, 5504, 580, 13, 1678, 1596, 29898, 21594, 29889, 29878, 3358, 29897, 13, 1678, 1596, 29898, 21594, 29889, 29886, 29893, 29885, 29897, 13, 1678, 1596, 580, 13, 2277, 29937, 13, 2 ]
waterbutler/providers/dropbox/settings.py	laurenrevere/waterbutler	0	149895	<reponame>laurenrevere/waterbutler try: from waterbutler import settings except ImportError: settings = {} config = settings.get('DROPBOX_PROVIDER_CONFIG', {}) BASE_URL = config.get('BASE_URL', 'https://api.dropbox.com/1/') BASE_CONTENT_URL = config.get('BASE_CONTENT_URL', 'https://api-content.dropbox.com/1/')	[ 1, 529, 276, 1112, 420, 29958, 433, 10732, 276, 9359, 29914, 13405, 4187, 1358, 13, 2202, 29901, 13, 1678, 515, 4094, 4187, 1358, 1053, 6055, 13, 19499, 16032, 2392, 29901, 13, 1678, 6055, 353, 6571, 13, 13, 2917, 353, 6055, 29889, 657, 877, 29928, 29366, 8456, 29990, 29918, 8618, 13044, 1001, 29918, 25903, 742, 426, 1800, 13, 13, 13, 25416, 29918, 4219, 353, 2295, 29889, 657, 877, 25416, 29918, 4219, 742, 525, 991, 597, 2754, 29889, 8865, 1884, 29889, 510, 29914, 29896, 29914, 1495, 13, 25416, 29918, 22412, 3919, 29918, 4219, 353, 2295, 29889, 657, 877, 25416, 29918, 22412, 3919, 29918, 4219, 742, 525, 991, 597, 2754, 29899, 3051, 29889, 8865, 1884, 29889, 510, 29914, 29896, 29914, 1495, 13, 2 ]
restrictive_growth_strings.py	oversj96/PyKabe	1	36889	"""restrictive_growth_strings.py: Constructs the lexicographic restrictive growth string representation of all the way to partition a set, given the length of the set.""" __author__ = "<NAME>" __copyright__ = "oversj96.github.io" def set_to_zero(working_set, index): """Given a set and an index, set all elements to 0 after the index.""" if index == len(working_set) - 1: return working_set else: for i in range(index + 1, len(working_set)): working_set[i] = 0 return working_set def update_b_row(a_row, b_row): """Update b row to reflect the max value of a sequence range in row a""" for i in range(1, len(a_row)): b_row[i - 1] = max(a_row[:i]) + 1 return b_row def restrictive_growth_strings(length): """Returns the set of all partitions of a set in a lexicographic integer format. The algorithm is based on Donald Knuth's volume 4A on combinatoric algorithms. Algorithm H.""" n = length - 1 a_string = [0 for i in range(0, length)] b_string = [1 for i in range(0, n)] lexico_string = [a_string.copy()] incrementable = True while incrementable: incrementable = False for index in range(n, 0, -1): if a_string[index] < n and a_string[index] < b_string[index - 1]: incrementable = True a_string[index] += 1 a_string = set_to_zero(a_string, index) b_string = update_b_row(a_string, b_string) lexico_string.append(a_string.copy()) break return lexico_string if __name__ == "__main__": """A quick way to check if the module works correctly is to compare the partition count to the bell number of the correct 'n' size. For a set of 7 elements, the partition count should be 877 by Bell's numbers.""" strings = restrictive_growth_strings(7) for string in strings: print(string) print(len(strings))	[ 1, 9995, 5060, 4146, 573, 29918, 29887, 798, 386, 29918, 19651, 29889, 2272, 29901, 1281, 4984, 29879, 278, 19566, 293, 12122, 13, 9250, 573, 14321, 1347, 8954, 310, 599, 278, 982, 304, 8877, 263, 731, 29892, 13, 2183, 278, 3309, 310, 278, 731, 1213, 15945, 13, 13, 1649, 8921, 1649, 353, 9872, 5813, 11903, 13, 1649, 8552, 1266, 1649, 353, 376, 29877, 874, 29926, 29929, 29953, 29889, 3292, 29889, 601, 29908, 13, 13, 13, 1753, 731, 29918, 517, 29918, 9171, 29898, 22899, 29918, 842, 29892, 2380, 1125, 13, 1678, 9995, 29954, 5428, 263, 731, 322, 385, 2380, 29892, 731, 599, 3161, 304, 29871, 29900, 1156, 278, 2380, 1213, 15945, 13, 1678, 565, 2380, 1275, 7431, 29898, 22899, 29918, 842, 29897, 448, 29871, 29896, 29901, 13, 4706, 736, 1985, 29918, 842, 13, 1678, 1683, 29901, 13, 4706, 363, 474, 297, 3464, 29898, 2248, 718, 29871, 29896, 29892, 7431, 29898, 22899, 29918, 842, 22164, 13, 9651, 1985, 29918, 842, 29961, 29875, 29962, 353, 29871, 29900, 13, 4706, 736, 1985, 29918, 842, 13, 13, 13, 1753, 2767, 29918, 29890, 29918, 798, 29898, 29874, 29918, 798, 29892, 289, 29918, 798, 1125, 13, 1678, 9995, 6422, 289, 1948, 304, 9432, 278, 4236, 995, 310, 263, 5665, 3464, 297, 1948, 263, 15945, 29908, 13, 1678, 363, 474, 297, 3464, 29898, 29896, 29892, 7431, 29898, 29874, 29918, 798, 22164, 13, 4706, 289, 29918, 798, 29961, 29875, 448, 29871, 29896, 29962, 353, 4236, 29898, 29874, 29918, 798, 7503, 29875, 2314, 718, 29871, 29896, 13, 1678, 736, 289, 29918, 798, 13, 13, 13, 1753, 9250, 573, 29918, 29887, 798, 386, 29918, 19651, 29898, 2848, 1125, 13, 1678, 9995, 11609, 29879, 278, 731, 310, 599, 23629, 310, 263, 731, 297, 263, 19566, 293, 12122, 6043, 3402, 29889, 13, 1678, 450, 5687, 338, 2729, 373, 18935, 8360, 2806, 29915, 29879, 7977, 29871, 29946, 29909, 373, 5769, 1061, 293, 14009, 29889, 13, 1678, 29068, 379, 1213, 15945, 13, 1678, 302, 353, 3309, 448, 29871, 29896, 13, 1678, 263, 29918, 1807, 353, 518, 29900, 363, 474, 297, 3464, 29898, 29900, 29892, 3309, 4638, 13, 1678, 289, 29918, 1807, 353, 518, 29896, 363, 474, 297, 3464, 29898, 29900, 29892, 302, 4638, 13, 1678, 454, 13821, 29918, 1807, 353, 518, 29874, 29918, 1807, 29889, 8552, 580, 29962, 13, 1678, 11924, 519, 353, 5852, 13, 1678, 1550, 11924, 519, 29901, 13, 4706, 11924, 519, 353, 7700, 13, 4706, 363, 2380, 297, 3464, 29898, 29876, 29892, 29871, 29900, 29892, 448, 29896, 1125, 13, 9651, 565, 263, 29918, 1807, 29961, 2248, 29962, 529, 302, 322, 263, 29918, 1807, 29961, 2248, 29962, 529, 289, 29918, 1807, 29961, 2248, 448, 29871, 29896, 5387, 13, 18884, 11924, 519, 353, 5852, 13, 18884, 263, 29918, 1807, 29961, 2248, 29962, 4619, 29871, 29896, 13, 18884, 263, 29918, 1807, 353, 731, 29918, 517, 29918, 9171, 29898, 29874, 29918, 1807, 29892, 2380, 29897, 13, 18884, 289, 29918, 1807, 353, 2767, 29918, 29890, 29918, 798, 29898, 29874, 29918, 1807, 29892, 289, 29918, 1807, 29897, 13, 18884, 454, 13821, 29918, 1807, 29889, 4397, 29898, 29874, 29918, 1807, 29889, 8552, 3101, 13, 18884, 2867, 13, 1678, 736, 454, 13821, 29918, 1807, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 9995, 29909, 4996, 982, 304, 1423, 565, 278, 3883, 1736, 5149, 338, 304, 7252, 13, 1678, 278, 8877, 2302, 304, 278, 22623, 1353, 310, 278, 1959, 525, 29876, 29915, 2159, 29889, 13, 1678, 1152, 263, 731, 310, 29871, 29955, 3161, 29892, 278, 8877, 2302, 881, 367, 29871, 29947, 29955, 29955, 491, 10914, 29915, 29879, 3694, 1213, 15945, 13, 1678, 6031, 353, 9250, 573, 29918, 29887, 798, 386, 29918, 19651, 29898, 29955, 29897, 13, 1678, 363, 1347, 297, 6031, 29901, 13, 4706, 1596, 29898, 1807, 29897, 13, 1678, 1596, 29898, 2435, 29898, 19651, 876, 13, 2 ]
tests/test_inchi.py	ginkgobioworks/pychemy	3	25988	<filename>tests/test_inchi.py from __future__ import print_function import unittest import six from pychemy.inchi import InChI class InchiTest(unittest.TestCase): def test_unicode_inchi(self): inchi_str = six.u('InChI=1S/C14H18O8/c1-20-9-4-7(5-15)2-3-8(9)21-14-13(19)12(18)11(17)10(6-16)22-14/h2-5,10-14,16-19H,6H2,1H3/t10-,11-,12+,13-,14-/m1/s1') inchi = InChI(inchi_str) formula = inchi.formula self.assertEqual(formula.formula, 'C14H18O8')	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 262, 4161, 29889, 2272, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 5215, 443, 27958, 13, 13, 5215, 4832, 13, 3166, 282, 3376, 6764, 29889, 262, 4161, 1053, 512, 1451, 29902, 13, 13, 1990, 512, 4161, 3057, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 29871, 822, 1243, 29918, 2523, 356, 29918, 262, 4161, 29898, 1311, 1125, 13, 1678, 297, 4161, 29918, 710, 353, 4832, 29889, 29884, 877, 797, 1451, 29902, 29922, 29896, 29903, 29914, 29907, 29896, 29946, 29950, 29896, 29947, 29949, 29947, 29914, 29883, 29896, 29899, 29906, 29900, 29899, 29929, 29899, 29946, 29899, 29955, 29898, 29945, 29899, 29896, 29945, 29897, 29906, 29899, 29941, 29899, 29947, 29898, 29929, 29897, 29906, 29896, 29899, 29896, 29946, 29899, 29896, 29941, 29898, 29896, 29929, 29897, 29896, 29906, 29898, 29896, 29947, 29897, 29896, 29896, 29898, 29896, 29955, 29897, 29896, 29900, 29898, 29953, 29899, 29896, 29953, 29897, 29906, 29906, 29899, 29896, 29946, 29914, 29882, 29906, 29899, 29945, 29892, 29896, 29900, 29899, 29896, 29946, 29892, 29896, 29953, 29899, 29896, 29929, 29950, 29892, 29953, 29950, 29906, 29892, 29896, 29950, 29941, 29914, 29873, 29896, 29900, 15767, 29896, 29896, 15767, 29896, 29906, 29974, 29892, 29896, 29941, 15767, 29896, 29946, 29899, 29914, 29885, 29896, 29914, 29879, 29896, 1495, 13, 1678, 297, 4161, 353, 512, 1451, 29902, 29898, 262, 4161, 29918, 710, 29897, 13, 1678, 7063, 353, 297, 4161, 29889, 689, 2497, 13, 1678, 1583, 29889, 9294, 9843, 29898, 689, 2497, 29889, 689, 2497, 29892, 525, 29907, 29896, 29946, 29950, 29896, 29947, 29949, 29947, 1495, 13, 2 ]
aser/server/cli.py	HKUST-KnowComp/CSKB-Population	13	170992	<gh_stars>10-100 def main(): from aser.server import ASERServer from aser.utils.config import get_server_args_parser parser = get_server_args_parser() args = parser.parse_args() ASERServer(args)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 1753, 1667, 7295, 13, 1678, 515, 408, 261, 29889, 2974, 1053, 3339, 1001, 6004, 13, 1678, 515, 408, 261, 29889, 13239, 29889, 2917, 1053, 679, 29918, 2974, 29918, 5085, 29918, 16680, 13, 1678, 13812, 353, 679, 29918, 2974, 29918, 5085, 29918, 16680, 580, 13, 1678, 6389, 353, 13812, 29889, 5510, 29918, 5085, 580, 13, 1678, 3339, 1001, 6004, 29898, 5085, 29897, 13, 13, 2 ]
order/models/customers.py	fbsamples/cp_reference	2	26310	<reponame>fbsamples/cp_reference # Copyright 2004-present, Facebook. All Rights Reserved. from django.db import models from core.models import BaseModel class Customer(BaseModel): """Represent a single customer instance fields: store: the store this customer belongs to full_name: customer full name email: customer email addr: customer shipping addr """ store = models.ForeignKey("shop.Store", null=True, on_delete=models.SET_NULL) full_name = models.CharField(max_length=255) email = models.CharField(max_length=255) addr = models.TextField(blank=True, null=True) def __str__(self): return self.full_name	[ 1, 529, 276, 1112, 420, 29958, 29888, 5824, 9422, 29914, 6814, 29918, 5679, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29900, 29946, 29899, 6338, 29892, 13327, 29889, 2178, 26863, 2538, 9841, 29889, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 3166, 7136, 29889, 9794, 1053, 7399, 3195, 13, 13, 13, 1990, 21886, 29898, 5160, 3195, 1125, 13, 1678, 9995, 1123, 6338, 263, 2323, 11962, 2777, 13, 13, 1678, 4235, 29901, 13, 1678, 3787, 29901, 278, 3787, 445, 11962, 14393, 304, 13, 1678, 2989, 29918, 978, 29901, 11962, 2989, 1024, 13, 1678, 4876, 29901, 11962, 4876, 13, 1678, 28915, 29901, 11962, 528, 17347, 28915, 13, 1678, 9995, 13, 13, 1678, 3787, 353, 4733, 29889, 27755, 2558, 703, 19032, 29889, 9044, 613, 1870, 29922, 5574, 29892, 373, 29918, 8143, 29922, 9794, 29889, 10490, 29918, 10074, 29897, 13, 1678, 2989, 29918, 978, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 4876, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 28915, 353, 4733, 29889, 15778, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 1583, 29889, 8159, 29918, 978, 13, 2 ]
pysoundcloud/client.py	omarcostahamido/PySoundCloud	4	14144	<filename>pysoundcloud/client.py import re import requests from typing import Union from pysoundcloud.soundcloudplaylists import SoundCloudPlaylists from pysoundcloud.soundcloudsearchresults import SoundCloudSearchResults from pysoundcloud.soundcloudlikedtracks import SoundCloudLikedTracks from pysoundcloud.soundcloudplaylist import SoundCloudPlaylist from pysoundcloud.soundcloudtrack import SoundCloudTrack from pysoundcloud.soundcloudrelatedtracks import SoundCloudRelatedTracks class Client: base_url: str = "https://api-v2.soundcloud.com/" client_id: str = "" """ :var base_url: The base url for all requests to the SoundCloud API :var client_id: The client ID to use with the SoundCloud API """ def __init__(self, client_id: str) -> None: """ Setup the SoundCloud client to interact with the API :param client_id: Your SoundCloud client ID :return: None """ self.client_id = client_id def search(self, query: str, limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudSearchResults]: """ Search SoundCloud for the specified query For some reason it doesn't always work and I have no clue why :param query: The query to search for :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudSearchResults, or False if response is not 200 """ parameters = {"q": query, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "search" response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudSearchResults(response, client_id=self.client_id, parent=self) def track(self, track_id: int) -> Union[bool, SoundCloudTrack]: """ Gets data about the track with the specified track_id :param track_id: The track id to search for :return: a SoundCloudTrack with data about the track, or False if response is not 200 """ parameters = {"client_id": self.client_id} url = self.base_url + "tracks/{}".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudTrack(response.json(), self.client_id, parent=self) def related(self, track_id: int, limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudRelatedTracks]: """ Gets tracks related to the specified track_id :param track_id: The track id to find related tracks for :param limit: The number of tracks to find :param offset: The number of tracks to search for from zero, so offset 10 and limit 10 means find tracks 10-20 :return: SoundCloudRelatedTracks with the tracks, or False if response is not 200 """ parameters = {"limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/related".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudRelatedTracks(response.json(), self.client_id) def playlists(self, track_id: int, representation: str = "mini", limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudPlaylists]: """ Gets playlists containing a specified track :param track_id: The track ID to find playlists containing :param representation: The type of representation (either full or mini) :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudPlaylists containing the track, or False if response is not 200 """ parameters = {"representation": representation, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/playlists_without_albums".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudPlaylists(response.json(), self.client_id, parent=self) def albums(self, track_id: int, representation: str = "mini", limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudPlaylists]: """ Gets albums containing a specified track :param track_id: The track ID to find albums containing :param representation: The type of representation (either full or mini) :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudPlaylists containing the track, or False if response is not 200 """ parameters = {"representation": representation, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/albums".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False if (len(response.json()["collection"]) == 0): return False return SoundCloudPlaylists(response.json(), self.client_id) def comments(self): """ .. todo:: Add a function to get comments on a specific track or by a specific user """ pass # Todo: add comments def web_profiles(self): """ .. todo:: Add a function to get the "web profiles" of a specific user """ pass # Todo: add web_profiles def liked_tracks(self, user_id: int, limit: int = 24, offset: int = 0) -> Union[bool, SoundCloudLikedTracks]: """ Gets the user's liked tracks :param user_id: The ID of the user to find liked tracks for :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudLikedTracks containing all the tracks, or False if response is not 200 """ parameters = {"client_id": self.client_id, "limit": limit, "offset": offset} url = self.base_url + "users/{}/track_likes".format(user_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudLikedTracks(response, client_id=self.client_id) def playlist(self, playlist_id: int = None, playlist_url: str = None, representation: str = "full", secret_token: str = None) -> Union[bool, SoundCloudPlaylist]: """ Get a playlist based on a specified playlist_id or playlist_url :param playlist_id: The ID of the playlist :param playlist_url: The URL of the playlist :param representation: The playlist representation (either fill or mini) :param secret_token: An optional secret token :return: A SoundCloudPlaylist, or False if response is not 200 """ if (playlist_id is None): if (playlist_url is not None): response = requests.get(playlist_url) patterns = [ r'<meta property="twitter:app:url:(?:googleplay\|iphone\|ipad)' r'content="soundcloud:\/\/playlists:([0-9]+)">', r'<meta property="twitter:player" content="https:\/\/w\.soundcloud\.com\/player\/\?url=https' r'%3(?:a\|A)%2(?:f\|F)%2(?:f\|F)api\.soundcloud\.com%2(?:f\|F)playlists%2(?:f\|F)([0-9]+)', r'<meta property="al:(?:ios\|android):url" content="soundcloud:\/\/playlists:([0-9]+)">', r'<link 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app/api/handler/PersonSearch.py	jormanfernandez/crud	0	110207	import falcon import json from data.operators.PersonOperator import PersonOperator from utils.logger import Log from utils.form.fields import Fields fields = Fields() class PersonSearchHandler(object): """ It handles the request to no specific persons Args: object (object): No info """ def on_get(self, req, resp, mode, value): """ Handles the GET for search of persons and gives a list of people that match the search Args: req (Falcon HTTP Request): Request received by the server resp (Falcon HTTP Response): Response constructed by the server mode (str): The search method to be used (name, lastname, phone, email) value (str, int): The value to be search Returns: None """ Log(f"GET Request Received", req=req) resp.content_type = "application/json" if mode not in getattr(fields, "field"): resp.status = falcon.HTTP_400 resp.body = json.dumps({ "msg": "Search method not allowed" }) else: resp.status = falcon.HTTP_200 resp.body = json.dumps(PersonOperator.searchBy(key=mode, value=value), ensure_ascii=False) resp.body += "\n"	[ 1, 1053, 11092, 535, 13, 5215, 4390, 13, 3166, 848, 29889, 3372, 4097, 29889, 7435, 26486, 1053, 5196, 26486, 13, 3166, 3667, 29879, 29889, 21707, 1053, 4522, 13, 3166, 3667, 29879, 29889, 689, 29889, 9621, 1053, 8989, 29879, 13, 13, 9621, 353, 8989, 29879, 580, 13, 13, 1990, 5196, 7974, 4598, 29898, 3318, 1125, 13, 1678, 9995, 13, 1678, 739, 17766, 278, 2009, 304, 694, 2702, 12407, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 1203, 313, 3318, 1125, 1939, 5235, 13, 1678, 9995, 13, 1678, 822, 373, 29918, 657, 29898, 1311, 29892, 12428, 29892, 4613, 29892, 4464, 29892, 995, 1125, 13, 4706, 9995, 13, 4706, 5166, 793, 278, 12354, 363, 2740, 310, 12407, 322, 4076, 263, 1051, 310, 2305, 393, 1993, 278, 2740, 13, 13, 4706, 826, 3174, 29901, 13, 9651, 12428, 313, 29943, 284, 535, 7331, 10729, 1125, 10729, 4520, 491, 278, 1923, 13, 9651, 4613, 313, 29943, 284, 535, 7331, 13291, 1125, 13291, 13319, 491, 278, 1923, 13, 9651, 4464, 313, 710, 1125, 450, 2740, 1158, 304, 367, 1304, 313, 978, 29892, 1833, 978, 29892, 9008, 29892, 4876, 29897, 13, 9651, 995, 313, 710, 29892, 938, 1125, 450, 995, 304, 367, 2740, 13, 13, 4706, 16969, 29901, 13, 9651, 6213, 13, 4706, 9995, 13, 4706, 4522, 29898, 29888, 29908, 7194, 10729, 24328, 2347, 613, 12428, 29922, 7971, 29897, 13, 4706, 4613, 29889, 3051, 29918, 1853, 353, 376, 6214, 29914, 3126, 29908, 13, 13, 4706, 565, 4464, 451, 297, 679, 5552, 29898, 9621, 29892, 376, 2671, 29908, 1125, 13, 9651, 4613, 29889, 4882, 353, 11092, 535, 29889, 10493, 29918, 29946, 29900, 29900, 13, 9651, 4613, 29889, 2587, 353, 4390, 29889, 29881, 17204, 3319, 13, 18884, 376, 7645, 1115, 376, 7974, 1158, 451, 6068, 29908, 13, 9651, 5615, 13, 4706, 1683, 29901, 13, 9651, 4613, 29889, 4882, 353, 11092, 535, 29889, 10493, 29918, 29906, 29900, 29900, 13, 9651, 4613, 29889, 2587, 353, 4390, 29889, 29881, 17204, 29898, 7435, 26486, 29889, 4478, 2059, 29898, 1989, 29922, 8513, 29892, 995, 29922, 1767, 511, 9801, 29918, 294, 18869, 29922, 8824, 29897, 13, 308, 13, 4706, 4613, 29889, 2587, 4619, 6634, 29876, 29908, 13, 268, 2 ]
taut_euler_class.py	henryseg/Veering	2	7614	<reponame>henryseg/Veering # # taut_euler_class.py # from file_io import parse_data_file, write_data_file from taut import liberal, isosig_to_tri_angle from transverse_taut import is_transverse_taut from sage.matrix.constructor import Matrix from sage.modules.free_module_element import vector from sage.arith.misc import gcd from sage.arith.functions import lcm # # Goal - given a transverse taut triangulation, decide if the # associated "absolute" euler class is torsion or not. If it is # torsion, determine its order. # # Contents and overview: # 1. References. # # 2. Background. # # 3. Helper functions. # # 4. Truncate. We build the correct "truncated" cell structure \calT' # from (M, \calT) and give generators for the cochain groups # C^k(\calT', \ZZ) (k = 1, 2). # # 5. Representative. We find a two-cocycle E \in Z^2(\calT', \ZZ) # that represents E(\calT) \in H^2(M, \ZZ). # # 6. Coboundary. We find the matrix for the coboundary operator # \delta^1. # # 7. Linear algebra. We solve the linear problem to decide if E is a # coboundary - that is, if E lies in B^2(\calT', \ZZ) - that is, if E # is in the image of \delta^1. # # 8. Remarks. # # 9. Calling code # # 1. References. # # <NAME> - Orderability and Dehn filling # Ghys - Groups acting on the circle # Thurston - A norm for the homology of three-manifolds # <NAME> - Foliations, chapter four # 2. Background: # Suppose that (M, \calT) is a transverse taut triangulation. Then # \calT^{2} is the "horizontal branched surface". This caries various # laminations, which extend to foliations on M. All of these have the # same Euler class, which we will denote E(\calT) \in H^2(M, \ZZ). # Suppose that \calF is a carried foliation and let UT\calF be the # unit tangent bundle over \calF. The Euler class E vanishes exactly # when UT\calF has a section; that is, when the unit tangent bundle is # trivialisable. # Recall: # Suppose that X is an F-bundle over B. We have # # i # F -------> X <--. # \| \| # \| \| # p\| \|s # \| \| # v \| # B ---' # # So s \from B \to X is a \emph{section} if p \circ s = Id_B # 3. Helper functions def diagonal(D): return [D[i][i] for i in range(min(D.dimensions()))] # 4. Truncate. # Suppose that M is a connected, cusped, oriented three-manifold. Let # C = C(M) \geq 1 be the number of cusps of M. Suppose that \calT is a # transverse taut ideal triangulation of M. Let T = T(\calT) \geq 1 # be the number of tetrahedra of \calT. # We use Regina to number and orient the edges \{e_i\}_{i = 0}^{T-1}, # the faces \{f_i\}_{i = 0}^{2T-1}, and the tetrahedra \{t_i\}_{i = # 0}^{T-1} of \calT. We call all of these \emph{ideal} cells. Note # that the transverse structure also gives us co-orientations of the # e_i and the f_i, called "upwards" # We remove a small open neighbourbood of all ideal vertices of all # model tetrahedra. This gives the \emph{truncated} cell structure # \calT'. The remains of the ideal cells are called \emph{truncated} # cells; we abuse and reuse the notations e_i and f_i for these. The # truncated cells inherit orientations and co-orientations. The new # cells are called \emph{peripheral} cells. We number these as # follows: # e_{ij} is the peripheral edge cutting vertex v_j off of ideal face f_i # f_{ij} is the peripheral face cutting vertex v_j off of ideal tetrahedron t_i # Note that every truncated face is combinatorially a hexagon. The # boundary of this hexagon contains three truncated edges alternating # with three peripheral edges. We orient each peripheral edge e_{ij} # so that the orientation of e_{ij} agrees with the orientation # induced by \bdy f_i. We orient each peripheral face f_{ij} # anti-clockwise, as viewed from infinity (that is, from outside of # M). Also, we equip e_{ij} and f_{ij} with co-orientations pointing # out of M, called "outward". # e_{i0} # --- # / \ # e_2 / \ e_1 # / \ # / f_i \ # \ / # e_{i1} --------- e_{i2} # e_0 # For an edge e or a face f we use e^* and f^* to denote the dual in # C^1(\calT', \ZZ) or C^2(\calT', \ZZ). Thus \{e^_i\} \cup # \{e^_{ij}\} generates C^1(\calT', \ZZ) while \{f^_i\} \cup # \{f^_{ij}\} generates C^2(\calT', \ZZ). # For more pictures, see # /Veering_code/NotesPictures/euler_notes_from_nathan.jpg # 5. Representative # We now construct a two-cocycle E \in Z^2(\calT', \ZZ). For every # peripheral face f we take # E(f) = 0. # \begin{remark} # To see that this is correct, let \calF be any foliation of M, # transverse to the boundary. Suppose that f is the given peripheral # triangle. We have a section of the restriction of UT\calF to \bdy # f; namely the outward field. This extends over f to give a section # of UT\calF restricted to f. So there is no obstruction to the # extension. See below for a more precise discussion in terms of # "Poincar\'e-Hopf index". # \end{remark} # Now suppose that f is a truncated face. Suppose that e_0, e_1, e_2 # are its three truncated edges. Recall that these are all oriented. # Let AC(f) be the number of the edges e_0, e_1, e_2 that are # oriented anti-clockwise (that is, agree with their induced # orientation coming from f). We take # E(f) = AC(f) - 2 # If we flip the transverse direction: AC(f') = 3 - AC(f), # so E(f') = AC(f') - 2 = 1 - AC(f) = 2 - AC(f) - 1 = -E(f) - 1 # \begin{remark} # Here is one way to remember (and explain!) this rule. Suppose that # f is the given truncated face. Suppose that s is a section of UTf \| # \bdy f. Then index(s) is the total rotation of s with respect to # the tangent field, _plus_ one. This can be rephrased in terms of # the index of tangent vector fields extending s over all of f. # Our choices of orientations of edges determine a section of UTf \| # \bdy f. Since all of the boundary edges e_{ij} of f are oriented # the same way, we choose a standard framing there; Nathan tells us to # just use the outward pointing section on all of the e_{ij}. Our # choice of section on e_0 (say) has to (a) depend only on the # orientation of e_0 and (b) has to be outward at the endpoints of # e_0. The simplest choice is the section that rotates by +\pi with # respect to the tangent along \bdy f_i, as we move forward along e_0. # So s points _back_ at the beginning of e_0, points _right_ in the # middle of e_0, and points _forwards_ at the end of e_0. The total # rotation of the resulting field (with respect to the tangent field) # is AC(f) - 3. Thus E(f) = AC(f) - 2 is the index. You can check # this works by drawing the four possible pictures and computing the index # of any extension of s over f. # \end{remark} # Claim: \delta^2 E = 0. # That is, E is a cocycle. # Proof of claim: Fix a truncated tetrahedron t and fix some oriention # of its truncated edges. A direct calculation shows that # \delta E (t) = E \bdy t = 0. # Likewise, a direct computation shows that switching the orientation # of a single edge leaves E \bdy t unchanged. QED. ### It would be nice to have a less computational proof! def euler_cocycle(tri, angle): """ Given a regina triangulation "tri", with oriented edges, and a transverse taut angle structure "angle", returns the associated two-cocycle E representing the Euler class E(tri). """ assert is_transverse_taut(tri, angle) face_coorientations = is_transverse_taut(tri, angle, return_type = "face_coorientations") # E will be a _row_ vector, because it eats column vectors. E = [] # First deal with the truncated faces for face in tri.faces(2): # 2 = dimension # First we compute the number of Regina oriented edges that agree with the Regina orientation on face AC = 0 for i in range(3): perm = face.faceMapping(1, i) # print perm[0], perm[1] if perm[1] == ((perm[0] + 1) % 3): # the edge and face orientations agree so, AC = AC + 1 # print "AC", AC # Now we condition on whether or not Regina and angle agree on the (co-)orientation of the face. if face_coorientations[face.index()] == 1: E.append(AC - 2) else: E.append(1 - AC) # Now deal with the peripheral faces for tet in tri.tetrahedra(): for j in range(4): E.append(0) return E # 6. Coboundary # Suppose that e is a truncated edge. Let LF be the set of truncated # faces to the left of e and let RF be the set of faces to the right. Then # \delta e^* = \sum_{f \in LF} f^* - \sum_{f \in RF} f^. # Suppose that e is a peripheral edge. So there is a unique truncated # face f meeting e. Note that f is to the left of e. There are # also a pair of boundary faces meeting e: say f' _above_ e and f'' # _below_ e. Then # \delta e^ = f^* + (f')^* - (f'')^. def coboundary(tri, angle): """ Given a triangulation "tri" (T), with oriented edges, and a transverse taut angle structure "angle", returns the co-boundary operator delta^1 \from C^1(T', ZZ) \to C^2(T', ZZ), as a matrix, for the truncated triangulation T'. Note that, strictly speaking, we don't need to use "angle" for this, but we use it to determine orientation on faces for the Euler class, so we might as well use it again here. """ # \delta^1 takes row vectors (functions on edges) and spits out # row vectors (functions on faces). So, if c is a one-cochain # then c \cdot \delta is a two-cochain. delta = [] assert is_transverse_taut(tri, angle) tet_vert_coorientations = is_transverse_taut(tri, angle, return_type = "tet_vert_coorientations") face_coorientations = is_transverse_taut(tri, angle, return_type = "face_coorientations") for edge in tri.edges(): # A row for every truncated edge row = [] for face in tri.triangles(): # A row entry for every truncated face count = 0 for i in range(3): if face.edge(i) == edge: perm = face.faceMapping(1, i) if perm[1] == ((perm[0] + 1) % 3): # the edge and face orientations agree so, count += 1 else: count -= 1 row.append(count face_coorientations[face.index()]) # +1 if face is to the left of the edge, -1 if face is to # the right of the edge, using Regina's edge orientation # when viewed from above (using the transverse taut notion # of up) # ,'\| # ,' \| # ,' \| # ,' CCW \| gets a +1 # `. ^ # `. \| # `. \| # `.\| for tet in tri.simplices(): for i in range(4): row.append(0) delta.append(row) for face in tri.triangles(): face_embeddings = [] for j in range(2): face_embeddings.append( face.embedding(j) ) for i in range(3): # vertices of the face # A row for every peripheral edge row = [] for face2 in tri.triangles(): # A row entry for every truncated face if face2 == face: row.append(1) else: row.append(0) for tet in tri.simplices(): for k in range(4): # A row entry for every peripheral face count = 0 for j in range(2): if (tet == face_embeddings[j].simplex()) and (face_embeddings[j].vertices()[i] == k): # the tetrahedron is on the jth side of the # face and the ith vertex of face is the kth # vertex of tet face_num_in_tet = face_embeddings[j].vertices()[3] count -= tet_vert_coorientations[tet.index()][face_num_in_tet] # tet_vert_coorientations is +1 if # coorientation on face points out of the # tetrahedron, and we want count += 1 if # the peripheral face is above the # peripheral edge row.append(count) delta.append(row) return delta # 7. Linear algebra # We ask: is there a one-cocycle C \in C^1(\calT', \ZZ) so that # \delta C = E? If so, then [E] = E(\calT) is zero in H^2, as # desired. # This is a linear algebra problem, so can be solved by, say, sage. def order_of_euler_class(delta, E): """ Given the coboundary operator delta and an Euler two-cocycle E, returns k if [E] is k--torsion. By convention, returns zero if [E] is non-torsion. Note that the trivial element is 1--torsion. """ delta = Matrix(delta) E = vector(E) # Note that E is a coboundary if there is a one-cocycle C solving # # E = Cdelta # # We can find C (if it exists at all) using Smith normal form. D, U, V = delta.smith_form() assert D == UdeltaV # So we are trying to solve # # Cdelta = CU.inverse()DV.inverse() = E # # for a one-cochain C. Multiply by V to get # # CdeltaV = CU.inverse()D = EV # # Now set # # B = CU.inverse(), and so BU = C # # and rewrite to get # # BUdeltaV = BD = EV # # So define E' by: Ep = EV # Finally we attempt to solve B * D = Ep. Note that D is # diagonal: so if we can solve all of the equations # B[i] * D[i][i] == Ep[i] # with B[i] integers, then [E] = 0 in cohomology. diag = diagonal(D) if any( (diag[i] == 0 and Ep[i] != 0) for i in range(len(Ep)) ): return 0 # All zeros are at the end in Smith normal form. Since we've # passed the above we can now remove them. first_zero = diag.index(0) diag = diag[:first_zero] Ep = Ep[:first_zero] # Since diag[i] is (now) never zero we can divide to get the # fractions Ep[i]/diag[i] and then find the scaling that makes # them simultaneously integral. denoms = [ diag[i] / gcd(Ep[i], diag[i]) for i in range(len(Ep)) ] return lcm(denoms) # 8. Remarks # a) Here is a nice trick that proves [E] = 0 in some cases. Suppose # that \gamma is an oriented path in \bdy M. Suppose that \gamma is # transverse to the one-skeleton of \calT'. We form a one-cocycle # D_\gamma by adding up the boundary edges that \gamma crosses, with # sign. The sign is positive if \gamma crosses from below to above, # and negative otherwise. Note that \delta D_\gamma vanishes on all # boundary faces. # b) <NAME> says that we should take the paths that go up # through the centres of tetrahedra and take the Poincare dual. BUT I # think this is not what we want... Marc is thinking of the relative # Euler class as discussed on page 390 of his paper "Taut ideal # triangulations of three-manifolds". The relative Euler class lives # in H^2(M, \bdy M), so is Poincare dual to an element of H_1(M), # represented by a collection of loops. # c) [2019-03-31] It seems that, for transverse veering triangulations # in the 16 census, the Euler class is always zero or two-torsion. # Note that there are manifolds M in the census where H^2(M, \ZZ) has # positive rank... What about odd torsion? # Question: If the veering triangulation is edge-orientable, does the # Euler class vanish? # Answer: Yes. Here is a version of a discussion with Nathan # [2020-04-03] - he says the following: # Suppose that F is a foliation carried by the horizontal branched # surface. Let UTF be the unit tangent bundle to F. We think of # e(UTF) as being the obstruction to UTF having a section. Let G be # the foliation carried by the upper (aka green) branched surface. If # G is transversely orientable (aka edge-orientability of the veering # triangulation) then G \cap F gives the desired section, and e(UTF) = # 0. Note that G \cap F gives, for every point, a pair of points in # the unit tangent circle. So let PUTF be the projective unit tangent # bundle to F. This definitely has a section, so e(PUTF) = 0. Now, # the bundle UTF is a double cover of the bundle PUTF. # Claim: The euler class is multiplicative with respect to covers (in # both senses). # With the claim in hand, we have # 2 * e(UTF) = e(PUTF) = 0 # We deduce that e(UTF) is either zero or two-torsion. # 9. Calling code @liberal def order_of_euler_class_wrapper(tri, angle): """ Returns the order of the euler class. """ return order_of_euler_class(coboundary(tri, angle), euler_cocycle(tri, angle)) def compute_order_of_euler_classes(file_in, number=None, file_out=None): data_in = parse_data_file(file_in) data_in = [line.split(" ") for line in data_in] if number != None: data_in = data_in[:number] data_out = [] evil = [] for i, line in enumerate(data_in): if i % 50 == 0: print( ((1.0i)/(1.0len(data_in)), len(data_out)) ) sig = line[0] tri, angle = isosig_to_tri_angle(sig) # angle = [int(letter) for letter in angle_s] curr_euler = order_of_euler_class(coboundary(tri, angle), euler_cocycle(tri, angle)) if curr_euler == "non-torsion": evil.append(sig) print(sig + " has non-torsion Euler class!!!!") elif curr_euler == 1: # order is one so [E] = 0. 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tests/test_utils.py	nhoffman/static_html_demo	0	104010	<filename>tests/test_utils.py<gh_stars>0 """ Test utils module. """ import logging from static_html_demo.utils import opener from __init__ import TestBase, get_testfile log = logging.getLogger(__name__) class TestOpener(TestBase): def setUp(self): with open(get_testfile('lorem.txt')) as f: self.firstline = f.next() def test01(self): for suffix in ['txt', 'gz', 'bz2']: fn = get_testfile('lorem.'+suffix) fobj = opener(fn) self.assertEqual(fobj.next(), self.firstline) def test02(self): for suffix in ['txt', 'gz', 'bz2']: fn = get_testfile('lorem.'+suffix) fobj = opener(fn, 'r') self.assertEqual(fobj.next(), self.firstline)	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 13239, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 3057, 3667, 29879, 3883, 29889, 13, 15945, 29908, 13, 13, 5215, 12183, 13, 13, 3166, 2294, 29918, 1420, 29918, 17482, 29889, 13239, 1053, 1015, 759, 13, 13, 3166, 4770, 2344, 1649, 1053, 4321, 5160, 29892, 679, 29918, 1688, 1445, 13, 1188, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1990, 4321, 11746, 759, 29898, 3057, 5160, 1125, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 411, 1722, 29898, 657, 29918, 1688, 1445, 877, 29880, 3668, 29889, 3945, 8785, 408, 285, 29901, 13, 9651, 1583, 29889, 4102, 1220, 353, 285, 29889, 4622, 580, 13, 13, 1678, 822, 1243, 29900, 29896, 29898, 1311, 1125, 13, 4706, 363, 25557, 297, 6024, 3945, 742, 525, 18828, 742, 525, 29890, 29920, 29906, 2033, 29901, 13, 9651, 7876, 353, 679, 29918, 1688, 1445, 877, 29880, 3668, 6169, 29974, 2146, 600, 861, 29897, 13, 9651, 285, 5415, 353, 1015, 759, 29898, 9144, 29897, 13, 9651, 1583, 29889, 9294, 9843, 29898, 29888, 5415, 29889, 4622, 3285, 1583, 29889, 4102, 1220, 29897, 13, 13, 1678, 822, 1243, 29900, 29906, 29898, 1311, 1125, 13, 4706, 363, 25557, 297, 6024, 3945, 742, 525, 18828, 742, 525, 29890, 29920, 29906, 2033, 29901, 13, 9651, 7876, 353, 679, 29918, 1688, 1445, 877, 29880, 3668, 6169, 29974, 2146, 600, 861, 29897, 13, 9651, 285, 5415, 353, 1015, 759, 29898, 9144, 29892, 525, 29878, 1495, 13, 9651, 1583, 29889, 9294, 9843, 29898, 29888, 5415, 29889, 4622, 3285, 1583, 29889, 4102, 1220, 29897, 13, 2 ]
src/scout_apm/core/cli/core_agent_manager.py	tim-schilling/scout_apm_python	60	168454	<gh_stars>10-100 # coding=utf-8 from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging from scout_apm.core import CoreAgentManager logger = logging.getLogger(__name__) def main(argv=None): parser = argparse.ArgumentParser() parser.add_argument( "-v", "--verbose", help="increase output verbosity", action="count" ) subparsers = parser.add_subparsers( title="subcommands", description="valid subcommands", dest="subparser" ) subparsers.add_parser("download") subparsers.add_parser("launch") args = parser.parse_args(argv) if args.verbose is not None: if args.verbose >= 2: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) core_agent_manager = CoreAgentManager() getattr(core_agent_manager, args.subparser)()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 14137, 29922, 9420, 29899, 29947, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 8542, 29892, 1596, 29918, 2220, 29892, 29104, 29918, 20889, 1338, 13, 13, 5215, 1852, 5510, 13, 5215, 12183, 13, 13, 3166, 885, 449, 29918, 481, 29885, 29889, 3221, 1053, 10239, 19661, 3260, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1753, 1667, 29898, 19218, 29922, 8516, 1125, 13, 1678, 13812, 353, 1852, 5510, 29889, 15730, 11726, 580, 13, 1678, 13812, 29889, 1202, 29918, 23516, 29898, 13, 4706, 11663, 29894, 613, 376, 489, 369, 15828, 613, 1371, 543, 262, 1037, 559, 1962, 9750, 359, 537, 613, 3158, 543, 2798, 29908, 13, 1678, 1723, 13, 13, 1678, 1014, 862, 4253, 353, 13812, 29889, 1202, 29918, 1491, 862, 4253, 29898, 13, 4706, 3611, 543, 1491, 26381, 613, 6139, 543, 3084, 1014, 26381, 613, 2731, 543, 1491, 16680, 29908, 13, 1678, 1723, 13, 1678, 1014, 862, 4253, 29889, 1202, 29918, 16680, 703, 10382, 1159, 13, 1678, 1014, 862, 4253, 29889, 1202, 29918, 16680, 703, 15343, 1159, 13, 13, 1678, 6389, 353, 13812, 29889, 5510, 29918, 5085, 29898, 19218, 29897, 13, 13, 1678, 565, 6389, 29889, 369, 15828, 338, 451, 6213, 29901, 13, 4706, 565, 6389, 29889, 369, 15828, 6736, 29871, 29906, 29901, 13, 9651, 12183, 29889, 16121, 3991, 29898, 5563, 29922, 21027, 29889, 18525, 29897, 13, 4706, 1683, 29901, 13, 9651, 12183, 29889, 16121, 3991, 29898, 5563, 29922, 21027, 29889, 11690, 29897, 13, 13, 1678, 7136, 29918, 14748, 29918, 12847, 353, 10239, 19661, 3260, 580, 13, 1678, 679, 5552, 29898, 3221, 29918, 14748, 29918, 12847, 29892, 6389, 29889, 1491, 16680, 29897, 580, 13, 2 ]
retry_requests.py	cube3x3x3/get_radio	0	197518	import logging import unittest import requests logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) def custom_session( retries=5, backoff_factor=0.5, status_forcelist=(500, 502, 504), session=None, ): session = session or requests.Session() retry = requests.packages.urllib3.util.retry.Retry( total=retries, read=retries, connect=retries, backoff_factor=backoff_factor, status_forcelist=status_forcelist, ) adapter = requests.adapters.HTTPAdapter(max_retries=retry) session.mount('http://', adapter) session.mount('https://', adapter) return session class TestRequestsMethods(unittest.TestCase): def test_retry(self): logger.info('start test_retry') with self.assertRaises(requests.exceptions.ConnectionError) as x: response = custom_session(2, 0.2).get( 'http://localhost:9999', ) logger.info('test_retry %s', x.exception) logger.info('end test_retry') def test_timeout(self): logger.info('start test_timeout2') with self.assertRaises(requests.exceptions.Timeout) as x: response = custom_session(2, 0.2).get( 'http://httpbin.org/delay/10', timeout=0.001 ) logger.info('test_timeout2 %s', x.exception) logger.info('end test_timeout2') def test_status500(self): logger.info('start test_status') with self.assertRaises(requests.exceptions.RetryError) as x: response = custom_session(2, 0.2).get( 'http://httpbin.org/status/500', ) logger.info('test_status500 %s', x.exception) if __name__ == '__main__': logging.basicConfig(level=logging.INFO) unittest.main(verbosity=2)	[ 1, 1053, 12183, 30004, 13, 5215, 443, 27958, 30004, 13, 5215, 7274, 30004, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 8443, 13, 21707, 29889, 842, 10108, 29898, 21027, 29889, 11690, 8443, 13, 30004, 13, 1753, 2888, 29918, 7924, 29898, 30004, 13, 1678, 3240, 2722, 29922, 29945, 11167, 13, 1678, 1250, 2696, 29918, 19790, 29922, 29900, 29889, 29945, 11167, 13, 1678, 4660, 29918, 1454, 2242, 391, 7607, 29945, 29900, 29900, 29892, 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janny/utils.py	icyphox/janny	5	117830	import json import re from datetime import timedelta from types import SimpleNamespace from janny.config import API_HOST from janny.auth import SESSION def get(path: str) -> SimpleNamespace: """Convert a JSON response into a Python object""" s = SESSION data = s.get(API_HOST + path).content obj = json.loads(data, object_hook=lambda d: SimpleNamespace(*d)) return obj timedelta_regex = ( r"((?P<days>-?\d+)d)?" r"((?P<hours>-?\d+)h)?" r"((?P<minutes>-?\d+)m)?" ) timedelta_pattern = re.compile(timedelta_regex, re.IGNORECASE) def parse_delta(delta: str) -> timedelta: """Parses a human readable timedelta (3d5h19m) into a datetime.timedelta. Delta includes: Xd days * Xh hours * Xm minutes Values can be negative following timedelta's rules. Eg: -5h-30m """ match = timedelta_pattern.match(delta) if match: parts = {k: int(v) for k, v in match.groupdict().items() if v} return timedelta(**parts) return timedelta(0) # List of running threads. Storing it here to prevent circular imports. RUNNING = list()	[ 1, 1053, 4390, 13, 5215, 337, 13, 3166, 12865, 1053, 5335, 287, 2554, 13, 3166, 4072, 1053, 12545, 23335, 13, 13, 3166, 432, 14763, 29889, 2917, 1053, 3450, 29918, 20832, 13, 3166, 432, 14763, 29889, 5150, 1053, 3725, 13507, 13, 13, 13, 1753, 679, 29898, 2084, 29901, 851, 29897, 1599, 12545, 23335, 29901, 13, 1678, 9995, 18455, 263, 4663, 2933, 964, 263, 5132, 1203, 15945, 29908, 13, 1678, 269, 353, 3725, 13507, 13, 1678, 848, 353, 269, 29889, 657, 29898, 8787, 29918, 20832, 718, 2224, 467, 3051, 13, 1678, 5446, 353, 4390, 29889, 18132, 29898, 1272, 29892, 1203, 29918, 20849, 29922, 2892, 270, 29901, 12545, 23335, 29898, 1068, 29881, 876, 13, 1678, 736, 5446, 13, 13, 13, 9346, 287, 2554, 29918, 13087, 353, 313, 13, 1678, 364, 29908, 3552, 29973, 29925, 29966, 16700, 29958, 29899, 29973, 29905, 29881, 28135, 29881, 29897, 3026, 364, 29908, 3552, 29973, 29925, 29966, 29882, 2470, 29958, 29899, 29973, 29905, 29881, 28135, 29882, 29897, 3026, 364, 29908, 3552, 29973, 29925, 29966, 1195, 2667, 29958, 29899, 29973, 29905, 29881, 28135, 29885, 29897, 3026, 13, 29897, 13, 13, 9346, 287, 2554, 29918, 11037, 353, 337, 29889, 12198, 29898, 9346, 287, 2554, 29918, 13087, 29892, 337, 29889, 6259, 6632, 1525, 23487, 29897, 13, 13, 13, 1753, 6088, 29918, 4181, 29898, 4181, 29901, 851, 29897, 1599, 5335, 287, 2554, 29901, 13, 1678, 9995, 29925, 1503, 267, 263, 5199, 19909, 5335, 287, 2554, 313, 29941, 29881, 29945, 29882, 29896, 29929, 29885, 29897, 964, 263, 12865, 29889, 9346, 287, 2554, 29889, 13, 1678, 360, 2554, 7805, 29901, 13, 1678, 334, 1060, 29881, 3841, 13, 1678, 334, 1060, 29882, 6199, 13, 1678, 334, 1060, 29885, 6233, 13, 1678, 2630, 1041, 508, 367, 8178, 1494, 5335, 287, 2554, 29915, 29879, 6865, 29889, 14304, 29901, 448, 29945, 29882, 29899, 29941, 29900, 29885, 13, 1678, 9995, 13, 1678, 1993, 353, 5335, 287, 2554, 29918, 11037, 29889, 4352, 29898, 4181, 29897, 13, 1678, 565, 1993, 29901, 13, 4706, 5633, 353, 426, 29895, 29901, 938, 29898, 29894, 29897, 363, 413, 29892, 325, 297, 1993, 29889, 2972, 8977, 2141, 7076, 580, 565, 325, 29913, 13, 4706, 736, 5335, 287, 2554, 29898, 1068, 20895, 29897, 13, 1678, 736, 5335, 287, 2554, 29898, 29900, 29897, 13, 13, 13, 29937, 2391, 310, 2734, 9717, 29889, 624, 8253, 372, 1244, 304, 5557, 19308, 24802, 29889, 13, 29934, 3904, 29940, 4214, 353, 1051, 580, 13, 2 ]
w11-functional-programming/teach-sort-lambda/pupils.py	carloswm85/2021-cs111-programming-with-functions	0	145744	import csv # Each row in the pupils.csv file contains three elements. # These are the indexes of the elements in each row. GIVEN_NAME_INDEX = 0 SURNAME_INDEX = 1 BIRTHDATE_INDEX = 2 def main(): path = "E:/GitHub/2021-cs111-programming-with-functions/w11-functional-programming/teach-sort/pupils.csv" # Call the read_compound_list function to read the pupils.csv file into a list named students_list. students_list = read_compound_list(path) # Write a lambda function that will extract the birthdate from a student. birthdate_func = lambda birthdate: birthdate[BIRTHDATE_INDEX] given_name_func = lambda given_name: given_name[GIVEN_NAME_INDEX] birthdate_func_2 = lambda birthdate: birthdate[BIRTHDATE_INDEX] # Write a call to the sorted function that will sort the students_list by birthdate from oldest to youngest. sorted_students_list_bd = sorted(students_list, key = birthdate_func) sorted_students_list_gn = sorted(students_list, key = given_name_func) sorted_students_list_bd_2 = sorted(students_list, key = birthdate_func_2) print("\nLISTA #1:") print_list(students_list) # Print the students_list by calling the print_list function. print("\nLISTA #2:") print_list(sorted_students_list_bd) # Within the main function, replace the code that sorts the students_list by birthdate, with code that sorts the students_list by given name. print("\nLISTA #3:") print_list(sorted_students_list_gn) # Within the main function, replace the code that sorts the students_list by birthdate, with code that sorts the students_list by birth month and day. In other words, the code should sort the students_list by birthdate but ignore the year when a student was born. print("\nLISTA #4:") print_list(sorted_students_list_bd_2) def read_compound_list(filename): """Read the text from a CSV file into a compound list. The compound list will contain small lists. Each small list will contain the data from one row of the CSV file. Parameter filename: the name of the CSV file to read. Return: the compound list """ # Create an empty list. compound_list = [] # Open the CSV file for reading. with open(filename, "rt") as csv_file: # Use the csv module to create a reader # object that will read from the opened file. reader = csv.reader(csv_file) # The first line of the CSV file contains column headings # and not a student's I-Number and name, so this statement # skips the first line of the CSV file. next(reader) # Process each row in the CSV file. for row in reader: # Append the current row at the end of the compound list. compound_list.append(row) return compound_list def print_list(lista): for element in lista: print(element) if __name__ == "__main__": main()	[ 1, 1053, 11799, 13, 13, 13, 29937, 7806, 1948, 297, 278, 23449, 2719, 29889, 7638, 934, 3743, 2211, 3161, 29889, 13, 29937, 4525, 526, 278, 18111, 310, 278, 3161, 297, 1269, 1948, 29889, 13, 29954, 5667, 1430, 29918, 5813, 29918, 27992, 353, 29871, 29900, 13, 29903, 4574, 5813, 29918, 27992, 353, 29871, 29896, 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chaban/bots/telegram_bot.py	ibrag8998/botchaban	1	92335	import typing as typ import httpx from chaban.config import global_settings, settings from chaban.core.exceptions import HttpMethodNotAllowed, TelegramAPIError from chaban.handlers.base import mh_registry from chaban.utils import MetaSingleton from .telegram_methods import TelegramMethodsMixin class TelegramBot(TelegramMethodsMixin, metaclass=MetaSingleton): """ Main telegram bot class. All methods related to telegram api are defined in ``TelegramMethodsMixin``. On init, get ``TELEGRAM_TOKEN`` from settings. Settings are getting that token from env. """ _allowed_http_methods = global_settings.TELEGRAM_ALLOWED_HTTP_METHODS def __init__(self): # bot don't need the token as a separate constant, token is only used as part of # the telegram api endpoint url self._endpoint = "https://api.telegram.org/bot{}/".format( settings.TELEGRAM_TOKEN ) def _build_url(self, method_name: str) -> str: return self._endpoint + method_name.lstrip("/") def request( self, method_name: str, http_method: str = "get", **kwargs ) -> typ.Dict[str, typ.Any]: """ Perform an HTTP :param http_method: request and pass the kwargs as params. Returns a JSON. """ http_method = http_method.lower() if http_method not in self._allowed_http_methods: raise HttpMethodNotAllowed return httpx.request( http_method, self._build_url(method_name), params=kwargs ).json() def start_polling(self): for message in self._poll_updates(): mh_registry.get_handler_and_handle(message) def _poll_updates(self) -> typ.Iterator[typ.Dict[str, typ.Any]]: """ Main loop. Getting updates from telegram, handling offset, yielding each update's message """ # set offset to 0 offset = 0 # start loop while True: # get json response from telegram resp = self.get_updates(offset=offset) # if not ok, raise the error if not resp["ok"]: raise TelegramAPIError("Response JSON: {}".format(resp)) # iterate through updates from resp json for update in resp["result"]: # update offset offset = max(offset, update["update_id"] + 1) # yield message yield update.get("message", update.get("edited_message"))	[ 1, 1053, 19229, 408, 2393, 13, 13, 5215, 1732, 29916, 13, 13, 3166, 521, 10910, 29889, 2917, 1053, 5534, 29918, 11027, 29892, 6055, 13, 3166, 521, 10910, 29889, 3221, 29889, 11739, 29879, 1053, 9056, 4062, 3664, 15930, 287, 29892, 9699, 1393, 8787, 2392, 13, 3166, 521, 10910, 29889, 3179, 9306, 29889, 3188, 1053, 286, 29882, 29918, 1727, 6020, 13, 3166, 521, 10910, 29889, 13239, 1053, 20553, 10873, 11285, 13, 13, 3166, 869, 15494, 1393, 29918, 23515, 1053, 9699, 1393, 26112, 29924, 861, 262, 13, 13, 13, 1990, 9699, 1393, 29933, 327, 29898, 29911, 6146, 1393, 26112, 29924, 861, 262, 29892, 1539, 562, 605, 29922, 19346, 10873, 11285, 1125, 13, 1678, 9995, 13, 1678, 4241, 4382, 1393, 9225, 770, 29889, 13, 13, 1678, 2178, 3519, 4475, 304, 4382, 1393, 7882, 526, 3342, 297, 4954, 29911, 6146, 1393, 26112, 29924, 861, 262, 29952, 1412, 13, 13, 1678, 1551, 2069, 29892, 679, 4954, 4330, 1307, 29954, 25058, 29918, 4986, 29968, 1430, 16159, 515, 6055, 29889, 13, 1678, 19215, 526, 2805, 393, 5993, 515, 8829, 29889, 13, 1678, 9995, 13, 13, 1678, 903, 24622, 29918, 1124, 29918, 23515, 353, 5534, 29918, 11027, 29889, 4330, 1307, 29954, 25058, 29918, 1964, 27998, 3352, 29918, 10493, 29918, 2303, 4690, 29949, 8452, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 396, 9225, 1016, 29915, 29873, 817, 278, 5993, 408, 263, 5004, 4868, 29892, 5993, 338, 871, 1304, 408, 760, 310, 13, 4706, 396, 278, 4382, 1393, 7882, 16248, 3142, 13, 4706, 1583, 3032, 29734, 353, 376, 991, 597, 2754, 29889, 15494, 1393, 29889, 990, 29914, 7451, 29912, 6822, 1642, 4830, 29898, 13, 9651, 6055, 29889, 4330, 1307, 29954, 25058, 29918, 4986, 29968, 1430, 13, 4706, 1723, 13, 13, 1678, 822, 903, 4282, 29918, 2271, 29898, 1311, 29892, 1158, 29918, 978, 29901, 851, 29897, 1599, 851, 29901, 13, 4706, 736, 1583, 3032, 29734, 718, 1158, 29918, 978, 29889, 29880, 17010, 11974, 1159, 13, 13, 1678, 822, 2009, 29898, 13, 4706, 1583, 29892, 1158, 29918, 978, 29901, 851, 29892, 1732, 29918, 5696, 29901, 851, 353, 376, 657, 613, 3579, 19290, 13, 1678, 1723, 1599, 2393, 29889, 21533, 29961, 710, 29892, 2393, 29889, 10773, 5387, 13, 4706, 9995, 13, 4706, 27313, 385, 7331, 584, 3207, 1732, 29918, 5696, 29901, 2009, 322, 1209, 278, 9049, 5085, 408, 8636, 29889, 13, 13, 4706, 16969, 263, 4663, 29889, 13, 4706, 9995, 13, 4706, 1732, 29918, 5696, 353, 1732, 29918, 5696, 29889, 13609, 580, 13, 13, 4706, 565, 1732, 29918, 5696, 451, 297, 1583, 3032, 24622, 29918, 1124, 29918, 23515, 29901, 13, 9651, 12020, 9056, 4062, 3664, 15930, 287, 13, 13, 4706, 736, 1732, 29916, 29889, 3827, 29898, 13, 9651, 1732, 29918, 5696, 29892, 1583, 3032, 4282, 29918, 2271, 29898, 5696, 29918, 978, 511, 8636, 29922, 19290, 13, 4706, 13742, 3126, 580, 13, 13, 1678, 822, 1369, 29918, 3733, 1847, 29898, 1311, 1125, 13, 4706, 363, 2643, 297, 1583, 3032, 29886, 3028, 29918, 786, 15190, 7295, 13, 9651, 286, 29882, 29918, 1727, 6020, 29889, 657, 29918, 13789, 29918, 392, 29918, 8411, 29898, 4906, 29897, 13, 13, 1678, 822, 903, 29886, 3028, 29918, 786, 15190, 29898, 1311, 29897, 1599, 2393, 29889, 20277, 29961, 22449, 29889, 21533, 29961, 710, 29892, 2393, 29889, 10773, 5262, 29901, 13, 4706, 9995, 13, 4706, 4241, 2425, 29889, 13, 13, 4706, 24162, 11217, 515, 4382, 1393, 29892, 11415, 9210, 29892, 7709, 292, 1269, 2767, 29915, 29879, 2643, 13, 4706, 9995, 13, 4706, 396, 731, 9210, 304, 29871, 29900, 13, 4706, 9210, 353, 29871, 29900, 13, 13, 4706, 396, 1369, 2425, 13, 4706, 1550, 5852, 29901, 13, 9651, 396, 679, 4390, 2933, 515, 4382, 1393, 13, 9651, 4613, 353, 1583, 29889, 657, 29918, 786, 15190, 29898, 10289, 29922, 10289, 29897, 13, 13, 9651, 396, 565, 451, 3431, 29892, 12020, 278, 1059, 13, 9651, 565, 451, 4613, 3366, 554, 3108, 29901, 13, 18884, 12020, 9699, 1393, 8787, 2392, 703, 5103, 4663, 29901, 6571, 1642, 4830, 29898, 13713, 876, 13, 13, 9651, 396, 13649, 1549, 11217, 515, 4613, 4390, 13, 9651, 363, 2767, 297, 4613, 3366, 2914, 3108, 29901, 13, 18884, 396, 2767, 9210, 13, 18884, 9210, 353, 4236, 29898, 10289, 29892, 2767, 3366, 5504, 29918, 333, 3108, 718, 29871, 29896, 29897, 13, 18884, 396, 7709, 2643, 13, 18884, 7709, 2767, 29889, 657, 703, 4906, 613, 2767, 29889, 657, 703, 287, 1573, 29918, 4906, 5783, 13, 2 ]
tests/build_feedstock_test.py	jayfurmanek/open-ce-builder	0	1609967	<gh_stars>0 # *************************************************************** # (C) Copyright IBM Corp. 2020, 2021. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ************************************************************* import os import pathlib import logging import pytest from importlib.util import spec_from_loader, module_from_spec from importlib.machinery import SourceFileLoader test_dir = pathlib.Path(__file__).parent.absolute() spec = spec_from_loader("opence", SourceFileLoader("opence", os.path.join(test_dir, '..', 'open_ce', 'open-ce'))) opence = module_from_spec(spec) spec.loader.exec_module(opence) import helpers import open_ce.utils as utils from open_ce.errors import OpenCEError import open_ce.build_feedstock as build_feedstock def test_build_feedstock_default(mocker): """ Tests that the default arguments for 'build_feedstock' generate the correct 'conda_build.api.build' input args. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) expect_recipe = os.path.join(os.getcwd(),'recipe') expect_config = {'variant_config_files' : [], 'output_folder' : utils.DEFAULT_OUTPUT_FOLDER} mocker.patch( 'conda_build.api.build', side_effect=(lambda x, kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, expect_config=expect_config, kwargs)) ) opence._main(["build", build_feedstock.COMMAND]) def test_build_feedstock_failure(mocker): """ Tests that a 'conda_build.api.build' failure is handled correctly. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) mocker.patch( 'conda_build.api.build', side_effect=ValueError("invalid literal for int() with base 10: 'xy'") #using ValueError to simulate a failure. ) with pytest.raises(OpenCEError) as exc: opence._main(["build", build_feedstock.COMMAND]) assert "Unable to build recipe: test_recipe" in str(exc.value) def test_build_feedstock_working_dir(mocker): """ Tests that the 'working_dir' argument is correctly handled and the original working directory is restored after execution. """ dirTracker = helpers.DirTracker("/test/starting_dir") mocker.patch( 'os.getcwd', side_effect=dirTracker.mocked_getcwd ) mocker.patch( 'os.path.exists', return_value=False ) mocker.patch( 'conda_build.api.build', return_value=[] ) working_dir = "/test/my_work_dir" mocker.patch( 'os.chdir', side_effect=(lambda x: dirTracker.validate_chdir(x, expected_dirs=[working_dir, # First the working directory should be changed to the arg. "/test/starting_dir"])) # And then changed back to the starting directory. ) opence._main(["build", build_feedstock.COMMAND, "--working_directory", working_dir]) def test_build_feedstock_config_file(mocker): """ Tests that the 'recipe_config_file' argument is correctly handled.. """ expect_recipe = os.path.join(os.getcwd(),'cuda_recipe_path') #Checks that the value from the input config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, kwargs)) ) opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", os.path.join(test_dir, "my_config.yaml"), "--build_type", "cuda"]) def test_build_feedstock_default_config_file(mocker): """ Tests that the default config file is loaded when no argument is specified. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=True #True for default config file. ) expect_recipe = os.path.join(os.getcwd(),'variants_from_default_config')#Checks that the value from the default config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, kwargs)) ) test_recipe_config = {'recipes' : [{'name' : 'my_variant', 'path' : 'variants_from_default_config'}]} mocker.patch('open_ce.conda_utils.render_yaml', return_value=test_recipe_config) opence._main(["build", build_feedstock.COMMAND]) def test_build_feedstock_nonexist_config_file(mocker): """ Tests that execution fails and the correct error message is shown if the default config file doesn't exist. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) with pytest.raises(OpenCEError) as exc: opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", "my_config.yml"]) assert "Unable to open provided config file: my_config.yml" in str(exc.value) def test_recipe_config_file_for_inapplicable_configuration(mocker, caplog): """ Tests the case when build is triggered for a configuration for which no recipes are applicable. """ expect_recipe = os.path.join(os.getcwd(),'cuda_recipe_path') #Checks that the value from the input config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, kwargs)) ) opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", os.path.join(test_dir, "my_config.yaml"), "--python_versions", "4.1"]) assert ("OPEN-CE", logging.INFO, "No recipe to build for given configuration.") in caplog.record_tuples def test_build_feedstock_local_src_dir_args(mocker): """ Tests that providing the local_src_dir argument sets the LOCAL_SRC_DIR environment variable correctly. """ mocker.patch( 'os.path.exists', return_value=True ) build_feedstock._set_local_src_dir("my_src_dir", None, None) assert os.environ["LOCAL_SRC_DIR"] == "my_src_dir" def test_build_feedstock_local_src_dir_args_fail(mocker): """ Tests that providing the local_src_dir argument to a non-existant file fails properly. """ mocker.patch( 'os.path.exists', return_value=False ) with pytest.raises(OpenCEError) as exc: build_feedstock._set_local_src_dir("my_src_dir", { 'local_src_dir' : "my_other_src_dir" }, None) assert "local_src_dir path \"my_src_dir\" specified doesn't exist" in str(exc.value) def test_build_feedstock_local_src_dir_recipe(mocker): """ Tests that providing the local_src_dir in a recipe sets the LOCAL_SRC_DIR environment variable correctly. """ mocker.patch( 'os.path.exists', return_value=True ) build_feedstock._set_local_src_dir(None, { 'local_src_dir' : "my_other_src_dir" } , "/test/location/recipe.yaml") assert os.environ["LOCAL_SRC_DIR"] == "/test/location/my_other_src_dir" def test_build_feedstock_extra_args(mocker): """ Tests that additional arguments add the expected values to the 'conda_build.api.build' arguments. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=True ) expect_config = { 'channel_urls' : ['/test/test_recipe/condabuild', 'test_channel', 'test_channel_2', 'test_channel_from_config']} expect_variants = {'python': '3.6', 'build_type': 'cpu', 'mpi_type': 'openmpi'} reject_recipe = os.path.join(os.getcwd(),'test_recipe_extra') mocker.patch( 'conda_build.api.build', side_effect=(lambda x, kwargs: helpers.validate_conda_build_args(x, expect_config=expect_config, expect_variants=expect_variants, reject_recipe=reject_recipe, **kwargs)) ) test_recipe_config = { 'recipes' : [{ 'name' : 'my_project', 'path' : 'recipe'}, { 'name' : 'my_variant', 'path': 'variants'}, { 'name' : 'test_recipe_extra', 'path' : 'extra'}], 'channels' : ['test_channel_from_config']} mocker.patch('open_ce.conda_utils.render_yaml', return_value=test_recipe_config) arg_input = ["build", build_feedstock.COMMAND, "--channels", "test_channel", "--channels", "test_channel_2", "--recipes", "my_project,my_variant", "--python_versions", "3.6", "--build_types", "cpu", "--mpi_types", "openmpi", "--cuda_versions", "10.2"] opence._main(arg_input) def test_build_feedstock_if_no_conda_build(mocker): ''' Test that build_feedstock should fail if conda_build isn't present ''' mocker.patch('pkg_resources.get_distribution', return_value=None) with pytest.raises(OpenCEError): assert opence._main(["build", build_feedstock.COMMAND]) == 1 def test_build_feedstock_conda_pkg_format(mocker): ''' Test that the --conda_pkg_format argument is correctly handled. 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resources/migrations/0021_change_image_file_to_char.py	City-of-Helsinki/berth-reservations	3	65894	<reponame>City-of-Helsinki/berth-reservations # Generated by Django 3.1 on 2020-10-05 12:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("resources", "0020_change_berth_number_to_char"), ] operations = [ migrations.AlterField( model_name="harbor", name="image_file", field=models.CharField( blank=True, max_length=400, null=True, verbose_name="Image file" ), ), migrations.AlterField( model_name="winterstoragearea", name="image_file", field=models.CharField( blank=True, max_length=400, null=True, verbose_name="Image file" ), ), ]	[ 1, 529, 276, 1112, 420, 29958, 16885, 29899, 974, 29899, 29950, 1379, 682, 29875, 29914, 495, 386, 29899, 690, 6972, 800, 13, 29937, 3251, 630, 491, 15337, 29871, 29941, 29889, 29896, 373, 29871, 29906, 29900, 29906, 29900, 29899, 29896, 29900, 29899, 29900, 29945, 29871, 29896, 29906, 29901, 29946, 29955, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 4852, 13237, 613, 376, 29900, 29900, 29906, 29900, 29918, 3167, 29918, 495, 386, 29918, 4537, 29918, 517, 29918, 3090, 4968, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 543, 8222, 4089, 613, 13, 9651, 1024, 543, 3027, 29918, 1445, 613, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 13, 18884, 9654, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29946, 29900, 29900, 29892, 1870, 29922, 5574, 29892, 26952, 29918, 978, 543, 2940, 934, 29908, 13, 9651, 10353, 13, 4706, 10353, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 543, 29893, 1639, 12925, 6203, 613, 13, 9651, 1024, 543, 3027, 29918, 1445, 613, 13, 9651, 1746, 29922, 9794, 29889, 27890, 29898, 13, 18884, 9654, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29946, 29900, 29900, 29892, 1870, 29922, 5574, 29892, 26952, 29918, 978, 543, 2940, 934, 29908, 13, 9651, 10353, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
examples/DogsVsCats.py	YiweiLi4/tensorbay-python-sdk	0	130343	<filename>examples/DogsVsCats.py #!/usr/bin/env python3 # # Copyright 2021 Graviti. Licensed under MIT License. # # pylint: disable=wrong-import-position # pylint: disable=wrong-import-order # pylint: disable=not-callable # pylint: disable=ungrouped-imports # pylint: disable=import-error # pylint: disable=pointless-string-statement # pylint: disable=invalid-name # pylint: disable=unused-import # flake8: noqa: F401 """This files includes the python code example in dogsvscats.rst.""" """Authorize a Client Instance""" from tensorbay import GAS ACCESS_KEY = "Accesskey-*****" gas = GAS(ACCESS_KEY) """""" """Create Dataset""" gas.create_dataset("DogsVsCats") """""" """Organize Dataset / regular import""" from tensorbay.dataset import Data, Dataset from tensorbay.label import Classification """""" """Organize dataset / import dataloader""" from tensorbay.opendataset import DogsVsCats dataset = DogsVsCats("path/to/dataset/directory") """""" """Upload Dataset""" dataset_client = gas.upload_dataset(dataset) dataset_client.commit("initial commit") """""" """Read Dataset / get dataset""" dataset_client = gas.get_dataset("DogsVsCats") """""" """Read Dataset / list segment names""" dataset_client.list_segment_names() """""" """Read Dataset Class / get a segment""" train_segment = dataset.get_segment_by_name("train") first_segment = dataset[0] """""" """Read Dataset / get segment""" from tensorbay.dataset import Segment train_segment = Segment("train", dataset_client) """""" """Read Dataset / get data""" data = train_segment[0] """""" """Read Dataset / get label""" category = data.label.classification.category """""" """Delete Dataset""" gas.delete_dataset("DogsVsCats") """"""	[ 1, 529, 9507, 29958, 19057, 29914, 29928, 12099, 29963, 29879, 29907, 1446, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29896, 4989, 29894, 4812, 29889, 10413, 21144, 1090, 341, 1806, 19245, 29889, 13, 29937, 13, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 15866, 549, 29899, 5215, 29899, 3283, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 15866, 549, 29899, 5215, 29899, 2098, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 1333, 29899, 4804, 519, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 348, 2972, 287, 29899, 326, 4011, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 5215, 29899, 2704, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 3149, 2222, 29899, 1807, 29899, 20788, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 20965, 29899, 978, 13, 29937, 282, 2904, 524, 29901, 11262, 29922, 348, 3880, 29899, 5215, 13, 29937, 17422, 446, 29947, 29901, 694, 25621, 29901, 383, 29946, 29900, 29896, 13, 13, 15945, 29908, 4013, 2066, 7805, 278, 3017, 775, 1342, 297, 26361, 29894, 1557, 1446, 29889, 29878, 303, 1213, 15945, 13, 13, 15945, 29908, 13720, 675, 263, 12477, 2799, 749, 15945, 29908, 13, 3166, 12489, 27495, 1053, 402, 3289, 13, 13, 2477, 23524, 29918, 10818, 353, 376, 6638, 1989, 29899, 2328, 20605, 13, 25496, 353, 402, 3289, 29898, 2477, 23524, 29918, 10818, 29897, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 4391, 13373, 24541, 15945, 29908, 13, 25496, 29889, 3258, 29918, 24713, 703, 29928, 12099, 29963, 29879, 29907, 1446, 1159, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 27356, 675, 13373, 24541, 847, 4943, 1053, 15945, 29908, 13, 3166, 12489, 27495, 29889, 24713, 1053, 3630, 29892, 13373, 24541, 13, 3166, 12489, 27495, 29889, 1643, 1053, 23236, 13, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 27356, 675, 8783, 847, 1053, 1418, 7003, 1664, 15945, 29908, 13, 3166, 12489, 27495, 29889, 459, 355, 271, 24541, 1053, 360, 12099, 29963, 29879, 29907, 1446, 13, 13, 24713, 353, 360, 12099, 29963, 29879, 29907, 1446, 703, 2084, 29914, 517, 29914, 24713, 29914, 12322, 1159, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 17553, 13373, 24541, 15945, 29908, 13, 24713, 29918, 4645, 353, 10489, 29889, 9009, 29918, 24713, 29898, 24713, 29897, 13, 24713, 29918, 4645, 29889, 15060, 703, 11228, 9063, 1159, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 847, 679, 8783, 15945, 29908, 13, 24713, 29918, 4645, 353, 10489, 29889, 657, 29918, 24713, 703, 29928, 12099, 29963, 29879, 29907, 1446, 1159, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 847, 1051, 10768, 2983, 15945, 29908, 13, 24713, 29918, 4645, 29889, 1761, 29918, 28192, 29918, 7039, 580, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 4134, 847, 679, 263, 10768, 15945, 29908, 13, 14968, 29918, 28192, 353, 8783, 29889, 657, 29918, 28192, 29918, 1609, 29918, 978, 703, 14968, 1159, 13, 4102, 29918, 28192, 353, 8783, 29961, 29900, 29962, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 847, 679, 10768, 15945, 29908, 13, 3166, 12489, 27495, 29889, 24713, 1053, 6667, 358, 13, 13, 14968, 29918, 28192, 353, 6667, 358, 703, 14968, 613, 8783, 29918, 4645, 29897, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 847, 679, 848, 15945, 29908, 13, 1272, 353, 7945, 29918, 28192, 29961, 29900, 29962, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 6359, 13373, 24541, 847, 679, 3858, 15945, 29908, 13, 7320, 353, 848, 29889, 1643, 29889, 1990, 2450, 29889, 7320, 13, 15945, 15945, 15945, 13, 13, 15945, 29908, 12498, 13373, 24541, 15945, 29908, 13, 25496, 29889, 8143, 29918, 24713, 703, 29928, 12099, 29963, 29879, 29907, 1446, 1159, 13, 15945, 15945, 15945, 13, 2 ]
django_storage_url/base.py	hoopoe/django-storage-url	2	117242	<reponame>hoopoe/django-storage-url from django.conf import settings from django.utils.functional import LazyObject import furl from .backends import get_storage_class from .backends.not_implemented import NotImplementedStorage class _DSNConfiguredStorage(LazyObject): def _setup(self): dsn = getattr(settings, self._setting_name, None) if not dsn: args = [] storage_class = NotImplementedStorage else: url = furl.furl(dsn) storage_class = get_storage_class(url.scheme) args = [url] # Django >= 1.9 now knows about LazyObject and sets them up before # serializing them. To work around this behavior, the storage class # itself needs to be deconstructible. storage_class = type( storage_class.__name__, (storage_class,), {"deconstruct": self._deconstructor}, ) self._wrapped = storage_class(*args) def dsn_configured_storage_class(setting_name): path = "{}.{}".format( dsn_configured_storage.__module__, dsn_configured_storage.__name__ ) return type( "DSNConfiguredStorage", (_DSNConfiguredStorage,), { "_setting_name": setting_name, "_deconstructor": lambda self: (path, [setting_name], {}), }, ) def dsn_configured_storage(setting_name): return dsn_configured_storage_class(setting_name)() def get_storage(dsn): url = furl.furl(dsn) storage_class = get_storage_class(url.scheme) return storage_class(url)	[ 1, 529, 276, 1112, 420, 29958, 1251, 459, 7297, 29914, 14095, 29899, 12925, 29899, 2271, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 13239, 29889, 2220, 284, 1053, 19575, 29891, 2061, 13, 13, 5215, 285, 2271, 13, 13, 3166, 869, 1627, 1975, 1053, 679, 29918, 12925, 29918, 1990, 13, 3166, 869, 1627, 1975, 29889, 1333, 29918, 326, 2037, 287, 1053, 2216, 1888, 2037, 287, 10486, 13, 13, 13, 1990, 903, 8452, 29940, 3991, 2955, 10486, 29898, 29931, 24683, 2061, 1125, 13, 1678, 822, 903, 14669, 29898, 1311, 1125, 13, 4706, 270, 16586, 353, 679, 5552, 29898, 11027, 29892, 1583, 3032, 26740, 29918, 978, 29892, 6213, 29897, 13, 4706, 565, 451, 270, 16586, 29901, 13, 9651, 6389, 353, 5159, 13, 9651, 8635, 29918, 1990, 353, 2216, 1888, 2037, 287, 10486, 13, 4706, 1683, 29901, 13, 9651, 3142, 353, 285, 2271, 29889, 29888, 2271, 29898, 6289, 29876, 29897, 13, 9651, 8635, 29918, 1990, 353, 679, 29918, 12925, 29918, 1990, 29898, 2271, 29889, 816, 2004, 29897, 13, 9651, 6389, 353, 518, 2271, 29962, 13, 13, 4706, 396, 15337, 6736, 29871, 29896, 29889, 29929, 1286, 9906, 1048, 19575, 29891, 2061, 322, 6166, 963, 701, 1434, 13, 4706, 396, 7797, 5281, 963, 29889, 1763, 664, 2820, 445, 6030, 29892, 278, 8635, 770, 13, 4706, 396, 3528, 4225, 304, 367, 316, 11433, 1821, 29889, 13, 4706, 8635, 29918, 1990, 353, 1134, 29898, 13, 9651, 8635, 29918, 1990, 17255, 978, 1649, 29892, 13, 9651, 313, 12925, 29918, 1990, 29892, 511, 13, 9651, 8853, 311, 11433, 1115, 1583, 3032, 311, 27821, 1118, 13, 4706, 1723, 13, 4706, 1583, 3032, 29893, 336, 2986, 353, 8635, 29918, 1990, 10456, 5085, 29897, 13, 13, 13, 1753, 270, 16586, 29918, 2917, 2955, 29918, 12925, 29918, 1990, 29898, 26740, 29918, 978, 1125, 13, 1678, 2224, 353, 29850, 1836, 8875, 1642, 4830, 29898, 13, 4706, 270, 16586, 29918, 2917, 2955, 29918, 12925, 17255, 5453, 1649, 29892, 270, 16586, 29918, 2917, 2955, 29918, 12925, 17255, 978, 1649, 13, 1678, 1723, 13, 1678, 736, 1134, 29898, 13, 4706, 376, 8452, 29940, 3991, 2955, 10486, 613, 13, 4706, 9423, 8452, 29940, 3991, 2955, 10486, 29892, 511, 13, 4706, 426, 13, 9651, 11119, 26740, 29918, 978, 1115, 4444, 29918, 978, 29892, 13, 9651, 11119, 311, 27821, 1115, 14013, 1583, 29901, 313, 2084, 29892, 518, 26740, 29918, 978, 1402, 6571, 511, 13, 4706, 2981, 13, 1678, 1723, 13, 13, 13, 1753, 270, 16586, 29918, 2917, 2955, 29918, 12925, 29898, 26740, 29918, 978, 1125, 13, 1678, 736, 270, 16586, 29918, 2917, 2955, 29918, 12925, 29918, 1990, 29898, 26740, 29918, 978, 29897, 580, 13, 13, 13, 1753, 679, 29918, 12925, 29898, 6289, 29876, 1125, 13, 1678, 3142, 353, 285, 2271, 29889, 29888, 2271, 29898, 6289, 29876, 29897, 13, 1678, 8635, 29918, 1990, 353, 679, 29918, 12925, 29918, 1990, 29898, 2271, 29889, 816, 2004, 29897, 13, 1678, 736, 8635, 29918, 1990, 29898, 2271, 29897, 13, 2 ]
grpclib/server.py	panaetov/grpclib	0	12979	import abc import socket import logging import asyncio import warnings import h2.config import h2.exceptions from .utils import DeadlineWrapper from .const import Status from .stream import send_message, recv_message from .stream import StreamIterator from .metadata import Metadata, Deadline from .protocol import H2Protocol, AbstractHandler from .exceptions import GRPCError, ProtocolError from .encoding.base import GRPC_CONTENT_TYPE from .encoding.proto import ProtoCodec log = logging.getLogger(__name__) class Stream(StreamIterator): """ Represents gRPC method call – HTTP/2 request/stream, and everything you need to communicate with client in order to handle this request. As you can see, every method handler accepts single positional argument - stream: .. code-block:: python async def MakeLatte(self, stream: grpclib.server.Stream): task: cafe_pb2.LatteOrder = await stream.recv_message() ... await stream.send_message(empty_pb2.Empty()) This is true for every gRPC method type. """ # stream state _send_initial_metadata_done = False _send_message_count = 0 _send_trailing_metadata_done = False _cancel_done = False def __init__(self, stream, cardinality, codec, recv_type, send_type, , metadata, deadline=None): self._stream = stream self._cardinality = cardinality self._codec = codec self._recv_type = recv_type self._send_type = send_type self.metadata = metadata self.deadline = deadline async def recv_message(self): """Coroutine to receive incoming message from the client. If client sends UNARY request, then you can call this coroutine only once. If client sends STREAM request, then you should call this coroutine several times, until it returns None. To simplify your code in this case, :py:class:`Stream` class implements async iteration protocol, so you can use it like this: .. code-block:: python async for massage in stream: do_smth_with(message) or even like this: .. code-block:: python messages = [msg async for msg in stream] HTTP/2 has flow control mechanism, so server will acknowledge received DATA frames as a message only after user consumes this coroutine. :returns: message """ return await recv_message(self._stream, self._codec, self._recv_type) async def send_initial_metadata(self): """Coroutine to send headers with initial metadata to the client. In gRPC you can send initial metadata as soon as possible, because gRPC doesn't use `:status` pseudo header to indicate success or failure of the current request. gRPC uses trailers for this purpose, and trailers are sent during :py:meth:`send_trailing_metadata` call, which should be called in the end. .. note:: This coroutine will be called implicitly during first :py:meth:`send_message` coroutine call, if not called before explicitly. """ if self._send_initial_metadata_done: raise ProtocolError('Initial metadata was already sent') await self._stream.send_headers([ (':status', '200'), ('content-type', (GRPC_CONTENT_TYPE + '+' + self._codec.__content_subtype__)), ]) self._send_initial_metadata_done = True async def send_message(self, message, kwargs): """Coroutine to send message to the client. If server sends UNARY response, then you should call this coroutine only once. If server sends STREAM response, then you can call this coroutine as many times as you need. :param message: message object """ if 'end' in kwargs: warnings.warn('"end" argument is deprecated, use ' '"stream.send_trailing_metadata" explicitly', stacklevel=2) end = kwargs.pop('end', False) assert not kwargs, kwargs if not self._send_initial_metadata_done: await self.send_initial_metadata() if not self._cardinality.server_streaming: if self._send_message_count: raise ProtocolError('Server should send exactly one message ' 'in response') await send_message(self._stream, self._codec, message, self._send_type) self._send_message_count += 1 if end: await self.send_trailing_metadata() async def send_trailing_metadata(self, , status=Status.OK, status_message=None): """Coroutine to send trailers with trailing metadata to the client. This coroutine allows sending trailers-only responses, in case of some failure conditions during handling current request, i.e. when ``status is not OK``. .. note:: This coroutine will be called implicitly at exit from request handler, with appropriate status code, if not called explicitly during handler execution. :param status: resulting status of this coroutine call :param status_message: description for a status """ if self._send_trailing_metadata_done: raise ProtocolError('Trailing metadata was already sent') if not self._send_message_count and status is Status.OK: raise ProtocolError('{!r} requires non-empty response' .format(status)) if self._send_initial_metadata_done: headers = [] else: # trailers-only response headers = [(':status', '200')] headers.append(('grpc-status', str(status.value))) if status_message is not None: headers.append(('grpc-message', status_message)) await self._stream.send_headers(headers, end_stream=True) self._send_trailing_metadata_done = True if status != Status.OK and self._stream.closable: self._stream.reset_nowait() async def cancel(self): """Coroutine to cancel this request/stream. Server will send RST_STREAM frame to the client, so it will be explicitly informed that there is nothing to expect from the server regarding this request/stream. """ if self._cancel_done: raise ProtocolError('Stream was already cancelled') await self._stream.reset() # TODO: specify error code self._cancel_done = True async def __aenter__(self): return self async def __aexit__(self, exc_type, exc_val, exc_tb): if ( self._send_trailing_metadata_done or self._cancel_done or self._stream._transport.is_closing() ): # to suppress exception propagation return True if exc_val is not None: if isinstance(exc_val, GRPCError): status = exc_val.status status_message = exc_val.message elif isinstance(exc_val, Exception): status = Status.UNKNOWN status_message = 'Internal Server Error' else: # propagate exception return elif not self._send_message_count: status = Status.UNKNOWN status_message = 'Empty response' else: status = Status.OK status_message = None try: await self.send_trailing_metadata(status=status, status_message=status_message) except h2.exceptions.StreamClosedError: pass # to suppress exception propagation return True async def request_handler(mapping, _stream, headers, codec, release_stream): try: headers_map = dict(headers) if headers_map[':method'] != 'POST': await _stream.send_headers([ (':status', '405'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return content_type = headers_map.get('content-type') if content_type is None: await _stream.send_headers([ (':status', '415'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Missing content-type header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return base_content_type, _, sub_type = content_type.partition('+') sub_type = sub_type or ProtoCodec.__content_subtype__ if ( base_content_type != GRPC_CONTENT_TYPE or sub_type != codec.__content_subtype__ ): await _stream.send_headers([ (':status', '415'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Unacceptable content-type header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return if headers_map.get('te') != 'trailers': await _stream.send_headers([ (':status', '400'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Required "te: trailers" header is missing'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return h2_path = headers_map[':path'] method = mapping.get(h2_path) if method is None: await _stream.send_headers([ (':status', '200'), ('grpc-status', str(Status.UNIMPLEMENTED.value)), ('grpc-message', 'Method not found'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return metadata = Metadata.from_headers(headers) try: deadline = Deadline.from_metadata(metadata) except ValueError: await _stream.send_headers([ (':status', '200'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Invalid grpc-timeout header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return async with Stream(_stream, method.cardinality, codec, method.request_type, method.reply_type, metadata=metadata, deadline=deadline) as stream: deadline_wrapper = None try: if deadline: deadline_wrapper = DeadlineWrapper() with deadline_wrapper.start(deadline): with deadline_wrapper: await method.func(stream) else: await method.func(stream) except asyncio.TimeoutError: if deadline_wrapper and deadline_wrapper.cancelled: log.exception('Deadline exceeded') raise GRPCError(Status.DEADLINE_EXCEEDED) else: log.exception('Timeout occurred') raise except asyncio.CancelledError: log.exception('Request was cancelled') raise except Exception: log.exception('Application error') raise except Exception: log.exception('Server error') finally: release_stream() class _GC(abc.ABC): _gc_counter = 0 @property @abc.abstractmethod def __gc_interval__(self): raise NotImplementedError @abc.abstractmethod def __gc_collect__(self): pass def __gc_step__(self): self._gc_counter += 1 if not (self._gc_counter % self.__gc_interval__): self.__gc_collect__() class Handler(_GC, AbstractHandler): __gc_interval__ = 10 closing = False def __init__(self, mapping, codec, , loop): self.mapping = mapping self.codec = codec self.loop = loop self._tasks = {} self._cancelled = set() def __gc_collect__(self): self._tasks = {s: t for s, t in self._tasks.items() if not t.done()} self._cancelled = {t for t in self._cancelled if not t.done()} def accept(self, stream, headers, release_stream): self.__gc_step__() self._tasks[stream] = self.loop.create_task( request_handler(self.mapping, stream, headers, self.codec, release_stream) ) def cancel(self, stream): task = self._tasks.pop(stream) task.cancel() self._cancelled.add(task) def close(self): for task in self._tasks.values(): task.cancel() self._cancelled.update(self._tasks.values()) self.closing = True async def wait_closed(self): if self._cancelled: await asyncio.wait(self._cancelled, loop=self.loop) def check_closed(self): self.__gc_collect__() return not self._tasks and not self._cancelled class Server(_GC, asyncio.AbstractServer): """ HTTP/2 server, which uses gRPC service handlers to handle requests. Handler is a subclass of the abstract base class, which was generated from .proto file: .. code-block:: python class CoffeeMachine(cafe_grpc.CoffeeMachineBase): async def MakeLatte(self, stream): task: cafe_pb2.LatteOrder = await stream.recv_message() ... await stream.send_message(empty_pb2.Empty()) server = Server([CoffeeMachine()], loop=loop) """ __gc_interval__ = 10 def __init__(self, handlers, , loop, codec=None): """ :param handlers: list of handlers :param loop: asyncio-compatible event loop """ mapping = {} for handler in handlers: mapping.update(handler.__mapping__()) self._mapping = mapping self._loop = loop self._codec = codec or ProtoCodec() self._config = h2.config.H2Configuration( client_side=False, header_encoding='utf-8', ) self._tcp_server = None self._handlers = set() def __gc_collect__(self): self._handlers = {h for h in self._handlers if not (h.closing and h.check_closed())} def _protocol_factory(self): self.__gc_step__() handler = Handler(self._mapping, self._codec, loop=self._loop) self._handlers.add(handler) return H2Protocol(handler, self._config, loop=self._loop) async def start(self, host=None, port=None, *, family=socket.AF_UNSPEC, flags=socket.AI_PASSIVE, sock=None, backlog=100, ssl=None, reuse_address=None, reuse_port=None): """Coroutine to start the server. :param host: can be a string, containing IPv4/v6 address or domain name. If host is None, server will be bound to all available interfaces. :param port: port number. :param family: can be set to either :py:data:`python:socket.AF_INET` or :py:data:`python:socket.AF_INET6` to force the socket to use IPv4 or IPv6. If not set it will be determined from host. :param flags: is a bitmask for :py:meth:`~python:asyncio.AbstractEventLoop.getaddrinfo`. :param sock: sock can optionally be specified in order to use a preexisting socket object. If specified, host and port should be omitted (must be None). :param backlog: is the maximum number of queued connections passed to listen(). :param ssl: can be set to an :py:class:`~python:ssl.SSLContext` to enable SSL over the accepted connections. :param reuse_address: tells the kernel to reuse a local socket in TIME_WAIT state, without waiting for its natural timeout to expire. :param reuse_port: tells the kernel to allow this endpoint to be bound to the same port as other existing endpoints are bound to, so long as they all set this flag when being created. """ if self._tcp_server is not None: raise RuntimeError('Server is already started') self._tcp_server = await self._loop.create_server( self._protocol_factory, host, port, family=family, flags=flags, sock=sock, backlog=backlog, ssl=ssl, reuse_address=reuse_address, reuse_port=reuse_port ) def close(self): """Stops accepting new connections, cancels all currently running requests. 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pg_dicreate.py	zhuyeaini9/pytorch_test	0	23677	<reponame>zhuyeaini9/pytorch_test<filename>pg_dicreate.py import torch import torch.nn.functional as F import torch.nn as nn import gym from gym import spaces import torch.optim as optim from torch.distributions import Categorical import random import numpy as np class Net(nn.Module): def __init__(self, input_space, output_space): super(Net, self).__init__() self.fc1 = nn.Linear(input_space, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, output_space) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.softmax(self.fc3(x), dim=-1) return x class PGAgent(object): def __init__(self, env_name,batch_size = 3): self.m_batch_size = batch_size self.m_env = gym.make(env_name) self.m_env._max_episode_steps = 1000 self.m_action_type = self.get_action_type() self.m_action_size = self.get_action_size() self.m_net = Net(self.get_input_space(), self.get_output_space()) self.m_adam = optim.Adam(params=self.m_net.parameters(), lr=0.01) self.m_batch_reward = [] self.m_batch_state = [] self.m_batch_action = [] def get_input_space(self): return self.m_env.observation_space.shape[0] def get_action_size(self): if self.m_action_type == 1: return self.m_env.action_space.n def get_output_space(self): if self.m_action_type == 1: return self.m_env.action_space.n def get_action_type(self): if isinstance(self.m_env.action_space, spaces.Discrete): action_type = 1 else: action_type = 2 return action_type def get_action(self, police_action, step_index, step_all): if self.m_action_type == 1: action_distribution = Categorical(police_action) return action_distribution.sample().item() def calculate_discounted_rewards_normal(self, reward_list): re = self.calculate_discounted_rewards(reward_list) return self.normalise_rewards(re) def calculate_discounted_rewards(self, reward_list): cur_re = 0 re_discounted_reward = [] for re in reversed(reward_list): re = re + 0.99 * cur_re cur_re = re re_discounted_reward.append(re) re_discounted_reward.reverse() return re_discounted_reward def normalise_rewards(self, rewards): mean_reward = np.mean(rewards) std_reward = np.std(rewards) return (rewards - mean_reward) / (std_reward + 1e-8) def reset(self): self.m_batch_state = [] self.m_batch_action = [] self.m_batch_reward = [] def step(self, step_index, step_all): self.reset() state = self.m_env.reset() batch = 0 reward_record = [] reward_list = [] action_list = [] state_list = [] while True: #be careful of detach out_action = self.m_net(torch.tensor(state).float().unsqueeze(dim=0)).detach() out_action = out_action.squeeze(0) tar_action = self.get_action(out_action, step_index, step_all) new_state, reward, done, _ = self.m_env.step(tar_action) reward_list.append(reward) action_list.append(tar_action) state_list.append(state) state = new_state if done: batch += 1 self.m_batch_reward.extend(self.calculate_discounted_rewards_normal(reward_list)) self.m_batch_action.extend(action_list) self.m_batch_state.extend(state_list) reward_record.append(sum(reward_list)) reward_list = [] action_list = [] state_list = [] state = self.m_env.reset() if batch == self.m_batch_size: break print(step_index, reward_record) state_tensor = torch.FloatTensor(self.m_batch_state) reward_tensor = torch.FloatTensor(self.m_batch_reward) action_tensor = torch.LongTensor(self.m_batch_action) # Calculate loss log_prob = torch.log(self.m_net(state_tensor)) selected_logprobs = reward_tensor * log_prob[np.arange(len(action_tensor)), action_tensor] loss = -selected_logprobs.mean() self.m_adam.zero_grad() # Calculate gradients loss.backward() # Apply gradients self.m_adam.step() def run_n_step(self, n): for i in range(n): self.step(i, n) agent = PGAgent('CartPole-v0') agent.run_n_step(300)	[ 1, 529, 276, 1112, 420, 29958, 29920, 6905, 4099, 475, 29875, 29929, 29914, 2272, 7345, 305, 29918, 1688, 29966, 9507, 29958, 4061, 29918, 27774, 3015, 29889, 2272, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 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boo/dataframe/canonic.py	AirVetra/boo	1	47840	import numpy import pandas as pd from boo.columns import SHORT_COLUMNS from boo.errors import UnclassifiableCodeError QUOTE_CHAR = '"' EMPTY = int(0) NUMERIC_COLUMNS = SHORT_COLUMNS.numeric def adjust_rub(df, cols=NUMERIC_COLUMNS): rows = (df.unit == "385") df.loc[rows, cols] = df.loc[rows, cols].multiply(1000) df.loc[rows, "unit"] = "384" rows = (df.unit == "383") df.loc[rows, cols] = df.loc[rows, cols].divide(1000).round(0).astype(int) df.loc[rows, "unit"] = "384" return df def dequote(name: str): """Split company name to organisation and title.""" # Warning: will not work well on company names with more than 4 quotechars parts = name.split(QUOTE_CHAR) org = parts[0].strip() cnt = name.count(QUOTE_CHAR) if cnt == 2: title = parts[1].strip() elif cnt > 2: title = QUOTE_CHAR.join(parts[1:]) else: title = name return org, title.strip() def replace_names(title: str): return title .replace( "ПУБЛИЧНОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО", "ПАО") .replace( "ОТКРЫТОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО", "ОАО") .replace( "АКЦИОНЕРНОЕ ОБЩЕСТВО ЭНЕРГЕТИКИ И ЭЛЕКТРИФИКАЦИИ", "AO энерго") .replace( "НЕФТЕПЕРЕРАБАТЫВАЮЩИЙ ЗАВОД", "НПЗ") .replace( "ГЕНЕРИРУЮЩАЯ КОМПАНИЯ ОПТОВОГО РЫНКА ЭЛЕКТРОЭНЕРГИИ", "ОГК") .replace( "ГОРНО-ОБОГАТИТЕЛЬНЫЙ КОМБИНАТ", "ГОК") def add_title(df): s_ = df.name.apply(dequote) df['org'] = s_.apply(lambda x: x[0]) df['title'] = s_.apply(lambda x: replace_names(x[1])) return df def rename_rows(df): RENAME_DICT = { '2460066195': "РусГидро", '4716016979': "ФСК ЕЭС", '7702038150': "Московский метрополитен", '7721632827': "Концерн Росэнергоатом", '7706664260': "Атомэнергопром", '7703683145': "Холдинг ВТБ Капитал АЙ БИ", '9102048801': "Черноморнефтегаз", '7736036626': "РИТЭК" } keys = RENAME_DICT.keys() ix = df.index.isin(keys) if not ix.any(): return df sub = df.loc[ix, 'title'] for k, v in RENAME_DICT.items(): if k in sub.index: sub.loc[k] = v df.loc[ix, 'title'] = sub return df def split_okved(code_string: str): """Get 3 levels of OKVED codes from code_string.""" if code_string.count(".") > 2: raise UnclassifiableCodeError(code_string) try: codes = [int(x) for x in code_string.split(".")] except ValueError: raise UnclassifiableCodeError(code_string) return codes + [0] * (3 - len(codes)) def add_okved_subcode(df): df['ok1'], df['ok2'], df['ok3'] = zip(df.okved.apply(split_okved)) return df def fst(x): try: return int(x[0:2]) except TypeError: return 0 def add_region(df): df['region'] = df.inn.apply(fst) return df def more_columns(df): return add_okved_subcode(add_region(add_title(df))) def canonic_df(df): """Преобразовать данные внтури датафрейма: - Привести все строки к одинаковым единицам измерения (тыс. руб.) - Убрать неиспользуемые колонки (date_revised, report_type) - Новые колонки: короткое название компании * три уровня кода ОКВЭД * регион (по ИНН) """ df_ = add_okved_subcode(add_region(add_title(df))) df_ = adjust_rub(df_) df_ = df_.set_index('inn') df_ = rename_rows(df_) return df_[canonic_columns()] def canonic_columns(numeric=SHORT_COLUMNS.numeric): return (['title', 'org', 'okpo', 'okopf', 'okfs', 'okved'] + ['unit'] + ['ok1', 'ok2', 'ok3', 'region'] + numeric) def is_numeric_column(name, numeric=SHORT_COLUMNS.numeric): return name in numeric def columns_typed_as_integer(numeric=SHORT_COLUMNS.numeric): return numeric + ['ok1', 'ok2', 'ok3', 'region'] def canonic_dtypes(): def switch(col): int_columns = columns_typed_as_integer() return numpy.int64 if (col in int_columns) else str result = {col: switch(col) for col in canonic_columns()} result['inn'] = str return result	[ 1, 1053, 12655, 13, 5215, 11701, 408, 10518, 13, 3166, 1045, 29877, 29889, 13099, 1053, 24972, 8476, 29918, 15032, 5005, 3059, 13, 3166, 1045, 29877, 29889, 12523, 1053, 853, 1990, 28677, 3399, 2392, 13, 13, 13, 13356, 2891, 29923, 29918, 11282, 353, 18793, 29915, 13, 29923, 3580, 15631, 353, 938, 29898, 29900, 29897, 13, 13967, 1001, 2965, 29918, 15032, 5005, 3059, 353, 24972, 8476, 29918, 15032, 5005, 3059, 29889, 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src/obmak_debug.py	jeppeter/py-obcode	0	44426	#! /usr/bin/env python import sys import os import extargsparse import re import time ##importdebugstart sys.path.append(os.path.abspath(os.path.dirname(__file__))) from strparser import * from filehdl import * from fmthdl import * from extract_ob import * from obmaklib import * ##importdebugend REPLACE_IMPORT_LIB=1 REPLACE_STR_PARSER=1 REPLACE_FILE_HDL=1 REPLACE_FMT_HDL=1 REPLACE_EXTRACT_OB=1 REPLACE_OBMAK_LIB=1 def main(): commandline=''' { "verbose\|v" : "+", "version\|V" : false, "dump\|D" : "obcode.json", "benchmark\|B" : false, "makob<makob_handler>##srcfile to give the other code file ,this need environment variable MAKOB_FILE to get the default (makob.json)##" : { "namemin" : 5, "namemax" : 20, "$" : "+" }, "unmakob<unmakob_handler>##dstfile to give the origin ,this need environment variable MAKOB_FILE to get the default (makob.json)##" : { "short" : false, "$" : "+" }, "basename<basename_handler>##to make basename##" : { "$" : "+" }, "obtrans<obtrans_handler>##translate the srcdir to dstdir in makob file##" : { "srcdir" : "", "dstdir" : "", "$" : "+" }, "oblist<oblist_handler>##to list files ob files##" : { "$" : "" }, "obuntrans<obuntrans_handler>##inputfile [outputfile] to trans file from MAKOB_FILE##" : { "$" : "+" }, "obunfunc<obunfunc_handler>##inputfile;outfile;funcs... to set obfuncs##" : { "$" : "+" } } ''' d = dict() d['version'] = "VERSION_RELACE_STRING" options = extargsparse.ExtArgsOptions(d) stime = time.time() parser = extargsparse.ExtArgsParse(options) parser.load_command_line_string(commandline) args = parser.parse_command_line(None,parser) if args.version: sys.stdout.write('%s\n'%(options.version)) sys.exit(0) if args.benchmark: etime = time.time() sys.stderr.write('run %s time %s second\n'%(sys.argv[1:],etime - stime)) return ##importdebugstart from obrelease import import re def debug_release(): if '-v' in sys.argv[1:]: #sys.stderr.write('will make verbose\n') loglvl = logging.DEBUG if logging.root is not None and len(logging.root.handlers) > 0: logging.root.handlers = [] logging.basicConfig(level=loglvl,format='%(asctime)s:%(filename)s:%(funcName)s:%(lineno)d\t%(message)s') topdir = os.path.abspath(os.path.join(os.path.dirname(__file__),'..')) tofile= os.path.abspath(os.path.join(topdir,'obmak.py')) curdir = os.path.abspath(os.path.dirname(__file__)) rlfiles = ReleaseFiles(__file__) rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'strparser.py')),r'REPLACE_STR_PARSER=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'fmthdl.py')),r'REPLACE_FMT_HDL=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'filehdl.py')),r'REPLACE_FILE_HDL=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'obmaklib.py')),r'REPLACE_OBMAK_LIB=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'extract_ob.py')),r'REPLACE_EXTRACT_OB=1') if len(sys.argv) > 2: for k in sys.argv[1:]: if not k.startswith('-'): tofile = k break versionfile = os.path.abspath(os.path.join(topdir,'VERSION')) if not os.path.exists(versionfile): raise Exception('can not find VERSION file') with open(versionfile,'r') as f: for l in f: l = l.rstrip('\r\n') vernum = l break #logging.info('str_c\n%s'%(strparser_c)) sarr = re.split('\.',vernum) if len(sarr) != 3: raise Exception('version (%s) not format x.x.x'%(vernum)) VERSIONNUMBER = vernum import_rets = fromat_ext_import_files(__file__,rlfiles.get_includes()) logging.info('import_rets\n%s'%(import_rets)) rlfiles.add_repls(r'VERSION_RELACE_STRING',VERSIONNUMBER) rlfiles.add_repls(r'debug_main','main') rlfiles.add_repls(r'REPLACE_IMPORT_LIB=1',make_string_slash_ok(import_rets)) #logging.info('repls %s'%(repls.keys())) disttools.release_file('__main__',tofile,[],[[r'##importdebugstart.',r'##importdebugend.']],[],rlfiles.get_repls()) return def debug_main(): if '--release' in sys.argv[1:]: debug_release() return main() return ##importdebugend if __name__ == '__main__': debug_main()	[ 1, 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snmn/nmn.py	jiangycTarheel/NMN-MultiHopQA	20	86347	<filename>snmn/nmn.py<gh_stars>10-100 import numpy as np import tensorflow as tf from tensorflow import convert_to_tensor as to_T, newaxis as ax from tensorflow.python.ops import tensor_array_ops, control_flow_ops from my.tensorflow.ops import bi_cudnn_rnn_encoder from snmn.config import cfg from snmn.util.cnn import fc_layer as fc, conv_layer as conv from basic.attention_modules import weighted_biattention from my.tensorflow.nn import dense from my.tensorflow.nn import linear_logits MODULE_INPUT_NUM = { '_NoOp': 2, '_Find': 0, '_Relocate': 1, '_Compare': 2, } MODULE_OUTPUT_NUM = { '_NoOp': 2, '_Find': 1, '_Relocate': 1, '_Compare': 1, } class NMN: def __init__(self, config, kb_batch, kb_mask_batch, kb_len_batch, c_list, cv_list, q, q_mask, module_names, module_prob_list, is_train, q_len, max_para_size=None, scope='NMN', reuse=None): """ NMN v4 with an attention stack """ with tf.variable_scope(scope, reuse=reuse): self.is_train = is_train self.main_config = config self.kb_batch = kb_batch self.kb_mask_batch = kb_mask_batch self.kb_len_batch = kb_len_batch self.c_list = c_list self.cv_list = cv_list self.q = q self.q_len = q_len self.q_mask = q_mask self.module_prob_list = module_prob_list self.kb_dim = cfg.MODEL.KB_DIM if self.main_config.nmn_relocate_move_ptr: MODULE_OUTPUT_NUM['_Relocate'] = MODULE_INPUT_NUM['_Relocate'] + 1 else: MODULE_OUTPUT_NUM['_Relocate'] = MODULE_INPUT_NUM['_Relocate'] self.T_ctrl = cfg.MODEL.T_CTRL self.mem_dim = cfg.MODEL.NMN.MEM_DIM self.N = tf.shape(kb_batch)[0] self.M = tf.shape(kb_batch)[1] self.JX = tf.shape(kb_batch)[2] self.att_shape = to_T([self.N, self.M, self.JX, 1]) self.max_para_size = max_para_size self.stack_len = cfg.MODEL.NMN.STACK.LENGTH # The initialial stack values are all zeros everywhere self.att_stack_init = tf.zeros( to_T([self.N, self.M, self.JX, self.kb_dim, self.stack_len])) # The initial stack pointer points to the stack bottom self.stack_ptr_init = tf.one_hot( tf.zeros(to_T([self.N]), tf.int32), self.stack_len) self.mem_init = tf.zeros(to_T([self.N, self.mem_dim])) # zero-outputs that can be easily used by the modules self.att_zero = tf.zeros(self.att_shape, tf.float32) if config.nmn_mem_init == 'zero': self.mem_zero = tf.zeros(to_T([self.N, self.mem_dim]), tf.float32) elif config.nmn_mem_init == 'random': self.mem_zero = tf.random.uniform(to_T([self.N, self.mem_dim]), minval=-100, maxval=100, dtype=tf.float32) else: raise NotImplementedError self.score_zero = tf.zeros(to_T([self.N, 1]), tf.float32) # the set of modules and functions (e.g. "_Find" -> Find) self.module_names = module_names self.module_funcs = [getattr(self, m[1:]) for m in module_names] self.num_module = len(module_names) self.module_validity_mat = _build_module_validity_mat(module_names) # unroll the modules with a fixed number of timestep T_ctrl self.att_list = [] self.att_stack_list = [] self.stack_ptr_list = [] self.mem_list = [] self.score_list = [] self.att_in_1, self.att_in_2 = None, None att_stack_prev = self.att_stack_init stack_ptr_prev = self.stack_ptr_init mem_prev = self.mem_init self.module_validity = [] for t in range(self.T_ctrl): c_i = self.c_list[t] cv_i = self.cv_list[t] module_prob = self.module_prob_list[t] # only keep the prob of valid modules (i.e. those won't cause # stack underflow or overflow. e.g. _Filter can't be run at # t = 0 since the stack is empty). if cfg.MODEL.NMN.VALIDATE_MODULES: module_validity = tf.matmul( stack_ptr_prev, self.module_validity_mat) if cfg.MODEL.NMN.HARD_MODULE_VALIDATION: module_validity = tf.round(module_validity) self.module_validity.append(module_validity) module_prob = module_validity module_prob /= tf.reduce_sum( module_prob, axis=1, keepdims=True) self.module_prob_list[t] = module_prob # run all the modules, and average their results wrt module_w res = [f(att_stack_prev, stack_ptr_prev, mem_prev, c_i, cv_i, t, reuse=(t > 0)) for f in self.module_funcs] att_stack_avg = tf.reduce_sum( module_prob[:, ax, ax, ax, ax, :] tf.stack([r[0] for r in res], axis=-1), axis=-1) stack_ptr_avg = _sharpen_ptr(tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[1] for r in res], axis=2), axis=-1)) mem_avg = tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[2] for r in res], axis=2), axis=-1) score_avg = tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[3] for r in res], axis=2), axis=-1) self.att_list.append(_read_from_stack(att_stack_avg, stack_ptr_avg)) self.att_stack_list.append(att_stack_avg) self.stack_ptr_list.append(stack_ptr_avg) self.mem_list.append(mem_avg) self.score_list.append(score_avg) att_stack_prev = att_stack_avg stack_ptr_prev = stack_ptr_avg mem_prev = mem_avg self.att_last = self.att_list[-1] self.att_first = self.att_list[0] self.att_second = self.att_list[1] self.mem_last = self.mem_list[-1] self.score_last = self.score_list[-1] def NoOp(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='NoOp', reuse=None): """ Does nothing. It leaves the stack pointer, the stack and mem vector as-is. """ return att_stack, stack_ptr, mem_in, self.score_zero def Find(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Find', reuse=None): """ Performs localization, and updates memory vector. """ with tf.variable_scope(scope, reuse=reuse): # Get attention # 1) linearly map the controller vectors to the KB dimension # 2) elementwise product with KB # 3) 1x1 convolution to get attention logits c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, \ is_train=self.is_train, dropout=self.main_config.nmn_dropout) kb_batch = self.kb_batch elt_prod = tf.nn.l2_normalize( kb_batch * c_mapped[:, ax, ax, :], axis=-1) att_out = self.apply_attention(elt_prod, cv_i, t) # Push to stack stack_ptr = _move_ptr_fw(stack_ptr) att_stack = _write_to_stack(att_stack, stack_ptr, att_out) # Find bridge entity if t == 0 and (self.main_config.supervise_bridge_entity): g1, _ = bi_cudnn_rnn_encoder('lstm', self.main_config.hidden_size, 1, 1-self.main_config.input_keep_prob, \ tf.squeeze(att_out, axis=1), tf.squeeze(self.kb_len_batch, axis=1), self.is_train) g1 = g1[:, ax, :, :] bridge_logits = linear_logits([g1, att_out], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train, scope='logits_bridge') self.bridge_logits = tf.squeeze(bridge_logits, axis=1) bridge_attn = tf.nn.softmax(self.bridge_logits) self.bridge_attn = bridge_attn new_mem = tf.einsum('ijk,ij->ik', tf.squeeze(kb_batch, axis=1), bridge_attn) if t == 0: self.inferred_bridge = new_mem return att_stack, stack_ptr, self.mem_zero, self.score_zero def Compare(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Compare', reuse=None): # Pop from stack att_in_2 = tf.squeeze(_read_from_stack(att_stack, stack_ptr), axis=-1) stack_ptr = _move_ptr_bw(stack_ptr) att_in_1 = tf.squeeze(_read_from_stack(att_stack, stack_ptr), axis=-1) #stack_ptr = _move_ptr_bw(stack_ptr) stack_ptr = _move_ptr_fw(stack_ptr) with tf.variable_scope(scope, reuse=reuse): att_prob_in_1 = linear_logits([att_in_1], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train) att_prob_in_2 = linear_logits([att_in_2], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train, reuse=True) att_prob_in_1, att_prob_in_2 = tf.squeeze(att_prob_in_1, axis=1), tf.squeeze(att_prob_in_2, axis=1) self.att_in_1 = att_prob_in_1 self.att_in_2 = att_prob_in_2 c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, is_train=self.is_train, dropout=self.main_config.nmn_dropout) kb_att_in_1 = _extract_softmax_avg(self.kb_batch, att_prob_in_1[:, ax, :]) kb_att_in_2 = _extract_softmax_avg(self.kb_batch, att_prob_in_2[:, ax, :]) fc_in_1 = tf.concat([c_mapped, c_mapped * kb_att_in_1, c_mappedkb_att_in_2, kb_att_in_1-kb_att_in_2], axis=1) mem_out = tf.nn.tanh(fc('fc_mem_out_1', fc_in_1, output_dim=self.mem_dim, is_train=self.is_train, dropout=self.main_config.nmn_dropout)) return att_stack, stack_ptr, mem_out, self.score_zero def Relocate(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Transform', reuse=None): """ Relocates the previous attention, and updates memory vector. """ kb_batch = self.kb_batch with tf.variable_scope(scope, reuse=reuse): c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, is_train=self.is_train, dropout=self.main_config.nmn_dropout) # Get attention # 1) linearly map the controller vectors to the KB dimension # 2) extract attended features from the input attention # 2) elementwise product with KB # 3) 1x1 convolution to get attention logits if t == 0: elt_prod = tf.nn.l2_normalize( kb_batch c_mapped[:, ax, ax, :], axis=-1) else: elt_prod = tf.nn.l2_normalize( kb_batch * c_mapped[:, ax, ax, :] * self.inferred_bridge[:, ax, ax, :], axis=-1) att_out = self.apply_attention(elt_prod, cv_i, t, module='relocate') # Push to stack if self.main_config.nmn_relocate_move_ptr: stack_ptr = _move_ptr_fw(stack_ptr) # cancel-out above att_stack = _write_to_stack(att_stack, stack_ptr, att_out) return att_stack, stack_ptr, self.mem_zero, self.score_zero def apply_attention(self, h, u_weights, t, module='find'): if self.main_config.nmn_attention_type == 'conv': out = _1x1conv('conv_att_out', h, output_dim=1) logits = apply_mask(out, self.kb_mask_batch) elif self.main_config.nmn_attention_type == 'biattn': out = self.bi_attention(h, u_weights, t, module=module) else: raise NotImplementedError return out def bi_attention(self, h, u_weights, t, module='find'): u_weights = tf.transpose(tf.squeeze(u_weights, axis=-1), perm=[1, 0]) q, q_mask = self.q, self.q_mask p0, context_dim, weight_one = weighted_biattention( h, q, self.kb_dim, h_mask=self.kb_mask_batch, u_mask=tf.cast(q_mask, 'bool'), u_weights=u_weights, scope='biattn') if module == 'find' and t == 1: self.weight_one = weight_one p0 = dense(p0, self.main_config.hidden_size2, scope='biattn_dense') return p0 def _move_ptr_fw(stack_ptr): """ Move the stack pointer forward (i.e. to push to stack). """ # Note: in TF, conv1d is implemented as auto-correlation (instead of # mathmatical convolution), so no flipping of the filter. filter_fw = to_T(np.array([1, 0, 0], np.float32).reshape((3, 1, 1))) new_stack_ptr = tf.squeeze( tf.nn.conv1d(stack_ptr[..., ax], filter_fw, 1, 'SAME'), axis=[2]) # when the stack pointer is already at the stack top, keep # the pointer in the same location (otherwise the pointer will be all zero) if cfg.MODEL.NMN.STACK.GUARD_STACK_PTR: stack_len = cfg.MODEL.NMN.STACK.LENGTH stack_top_mask = tf.one_hot(stack_len - 1, stack_len) new_stack_ptr += stack_top_mask stack_ptr return new_stack_ptr def _move_ptr_bw(stack_ptr): """ Move the stack pointer backward (i.e. to pop from stack). """ # Note: in TF, conv1d is implemented as auto-correlation (instead of # mathmatical convolution), so no flipping of the filter. filter_fw = to_T(np.array([0, 0, 1], np.float32).reshape((3, 1, 1))) new_stack_ptr = tf.squeeze( tf.nn.conv1d(stack_ptr[..., ax], filter_fw, 1, 'SAME'), axis=[2]) # when the stack pointer is already at the stack bottom, keep # the pointer in the same location (otherwise the pointer will be all zero) if cfg.MODEL.NMN.STACK.GUARD_STACK_PTR: stack_len = cfg.MODEL.NMN.STACK.LENGTH stack_bottom_mask = tf.one_hot(0, stack_len) new_stack_ptr += stack_bottom_mask * stack_ptr return new_stack_ptr def _read_from_stack(att_stack, stack_ptr): """ Read the value at the given stack pointer. """ stack_ptr_expand = stack_ptr[:, ax, ax, ax, :] # The stack pointer is a one-hot vector, so just do dot product att = tf.reduce_sum(att_stack * stack_ptr_expand, axis=-1, keepdims=True) return att def _write_to_stack(att_stack, stack_ptr, att): """ Write value 'att' into the stack at the given stack pointer. Note that the result needs to be assigned back to att_stack """ stack_ptr_expand = stack_ptr[:, ax, ax, ax, :] att_stack = att[:, :, :, :, ax] * stack_ptr_expand + att_stack * (1 - stack_ptr_expand) return att_stack def _sharpen_ptr(stack_ptr): """ Sharpen the stack pointers into (nearly) one-hot vectors, using argmax or softmax. The stack values should always sum up to one for each instance. """ hard = cfg.MODEL.NMN.STACK.USE_HARD_SHARPEN if hard: # hard (non-differentiable) sharpening with argmax new_stack_ptr = tf.one_hot( tf.argmax(stack_ptr, axis=1), tf.shape(stack_ptr)[1]) else: # soft (differentiable) sharpening with softmax temperature = cfg.MODEL.NMN.STACK.SOFT_SHARPEN_TEMP new_stack_ptr = tf.nn.softmax(stack_ptr / temperature) return new_stack_ptr def _1x1conv(name, bottom, output_dim, reuse=None): return conv(name, bottom, kernel_size=1, stride=1, output_dim=output_dim, reuse=reuse) def apply_mask(x, mask): return x - 1e30 * (1. - tf.cast(mask[:, :, :, ax], 'float32')) def _spatial_softmax(att_raw): att_shape = tf.shape(att_raw) N = att_shape[0] att_softmax = tf.nn.softmax(tf.reshape(att_raw, to_T([N, -1])), axis=1) att_softmax = tf.reshape(att_softmax, att_shape) return att_softmax def _extract_softmax_avg(kb_batch, att_raw): att_softmax = _spatial_softmax(att_raw)[:, :, :, ax] return tf.reduce_sum(kb_batch * att_softmax, axis=[1, 2]) def _build_module_validity_mat(module_names): """ Build a module validity matrix, ensuring that only valid modules will have non-zero probabilities. A module is only valid to run if there are enough attentions to be popped from the stack, and have space to push into (e.g. _Find), so that stack will not underflow or overflow by design. module_validity_mat is a stack_len x num_module matrix, and is used to multiply with stack_ptr to get validity boolean vector for the modules. """ stack_len = cfg.MODEL.NMN.STACK.LENGTH module_validity_mat = np.zeros((stack_len, len(module_names)), np.float32) for n_m, m in enumerate(module_names): # a module can be run only when stack ptr position satisfies # (min_ptr_pos <= ptr <= max_ptr_pos), where max_ptr_pos is inclusive # 1) minimum position: # stack need to have MODULE_INPUT_NUM[m] things to pop from min_ptr_pos = MODULE_INPUT_NUM[m] # the stack ptr diff=(MODULE_OUTPUT_NUM[m] - MODULE_INPUT_NUM[m]) # ensure that ptr + diff <= stack_len - 1 (stack top) max_ptr_pos = ( stack_len - 1 + MODULE_INPUT_NUM[m] - MODULE_OUTPUT_NUM[m]) module_validity_mat[min_ptr_pos:max_ptr_pos+1, n_m] = 1. return to_T(module_validity_mat)	[ 1, 529, 9507, 29958, 16586, 23521, 29914, 22882, 29876, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 12655, 408, 7442, 13, 5215, 26110, 408, 15886, 13, 3166, 26110, 1053, 3588, 29918, 517, 29918, 20158, 408, 304, 29918, 29911, 29892, 716, 8990, 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bhr_site/urls.py	amerck/bhr-site	23	172853	from django.conf.urls import include, url from django.contrib import admin from django.views.generic.base import RedirectView from django.contrib.auth import views as auth_views urlpatterns = [ # Examples: # url(r'^$', 'bhr_site.views.home', name='home'), # url(r'^blog/', include('blog.urls')), url(r'^admin/', admin.site.urls), url(r'^bhr/', include('bhr.urls')), url(r'^accounts/login/$', auth_views.LoginView.as_view(), {'template_name': 'login.html'}, name='accounts_login'), url(r'^accounts/logout/$', auth_views.LogoutView.as_view(), name='logout'), url(r'^$', RedirectView.as_view(url='/bhr', permanent=False), name='siteroot'), ]	[ 1, 515, 9557, 29889, 5527, 29889, 26045, 1053, 3160, 29892, 3142, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 3166, 9557, 29889, 7406, 29889, 19206, 29889, 3188, 1053, 4367, 1088, 1043, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 8386, 408, 4817, 29918, 7406, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 396, 1222, 9422, 29901, 13, 1678, 396, 3142, 29898, 29878, 29915, 29985, 29938, 742, 525, 29890, 1092, 29918, 2746, 29889, 7406, 29889, 5184, 742, 1024, 2433, 5184, 5477, 13, 1678, 396, 3142, 29898, 29878, 29915, 29985, 7312, 29914, 742, 3160, 877, 7312, 29889, 26045, 1495, 511, 13, 13, 1678, 3142, 29898, 29878, 29915, 29985, 6406, 29914, 742, 4113, 29889, 2746, 29889, 26045, 511, 13, 1678, 3142, 29898, 29878, 29915, 29985, 29890, 1092, 29914, 742, 3160, 877, 29890, 1092, 29889, 26045, 1495, 511, 13, 1678, 3142, 29898, 29878, 29915, 29985, 10149, 29879, 29914, 7507, 13346, 742, 4817, 29918, 7406, 29889, 11049, 1043, 29889, 294, 29918, 1493, 3285, 11117, 6886, 29918, 978, 2396, 525, 7507, 29889, 1420, 16675, 1024, 2433, 10149, 29879, 29918, 7507, 5477, 13, 1678, 3142, 29898, 29878, 29915, 29985, 10149, 29879, 29914, 1188, 449, 13346, 742, 4817, 29918, 7406, 29889, 3403, 449, 1043, 29889, 294, 29918, 1493, 3285, 1024, 2433, 1188, 449, 5477, 13, 13, 1678, 3142, 29898, 29878, 29915, 29985, 29938, 742, 4367, 1088, 1043, 29889, 294, 29918, 1493, 29898, 2271, 2433, 29914, 29890, 1092, 742, 17667, 29922, 8824, 511, 1024, 2433, 29879, 277, 1489, 327, 5477, 13, 29962, 13, 2 ]
NVIDIA/benchmarks/ssd/implementations/pytorch/utils.py	goswamig/training_results_v0.7	48	24705	<gh_stars>10-100 # MIT License # # Copyright (c) 2018 kuangliu # # 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. # # ------------------------------------------------------------------------------ # # MIT License # # Copyright (c) 2017 <NAME>, <NAME> # # 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. # # ------------------------------------------------------------------------------ # # Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torchvision import torchvision.transforms as transforms import torch.utils.data as data from PIL import Image from xml.etree import ElementTree import os import glob from pathlib import Path import numpy as np import random import itertools import torch.nn.functional as F try: import ujson as json except ImportError: import json import gc import time import bz2 import pickle from math import sqrt, ceil, cos, sin, pi from mlperf_logging.mllog import constants from mlperf_logger import log_event from SSD import _C as C from fused_color_jitter import FusedColorJitter # This function is from https://github.com/kuangliu/pytorch-ssd def calc_iou_tensor(box1, box2): """ Calculation of IoU based on two boxes tensor, Reference to https://github.com/kuangliu/pytorch-ssd input: box1 (N, 4) box2 (M, 4) output: IoU (N, M) """ N = box1.size(0) M = box2.size(0) be1 = box1.unsqueeze(1).expand(-1, M, -1) be2 = box2.unsqueeze(0).expand(N, -1, -1) # Left Top & Right Bottom lt = torch.max(be1[:,:,:2], be2[:,:,:2]) #mask1 = (be1[:,:, 0] < be2[:,:, 0]) ^ (be1[:,:, 1] < be2[:,:, 1]) #mask1 = ~mask1 rb = torch.min(be1[:,:,2:], be2[:,:,2:]) #mask2 = (be1[:,:, 2] < be2[:,:, 2]) ^ (be1[:,:, 3] < be2[:,:, 3]) #mask2 = ~mask2 delta = rb - lt delta[delta < 0] = 0 intersect = delta[:,:,0]delta[:,:,1] #mask1.float()mask2.float() delta1 = be1[:,:,2:] - be1[:,:,:2] area1 = delta1[:,:,0]delta1[:,:,1] delta2 = be2[:,:,2:] - be2[:,:,:2] area2 = delta2[:,:,0]delta2[:,:,1] iou = intersect/(area1 + area2 - intersect) return iou # This class is from https://github.com/chauhan-utk/ssd.DomainAdaptation class SSDCropping(object): """ Cropping for SSD, according to original paper Choose between following 3 conditions: 1. Preserve the original image 2. Random crop minimum IoU is among 0.1, 0.3, 0.5, 0.7, 0.9 3. Random crop Reference to https://github.com/chauhan-utk/ssd.DomainAdaptation """ def __init__(self): self.sample_options = ( # Do nothing None, # min IoU, max IoU (0.1, None), (0.3, None), (0.5, None), (0.7, None), (0.9, None), # no IoU requirements (None, None), ) # Implementation uses 1 iteration to find a possible candidate, this # was shown to produce the same mAP as using more iterations. self.num_cropping_iterations = 1 log_event(key=constants.MAX_SAMPLES, value=self.num_cropping_iterations) def __call__(self, img, img_size, bboxes, labels): # Ensure always return cropped image while True: mode = random.choice(self.sample_options) if mode is None: return img, img_size, bboxes, labels htot, wtot = img_size min_iou, max_iou = mode min_iou = float("-inf") if min_iou is None else min_iou max_iou = float("+inf") if max_iou is None else max_iou # Implementation use 50 iteration to find possible candidate for _ in range(self.num_cropping_iterations): # suze of each sampled path in [0.1, 1] 0.30.3 approx. 0.1 w = random.uniform(0.3 , 1.0) h = random.uniform(0.3 , 1.0) if w/h < 0.5 or w/h > 2: continue # left 0 ~ wtot - w, top 0 ~ htot - h left = random.uniform(0, 1.0 - w) top = random.uniform(0, 1.0 - h) right = left + w bottom = top + h ious = calc_iou_tensor(bboxes, torch.tensor([[left, top, right, bottom]])) # tailor all the bboxes and return if not ((ious > min_iou) & (ious < max_iou)).all(): continue # discard any bboxes whose center not in the cropped image xc = 0.5(bboxes[:, 0] + bboxes[:, 2]) yc = 0.5(bboxes[:, 1] + bboxes[:, 3]) masks = (xc > left) & (xc < right) & (yc > top) & (yc < bottom) # if no such boxes, continue searching again if not masks.any(): continue bboxes[bboxes[:, 0] < left, 0] = left bboxes[bboxes[:, 1] < top, 1] = top bboxes[bboxes[:, 2] > right, 2] = right bboxes[bboxes[:, 3] > bottom, 3] = bottom #print(left, top, right, bottom) #print(labels, bboxes, masks) bboxes = bboxes[masks, :] labels = labels[masks] left_idx = int(leftwtot) top_idx = int(tophtot) right_idx = int(rightwtot) bottom_idx = int(bottomhtot) #print(left_idx,top_idx,right_idx,bottom_idx) #img = img[:, top_idx:bottom_idx, left_idx:right_idx] img = img.crop((left_idx, top_idx, right_idx, bottom_idx)) bboxes[:, 0] = (bboxes[:, 0] - left)/w bboxes[:, 1] = (bboxes[:, 1] - top)/h bboxes[:, 2] = (bboxes[:, 2] - left)/w bboxes[:, 3] = (bboxes[:, 3] - top)/h htot = bottom_idx - top_idx wtot = right_idx - left_idx return img, (htot, wtot), bboxes, labels # Don't need to cast to float, already there (from FusedColorJitter) class ToTensor(object): def __init__(self): pass def __call__(self, img): img = torch.Tensor(np.array(img)) # Transform from HWC to CHW img = img.permute(2, 0 ,1).div(255) return img class RandomHorizontalFlip(object): def __init__(self, p=0.5): self.p = p def __call__(self, image, bboxes): if random.random() < self.p: bboxes[:, 0], bboxes[:, 2] = 1.0 - bboxes[:, 2], 1.0 - bboxes[:, 0] return image.transpose(Image.FLIP_LEFT_RIGHT), bboxes return image, bboxes # Do data augumentation class SSDTransformer(object): """ SSD Data Augumentation, according to original paper Composed by several steps: Cropping Resize Flipping Jittering """ def __init__(self, size = (300, 300), val=False): # define vgg16 mean self.size = size self.val = val self.crop = SSDCropping() self.img_trans = transforms.Compose([ transforms.Resize(self.size), #transforms.ColorJitter(brightness=0.125, contrast=0.5, # saturation=0.5, hue=0.05 #), #transforms.ToTensor(), FusedColorJitter(), ToTensor(), ]) self.hflip = RandomHorizontalFlip() # All Pytorch Tensor will be normalized # https://discuss.pytorch.org/t/how-to-preprocess-input-for-pre-trained-networks/683 normalization_mean = [0.485, 0.456, 0.406] normalization_std = [0.229, 0.224, 0.225] self.normalize = transforms.Normalize(mean=normalization_mean, std=normalization_std) self.trans_val = transforms.Compose([ transforms.Resize(self.size), transforms.ToTensor(), self.normalize,]) def __call__(self, img, img_size, bbox=None, label=None, max_num=200): #img = torch.tensor(img) if self.val: bbox_out = torch.zeros(max_num, 4) label_out = torch.zeros(max_num, dtype=torch.long) bbox_out[:bbox.size(0), :] = bbox label_out[:label.size(0)] = label return self.trans_val(img), img_size, bbox_out, label_out # random crop img, img_size, bbox, label = self.crop(img, img_size, bbox, label) # random horiz. flip img, bbox = self.hflip(img, bbox) # [Resize, ColorJitter, ToTensor] img = self.img_trans(img).contiguous() img = self.normalize(img) return img, img_size, bbox, label # Implement a datareader for COCO dataset class COCODetection(data.Dataset): def __init__(self, img_folder, annotate_file, transform=None, data=None): self.img_folder = img_folder self.annotate_file = annotate_file if data: self.data = data else: # Start processing annotation with open(annotate_file) as fin: # loading huge json files tends to cause the gc (cycle collector) to # waste a lot of time so: gc_old = gc.isenabled() gc.disable() self.data = json.load(fin) if gc_old: gc.enable() self.images = {} self.label_map = {} self.label_info = {} #print("Parsing COCO data...") start_time = time.time() # 0 stand for the background cnt = 0 self.label_info[cnt] = "background" for cat in self.data["categories"]: cnt += 1 self.label_map[cat["id"]] = cnt self.label_info[cnt] = cat["name"] # build inference for images for img in self.data["images"]: img_id = img["id"] img_name = img["file_name"] img_size = (img["height"],img["width"]) #print(img_name) if img_id in self.images: raise Exception("dulpicated image record") self.images[img_id] = (img_name, img_size, []) # read bboxes for bboxes in self.data["annotations"]: img_id = bboxes["image_id"] category_id = bboxes["category_id"] bbox = bboxes["bbox"] bbox_label = self.label_map[bboxes["category_id"]] self.images[img_id][2].append((bbox, bbox_label)) for k, v in list(self.images.items()): if len(v[2]) == 0: #print("empty image: {}".format(k)) self.images.pop(k) self.img_keys = list(self.images.keys()) self.transform = transform #print("End parsing COCO data, total time {}".format(time.time()-start_time)) @property def labelnum(self): return len(self.label_info) @staticmethod def load(pklfile): #print("Loading from {}".format(pklfile)) with bz2.open(pklfile, "rb") as fin: ret = pickle.load(fin) return ret def save(self, pklfile): #print("Saving to {}".format(pklfile)) with bz2.open(pklfile, "wb") as fout: pickle.dump(self, fout) def __len__(self): return len(self.images) def __getitem__(self, idx): img_id = self.img_keys[idx] img_data = self.images[img_id] fn = img_data[0] img_path = os.path.join(self.img_folder, fn) s = time.time() img = Image.open(img_path).convert("RGB") e = time.time() decode_time = e - s htot, wtot = img_data[1] bbox_sizes = [] bbox_labels = [] #for (xc, yc, w, h), bbox_label in img_data[2]: for (l,t,w,h), bbox_label in img_data[2]: r = l + w b = t + h #l, t, r, b = xc - 0.5w, yc - 0.5h, xc + 0.5w, yc + 0.5h bbox_size = (l/wtot, t/htot, r/wtot, b/htot) # filter out zero-size bboxes if l == r or t == b: continue bbox_sizes.append(bbox_size) bbox_labels.append(bbox_label) bbox_sizes = torch.tensor(bbox_sizes) bbox_labels = torch.tensor(bbox_labels) s = time.time() if self.transform != None: img, (htot, wtot), bbox_sizes, bbox_labels = \ self.transform(img, (htot, wtot), bbox_sizes, bbox_labels) else: pass # img = transforms.ToTensor()(img) return img, img_id, (htot, wtot), bbox_sizes, bbox_labels	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 341, 1806, 19245, 13, 29937, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29947, 413, 29884, 574, 492, 29884, 13, 29937, 13, 29937, 20894, 2333, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 263, 3509, 13, 29937, 310, 445, 7047, 322, 6942, 5106, 2066, 313, 1552, 376, 6295, 14093, 4968, 304, 5376, 13, 29937, 297, 278, 18540, 1728, 24345, 29892, 3704, 1728, 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world_creator/objects.py	lilSpeedwagon/zaWRka-project	1	48455	<gh_stars>1-10 #!/usr/bin/env python3 from data_structures import * import copy import logging as log import converter import math as m from enum import Enum import os APP_VERSION = 1.0 class SignsTypes(Enum): STOP = "stop sign" ONLY_FORWARD = "only forward sign" ONLY_RIGHT = "only right sign" ONLY_LEFT = "only left sign" FORWARD_OR_RIGHT = "forward or right sign" FORWARD_OR_LEFT = "forward or left sign" class ImagesPaths(): PATH_TO_IMAGE = 'models' STOP = os.path.join(PATH_TO_IMAGE, 'brick-sign/brick.png') ONLY_FORWARD = os.path.join(PATH_TO_IMAGE, 'forward-sign/forward.png') ONLY_LEFT = os.path.join(PATH_TO_IMAGE, 'left-sign/left.png') ONLY_RIGHT = os.path.join(PATH_TO_IMAGE, 'right-sign/right.png') FORWARD_OR_LEFT = os.path.join(PATH_TO_IMAGE, 'forward-left-sign/frwd_left.png') FORWARD_OR_RIGHT = os.path.join(PATH_TO_IMAGE, 'forward-right-sign/frwd_right.png') def sign_path_to_sign_type(img_path): if img_path is ImagesPaths.STOP: return SignsTypes.STOP.value elif img_path is ImagesPaths.ONLY_FORWARD: return SignsTypes.ONLY_FORWARD.value elif img_path is ImagesPaths.ONLY_LEFT: return SignsTypes.ONLY_LEFT.value elif img_path is ImagesPaths.ONLY_RIGHT: return SignsTypes.ONLY_RIGHT.value elif img_path is ImagesPaths.FORWARD_OR_LEFT: return SignsTypes.FORWARD_OR_LEFT.value elif img_path is ImagesPaths.FORWARD_OR_RIGHT: return SignsTypes.FORWARD_OR_RIGHT.value else: return " " def sign_type_to_sign_path(sign_type): if sign_type == SignsTypes.STOP.value: return ImagesPaths.STOP elif sign_type == SignsTypes.ONLY_FORWARD.value: return ImagesPaths.ONLY_FORWARD elif sign_type == SignsTypes.ONLY_LEFT.value: return ImagesPaths.ONLY_LEFT elif sign_type == SignsTypes.ONLY_RIGHT.value: return ImagesPaths.ONLY_RIGHT elif sign_type == SignsTypes.FORWARD_OR_LEFT.value: return ImagesPaths.FORWARD_OR_LEFT elif sign_type == SignsTypes.FORWARD_OR_RIGHT.value: return ImagesPaths.FORWARD_OR_RIGHT else: return " " class ObjectType(Enum): START = 10, WALL = 11, BOX = 12, SQUARE = 13, SIGN = 14, TRAFFIC_LIGHT = 15 class CellQuarter(Enum): RIGHT_TOP = 0 RIGHT_BOT = 1 LEFT_TOP = 2 LEFT_BOT = 3 class MapParams: def __init__(self, n_cells: Size2D, cell_sz: Size2D): self.n_cells = n_cells self.cell_sz = cell_sz self.phys_size = Size2D(self.n_cells.x * self.cell_sz.x, self.n_cells.y * self.cell_sz.y) print("World cells: count={0},size={1}".format(self.n_cells, self.cell_sz)) def serialize(self): data = { 'cell_cnt': self.n_cells.as_list(), 'cell_sz': self.cell_sz.as_list() } return data def __str__(self): return 'Map params: count({}) / size({})'.format(self.n_cells, self.cell_sz) @staticmethod def deserialize(data: dict): return MapParams(Point2D.from_list(data['cell_cnt']), Point2D.from_list(data['cell_sz'])) class Object: def render(self): pass def serialized(self): pass @staticmethod def deserialize(data: dict): if data['name'] not in SERIALIZATION_SUPPORT: log.error('Object type \'{}\' not found'.format(data['name'])) return None return SERIALIZATION_SUPPORT[data['name']].deserialize(data) class Wall(): TYPE = ObjectType.WALL def __init__(self, point1, point2): self.p1 = point1 self.p2 = point2 def __str__(self): return "[({}) p1 = {}, p2 = {}]".format(type(self), self.p1, self.p2) def distance_2_point(self, pnt): import numpy from numpy import arccos, array, dot, pi, cross from numpy.linalg import det, norm A = numpy.array(self.p1.as_list()) B = numpy.array(self.p2.as_list()) P = numpy.array(pnt.as_list()) if arccos(dot((P - A) / norm(P - A), (B - A) / norm(B - A))) > m.pi / 2: return norm(P - A) if arccos(dot((P - B) / norm(P - B), (A - B) / norm(A - B))) > m.pi / 2: return norm(P - B) return norm(cross(A-B, A-P))/norm(B-A) def render(self, qp): qp.drawWallLine(self.p1, self.p2, color=(0, 0, 0)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pnts': self.p1.as_list() + self.p2.as_list() } return data @staticmethod def deserialize(data: dict): return Wall(Point2D.from_list(data['pnts'][0:2]), Point2D.from_list(data['pnts'][2:4])) class Sign(Object): TYPE = ObjectType.SIGN def __init__(self, pos, orient, signType): self.pos = pos self.type = signType self.orient = orient def __str__(self): return "[({}) pose = {}, orient = {}, type = {}]".format(type(self), self.pos, self.orient, self.type) def render(self, qp): qp.drawQuarterImg(self.pos, self.orient, sign_type_to_sign_path(self.type)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pos': self.pos.as_list(), 'orient': self.orient.value, 'type': self.type } return data @staticmethod def deserialize(data: dict): return Sign(Point2D.from_list(data['pos']), CellQuarter(data['orient']), data['type']) class Box(Object): TYPE = ObjectType.BOX def __init__(self, pos: Point2D): self.pos = pos def render(self, qp): qp.fillCell(self.pos, color=(150, 150, 150)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pos': self.pos.as_list() } return data @staticmethod def deserialize(data: dict): return Box(Point2D.from_list(data['pos'])) SERIALIZATION_SUPPORT = { 'wall': Wall, 'sign': Sign, 'box': Box }	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 3166, 848, 29918, 4984, 1973, 1053, 334, 13, 5215, 3509, 13, 5215, 12183, 408, 1480, 13, 5215, 29105, 13, 5215, 5844, 408, 286, 13, 3166, 14115, 1053, 1174, 398, 13, 5215, 2897, 13, 13, 20576, 29918, 16358, 353, 29871, 29896, 29889, 29900, 13, 13, 1990, 9954, 29879, 10562, 29898, 16854, 1125, 13, 1678, 6850, 4590, 353, 376, 9847, 1804, 29908, 13, 1678, 6732, 16786, 29918, 22051, 29956, 17011, 353, 376, 6194, 6375, 1804, 29908, 13, 1678, 6732, 16786, 29918, 22789, 3912, 353, 376, 6194, 1492, 1804, 29908, 13, 1678, 6732, 16786, 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main project.py	Kshitijkrishnadas/haribol	0	139569	import mysql.connector as sql conn=sql.connect(host='localhost',user='root',passwd='<PASSWORD>',database='electronic_shop') cur=conn.cursor() if conn.is_connected: print(' ELECTRONIC SHOP SYSTEM') print(' 1.Admin') print(' 2.Customers') choice=int(input(' Enter the choice: ')) if choice == 1: pw = '<PASSWORD>' passwrd = input('Enter the password: ') if passwrd == pw: print(' 1.Enter data for Items') print(' 2.Enter data for Labour') print(' 3.Enter data for Customers') choose = int(input('Enter the choice for adding data: ')) if choose == 1: item = input('Enter the name of item: ') availability = int(input('Availability of the item: ')) price = int(input('Enter the price of item in rupees: ')) cur.execute("INSERT INTO ITEMS VALUES('" + item + "'," + str(availability) + "," + str(price) + ")") print('ENTRY SUCCESSFUL.') conn.commit() elif choose == 2: name = input('Enter the name of labour: ') age = int(input('Enter the age: ')) place = input('Enter the place of the labour: ') dept = input('Enter the name of the department: ') cur.execute("INSERT INTO LABOURS VALUES('" + name + "','" + str(age) + "','" + place + "','" + dept + "')") print('ENTRY SUCCESSFUL.') conn.commit() elif choose == 3: name = input('Enter the name of the customer: ') age = int(input('Enter the age: ')) place = input('Enter the place of the customer: ') item = input('Enter the item bought: ') cur.execute("INSERT INTO CUSTOMER VALUES('" + name + "','" + str(age) + "','" + place + "','" + item + "')") print('ENTRY SUCCESSFUL.') conn.commit() else: print('Invalid Login!') elif choice == 2: print(' 1.Display the list of Items.') print(' 2.Display the list of Labours.') print(' 3.Display the list of Customers.') ch = int(input('Enter the choice: ')) if ch == 1: print() cur.execute('SELECT * FROM ITEMS') dataa = cur.fetchall() print('List of items: ') for row in dataa: print() print('Item: ',row[0]) print('Availablity: ',row[1]) print('Price: ',row[2]) elif ch == 2: print() cur.execute('SELECT * FROM LABOURS') data = cur.fetchall() print('List of Labours:') for row in data: print() print('Name: ',row[0]) print('Age: ',row[1]) print('Place: ',row[2]) print('Department: ',row[3]) elif ch == 3: print() cur.execute('SELECT * FROM CUSTOMER') datas = cur.fetchall() print('List of customers:') for row in datas: print() print('Name: ',row[0]) print('Age: ',row[1]) print('Place: ',row[2]) print('Item bought: ',row[3])	[ 1, 1053, 5749, 29889, 11958, 2801, 408, 4576, 13, 13082, 29922, 2850, 29889, 6915, 29898, 3069, 2433, 7640, 742, 1792, 2433, 4632, 742, 3364, 9970, 2433, 29966, 25711, 17013, 29958, 742, 9803, 2433, 15436, 1617, 293, 29918, 19032, 1495, 13, 2764, 29922, 13082, 29889, 18127, 580, 13, 361, 11009, 29889, 275, 29918, 18045, 29901, 13, 1678, 1596, 877, 462, 462, 632, 382, 3281, 29934, 1164, 2965, 24972, 4590, 28962, 1254, 12665, 1495, 13, 1678, 1596, 877, 462, 462, 795, 29896, 29889, 12754, 1495, 13, 1678, 1596, 877, 462, 462, 795, 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gooey/gui/processor.py	Jacke/Gooey	13,430	1610714	import os import re import subprocess import sys from functools import partial from threading import Thread from gooey.gui import events from gooey.gui.pubsub import pub from gooey.gui.util.casting import safe_float from gooey.gui.util.taskkill import taskkill from gooey.util.functional import unit, bind class ProcessController(object): def __init__(self, progress_regex, progress_expr, hide_progress_msg,encoding, shell=True): self._process = None self.progress_regex = progress_regex self.progress_expr = progress_expr self.hide_progress_msg = hide_progress_msg self.encoding = encoding self.wasForcefullyStopped = False self.shell_execution = shell def was_success(self): self._process.communicate() return self._process.returncode == 0 def poll(self): if not self._process: raise Exception('Not started!') return self._process.poll() def stop(self): if self.running(): self.wasForcefullyStopped = True taskkill(self._process.pid) def running(self): return self._process and self.poll() is None def run(self, command): self.wasForcefullyStopped = False env = os.environ.copy() env["GOOEY"] = "1" env["PYTHONIOENCODING"] = self.encoding try: self._process = subprocess.Popen( command.encode(sys.getfilesystemencoding()), stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.STDOUT, shell=self.shell_execution, env=env) except: self._process = subprocess.Popen( command, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr = subprocess.STDOUT, shell = self.shell_execution, env=env) t = Thread(target=self._forward_stdout, args=(self._process,)) t.start() def _forward_stdout(self, process): ''' Reads the stdout of `process` and forwards lines and progress to any interested subscribers ''' while True: line = process.stdout.readline() if not line: break _progress = self._extract_progress(line) pub.send_message(events.PROGRESS_UPDATE, progress=_progress) if _progress is None or self.hide_progress_msg is False: pub.send_message(events.CONSOLE_UPDATE, msg=line.decode(self.encoding)) pub.send_message(events.EXECUTION_COMPLETE) def _extract_progress(self, text): ''' Finds progress information in the text using the user-supplied regex and calculation instructions ''' # monad-ish dispatch to avoid the if/else soup find = partial(re.search, string=text.strip().decode(self.encoding)) regex = unit(self.progress_regex) match = bind(regex, find) result = bind(match, self._calculate_progress) return result def _calculate_progress(self, match): ''' Calculates the final progress value found by the regex ''' if not self.progress_expr: return safe_float(match.group(1)) else: return self._eval_progress(match) def _eval_progress(self, match): ''' Runs the user-supplied progress calculation rule ''' _locals = {k: safe_float(v) for k, v in match.groupdict().items()} if "x" not in _locals: _locals["x"] = [safe_float(x) for x in match.groups()] try: return int(eval(self.progress_expr, {}, _locals)) except: return 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thermo/property_package_constants.py	RoryKurek/thermo	380	117506	<filename>thermo/property_package_constants.py # -- coding: utf-8 -- '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2018, 2019 <NAME> <<EMAIL>> 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. .. warning:: These classes were a first attempt at rigorous multiphase equilibrium. They may be useful in some special cases but they are not complete and further development will not happen. They were never documented as well. It is recommended to switch over to the :obj:`thermo.flash` interface which seeks to be more modular, easier to maintain and extend, higher-performance, and easier to modify. ''' from __future__ import division __all__ = ['PropertyPackageConstants', 'IDEAL_PKG', 'NRTL_PKG', 'UNIFAC_PKG', 'UNIFAC_DORTMUND_PKG', 'PR_PKG', 'SRK_PKG'] from fluids.numerics import brenth, ridder, derivative, numpy as np from chemicals.utils import log, exp from fluids.constants import R, pi, N_A from chemicals.identifiers import IDs_to_CASs from chemicals.rachford_rice import flash_inner_loop from thermo.eos_mix import PRMIX, SRKMIX from thermo.eos import PR, SRK from thermo.chemical import Chemical from thermo.mixture import Mixture from thermo.property_package import * IDEAL_PKG = 'Ideal' NRTL_PKG = 'NRTL' UNIFAC_PKG = 'Unifac' UNIFAC_DORTMUND_PKG = 'Unifac Dortmund' PR_PKG = 'PR' SRK_PKG = 'SRK' property_packages = [IDEAL_PKG, NRTL_PKG, UNIFAC_PKG, UNIFAC_DORTMUND_PKG, PR_PKG, SRK_PKG] property_packages_cubic = [PR_PKG, SRK_PKG] property_package_to_eos = {PR_PKG: PRMIX, SRK_PKG: SRKMIX} property_package_to_eos_pures = {PR_PKG: PR, SRK_PKG: SRK} property_package_names_to_objs = {IDEAL_PKG: IdealCaloric, NRTL_PKG: Nrtl, # Not complete - enthalpy missing UNIFAC_PKG: UnifacCaloric, UNIFAC_DORTMUND_PKG: UnifacDortmundCaloric, PR_PKG: GceosBase, SRK_PKG: GceosBase, } class PropertyPackageConstants(object): '''Class to store kijs, as well as allow properties to be edited; load them from the database and then be ready to store them. ''' def __init__(self, mixture, name=IDEAL_PKG, kwargs): if isinstance(mixture, list): self.CASs = IDs_to_CASs(mixture) self.Chemicals = [Chemical(CAS) for CAS in self.CASs] elif isinstance(mixture, Mixture): self.Chemicals = mixture.Chemicals self.name = name if name not in property_packages_cubic: eos_mix = PRMIX eos = PR else: eos_mix = property_package_to_eos[name] eos = property_package_to_eos_pures[name] self.eos = eos self.eos_mix = eos_mix self.eos_in_a_box = [eos_mix] self.pkg_obj = property_package_names_to_objs[self.name] self.set_chemical_constants() self.set_Chemical_property_objects() self.set_TP_sources() self.kwargs = kwargs self.pkg = self.new_package() def new_package(self): pkg_args = {'VaporPressures': self.VaporPressures, 'Tms': self.Tms, 'Tbs': self.Tbs, 'Tcs': self.Tcs, 'Pcs': self.Pcs, 'omegas': self.omegas, 'VolumeLiquids': self.VolumeLiquids, 'HeatCapacityLiquids': self.HeatCapacityLiquids, 'HeatCapacityGases': self.HeatCapacityGases, 'EnthalpyVaporizations': self.EnthalpyVaporizations, 'VolumeLiquids': self.VolumeLiquids, 'VolumeGases': self.VolumeGases, 'eos': self.eos, 'eos_mix': self.eos_mix, 'MWs': self.MWs, 'atomss': self.atomss, 'Hfs': self.Hfgms, 'Gfs': self.Gfgms, } pkg_args.update(self.kwargs) if self.name == UNIFAC_PKG: pkg_args['UNIFAC_groups'] = self.UNIFAC_groups elif self.name == UNIFAC_DORTMUND_PKG: pkg_args['UNIFAC_groups'] = self.UNIFAC_Dortmund_groups return self.pkg_obj(pkg_args) def from_json(self, json): self.__dict__.update(json) self.set_Chemical_property_objects() self.set_TP_sources() transfer_methods = ['set_chemical_constants', 'set_Chemical_property_objects', 'set_TP_sources', 'UNIFAC_Dortmund_groups', 'UNIFAC_groups', 'atomss'] try: for method in transfer_methods: attr = Mixture.__dict__[method] setattr(PropertyPackageConstants, method, attr) except: for method in transfer_methods: setattr(PropertyPackageConstants, method, getattr(Mixture, method))	[ 1, 529, 9507, 29958, 721, 4346, 29914, 6799, 29918, 5113, 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mlcomp/parallelm/common/buff_to_lines.py	lisapm/mlpiper	7	172185	class BufferToLines(object): def __init__(self): self._acc_buff = "" self._last_line = "" self._in_middle_of_line = False def add(self, buff): self._acc_buff += buff.decode() self._in_middle_of_line = False if self._acc_buff[-1] == '\n' else True def lines(self): lines = self._acc_buff.split('\n') up_to_index = len(lines) - 2 if self._in_middle_of_line else len(lines) - 1 self._acc_buff = lines[-1] if self._in_middle_of_line else "" for iii in range(up_to_index): yield lines[iii]	[ 1, 29871, 13, 1990, 16534, 1762, 20261, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 3032, 5753, 29918, 28040, 353, 5124, 13, 4706, 1583, 3032, 4230, 29918, 1220, 353, 5124, 13, 4706, 1583, 3032, 262, 29918, 17662, 29918, 974, 29918, 1220, 353, 7700, 13, 13, 1678, 822, 788, 29898, 1311, 29892, 20487, 1125, 13, 4706, 1583, 3032, 5753, 29918, 28040, 4619, 20487, 29889, 13808, 580, 13, 4706, 1583, 3032, 262, 29918, 17662, 29918, 974, 29918, 1220, 353, 7700, 565, 1583, 3032, 5753, 29918, 28040, 14352, 29896, 29962, 1275, 11297, 29876, 29915, 1683, 5852, 13, 13, 1678, 822, 3454, 29898, 1311, 1125, 13, 4706, 3454, 353, 1583, 3032, 5753, 29918, 28040, 29889, 5451, 28909, 29876, 1495, 13, 4706, 701, 29918, 517, 29918, 2248, 353, 7431, 29898, 9012, 29897, 448, 29871, 29906, 565, 1583, 3032, 262, 29918, 17662, 29918, 974, 29918, 1220, 1683, 7431, 29898, 9012, 29897, 448, 29871, 29896, 13, 4706, 1583, 3032, 5753, 29918, 28040, 353, 3454, 14352, 29896, 29962, 565, 1583, 3032, 262, 29918, 17662, 29918, 974, 29918, 1220, 1683, 5124, 13, 13, 4706, 363, 474, 2236, 297, 3464, 29898, 786, 29918, 517, 29918, 2248, 1125, 13, 9651, 7709, 3454, 29961, 25609, 29962, 13, 2 ]
motion_detection.py	udelblue/cv	0	165772	import cv2 import numpy as np def main(): #window_name="Cam feed" #cv2.namedWindow(window_name) cap=cv2.VideoCapture(0) #filename = 'F:\sample.avi' #codec=cv2.VideoWriter_fourcc('X','V','I','D') #framerate=30 #resolution = (500,500) # VideoFileOutput = cv2.VideoWriter(filename,codec,framerate,resolution) if cap.isOpened(): ret,frame = cap.read() else: ret =False ret,frame1 = cap.read() ret,frame2 = cap.read() while ret: ret,frame = cap.read() #VideoFileOutput.write(frame) d=cv2.absdiff(frame1,frame2) grey=cv2.cvtColor(d,cv2.COLOR_BGR2GRAY) blur =cv2.GaussianBlur(grey,(5,5),0) ret,th=cv2.threshold(blur,20,255,cv2.THRESH_BINARY) dilated=cv2.dilate(th,np.ones((3,3),np.uint8),iterations=3) img,c,h=cv2.findContours(dilated,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(frame1,c,-1,(0,255,0),2) #cv2.imshow("win1",frame2) cv2.imshow("inter",frame1) if cv2.waitKey(40) == 27: break frame1 = frame2 ret,frame2= cap.read() cv2.destroyAllWindows() #VideoFileOutput.release() cap.release() main()	[ 1, 1053, 13850, 29906, 30004, 13, 5215, 12655, 408, 7442, 30004, 13, 1753, 1667, 7295, 30004, 13, 259, 396, 7165, 29918, 978, 543, 14353, 8343, 19451, 13, 259, 396, 11023, 29906, 29889, 17514, 5907, 29898, 7165, 29918, 978, 8443, 13, 259, 2117, 29922, 11023, 29906, 29889, 15167, 21133, 545, 29898, 29900, 8443, 13, 30004, 13, 259, 396, 9507, 353, 525, 29943, 3583, 11249, 29889, 17345, 29915, 30004, 13, 259, 396, 401, 29883, 29922, 11023, 29906, 29889, 15167, 10507, 29918, 17823, 617, 877, 29990, 3788, 29963, 3788, 29902, 3788, 29928, 1495, 30004, 13, 259, 396, 1341, 4183, 403, 29922, 29941, 29900, 30004, 13, 259, 396, 9778, 918, 353, 313, 29945, 29900, 29900, 29892, 29945, 29900, 29900, 8443, 13, 30004, 13, 396, 29871, 13987, 2283, 6466, 353, 13850, 29906, 29889, 15167, 10507, 29898, 9507, 29892, 401, 29883, 29892, 1341, 4183, 403, 29892, 9778, 918, 8443, 13, 30004, 13, 259, 6756, 13, 259, 565, 2117, 29889, 275, 6585, 287, 7295, 30004, 13, 418, 6756, 13, 418, 3240, 29892, 2557, 353, 2117, 29889, 949, 26471, 13, 30004, 13, 259, 1683, 29901, 30004, 13, 418, 3240, 353, 8824, 30004, 13, 30004, 13, 259, 3240, 29892, 2557, 29896, 353, 2117, 29889, 949, 26471, 13, 259, 3240, 29892, 2557, 29906, 353, 2117, 29889, 949, 26471, 13, 30004, 13, 259, 1550, 3240, 29901, 30004, 13, 418, 3240, 29892, 2557, 353, 2117, 29889, 949, 26471, 13, 418, 396, 15167, 2283, 6466, 29889, 3539, 29898, 2557, 8443, 13, 30004, 13, 418, 270, 29922, 11023, 29906, 29889, 370, 4928, 2593, 29898, 2557, 29896, 29892, 2557, 29906, 8443, 13, 30004, 13, 418, 18345, 29922, 11023, 29906, 29889, 11023, 29873, 3306, 29898, 29881, 29892, 11023, 29906, 29889, 15032, 1955, 29918, 29933, 14345, 29906, 29954, 22800, 8443, 13, 30004, 13, 418, 1999, 332, 353, 11023, 29906, 29889, 29954, 17019, 10358, 332, 29898, 7979, 29891, 22657, 29945, 29892, 29945, 511, 29900, 8443, 13, 418, 3240, 29892, 386, 29922, 11023, 29906, 29889, 386, 12268, 29898, 2204, 332, 29892, 29906, 29900, 29892, 29906, 29945, 29945, 29892, 11023, 29906, 29889, 4690, 1525, 7068, 29918, 29933, 1177, 19926, 8443, 13, 418, 21749, 630, 29922, 11023, 29906, 29889, 29881, 309, 403, 29898, 386, 29892, 9302, 29889, 2873, 3552, 29941, 29892, 29941, 511, 9302, 29889, 13470, 29947, 511, 1524, 800, 29922, 29941, 8443, 13, 418, 10153, 29892, 29883, 29892, 29882, 29922, 11023, 29906, 29889, 2886, 1323, 2470, 29898, 29881, 309, 630, 29892, 11023, 29906, 29889, 1525, 5659, 29918, 29911, 21661, 29892, 11023, 29906, 29889, 3210, 29909, 1177, 29918, 3301, 8618, 29990, 29918, 5425, 3580, 1307, 8443, 13, 418, 6756, 13, 418, 13850, 29906, 29889, 4012, 1323, 2470, 29898, 2557, 29896, 29892, 29883, 6653, 29896, 22657, 29900, 29892, 29906, 29945, 29945, 29892, 29900, 511, 29906, 8443, 13, 30004, 13, 418, 396, 11023, 29906, 29889, 326, 4294, 703, 5080, 29896, 613, 2557, 29906, 8443, 13, 418, 13850, 29906, 29889, 326, 4294, 703, 1639, 613, 2557, 29896, 8443, 13, 418, 6756, 13, 418, 565, 13850, 29906, 29889, 10685, 2558, 29898, 29946, 29900, 29897, 1275, 29871, 29906, 29955, 29901, 30004, 13, 308, 2867, 30004, 13, 418, 3515, 29896, 353, 3515, 29906, 30004, 13, 418, 3240, 29892, 2557, 29906, 29922, 2117, 29889, 949, 26471, 13, 259, 13850, 29906, 29889, 20524, 3596, 7685, 26471, 13, 259, 396, 15167, 2283, 6466, 29889, 14096, 26471, 13, 259, 2117, 29889, 14096, 26471, 13, 3396, 580, 259, 6756, 13, 1678, 6756, 13, 2 ]
leetcode/389.py	GihwanKim/Baekjoon	0	187508	""" File: 389.py Title: Find the Difference Difficulty: Easy URL: https://leetcode.com/problems/find-the-difference/ """ import unittest class Solution: def findTheDifference(self, s: str, t: str) -> str: sorted_s = list(sorted(s)) sorted_t = list(sorted(t)) for i in range(len(s)): if sorted_s[i] != sorted_t[i]: return sorted_t[i] return sorted_t[-1] class SolutionTestCase(unittest.TestCase): def test_example1(self): # Input s = "abcd" t = "abcde" # Output output = "e" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example2(self): # Input s = "" t = "y" # Output output = "y" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example3(self): # Input s = "a" t = "aa" # Output output = "a" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example3(self): # Input s = "ae" t = "aea" # Output output = "a" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) if __name__ == "__main__": unittest.main()	[ 1, 9995, 13, 1678, 3497, 29901, 29871, 29941, 29947, 29929, 29889, 2272, 13, 1678, 18527, 29901, 10987, 278, 360, 17678, 13, 1678, 360, 2593, 3953, 29891, 29901, 382, 8995, 13, 1678, 3988, 29901, 2045, 597, 280, 300, 401, 29889, 510, 29914, 17199, 29879, 29914, 2886, 29899, 1552, 29899, 29881, 17678, 29914, 13, 15945, 29908, 13, 13, 5215, 443, 27958, 13, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 1284, 1576, 29928, 17678, 29898, 1311, 29892, 269, 29901, 851, 29892, 260, 29901, 851, 29897, 1599, 851, 29901, 13, 4706, 12705, 29918, 29879, 353, 1051, 29898, 24582, 29898, 29879, 876, 13, 4706, 12705, 29918, 29873, 353, 1051, 29898, 24582, 29898, 29873, 876, 13, 4706, 363, 474, 297, 3464, 29898, 2435, 29898, 29879, 22164, 13, 9651, 565, 12705, 29918, 29879, 29961, 29875, 29962, 2804, 12705, 29918, 29873, 29961, 29875, 5387, 13, 18884, 736, 12705, 29918, 29873, 29961, 29875, 29962, 13, 4706, 736, 12705, 29918, 29873, 14352, 29896, 29962, 13, 13, 13, 1990, 24380, 3057, 8259, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 1243, 29918, 4773, 29896, 29898, 1311, 1125, 13, 4706, 396, 10567, 13, 4706, 269, 353, 376, 370, 2252, 29908, 13, 4706, 260, 353, 376, 10736, 311, 29908, 13, 4706, 396, 10604, 13, 4706, 1962, 353, 376, 29872, 29908, 13, 13, 4706, 1650, 353, 24380, 580, 13, 4706, 1583, 29889, 9294, 9843, 29898, 2929, 918, 29889, 2886, 1576, 29928, 17678, 29898, 29879, 29892, 260, 511, 1962, 29897, 13, 13, 1678, 822, 1243, 29918, 4773, 29906, 29898, 1311, 1125, 13, 4706, 396, 10567, 13, 4706, 269, 353, 5124, 13, 4706, 260, 353, 376, 29891, 29908, 13, 4706, 396, 10604, 13, 4706, 1962, 353, 376, 29891, 29908, 13, 13, 4706, 1650, 353, 24380, 580, 13, 4706, 1583, 29889, 9294, 9843, 29898, 2929, 918, 29889, 2886, 1576, 29928, 17678, 29898, 29879, 29892, 260, 511, 1962, 29897, 13, 13, 1678, 822, 1243, 29918, 4773, 29941, 29898, 1311, 1125, 13, 4706, 396, 10567, 13, 4706, 269, 353, 376, 29874, 29908, 13, 4706, 260, 353, 376, 7340, 29908, 13, 4706, 396, 10604, 13, 4706, 1962, 353, 376, 29874, 29908, 13, 13, 4706, 1650, 353, 24380, 580, 13, 4706, 1583, 29889, 9294, 9843, 29898, 2929, 918, 29889, 2886, 1576, 29928, 17678, 29898, 29879, 29892, 260, 511, 1962, 29897, 13, 13, 1678, 822, 1243, 29918, 4773, 29941, 29898, 1311, 1125, 13, 4706, 396, 10567, 13, 4706, 269, 353, 376, 3660, 29908, 13, 4706, 260, 353, 376, 3660, 29874, 29908, 13, 4706, 396, 10604, 13, 4706, 1962, 353, 376, 29874, 29908, 13, 13, 4706, 1650, 353, 24380, 580, 13, 4706, 1583, 29889, 9294, 9843, 29898, 2929, 918, 29889, 2886, 1576, 29928, 17678, 29898, 29879, 29892, 260, 511, 1962, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
parser/reader/__init__.py	cheery/better_web_language	136	56597	from stream import CStream from tokenizer import L2 from data import Expr, Literal, Position #import space #table = { # u'(': u'lp', u')': u'rp', # u'[': u'lb', u']': u'rb', # u'{': u'lc', u'}': u'rc', # u'and': u'and', u'or': u'or', u'not': u'not', # u'=': u'let', u':=': u'set', # u'<': u'chain', # u'>': u'chain', # u'<=': u'chain', # u'>=': u'chain', # u'==': u'chain', # u'!=': u'chain', # u'^': u'op', u'&': u'op', u'<<': u'op', # u'>>': u'op', u'!': u'op', u'': u'op', # u'/': u'op', u'%': u'op', u'+': u'op', # u'-': u'op', u'\|': u'op', u'++': u'op', # u':': u'symbol', # u'.': u'dot'} #binops = { # u'\|': 10, # u'^': 10, # u'&': 20, # u'<<': 30, u'>>': 40, # u'++': 40, u'+': 40, u'-': 40, # u'': 50, u'/': 50, u'%': 50, #} #right_binding = [] #prefixes = { # u'~': 90, # u'-': 90, # u'+': 90, #} #postfixes = { # u'!': 100, #} # #def read(source): # exps = [] # ts = L2(CStream(source), table) # while ts.filled: # if ts.position.col != 0: # raise space.Error(u"%s: layout error" % ts.first.start.repr()) # exps.append(toplevel(ts, 0)) # return exps # #def toplevel(ts, col): # head = expression(ts) # if head.dcf is not None and ts.filled: # if head.stop.lno == ts.position.lno: # head.dcf.capture = [toplevel(ts, col)] # elif ts.position.col > col: # head.dcf.capture = exps = [] # scol = ts.position.col # while ts.filled and ts.position.col == scol: # exp = toplevel(ts, scol) # exps.append(exp) # while ts.filled and ts.position.lno == exp.stop.lno and ts.position.col > scol: # exps.append(toplevel(ts, scol)) # return head # #def expressions(ts): # exps = [] # while ts.filled: # if match_some(ts.first, [u'rp', u'rb', u'rc']): # break # exps.append(expression(ts)) # return exps # #def expression(ts): # left = expression_and(ts) # if match(ts.first, u'or'): # op = ts.advance() # op.name = u'symbol' # right = expression(ts) # return Expr(left.start, right.stop, u'form', [op, left, right]) # return left # #def expression_and(ts): # left = expression_chain(ts) # if match(ts.first, u'and'): # op = ts.advance() # op.name = u'symbol' # right = expression_and(ts) # return Expr(left.start, right.stop, u'form', [op, left, right]) # return left # #def expression_chain(ts): # left = expression_bare(ts, 0) # if match(ts.first, u'chain'): # exps = [left] # while match(ts.first, u'chain'): # op = ts.advance() # op.name = u'symbol' # exps.append(op) # exps.append(expression_bare(ts, 0)) # left = Expr(exps[0].start, exps[len(exps)-1].stop, u'chain', exps) # return left # #def expression_bare(ts, rbp): # if on_prefix(ts): # op = ts.advance() # exp = expression_bare(ts, prefixes[op.value]) # op.name = u'symbol' # op.value = op.value+u'expr' # left = Expr(op.start, exp.stop, u'form', [op, exp]) # else: # left = terminal(ts) # while ts.filled: # if match(ts.first, u'dot'): # dot = ts.advance() # symbol = ts.advance() # if not match(symbol, u'symbol'): # raise space.Error(u"%s: expected symbol" % symbol.start.repr()) # left = Expr(left.start, symbol.stop, u'attr', [left, symbol]) # elif match(ts.first, u'lb') and left.stop.eq(ts.first.start): # lb = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rb'): # raise space.Error(u"%s: [] truncates at %s" % (lb.start.repr(), ts.position.repr())) # rb = ts.advance() # left = Expr(left.start, rb.stop, u'index', [left] + exps) # elif match_some(ts.first, [u'let', u'set']): # let = ts.advance() # exp = expression(ts) # left = Expr(left.start, exp.stop, let.name, [left, exp]) # elif match(ts.first, u'op') and match(ts.second, u'let') and ts.first.value in binops: # aug = ts.advance() # aug.name = u'symbol' # let = ts.advance() # exp = expression(ts) # left = Expr(left.start, exp.stop, u'aug', [aug, left, exp]) # else: # break # while ts.filled: # if on_binop(left, ts) and rbp < binops.get(ts.first.value, 0): # op = ts.advance() # op.name = u'symbol' # lbp = binops.get(op.value, 0) # right = expression_bare(ts, lbp - (ts.first.value in right_binding)) # left = Expr(left.start, right.stop, u'form', [op, left, right]) # elif on_postfix(left, ts) and rbp < postfixes.get(ts.first.value, 0): # op = ts.advance() # op.name = u'symbol' # lbp = postfixes.get(op.value, 0) # op.value = u'expr'+op.value # left = Expr(left.start, op.stop, u'form', [op, left]) # else: # break # return left # #def terminal(ts): # if match_some(ts.first, [u'symbol', u'string', u'int', u'hex', u'float']): # return ts.advance() # elif match(ts.first, u'lp'): # lp = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rp'): # raise space.Error(u"%s: form truncates at %s" % (lp.start.repr(), ts.position.repr())) # rp = ts.advance() # exp = Expr(lp.start, rp.stop, u'form', exps) # exp.dcf = exp # return exp # elif match(ts.first, u'lb'): # lb = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rb'): # raise space.Error(u"%s: list truncates at %s" % (lb.start.repr(), ts.position.repr())) # rb = ts.advance() # exp = Expr(lb.start, rb.stop, u'list', exps) # exp.dcf = exp # return exp # elif match(ts.first, u'lc'): # lc = ts.advance() # if match(ts.second, u'rc'): # exp = ts.advance() # exp.name = u'symbol' # else: # exp = expression(ts) # rc = ts.advance() # return exp # elif match(ts.first, u'not'): # op = ts.advance() # op.name = u'symbol' # exp = expression_chain(ts) # return Expr(op.start, exp.stop, u'form', [op, exp]) # if ts.filled: # raise space.Error(u"%s: expected term, got %s" % (ts.position.repr(), ts.first.value)) # raise space.Error(u"%s: expected term, got eof" % ts.position.repr()) # #def match_some(t, names): # return t is not None and t.name in names # #def match(t, name): # return t is not None and t.name == name # #def on_prefix(ts): # if match(ts.first, u'op') and ts.second is not None: # return ts.first.stop.eq(ts.second.start) # return False # #def on_binop(left, ts): # if match(ts.first, u'op') and ts.second is not None: # l = left.stop.eq(ts.first.start) # r = 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testfixtures/tests/test_roundcomparison.py	Alexhuszagh/XLDiscoverer	0	17953	# Copyright (c) 2014 Simplistix Ltd # See license.txt for license details. from decimal import Decimal from testfixtures import RoundComparison as R, compare, ShouldRaise from unittest import TestCase from ..compat import PY2, PY3 class Tests(TestCase): def test_equal_yes_rhs(self): self.assertTrue(0.123457 == R(0.123456, 5)) def test_equal_yes_lhs(self): self.assertTrue(R(0.123456, 5) == 0.123457) def test_equal_no_rhs(self): self.assertFalse(0.123453 == R(0.123456, 5)) def test_equal_no_lhs(self): self.assertFalse(R(0.123456, 5) == 0.123453) def test_not_equal_yes_rhs(self): self.assertFalse(0.123457 != R(0.123456, 5)) def test_not_equal_yes_lhs(self): self.assertFalse(R(0.123456, 5) != 0.123457) def test_not_equal_no_rhs(self): self.assertTrue(0.123453 != R(0.123456, 5)) def test_not_equal_no_lhs(self): self.assertTrue(R(0.123456, 5) != 0.123453) def test_equal_in_sequence_rhs(self): self.assertEqual((1, 2, 0.123457), (1, 2, R(0.123456, 5))) def test_equal_in_sequence_lhs(self): self.assertEqual((1, 2, R(0.123456, 5)), (1, 2, 0.123457)) def test_not_equal_in_sequence_rhs(self): self.assertNotEqual((1, 2, 0.1236), (1, 2, R(0.123456, 5))) def test_not_equal_in_sequence_lhs(self): self.assertNotEqual((1, 2, R(0.123456, 5)), (1, 2, 0.1236)) def test_not_numeric_rhs(self): with ShouldRaise(TypeError): 'abc' == R(0.123456, 5) def test_not_numeric_lhs(self): with ShouldRaise(TypeError): R(0.123456, 5) == 'abc' def test_repr(self): compare('<R:0.12346 to 5 digits>', repr(R(0.123456, 5))) def test_str(self): compare('<R:0.12346 to 5 digits>', repr(R(0.123456, 5))) def test_str_negative(self): if PY3: expected = '<R:123500 to -2 digits>' else: expected = '<R:123500.0 to -2 digits>' compare(expected, repr(R(123456, -2))) TYPE_ERROR_DECIMAL = TypeError( "Cannot compare <R:0.12346 to 5 digits> with <class 'decimal.Decimal'>" ) def test_equal_yes_decimal_to_float_rhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertTrue(Decimal("0.123457") == R(0.123456, 5)) def test_equal_yes_decimal_to_float_lhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertTrue(R(0.123456, 5) == Decimal("0.123457")) def test_equal_no_decimal_to_float_rhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertFalse(Decimal("0.123453") == R(0.123456, 5)) def test_equal_no_decimal_to_float_lhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertFalse(R(0.123456, 5) == Decimal("0.123453")) TYPE_ERROR_FLOAT = TypeError( "Cannot compare <R:0.12346 to 5 digits> with <class 'float'>" ) def test_equal_yes_float_to_decimal_rhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertTrue(0.123457 == R(Decimal("0.123456"), 5)) def test_equal_yes_float_to_decimal_lhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertTrue(R(Decimal("0.123456"), 5) == 0.123457) def test_equal_no_float_to_decimal_rhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertFalse(0.123453 == R(Decimal("0.123456"), 5)) def test_equal_no_float_to_decimal_lhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertFalse(R(Decimal("0.123456"), 5) == 0.123453) def test_integer_float(self): with ShouldRaise(TypeError, unless=PY2): 1 == R(1.000001, 5) def test_float_integer(self): with ShouldRaise(TypeError, unless=PY2): R(1.000001, 5) == 1 def test_equal_yes_integer_other_rhs(self): self.assertTrue(10 == R(11, -1)) def test_equal_yes_integer_lhs(self): self.assertTrue(R(11, -1) == 10) def test_equal_no_integer_rhs(self): self.assertFalse(10 == R(16, -1)) def test_equal_no_integer_lhs(self): self.assertFalse(R(16, -1) == 10) def test_equal_integer_zero_precision(self): self.assertTrue(1 == R(1, 0)) def test_equal_yes_negative_precision(self): self.assertTrue(149.123 == R(101.123, -2)) def test_equal_no_negative_precision(self): 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benchmarks/solve.py	Kludex/di	0	187816	"""Run this file like python benchmarks/solve.py It will print out results to the console and open web browser windows. """ import anyio from pyinstrument.profiler import Profiler from benchmarks.utils import GraphSize, SleepTimes, generate_dag from di import Container, Dependant, Depends from di.executors import ConcurrentAsyncExecutor, SimpleSyncExecutor INTERVAL = 10e-6 # 10 us async def async_bench(sleep: SleepTimes, graph: GraphSize, iters: int) -> None: container = Container(executor=ConcurrentAsyncExecutor()) solved = container.solve( Dependant(generate_dag(Depends, graph, sync=False, sleep=sleep)) ) p = Profiler() await container.execute_async(solved) p.start() for _ in range(iters): await container.execute_async(solved) p.stop() p.print() p.open_in_browser() def sync_bench(sleep: SleepTimes, graph: GraphSize, iters: int) -> None: container = Container(executor=SimpleSyncExecutor()) solved = container.solve( Dependant(generate_dag(Depends, graph, sync=True, sleep=sleep)) ) p = Profiler() container.execute_sync(solved) p.start() for _ in range(iters): container.execute_sync(solved) p.stop() p.print() p.open_in_browser() LARGE_GRAPH = GraphSize(25, 5, 5) SMALL_GRAPH = GraphSize(1, 1, 1) FAST_DEPS = SleepTimes(0, 0) SLOW_DEPS = SleepTimes(1e-3, 1e-3) if __name__ == "__main__": anyio.run(async_bench, FAST_DEPS, SMALL_GRAPH, 1_000) anyio.run(async_bench, FAST_DEPS, LARGE_GRAPH, 1_000) anyio.run(async_bench, SLOW_DEPS, SMALL_GRAPH, 1_000) anyio.run(async_bench, SLOW_DEPS, LARGE_GRAPH, 100) sync_bench(FAST_DEPS, SMALL_GRAPH, iters=1_000) sync_bench(FAST_DEPS, LARGE_GRAPH, iters=1_000) sync_bench(SLOW_DEPS, SMALL_GRAPH, iters=1_000) sync_bench(SLOW_DEPS, LARGE_GRAPH, iters=10)	[ 1, 9995, 6558, 445, 934, 763, 3017, 23513, 29879, 29914, 2929, 345, 29889, 2272, 13, 13, 3112, 674, 1596, 714, 2582, 304, 278, 2991, 322, 1722, 1856, 4714, 5417, 29889, 13, 15945, 29908, 13, 5215, 738, 601, 13, 3166, 11451, 2611, 15461, 29889, 771, 1777, 261, 1053, 23202, 261, 13, 13, 3166, 23513, 29879, 29889, 13239, 1053, 12367, 3505, 29892, 317, 5436, 29164, 29892, 5706, 29918, 24157, 13, 3166, 652, 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djl10n/views.py	ralkan/django-l10n	1	61672	<gh_stars>1-10 import json from django.conf import settings from django.template import Context, Template from django.http import HttpResponse from .helpers import get_translations js_catalog_template = r""" {% autoescape off %} (function (globals) { String.prototype.toTitleCase = function() { return this.replace( /([^\W_]+[^\s-_]) /g, function(txt){ return txt.charAt(0).toUpperCase() + txt.substr(1).toLowerCase(); }); } String.prototype.toUpperCaseFirstChar = function() { return this.substr(0, 1).toUpperCase() + this.substr(1); }; String.prototype.isUpperCase = function() { return this == this.toUpperCase(); }; String.prototype.isTitleCase = function() { return this == this.toTitleCase(); }; String.prototype.isUpperCaseFirstChar = function() { return this == this.toUpperCaseFirstChar(); }; var django = globals.django \|\| (globals.django = {}); /* gettext library / django.translations = {{ catalog_str }}; django.localize = function (key, fallback, placeholders) { fallback = typeof(fallback) == "undefined" ? key : fallback; var value = django.translations[key.toLowerCase()]; if (typeof(value) == 'undefined') { {% if not debug %} value = fallback; {% else %} value = '\{\{' + key + '\}\}'; {% endif %} } if (placeholders && placeholders['plural']) { var plural = parseInt(placeholders['plural']); var values = value.split('\|'); delete placeholders['plural']; if (plural == 1 \|\| values.length < 2) { value = value.split('\|')[0]; } else { value = value.split('\|')[1]; } } for (var ph in placeholders) { value = value.replace(':' + ph, placeholders[ph]); } if (key.isUpperCase()) { value = value.toUpperCase(); } else if (key.isTitleCase()) { value = value.toTitleCase(); } else if (key.isUpperCaseFirstChar()) { value = value.toUpperCaseFirstChar(); } return value; }; / formatting library */ globals.localize = django.localize; }(this)); {% endautoescape %} """ def render_javascript_catalog(translations=None): def _indent(s): return s.replace('\n', '\n ') template = Template(js_catalog_template) context = Context({ 'catalog_str': _indent(json.dumps( translations, sort_keys=True, indent=2)) if translations else {}, 'debug': settings.LOCALIZE_DEBUG }) return HttpResponse(template.render(context), 'text/javascript') def javascript_catalog(request, domain='globaljs', packages=None): locale = request.LANGUAGE_CODE translations = get_translations(locale, domain) return render_javascript_catalog(translations)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 4390, 13, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 29889, 6886, 1053, 15228, 29892, 25663, 13, 3166, 9557, 29889, 1124, 1053, 9056, 5103, 13, 13, 3166, 869, 3952, 6774, 1053, 679, 29918, 3286, 29880, 800, 13, 13, 13, 1315, 29918, 28045, 29918, 6886, 353, 364, 15945, 29908, 13, 18255, 4469, 21587, 1283, 15493, 13, 29898, 2220, 313, 23705, 1338, 29897, 426, 13, 29871, 1714, 29889, 16309, 29889, 517, 7030, 8259, 353, 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tests/__init__.py	jdeepe/Dynamodb-ORM-Demo	0	121281	from api.dataset.models import DataSchema, Dataset def verify_settings(model, p_key, settings): details = eval(model).get(p_key) for key, setting in settings.items(): print(getattr(details, key), setting) setting = setting if setting else None assert getattr(details, key) == setting def load_data(): load_dataschema_data() load_dataset() def load_dataschema_data(): DataSchema.create_table(read_capacity_units=1, write_capacity_units=1, wait=True) for i in range(50): name = "WorkOrder_v%d" % i data_schema = DataSchema(name) data = { "dataset": name, "status": "RETIRED", "description": "Schema for %s" % name, "creation_timestamp": "2017-04-24T11:38:41.164Z", "last_updated_timestamp": "2017-12-24T22:38:47.346Z", } for key in data: setattr(data_schema, key, data[key]) data_schema.save() def load_dataset(): for i in range(50): data = { "name": "Forecast_v%d" % i, "description": "Providing a demand forecast", "status": "ACTIVE", "type": "TRANSACTIONAL", "frequency": "DAILY", "classification": "Orange", "owner": "Forecast team", "owner_contact": "forecast@", "service_arn": "arn:aws:s3:::org.ent-data-lake", "location_pointer": "my.org/Forecasting/Forecast_v1", "creation_timestamp": "2017-01-12T11:39:43.164Z", "derived_from": None, "replaced_by": None, "from_date": "2017-01-03", "to_date": None, "schema_name": "Forecast_schema_v1", "schema_location": None, "data_lineage": None, "compliance": None, "enforce_sla": None, "sla_cron": "0 6 * * *", "tags": [ {"key": "org", "value": "CRM"}, {"key": "cost", "value": "SupplyChain"}, ], "retention": None, "encryption": None, "encryption_type": None, "encryption_kms_id": None, "cross_region_replication": None, "config_crr": None, "password": None, "s3_logging_enabled": None, "config_s3_logging": None, "requestor_pays_enabled": None, } name = "Forecast_v%d" % i dataset = Dataset(name) for key in data: setattr(dataset, key, data[key]) dataset.save()	[ 1, 515, 7882, 29889, 24713, 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lib/base_folder.py	collinahn/file_name_changer	1	1613963	# working dir 를 저장하는 싱글턴 클래스 from dataclasses import ( dataclass, field ) class MetaSingleton(type): _instances = {} def __call__(cls, args, kwargs): if cls not in cls._instances: cls._instances[cls] = super().__call__(args, **kwargs) return cls._instances[cls] @dataclass(frozen=True) class WorkingDir(metaclass=MetaSingleton): abs_path: str = field(default_factory='') rel_path: str = field(default_factory='') if __name__ == '__main__': import utils p1 = WorkingDir(utils.extract_dir(), utils.get_relative_path('.')) print(p1) # Config(name='John', age='25') p2 = WorkingDir('', '') print(p2) # Config(name='John', age='25') print(p1 == p2) # True print(p1 is p2) # True	[ 1, 396, 1985, 4516, 29871, 31517, 29871, 239, 163, 131, 31299, 30944, 31081, 29871, 239, 142, 180, 237, 187, 131, 240, 135, 183, 29871, 240, 132, 183, 238, 161, 155, 30784, 13, 13, 3166, 848, 13203, 1053, 313, 13, 1678, 848, 1990, 29892, 13, 1678, 1746, 13, 29897, 13, 13, 13, 1990, 20553, 10873, 11285, 29898, 1853, 1125, 13, 1678, 903, 2611, 2925, 353, 6571, 13, 13, 1678, 822, 4770, 4804, 12035, 25932, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 565, 1067, 29879, 451, 297, 1067, 29879, 3032, 2611, 2925, 29901, 13, 9651, 1067, 29879, 3032, 2611, 2925, 29961, 25932, 29962, 353, 2428, 2141, 1649, 4804, 1649, 10456, 5085, 29892, 3579, 19290, 29897, 13, 4706, 736, 1067, 29879, 3032, 2611, 2925, 29961, 25932, 29962, 13, 13, 13, 29992, 1272, 1990, 29898, 29888, 307, 2256, 29922, 5574, 29897, 13, 1990, 24176, 9170, 29898, 2527, 562, 605, 29922, 19346, 10873, 11285, 1125, 13, 1678, 6425, 29918, 2084, 29901, 851, 353, 1746, 29898, 4381, 29918, 14399, 2433, 1495, 13, 1678, 1104, 29918, 2084, 29901, 851, 353, 1746, 29898, 4381, 29918, 14399, 2433, 1495, 13, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 13, 1678, 1053, 3667, 29879, 13, 1678, 282, 29896, 353, 24176, 9170, 29898, 13239, 29889, 21111, 29918, 3972, 3285, 3667, 29879, 29889, 657, 29918, 22925, 29918, 2084, 877, 6169, 876, 13, 1678, 1596, 29898, 29886, 29896, 29897, 29871, 396, 12782, 29898, 978, 2433, 11639, 742, 5046, 2433, 29906, 29945, 1495, 13, 1678, 282, 29906, 353, 24176, 9170, 877, 742, 27255, 13, 1678, 1596, 29898, 29886, 29906, 29897, 29871, 396, 12782, 29898, 978, 2433, 11639, 742, 5046, 2433, 29906, 29945, 1495, 13, 13, 1678, 1596, 29898, 29886, 29896, 1275, 282, 29906, 29897, 29871, 396, 5852, 13, 1678, 1596, 29898, 29886, 29896, 338, 282, 29906, 29897, 29871, 396, 5852, 2 ]
changelogger/urls.py	zhelyabuzhsky/changelogger	1	45431	from django.conf.urls import include from django.contrib import admin from django.contrib.auth import views as auth_views from django.urls import path urlpatterns = [ path("", include("changelogs.urls")), path("admin/", admin.site.urls), path("accounts/", include("django.contrib.auth.urls")), path("login/", auth_views.LoginView.as_view()), path("logout/", auth_views.LogoutView.as_view()), path("oauth/", include("social_django.urls", namespace="social")), ]	[ 1, 515, 9557, 29889, 5527, 29889, 26045, 1053, 3160, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 8386, 408, 4817, 29918, 7406, 13, 3166, 9557, 29889, 26045, 1053, 2224, 13, 13, 2271, 11037, 29879, 353, 518, 13, 1678, 2224, 703, 613, 3160, 703, 305, 9477, 12099, 29889, 26045, 1159, 511, 13, 1678, 2224, 703, 6406, 29914, 613, 4113, 29889, 2746, 29889, 26045, 511, 13, 1678, 2224, 703, 10149, 29879, 29914, 613, 3160, 703, 14095, 29889, 21570, 29889, 5150, 29889, 26045, 1159, 511, 13, 1678, 2224, 703, 7507, 29914, 613, 4817, 29918, 7406, 29889, 11049, 1043, 29889, 294, 29918, 1493, 25739, 13, 1678, 2224, 703, 1188, 449, 29914, 613, 4817, 29918, 7406, 29889, 3403, 449, 1043, 29889, 294, 29918, 1493, 25739, 13, 1678, 2224, 703, 23106, 29914, 613, 3160, 703, 24911, 29918, 14095, 29889, 26045, 613, 7397, 543, 24911, 1159, 511, 13, 29962, 13, 2 ]
rnafbinv/mutator.py	matandro/RNAsfbinv	3	137545	<filename>rnafbinv/mutator.py #!/usr/bin/env python3 ''' enter description ''' import random from rnafbinv import IUPAC, tree_aligner from typing import Dict, Any import enum ''' 1) select motif? / select index -> identify motif? (single / double on stem) 2) identify sequence constrains ( / missing constraints) 3) select random possible mutation (addition, removal or modification) ''' class Action(enum.Enum): REPLACE = 1 ADD = 2 REMOVE = 3 def perturbate(current_sequence: str, match_tree: tree_aligner.Tree, options: Dict[str, Any]) -> str: min_length = len(options.get('target_structure')) - options.get('vlength') max_length = len(options.get('target_structure')) + options.get('vlength') actions = [Action.REPLACE] if len(current_sequence) < max_length: actions.append(Action.ADD) if len(current_sequence) > min_length: actions.append(Action.REMOVE) # mutated_sequence = simple_point_mutation(current_sequence, random.choice(actions)) mutated_sequence = multi_point_mutation(current_sequence, min_length, max_length, random.choice(actions)) return mutated_sequence def simple_point_mutation(old_sequence: str, action: Action=Action.REPLACE) -> str: index = random.randint(0, len(old_sequence) - 1) if action == Action.REPLACE: sequence = old_sequence[:index] + random.choice(IUPAC.IUPAC_RNA_BASE.replace(old_sequence[index], '')) + \ old_sequence[index + 1:] elif action == Action.ADD: sequence = old_sequence[:index] + random.choice(IUPAC.IUPAC_RNA_BASE.replace(old_sequence[index], '')) + \ old_sequence[index:] elif action == Action.REMOVE: sequence = old_sequence[:index] + old_sequence[index + 1:] return sequence def multi_point_mutation(old_sequence: str, min_length:int, max_length: int, action: Action=Action.REPLACE, max_size: int=5) -> str: def gen_sequence(gen_size: int, old_seq: str= None) -> str: res = '' # if we replace, select an index to be different for sure rand_loc = None if old_seq is not None: rand_loc = random.randint(0, len(old_seq) - 1) # generate new subseq for i in range(0, gen_size): selection = IUPAC.IUPAC_RNA_BASE if i == rand_loc: selection = selection.replace(old_seq[rand_loc], '') res += random.choice(selection) return res index = random.randint(0, len(old_sequence) - 1) dist = [] for i in range(1, max_size): dist += [i] * (max_size - i + 1) if action == Action.REPLACE: size = min(random.choice(dist), len(old_sequence) - index) new_part = gen_sequence(size, old_sequence[index : index + size]) sequence = old_sequence[:index] + new_part + old_sequence[index + size:] elif action == Action.ADD: size = min(random.choice(dist), max_length - len(old_sequence)) new_part = gen_sequence(size) sequence = old_sequence[:index] + new_part + old_sequence[index:] elif action == Action.REMOVE: size = min(random.choice(dist), len(old_sequence) - index, max(1, len(old_sequence) - min_length)) sequence = old_sequence[:index] + old_sequence[index + size:] return sequence	[ 1, 529, 9507, 29958, 29878, 1056, 29888, 2109, 29894, 29914, 6149, 1061, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 12008, 13, 5893, 6139, 13, 12008, 13, 13, 5215, 4036, 13, 3166, 364, 1056, 29888, 2109, 29894, 1053, 306, 4897, 2477, 29892, 5447, 29918, 2520, 261, 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29918, 16506, 29961, 2248, 718, 2159, 17531, 13, 1678, 736, 5665, 13, 2 ]
skp_edu_docker/code/cluster/preprocess/pre_node_feed_text2fasttext.py	TensorMSA/hoyai_docker	8	104401	from cluster.preprocess.pre_node_feed import PreNodeFeed import os,h5py import numpy as np class PreNodeFeedText2FastText(PreNodeFeed): """ """ def run(self, conf_data): """ override init class """ super(PreNodeFeedText2FastText, self).run(conf_data) self._init_node_parm(conf_data['node_id']) def _convert_data_format(self, file_path, index): """ just pass hdf5 file chunk :param file_path: :param index: :return: """ try: h5file = h5py.File(file_path, mode='r') raw_data = h5file['rawdata'] data_set = raw_data[index.start: index.stop] filtered_data = [data_set[np.logical_not(data_set == '#')].tolist()] return filtered_data except Exception as e: raise Exception(e) finally: h5file.close() def data_size(self): try: h5file = h5py.File(self.input_paths[self.pointer], mode='r') return h5file['rawdata'].len() except Exception as e: raise Exception(e) finally: h5file.close()	[ 1, 515, 9867, 29889, 1457, 5014, 29889, 1457, 29918, 3177, 29918, 18798, 1053, 4721, 4247, 29737, 13, 5215, 2897, 29892, 29882, 29945, 2272, 13, 5215, 12655, 408, 7442, 13, 13, 1990, 4721, 4247, 29737, 1626, 29906, 29943, 579, 1626, 29898, 6572, 4247, 29737, 1125, 13, 1678, 9995, 13, 13, 1678, 9995, 13, 1678, 822, 1065, 29898, 1311, 29892, 1970, 29918, 1272, 1125, 13, 4706, 9995, 13, 4706, 5712, 2069, 770, 13, 4706, 9995, 13, 4706, 2428, 29898, 6572, 4247, 29737, 1626, 29906, 29943, 579, 1626, 29892, 1583, 467, 3389, 29898, 5527, 29918, 1272, 29897, 13, 4706, 1583, 3032, 2344, 29918, 3177, 29918, 862, 29885, 29898, 5527, 29918, 1272, 1839, 3177, 29918, 333, 11287, 13, 13, 13, 1678, 822, 903, 13441, 29918, 1272, 29918, 4830, 29898, 1311, 29892, 934, 29918, 2084, 29892, 2380, 1125, 13, 4706, 9995, 13, 4706, 925, 1209, 298, 2176, 29945, 934, 19875, 13, 4706, 584, 3207, 934, 29918, 2084, 29901, 13, 4706, 584, 3207, 2380, 29901, 13, 4706, 584, 2457, 29901, 13, 4706, 9995, 13, 4706, 1018, 29901, 13, 9651, 298, 29945, 1445, 353, 298, 29945, 2272, 29889, 2283, 29898, 1445, 29918, 2084, 29892, 4464, 2433, 29878, 1495, 13, 9651, 10650, 29918, 1272, 353, 298, 29945, 1445, 1839, 1610, 1272, 2033, 13, 9651, 848, 29918, 842, 353, 10650, 29918, 1272, 29961, 2248, 29889, 2962, 29901, 2380, 29889, 9847, 29962, 13, 9651, 22289, 29918, 1272, 353, 518, 1272, 29918, 842, 29961, 9302, 29889, 1188, 936, 29918, 1333, 29898, 1272, 29918, 842, 1275, 16321, 1495, 1822, 25027, 391, 580, 29962, 13, 9651, 736, 22289, 29918, 1272, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 12020, 8960, 29898, 29872, 29897, 13, 4706, 7146, 29901, 13, 9651, 298, 29945, 1445, 29889, 5358, 580, 13, 13, 1678, 822, 848, 29918, 2311, 29898, 1311, 1125, 13, 4706, 1018, 29901, 13, 9651, 298, 29945, 1445, 353, 298, 29945, 2272, 29889, 2283, 29898, 1311, 29889, 2080, 29918, 24772, 29961, 1311, 29889, 17226, 1402, 4464, 2433, 29878, 1495, 13, 9651, 736, 298, 29945, 1445, 1839, 1610, 1272, 13359, 2435, 580, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 12020, 8960, 29898, 29872, 29897, 13, 4706, 7146, 29901, 13, 9651, 298, 29945, 1445, 29889, 5358, 580, 2 ]
source/NVDAObjects/IAccessible/adobeFlash.py	SWEN-712/screen-reader-brandonp728	0	63706	<reponame>SWEN-712/screen-reader-brandonp728 #NVDAObjects/IAccessible/adobeFlash.py #A part of NonVisual Desktop Access (NVDA) #Copyright (C) 2009 NVDA Contributors <http://www.nvda-project.org/> #This file is covered by the GNU General Public License. #See the file COPYING for more details. import winUser import oleacc from . import IAccessible, getNVDAObjectFromEvent from NVDAObjects import NVDAObjectTextInfo from NVDAObjects.behaviors import EditableTextWithoutAutoSelectDetection from comtypes import COMError, IServiceProvider, hresult from comtypes.gen.FlashAccessibility import ISimpleTextSelection, IFlashAccessibility from logHandler import log class InputTextFieldTextInfo(NVDAObjectTextInfo): def _getStoryText(self): return self.obj.value or "" def _getRawSelectionOffsets(self): try: return self.obj.ISimpleTextSelectionObject.GetSelection() except COMError as e: if e.hresult == hresult.E_FAIL: # The documentation says that an empty field should return 0 for both values, but instead, we seem to get E_FAIL. # An empty field still has a valid caret. return 0, 0 else: raise RuntimeError except AttributeError: raise RuntimeError def _getCaretOffset(self): # We want the active (moving) end of the selection. return self._getRawSelectionOffsets()[1] def _getSelectionOffsets(self): # This might be a backwards selection, but for now, we should always return the values in ascending order. return sorted(self._getRawSelectionOffsets()) class InputTextField(EditableTextWithoutAutoSelectDetection, IAccessible): TextInfo = InputTextFieldTextInfo class Root(IAccessible): def _get_presentationType(self): return self.presType_content def _get_treeInterceptorClass(self): import virtualBuffers.adobeFlash return virtualBuffers.adobeFlash.AdobeFlash #Flash root client has broken accParent, force to return the flash root window root IAccessible def _get_parent(self): return getNVDAObjectFromEvent(self.windowHandle,0,0) class PluginClientWithBrokenFocus(IAccessible): """The client of a Flash plugin with broken focus behaviour. #2546: In Flash protected mode, the Flash content is in another window beneath the plugin window. Unfortunately, Flash doesn't bother to set focus to this window. To work around this, when focus hits this object, focus is forced to the child. """ def event_gainFocus(self): try: self.firstChild.firstChild.setFocus() except AttributeError: super(PluginClientWithBrokenFocus, self).event_gainFocus() def findExtraOverlayClasses(obj, clsList): """Determine the most appropriate class if this is a Flash object. This works similarly to L{NVDAObjects.NVDAObject.findOverlayClasses} except that it never calls any other findOverlayClasses method. """ iaRole = obj.IAccessibleRole if obj.windowClassName == "GeckoPluginWindow" and iaRole == oleacc.ROLE_SYSTEM_CLIENT and obj.childCount == 1 and obj.firstChild.windowClassName == "GeckoFPSandboxChildWindow": clsList.append(PluginClientWithBrokenFocus) return try: servProv = obj.IAccessibleObject.QueryInterface(IServiceProvider) except COMError: return # Check whether this is the Flash root accessible. if iaRole == oleacc.ROLE_SYSTEM_CLIENT: try: servProv.QueryService(IFlashAccessibility._iid_, IFlashAccessibility) clsList.append(Root) except COMError: pass # If this is a client and IFlashAccessibility wasn't present, this is not a Flash object. return # Check whether this is a Flash input text field. try: # We have to fetch ISimpleTextSelectionObject in order to check whether this is an input text field, so store it on the instance. obj.ISimpleTextSelectionObject = servProv.QueryService(ISimpleTextSelection._iid_, ISimpleTextSelection) clsList.append(InputTextField) except COMError: 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29963, 7698, 2061, 29889, 2886, 3563, 8387, 27403, 29913, 5174, 393, 372, 2360, 5717, 738, 916, 1284, 3563, 8387, 27403, 1158, 29889, 13, 12, 15945, 29908, 13, 12, 423, 16727, 353, 5446, 29889, 29902, 6638, 1821, 16727, 13, 12, 361, 5446, 29889, 7165, 23607, 1275, 376, 7999, 27604, 16288, 5907, 29908, 322, 29871, 423, 16727, 1275, 288, 280, 5753, 29889, 1672, 1307, 29918, 14816, 1254, 12665, 29918, 27205, 3919, 322, 5446, 29889, 5145, 3981, 1275, 29871, 29896, 322, 5446, 29889, 4102, 5938, 29889, 7165, 23607, 1275, 376, 7999, 27604, 29943, 7024, 26738, 5938, 5907, 1115, 13, 12, 12, 25932, 1293, 29889, 4397, 29898, 16288, 4032, 3047, 29857, 1717, 20560, 29897, 13, 12, 12, 2457, 13, 13, 12, 2202, 29901, 13, 12, 12, 2140, 1184, 29894, 353, 5446, 29889, 29902, 6638, 1821, 2061, 29889, 3010, 10448, 29898, 29902, 3170, 6980, 29897, 13, 12, 19499, 23353, 2392, 29901, 13, 12, 12, 2457, 13, 13, 12, 29937, 5399, 3692, 445, 338, 278, 21967, 3876, 15579, 29889, 13, 12, 361, 29871, 423, 16727, 1275, 288, 280, 5753, 29889, 1672, 1307, 29918, 14816, 1254, 12665, 29918, 27205, 3919, 29901, 13, 12, 12, 2202, 29901, 13, 12, 12, 12, 2140, 1184, 29894, 29889, 3010, 3170, 29898, 6545, 29880, 1161, 6638, 4127, 3032, 29875, 333, 3383, 306, 8754, 1161, 6638, 4127, 29897, 13, 12, 12, 12, 25932, 1293, 29889, 4397, 29898, 10303, 29897, 13, 12, 12, 19499, 23353, 2392, 29901, 13, 12, 12, 12, 3364, 13, 12, 12, 29937, 960, 445, 338, 263, 3132, 322, 306, 8754, 1161, 6638, 4127, 9007, 29915, 29873, 2198, 29892, 445, 338, 451, 263, 21967, 1203, 29889, 13, 12, 12, 2457, 13, 13, 12, 29937, 5399, 3692, 445, 338, 263, 21967, 1881, 1426, 1746, 29889, 13, 12, 2202, 29901, 13, 12, 12, 29937, 1334, 505, 304, 6699, 8519, 326, 552, 1626, 15097, 2061, 297, 1797, 304, 1423, 3692, 445, 338, 385, 1881, 1426, 1746, 29892, 577, 3787, 372, 373, 278, 2777, 29889, 13, 12, 12, 5415, 29889, 3235, 326, 552, 1626, 15097, 2061, 353, 3348, 1184, 29894, 29889, 3010, 3170, 29898, 3235, 326, 552, 1626, 15097, 3032, 29875, 333, 3383, 8519, 326, 552, 1626, 15097, 29897, 13, 12, 12, 25932, 1293, 29889, 4397, 29898, 4290, 15778, 29897, 13, 12, 19499, 23353, 2392, 29901, 13, 12, 12, 3364, 13, 2 ]
utils.py	nhh1501/E2E_MLT_VN	0	59579	#!/usr/bin/python # encoding: utf-8 import torch import albumentations as A import torch.nn as nn from torch.autograd import Variable from torch.utils.data import Dataset import collections from PIL import Image, ImageFilter import matplotlib.pyplot as plt import math import random import numpy as np import cv2 import os import PIL from data_gen import generate_rbox, generate_rbox2 from data_gen import load_gt_annoataion from data_gen import get_images import torchvision.transforms as transforms import tensorflow as tf from data_gen import draw_box_points class ImgAugTransform: def __init__(self): self.aug = A.Compose([ A.OneOf([ A.Blur(blur_limit=10,p=0.5), A.MedianBlur(blur_limit=5,p=0.5), ]), A.OneOf([ A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=255, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=170, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=85, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=0, p=0.5), ]), # A.RandomContrast(limit=0.05, p=0.75), # A.RandomBrightness(limit=0.05, p=0.75), # A.RandomBrightnessContrast(contrast_limit=0.05, brightness_limit=0.05, p=0.75), ]) def __call__(self, img): img = np.array(img) transformed_img = self.aug(image=img)['image'] return Image.fromarray(transformed_img) # return transformed_img def to_array(img): img = np.asarray(img, dtype=np.float32) img /= 128 img -= 1 return img # return img def random_dilate(img): img = np.array(img) img = cv2.dilate(img, np.ones(shape=(random.randint(1, 3), random.randint(1, 3)), dtype=np.uint8)) return Image.fromarray(img) # return img def random_erode(img): img = np.array(img) img = cv2.erode(img, np.ones(shape=(random.randint(1, 3), random.randint(1, 3)), dtype=np.uint8)) return Image.fromarray(img) # return img def train_transforms(is_crnn = True): if is_crnn: transform = transforms.Compose([ transforms.RandomApply( [ random_dilate, ], p=0.15), transforms.RandomApply( [ random_erode, ], p=0.15), transforms.RandomAffine(degrees=3, scale=(0.95, 1.05), shear=3, resample=Image.NEAREST, fillcolor=(255,255,255)), # transforms.RandomApply( # [ # to_array, # ], # p=1), transforms.RandomApply( [ ImgAugTransform(), ], p=0.6), transforms.Grayscale(), transforms.ToTensor() ]) else: transform = transforms.Compose([ transforms.RandomApply( [ random_dilate, ], p=0.15), transforms.RandomApply( [ random_erode, ], p=0.15), transforms.RandomAffine(degrees=3, scale=(0.95, 1.05), shear=3, resample=Image.NEAREST, fillcolor=(255, 255, 255)), # transforms.RandomApply( # [ # to_array, # ], # p=1), transforms.RandomApply( [ ImgAugTransform(), ], p=0.6), transforms.ToTensor() ]) return transform def test_transforms(is_crnn = True): if is_crnn: transform = transforms.Compose([ transforms.Grayscale(), transforms.ToTensor() ]) else: transform = transforms.Compose([ transforms.ToTensor() ]) return transform def cut_image(img, new_size, word_gto): if len(word_gto) > 0: rep = True cnt = 0 while rep: if cnt > 30: return img text_poly = word_gto[random.randint(0, len(word_gto) - 1)] center = text_poly.sum(0) / 4 xs = int(center[0] - random.uniform(-100, 100) - new_size[1] / 2) xs = max(xs, 1) ys = int(center[1] - random.uniform(-100, 100) - new_size[0] / 2) ys = max(ys, 1) crop_rect = (xs, ys, xs + new_size[1], ys + new_size[0]) crop_img = img[crop_rect[1]:crop_rect[3], crop_rect[0]:crop_rect[2]] # cv2.imshow('dasd',crop_img) if crop_img.shape[0] == crop_img.shape[1]: rep = False else: cnt += 1 else: xs = int(random.uniform(0, img.shape[1])) ys = int(random.uniform(0, img.shape[0])) crop_rect = (xs, ys, xs + new_size[1], ys + new_size[0]) crop_img = img[crop_rect[1]:crop_rect[3], crop_rect[0]:crop_rect[2]] if len(word_gto) > 0: word_gto[:, :, 0] -= xs word_gto[:, :, 1] -= ys return crop_img class ocrDataset(Dataset): def __init__(self, root, norm_height = 48,in_train = True, target_transform=None,is_crnn = True): self.norm_height = norm_height self.path = self.get_path(root) self.root = root self.train_transform = train_transforms(is_crnn) self.test_transform = test_transforms(is_crnn) self.in_train = in_train self.target_transform = target_transform def get_path(self,data_path): base_dir = os.path.dirname(data_path) files_out = [] cnt = 0 with open(data_path) as f: while True: line = f.readline() if not line: break line = line.strip() if len(line) == 0: continue if not line[0] == '/': line = '{0}/{1}'.format(base_dir, line) files_out.append(line) cnt += 1 # if cnt > 100: # break return files_out def get_data(self,image_name): src_del = " " spl = image_name.split(" ") if len(spl) == 1: spl = image_name.split(",") src_del = "," image_name = spl[0].strip() # image_name = (spl[0] + ' '+ spl[1]).strip() gt_txt = '' if len(spl) > 1: gt_txt = "" delim = "" for k in range(1, len(spl)): gt_txt += delim + spl[k] delim = src_del if len(gt_txt) > 1 and gt_txt[0] == '"' and gt_txt[-1] == '"': gt_txt = gt_txt[1:-1] if image_name[len(image_name) - 1] == ',': image_name = image_name[0:-1] im = cv2.imread(image_name) im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) # im = im[2:im.shape[0]-2,:,:] # im_zero = np.zeros((self.norm_height,im.shape[1],3), np.uint8) if self.norm_height > im.shape[0]: im_samp = np.ones((self.norm_height,im.shape[1],3), np.uint8)255 rand = np.random.randint(0,self.norm_height - im.shape[0]) im_samp[rand:rand + im.shape[0],:,:] = im else: scale = self.norm_height / float(im.shape[0]) width = int(im.shape[1] scale) im_samp = cv2.resize(im, (int(width), self.norm_height)) image = PIL.Image.fromarray(np.uint8(im_samp)) # image = np.asarray(im, dtype=np.float32) # image /= 128 # image -= 1 #get labels # gt_labels = [] # for k in range(len(gt_txt)): # if gt_txt[k] in self.codec_rev: # gt_labels.append(self.codec_rev[gt_txt[k]]) # else: # gt_labels.append(3) # # return image,gt_labels return image,gt_txt def __len__(self): return len(self.path) def __getitem__(self, index): try: image,label = self.get_data(self.path[index]) except IOError: print('Corrupted image for %d' % index) return self[index + 1] if self.in_train : image = self.train_transform(image) else: image = self.test_transform(image) # image = np.array(image) if self.target_transform is not None: label = self.target_transform(label) return (image, label) class alignCollate(object): def __init__(self): pass def __call__(self, batch): images, labels = zip(batch) c = images[0].size(0) h = max([p.size(1) for p in images]) w = max([p.size(2) for p in images]) batch_images = torch.zeros(len(images), c, h, w).fill_(1) for i, image in enumerate(images): started_h = max(0, random.randint(0, h - image.size(1))) started_w = max(0, random.randint(0, w - image.size(2))) batch_images[i, :, started_h:started_h + image.size(1), started_w:started_w + image.size(2)] = image return batch_images, labels # Transform class E2Edataset(Dataset): def __init__(self, train_list, input_size=512, normalize = True): super(E2Edataset, self).__init__() self.image_list = np.array(get_images(train_list)) self.input_size = input_size self.normalize = normalize print('{} training images in {}'.format(self.image_list.shape[0], train_list)) self.transform = transforms.Compose([ transforms.ColorJitter(.3,.3,.3,.3), transforms.RandomGrayscale(p=0.1) ]) def __len__(self): return len(self.image_list) def __getitem__(self, index): im_name = self.image_list[index] im = cv2.imread(im_name) # 图片 # cv2.imshow('sad',im) txt_fn = im_name.replace(os.path.basename(im_name).split('.')[1], 'txt') base_name = os.path.basename(txt_fn) txt_fn_gt = '{0}/gt_{1}'.format(os.path.dirname(im_name), base_name) text_polys, text_tags, labels_txt = load_gt_annoataion(txt_fn_gt, True) # 载入标注信息 resize_w = self.input_size resize_h = self.input_size scaled = cut_image(im, (self.input_size, self.input_size), text_polys) if scaled.shape[0] == 0 or scaled.shape[1] == 0: scaled = im if scaled.shape[1] != resize_w or scaled.shape[0] != resize_h: ratio_img= min(scaled.shape[1]/scaled.shape[0],scaled.shape[0]/scaled.shape[1]) if scaled.shape[0] > scaled.shape[1]: resize_w =resize_w ratio_img scalex = scaled.shape[1] / resize_w scaley = scaled.shape[0] / resize_h scaled = cv2.resize(scaled, dsize=(int(resize_w), int(resize_h))) else : resize_h = resize_h * ratio_img scalex = scaled.shape[1] / resize_w scaley = scaled.shape[0] / resize_h scaled = cv2.resize(scaled, dsize=(int(resize_w), int(resize_h))) # continue if len(text_polys) > 0: text_polys[:, :, 0] /= scalex text_polys[:, :, 1] /= scaley scaled = cv2.copyMakeBorder(scaled, 0, self.input_size - scaled.shape[0], 0, self.input_size - scaled.shape[1], cv2.BORDER_CONSTANT,value=[255,255,255]) # scaled_show = scaled.copy() # for (i, c) in enumerate(text_polys): # draw_box_points(scaled_show, c, color=(0, 255, 0), thickness=1) # # instead of creating a new image, I simply modify the old one # # # # show the modified image with all the rectangles at the end. # cv2.imshow('img', scaled_show) pim = PIL.Image.fromarray(np.uint8(scaled)) if self.transform: pim = self.transform(pim) im = np.array(pim) new_h, new_w, _ = im.shape score_map, geo_map, training_mask, gt_idx, gt_out, labels_out = generate_rbox(im, (new_h, new_w), text_polys, text_tags, labels_txt, vis=False) im = np.asarray(im) ## if self.normalize: im = im.astype(np.float32) im /= 128 im -= 1 im = torch.from_numpy(im).permute(2,0,1) return im, score_map, geo_map, training_mask, gt_idx, gt_out, labels_out def E2Ecollate(batch): img = [] gt_boxes = [] texts = [] scores = [] training_masks = [] for per_batch in batch: img.append(per_batch[0]) scores.append(per_batch[1]) training_masks.append(per_batch[3]) gt_boxes.append(per_batch[5]) texts.append(per_batch[6]) return torch.stack(img, 0), gt_boxes, texts,scores,training_masks class BeamSearchDecoder(): def __init__(self, lib, corpus, chars, word_chars, beam_width=20, lm_type='Words', lm_smoothing=0.01, tfsess=None): word_beam_search_module = tf.load_op_library(lib) self.mat = tf.placeholder(tf.float32) corpus = open(corpus).read() chars = open(chars).read() word_chars = open(word_chars).read() self.beamsearch_decoder = word_beam_search_module.word_beam_search(self.mat, beam_width, lm_type, lm_smoothing, corpus, chars, word_chars) self.tfsess = tfsess or tf.Session() self.idx2char = dict(zip(range(0, len(chars)), chars)) def beamsearch(self, mat): mat = np.concatenate((mat[:, :, 1:], mat[:, :, :1]), axis=-1) results = self.tfsess.run(self.beamsearch_decoder, {self.mat: mat}) return results def decode(self, preds_idx): return [''.join([self.idx2char[idx] for idx in row if idx < len(self.idx2char)]) for row in preds_idx] if __name__ == '__main__': llist = './data/ICDAR2015.txt' data = E2Edataset(train_list=llist) E2Edataloader = torch.utils.data.DataLoader(data, batch_size=2, shuffle=False, collate_fn=E2Ecollate) for index, data in enumerate(E2Edataloader): im = data	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 29937, 8025, 29901, 23616, 29899, 29947, 13, 13, 5215, 4842, 305, 13, 5215, 27234, 942, 800, 408, 319, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 3166, 4842, 305, 29889, 1300, 468, 3665, 1053, 28736, 13, 3166, 4842, 305, 29889, 13239, 29889, 1272, 1053, 13373, 24541, 13, 5215, 16250, 13, 3166, 349, 6227, 1053, 7084, 29892, 7084, 5072, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 5844, 13, 5215, 4036, 13, 5215, 12655, 408, 7442, 13, 5215, 13850, 29906, 13, 5215, 2897, 13, 5215, 349, 6227, 13, 3166, 848, 29918, 1885, 1053, 5706, 29918, 29878, 1884, 29892, 5706, 29918, 29878, 1884, 29906, 13, 3166, 848, 29918, 1885, 1053, 2254, 29918, 4141, 29918, 7665, 532, 291, 13, 3166, 848, 29918, 1885, 1053, 679, 29918, 8346, 13, 5215, 4842, 305, 4924, 29889, 9067, 29879, 408, 4327, 29879, 13, 5215, 26110, 408, 15886, 13, 3166, 848, 29918, 1885, 1053, 4216, 29918, 1884, 29918, 9748, 13, 13, 13, 1990, 1954, 29887, 29909, 688, 13372, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 1583, 29889, 2987, 353, 319, 29889, 1523, 4220, 4197, 13, 13, 4706, 319, 29889, 6716, 2776, 4197, 13, 9651, 319, 29889, 10358, 332, 29898, 2204, 332, 29918, 13400, 29922, 29896, 29900, 29892, 29886, 29922, 29900, 29889, 29945, 511, 13, 9651, 319, 29889, 19302, 713, 10358, 332, 29898, 2204, 332, 29918, 13400, 29922, 29945, 29892, 29886, 29922, 29900, 29889, 29945, 511, 13, 4706, 4514, 511, 13, 4706, 319, 29889, 6716, 2776, 4197, 13, 9651, 319, 29889, 7967, 7989, 15063, 449, 29898, 3317, 29918, 5391, 267, 29922, 29955, 29892, 1375, 29918, 5391, 267, 29922, 29941, 29892, 1375, 29918, 3545, 29922, 29941, 29892, 1375, 29918, 2103, 29922, 29896, 29892, 4236, 29918, 3545, 29922, 29896, 29953, 29892, 4236, 29918, 2103, 29922, 29946, 29892, 13, 462, 9651, 5445, 29918, 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nnef/fold_os.py	lahplover/nnef	2	37534	import numpy as np import pandas as pd import torch from physics.protein_os import Protein import options from utils import write_pdb, write_pdb_sample, transform_profile, load_protein from physics.anneal import AnnealCoords, AnnealFrag # from physics.move import SampleICNext from physics.grad_minimizer import * from physics.dynamics import * import os import mdtraj as md from utils import test_setup import h5py ################################################# parser = options.get_fold_parser() args = options.parse_args_and_arch(parser) device, model, energy_fn, ProteinBase = test_setup(args) # position_weights = torch.zeros((1, args.seq_len + 1), device=device) # position_weights[:, 0:5] = 1 # energy_fn.energy_fn.position_weights = position_weights ################################################# data_path = 'data/fold/cullpdb_val_deep' protein_sample = pd.read_csv(f'{data_path}/sample.csv') pdb_selected = protein_sample['pdb'].values np.random.shuffle(pdb_selected) fold_engine = args.fold_engine mode = args.mode # sample_ic = SampleICNext(mode) exp_id = args.load_exp[-5:] save_dir = args.save_dir # if not os.path.exists(f'data/fold/{exp_id}'): # os.mkdir(f'data/fold/{exp_id}') if not os.path.exists(f'data/fold/{save_dir}'): os.mkdir(f'data/fold/{save_dir}') for pdb_id in pdb_selected: seq, coords_native, profile = load_protein(data_path, pdb_id, mode, device, args) protein_native = Protein(seq, coords_native, profile) energy_native = protein_native.get_energy(energy_fn).item() print('energy_native:', energy_native) rg2, collision = protein_native.get_rad_gyration(coords_native) print('native radius of gyration square:', rg2.item()) # residue_energy = protein_native.get_residue_energy(energy_fn) # print(residue_energy) # write_pdb(seq, coords_native, pdb_id, 'native', exp_id) protein = Protein(seq, coords_native.clone(), profile.clone()) if args.random_init: # random_coords_int = sample_ic.random_coords_int(len(seq)-3).to(device) # protein.update_coords_internal(random_coords_int) # extend_coords_int = torch.tensor([[5.367, 1.6, 0.0]], device=device).repeat((len(seq)-3, 1)) extend_coords_int = torch.tensor([[5.367, 0.1, 0.0]], device=device).repeat((len(seq)-3, 1)) protein.update_coords_internal(extend_coords_int) protein.update_cartesian_from_internal() coords_init = protein.coords energy_init = protein.get_energy(energy_fn).item() print('energy_init:', energy_init) # write_pdb(seq, coords_init, pdb_id, f'init_{mode}', exp_id) if fold_engine == 'anneal': # simulated annealing torch.set_grad_enabled(False) if args.anneal_type == 'int_one': annealer = AnnealCoords(energy_fn, protein, mode=mode, ic_move_std=args.ic_move_std, T_max=args.T_max, T_min=args.T_min, L=args.L) elif args.anneal_type == 'frag': frag_file = h5py.File(f'data/fragment/{pdb_id}/{pdb_id}_int.h5', 'r') query_pos = torch.tensor(frag_file['query_pos'][()], device=device) frag_int = torch.tensor(frag_file['coords_int'][()], device=device) annealer = AnnealFrag(energy_fn, protein, frag=(query_pos, frag_int), use_rg=args.use_rg, T_max=args.T_max, T_min=args.T_min, L=args.L) else: raise ValueError('anneal_type should be int_one / frag.') annealer.run() coords_best = annealer.x_best energy_best = annealer.energy_best sample = annealer.sample sample_energy = annealer.sample_energy elif fold_engine == 'grad': if args.x_type == 'cart': minimizer = GradMinimizerCartesian(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'internal': minimizer = GradMinimizerInternal(energy_fn, protein, lr=args.lr, num_steps=args.L, momentum=0.0) elif args.x_type == 'int_fast': minimizer = GradMinimizerIntFast(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'mixed': minimizer = GradMinimizerMixed(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'mix_fast': minimizer = GradMinimizerMixFast(energy_fn, protein, lr=args.lr, num_steps=args.L) else: raise ValueError('x_type should be cart / internal / mixed / int_fast / mix_fast.') minimizer.run() coords_best = minimizer.x_best energy_best = minimizer.energy_best sample = minimizer.sample sample_energy = minimizer.sample_energy elif fold_engine == 'dynamics': if args.x_type == 'cart': minimizer = Dynamics(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'internal': minimizer = DynamicsInternal(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'int_fast': minimizer = DynamicsIntFast(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'mixed': minimizer = DynamicsMixed(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'mix_fast': minimizer = DynamicsMixFast(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) else: raise ValueError('x_type should be cart / internal / mixed / int_fast / mix_fast.') minimizer.run() coords_best = minimizer.x_best energy_best = minimizer.energy_best sample = minimizer.sample sample_energy = minimizer.sample_energy else: raise ValueError('fold_engine should be anneal / grad / dynamics') # protein.update_coords(coords_best) # residue_energy = protein.get_residue_energy(energy_fn) # print(residue_energy) # write_pdb(seq, coords_best, pdb_id, f'best_{mode}', exp_id) # save sampled structures sample = [coords_native.cpu(), coords_best.cpu(), coords_init.cpu()] + sample sample_energy = [energy_native, energy_best, energy_init] + sample_energy # write_pdb_sample(seq, sample, pdb_id, 'sample', exp_id) # pd.DataFrame({'sample_energy': sample_energy}).to_csv(f'data/fold/{exp_id}/{pdb_id}_energy.csv', index=False) write_pdb_sample(seq, sample, pdb_id, 'sample', save_dir) # compute RMSD, sample_xyz = torch.stack(sample, 0).cpu().detach().numpy() print(sample_xyz.shape) t = md.Trajectory(xyz=sample_xyz, topology=None) t = t.superpose(t, frame=0) write_pdb_sample(seq, t.xyz, pdb_id, 'sample2', save_dir) sample_rmsd = md.rmsd(t, t, frame=0) # computation will change sample_xyz; print(f'best RMSD: {sample_rmsd[1]}') df = pd.DataFrame({'sample_energy': sample_energy, 'sample_rmsd': sample_rmsd}) df.to_csv(f'data/fold/{save_dir}/{pdb_id}_energy.csv', index=False)	[ 1, 1053, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 5215, 4842, 305, 13, 3166, 17558, 29889, 14676, 262, 29918, 359, 1053, 14409, 262, 13, 5215, 3987, 13, 3166, 3667, 29879, 1053, 2436, 29918, 29886, 2585, 29892, 2436, 29918, 29886, 2585, 29918, 11249, 29892, 4327, 29918, 10185, 29892, 2254, 29918, 14676, 262, 13, 3166, 17558, 29889, 11276, 284, 1053, 10558, 284, 7967, 4339, 29892, 10558, 284, 29943, 1431, 13, 29937, 515, 17558, 29889, 11631, 1053, 21029, 2965, 9190, 13, 3166, 17558, 29889, 5105, 29918, 1195, 326, 3950, 1053, 334, 13, 3166, 17558, 29889, 29881, 2926, 1199, 1053, 334, 13, 5215, 2897, 13, 5215, 22821, 3018, 29926, 408, 22821, 13, 3166, 3667, 29879, 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seatsvotes/cvtools.py	ljwolf/seatsvotes	0	45282	<filename>seatsvotes/cvtools.py import numpy as np import statsmodels as __sm import pandas as pd sm = __sm.api def leverage(results): """ Compute the leverage matrix from a WLS model: H = W^.5 X(X'WX)^-1X'W^.5 where W^.5 is the scalar square root. """ if isinstance(results.model, sm.WLS): W = np.diag(results.model.weights) elif isinstance(results.model, sm.OLS): W = np.eye(results.model.exog.shape[0]) X = results.model.exog emp_cov = results.cov_params(scale=1) unW_H = X.dot(emp_cov).dot(X.T) return (W.5).dot(unW_H).dot(W.5) def jackknife(candidate, full=False, kw): """ Estimate each leave-one-out model given the input data. Arguments --------- candidate: model statsmodels regression model or a pysal regression model. full : bool whether to return all reestimated models, or just the parameter estimates from the models. kw : keywords keyword arguments passed down to the resulting model estimation statement. If for a statsmodels model, should be separated into fit_kw and init_kw. """ if isinstance(candidate, __sm.base.wrapper.ResultsWrapper): out = _sm_jackknife(candidate, kw) if not full: out = pd.DataFrame(np.vstack([mod.params.reshape(1,-1) for mod in out]), columns=candidate.params.index) return out else: _pysal_jackknife(candidate, kw) def _sm_jackknife(results, init_kw=dict(), fit_kw=dict()): """ Jackknife the results object, meaning the model is re-estimated with a single observation left out each time. """ endog, exog = results.model.endog, results.model.exog if isinstance(results.model, sm.WLS): weights = results.model.weights elif isinstance(results.model, sm.OLS): weights = np.ones_like(endog) deletions = [sm.WLS(np.delete(endog, i), np.delete(exog, i, axis=0), weights=np.delete(weights, i), init_kw).fit(fit_kw) for i in range(len(endog))] return deletions def _pysal_jackknife(model, kw): raise NotImplementedError X = model.X Y = model.Y yend = getattr(model, 'yend', None) z = getattr(model, 'z', None) W = getattr(model, 'W', None) fit = type(model)	[ 1, 529, 9507, 29958, 344, 1446, 29894, 4769, 29914, 11023, 8504, 29889, 2272, 13, 5215, 12655, 408, 7442, 13, 5215, 22663, 9794, 408, 4770, 3844, 13, 5215, 11701, 408, 10518, 13, 13, 3844, 353, 4770, 3844, 29889, 2754, 13, 13, 1753, 454, 19698, 29898, 9902, 1125, 13, 1678, 9995, 13, 1678, 11796, 29872, 278, 454, 19698, 4636, 515, 263, 399, 8547, 1904, 29901, 13, 13, 1678, 379, 353, 399, 29985, 29889, 29945, 1060, 29898, 29990, 29915, 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org.beerware.empty.app/assets/axp.py	pmp-p/projects	0	27484	from android import * print(widget.Button) from android.widget import TextView	[ 1, 29871, 13, 3166, 1442, 1053, 334, 13, 13, 13, 2158, 29898, 8030, 29889, 3125, 29897, 13, 13, 13, 3166, 1442, 29889, 8030, 1053, 19633, 13, 2 ]
Lecture 12/Lecture12HWAssignment2.py	AtharvaJoshi21/PythonPOC	1	127065	# WAP to accept an unsorted list of integers from user and sort it using # a. Bubble Sort # b. Insertion Sort def BubbleSort(inputList): listCount = len(inputList) for x in range(listCount - 1): alreadySorted = True print('Iteration', x) for y in range(x+1, listCount - 1): if inputList[x] > inputList[y]: temp = inputList[x] inputList[x] = inputList[y] inputList[y] = temp alreadySorted = False else: alreadySorted = True if alreadySorted: continue return inputList def main(): unsortedList = eval(input('Please enter a list to be sorted: ')) print(BubbleSort(unsortedList)) if __name__ == '__main__': main()	[ 1, 396, 399, 3301, 304, 3544, 385, 443, 24582, 1051, 310, 11920, 515, 1404, 322, 2656, 372, 773, 29871, 13, 29937, 29871, 12, 29874, 29889, 350, 23232, 20025, 13, 29937, 29871, 12, 29890, 29889, 24505, 291, 20025, 13, 13, 1753, 350, 23232, 13685, 29898, 2080, 1293, 1125, 13, 1678, 1051, 3981, 353, 7431, 29898, 2080, 1293, 29897, 13, 1678, 363, 921, 297, 3464, 29898, 1761, 3981, 448, 29871, 29896, 1125, 13, 4706, 2307, 13685, 287, 353, 5852, 13, 4706, 1596, 877, 13463, 362, 742, 921, 29897, 13, 4706, 363, 343, 297, 3464, 29898, 29916, 29974, 29896, 29892, 1051, 3981, 448, 29871, 29896, 1125, 13, 9651, 565, 1881, 1293, 29961, 29916, 29962, 1405, 1881, 1293, 29961, 29891, 5387, 13, 18884, 5694, 353, 1881, 1293, 29961, 29916, 29962, 13, 18884, 1881, 1293, 29961, 29916, 29962, 353, 1881, 1293, 29961, 29891, 29962, 13, 18884, 1881, 1293, 29961, 29891, 29962, 353, 5694, 13, 18884, 2307, 13685, 287, 353, 7700, 13, 9651, 1683, 29901, 13, 18884, 2307, 13685, 287, 353, 5852, 13, 4706, 565, 2307, 13685, 287, 29901, 13, 9651, 6773, 13, 13, 1678, 736, 1881, 1293, 13, 13, 1753, 1667, 7295, 13, 1678, 443, 24582, 1293, 353, 19745, 29898, 2080, 877, 12148, 3896, 263, 1051, 304, 367, 12705, 29901, 525, 876, 13, 1678, 1596, 29898, 29933, 23232, 13685, 29898, 348, 24582, 1293, 876, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 2 ]
1-100/1-10/6-zigZagConversion/zigZagConversion.py	xuychen/Leetcode	0	44831	<reponame>xuychen/Leetcode<gh_stars>0 import operator class Solution(object): def convert(self, s, numRows): """ :type s: str :type numRows: int :rtype: str """ if numRows == 1: return s period = (numRows - 1) * 2 result = "" end = "" length = len(s) i = 0 while i * period < length: result += s[iperiod] if i period + numRows - 1< length: end += s[i * period + numRows - 1] i += 1 for i in range(1, numRows-1): j = 0 while j * period < length: lowerBase = j * period upperBase = lowerBase + period if (lowerBase + i < length): result += s[lowerBase+i] if (upperBase - i < length): result += s[upperBase-i] j += 1 return result + end def convert2(self, s, numRows): """ :type s: str :type numRows: int :rtype: str """ if numRows == 1: return s num_groups = 2 * (numRows - 1) strings = [""] * numRows for i in range(len(s)): mod = i % num_groups if mod > num_groups / 2: strings[num_groups-mod] += s[i] else: strings[mod] += s[i] return reduce(operator.__add__, strings)	[ 1, 529, 276, 1112, 420, 29958, 29916, 8631, 2724, 29914, 3226, 300, 401, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 5455, 13, 13, 1990, 24380, 29898, 3318, 1125, 13, 1678, 822, 3588, 29898, 1311, 29892, 269, 29892, 954, 10661, 1125, 13, 4706, 9995, 13, 4706, 584, 1853, 269, 29901, 851, 13, 4706, 584, 1853, 954, 10661, 29901, 938, 13, 4706, 584, 29878, 1853, 29901, 851, 13, 4706, 9995, 13, 13, 4706, 565, 954, 10661, 1275, 29871, 29896, 29901, 13, 9651, 736, 269, 13, 13, 4706, 3785, 353, 313, 1949, 10661, 448, 29871, 29896, 29897, 334, 29871, 29906, 13, 4706, 1121, 353, 5124, 13, 4706, 1095, 353, 5124, 13, 4706, 3309, 353, 7431, 29898, 29879, 29897, 13, 13, 4706, 474, 353, 29871, 29900, 13, 4706, 1550, 474, 334, 3785, 529, 3309, 29901, 13, 9651, 1121, 4619, 269, 29961, 29875, 29930, 19145, 29962, 13, 9651, 565, 474, 334, 3785, 718, 954, 10661, 448, 29871, 29896, 29966, 3309, 29901, 13, 18884, 1095, 4619, 269, 29961, 29875, 334, 3785, 718, 954, 10661, 448, 29871, 29896, 29962, 13, 9651, 474, 4619, 29871, 29896, 13, 13, 13, 4706, 363, 474, 297, 3464, 29898, 29896, 29892, 954, 10661, 29899, 29896, 1125, 13, 9651, 432, 353, 29871, 29900, 13, 9651, 1550, 432, 334, 3785, 529, 3309, 29901, 13, 18884, 5224, 5160, 353, 432, 334, 3785, 13, 18884, 7568, 5160, 353, 5224, 5160, 718, 3785, 13, 18884, 565, 313, 13609, 5160, 718, 474, 529, 3309, 1125, 13, 462, 1678, 1121, 4619, 269, 29961, 13609, 5160, 29974, 29875, 29962, 13, 462, 1678, 565, 313, 21064, 5160, 448, 474, 529, 3309, 1125, 13, 462, 4706, 1121, 4619, 269, 29961, 21064, 5160, 29899, 29875, 29962, 13, 13, 18884, 432, 4619, 29871, 29896, 13, 13, 4706, 736, 1121, 718, 1095, 13, 13, 1678, 822, 3588, 29906, 29898, 1311, 29892, 269, 29892, 954, 10661, 1125, 13, 4706, 9995, 13, 4706, 584, 1853, 269, 29901, 851, 13, 4706, 584, 1853, 954, 10661, 29901, 938, 13, 4706, 584, 29878, 1853, 29901, 851, 13, 4706, 9995, 13, 13, 4706, 565, 954, 10661, 1275, 29871, 29896, 29901, 13, 9651, 736, 269, 13, 13, 4706, 954, 29918, 13155, 353, 29871, 29906, 334, 313, 1949, 10661, 448, 29871, 29896, 29897, 13, 4706, 6031, 353, 6796, 3108, 334, 954, 10661, 13, 13, 4706, 363, 474, 297, 3464, 29898, 2435, 29898, 29879, 22164, 13, 9651, 878, 353, 474, 1273, 954, 29918, 13155, 13, 9651, 565, 878, 1405, 954, 29918, 13155, 847, 29871, 29906, 29901, 13, 18884, 6031, 29961, 1949, 29918, 13155, 29899, 1545, 29962, 4619, 269, 29961, 29875, 29962, 13, 9651, 1683, 29901, 13, 18884, 6031, 29961, 1545, 29962, 4619, 269, 29961, 29875, 29962, 13, 13, 4706, 736, 10032, 29898, 6891, 17255, 1202, 1649, 29892, 6031, 29897, 2 ]
testtube/renderer.py	blaix/testtube	0	95743	<filename>testtube/renderer.py """Renderer that outputs formatted messaging for end users.""" import sys from termcolor import colored from testtube.conf import Settings # For python3 support, we must use print as a function rather than a statement class Renderer(object): """Utility that outputs formatted messages.""" def failure(self, message=''): """Print the passed message in red.""" print(colored(message, 'red')) def notice(self, message=''): """Print the passed message.""" print(message) def success(self, message=''): """Print the passed message in green.""" print(colored(message, 'green')) def divider(self): """Print a divider.""" print('=' * 71) def report(self, results): """Print a test report.""" self.notice("Test Report\n") for count, group in enumerate(results, 1): results = (self._format_test(test, res) for test, res in group) results = (', ').join(results) self.notice("Test group %s:\t%s" % (count, results)) self.divider() def audible_alert(self, count): """Beep the number of times specified.""" sys.stdout.write('\a' * count) def _format_test(self, test, result): color = 'green' if result else 'red' files = '' if not test.all_files: files = ' (%s)' % Settings.short_path(test.changed) return colored('%s%s' % (test.name, files), color)	[ 1, 529, 9507, 29958, 1688, 29873, 4003, 29914, 9482, 261, 29889, 2272, 13, 15945, 29908, 21323, 393, 14391, 20917, 4473, 6751, 363, 1095, 4160, 1213, 15945, 13, 5215, 10876, 13, 13, 3166, 1840, 2780, 1053, 28684, 13, 13, 3166, 1243, 29873, 4003, 29889, 5527, 1053, 19215, 13, 13, 29937, 1152, 3017, 29941, 2304, 29892, 591, 1818, 671, 1596, 408, 263, 740, 3265, 1135, 263, 3229, 13, 13, 13, 1990, 26000, 261, 29898, 3318, 1125, 13, 13, 1678, 9995, 7270, 537, 393, 14391, 20917, 7191, 1213, 15945, 13, 13, 1678, 822, 10672, 29898, 1311, 29892, 2643, 2433, 29374, 13, 4706, 9995, 11816, 278, 4502, 2643, 297, 2654, 1213, 15945, 13, 4706, 1596, 29898, 2780, 287, 29898, 4906, 29892, 525, 1127, 8785, 13, 13, 1678, 822, 8369, 29898, 1311, 29892, 2643, 2433, 29374, 13, 4706, 9995, 11816, 278, 4502, 2643, 1213, 15945, 13, 4706, 1596, 29898, 4906, 29897, 13, 13, 1678, 822, 2551, 29898, 1311, 29892, 2643, 2433, 29374, 13, 4706, 9995, 11816, 278, 4502, 2643, 297, 7933, 1213, 15945, 13, 4706, 1596, 29898, 2780, 287, 29898, 4906, 29892, 525, 12692, 8785, 13, 13, 1678, 822, 1933, 1241, 29898, 1311, 1125, 13, 4706, 9995, 11816, 263, 1933, 1241, 1213, 15945, 13, 4706, 1596, 877, 2433, 334, 29871, 29955, 29896, 29897, 13, 13, 1678, 822, 3461, 29898, 1311, 29892, 2582, 1125, 13, 4706, 9995, 11816, 263, 1243, 3461, 1213, 15945, 13, 4706, 1583, 29889, 1333, 625, 703, 3057, 13969, 29905, 29876, 1159, 13, 13, 4706, 363, 2302, 29892, 2318, 297, 26985, 29898, 9902, 29892, 29871, 29896, 1125, 13, 9651, 2582, 353, 313, 1311, 3032, 4830, 29918, 1688, 29898, 1688, 29892, 620, 29897, 363, 1243, 29892, 620, 297, 2318, 29897, 13, 9651, 2582, 353, 313, 742, 525, 467, 7122, 29898, 9902, 29897, 13, 9651, 1583, 29889, 1333, 625, 703, 3057, 2318, 1273, 29879, 3583, 29873, 29995, 29879, 29908, 1273, 313, 2798, 29892, 2582, 876, 13, 13, 4706, 1583, 29889, 4563, 1241, 580, 13, 13, 1678, 822, 12990, 1821, 29918, 12888, 29898, 1311, 29892, 2302, 1125, 13, 4706, 9995, 3629, 1022, 278, 1353, 310, 3064, 6790, 1213, 15945, 13, 4706, 10876, 29889, 25393, 29889, 3539, 28909, 29874, 29915, 334, 2302, 29897, 13, 13, 1678, 822, 903, 4830, 29918, 1688, 29898, 1311, 29892, 1243, 29892, 1121, 1125, 13, 4706, 2927, 353, 525, 12692, 29915, 565, 1121, 1683, 525, 1127, 29915, 13, 4706, 2066, 353, 6629, 13, 13, 4706, 565, 451, 1243, 29889, 497, 29918, 5325, 29901, 13, 9651, 2066, 353, 525, 313, 29995, 29879, 16029, 1273, 19215, 29889, 12759, 29918, 2084, 29898, 1688, 29889, 15033, 29897, 13, 13, 4706, 736, 28684, 877, 29995, 29879, 29995, 29879, 29915, 1273, 313, 1688, 29889, 978, 29892, 2066, 511, 2927, 29897, 13, 2 ]
pipelinePhase.py	armbrustsamuel/PythonPipelineEmulator	0	166917	# PIPELINE PHASES import re import miscellaneous as misc import calculator as calc instructionMemory = [] # Search phase # @param program counter # @return a instruction such as "dadd","daddi","dsub" ... def search(pc): # Retrieve next instruction in memory instruction = misc.readInstructions("inputfile.txt") dummyReturn = [" "," "," "] if pc >= len(instruction): return dummyReturn return instruction[pc] #returnIntruction = ["lw", "$s1", "$s2", "$s3"] #return returnIntruction # Decode phase # @param # @return def decode(pc): # Decode instruction and call execute instructionMemory = misc.readInstructions("inputfile.txt") dummyDecode = [" "," "," "," "," "] # Adjust length if pc >= len(instructionMemory): return dummyDecode else: instructionMemory[pc].append(" ") # -- print(len(instructionMemory[pc])) if len(instructionMemory[pc]) > 3 : instructionMemory[pc][3] = instructionMemory[pc][2] if len(instructionMemory[pc]) > 2: instructionMemory[pc][2] = instructionMemory[pc][1] instructionMemory[pc][1] = instructionMemory[pc][0][5:].strip() instructionMemory[pc][0] = instructionMemory[pc][0][:5].strip() return instructionMemory[pc] # To be removed #misc.printInstruction(instructionMemory,pc) # Execute phase # @param # @return exit[code, message] # - 0 - correct execution , result of calculation # - 1 - wrong execution , "Wrong instruction" # - 2 - shift address , "ok" or "nok" def execute(opCode,reg1,reg2): # Read registers registers = misc.readRegisters('registers.txt') if reg1 != " ": op1 = registers[int(reg1)] if reg1 != " ": op2 = registers[int(reg2)] if opCode.find("dadd") != -1: return calc.sum(op1,op2) elif opCode.find("daddi") != -1: return calc.daddi(op1,op2) elif opCode.find("dsub") != -1: return calc.sub(op1,op2) elif opCode.find("lw") != -1: return "LW" elif opCode.find("dsubi") != -1: return calc.sub(int(op1),int(op2)) elif opCode.find("beqz") != -1: return calc.beqz(op1) elif opCode.find("bnez") != -1: return calc.bnez(op1) elif opCode.find("beq") != -1: return calc.beq(op1,op2) elif opCode.find("bne") != -1: return calc.bne(op1,op2) elif opCode.find("j") != -1: return "JUMP" elif opCode.find(" ") != -1: return 0 else: #print("Wrong instruction - review code") return 1 # MEMORY phase # Reuse already created function # Writeback # @param # @return def saveAtRegister(data, address, registers): registers[address] = data if ( misc.writeRegisters('registers.txt', registers) == 0): return 0 else: print("Error saving registers") return 1	[ 1, 396, 259, 349, 29902, 4162, 18521, 349, 29950, 8127, 29903, 13, 13, 5215, 337, 13, 5215, 3984, 3729, 23584, 408, 3984, 29883, 13, 5215, 3408, 1061, 408, 22235, 13, 13, 2611, 4080, 16015, 353, 5159, 13, 13, 29937, 11856, 8576, 13, 29937, 732, 3207, 1824, 6795, 13, 29937, 732, 2457, 263, 15278, 1316, 408, 376, 29881, 1202, 3284, 29881, 1202, 29875, 3284, 29881, 1491, 29908, 2023, 13, 1753, 2740, 29898, 6739, 1125, 13, 12, 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awstin/dynamodb/orm.py	k2bd/awstin	1	5047	<reponame>k2bd/awstin import uuid from abc import ABC, abstractmethod from collections import defaultdict from typing import Union from boto3.dynamodb.conditions import Attr as BotoAttr from boto3.dynamodb.conditions import Key as BotoKey from awstin.dynamodb.utils import from_decimal, to_decimal class NotSet: """ A value of an attribute on a data model is not present in a DynamoDB result """ def __str__(self): return "<<Attribute not set>>" def __repr__(self): return "<<Attribute not set>>" NOT_SET = NotSet() class BaseAttribute: def __init__(self, attribute_name: Union[str, None] = None): """ Parameters ---------- attribute_name : str, optional Name of the property in the DynamoDB table. Defaults to the name of the attribute on the DynamoModel class. """ # Set by user self._attribute_name = attribute_name # Set by Model self._name_on_model = None @property def _awstin_name(self): if self._attribute_name is not None: return self._attribute_name else: return self._name_on_model def __getattr__(self, name): """ Support for nested mapping queries """ try: return super().__getattr__(name) except AttributeError: return type(self)(attribute_name=f"{self._awstin_name}.{name}") def __getitem__(self, index): """ Support for nested container queries """ return type(self)(attribute_name=f"{self._awstin_name}[{index}]") # --- Query and scan filter expressions --- def begins_with(self, value): """ Filter results by a key or attribute beginning with a value Parameters ---------- value : str Starting string for returned results """ return self._query_type(self._awstin_name).begins_with(to_decimal(value)) def between(self, low, high): """ Filter results by range (inclusive) Parameters ---------- low : Any Low end of the range high : Any High end of the range """ return self._query_type(self._awstin_name).between( to_decimal(low), to_decimal(high), ) def __eq__(self, value): return self._query_type(self._awstin_name).eq(to_decimal(value)) def __gt__(self, value): return self._query_type(self._awstin_name).gt(to_decimal(value)) def __ge__(self, value): return self._query_type(self._awstin_name).gte(to_decimal(value)) def __lt__(self, value): return self._query_type(self._awstin_name).lt(to_decimal(value)) def __le__(self, value): return self._query_type(self._awstin_name).lte(to_decimal(value)) def attribute_type(self, value): """ Filter results by attribute type Parameters ---------- value : str Index for a DynamoDB attribute type (e.g. "N" for Number) """ return BotoAttr(self._awstin_name).attribute_type(to_decimal(value)) def contains(self, value): """ Filter results by attributes that are containers and contain the target value Parameters ---------- values : Any Result must contain this item """ return BotoAttr(self._awstin_name).contains(to_decimal(value)) def exists(self): """ Filter results by existence of an attribute """ return BotoAttr(self._awstin_name).exists() def in_(self, values): """ Filter results by existence in a set Parameters ---------- values : list of Any Allowed values of returned results """ in_values = [to_decimal(value) for value in values] return BotoAttr(self._awstin_name).is_in(in_values) def __ne__(self, value): return BotoAttr(self._awstin_name).ne(to_decimal(value)) def not_exists(self): """ Filter results by non-existence of an attribute """ return BotoAttr(self._awstin_name).not_exists() def size(self): """ Filter by size of a collection """ return Size(self._awstin_name) # --- Update expressions --- def set(self, expression): """ Set an attribute to a new value. Corresponds to SET as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand New value, or an expression defining a new value """ return SetOperator(self, UpdateOperand(expression)) def remove(self): """ Remove an attribute. Corresponds to REMOVE as part of the update expression in ``Table.update_item``. """ return RemoveOperator(self) def add(self, expression): """ Add to an attribute (numerical add or addition to a set). Corresponds to ADD as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand Value to add """ return AddOperator(self, UpdateOperand(expression)) def delete(self, expression): """ Delete part of a set attribute. Corresponds to DELETE as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand Value to delete """ return DeleteOperator(self, UpdateOperand(expression)) def __add__(self, other): return CombineOperand(UpdateOperand(self), UpdateOperand(other), "+") def __sub__(self, other): return CombineOperand(UpdateOperand(self), UpdateOperand(other), "-") def __radd__(self, other): return CombineOperand(UpdateOperand(other), UpdateOperand(self), "+") def __rsub__(self, other): return CombineOperand(UpdateOperand(other), UpdateOperand(self), "-") def if_not_exists(self, value): """ Conditionally return a value if this attribute doesn't exist on the model """ return IfNotExistsOperand(UpdateOperand(self), UpdateOperand(value)) class Key(BaseAttribute): """ Used to define and query hash and sort key attributes on a dynamodb table data model """ _query_type = BotoKey class Attr(BaseAttribute): """ Used to define and query non-key attributes on a dynamodb table data model """ _query_type = BotoAttr def size_query(self, args, kwargs): return BotoAttr(self._awstin_name).size() class Size(BaseAttribute): _query_type = size_query class DynamoModelMeta(type): def __getattribute__(self, name): attr = super().__getattribute__(name) if isinstance(attr, BaseAttribute): attr._name_on_model = name return attr else: return attr def _dynamodb_attributes(self): result = { getattr(self, attr)._awstin_name: attr for attr in dir(self) if isinstance(getattr(self, attr), BaseAttribute) } return result def _get_kwargs(self): """ Kwargs that should be passed to query, scan, get_item """ return { self._dynamo_projection(), self._index_kwargs(), } def _dynamo_projection(self): """ Attributes to request when retrieving data from DynamoDB Returns ------- dict kwargs to be passed to DynamoDB get attribute calls to employ a projection expression and placeholders """ placeholders = { "#" + str(uuid.uuid4())[:8]: value for value in self._dynamodb_attributes().keys() } expression = ", ".join(placeholders.keys()) return dict( ProjectionExpression=expression, ExpressionAttributeNames=placeholders, ) def _index_kwargs(self): if hasattr(self, "_index_name_"): return dict( IndexName=self._index_name_, ) else: return {} class DynamoModel(metaclass=DynamoModelMeta): """ Class defining an ORM model for a DynamoDB table. Subclasses must have a ``_table_name_`` attribute. Attributes making up the data model should be Attr or Key instances. Subclasses representing indexes should also have an ``_index_name_`` attribute """ def __init__(self, kwargs): """ Parameters ---------- kwargs : dict of (str, Any) Initialization of Attr and Key attributes. """ model_attrs = type(self)._dynamodb_attributes().values() for name in model_attrs: setattr(self, name, NOT_SET) for name, value in kwargs.items(): if name not in model_attrs: msg = f"{type(self)!r} has no attribute {name!r}" raise AttributeError(msg) setattr(self, name, value) @classmethod def deserialize(cls, data): """ Deserialize JSON into a DynamoModel subclass. Internally converts Decimal to float in the deserialization. Parameters ---------- data : dict of (str, Any) Serialized model Returns ------- DynamoModel The deserialized data model """ model_attrs = cls._dynamodb_attributes() result = cls() for attr in model_attrs.values(): setattr(result, attr, NOT_SET) for db_attr, value in data.items(): if db_attr in model_attrs.keys(): if type(value) in [list, set, tuple]: value = type(value)(from_decimal(v) for v in value) elif type(value) is dict: value = {from_decimal(k): from_decimal(v) for k, v in value.items()} else: value = from_decimal(value) setattr(result, model_attrs[db_attr], value) return result def serialize(self): """ Serialize a DynamoModel subclass to JSON that can be inserted into DynamoDB. Internally converts float to Decimal. Returns ------- dict of (str, Any) The serialized JSON entry """ model_attrs = type(self)._dynamodb_attributes() result = {} for dynamo_name, model_name in model_attrs.items(): value = getattr(self, model_name) if value is not NOT_SET: if type(value) in [list, set, tuple]: value = type(value)(to_decimal(v) for v in value) elif type(value) is dict: value = {to_decimal(k): to_decimal(v) for k, v in value.items()} else: value = to_decimal(value) result[dynamo_name] = value return result # ---- Update Operators class UpdateOperator(ABC): """ A representation of an UpdateItem expression """ def __and__(self, other): """ Combine two update expressions """ return CombineOperator(self, other) @abstractmethod def update_dict(self): pass @staticmethod def update_expression(update_dict): expressions = [] for operation in "SET", "ADD", "DELETE", "REMOVE": if update_dict.get(operation): expressions.append(operation + " " + ", ".join(update_dict[operation])) return " ".join(expressions) def serialize(self): """ Produce kwargs to be passed to DynamoDB Table.update_item. Keys and values are: "UpdateExpression": string representing the update expression "ExpressionAttributeNames": Placeholder map for attribute names "ExpressionAttributeValues": Placeholder map for attribute values Returns ------- dict Kwargs for update_item """ update_dict = self.update_dict() result = { "UpdateExpression": self.update_expression(update_dict), } if update_dict["ExpressionAttributeNames"]: result["ExpressionAttributeNames"] = update_dict["ExpressionAttributeNames"] if update_dict["ExpressionAttributeValues"]: result["ExpressionAttributeValues"] = update_dict[ "ExpressionAttributeValues" ] return result class CombineOperator(UpdateOperator): """ Combine two update expressions """ def __init__(self, left, right): self.left = left self.right = right def update_dict(self): result = defaultdict(list) ser_left = self.left.update_dict() ser_right = self.right.update_dict() items = list(ser_left.items()) + list(ser_right.items()) for key, values in items: if key in ["SET", "ADD", "DELETE", "REMOVE"]: result[key].extend(values) result["ExpressionAttributeNames"] = dict( ser_left["ExpressionAttributeNames"], ser_right["ExpressionAttributeNames"], ) result["ExpressionAttributeValues"] = dict( ser_left["ExpressionAttributeValues"], ser_right["ExpressionAttributeValues"], ) return result class SetOperator(UpdateOperator): """ Support for SET """ def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( serialized_operand["ExpressionAttributeNames"], serialized_attr["ExpressionAttributeNames"], ) return { "SET": [ f"{serialized_attr['UpdateExpression']} = " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } class AddOperator(UpdateOperator): def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( serialized_operand["ExpressionAttributeNames"], serialized_attr["ExpressionAttributeNames"], ) return { "ADD": [ f"{serialized_attr['UpdateExpression']} " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } class RemoveOperator(UpdateOperator): def __init__(self, attr): self.attr = attr def update_dict(self): serialized_attr = itemize_attr(self.attr) return { "REMOVE": [serialized_attr["UpdateExpression"]], "ExpressionAttributeNames": serialized_attr["ExpressionAttributeNames"], "ExpressionAttributeValues": {}, } class DeleteOperator(UpdateOperator): def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( serialized_operand["ExpressionAttributeNames"], serialized_attr["ExpressionAttributeNames"], ) return { "DELETE": [ f"{serialized_attr['UpdateExpression']} " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } # ---- Update Operands def serialize_operand(value): name = str(uuid.uuid4())[:8] if isinstance(value, UpdateOperand): return value.serialize() elif isinstance(value, BaseAttribute): return itemize_attr(value) elif type(value) in [list, set, tuple]: name = ":" + name value = type(value)([to_decimal(v) for v in value]) return { "UpdateExpression": name, "ExpressionAttributeNames": {}, "ExpressionAttributeValues": {name: value}, } else: name = ":" + name return { "UpdateExpression": name, "ExpressionAttributeNames": {}, "ExpressionAttributeValues": {name: to_decimal(value)}, } def itemize_attr(attr): # Separate indexes parts = [] current_section = "" for letter in attr._awstin_name: if letter == "[": parts.append(current_section) current_section = "[" elif letter == "]": parts.append(current_section + "]") current_section = "" else: current_section += letter if current_section: parts.append(current_section) serialized = "" name_map = {} # Separate attributes for part in parts: if "[" in part and "]" in part: serialized += part else: if part.startswith("."): serialized += "." part = part[1:] sections = part.split(".") serialized_sections = [] for section in sections: name = "#" + str(uuid.uuid4())[:8] name_map[name] = section serialized_sections.append(name) serialized += ".".join(serialized_sections) result = { "UpdateExpression": serialized, "ExpressionAttributeNames": name_map, "ExpressionAttributeValues": {}, } return result class UpdateOperand: """ Inner part of an update expression """ def __init__(self, value): self.value = value def serialize(self): return serialize_operand(self.value) class CombineOperand(UpdateOperand): """ Add or subtact two expressions """ def __init__(self, left, right, symbol): self.left = left self.right = right self.symbol = symbol def serialize(self): ser_left = serialize_operand(self.left) ser_right = serialize_operand(self.right) expression = ( f"{ser_left['UpdateExpression']} " f"{self.symbol} " f"{ser_right['UpdateExpression']}" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( ser_left["ExpressionAttributeNames"], ser_right["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( ser_left["ExpressionAttributeValues"], ser_right["ExpressionAttributeValues"], ), } class IfNotExistsOperand(UpdateOperand): """ Set a value if the given attribute does not exist """ def __init__(self, attr, value): self.attr = attr self.value = value def serialize(self): ser_attr = serialize_operand(self.attr) ser_value = serialize_operand(self.value) expression = ( f"if_not_exists({ser_attr['UpdateExpression']}, " f"{ser_value['UpdateExpression']})" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( ser_attr["ExpressionAttributeNames"], ser_value["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( ser_attr["ExpressionAttributeValues"], ser_value["ExpressionAttributeValues"], ), } class ListAppendOperand(UpdateOperand): """ Combine two lists """ def __init__(self, left, right): self.left = left self.right = right def serialize(self): ser_left = serialize_operand(self.left) ser_right = serialize_operand(self.right) expression = ( f"list_append({ser_left['UpdateExpression']}, " f"{ser_right['UpdateExpression']})" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( ser_left["ExpressionAttributeNames"], ser_right["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( ser_left["ExpressionAttributeValues"], *ser_right["ExpressionAttributeValues"], ), } def list_append(left, right): """ Set a value to the combination of two lists in an update expression """ return ListAppendOperand(UpdateOperand(left), UpdateOperand(right))	[ 1, 529, 276, 1112, 420, 29958, 29895, 29906, 6448, 29914, 1450, 303, 262, 13, 5215, 318, 5416, 13, 3166, 25638, 1053, 16417, 29892, 9846, 5696, 13, 3166, 16250, 1053, 2322, 8977, 13, 3166, 19229, 1053, 7761, 13, 13, 3166, 289, 3747, 29941, 29889, 29881, 2926, 10396, 29889, 1116, 2187, 1053, 2180, 509, 408, 350, 3747, 25098, 13, 3166, 289, 3747, 29941, 29889, 29881, 2926, 10396, 29889, 1116, 2187, 1053, 7670, 408, 350, 3747, 2558, 13, 13, 3166, 3773, 303, 262, 29889, 29881, 2926, 10396, 29889, 13239, 1053, 515, 29918, 7099, 3039, 29892, 304, 29918, 7099, 3039, 13, 13, 13, 1990, 2216, 2697, 29901, 13, 1678, 9995, 13, 1678, 319, 995, 310, 385, 5352, 373, 263, 848, 1904, 338, 451, 2198, 297, 263, 22554, 29877, 4051, 1121, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 376, 9314, 6708, 451, 731, 6778, 29908, 13, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 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tests/testapp/urls.py	matthiask/django-keyed-urls	0	105180	<reponame>matthiask/django-keyed-urls from django.conf.urls import include, patterns, url from django.conf.urls.i18n import i18n_patterns from django.contrib import admin admin.autodiscover() urlpatterns = patterns( '', url(r'^admin/', include(admin.site.urls)), ) urlpatterns += i18n_patterns( '', url(r'^ku/', include('keyed_urls.urls')), )	[ 1, 529, 276, 1112, 420, 29958, 2922, 386, 29875, 1278, 29914, 14095, 29899, 1989, 287, 29899, 26045, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 3160, 29892, 15038, 29892, 3142, 13, 3166, 9557, 29889, 5527, 29889, 26045, 29889, 29875, 29896, 29947, 29876, 1053, 474, 29896, 29947, 29876, 29918, 11037, 29879, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 13, 13, 6406, 29889, 1300, 397, 10669, 957, 580, 13, 13, 13, 2271, 11037, 29879, 353, 15038, 29898, 13, 1678, 15516, 13, 1678, 3142, 29898, 29878, 29915, 29985, 6406, 29914, 742, 3160, 29898, 6406, 29889, 2746, 29889, 26045, 8243, 13, 29897, 13, 13, 2271, 11037, 29879, 4619, 474, 29896, 29947, 29876, 29918, 11037, 29879, 29898, 13, 1678, 15516, 13, 1678, 3142, 29898, 29878, 29915, 29985, 2120, 29914, 742, 3160, 877, 1989, 287, 29918, 26045, 29889, 26045, 1495, 511, 13, 29897, 13, 2 ]
LED2Net/Projection/Cube2Equirec.py	zhigangjiang/LED2-Net	57	96369	import os import sys import cv2 import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import scipy.misc as sic class Cube2Equirec(nn.Module): def __init__(self, cube_length, equ_h): super().__init__() self.cube_length = cube_length self.equ_h = equ_h equ_w = equ_h * 2 self.equ_w = equ_w theta = (np.arange(equ_w) / (equ_w-1) - 0.5) * 2 np.pi phi = (np.arange(equ_h) / (equ_h-1) - 0.5) np.pi theta, phi = np.meshgrid(theta, phi) x = np.sin(theta) * np.cos(phi) y = np.sin(phi) z = np.cos(theta) * np.cos(phi) xyz = np.concatenate([x[..., None], y[..., None], z[..., None]], axis=-1) planes = np.asarray([ [0, 0, 1, 1], # z = -1 [0, 1, 0, -1], # y = 1 [0, 0, 1, -1], # z = 1 [1, 0, 0, 1], # x = -1 [1, 0, 0, -1], # x = 1 [0, 1, 0, 1] # y = -1 ]) r_lst = np.array([ [0, 1, 0], [0.5, 0, 0], [0, 0, 0], [0, 0.5, 0], [0, -0.5, 0], [-0.5, 0, 0] ]) * np.pi f = cube_length / 2.0 self.K = np.array([ [f, 0, (cube_length-1)/2.0], [0, f, (cube_length-1)/2.0], [0, 0, 1] ]) self.R_lst = [cv2.Rodrigues(x)[0] for x in r_lst] self.mask, self.XY = self._intersection(xyz, planes) def forward(self, x, mode='bilinear'): assert mode in ['nearest', 'bilinear'] assert x.shape[0] % 6 == 0 equ_count = x.shape[0] // 6 equi = torch.zeros(equ_count, x.shape[1], self.equ_h, self.equ_w).to(x.device) for i in range(6): now = x[i::6, ...] mask = self.mask[i].to(x.device) mask = mask[None, ...].repeat(equ_count, x.shape[1], 1, 1) XY = (self.XY[i].to(x.device)[None, None, :, :].repeat(equ_count, 1, 1, 1) / (self.cube_length-1) - 0.5) * 2 sample = F.grid_sample(now, XY, mode=mode, align_corners=True)[..., 0, :] equi[mask] = sample.view(-1) return equi def _intersection(self, xyz, planes): abc = planes[:, :-1] depth = -planes[:, 3][None, None, ...] / np.dot(xyz, abc.T) depth[depth < 0] = np.inf arg = np.argmin(depth, axis=-1) depth = np.min(depth, axis=-1) pts = depth[..., None] * xyz mask_lst = [] mapping_XY = [] for i in range(6): mask = arg == i mask = np.tile(mask[..., None], [1, 1, 3]) XY = np.dot(np.dot(pts[mask].reshape([-1, 3]), self.R_lst[i].T), self.K.T) XY = np.clip(XY[..., :2].copy() / XY[..., 2:], 0, self.cube_length-1) mask_lst.append(mask[..., 0]) mapping_XY.append(XY) mask_lst = [torch.BoolTensor(x) for x in mask_lst] mapping_XY = [torch.FloatTensor(x) for x in mapping_XY] return mask_lst, mapping_XY if __name__ == '__main__': import matplotlib.pyplot as plt batch = torch.zeros(12, 3, 256, 256) + 20 c2e = Cube2Equirec(256, 512) equi = c2e(batch) plt.imshow(equi[0, ...].permute(1, 2, 0).cpu().numpy()) plt.show()	[ 1, 1053, 2897, 13, 5215, 10876, 13, 5215, 13850, 29906, 13, 5215, 5844, 13, 5215, 12655, 408, 7442, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 5215, 4842, 305, 29889, 15755, 29889, 2220, 284, 408, 383, 13, 3166, 4842, 305, 29889, 1300, 468, 3665, 1053, 28736, 13, 5215, 4560, 2272, 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favicons/_constants.py	checktheroads/favicons	7	197913	<filename>favicons/_constants.py<gh_stars>1-10 """Static values for one way import.""" SUPPORTED_FORMATS = (".svg", ".jpeg", ".jpg", ".png", ".tiff", ".tif") HTML_LINK = '<link rel="{rel}" type="{type}" href="{href}" />' ICON_TYPES = ( {"image_fmt": "ico", "rel": None, "dimensions": (64, 64), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (16, 16), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (32, 32), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (64, 64), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (96, 96), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (180, 180), "prefix": "favicon"}, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (57, 57), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (60, 60), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (72, 72), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (76, 76), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (114, 114), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (120, 120), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (144, 144), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (152, 152), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (167, 167), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (180, 180), "prefix": "apple-touch-icon", }, {"image_fmt": "png", "rel": None, "dimensions": (70, 70), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (270, 270), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (310, 310), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (310, 150), "prefix": "mstile"}, {"image_fmt": "png", "rel": "shortcut icon", "dimensions": (196, 196), "prefix": "favicon"}, )	[ 1, 529, 9507, 29958, 29888, 485, 27078, 19891, 3075, 1934, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 17046, 1819, 363, 697, 982, 1053, 1213, 15945, 13, 13, 29903, 4897, 15082, 3352, 29918, 19094, 1299, 29903, 353, 313, 1642, 15120, 613, 11393, 26568, 613, 11393, 6173, 613, 11393, 2732, 613, 11393, 29873, 2593, 613, 11393, 29873, 361, 1159, 13, 13, 7020, 29918, 23714, 29968, 353, 12801, 2324, 1104, 10724, 2674, 5038, 1134, 10724, 1853, 5038, 2822, 10724, 12653, 5038, 2900, 29915, 13, 13, 2965, 1164, 29918, 15631, 29925, 2890, 353, 313, 13, 1678, 8853, 3027, 29918, 23479, 1115, 376, 1417, 613, 376, 2674, 1115, 6213, 29892, 376, 6229, 5580, 1115, 313, 29953, 29946, 29892, 29871, 29953, 29946, 511, 376, 13506, 1115, 376, 29888, 485, 4144, 10758, 13, 1678, 8853, 3027, 29918, 23479, 1115, 376, 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unittest/buildtool/util_test.py	rantav/spinnaker	0	140841	<gh_stars>0 # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=missing-docstring import datetime import logging import os import shutil import tempfile import unittest from buildtool import ( ensure_dir_exists, timedelta_string, write_to_path) class TestRunner(unittest.TestCase): @classmethod def setUpClass(cls): cls.base_temp_dir = tempfile.mkdtemp(prefix='buildtool.util_test') @classmethod def tearDownClass(cls): shutil.rmtree(cls.base_temp_dir) def test_ensure_dir(self): want = os.path.join(self.base_temp_dir, 'ensure', 'a', 'b', 'c') self.assertFalse(os.path.exists(want)) ensure_dir_exists(want) self.assertTrue(os.path.exists(want)) # Ok if already exists ensure_dir_exists(want) self.assertTrue(os.path.exists(want)) def test_write_to_path(self): path = os.path.join(self.base_temp_dir, 'test_write', 'file') content = 'First Line\nSecond Line' write_to_path(content, path) with open(path, 'r') as f: self.assertEquals(content, f.read()) def test_deltatime_string(self): timedelta = datetime.timedelta tests = [ (timedelta(1, 60 * 60 * 4 + 60 * 5 + 2, 123456), 'days=1 + 04:05:02'), (timedelta(1, 60 * 5 + 2, 123456), 'days=1 + 00:05:02'), (timedelta(1, 2, 123456), 'days=1 + 00:00:02'), (timedelta(0, 60 * 60 * 4 + 60 * 5 + 2, 123456), '04:05:02'), (timedelta(0, 60 * 5 + 2, 123456), '05:02'), (timedelta(0, 2, 123456), '2.123 secs') ] for test in tests: self.assertEquals(test[1], timedelta_string(test[0])) if __name__ == '__main__': logging.basicConfig( format='%(levelname).1s %(asctime)s.%(msecs)03d %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG) unittest.main(verbosity=2)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29955, 5087, 9266, 29889, 2178, 26863, 2538, 9841, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 29889, 29900, 13, 29937, 13, 29937, 25870, 3734, 491, 22903, 4307, 470, 15502, 304, 297, 5007, 29892, 7047, 13, 29937, 13235, 1090, 278, 19245, 338, 13235, 373, 385, 376, 3289, 8519, 29908, 350, 3289, 3235, 29892, 13, 29937, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29059, 6323, 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notebooks/bqml/track_meta.py	roannav/learntools	359	32140	# See also examples/example_track/track_meta.py for a longer, commented example track = dict( author_username='dansbecker', course_name='Machine Learning', course_url='https://www.kaggle.com/learn/intro-to-machine-learning' ) lessons = [ dict( topic='Your First BiqQuery ML Model', ), ] notebooks = [ dict( filename='tut1.ipynb', lesson_idx=0, type='tutorial', scriptid=4076893, ), dict( filename='ex1.ipynb', lesson_idx=0, type='exercise', scriptid=4077160, ), ]	[ 1, 396, 2823, 884, 6455, 29914, 4773, 29918, 11294, 29914, 11294, 29918, 7299, 29889, 2272, 363, 263, 5520, 29892, 19952, 1342, 13, 11294, 353, 9657, 29898, 13, 1678, 4148, 29918, 6786, 2433, 29881, 550, 915, 4937, 742, 13, 1678, 3236, 29918, 978, 2433, 29076, 29257, 742, 13, 1678, 3236, 29918, 2271, 2433, 991, 597, 1636, 29889, 29895, 351, 6234, 29889, 510, 29914, 19668, 29914, 23333, 29899, 517, 29899, 23523, 29899, 21891, 29915, 13, 29897, 13, 13, 2222, 787, 353, 518, 13, 4706, 9657, 29898, 13, 9651, 11261, 2433, 10858, 3824, 3457, 29939, 3010, 23158, 8125, 742, 13, 9651, 10353, 13, 29962, 13, 13, 1333, 19273, 29879, 353, 518, 13, 1678, 9657, 29898, 13, 4706, 10422, 2433, 29873, 329, 29896, 29889, 666, 948, 29890, 742, 13, 4706, 3109, 265, 29918, 13140, 29922, 29900, 29892, 13, 4706, 1134, 2433, 12631, 742, 13, 4706, 2471, 333, 29922, 29946, 29900, 29955, 29953, 29947, 29929, 29941, 29892, 13, 4706, 10353, 13, 1678, 9657, 29898, 13, 4706, 10422, 2433, 735, 29896, 29889, 666, 948, 29890, 742, 13, 4706, 3109, 265, 29918, 13140, 29922, 29900, 29892, 13, 4706, 1134, 2433, 735, 6269, 895, 742, 13, 4706, 2471, 333, 29922, 29946, 29900, 29955, 29955, 29896, 29953, 29900, 29892, 13, 4706, 10353, 13, 29962, 13, 13, 13, 2 ]
Exercicios/068-ParouImparV2.py	LuisDjay/Python-Curso-em-Video	0	61669	<filename>Exercicios/068-ParouImparV2.py print('=====Desafio 068=====') from random import randint cont = 0 while True: jog = int(input('Diga Um valor: ')) resp = str(input('Par ou Ímpar? [P/I] ').upper()) comp = randint(0, 10) result = jog + comp result2 = result % 2 if result2 == 0 and resp == 'P': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu PAR') print(f'Você Venceu!') print(f'Vamos jogar novamente') elif result2 == 0 and resp == 'I': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu PAR') print(f'Você Perdeu!') break elif result2 != 0 and resp == 'I': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu IMPAR') print(f'Você Venceu!') print(f'Vamos jogar novamente') cont += 1 elif result2 != 0 and resp == 'P': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu IMPAR') print(f'Você Perdeu!') break cont += 1 print(f'Você venceu {cont} vezes.')	[ 1, 529, 9507, 29958, 1252, 6269, 19382, 29914, 29900, 29953, 29947, 29899, 2177, 283, 1888, 862, 29963, 29906, 29889, 2272, 13, 2158, 877, 2751, 29922, 4002, 2142, 601, 29871, 29900, 29953, 29947, 2751, 29922, 1495, 13, 3166, 4036, 1053, 20088, 524, 13, 13, 1285, 353, 29871, 29900, 13, 8000, 5852, 29901, 13, 1678, 16812, 353, 938, 29898, 2080, 877, 29928, 4324, 6379, 16497, 29901, 525, 876, 13, 1678, 4613, 353, 851, 29898, 2080, 877, 2177, 2123, 27614, 29885, 862, 29973, 518, 29925, 29914, 29902, 29962, 525, 467, 21064, 3101, 13, 1678, 752, 353, 20088, 524, 29898, 29900, 29892, 29871, 29896, 29900, 29897, 13, 1678, 1121, 353, 16812, 718, 752, 13, 1678, 1121, 29906, 353, 1121, 1273, 29871, 29906, 13, 1678, 565, 1121, 29906, 1275, 29871, 29900, 322, 4613, 1275, 525, 29925, 2396, 13, 4706, 1596, 29898, 29888, 29915, 29963, 25802, 435, 468, 283, 426, 29926, 468, 29913, 321, 288, 2912, 3136, 16812, 283, 426, 2388, 1836, 438, 3001, 904, 426, 2914, 29913, 897, 29884, 349, 1718, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 542, 30037, 478, 663, 29884, 29991, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 14054, 16812, 279, 2420, 2503, 1495, 13, 1678, 25342, 1121, 29906, 1275, 29871, 29900, 322, 4613, 1275, 525, 29902, 2396, 13, 4706, 1596, 29898, 29888, 29915, 29963, 25802, 435, 468, 283, 426, 29926, 468, 29913, 321, 288, 2912, 3136, 16812, 283, 426, 2388, 1836, 438, 3001, 904, 426, 2914, 29913, 897, 29884, 349, 1718, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 542, 30037, 2431, 311, 29884, 29991, 1495, 13, 4706, 2867, 13, 1678, 25342, 1121, 29906, 2804, 29871, 29900, 322, 4613, 1275, 525, 29902, 2396, 13, 4706, 1596, 29898, 29888, 29915, 29963, 25802, 435, 468, 283, 426, 29926, 468, 29913, 321, 288, 2912, 3136, 16812, 283, 426, 2388, 1836, 438, 3001, 904, 426, 2914, 29913, 897, 29884, 306, 3580, 1718, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 542, 30037, 478, 663, 29884, 29991, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 14054, 16812, 279, 2420, 2503, 1495, 13, 4706, 640, 4619, 29871, 29896, 13, 1678, 25342, 1121, 29906, 2804, 29871, 29900, 322, 4613, 1275, 525, 29925, 2396, 13, 4706, 1596, 29898, 29888, 29915, 29963, 25802, 435, 468, 283, 426, 29926, 468, 29913, 321, 288, 2912, 3136, 16812, 283, 426, 2388, 1836, 438, 3001, 904, 426, 2914, 29913, 897, 29884, 306, 3580, 1718, 1495, 13, 4706, 1596, 29898, 29888, 29915, 29963, 542, 30037, 2431, 311, 29884, 29991, 1495, 13, 4706, 2867, 13, 1678, 640, 4619, 29871, 29896, 13, 2158, 29898, 29888, 29915, 29963, 542, 30037, 325, 663, 29884, 426, 1285, 29913, 7763, 267, 29889, 1495, 13, 2 ]
page/playground_page.py	fnrcum/pytest_playwright_framework	0	1608143	from playwright.sync_api import Page class PlaygroundPage: def __init__(self, page: Page): self.page = page @property def bad_button(self): return self.page.wait_for_selector("#badButton")	[ 1, 515, 1708, 29893, 1266, 29889, 16593, 29918, 2754, 1053, 9305, 13, 13, 13, 1990, 7412, 2057, 5074, 29901, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1813, 29901, 9305, 1125, 13, 4706, 1583, 29889, 3488, 353, 1813, 13, 13, 1678, 732, 6799, 13, 1678, 822, 4319, 29918, 3092, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 3488, 29889, 10685, 29918, 1454, 29918, 14357, 14822, 12313, 3125, 1159, 2 ]
api/authentication/tests.py	app-generator/api-server-django-sample	2	76597	from django.urls import reverse from rest_framework.test import APITestCase from rest_framework import status class AuthenticationTest(APITestCase): base_url_register = reverse("api-users:register-list") base_url_login = reverse("api-users:login-list") base_url_logout = reverse("api-users:logout-list") base_url_check_session = reverse("api-users:check-session-list") data_register = {"username": "test", "password": "<PASSWORD>", "email": "<EMAIL>"} data_login = {"password": "<PASSWORD>", "email": "<EMAIL>"} def test_register(self): response = self.client.post( f"{self.base_url_register}", data=self.data_register ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) response_data = response.json() self.assertEqual(response_data["success"], True) def test_login(self): response = self.client.post(f"{self.base_url_login}", data=self.data_login) self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["success"], True) def test_logout(self): # Login to retrieve token response = self.client.post(f"{self.base_url_login}", data=self.data_login) response_data = response.json() token = response_data["token"] self.client.credentials(HTTP_AUTHORIZATION=token) # Logout response = self.client.post(f"{self.base_url_logout}", data={"token": token}) self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["success"], True) def test_check_session(self): # Login to retrieve token response = self.client.post(f"{self.base_url_login}", data=self.data_login) response_data = response.json() token = response_data["token"] self.client.credentials(HTTP_AUTHORIZATION=token) # Check session response = self.client.post(f"{self.base_url_check_session}") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() 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thuum/__main__.py	nickcoutsos/thuum	0	40802	import argparse import functools import sys from tornado import ( httpclient, ioloop, ) def add_package_to_path(): if not (__name__ == "__main__" and __package__ == ""): return import os sys.path.append(os.path.abspath(os.path.join( os.path.abspath(__file__), "..", ".."))) add_package_to_path() from thuum import ( reporters, runners, stats, ) class UsageError(Exception): def __init__(self, message, parser): super(UsageError, self).__init__(message) self.parser = parser class AddBody(argparse.Action): def __call__(self, parser, namespace, body, option_string=None): if namespace.body is not None: raise UsageError("Cannot specify -b/--body more than once.", parser) if namespace.method not in ("PATCH", "POST", "PUT"): raise UsageError( "Cannot specify -b/--body with %r." % namespace.method, parser) if body.startswith("py:"): pass elif body.startswith("@"): pass namespace.body = body class AddHeader(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): header = tuple(values.split(":")) namespace.headers.append(header) if len(header) != 2: raise UsageError("Headers must be of the form 'name:value'", parser) class StoreMappedChoice(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): choice = self.choices[values] setattr(namespace, self.dest, choice) def get_argument_parser(parser=None): parser = parser or argparse.ArgumentParser( description="Simple HTTP Load runner.") parser.add_argument( "-m", "--method", choices=("DELETE", "GET", "HEAD", "OPTIONS", "POST", "PUT"), help="HTTP Method to use for request", default="GET") parser.add_argument( "-b", "--body", action=AddBody, default=None, help=( "Request body. Prefix with 'py:' to indicate a fully-qualified " "python callable. Prefix with '@' to indicate a file path." )) parser.add_argument( "-c", "--concurrency", help="Number of requests to make concurrently.", dest="concurrency", default=1, type=int) parser.add_argument( "-H", "--header", dest="headers", help="Custom header. name:value", default=[], action=AddHeader) parser.add_argument( "--reporter", dest="reporter_class", help="Stats report format.", action=StoreMappedChoice, default=reporters.TerminalReporter, choices={ "csv": reporters.CSVReporter, "json": reporters.JSONReporter, "term": reporters.TerminalReporter, }) group = parser.add_mutually_exclusive_group(required=True) group.add_argument( "-n", "--requests", help="Number of requests", type=int) group.add_argument( "-d", "--duration", help="Run load test for specified length of time.", type=float) parser.add_argument("url", help="URL to hit") return parser def main(argv=sys.argv[1:], stdout=sys.stdout): parser = get_argument_parser() try: args = parser.parse_args(argv) except UsageError as exception: sys.exit("%s\n\n%s" % ( exception.message, exception.parser.format_usage() )) httpclient.AsyncHTTPClient.configure(None, max_clients=args.concurrency) client = httpclient.AsyncHTTPClient(io_loop=ioloop.IOLoop.current()) try: make_request = functools.partial( httpclient.HTTPRequest, args.url, args.method, args.headers, args.body) if args.duration: runner = runners.DurationRunner(client, make_request, args.duration) else: runner = runners.QuantityRunner(client, make_request, args.requests) reporter = args.reporter_class(stdout) progress = functools.partial(reporter.progress, runner) tracker = stats.Tracker(runner) tracker.events.on("request_finished", reporter.record) tracker.events.on("tests_finished", lambda t: progress()) tracker.events.on("tests_finished", reporter.summarize) client.io_loop.add_callback(progress) ioloop.PeriodicCallback(progress, 500, client.io_loop).start() runner.run() except KeyboardInterrupt: sys.exit("Tests interrupted.") if __name__ == "__main__": 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silica/transformations/replace_symbols.py	leonardt/fsm_dsl	0	195340	<reponame>leonardt/fsm_dsl import ast from copy import deepcopy class SymbolReplacer(ast.NodeTransformer): def __init__(self, symbol_table, ctx): self.symbol_table = symbol_table self.ctx = ctx def visit_Name(self, node): if node.id in self.symbol_table: if self.ctx is None: new_node = deepcopy(self.symbol_table[node.id]) new_node.ctx = node.ctx return new_node elif isinstance(node.ctx, self.ctx): return deepcopy(self.symbol_table[node.id]) return node def replace_symbols(tree, symbol_table, ctx=None): return SymbolReplacer(symbol_table, ctx).visit(tree)	[ 1, 529, 276, 1112, 420, 29958, 280, 265, 538, 29873, 29914, 29888, 3844, 29918, 29881, 2536, 13, 5215, 8717, 13, 3166, 3509, 1053, 6483, 8552, 13, 13, 13, 1990, 23858, 5612, 433, 2265, 29898, 579, 29889, 4247, 13372, 261, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 5829, 29918, 2371, 29892, 12893, 1125, 13, 4706, 1583, 29889, 18098, 29918, 2371, 353, 5829, 29918, 2371, 13, 4706, 1583, 29889, 13073, 353, 12893, 13, 13, 1678, 822, 6493, 29918, 1170, 29898, 1311, 29892, 2943, 1125, 13, 4706, 565, 2943, 29889, 333, 297, 1583, 29889, 18098, 29918, 2371, 29901, 13, 9651, 565, 1583, 29889, 13073, 338, 6213, 29901, 13, 18884, 716, 29918, 3177, 353, 6483, 8552, 29898, 1311, 29889, 18098, 29918, 2371, 29961, 3177, 29889, 333, 2314, 13, 18884, 716, 29918, 3177, 29889, 13073, 353, 2943, 29889, 13073, 13, 18884, 736, 716, 29918, 3177, 13, 9651, 25342, 338, 8758, 29898, 3177, 29889, 13073, 29892, 1583, 29889, 13073, 1125, 13, 18884, 736, 6483, 8552, 29898, 1311, 29889, 18098, 29918, 2371, 29961, 3177, 29889, 333, 2314, 13, 4706, 736, 2943, 13, 13, 1753, 5191, 29918, 18098, 29879, 29898, 8336, 29892, 5829, 29918, 2371, 29892, 12893, 29922, 8516, 1125, 13, 1678, 736, 23858, 5612, 433, 2265, 29898, 18098, 29918, 2371, 29892, 12893, 467, 1730, 277, 29898, 8336, 29897, 13, 2 ]
1701-1800/1711-1720/1711-countGoodMeals/countGoodMeals.py	xuychen/Leetcode	0	27963	<gh_stars>0 from collections import defaultdict class Solution(object): def countPairs(self, deliciousness): """ :type deliciousness: List[int] :rtype: int """ max_sum = max(deliciousness) * 2 count = 0 dictionary = defaultdict(int) for value in deliciousness: summation = 1 while summation <= max_sum: count = (count + dictionary[summation-value]) % 1000000007 summation <<= 1 dictionary[value] += 1 return count	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 16250, 1053, 2322, 8977, 13, 13, 1990, 24380, 29898, 3318, 1125, 13, 1678, 822, 2302, 29925, 7121, 29898, 1311, 29892, 628, 14803, 2264, 1125, 13, 4706, 9995, 13, 4706, 584, 1853, 628, 14803, 2264, 29901, 2391, 29961, 524, 29962, 13, 4706, 584, 29878, 1853, 29901, 938, 13, 4706, 9995, 13, 13, 4706, 4236, 29918, 2083, 353, 4236, 29898, 6144, 14803, 2264, 29897, 334, 29871, 29906, 13, 4706, 2302, 353, 29871, 29900, 13, 4706, 8600, 353, 2322, 8977, 29898, 524, 29897, 13, 13, 4706, 363, 995, 297, 628, 14803, 2264, 29901, 13, 9651, 22792, 362, 353, 29871, 29896, 13, 9651, 1550, 22792, 362, 5277, 4236, 29918, 2083, 29901, 13, 18884, 2302, 353, 313, 2798, 718, 8600, 29961, 2083, 29885, 362, 29899, 1767, 2314, 1273, 29871, 29896, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 29955, 13, 18884, 22792, 362, 3532, 29922, 29871, 29896, 13, 13, 9651, 8600, 29961, 1767, 29962, 4619, 29871, 29896, 13, 13, 4706, 736, 2302, 13, 2 ]
modin/pandas/datetimes.py	alclol/modin	0	68697	<reponame>alclol/modin # Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership. The Modin Development Team licenses this file to you 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 pandas from .dataframe import DataFrame from .series import Series def to_datetime( arg, errors="raise", dayfirst=False, yearfirst=False, utc=None, format=None, exact=True, unit=None, infer_datetime_format=False, origin="unix", cache=True, ): """Convert the arg to datetime format. If not Ray DataFrame, this falls back on pandas. Args: errors ('raise' or 'ignore'): If 'ignore', errors are silenced. Pandas blatantly ignores this argument so we will too. dayfirst (bool): Date format is passed in as day first. yearfirst (bool): Date format is passed in as year first. utc (bool): retuns a UTC DatetimeIndex if True. box (bool): If True, returns a DatetimeIndex. format (string): strftime to parse time, eg "%d/%m/%Y". exact (bool): If True, require an exact format match. unit (string, default 'ns'): unit of the arg. infer_datetime_format (bool): Whether or not to infer the format. origin (string): Define the reference date. 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tests/test_gpreg.py	cdgreenidge/gdec	0	12283	<filename>tests/test_gpreg.py """Test gpreg.py.""" from typing import Tuple import numpy as np import pytest from gdec import gpreg, npgp @pytest.fixture(scope="module") def dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: np.random.seed(42) amplitude = 1.0 lengthscale = 12 sigma = 0.5 n = 128 def spectrum_fn(w: np.ndarray) -> np.ndarray: return npgp.rbf_spectrum(w, amplitude, lengthscale) basis, freqs = npgp.real_fourier_basis(n) coef_vars = npgp.rbf_spectrum(freqs, amplitude, lengthscale) z = np.arange(n) w = np.sqrt(coef_vars) * np.random.randn(n) f = basis @ w x = z.repeat(1) f_x = f.repeat(1) y = sigma * np.random.randn(*f_x.shape) + f_x return x[:, None], y, z[:, None], f def test_you_can_train_periodic_gp_regression_on_the_synthetic_dataset(dataset): X, y, z, f = dataset grid_size = np.unique(X).size model = gpreg.PeriodicGPRegression() model.fit(X, y, grid_size=grid_size) f_est = model.predict(z) error = np.max(np.abs(f - f_est)) assert error < 0.3 def test_training_pid_on_float_dataset_raises_value_error(dataset): X, y, _, _ = dataset X = X.astype(np.float32) grid_size = np.unique(X).size model = gpreg.PeriodicGPRegression() with pytest.raises(ValueError): model.fit(X, y, grid_size=grid_size)	[ 1, 529, 9507, 29958, 21150, 29914, 1688, 29918, 29887, 1457, 29887, 29889, 2272, 13, 15945, 29908, 3057, 330, 1457, 29887, 29889, 2272, 1213, 15945, 13, 3166, 19229, 1053, 12603, 552, 13, 13, 5215, 12655, 408, 7442, 13, 5215, 11451, 1688, 13, 13, 3166, 330, 7099, 1053, 330, 1457, 29887, 29892, 302, 4061, 29886, 13, 13, 13, 29992, 2272, 1688, 29889, 7241, 15546, 29898, 6078, 543, 5453, 1159, 13, 1753, 8783, 580, 1599, 12603, 552, 29961, 9302, 29889, 299, 2378, 29892, 7442, 29889, 299, 2378, 29892, 7442, 29889, 299, 2378, 29892, 7442, 29889, 299, 2378, 5387, 13, 1678, 7442, 29889, 8172, 29889, 26776, 29898, 29946, 29906, 29897, 13, 1678, 28347, 353, 29871, 29896, 29889, 29900, 13, 1678, 3309, 7052, 353, 29871, 29896, 29906, 13, 1678, 269, 2934, 353, 29871, 29900, 29889, 29945, 13, 1678, 302, 353, 29871, 29896, 29906, 29947, 13, 13, 1678, 822, 18272, 29918, 9144, 29898, 29893, 29901, 7442, 29889, 299, 2378, 29897, 1599, 7442, 29889, 299, 2378, 29901, 13, 4706, 736, 302, 4061, 29886, 29889, 29878, 1635, 29918, 21494, 5848, 29898, 29893, 29892, 28347, 29892, 3309, 7052, 29897, 13, 13, 1678, 8405, 29892, 3005, 29939, 29879, 353, 302, 4061, 29886, 29889, 6370, 29918, 29888, 283, 4336, 29918, 6500, 275, 29898, 29876, 29897, 13, 1678, 1302, 1389, 29918, 16908, 353, 302, 4061, 29886, 29889, 29878, 1635, 29918, 21494, 5848, 29898, 29888, 7971, 29879, 29892, 28347, 29892, 3309, 7052, 29897, 13, 1678, 503, 353, 7442, 29889, 279, 927, 29898, 29876, 29897, 13, 13, 1678, 281, 353, 7442, 29889, 3676, 29898, 1111, 1389, 29918, 16908, 29897, 334, 7442, 29889, 8172, 29889, 9502, 29876, 29898, 29876, 29897, 13, 1678, 285, 353, 8405, 732, 281, 13, 13, 1678, 921, 353, 503, 29889, 14358, 29898, 29896, 29897, 13, 1678, 285, 29918, 29916, 353, 285, 29889, 14358, 29898, 29896, 29897, 13, 1678, 343, 353, 269, 2934, 334, 7442, 29889, 8172, 29889, 9502, 29876, 10456, 29888, 29918, 29916, 29889, 12181, 29897, 718, 285, 29918, 29916, 13, 1678, 736, 921, 7503, 29892, 6213, 1402, 343, 29892, 503, 7503, 29892, 6213, 1402, 285, 13, 13, 13, 1753, 1243, 29918, 6293, 29918, 3068, 29918, 14968, 29918, 19145, 293, 29918, 29887, 29886, 29918, 276, 11476, 29918, 265, 29918, 1552, 29918, 19274, 386, 7492, 29918, 24713, 29898, 24713, 1125, 13, 1678, 1060, 29892, 343, 29892, 503, 29892, 285, 353, 8783, 13, 1678, 6856, 29918, 2311, 353, 7442, 29889, 13092, 29898, 29990, 467, 2311, 13, 1678, 1904, 353, 330, 1457, 29887, 29889, 29853, 293, 19903, 4597, 23881, 580, 13, 1678, 1904, 29889, 9202, 29898, 29990, 29892, 343, 29892, 6856, 29918, 2311, 29922, 7720, 29918, 2311, 29897, 13, 1678, 285, 29918, 342, 353, 1904, 29889, 27711, 29898, 29920, 29897, 13, 1678, 1059, 353, 7442, 29889, 3317, 29898, 9302, 29889, 6897, 29898, 29888, 448, 285, 29918, 342, 876, 13, 1678, 4974, 1059, 529, 29871, 29900, 29889, 29941, 13, 13, 13, 1753, 1243, 29918, 26495, 29918, 5935, 29918, 265, 29918, 7411, 29918, 24713, 29918, 336, 4637, 29918, 1767, 29918, 2704, 29898, 24713, 1125, 13, 1678, 1060, 29892, 343, 29892, 17117, 903, 353, 8783, 13, 1678, 1060, 353, 1060, 29889, 579, 668, 29898, 9302, 29889, 7411, 29941, 29906, 29897, 13, 1678, 6856, 29918, 2311, 353, 7442, 29889, 13092, 29898, 29990, 467, 2311, 13, 1678, 1904, 353, 330, 1457, 29887, 29889, 29853, 293, 19903, 4597, 23881, 580, 13, 1678, 411, 11451, 1688, 29889, 336, 4637, 29898, 1917, 2392, 1125, 13, 4706, 1904, 29889, 9202, 29898, 29990, 29892, 343, 29892, 6856, 29918, 2311, 29922, 7720, 29918, 2311, 29897, 13, 2 ]
evaluate.py	OlinSTEP/silent_speech	0	144329	import sys import os import logging import torch from torch import nn from transduction_model import test, save_output, Model from read_emg import EMGDataset from asr import evaluate from data_utils import phoneme_inventory, print_confusion from absl import flags FLAGS = flags.FLAGS flags.DEFINE_list('models', [], 'identifiers of models to evaluate') class EnsembleModel(nn.Module): def __init__(self, models): super().__init__() self.models = nn.ModuleList(models) def forward(self, x, x_raw, sess): ys = [] ps = [] for model in self.models: y, p = model(x, x_raw, sess) ys.append(y) ps.append(p) return torch.stack(ys,0).mean(0), torch.stack(ps,0).mean(0) def main(): os.makedirs(FLAGS.output_directory, exist_ok=True) logging.basicConfig(handlers=[ logging.FileHandler(os.path.join(FLAGS.output_directory, 'eval_log.txt'), 'w'), logging.StreamHandler() ], level=logging.INFO, format="%(message)s") testset = EMGDataset(test=True) device = 'cuda' if torch.cuda.is_available() and not FLAGS.debug else 'cpu' models = [] for fname in FLAGS.models: state_dict = torch.load(fname) n_sess = 1 if FLAGS.no_session_embed else state_dict["session_emb.weight"].size(0) model = Model(testset.num_features, testset.num_speech_features, len(phoneme_inventory), n_sess).to(device) model.load_state_dict(state_dict) models.append(model) ensemble = EnsembleModel(models) _, _, confusion = test(ensemble, testset, device) print_confusion(confusion) for i, datapoint in enumerate(testset): save_output(ensemble, datapoint, os.path.join(FLAGS.output_directory, f'example_output_{i}.wav'), device) evaluate(testset, FLAGS.output_directory) if __name__ == "__main__": FLAGS(sys.argv) main()	[ 1, 1053, 10876, 13, 5215, 2897, 13, 5215, 12183, 13, 13, 5215, 4842, 305, 13, 3166, 4842, 305, 1053, 302, 29876, 13, 13, 3166, 1301, 700, 428, 29918, 4299, 1053, 1243, 29892, 4078, 29918, 4905, 29892, 8125, 13, 3166, 1303, 29918, 331, 29887, 1053, 27295, 29954, 16390, 24541, 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RNAPuzzles/RNAPuzzles/settings.py	whinyadventure/RNA-Puzzles	0	174781	""" Django settings for RNAPuzzles project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = os.getenv("SECRET_KEY", '<KEY>') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = os.getenv("DEBUG", "False") == "True" ALLOWED_HOSTS = ["127.0.0.1", "django-service"] LOGOUT_REDIRECT_URL = '/' DOMAIN_URL = "lepus.cs.put.poznan.pl" LOGIN_URL = "/accounts/signin/" # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'martor', 'crispy_forms', 'guardian', 'publications', 'captcha', 'tempus_dominus', 'rnapuzzles', ] CRISPY_TEMPLATE_PACK = 'bootstrap4' AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', # default 'guardian.backends.ObjectPermissionBackend', ) ANONYMOUS_USER_NAME = "<EMAIL>" MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'rnapuzzles.middleware.request.GuardianAuthenticationMiddleware', ] ROOT_URLCONF = 'RNAPuzzles.urls' TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', ) TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, "rnapuzzles/templates")], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'RNAPuzzles.wsgi.application' AUTH_USER_MODEL = 'rnapuzzles.CustomUser' if os.getenv("EMAIL_HOST_USER") and os.getenv("EMAIL_HOST_PASSWORD"): EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_USE_TLS = True EMAIL_PORT = 587 EMAIL_HOST_USER = os.getenv("EMAIL_HOST_USER",False) EMAIL_HOST_PASSWORD = os.getenv("EMAIL_HOST_PASSWORD",False) else: EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' from django.contrib.messages import constants as messages MESSAGE_TAGS = { messages.DEBUG: 'alert-info', messages.INFO: 'alert-info', messages.SUCCESS: 'alert-success', messages.WARNING: 'alert-warning', messages.ERROR: 'alert-danger', } # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.getenv("DB_NAME", 'rna'), 'USER': os.getenv("DB_USER",'postgres'), 'PASSWORD': os.getenv("DB_PASSWORD",'password'), 'HOST': os.getenv("DB_HOST", 'localhost'), 'PORT': os.getenv("DB_PORT", '5432'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ #LANGUAGE_CODE = 'en-GB' TIME_ZONE = 'CET' #USE_I18N = True USE_L10N = False USE_TZ = True DATETIME_FORMAT = 'd M Y, H:i ' DATETIME_INPUT_FORMATS = ['%d-%m-%Y %H:%M'] TEMPUS_DOMINUS_LOCALIZE = False TEMPUS_DOMINUS_INCLUDE_ASSETS = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) STATIC_URL = '/static/' STATIC_ROOT = "/var/www/html/"#os.path.join(PROJECT_ROOT, 'static') MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media') MEDIA_SUBMISSIONS = "submissions" STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) STATICFILES_DIRS = ( os.path.join(BASE_DIR, "rnapuzzles/static"), ) # Global martor settings # Input: string boolean, `true/false` MARTOR_ENABLE_CONFIGS = { 'imgur': 'false', # to enable/disable imgur/custom uploader. 'mention': 'false', # to enable/disable mention 'jquery': 'true', # to include/revoke jquery (require for admin default django) 'living': 'false', # to enable/disable live updates in preview 'spellcheck': 'true', } # To setup the martor editor with label or not (default is False) MARTOR_ENABLE_LABEL = True # Imgur API Keys MARTOR_IMGUR_CLIENT_ID = 'your-client-id' MARTOR_IMGUR_API_KEY = 'your-api-key' # Safe Mode MARTOR_MARKDOWN_SAFE_MODE = True # default # Markdownify MARTOR_MARKDOWNIFY_FUNCTION = 'martor.utils.markdownify' # default MARTOR_MARKDOWNIFY_URL = '/martor/markdownify/' # default # Markdown extensions (default) MARTOR_MARKDOWN_EXTENSIONS = [ 'markdown.extensions.extra', 'markdown.extensions.nl2br', 'markdown.extensions.smarty', 'markdown.extensions.fenced_code', # Custom markdown extensions. 'martor.extensions.urlize', 'martor.extensions.del_ins', # ~~strikethrough~~ and ++underscores++ 'martor.extensions.mention', # to parse markdown mention 'martor.extensions.emoji', # to parse markdown emoji 'martor.extensions.mdx_video', # to parse embed/iframe video ] # Markdown Extensions Configs MARTOR_MARKDOWN_EXTENSION_CONFIGS = {} # Markdown urls MARTOR_UPLOAD_URL = '/martor/uploader/' # default MARTOR_SEARCH_USERS_URL = '/martor/search-user/' # default # Markdown Extensions # MARTOR_MARKDOWN_BASE_EMOJI_URL = 'https://www.webfx.com/tools/emoji-cheat-sheet/graphics/emojis/' # from webfx MARTOR_MARKDOWN_BASE_EMOJI_URL = 'https://github.githubassets.com/images/icons/emoji/' # default from github MARTOR_MARKDOWN_BASE_MENTION_URL = 'https://python.web.id/author/' SILENCED_SYSTEM_CHECKS = ['captcha.recaptcha_test_key_error'] # RabbitMq CELERY_BROKER_URL = 'amqp://{user}:{password}@{host}:{port}'.format( host=os.getenv('RABBITMQ_HOST', 'localhost'), port=os.getenv('RABBITMQ_PORT', '5672'), user=os.getenv("RABBITMQ_USER", "user"), password=os.getenv("RABBITMQ_PASSWORD", "password") ) CELERY_ACCEPT_CONTENT = ['application/json'] CELERY_TASK_SERIALIZER = 'json' CELERY_RESULT_SERIALIZER = 'json'	[ 1, 9995, 13, 29928, 5364, 6055, 363, 390, 29940, 3301, 18813, 793, 2060, 29889, 13, 13, 24565, 491, 525, 14095, 29899, 6406, 1369, 4836, 29915, 773, 15337, 29871, 29906, 29889, 29906, 29889, 13, 13, 2831, 901, 2472, 373, 445, 934, 29892, 1074, 13, 991, 597, 2640, 29889, 19776, 574, 26555, 622, 29889, 510, 29914, 264, 29914, 29906, 29889, 29906, 29914, 3332, 1199, 29914, 11027, 29914, 13, 13, 2831, 278, 2989, 1051, 310, 6055, 322, 1009, 1819, 29892, 1074, 13, 991, 597, 2640, 29889, 19776, 574, 26555, 622, 29889, 510, 29914, 264, 29914, 29906, 29889, 29906, 29914, 999, 29914, 11027, 29914, 13, 15945, 29908, 13, 13, 5215, 2897, 13, 13, 29937, 8878, 10898, 2768, 278, 2060, 763, 445, 29901, 2897, 29889, 2084, 29889, 7122, 29898, 25416, 29918, 9464, 29892, 29757, 13, 25416, 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python/depthcharge/register/cp.py	youssefms/depthcharge	0	61757	# SPDX-License-Identifier: BSD-3-Clause # Depthcharge: <https://github.com/nccgroup/depthcharge> """ Implements FDTCrashRegisterReader """ from ..operation import Operation from .data_abort import DataAbortRegisterReader class CpCrashRegisterReader(DataAbortRegisterReader): """ This is a :py:class:`~.DataAbortRegisterReader` that uses the ``cp`` console command to trigger the Data Abort used to read registers. """ _required = { 'arch': 'ARM', 'commands': ['cp'], 'crash_or_reboot': True, } @classmethod def rank(cls, **_kwargs): return 10 # This is by intent; we are not using another memory reader. # pylint: disable=method-hidden def _trigger_data_abort(self): cmd = 'cp.l {addr:x} {addr:x} 1'.format(addr=self._crash_addr) return self._ctx.send_command(cmd) Operation.register(CpCrashRegisterReader)	[ 1, 396, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 350, 7230, 29899, 29941, 29899, 20216, 1509, 13, 29937, 10034, 386, 23367, 29901, 529, 991, 597, 3292, 29889, 510, 29914, 29876, 617, 2972, 29914, 19488, 23367, 29958, 13, 13, 15945, 29908, 13, 1888, 9711, 383, 29928, 9472, 29878, 1161, 15213, 6982, 13, 15945, 29908, 13, 13, 3166, 6317, 16453, 1053, 20462, 13, 3166, 869, 1272, 29918, 370, 441, 1053, 3630, 4920, 441, 15213, 6982, 13, 13, 13, 1990, 315, 29886, 20647, 1161, 15213, 6982, 29898, 1469, 4920, 441, 15213, 6982, 1125, 13, 1678, 9995, 13, 1678, 910, 338, 263, 584, 2272, 29901, 1990, 18078, 30022, 29889, 1469, 4920, 441, 15213, 6982, 29952, 393, 3913, 278, 4954, 6814, 16159, 13, 1678, 2991, 1899, 304, 7135, 278, 3630, 1976, 441, 1304, 304, 1303, 28975, 29889, 13, 1678, 9995, 13, 13, 1678, 903, 12403, 353, 426, 13, 4706, 525, 1279, 2396, 525, 1718, 29924, 742, 13, 4706, 525, 26381, 2396, 6024, 6814, 7464, 13, 4706, 525, 7283, 1161, 29918, 272, 29918, 276, 4777, 2396, 5852, 29892, 13, 1678, 500, 13, 13, 1678, 732, 1990, 5696, 13, 1678, 822, 7115, 29898, 25932, 29892, 3579, 29918, 19290, 1125, 13, 4706, 736, 29871, 29896, 29900, 13, 13, 1678, 396, 910, 338, 491, 7609, 29936, 591, 526, 451, 773, 1790, 3370, 9591, 29889, 13, 1678, 396, 282, 2904, 524, 29901, 11262, 29922, 5696, 29899, 10892, 13, 1678, 822, 903, 21001, 29918, 1272, 29918, 370, 441, 29898, 1311, 1125, 13, 4706, 9920, 353, 525, 6814, 29889, 29880, 426, 10030, 29901, 29916, 29913, 426, 10030, 29901, 29916, 29913, 29871, 29896, 4286, 4830, 29898, 10030, 29922, 1311, 3032, 7283, 1161, 29918, 10030, 29897, 13, 4706, 736, 1583, 3032, 13073, 29889, 6717, 29918, 6519, 29898, 9006, 29897, 13, 13, 13, 10925, 29889, 9573, 29898, 29907, 29886, 20647, 1161, 15213, 6982, 29897, 13, 2 ]
Day01-15/code/Day15/excel1.py	EngrSaad2/Python-100-Days	6	44704	<reponame>EngrSaad2/Python-100-Days<gh_stars>1-10 """ 创建Excel文件 Version: 0.1 Author: 骆昊 Date: 2018-03-26 """ from openpyxl import Workbook from openpyxl.worksheet.table import Table, TableStyleInfo workbook = Workbook() sheet = workbook.active data = [ [1001, '白元芳', '男', '13123456789'], [1002, '白洁', '女', '13233445566'] ] sheet.append(['学号', '姓名', '性别', '电话']) for row in data: sheet.append(row) tab = Table(displayName="Table1", ref="A1:E5") tab.tableStyleInfo = TableStyleInfo( name="TableStyleMedium9", showFirstColumn=False, showLastColumn=False, showRowStripes=True, showColumnStripes=True) sheet.add_table(tab) workbook.save('./res/全班学生数据.xlsx')	[ 1, 529, 276, 1112, 420, 29958, 2369, 629, 17618, 328, 29906, 29914, 11980, 29899, 29896, 29900, 29900, 29899, 25991, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 13, 31441, 30886, 22926, 30333, 30631, 13, 13, 6594, 29901, 29871, 29900, 29889, 29896, 13, 13720, 29901, 29871, 236, 173, 137, 233, 155, 141, 13, 2539, 29901, 29871, 29906, 29900, 29896, 29947, 29899, 29900, 29941, 29899, 29906, 29953, 13, 15945, 29908, 13, 3166, 1722, 2272, 15524, 1053, 5244, 2909, 13, 3166, 1722, 2272, 15524, 29889, 1287, 9855, 29889, 2371, 1053, 6137, 29892, 6137, 5568, 3401, 13, 13, 1287, 2909, 353, 5244, 2909, 580, 13, 9855, 353, 664, 2909, 29889, 4925, 13, 1272, 353, 518, 13, 1678, 518, 29896, 29900, 29900, 29896, 29892, 525, 30868, 30824, 235, 141, 182, 742, 525, 31203, 742, 525, 29896, 29941, 29896, 29906, 29941, 29946, 29945, 29953, 29955, 29947, 29929, 7464, 13, 1678, 518, 29896, 29900, 29900, 29906, 29892, 525, 30868, 233, 183, 132, 742, 525, 30647, 742, 525, 29896, 29941, 29906, 29941, 29941, 29946, 29946, 29945, 29945, 29953, 29953, 2033, 13, 29962, 13, 9855, 29889, 4397, 18959, 30415, 30850, 742, 525, 232, 170, 150, 30548, 742, 525, 30952, 232, 139, 174, 742, 525, 31679, 31852, 11287, 13, 1454, 1948, 297, 848, 29901, 13, 1678, 9869, 29889, 4397, 29898, 798, 29897, 13, 3891, 353, 6137, 29898, 4990, 1170, 543, 3562, 29896, 613, 2143, 543, 29909, 29896, 29901, 29923, 29945, 1159, 13, 13, 3891, 29889, 2371, 5568, 3401, 353, 6137, 5568, 3401, 29898, 13, 1678, 1024, 543, 3562, 5568, 19302, 1974, 29929, 613, 1510, 6730, 4409, 29922, 8824, 29892, 13, 1678, 1510, 8897, 4409, 29922, 8824, 29892, 1510, 4301, 855, 374, 5547, 29922, 5574, 29892, 1510, 4409, 855, 374, 5547, 29922, 5574, 29897, 13, 9855, 29889, 1202, 29918, 2371, 29898, 3891, 29897, 13, 1287, 2909, 29889, 7620, 877, 6904, 690, 29914, 30753, 234, 146, 176, 30415, 30486, 30354, 30763, 29889, 20267, 29916, 1495, 13, 2 ]
playsound.py	Tony1527/playsound	2	89492	<reponame>Tony1527/playsound class PlaysoundException(Exception): pass def _playsoundWin(sound, block = True): ''' Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on Windows 7 with Python 2.7. Probably works with more file formats. Probably works on Windows XP thru Windows 10. Probably works with all versions of Python. Inspired by (but not copied from) <NAME> <<EMAIL>>'s mp3play: https://github.com/michaelgundlach/mp3play I never would have tried using windll.winmm without seeing his code. ''' from ctypes import c_buffer, windll from random import random from time import sleep from sys import getfilesystemencoding def winCommand(command): buf = c_buffer(255) command = ' '.join(command).encode(getfilesystemencoding()) errorCode = int(windll.winmm.mciSendStringA(command, buf, 254, 0)) if errorCode: errorBuffer = c_buffer(255) windll.winmm.mciGetErrorStringA(errorCode, errorBuffer, 254) exceptionMessage = ('\n Error ' + str(errorCode) + ' for command:' '\n ' + command.decode() + '\n ' + errorBuffer.value.decode()) raise PlaysoundException(exceptionMessage) return buf.value alias = 'playsound_' + str(random()) winCommand('open "' + sound + '" alias', alias) winCommand('set', alias, 'time format milliseconds') durationInMS = winCommand('status', alias, 'length') winCommand('play', alias, 'from 0 to', durationInMS.decode()) if block: sleep(float(durationInMS) / 1000.0) def _playsoundOSX(sound, block = True): ''' Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on OS X 10.11 with Python 2.7. Probably works with anything QuickTime supports. Probably works on OS X 10.5 and newer. Probably works with all versions of Python. Inspired by (but not copied from) Aaron's Stack Overflow answer here: http://stackoverflow.com/a/34568298/901641 I never would have tried using AppKit.NSSound without seeing his code. ''' from AppKit import NSSound from Foundation import NSURL from time import sleep if '://' not in sound: if not sound.startswith('/'): from os import getcwd sound = getcwd() + '/' + sound sound = 'file://' + sound url = NSURL.URLWithString_(sound) nssound = NSSound.alloc().initWithContentsOfURL_byReference_(url, True) if not nssound: raise IOError('Unable to load sound named: ' + sound) nssound.play() if block: sleep(nssound.duration()) def _playsoundNix(sound, block=True): """Play a sound using GStreamer. Inspired by this: https://gstreamer.freedesktop.org/documentation/tutorials/playback/playbin-usage.html """ if not block: raise NotImplementedError( "block=False cannot be used on this platform yet") # pathname2url escapes non-URL-safe characters import os try: from urllib.request import pathname2url except ImportError: # python 2 from urllib import pathname2url import gi gi.require_version('Gst', '1.0') from gi.repository import Gst Gst.init(None) playbin = Gst.ElementFactory.make('playbin', 'playbin') if sound.startswith(('http://', 'https://')): playbin.props.uri = sound else: playbin.props.uri = 'file://' + pathname2url(os.path.abspath(sound)) set_result = playbin.set_state(Gst.State.PLAYING) if set_result != Gst.StateChangeReturn.ASYNC: raise PlaysoundException( "playbin.set_state returned " + repr(set_result)) # FIXME: use some other bus method than poll() with block=False # https://lazka.github.io/pgi-docs/#Gst-1.0/classes/Bus.html bus = playbin.get_bus() bus.poll(Gst.MessageType.EOS, Gst.CLOCK_TIME_NONE) playbin.set_state(Gst.State.NULL) from platform import system system = system() if system == 'Windows': playsound = _playsoundWin elif system == 'Darwin': playsound = _playsoundOSX else: playsound = _playsoundNix del system from ctypes import c_buffer, windll from random import random from time import sleep from sys import getfilesystemencoding def winCommand(command): buf = c_buffer(255) command = ' '.join(command).encode(getfilesystemencoding()) errorCode = int(windll.winmm.mciSendStringA(command, buf, 254, 0)) if errorCode: errorBuffer = c_buffer(255) windll.winmm.mciGetErrorStringA(errorCode, errorBuffer, 254) exceptionMessage = ('\n Error ' + str(errorCode) + ' for command:' '\n ' + command.decode() + '\n ' + errorBuffer.value.decode()) raise PlaysoundException(exceptionMessage) return buf.value from threading import Thread,Event,Lock from queue import Queue,Empty from collections import deque ''' music class which uses windows mci to play the music ''' class _music(object): __alias=None __running_idx=None __sound=None __start=None __end=None __is_repeat=False __id=-1 music_list=None ''' initialize the music object ''' def __init__(self,sound,id): self.__alias=['',''] self.__running_idx=0 self.__id=id self.preload(sound) def set_music_list(self,music_list): self.music_list = music_list def __eq__(self,value): return self.__id==value ''' clear the music object music will be closed ''' def close(self): self.stop() self.__clear() ''' get id of music music will not be affected ''' def get_id(self): return self.__id ''' return whether music plays repeatly music will not be affected ''' def is_repeat(self): return self.__is_repeat ''' return the range from start to end music will not be affected ''' def length(self): if self.__check_alias(): return self.__end-self.__start ''' return the mode of the music object music will not be affected ''' def mode(self): if self.__check_alias(): return winCommand('status',self.__get_alias(),'mode').decode() ''' pause the music music will be paused ''' def pause(self): if self.__check_alias(): winCommand('pause '+self.__get_alias()) ''' play the music from start to end music will be playing ''' def play(self,start=0,end=-1): self.__start,self.__end=self.__parse_start_end(start,end,self.total_length()) self.__play_implement(self.__start,self.__end) ''' return the position of the music music will not be affected ''' def position(self): if self.__check_alias(): return int(winCommand('status',self.__get_alias(),'position').decode()) ''' preload the music information ''' def preload(self,sound): self.__sound=sound for i in range(2): self.__alias[i]='playsound_'+str(random()) winCommand('open "'+self.__sound+'" alias',self.__alias[i]) winCommand('set',self.__alias[i],'time format milliseconds') length=self.total_length() self.__start=0 self.__end=length return length ''' resume playing music will be playing ''' def resume(self): if self.__check_alias(): if self.__is_repeat: self.__play_implement(self.position(),self.__end) else: winCommand('resume '+self.__get_alias()) ''' seek the music to pos. music will bee paused ''' def seek(self,pos): if self.__check_alias(): if pos>self.__end or pos<self.__start: raise PlaysoundException('position exceed range') winCommand('seek',self.__get_alias(),'to',str(pos)) winCommand('play',self.__get_alias(),'from '+ str(pos) +' to',str(self.__end)) self.pause() ''' set repeat flag of the music music will repeatly play ''' def set_repeat(self,repeat): self.__is_repeat=repeat ''' set id for music object music will not be affected ''' def set_id(self,id): self.__id=id ''' stop the music. music will be stopped ''' def stop(self): if self.__check_alias(): self.seek(self.__start) winCommand('stop '+self.__get_alias()) ''' total_length of the music object, the difference that total_length is the range is total music, but length is only range from start to end music will not be affected ''' def total_length(self): if self.__check_alias(): return int(winCommand('status',self.__get_alias(),'length').decode()) ''' update the record time of the music, ''' def update_mode(self,delay=0): mod = self.mode() if mod =='playing': #if self.__end-self.position()<delay then repeat the music if self.__is_repeat==True: if self.__end-self.position()<=delay: self.__running_idx=(self.__running_idx+1)%2 self.__play_implement(self.__start,self.__end) return mod def __get_alias(self): return self.__alias[self.__running_idx] def __check_alias(self): if self.__get_alias()!='': return True def __parse_start_end(self,start,end,length): if not (isinstance(start,int) and isinstance(end,int)): raise PlaysoundException('start and end must be int') _start=0 _end=0 if end==-1: _end = length elif end<=length: _end = end else: raise PlaysoundException('music range exceed limits') if start<0 or start>length: raise PlaysoundException('music range exceed limits') elif _end<start: raise PlaysoundException('end must be bigger than start') else: _start=start return _start,_end def __del__(self): self.__clear() def __clear(self): if self.__check_alias(): for i in range(2): winCommand('close '+self.__alias[i]) self.__alias=['',''] self.__start=None self.__end=None self.__is_repeat=False def __play_implement(self,start,end): winCommand('play',self.__get_alias(),'from '+ str(start) +' to',str(end)) def print(self): if self.__check_alias(): def format_miliseconds(t): return '%d:%d:%d.%d'%(t//3600000,(t%3600000)//60000,(t%60000)//1000,t%1000) print('music name:',self.__sound) print('mode：',self.mode()) print('total_length：',self.total_length()) print('position:',str(self.position())) print('start - end: {} - {}'.format(format_miliseconds(self.__start),format_miliseconds(self.__end))) ''' singleton ''' class _singleton(object): _mutex=Lock() def __init__(self): pass @classmethod def GetInstance(cls,args,kwargs): if not hasattr(cls,'_instance'): cls._mutex.acquire() if not hasattr(cls,'_instance'): cls._instance = cls() print('create instance',cls._instance) cls._mutex.release() return cls._instance ''' music tag is used to send message for music manager ''' class _music_tag(object): id=-1 #id is the connection between music player and _music object operator='' #operator of _music object args=None #parameters block_event=None block=False retval=None #return value for some methods of music player music_list=None #special deal with music list def __init__(self,id,operator,block=False,args): self.id=id self.operator = operator self.args = args if block: self.block_event=Event() self.block=True def set_music_list(self,music_list): self.music_list = music_list ''' music player is the client who sends music tags to music manager which indeed plays music. music player controls music once you open the music. ''' class music_player(object): __id=-1 #identity of every _music object __music=None #sound static_id=0 #static variables mutex=Lock() #lock of static_id music_list=None #this music player belong to which music list def __init__(self,music_list=None): ''' if music player belongs to one of music list,then set music_list, otherwise you can ignore music_list parameter ''' self.music_list = music_list def get_music(self): ''' get name of sound ''' return self.__music def close(self): ''' close sound ''' self.__send('close',False) self.__id=-1 def length(self): ''' get the length of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('length',True) def mode(self): ''' get the mode of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('mode',True) def open(self,music): ''' open the music ''' self.__music=music self.mutex.acquire() self.__id=music_player.static_id music_player.static_id=music_player.static_id+1 self.mutex.release() self.__send('open',False,self.__music,self.__id) def pause(self): ''' pause the music ''' self.__send('pause',False) def play(self,start=0,end=-1): ''' play the music ''' self.__send('play',False,start,end) def position(self): ''' get the mode of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('position',True) def resume(self): ''' resume the music ''' self.__send('resume',False) def seek(self,pos): ''' seek the music to pos, which is defined in miliseconds ''' self.__send('seek',False,pos) def set_repeat(self,repeat): ''' play music repeatly ''' self.__send('set_repeat',False,repeat) def stop(self): ''' stop the music ''' self.__send('stop',False) def total_length(self): ''' get the total length of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('total_length',True) def __send(self,operator,block,args): ''' send music tag to music manager ''' if self.__id==-1: raise PlaysoundException('No music has been opened') tag=_music_tag(self.__id,operator,block,args) tag.music_list=self.music_list return music_manager.GetInstance().put_tag(tag) class music_list(object): __music_list=deque() def append_music(self,sound,repeat=False): music = music_player(self) music.open(sound) music.set_repeat(repeat) self.__music_list.append(music) if len(self.__music_list)==1: self.top().play() def play_next(self): if len(self.__music_list)>=2: self.__music_list[1].play() self.__music_list.popleft().close() def pause_music(self): if len(self.__music_list)>0 and self.mode()=='playing': self.top().pause() def resume_music(self): if len(self.__music_list)>0 and self.mode()=='paused': self.top().resume() def mode(self): return self.top().mode() def top(self): return self.__music_list[0] class music_manager(_singleton): __mutex=Lock() __sounds=[] # __music_list=[] __tag_queue=Queue() __running_event=Event() __end_running_event=Event() def __init__(self): self.reset_event() def reset_event(self): self.__running_event.set() self.__end_running_event.clear() def put_tag(self,tag): ''' push a music tag to music_manager @warning: if tag.block is True ,this method will block current thread until music manager respond this functions ''' if tag.block: tag.block_event.clear() self.__tag_queue.put(tag) if tag.block: tag.block_event.wait() return tag.retval def get_tag(self): try: #if there is no task for music player, then sleep the music manager, #otherwise get a tag immediately if len(self.__sounds)>0: tag=self.__tag_queue.get_nowait() else: tag=self.__tag_queue.get() retval=None if tag.operator == 'open': m=self.__add_music(tag.args) m.set_music_list(tag.music_list) elif tag.operator == 'close': #remove the music from self.__sounds self.__rm_music(tag.id) else: (idx,item)=self.__get_music_idx_and_item(tag.id) #reflect retval=getattr(item,tag.operator)(tag.args) #set return values in tag if tag.block==True: tag.retval=retval tag.block_event.set() except Empty: pass def __add_music(self,sound,id): m=_music(sound,id) self.__mutex.acquire() self.__sounds.append(m) self.__mutex.release() return m def __rm_music(self,id): idx,rm_item=self.__get_music_idx_and_item(id) rm_item.close() rm_item.set_id(-1) self.__mutex.acquire() self.__sounds.pop(idx) self.__mutex.release() def __get_music_idx_and_item(self,id): for i,x in enumerate(self.__sounds): if x.get_id()==id: return i,x raise PlaysoundException('Unknown music object found') @classmethod def start(cls): ''' start the music manager ''' Thread(target=music_manager._start_music_manager_impl).start() @classmethod def stop(cls): ''' stop the music manager ''' manager = cls.GetInstance() manager.__running_event.clear() manager.__end_running_event.wait() manager.reset_event() print('stop manager',manager) ''' main loop of music manager ''' @classmethod def _start_music_manager_impl(cls): manager = cls.GetInstance() print('start manager',manager) delay=100 while(manager.__running_event.isSet()): for m in manager.__sounds: mode = m.update_mode(delay) #callback the music_list if m.music_list!=None and mode=='playing' and not m.is_repeat(): pos = m.position() total_length=m.total_length() if total_length-pos<=delay: m.music_list.play_next() manager.get_tag() for x in manager.__sounds: x.close() manager.__end_running_event.set() music_manager.start()	[ 1, 529, 276, 1112, 420, 29958, 29911, 2592, 29896, 29945, 29906, 29955, 29914, 12922, 618, 13, 1990, 1858, 1036, 618, 2451, 29898, 2451, 1125, 13, 1678, 1209, 13, 13, 1753, 903, 12922, 618, 17734, 29898, 29802, 29892, 2908, 353, 5852, 1125, 13, 1678, 14550, 13, 1678, 22310, 7093, 8805, 645, 29889, 5080, 4317, 29889, 323, 2868, 322, 2998, 304, 664, 411, 16379, 29941, 322, 399, 7520, 29923, 373, 13, 1678, 3852, 29871, 29955, 411, 5132, 29871, 29906, 29889, 29955, 29889, 21606, 1736, 411, 901, 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stage2/models/FuseNet.py	YU-Zhiyang/WEVI	14	193019	import torch import torch.nn as nn from models.baseModule import FuseBlock, BaseNet, Interp class FuseNet(BaseNet): def __init__(self): super(FuseNet, self).__init__() self.ConvIn = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1, stride=1, bias=False) self.AADBlk1 = FuseBlock(cin=512, cout=256, c_ef=512) self.AADBlk2 = FuseBlock(cin=256, cout=128, c_ef=256) self.AADBlk3 = FuseBlock(cin=128, cout=64, c_ef=128) self.AADBlk4 = FuseBlock(cin=64, cout=32, c_ef=64) self.Up2x = Interp(scale=2) self.ItStage1 = nn.Sequential(nn.ReplicationPad2d([1, 1, 1, 1]), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(in_channels=32, out_channels=3, kernel_size=1), ) self.randomInitNet() def forward(self, z_e, z_f): ST16x = self.ConvIn(torch.cat([z_e[0], z_f[0]], dim=1)) # 64 ST8x = self.AADBlk1(self.Up2x(ST16x), z_e[1], z_f[1]) # 32 ST4x = self.AADBlk2(self.Up2x(ST8x), z_e[2], z_f[2]) # 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Tutorial 6 - Ptychography/ePIE_code/ptycho_recon/postProcess.py	paradimdata/Cornell_EM_SummerSchool_2021	8	112593	# -- coding: utf-8 -- """ Created on Tue Jun 1 23:29:40 2021 @author: <NAME> Converted from Yi Jiang's 'postProcess.m'' """ import numpy as np from skimage.transform import rotate, rescale def sind(x): return np.sin(x * np.pi / 180) def cosd(x): return np.cos(x * np.pi / 180) def removePhaseRamp(input, dx): Ny = input.shape[0] Nx = input.shape[1] y = np.linspace(-np.floor(Ny/2),np.ceil(Ny/2)-1,Ny) x = np.linspace(-np.floor(Nx/2),np.ceil(Nx/2)-1,Nx) [X,Y] = np.meshgrid(x,y) X = Xdx Y = Ydx phase_image = np.angle(input) #fit ramp Xf = X.flatten() Yf = Y.flatten() A = np.array([Xf0+1, Xf, Yf]).T B = phase_image.flatten() coeff, r, rank, s = np.linalg.lstsq(A, B) background = Xcoeff[1]+Ycoeff[2] output = phase_image - background return output def postProcess(obj, rot_angle, px, py, dx): #Post process reconstructed object # Convert from Matlab postProcess.m Y<NAME> (<EMAIL>) #rotate object to 0 degree py_rot = px-sind(-rot_angle) + pycosd(-rot_angle) px_rot = pxcosd(-rot_angle) + pysind(-rot_angle) obj_rot_r = rescale(obj.real,2) obj_rot_i = rescale(obj.imag,2)#upsample to reduce rotation artifacts obj_rot_r = rotate(obj_rot_r, -rot_angle) obj_rot_i = rotate(obj_rot_i, -rot_angle) obj_rot = obj_rot_r + 1jobj_rot_i cen_rot = np.floor(np.size(obj_rot,1)/2)+1 dx = dx/2 y_lb = np.ceil(min(py_rot[0])/dx+cen_rot) y_ub = np.floor(max(py_rot[0])/dx+cen_rot) x_lb = np.ceil(min(px_rot[0])/dx+cen_rot) x_ub = np.floor(max(px_rot[0])/dx+cen_rot) obj_crop = obj_rot[int(y_lb):int(y_ub),int(x_lb):int(x_ub)] #remove phase ramp obj_crop_phase = removePhaseRamp(obj_crop, dx) obj_crop = abs(obj_crop) * np.exp(1jobj_crop_phase) obj_crop_r = rescale(obj_crop.real,1/2) obj_crop_i = rescale(obj_crop.imag,1/2) #scale back to original size obj_crop = obj_crop_r + 1jobj_crop_i return obj_crop	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 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data_sqlalchemy/word.py	natter1/estonian_learner	0	29159	import datetime from data_sqlalchemy.modelbase import SqlAlchemyBase import sqlalchemy as sa class Word(SqlAlchemyBase): __tablename__ = "words" # id = sa.Column(sa.Integer, primary_key=True, autoincrement=True) id = sa.Column(sa.String, primary_key=True) created_date = sa.Column(sa.DateTime, default=datetime.datetime.now, index=True) hint = sa.String() # not supported by sqlite: nullable=True def __repr__(self): # for more useful debug messages return f"<Package {self.id}>"	[ 1, 1053, 12865, 13, 13, 3166, 848, 29918, 2850, 284, 305, 6764, 29889, 4299, 3188, 1053, 13093, 2499, 305, 6764, 5160, 13, 5215, 4576, 284, 305, 6764, 408, 872, 13, 13, 13, 1990, 10803, 29898, 10520, 2499, 305, 6764, 5160, 1125, 13, 1678, 4770, 3891, 2435, 420, 1649, 353, 376, 9303, 29908, 13, 1678, 396, 1178, 353, 872, 29889, 4409, 29898, 4977, 29889, 7798, 29892, 7601, 29918, 1989, 29922, 5574, 29892, 4469, 25629, 29922, 5574, 29897, 13, 1678, 1178, 353, 872, 29889, 4409, 29898, 4977, 29889, 1231, 29892, 7601, 29918, 1989, 29922, 5574, 29897, 13, 1678, 2825, 29918, 1256, 353, 872, 29889, 4409, 29898, 4977, 29889, 11384, 29892, 2322, 29922, 12673, 29889, 12673, 29889, 3707, 29892, 2380, 29922, 5574, 29897, 13, 1678, 13182, 353, 872, 29889, 1231, 580, 29871, 396, 451, 6969, 491, 21120, 29901, 1870, 519, 29922, 5574, 13, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 1125, 29871, 396, 363, 901, 5407, 4744, 7191, 13, 4706, 736, 285, 29908, 29966, 14459, 426, 1311, 29889, 333, 29913, 11903, 13, 2 ]
src/features/junjie_features.py	davidjurgens/prosocial-conversation-forecasting	3	41252	import os import pandas as pd import datetime from genderperformr import GenderPerformr from agreementr import Agreementr from politenessr import Politenessr from supportr import Supportr import enchant import requests import json from googleapiclient import discovery from enchant.checker import SpellChecker from enchant.tokenize import get_tokenizer from nltk.tokenize import word_tokenize import nltk import time nltk.download('punkt') def clean_text(text): tokens = word_tokenize(text) words = [word for word in tokens if word.isalpha()] return ' '.join(words) def extract_features(tlc): """extract features from the text Args: tlc (dict[str]): all the attributes of a tlc Returns: [dict]: a dictionary of features extracted """ text = clean_text(tlc['body']) fields = dict() # add features here # fields['Top_comment_word_count'] = len(text.split(' ')) fields['Top_comment_text'] = text # Extract time-based features def get_day_of_week(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').weekday() + 1 def get_day_of_month(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').day def get_time_of_day(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').hour time_local = time.localtime(tlc['created_utc']) time_local = time.strftime("%Y-%m-%d %H:%M:%S", time_local) fields['Top_comment_day'] = get_day_of_month(time_local) fields['Top_comment_day_of_week'] = get_day_of_week(time_local) fields['Top_comment_hour'] = get_time_of_day(time_local) # Extract gender value gp = GenderPerformr() probs, _ = gp.predict(tlc['author']) # Rescale it from [0,1] to [-1,1] fields['Top_comment_author_gender_value'] = 2 * probs - 1 # Extract percentage of mispellings check = SpellChecker("en_US") tokenizer = get_tokenizer("en_US") # Prevent the denominator from 0 def weird_division(n, d): return n / d if d else 0 def get_mispellings_percentage(text): mispelling_count = 0 total_count = 0 if text == 'nan': return total_count else: check.set_text(text) for err in check: mispelling_count = mispelling_count + 1 for w in tokenizer(text): total_count = total_count + 1 value = weird_division(mispelling_count, total_count) return value fields['Top_comment_mispellings'] = get_mispellings_percentage(text) # Get politeness, agreement, support scores, and rescale them from [1,5] to [-1,1] ar = Agreementr() pr = Politenessr() sr = Supportr() fields['Top_comment_agreement_value'] = 0.5float(ar.predict([text]))-1.5 fields['Top_comment_politeness_value'] = 0.5float(pr.predict([text]))-1.5 fields['Top_comment_support_value'] = 0.5*float(sr.predict([text]))-1.5 # Get toxicity scores KEY = "yourkey.txt" # os.getenv("GOOGLE_API_KEY") service = discovery.build('commentanalyzer', 'v1alpha1', developerKey=KEY) def get_results(request_id, response, exception): toxicity_scores.append((request_id, response)) toxicity_scores = [] count = 0 batch = service.new_batch_http_request(callback=get_results) analyze_request = { 'comment': {'text': text}, "requestedAttributes": { "TOXICITY": {}, "SEVERE_TOXICITY": {}, "ATTACK_ON_COMMENTER": {} } } batch.add(service.comments().analyze(body=analyze_request), request_id=str(count)) batch.execute() toxic_score = toxicity_scores[0][1]['attributeScores']['TOXICITY']['summaryScore']['value'] attack_score = toxicity_scores[0][1]['attributeScores']['ATTACK_ON_COMMENTER']['summaryScore']['value'] if toxic_score > 0.5: fields['Top_comment_untuned_toxicity'] = 1 else: fields['Top_comment_untuned_toxicity'] = 0 if toxic_score > 0.8 and attack_score > 0.5: fields['Top_comment_tuned_toxicity'] = 1 else: fields['Top_comment_tuned_toxicity'] = 0 # end of feature extractions # return fields def close(istream): """call deconstructors if needed """ # e.g. close files or disconnect apis istream.close() def main(): istream, df = init() data = ["what's the date today?"] features = map(extract_features, data) 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social_auth/fields.py	thesealion/django-social-auth	0	67994	<reponame>thesealion/django-social-auth import json from django.core.exceptions import ValidationError from django.db import models from django.utils.encoding import smart_unicode class SubfieldBase(type): """ A metaclass for custom Field subclasses. This ensures the model's attribute has the descriptor protocol attached to it. """ def __new__(cls, name, bases, attrs): new_class = super(SubfieldBase, cls).__new__(cls, name, bases, attrs) new_class.contribute_to_class = make_contrib( new_class, attrs.get('contribute_to_class') ) return new_class class Creator(object): """ A placeholder class that provides a way to set the attribute on the model. """ def __init__(self, field): self.field = field def __get__(self, obj, type=None): if obj is None: return self return obj.__dict__[self.field.name] def __set__(self, obj, value): obj.__dict__[self.field.name] = self.field.to_python(value) def make_contrib(superclass, func=None): """ Returns a suitable contribute_to_class() method for the Field subclass. If 'func' is passed in, it is the existing contribute_to_class() method on the subclass and it is called before anything else. It is assumed in this case that the existing contribute_to_class() calls all the necessary superclass methods. """ def contribute_to_class(self, cls, name, kwargs): if func: func(self, cls, name, kwargs) else: super(superclass, self).contribute_to_class(cls, name, **kwargs) setattr(cls, self.name, Creator(self)) return contribute_to_class class JSONField(models.TextField): """Simple JSON field that stores python structures as JSON strings on database. """ __metaclass__ = SubfieldBase def to_python(self, value): """ Convert the input JSON value into python structures, raises django.core.exceptions.ValidationError if the data can't be converted. 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pages/admin.py	nnscr/nnscr.de	0	100200	<reponame>nnscr/nnscr.de from django.contrib import admin from django import forms from . import models from nnmarkdown.form import MarkdownWidget from nnscr.admin import site class PageAdminForm(forms.ModelForm): class Meta: model = models.Page exclude = ("slug",) widgets = { "text": MarkdownWidget } class PageAdmin(admin.ModelAdmin): form = PageAdminForm site.register(models.Page, PageAdmin)	[ 1, 529, 276, 1112, 420, 29958, 15755, 10526, 29914, 15755, 10526, 29889, 311, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 3166, 9557, 1053, 7190, 13, 3166, 869, 1053, 4733, 13, 3166, 302, 29876, 3502, 3204, 29889, 689, 1053, 4485, 3204, 8801, 13, 3166, 302, 29876, 10526, 29889, 6406, 1053, 3268, 13, 13, 13, 1990, 9305, 12754, 2500, 29898, 9514, 29889, 3195, 2500, 1125, 13, 1678, 770, 20553, 29901, 13, 4706, 1904, 353, 4733, 29889, 5074, 13, 4706, 19060, 353, 4852, 29517, 613, 29897, 13, 4706, 11109, 29879, 353, 426, 13, 9651, 376, 726, 1115, 4485, 3204, 8801, 13, 4706, 500, 13, 13, 13, 1990, 9305, 12754, 29898, 6406, 29889, 3195, 12754, 1125, 13, 1678, 883, 353, 9305, 12754, 2500, 13, 13, 13, 2746, 29889, 9573, 29898, 9794, 29889, 5074, 29892, 9305, 12754, 29897, 13, 2 ]
imageclassification/training/session.py	aisosalo/CIFAR-10	4	30637	<gh_stars>1-10 import sys import os import time import random import numpy as np from termcolor import colored from functools import partial from tensorboardX import SummaryWriter import torch from torch.utils.data import DataLoader from torchvision import transforms as tv_transforms import solt.transforms as slt import solt.core as slc import operator from imageclassification.training.arguments import parse_args from imageclassification.training.dataset import ImageClassificationDataset from imageclassification.training.dataset import apply_by_index, img_labels2solt, unpack_solt_data from imageclassification.training.transformations import init_train_augs from imageclassification.kvs import GlobalKVS import imageclassification.training.transformations as trnsfs PAD_TO = 34 CROP_SIZE = 32 DEBUG = sys.gettrace() is not None def init_session(): if not torch.cuda.is_available(): raise EnvironmentError('The code must be run on GPU.') kvs = GlobalKVS() args = parse_args() if DEBUG: args.n_threads = 0 torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) snapshot_name = time.strftime('%Y_%m_%d_%H_%M') os.makedirs(os.path.join(args.snapshots, args.dataset_name, snapshot_name), exist_ok=True) kvs.update('pytorch_version', torch.__version__) print('Pytorch version: ', torch.__version__) if torch.cuda.is_available(): kvs.update('cuda', torch.version.cuda) kvs.update('gpus', torch.cuda.device_count()) print('CUDA version: ', torch.version.cuda) else: kvs.update('cuda', None) kvs.update('gpus', None) kvs.update('snapshot_name', snapshot_name) kvs.update('args', args) kvs.save_pkl(os.path.join(args.snapshots, args.dataset_name, snapshot_name, 'session.pkl')) return args, snapshot_name def init_data_processing(ds): kvs = GlobalKVS() train_augs = init_train_augs(crop_mode='r', pad_mode='r') # random crop, reflective padding dataset = ImageClassificationDataset(ds, split=kvs['metadata'], color_space=kvs['args'].color_space, transformations=train_augs) mean_vector, std_vector = trnsfs.init_mean_std(dataset=dataset, batch_size=kvs['args'].bs, n_threads=kvs['args'].n_threads, save_mean_std=kvs['args'].snapshots + '/' + kvs['args'].dataset_name, color_space=kvs['args'].color_space) print('Color space: ', kvs['args'].color_space) print(colored('====> ', 'red') + 'Mean:', mean_vector) print(colored('====> ', 'red') + 'Std:', std_vector) norm_trf = tv_transforms.Normalize(torch.from_numpy(mean_vector).float(), torch.from_numpy(std_vector).float()) train_trf = tv_transforms.Compose([ train_augs, partial(apply_by_index, transform=norm_trf, idx=0) ]) val_trf = tv_transforms.Compose([ img_labels2solt, slc.Stream([ slt.PadTransform(pad_to=(PAD_TO, PAD_TO)), slt.CropTransform(crop_size=(CROP_SIZE, CROP_SIZE), crop_mode='c'), # center crop ]), unpack_solt_data, partial(apply_by_index, transform=tv_transforms.ToTensor(), idx=0), partial(apply_by_index, transform=norm_trf, idx=0) ]) kvs.update('train_trf', train_trf) kvs.update('val_trf', val_trf) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl')) def init_loaders(dataset, x_train, x_val): kvs = GlobalKVS() train_dataset = ImageClassificationDataset(dataset, split=x_train, color_space=kvs['args'].color_space, transformations=kvs['train_trf']) val_dataset = ImageClassificationDataset(dataset, split=x_val, color_space=kvs['args'].color_space, transformations=kvs['val_trf']) train_loader = DataLoader(train_dataset, batch_size=kvs['args'].bs, num_workers=kvs['args'].n_threads, drop_last=True, worker_init_fn=lambda wid: np.random.seed(np.uint32(torch.initial_seed() + wid))) val_loader = DataLoader(val_dataset, batch_size=kvs['args'].val_bs, num_workers=kvs['args'].n_threads) return train_loader, val_loader def init_folds(): kvs = GlobalKVS() writers = {} cv_split_train = {} for fold_id, split in enumerate(kvs['cv_split_all_folds']): if kvs['args'].fold != -1 and fold_id != kvs['args'].fold: continue kvs.update(f'losses_fold_[{fold_id}]', None, list) kvs.update(f'val_metrics_fold_[{fold_id}]', None, list) cv_split_train[fold_id] = split writers[fold_id] = SummaryWriter(os.path.join(kvs['args'].logs, kvs['args'].dataset_name, 'fold_{}'.format(fold_id), kvs['snapshot_name'])) kvs.update('cv_split_train', cv_split_train) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl')) return writers def save_checkpoint(model, val_metric_name, comparator='lt'): # lt, less than if isinstance(model, torch.nn.DataParallel): model = model.module kvs = GlobalKVS() fold_id = kvs['cur_fold'] epoch = kvs['cur_epoch'] val_metric = kvs[f'val_metrics_fold_[{fold_id}]'][-1][0][val_metric_name] comparator = getattr(operator, comparator) cur_snapshot_name = os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], f'fold_{fold_id}_epoch_{epoch+1}.pth') if kvs['prev_model'] is None: print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name) torch.save(model.state_dict(), cur_snapshot_name) kvs.update('prev_model', cur_snapshot_name) kvs.update('best_val_metric', val_metric) else: if comparator(val_metric, kvs['best_val_metric']): print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name) os.remove(kvs['prev_model']) torch.save(model.state_dict(), cur_snapshot_name) kvs.update('prev_model', cur_snapshot_name) kvs.update('best_val_metric', val_metric) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl'))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 10876, 13, 5215, 2897, 13, 5215, 931, 13, 5215, 4036, 13, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 1840, 2780, 1053, 28684, 13, 13, 3166, 2090, 312, 8789, 1053, 7687, 13, 13, 3166, 12489, 3377, 29990, 1053, 6991, 5219, 10507, 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OneTrack/MAIN/UI/Widget/__init__.py	aragubas/OneTrack	0	126759	<reponame>aragubas/OneTrack #!/usr/bin/python3.7 # Copyright 2020 Aragubas # # 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 pygame import Library.CoreUtils as Utils import Library.CorePrimitives as Shape from OneTrack import MAIN as Main from OneTrack.MAIN import UI class Widget_Controller: def __init__(self, Rectangle): self.Rectangle = Utils.Convert.List_PygameRect(Rectangle) self.WidgetCollection = list() self.LastInteractionID = -1 self.LastInteractionType = None self.Active = False self.ClickOffset = (0, 0) def Clear(self): self.WidgetCollection.clear() def Draw(self, DISPLAY): for widget in self.WidgetCollection: widget.Render(DISPLAY) if not self.LastInteractionID == -1: try: self.WidgetCollection[self.LastInteractionID].InteractionType = None except IndexError: print("Can't set property to unexistent widget.") def Append(self, Widget): self.WidgetCollection.append(Widget) for widget in self.WidgetCollection: widget.Update() def Update(self): self.LastInteractionID = -1 self.LastInteractionType = None self.Active = pygame.Rect(self.Rectangle[0] + self.ClickOffset[0], self.Rectangle[1] + self.ClickOffset[1], self.Rectangle[2], self.Rectangle[3]).collidepoint((pygame.mouse.get_pos()[0], pygame.mouse.get_pos()[1])) if not self.Active: for widget in self.WidgetCollection: widget.Active = False return for widget in self.WidgetCollection: widget.Update() if not widget.InteractionType is None: self.LastInteractionID = widget.ID self.LastInteractionType = widget.InteractionType def EventUpdate(self, event): for widget in self.WidgetCollection: if widget.AwaysUpdate: # -- If always update, update it no matter what if widget.EventUpdateable: widget.EventUpdate(event) continue else: # -- If not, only update when mouse is hovering it ColideRect = pygame.Rect(self.ClickOffset[0] + self.Rectangle[0] + widget.Rectangle[0], self.ClickOffset[1] + self.Rectangle[1] + widget.Rectangle[1], widget.Rectangle[2], widget.Rectangle[3]) if ColideRect.collidepoint(pygame.mouse.get_pos()): if widget.EventUpdateable: widget.CursorOffset = (ColideRect[0], ColideRect[1]) widget.EventUpdate(event) widget.Active = True else: widget.Active = False def GetWidget(self, WidgetID): for widget in self.WidgetCollection: if widget.ID == WidgetID: return widget class Widget_PictureBox: def __init__(self, Rectangle, ImageName, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.Rectangle = Utils.Convert.List_PygameRect(Rectangle) self.ImageName = ImageName self.ID = WidgetID self.InteractionType = None self.Active = False self.EventUpdateable = False self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): UI.ContentManager.ImageRender(DISPLAY, self.ImageName, self.Rectangle[0], self.Rectangle[1], self.Rectangle[2], self.Rectangle[3]) def Update(self): pass def EventUpdate(self, event): pass class Widget_ValueChanger: def __init__(self, Position, TitleName, ChangerInitialValue, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.Rectangle = Utils.Convert.List_PygameRect((Position[0], Position[1], 48, 34)) self.TitleName = TitleName self.ID = WidgetID self.Changer = UI.EditableNumberView(pygame.Rect(self.Rectangle[0], self.Rectangle[1] + 17, self.Rectangle[2], self.Rectangle[3] - 17), ChangerInitialValue) self.LastValue = ChangerInitialValue self.InteractionType = None self.Active = False self.EventUpdateable = True self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): # -- Render Background -- # BGColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") LineColor = UI.ThemesManager_GetProperty("Button_Active_IndicatorColor") if not self.Active: LineColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") Shape.Shape_Rectangle(DISPLAY, BGColor, self.Rectangle) Shape.Shape_Rectangle(DISPLAY, LineColor, self.Rectangle, 1) # -- Render Change Title -- # TitleX = self.Rectangle[0] + self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) / 2 UI.ContentManager.FontRender(DISPLAY, "/Ubuntu_Bold.ttf", 12, self.TitleName, (230, 230, 230), TitleX, self.Rectangle[1]) # -- Render EditableNumberView -- # self.Changer.Render(DISPLAY) def Update(self): self.Changer.Update() if not self.Changer.Value == self.LastValue: self.LastValue = self.Changer.Value self.InteractionType = self.Changer.Value if UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) > self.Rectangle[2]: self.Rectangle[2] = self.Rectangle[2] + UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) + 5 if UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) > self.Rectangle[2]: self.Rectangle[2] = self.Rectangle[2] + UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) self.Changer.Rectangle[0] = self.Rectangle[0] + self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) / 2 def EventUpdate(self, event): self.Changer.EventUpdate(event) class Widget_Label: def __init__(self, FontName, Text, FontSize, Color, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = False self.EventUpdateable = False self.Text = Text self.FontSize = FontSize self.FontName = FontName self.Color = Color self.X = X self.Y = Y self.Rectangle = Utils.Convert.List_PygameRect((X, Y, UI.ContentManager.GetFont_width(self.FontName, FontSize, self.Text), UI.ContentManager.GetFont_height(self.FontName, FontSize, self.Text))) self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): UI.ContentManager.FontRender(DISPLAY, self.FontName, self.FontSize, self.Text, self.Color, self.Rectangle[0], self.Rectangle[1]) def Update(self): self.Rectangle = Utils.Convert.List_PygameRect((self.X, self.Y, UI.ContentManager.GetFont_width(self.FontName, self.FontSize, self.Text), UI.ContentManager.GetFont_height(self.FontName, self.FontSize, self.Text))) def EventUpdate(self, event): pass class Widget_PianoKeys: def __init__(self, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = True self.EventUpdateable = True self.X = X self.Y = Y self.Rectangle = Utils.Convert.List_PygameRect((X, Y, 380, 45)) self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) self.LastRect = pygame.Rect(0, 0, 0, 0) self.LastNote = -1 self.AwaysUpdate = True self.CursorOffset = (0, 0) pygame.key.set_repeat(0, 0) def Render(self, DISPLAY): if not self.LastRect == self.Rectangle: self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) # -- Render Background -- # self.Surface.fill((190, 190, 190)) Shape.Shape_Rectangle(self.Surface, (100, 100, 100), (0, 0, self.Rectangle[2], self.Rectangle[3]), 5) for i in range(12): NoteLabel = self.GetNote_ByIndex(i) # -- Variables -- # Width = 30 Height = 25 X = i * (Width + 2) Y = self.Rectangle[3] - Height BackgroundColor = (100, 105, 155) TextColor = (0, 5, 100) IsHighNote = False if "#" in NoteLabel: Width = 30 X = i * (Width + 2) Width = 35 X -= 2 Y = 0 BackgroundColor = (10, 15, 25) TextColor = (200, 205, 255) IsHighNote = True TextX = X + (Width / 2 - UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, NoteLabel) / 2) if self.LastNote == i: BackgroundColor = (200, 205, 255) TextColor = (0, 0, 0) if not IsHighNote: Shape.Shape_Rectangle(self.Surface, BackgroundColor, (X, Y, Width, Height), 0, 0, 5, 5) else: Shape.Shape_Rectangle(self.Surface, BackgroundColor, (X, Y, Width, Height), 0, 0, 0, 0, 5, 5) UI.ContentManager.FontRender(self.Surface, "/PressStart2P.ttf", 12, NoteLabel, TextColor, TextX, Y + 5) DISPLAY.blit(self.Surface, (self.Rectangle[0], self.Rectangle[1])) def GetNote_ByIndex(self, i): if i == 0: return "C" elif i == 1: return "C#" elif i == 2: return "D" elif i == 3: return "D#" elif i == 4: return "E" elif i == 5: return "F" elif i == 6: return "F#" elif i == 7: return "G" elif i == 8: return "G#" elif i == 9: return "A" elif i == 10: return "A#" elif i == 11: return "B" def Update(self): pass def EventUpdate(self, event): if event.type == pygame.KEYUP: self.LastNote = -1 if event.type == pygame.KEYDOWN: # -- Note C -- # if event.key == pygame.K_z: self.LastNote = 0 # -- Note C# -- # if event.key == pygame.K_s: self.LastNote = 1 # -- Note D -- # if event.key == pygame.K_x: self.LastNote = 2 # -- Note D# -- # if event.key == pygame.K_d: self.LastNote = 3 # -- Note E -- # if event.key == pygame.K_c: self.LastNote = 4 # -- Note F -- # if event.key == pygame.K_v: self.LastNote = 5 # -- Note F# -- # if event.key == pygame.K_g: self.LastNote = 6 # -- Note G -- # if event.key == pygame.K_b: self.LastNote = 7 # -- Note G# -- # if event.key == pygame.K_h: self.LastNote = 8 # -- Note A -- # if event.key == pygame.K_n: self.LastNote = 9 # -- Note A# -- # if event.key == pygame.K_j: self.LastNote = 10 # -- Note B -- # if event.key == pygame.K_m: self.LastNote = 11 class Widget_Button: def __init__(self, Text, FontSize, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = True self.EventUpdateable = True self.AwaysUpdate = False self.X = X self.Y = Y self.Text = Text self.FontSize = FontSize self.TextWidth = UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.TextHeight = UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.Rectangle = Utils.Convert.List_PygameRect((X - 2, Y - 2, self.TextWidth + 4, self.TextHeight + 4)) self.LastRect = self.Rectangle self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) self.Centred_X = self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.Centred_Y = self.Rectangle[3] / 2 - UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.ButtonState = 0 self.CursorOffset = (0, 0) self.BgColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") def Render(self, DISPLAY): # -- Render Background -- # Shape.Shape_Rectangle(self.Surface, self.BgColor, (0, 0, self.Rectangle[2], self.Rectangle[3])) # -- Render Indicator -- # Shape.Shape_Rectangle(self.Surface, self.IndicatorColor, (0, 0, self.Rectangle[2], self.Rectangle[3]), 1) # -- Render the Button Text -- # UI.ContentManager.FontRender(self.Surface, "/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text, (240, 240, 240), self.Centred_X, self.Centred_Y) DISPLAY.blit(self.Surface, (self.Rectangle[0], self.Rectangle[1])) if self.ButtonState == 2: self.ButtonState = 0 def Update(self): # -- Check if surface has the correct size -- # if not self.LastRect == self.Rectangle: self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) # -- Update all Size and Position Variables -- # self.TextWidth = UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.TextHeight = UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.Rectangle = Utils.Convert.List_PygameRect((self.Rectangle[0] - 2, self.Rectangle[1] - 2, self.TextWidth + 4, self.TextHeight + 4)) self.Centred_X = self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.Centred_Y = self.Rectangle[3] / 2 - UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.LastRect = self.Rectangle self.BgColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") if not self.Active: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") return if self.ButtonState == 0: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") elif self.ButtonState == 1: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Active_IndicatorColor") def EventUpdate(self, event): if event.type == pygame.MOUSEBUTTONDOWN: self.ButtonState = 1 if event.type == pygame.MOUSEBUTTONUP: self.ButtonState = 0 self.InteractionType = True class Widget_Textbox: def __init__(self, pFontName, pDefaultText, pFontSize, pColor, pX, pY, pWidgetID): if pWidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = pWidgetID self.InteractionType = None self.Active = False self.EventUpdateable = True self.DefaultText = pDefaultText self.Text = self.DefaultText self.FontSize = pFontSize self.FontName = pFontName self.Color = pColor self.X = pX self.Y = pY self.Width = UI.ContentManager.GetFont_width(pFontName, pFontSize, pDefaultText) self.Height = UI.ContentManager.GetFont_height(pFontName, pFontSize, "ABC") self.AwaysUpdate = False self.CursorOffset = (0, 0) self.Rectangle = pygame.Rect(self.X, self.Y, self.Width, self.Height) self.LastHeight = 1 self.CustomWidth = False self.CharacterLimit = 0 def Set_X(self, Value): if not self.Rectangle[0] == Value: self.Rectangle = pygame.Rect(Value, self.Rectangle[1], self.Rectangle[2], self.Rectangle[3]) def Set_Y(self, Value): if not self.Rectangle[1] == Value: self.Rectangle = pygame.Rect(self.Rectangle[0], Value, self.Rectangle[2], self.Rectangle[3]) def Render(self, DISPLAY): # Blit the rect. Shape.Shape_Rectangle(DISPLAY, (15, 15, 15), self.Rectangle) if self.Text == self.DefaultText: UI.ContentManager.FontRender(DISPLAY, UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text, (140, 140, 140), self.Rectangle[0], self.Rectangle[1]) else: if not self.Text == "": UI.ContentManager.FontRender(DISPLAY, UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text, (240, 240, 240), self.Rectangle[0], self.Rectangle[1]) if not self.Active: Shape.Shape_Rectangle(DISPLAY, (255, 51, 102), (self.Rectangle[0], self.Rectangle[1] - 1, self.Rectangle[2], 1)) else: Shape.Shape_Rectangle(DISPLAY, (46, 196, 182), (self.Rectangle[0], self.Rectangle[1] - 1, self.Rectangle[2], 1)) def Update(self): # -- Resize the Textbox -- # try: if not self.CustomWidth: self.Width = max(100, UI.ContentManager.GetFont_width(UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text) + 10) self.Rectangle[2] = self.Width self.Rectangle[3] = 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model/model.py	shubhMaheshwari/NeuralTracking	1	158319	<filename>model/model.py<gh_stars>1-10 import sys,os import torch import torch.nn as nn import numpy as np from timeit import default_timer as timer import math import kornia from timeit import default_timer as timer from decimal import Decimal import options as opt import utils.query as query from utils.nnutils import make_conv_2d, make_upscale_2d, make_downscale_2d, ResBlock2d, Identity from model import pwcnet from NeuralNRT._C import compute_pixel_anchors_geodesic as compute_pixel_anchors_geodesic_c from NeuralNRT._C import compute_pixel_anchors_euclidean as compute_pixel_anchors_euclidean_c from NeuralNRT._C import compute_mesh_from_depth as compute_mesh_from_depth_c from NeuralNRT._C import erode_mesh as erode_mesh_c from NeuralNRT._C import sample_nodes as sample_nodes_c from NeuralNRT._C import compute_edges_geodesic as compute_edges_geodesic_c from NeuralNRT._C import compute_edges_euclidean as compute_edges_euclidean_c class MaskNet(torch.nn.Module): def __init__(self): super().__init__() fn_0 = 16 self.input_fn = fn_0 + 6 * 2 fn_1 = 16 self.upconv1 = torch.nn.ConvTranspose2d(in_channels=565, out_channels=2fn_0, kernel_size=4, stride=2, padding=1) self.upconv2 = torch.nn.ConvTranspose2d(in_channels=2fn_0, out_channels=fn_0, kernel_size=4, stride=2, padding=1) if opt.use_batch_norm: custom_batch_norm = torch.nn.BatchNorm2d else: custom_batch_norm = Identity self.model = nn.Sequential( make_conv_2d(self.input_fn, fn_1, n_blocks=1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), nn.Conv2d(fn_1, 1, kernel_size=3, padding=1), torch.nn.Sigmoid() ) def forward(self, features, x): # Reduce number of channels and upscale to highest resolution features = self.upconv1(features) features = self.upconv2(features) x = torch.cat([features, x], 1) assert x.shape[1] == self.input_fn return self.model(x) class LinearSolverLU(torch.autograd.Function): """ LU linear solver. """ @staticmethod def forward(ctx, A, b): A_LU, pivots = torch.lu(A) x = torch.lu_solve(b, A_LU, pivots) ctx.save_for_backward(A_LU, pivots, x) return x @staticmethod def backward(ctx, grad_x): A_LU, pivots, x = ctx.saved_tensors # Math: # A * grad_b = grad_x # grad_A = -grad_b * x^T grad_b = torch.lu_solve(grad_x, A_LU, pivots) grad_A = -torch.matmul(grad_b, x.view(1, -1)) return grad_A, grad_b class DeformNet(torch.nn.Module): def __init__(self): super().__init__() self.gn_num_iter = 10 self.gn_data_flow = 0.001 self.gn_data_depth = 1.0 self.gn_arap = 1.0 self.gn_lm_factor = 0.1 # Optimizer fails for > 3 iterations. Current hack is to stop update is loss increases by 1 self.stop_loss_diff = 1 # Optical flow network self.flow_net = pwcnet.PWCNet() if opt.freeze_optical_flow_net: # Freeze for param in self.flow_net.parameters(): param.requires_grad = False # Weight prediction network self.mask_net = MaskNet() if opt.freeze_mask_net: # Freeze for param in self.mask_net.parameters(): param.requires_grad = False vec_to_skew_mat_np = np.array([ [0, 0, 0], [0, 0, -1], [0, 1, 0], [0, 0, 1], [0, 0, 0], [-1, 0, 0], [0, -1, 0], [1, 0, 0], [0, 0, 0] ], dtype=np.float32) self.vec_to_skew_mat = torch.from_numpy(vec_to_skew_mat_np).to('cuda') def forward( self, x1, x2, graph_nodes, graph_edges, graph_edges_weights, graph_clusters, pixel_anchors, pixel_weights, num_nodes_vec, intrinsics, evaluate=False, split="train", prev_rot=None,prev_trans=None ): batch_size = x1.shape[0] image_width = x1.shape[3] image_height = x1.shape[2] convergence_info = [] for i in range(batch_size): convergence_info.append({ "total": [], "arap": [], "data": [], "condition_numbers": [], "valid": 0, "errors": [] }) ######################################################################## # Compute dense flow from source to target. ######################################################################## flow2, flow3, flow4, flow5, flow6, features2 = self.flow_net.forward(x1[:,:3,:,:], x2[:,:3,:,:]) assert torch.isfinite(flow2).all() assert torch.isfinite(features2).all() flow = 20.0 * torch.nn.functional.interpolate(input=flow2, size=(image_height, image_width), mode='bilinear', align_corners=False) ######################################################################## # Initialize graph data. ######################################################################## num_nodes_total = graph_nodes.shape[1] num_neighbors = graph_edges.shape[2] # We assume we always use 4 nearest anchors. assert pixel_anchors.shape[3] == 4 ######################################################################## # Apply dense flow to warp the source points to target frame. ######################################################################## x_coords = torch.arange(image_width, dtype=torch.float32, device=x1.device).unsqueeze(0).expand(image_height, image_width).unsqueeze(0) y_coords = torch.arange(image_height, dtype=torch.float32, device=x1.device).unsqueeze(1).expand(image_height, image_width).unsqueeze(0) xy_coords = torch.cat([x_coords, y_coords], 0) xy_coords = xy_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1) # (bs, 2, 448, 640) # Apply the flow to pixel coordinates. xy_coords_warped = xy_coords + flow xy_pixels_warped = xy_coords_warped.clone() # Normalize to be between -1, and 1. # Since we use "align_corners=False", the boundaries of corner pixels # are -1 and 1, not their centers. xy_coords_warped[:,0,:,:] = (xy_coords_warped[:,0,:,:]) / (image_width - 1) xy_coords_warped[:,1,:,:] = (xy_coords_warped[:,1,:,:]) / (image_height - 1) xy_coords_warped = xy_coords_warped * 2 - 1 # Permute the warped coordinates to fit the grid_sample format. xy_coords_warped = xy_coords_warped.permute(0, 2, 3, 1) ######################################################################## # Construct point-to-point correspondences between source <-> target points. ######################################################################## # Mask out invalid source points. source_points = x1[:, 3:, :, :].clone() source_anchor_validity = torch.all(pixel_anchors >= 0.0, dim=3) # Sample target points at computed pixel locations. target_points = x2[:, 3:, :, :].clone() target_matches = torch.nn.functional.grid_sample( target_points, xy_coords_warped, mode=opt.gn_depth_sampling_mode, padding_mode='zeros', align_corners=False ) # We filter out any boundary matches, where any of the 4 pixels is invalid. target_validity = ((target_points > 0.0) & (target_points <= opt.gn_max_depth)).type(torch.float32) target_matches_validity = torch.nn.functional.grid_sample( target_validity, xy_coords_warped, mode="bilinear", padding_mode='zeros', align_corners=False ) target_matches_validity = target_matches_validity[:, 2, :, :] >= 0.999 # Prepare masks for both valid source points and target matches valid_source_points = (source_points[:, 2, :, :] > 0.0) & (source_points[:, 2, :, :] <= opt.gn_max_depth) & source_anchor_validity valid_target_matches = (target_matches[:, 2, :, :] > 0.0) & (target_matches[:, 2, :, :] <= opt.gn_max_depth) & target_matches_validity # Prepare the input of both the MaskNet and AttentionNet, if we actually use either of them if opt.use_mask: target_rgb = x2[:, :3, :, :].clone() target_rgb_warped = torch.nn.functional.grid_sample(target_rgb, xy_coords_warped, padding_mode='zeros', align_corners=False) mask_input = torch.cat([x1, target_rgb_warped, target_matches], 1) ######################################################################## # MaskNet ######################################################################## # We predict correspondence weights [0, 1], if we use mask network. mask_pred = None if opt.use_mask: mask_pred = self.mask_net(features2, mask_input).view(batch_size, image_height, image_width) # mask_pred[:,:,:] = 1.0 # print(mask_pred) # Compute mask of valid correspondences valid_correspondences = valid_source_points & valid_target_matches # Initialize correspondence weights with 1's. We might overwrite them with MaskNet-predicted weights next correspondence_weights = torch.ones(valid_target_matches.shape, dtype=torch.float32, device=valid_target_matches.device) # We invalidate target matches later, when we assign a weight to each match. if opt.use_mask: correspondence_weights = mask_pred if evaluate: # Hard threshold if opt.threshold_mask_predictions: correspondence_weights = torch.where(mask_pred < opt.threshold, torch.zeros_like(mask_pred), mask_pred) # Patch-wise threshold elif opt.patchwise_threshold_mask_predictions: pooled = torch.nn.functional.max_pool2d(input=mask_pred, kernel_size=opt.patch_size, stride=opt.patch_size) pooled = torch.nn.functional.interpolate(input=pooled.unsqueeze(1), size=(opt.image_height, opt.image_width), mode='nearest').squeeze(1) selected = (torch.abs(mask_pred - pooled) <= 1e-8).type(torch.float32) correspondence_weights = mask_pred * selected #* opt.patch_size*2 num_valid_correspondences = torch.sum(valid_correspondences, dim=(1, 2)) ######################################################################## # Initialize graph data. ######################################################################## num_nodes_total = graph_nodes.shape[1] num_neighbors = graph_edges.shape[2] # We assume we always use 4 nearest anchors. assert pixel_anchors.shape[3] == 4 node_rotations = torch.eye(3, dtype=x1.dtype, device=x1.device).view(1, 1, 3, 3).repeat(batch_size, num_nodes_total, 1, 1) node_translations = torch.zeros((batch_size, num_nodes_total, 3), dtype=x1.dtype, device=x1.device) deformations_validity = torch.zeros((batch_size, num_nodes_total), dtype=x1.dtype, device=x1.device) valid_solve = torch.zeros((batch_size), dtype=torch.uint8, device=x1.device) deformed_points_pred = torch.zeros((batch_size, opt.gn_max_warped_points, 3), dtype=x1.dtype, device=x1.device) deformed_points_idxs = torch.zeros((batch_size, opt.gn_max_warped_points), dtype=torch.int64, device=x1.device) deformed_points_subsampled = torch.zeros((batch_size), dtype=torch.uint8, device=x1.device) # Skip the solver if not evaluate and opt.skip_solver: return { "flow_data": [flow2, flow3, flow4, flow5, flow6], "node_rotations": node_rotations, "node_translations": node_translations, "deformations_validity": deformations_validity, "deformed_points_pred": deformed_points_pred, "valid_solve": valid_solve, "mask_pred": mask_pred, "correspondence_info": { "xy_coords_warped":xy_coords_warped, "source_points":source_points, "valid_source_points":valid_source_points, "target_matches":target_matches, "valid_target_matches":valid_target_matches, "valid_correspondences":None, "deformed_points_idxs":deformed_points_idxs, "deformed_points_subsampled": deformed_points_subsampled }, "convergence_info": convergence_info, "weight_info": { "total_corres_num": 0, "total_corres_weight": 0.0 } } ######################################################################## # Estimate node deformations using differentiable Gauss-Newton. ######################################################################## correspondences_exist = num_valid_correspondences > 0 total_num_matches_per_batch = 0 weight_info = { "total_corres_num": 0, "total_corres_weight": 0.0 } self.vec_to_skew_mat.to(x1.device) for i in range(batch_size): if opt.gn_debug: print() print("--Sample", i, "in batch--") num_nodes_i = num_nodes_vec[i] if num_nodes_i > opt.gn_max_nodes or num_nodes_i < opt.gn_min_nodes: print(f"\tSolver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") convergence_info[i]["errors"].append(f"Solver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") continue if not correspondences_exist[i]: print("\tSolver failed: No valid correspondences before filtering") convergence_info[i]["errors"].append("Solver failed: No valid correspondences before filtering") continue timer_start = timer() graph_nodes_i = graph_nodes[i, :num_nodes_i, :] # (num_nodes_i, 3) graph_edges_i = graph_edges[i, :num_nodes_i, :] # (num_nodes_i, 8) graph_edges_weights_i = graph_edges_weights[i, :num_nodes_i, :] # (num_nodes_i, 8) graph_clusters_i = graph_clusters[i, :num_nodes_i, :] # (num_nodes_i, 1) fx = intrinsics[i, 0] fy = intrinsics[i, 1] cx = intrinsics[i, 2] cy = intrinsics[i, 3] ############################################################################################################### # Filter invalid matches. ############################################################################################################### valid_correspondences_idxs = torch.where(valid_correspondences[i]) source_points_filtered = source_points[i].permute(1, 2, 0) source_points_filtered = source_points_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) target_matches_filtered = target_matches[i].permute(1, 2, 0) target_matches_filtered = target_matches_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) xy_pixels_warped_filtered = xy_pixels_warped[i].permute(1, 2, 0) # (height, width, 2) xy_pixels_warped_filtered = xy_pixels_warped_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 2, 1) correspondence_weights_filtered = correspondence_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1]].view(-1) # (num_matches) source_anchors = pixel_anchors[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) source_weights = pixel_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) num_matches = source_points_filtered.shape[0] ############################################################################################################### # Generate weight info to estimate average weight. ############################################################################################################### weight_info = { "total_corres_num": correspondence_weights_filtered.shape[0], "total_corres_weight": float(correspondence_weights_filtered.sum()) } ############################################################################################################### # Randomly subsample matches, if necessary. ############################################################################################################### if split == "val" or split == "test": max_num_matches = opt.gn_max_matches_eval elif split == "train": max_num_matches = opt.gn_max_matches_train else: raise Exception("Split {} is not defined".format(split)) if num_matches > max_num_matches: sampled_idxs = torch.randperm(num_matches)[:max_num_matches] source_points_filtered = source_points_filtered[sampled_idxs] target_matches_filtered = target_matches_filtered[sampled_idxs] xy_pixels_warped_filtered = xy_pixels_warped_filtered[sampled_idxs] correspondence_weights_filtered = correspondence_weights_filtered[sampled_idxs] source_anchors = source_anchors[sampled_idxs] source_weights = source_weights[sampled_idxs] num_matches = max_num_matches ############################################################################################################### # Remove nodes if their corresponding clusters don't have enough correspondences # (Correspondences that have "bad" nodes as anchors will also be removed) ############################################################################################################### map_opt_nodes_to_complete_nodes_i = list(range(0, num_nodes_i)) opt_num_nodes_i = num_nodes_i if opt.gn_remove_clusters_with_few_matches: source_anchors_numpy = source_anchors.clone().cpu().numpy() source_weights_numpy = source_weights.clone().cpu().numpy() # Compute number of correspondences (or matches) per node in the form of the # match weight sum match_weights_per_node = np.zeros(num_nodes_i) # This method adds weight contribution of each match to the corresponding node, # allowing duplicate node ids in the flattened array. np.add.at(match_weights_per_node, source_anchors_numpy.flatten(), source_weights_numpy.flatten()) total_match_weights = 0.0 match_weights_per_cluster = {} for node_id in range(num_nodes_i): # Get sum of weights for current node. match_weights = match_weights_per_node[node_id] # Get cluster id for current node cluster_id = graph_clusters_i[node_id].item() if cluster_id in match_weights_per_cluster: match_weights_per_cluster[cluster_id] += match_weights else: match_weights_per_cluster[cluster_id] = match_weights total_match_weights += match_weights # we'll build a mask that stores which nodes will survive valid_nodes_mask_i = torch.ones((num_nodes_i), dtype=torch.bool, device=x1.device) # if not enough matches in a cluster, mark all cluster's nodes for removal node_ids_for_removal = [] for cluster_id, cluster_match_weights in match_weights_per_cluster.items(): if opt.gn_debug: print('cluster_id', cluster_id, cluster_match_weights) if cluster_match_weights < opt.gn_min_num_correspondences_per_cluster: x = torch.where(graph_clusters_i == cluster_id)[0].tolist() node_ids_for_removal += x if opt.gn_debug: print("node_ids_for_removal", node_ids_for_removal) if len(node_ids_for_removal) > 0: # Mark invalid nodes valid_nodes_mask_i[node_ids_for_removal] = False # Kepp only nodes and edges for valid nodes graph_nodes_i = graph_nodes_i[valid_nodes_mask_i.squeeze()] graph_edges_i = graph_edges_i[valid_nodes_mask_i.squeeze()] graph_edges_weights_i = graph_edges_weights_i[valid_nodes_mask_i.squeeze()] # Update number of nodes opt_num_nodes_i = graph_nodes_i.shape[0] # Get mask of correspondences for which any one of their anchors is an invalid node valid_corresp_mask = torch.ones((num_matches), dtype=torch.bool, device=x1.device) for node_id_for_removal in node_ids_for_removal: valid_corresp_mask = valid_corresp_mask & torch.all(source_anchors != node_id_for_removal, axis=1) source_points_filtered = source_points_filtered[valid_corresp_mask] target_matches_filtered = target_matches_filtered[valid_corresp_mask] xy_pixels_warped_filtered = xy_pixels_warped_filtered[valid_corresp_mask] correspondence_weights_filtered = correspondence_weights_filtered[valid_corresp_mask] source_anchors = source_anchors[valid_corresp_mask] source_weights = source_weights[valid_corresp_mask] num_matches = source_points_filtered.shape[0] # Update node_ids in edges and anchors by mapping old indices to new indices map_opt_nodes_to_complete_nodes_i = [] node_count = 0 for node_id, is_node_valid in enumerate(valid_nodes_mask_i): if is_node_valid: graph_edges_i[graph_edges_i == node_id] = node_count source_anchors[source_anchors == node_id] = node_count map_opt_nodes_to_complete_nodes_i.append(node_id) node_count += 1 if num_matches == 0: if opt.gn_debug: print("\tSolver failed: No valid correspondences") convergence_info[i]["errors"].append("Solver failed: No valid correspondences after filtering") continue if opt_num_nodes_i > opt.gn_max_nodes or opt_num_nodes_i < opt.gn_min_nodes: if opt.gn_debug: print(f"\tSolver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") convergence_info[i]["errors"].append(f"Solver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") continue # Since source anchor ids need to be long in order to be used as indices, # we need to convert them. assert torch.all(source_anchors >= 0) source_anchors = source_anchors.type(torch.int64) ############################################################################################################### # Filter invalid edges. ############################################################################################################### node_ids = torch.arange(opt_num_nodes_i, dtype=torch.int32, device=x1.device).view(-1, 1).repeat(1, num_neighbors) # (opt_num_nodes_i, num_neighbors) graph_edge_pairs = torch.cat([node_ids.view(-1, num_neighbors, 1), graph_edges_i.view(-1, num_neighbors, 1)], 2) # (opt_num_nodes_i, num_neighbors, 2) valid_edges = graph_edges_i >= 0 valid_edge_idxs = torch.where(valid_edges) graph_edge_pairs_filtered = graph_edge_pairs[valid_edge_idxs[0], valid_edge_idxs[1], :].type(torch.int64) graph_edge_weights_pairs = graph_edges_weights_i[valid_edge_idxs[0], valid_edge_idxs[1]] num_edges_i = graph_edge_pairs_filtered.shape[0] ############################################################################################################### # Execute Gauss-Newton solver. ############################################################################################################### num_gn_iter = self.gn_num_iter lambda_data_flow = math.sqrt(self.gn_data_flow) lambda_data_depth = math.sqrt(self.gn_data_depth) lambda_arap = math.sqrt(self.gn_arap) lm_factor = self.gn_lm_factor # The parameters in GN solver are 3 parameters for rotation and 3 parameters for # translation for every node. All node rotation parameters are listed first, and # then all node translation parameters are listed. # x = [w_current_all, t_current_all] if prev_rot is None and prev_trans is None: R_current = torch.eye(3, dtype=x1.dtype, device=x1.device).view(1, 3, 3).repeat(opt_num_nodes_i, 1, 1) t_current = torch.zeros((opt_num_nodes_i, 3, 1), dtype=x1.dtype, device=x1.device) else: R_current = torch.tensor(prev_rot[map_opt_nodes_to_complete_nodes_i, :, :],dtype=x1.dtype,device=x1.device).view(opt_num_nodes_i, 3, 3) t_current = torch.tensor(prev_trans[map_opt_nodes_to_complete_nodes_i, :],dtype=x1.dtype,device=x1.device).view(opt_num_nodes_i, 3, 1) if opt.gn_debug: print("\tNum. matches: {0} \|\| Num. nodes: {1} \|\| Num. edges: {2}".format(num_matches, opt_num_nodes_i, num_edges_i)) # Helper structures. data_increment_vec_0_3 = torch.arange(0, num_matches 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) data_increment_vec_1_3 = torch.arange(1, num_matches * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) data_increment_vec_2_3 = torch.arange(2, num_matches * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) if num_edges_i > 0: arap_increment_vec_0_3 = torch.arange(0, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_increment_vec_1_3 = torch.arange(1, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_increment_vec_2_3 = torch.arange(2, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_one_vec = torch.ones((num_edges_i), dtype=x1.dtype, device=x1.device) ill_posed_system = False for gn_i in range(num_gn_iter): if gn_i % 3 == 2: lm_factor /= 2; timer_data_start = timer() ########################################## # Compute data residual and jacobian. ########################################## jacobian_data = torch.zeros((num_matches * 3, opt_num_nodes_i * 6), dtype=x1.dtype, device=x1.device) # (num_matches3, opt_num_nodes_i6) deformed_points = torch.zeros((num_matches, 3, 1), dtype=x1.dtype, device=x1.device) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors[:, k] # (num_matches) nodes_k = graph_nodes_i[node_idxs_k].view(num_matches, 3, 1) # (num_matches, 3, 1) # Compute deformed point contribution. rotated_points_k = torch.matmul(R_current[node_idxs_k], source_points_filtered - nodes_k) # (num_matches, 3, 1) = (num_matches, 3, 3) * (num_matches, 3, 1) deformed_points_k = rotated_points_k + nodes_k + t_current[node_idxs_k] deformed_points += source_weights[:, k].view(num_matches, 1, 1).repeat(1, 3, 1) * deformed_points_k # (num_matches, 3, 1) # Get necessary components of deformed points. eps = 1e-7 # Just as good practice, although matches should all have valid depth at this stage deformed_x = deformed_points[:, 0, :].view(num_matches) # (num_matches) deformed_y = deformed_points[:, 1, :].view(num_matches) # (num_matches) deformed_z_inverse = torch.div(1.0, deformed_points[:, 2, :].view(num_matches) + eps) # (num_matches) fx_mul_x = fx * deformed_x # (num_matches) fy_mul_y = fy * deformed_y # (num_matches) fx_div_z = fx * deformed_z_inverse # (num_matches) fy_div_z = fy * deformed_z_inverse # (num_matches) fx_mul_x_div_z = fx_mul_x * deformed_z_inverse # (num_matches) fy_mul_y_div_z = fy_mul_y * deformed_z_inverse # (num_matches) minus_fx_mul_x_div_z_2 = -fx_mul_x_div_z * deformed_z_inverse # (num_matches) minus_fy_mul_y_div_z_2 = -fy_mul_y_div_z * deformed_z_inverse # (num_matches) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors[:, k] # (num_matches) nodes_k = graph_nodes_i[node_idxs_k].view(num_matches, 3, 1) # (num_matches, 3, 1) weights_k = source_weights[:, k] * correspondence_weights_filtered # (num_matches) # Compute skew symetric part. rotated_points_k = torch.matmul(R_current[node_idxs_k], source_points_filtered - nodes_k) # (num_matches, 3, 1) = (num_matches, 3, 3) * (num_matches, 3, 1) weighted_rotated_points_k = weights_k.view(num_matches, 1, 1).repeat(1, 3, 1) * rotated_points_k # (num_matches, 3, 1) skew_symetric_mat_data = -torch.matmul(self.vec_to_skew_mat, weighted_rotated_points_k).view(num_matches, 3, 3) # (num_matches, 3, 3) # Compute jacobian wrt. TRANSLATION. # FLOW PART jacobian_data[data_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 0] += lambda_data_flow * weights_k * fx_div_z # (num_matches) jacobian_data[data_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_flow * weights_k * minus_fx_mul_x_div_z_2 # (num_matches) jacobian_data[data_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 1] += lambda_data_flow * weights_k * fy_div_z # (num_matches) jacobian_data[data_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_flow * weights_k * minus_fy_mul_y_div_z_2 # (num_matches) # DEPTH PART jacobian_data[data_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_depth * weights_k # (num_matches) # Compute jacobian wrt. ROTATION. # FLOW PART jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 0] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 0] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 1] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 1] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 2] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 2] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 2] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 0] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 0] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 1] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 1] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 2] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 2] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 2] # DEPTH PART jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 0] += lambda_data_depth * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 1] += lambda_data_depth * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 2] += lambda_data_depth * skew_symetric_mat_data[:, 2, 2] assert torch.isfinite(jacobian_data).all(), jacobian_data res_data = torch.zeros((num_matches * 3, 1), dtype=x1.dtype, device=x1.device) # FLOW PART res_data[data_increment_vec_0_3, 0] = lambda_data_flow * correspondence_weights_filtered * (fx_mul_x_div_z + cx - xy_pixels_warped_filtered[:, 0, :].view(num_matches)) res_data[data_increment_vec_1_3, 0] = lambda_data_flow * correspondence_weights_filtered * (fy_mul_y_div_z + cy - xy_pixels_warped_filtered[:, 1, :].view(num_matches)) # DEPTH PART res_data[data_increment_vec_2_3, 0] = lambda_data_depth * correspondence_weights_filtered * (deformed_points[:, 2, :] - target_matches_filtered[:, 2, :]).view(num_matches) if opt.gn_print_timings: print("\t\tData term: {:.3f} s".format(timer() - timer_data_start)) timer_arap_start = timer() ########################################## # Compute arap residual and jacobian. ########################################## if num_edges_i > 0: jacobian_arap = torch.zeros((num_edges_i * 3, opt_num_nodes_i * 6), dtype=x1.dtype, device=x1.device) # (num_edges_i3, opt_num_nodes_i6) node_idxs_0 = graph_edge_pairs_filtered[:, 0] # i node node_idxs_1 = graph_edge_pairs_filtered[:, 1] # j node w = torch.ones_like(graph_edge_weights_pairs) if opt.gn_use_edge_weighting: # Since graph edge weights sum up to 1 for all neighbors, we multiply # it by the number of neighbors to make the setting in the same scale # as in the case of not using edge weights (they are all 1 then). w = float(num_neighbors) * graph_edge_weights_pairs w_repeat = w.unsqueeze(-1).repeat(1, 3).unsqueeze(-1) w_repeat_repeat = w_repeat.repeat(1, 1, 3) nodes_0 = graph_nodes_i[node_idxs_0].view(num_edges_i, 3, 1) nodes_1 = graph_nodes_i[node_idxs_1].view(num_edges_i, 3, 1) # Compute residual. rotated_node_delta = torch.matmul(R_current[node_idxs_0], nodes_1 - nodes_0) # (num_edges_i, 3) res_arap = lambda_arap * w_repeat * (rotated_node_delta + nodes_0 + t_current[node_idxs_0] - (nodes_1 + t_current[node_idxs_1])) res_arap = res_arap.view(num_edges_i * 3, 1) # Compute jacobian wrt. translations. jacobian_arap[arap_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 0] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 1] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 2] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 0] += -lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 1] += -lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 2] += -lambda_arap * w * arap_one_vec # (num_edges_i) # Compute jacobian wrt. rotations. # Derivative wrt. R_1 is equal to 0. skew_symetric_mat_arap = -lambda_arap * w_repeat_repeat * torch.matmul(self.vec_to_skew_mat, rotated_node_delta).view(num_edges_i, 3, 3) # (num_edges_i, 3, 3) jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 0, 0] jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 0, 1] jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 0, 2] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 1, 0] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 1, 1] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 1, 2] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 2, 0] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 2, 1] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 2, 2] assert torch.isfinite(jacobian_arap).all(), jacobian_arap if opt.gn_print_timings: print("\t\tARAP term: {:.3f} s".format(timer() - timer_arap_start)) ########################################## # Solve linear system. ########################################## if num_edges_i > 0: res = torch.cat((res_data, res_arap), 0) jac = torch.cat((jacobian_data, jacobian_arap), 0) else: res = res_data jac = jacobian_data timer_system_start = timer() # Compute A = J^TJ and b = -J^Tr. jac_t = torch.transpose(jac, 0, 1) A = torch.matmul(jac_t, jac) b = torch.matmul(-jac_t, res) # Solve linear system Ax = b. A = A + torch.eye(A.shape[0], dtype=A.dtype, device=A.device) * lm_factor assert torch.isfinite(A).all(), A if opt.gn_print_timings: print("\t\tSystem computation: {:.3f} s".format(timer() - timer_system_start)) timer_cond_start = timer() # Check the determinant/condition number. # If unstable, we break optimization. if opt.gn_check_condition_num: with torch.no_grad(): # Condition number. values, _ = torch.eig(A) real_values = values[:, 0] assert torch.isfinite(real_values).all(), real_values max_eig_value = torch.max(torch.abs(real_values)) min_eig_value = torch.min(torch.abs(real_values)) condition_number = max_eig_value / min_eig_value condition_number = condition_number.item() convergence_info[i]["condition_numbers"].append(condition_number) if opt.gn_break_on_condition_num and (not math.isfinite(condition_number) or condition_number > opt.gn_max_condition_num): print("\t\tToo high condition number: {0:e} (max: {1:.3f}, min: {2:.3f}). Discarding sample".format(condition_number, max_eig_value.item(), min_eig_value.item())) convergence_info[i]["errors"].append("Too high condition number: {0:e} (max: {1:.3f}, min: {2:.3f}). Discarding sample".format(condition_number, max_eig_value.item(), min_eig_value.item())) ill_posed_system = True break elif opt.gn_debug: print("\t\tCondition number: {0:e} (max: {1:.3f}, min: {2:.3f})".format(condition_number, max_eig_value.item(), min_eig_value.item())) if opt.gn_print_timings: print("\t\tComputation of cond. num.: {:.3f} s".format(timer() - timer_cond_start)) timer_solve_start = timer() linear_solver = LinearSolverLU.apply try: x = linear_solver(A, b) except RuntimeError as e: ill_posed_system = True print("\t\tSolver failed: Ill-posed system!", e) convergence_info[i]["errors"].append("Solver failed: Ill-posed system!") break if not torch.isfinite(x).all(): ill_posed_system = True print("\t\tSolver failed: Non-finite solution x!") convergence_info[i]["errors"].append("Solver failed: Non-finite solution x!") break if opt.gn_print_timings: print("\t\tLinear solve: {:.3f} s".format(timer() - timer_solve_start)) loss_data = torch.norm(res_data).item() loss_total = torch.norm(res).item() if len(convergence_info[i]["total"]): if loss_total - convergence_info[i]["total"][-1] > self.stop_loss_diff: print("As loss is greater than before breaking optimization") break convergence_info[i]["data"].append(loss_data) convergence_info[i]["total"].append(loss_total) # Increment the current rotation and translation. R_inc = kornia.angle_axis_to_rotation_matrix(x[:opt_num_nodes_i3].view(opt_num_nodes_i, 3)) t_inc = x[opt_num_nodes_i3:].view(opt_num_nodes_i, 3, 1) R_current = torch.matmul(R_inc, R_current) t_current = t_current + t_inc if num_edges_i > 0: loss_arap = torch.norm(res_arap).item() convergence_info[i]["arap"].append(loss_arap) if opt.gn_debug: if num_edges_i > 0: print("\t\t-->Iteration: {0}. Lm:{1:.3f} Loss: \tdata = {2:.3f}, \tarap = {3:.3f}, \ttotal = {4:.3f}".format(gn_i, lm_factor,loss_data, loss_arap, loss_total)) else: print("\t\t-->Iteration: {0}. Loss: \tdata = {1:.3f}, \ttotal = {2:.3f}".format(gn_i, loss_data, loss_total)) ############################################################################################################### # Write the solutions. ############################################################################################################### if not ill_posed_system and torch.isfinite(res).all(): node_rotations[i, map_opt_nodes_to_complete_nodes_i, :, :] = R_current.view(opt_num_nodes_i, 3, 3) node_translations[i, map_opt_nodes_to_complete_nodes_i, :] = t_current.view(opt_num_nodes_i, 3) deformations_validity[i, map_opt_nodes_to_complete_nodes_i] = 1 valid_solve[i] = 1 ############################################################################################################### # Warp all valid source points using estimated deformations. ############################################################################################################### if valid_solve[i]: # Filter out any invalid pixel anchors, and invalid source points. source_points_i = source_points[i].permute(1, 2, 0) source_points_i = source_points_i[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) source_anchors_i = pixel_anchors[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) source_weights_i = pixel_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) num_points = source_points_i.shape[0] # Filter out points randomly, if too many are still left. if num_points > opt.gn_max_warped_points: sampled_idxs = torch.randperm(num_points)[:opt.gn_max_warped_points] source_points_i = source_points_i[sampled_idxs] source_anchors_i = source_anchors_i[sampled_idxs] source_weights_i = source_weights_i[sampled_idxs] num_points = opt.gn_max_warped_points deformed_points_idxs[i] = sampled_idxs deformed_points_subsampled[i] = 1 source_anchors_i = source_anchors_i.type(torch.int64) # Now we deform all source points. deformed_points_i = torch.zeros((num_points, 3, 1), dtype=x1.dtype, device=x1.device) graph_nodes_complete_i = graph_nodes[i, :num_nodes_i, :] R_final = node_rotations[i, :num_nodes_i, :, :].view(num_nodes_i, 3, 3) t_final = node_translations[i, :num_nodes_i, :].view(num_nodes_i, 3, 1) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors_i[:, k] # (num_points) nodes_k = graph_nodes_complete_i[node_idxs_k].view(num_points, 3, 1) # (num_points, 3, 1) # Compute deformed point contribution. rotated_points_k = torch.matmul(R_final[node_idxs_k], source_points_i - nodes_k) # (num_points, 3, 1) = (num_points, 3, 3) * (num_points, 3, 1) deformed_points_k = rotated_points_k + nodes_k + t_final[node_idxs_k] deformed_points_i += source_weights_i[:, k].view(num_points, 1, 1).repeat(1, 3, 1) * deformed_points_k # (num_points, 3, 1) deformed_points_i = deformed_points_i.view(num_points, 3) # Store the results. deformed_points_pred[i, :num_points, :] = deformed_points_i.view(1, num_points, 3) if valid_solve[i]: total_num_matches_per_batch += num_matches if opt.gn_debug: if int(valid_solve[i].cpu().numpy()): print("\t\tValid solve ({:.3f} s)".format(timer() - timer_start)) else: print("\t\tInvalid solve ({:.3f} s)".format(timer() - timer_start)) convergence_info[i]["valid"] = int(valid_solve[i].item()) ############################################################################################################### # We invalidate complete batch if we have too many matches in total (otherwise backprop crashes) ############################################################################################################### if not evaluate and total_num_matches_per_batch > opt.gn_max_matches_train_per_batch: print("\t\tSolver failed: Too many matches per batch: {}".format(total_num_matches_per_batch)) for i in range(batch_size): convergence_info[i]["errors"].append("Solver failed: Too many matches per batch: {}".format(total_num_matches_per_batch)) valid_solve[i] = 0 return { "flow_data": [flow2, flow3, flow4, flow5, flow6], "node_rotations": node_rotations, "node_translations": node_translations, "deformations_validity": deformations_validity, "deformed_points_pred": deformed_points_pred, "valid_solve": valid_solve, "mask_pred": mask_pred, "correspondence_info": { "xy_coords_warped":xy_coords_warped, "source_points":source_points, "valid_source_points":valid_source_points, "target_matches":target_matches, "valid_target_matches":valid_target_matches, "valid_correspondences":valid_correspondences, "deformed_points_idxs":deformed_points_idxs, "deformed_points_subsampled":deformed_points_subsampled }, "convergence_info": convergence_info, "weight_info": weight_info }	[ 1, 529, 9507, 29958, 4299, 29914, 4299, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 10876, 29892, 359, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 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datasets/nhse_stats/topics/msa.py	nhsengland/publish-o-matic	0	198135	""" Scrapes the data at http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/ """ import calendar import datetime import re from lxml.html import fromstring, tostring import requests import slugify from publish.lib.helpers import to_markdown, anchor_to_resource TITLE_ROOT = "Mixed-Sex Accommodation Data" ROOT = "http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/" DATE_RE = re.compile(".\s(.)\s(\d{4})") MONTHS_LOOKUP = dict((v,k) for k,v in enumerate(calendar.month_name)) def process_block(p, title, description, current_year): if not current_year: return None dataset = { "title": "{} - {}".format(TITLE_ROOT, title), "notes": description, "tags": ["Statistics", current_year], "resources": [], "origin": "http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/", "groups": ["msa"] } for resource in p.cssselect('a'): r = anchor_to_resource(resource) if r['format'] == 'XLSM': r['format'] = 'XLS' dataset["resources"].append(r) dataset["name"] = slugify.slugify(dataset['title']).lower() return dataset def process_latest(datasets, latest): """ We process the latest data as a special case because it is all munged together in a separate block. We need to find the links, parse them, try and group them by name, and then decide how we're going to label the dataset. """ for anchor in latest: resource = anchor_to_resource(anchor) y = int(string_to_date(resource['description'])[:4]) finder = "{}-{}".format(y-1, str(y)[2:4]) finder = "{} - {}".format(TITLE_ROOT, finder) # We can find the first dataset in the list (datasets) whose # title starts with finder as the most recent years go at # the top of the list on the page. for dataset in datasets: if dataset['title'].startswith(finder): print "We think ", resource['description'], "goes in", dataset['title'] dataset['resources'].insert(0, resource) break def string_to_date(s, start=True): m = DATE_RE.match(s) if not m: return "" if not len(m.groups()) == 2: return "" mnth = MONTHS_LOOKUP.get(m.groups()[0].strip(), "") year = int(m.groups()[1].strip()) if not mnth: return "" day = 1 _, mend = calendar.monthrange(year, mnth) if not start: day = mend d = datetime.datetime(year=year, month=mnth, day=day) return d.strftime("%Y-%m-%d") def process_dates(datasets): for dataset in datasets: start_date = string_to_date(dataset["resources"][-1]['description']) end_date = string_to_date(dataset["resources"][0]['description'], start=False) dataset["coverage_start_date"] = start_date dataset["coverage_end_date"] = end_date dataset["frequency"] = "Monthly" qrtrs = [1 for r in dataset['resources'] if 'Quarter' in r['description']] if any(qrtrs): dataset["frequency"] = "Quarterly" def scrape(workspace): print "Scraping MSA with workspace {}".format(workspace) datasets = [] page = requests.get(ROOT) html = fromstring(page.content) center = html.cssselect('.column.center')[0] paras = list(center.cssselect('P')) current_year = None # Iterate through all of the Ps. From here until we find a <strong> # is the description description = [] num_p = 0 for p in paras: if len(p.cssselect('STRONG')) > 0: break num_p += 1 description.append(tostring(p)) description = to_markdown(''.join(description)) latest_data_links = [] # Process the individual datasets current_label = "" generator = (p for p in paras[num_p:]) for p in generator: strong = p.cssselect('STRONG') if len(strong) > 0: current_label = strong[0].text_content().strip() else: if current_label == 'Latest Data': latest_data_links = p.cssselect('a') continue datasets.append(process_block(p, current_label, description, current_label)) datasets = filter(lambda x: x is not None, datasets) process_dates(datasets) process_latest(datasets, latest_data_links) print len(datasets) return datasets	[ 1, 9995, 2522, 2390, 267, 278, 848, 472, 1732, 597, 1636, 29889, 996, 1049, 29889, 29876, 9499, 29889, 2679, 29914, 6112, 6765, 29914, 6112, 391, 936, 29899, 1287, 29899, 598, 294, 29914, 29885, 11925, 29899, 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1678, 1596, 376, 4421, 2390, 292, 341, 8132, 411, 664, 3493, 6571, 1642, 4830, 29898, 1287, 3493, 29897, 13, 13, 1678, 20035, 353, 5159, 13, 13, 1678, 1813, 353, 7274, 29889, 657, 29898, 21289, 29897, 13, 1678, 3472, 353, 515, 1807, 29898, 3488, 29889, 3051, 29897, 13, 13, 1678, 4818, 353, 3472, 29889, 4268, 2622, 12839, 4914, 29889, 5064, 29861, 29900, 29962, 13, 13, 1678, 610, 294, 353, 1051, 29898, 5064, 29889, 4268, 2622, 877, 29925, 8785, 13, 13, 1678, 1857, 29918, 6360, 353, 6213, 13, 13, 1678, 396, 20504, 403, 1549, 599, 310, 278, 10144, 29889, 3645, 1244, 2745, 591, 1284, 263, 529, 1110, 29958, 13, 1678, 396, 338, 278, 6139, 13, 1678, 6139, 353, 5159, 13, 1678, 954, 29918, 29886, 353, 29871, 29900, 13, 1678, 363, 282, 297, 610, 294, 29901, 13, 4706, 565, 7431, 29898, 29886, 29889, 4268, 2622, 877, 10810, 20614, 8785, 1405, 29871, 29900, 29901, 13, 9651, 2867, 13, 4706, 954, 29918, 29886, 4619, 29871, 29896, 13, 4706, 6139, 29889, 4397, 29898, 517, 1807, 29898, 29886, 876, 13, 1678, 6139, 353, 304, 29918, 3502, 3204, 877, 4286, 7122, 29898, 8216, 876, 13, 13, 1678, 9281, 29918, 1272, 29918, 4965, 353, 5159, 13, 13, 1678, 396, 10554, 278, 5375, 20035, 13, 1678, 1857, 29918, 1643, 353, 5124, 13, 1678, 15299, 353, 313, 29886, 363, 282, 297, 610, 294, 29961, 1949, 29918, 29886, 29901, 2314, 13, 1678, 363, 282, 297, 15299, 29901, 13, 4706, 4549, 353, 282, 29889, 4268, 2622, 877, 10810, 20614, 1495, 13, 4706, 565, 7431, 29898, 1110, 29897, 1405, 29871, 29900, 29901, 13, 9651, 1857, 29918, 1643, 353, 4549, 29961, 29900, 1822, 726, 29918, 3051, 2141, 17010, 580, 13, 4706, 1683, 29901, 13, 9651, 565, 1857, 29918, 1643, 1275, 525, 13992, 342, 3630, 2396, 13, 18884, 9281, 29918, 1272, 29918, 4965, 353, 282, 29889, 4268, 2622, 877, 29874, 1495, 13, 18884, 6773, 13, 13, 9651, 20035, 29889, 4397, 29898, 5014, 29918, 1271, 29898, 29886, 29892, 1857, 29918, 1643, 29892, 6139, 29892, 1857, 29918, 1643, 876, 13, 13, 1678, 20035, 353, 4175, 29898, 2892, 921, 29901, 921, 338, 451, 6213, 29892, 20035, 29897, 13, 1678, 1889, 29918, 15190, 29898, 14538, 1691, 29897, 13, 1678, 1889, 29918, 12333, 29898, 14538, 1691, 29892, 9281, 29918, 1272, 29918, 4965, 29897, 13, 13, 1678, 1596, 7431, 29898, 14538, 1691, 29897, 13, 1678, 736, 20035, 13, 2 ]
test/test_ostool.py	JukeboxPipeline/jukebox-core	2	139610	from jukeboxcore import ostool def test_detect_sys(): """ Detect Platform :returns: None :rtype: None :raises: None """ ostool.detect_sys() def test_get_interface(): """ Get PlatformInterface for your Platform :returns: None :rtype: None :raises: None """ ostool.get_interface() def test_interface(): """ Run PlatformInterface methods :returns: None :rtype: None :raises: None """ inter = ostool.get_interface() loc = inter.get_maya_location() assert loc site = inter.get_maya_sitepackage_dir() assert site mayapy = inter.get_maya_python() assert mayapy exe = inter.get_maya_exe() assert exe	[ 1, 515, 3623, 446, 1884, 3221, 1053, 14750, 1507, 13, 13, 13, 1753, 1243, 29918, 4801, 522, 29918, 9675, 7295, 13, 1678, 9995, 5953, 522, 28096, 13, 13, 1678, 584, 18280, 29901, 6213, 13, 1678, 584, 29878, 1853, 29901, 6213, 13, 1678, 584, 336, 4637, 29901, 6213, 13, 1678, 9995, 13, 1678, 14750, 1507, 29889, 4801, 522, 29918, 9675, 580, 13, 13, 13, 1753, 1243, 29918, 657, 29918, 13248, 7295, 13, 1678, 9995, 3617, 28096, 10448, 363, 596, 28096, 13, 13, 1678, 584, 18280, 29901, 6213, 13, 1678, 584, 29878, 1853, 29901, 6213, 13, 1678, 584, 336, 4637, 29901, 6213, 13, 1678, 9995, 13, 1678, 14750, 1507, 29889, 657, 29918, 13248, 580, 13, 13, 13, 1753, 1243, 29918, 13248, 7295, 13, 1678, 9995, 7525, 28096, 10448, 3519, 13, 13, 1678, 584, 18280, 29901, 6213, 13, 1678, 584, 29878, 1853, 29901, 6213, 13, 1678, 584, 336, 4637, 29901, 6213, 13, 1678, 9995, 13, 1678, 1006, 353, 14750, 1507, 29889, 657, 29918, 13248, 580, 13, 1678, 1180, 353, 1006, 29889, 657, 29918, 29885, 9010, 29918, 5479, 580, 13, 1678, 4974, 1180, 13, 1678, 3268, 353, 1006, 29889, 657, 29918, 29885, 9010, 29918, 2746, 5113, 29918, 3972, 580, 13, 1678, 4974, 3268, 13, 1678, 1122, 27580, 353, 1006, 29889, 657, 29918, 29885, 9010, 29918, 4691, 580, 13, 1678, 4974, 1122, 27580, 13, 1678, 429, 29872, 353, 1006, 29889, 657, 29918, 29885, 9010, 29918, 8097, 580, 13, 1678, 4974, 429, 29872, 13, 2 ]
wxnat/tests/__init__.py	pauldmccarthy/wxnatpy	1	56129	<gh_stars>1-10 #!/usr/bin/env python # # __init__.py - unit tests for wxnatpy # # Author: <NAME> <<EMAIL>> # import time import wx def run_with_wx(func, args, kwargs): propagateRaise = kwargs.pop('propagateRaise', True) startingDelay = kwargs.pop('startingDelay', 500) finishingDelay = kwargs.pop('finishingDelay', 500) callAfterApp = kwargs.pop('callAfterApp', None) result = [None] raised = [None] app = wx.App() frame = wx.Frame(None) if callAfterApp is not None: callAfterApp() def wrap(): try: if func is not None: result[0] = func(args, **kwargs) except Exception as e: print(e) raised[0] = e finally: def finish(): frame.Destroy() app.ExitMainLoop() wx.CallLater(finishingDelay, finish) frame.Show() wx.CallLater(startingDelay, wrap) app.MainLoop() if raised[0] and propagateRaise: raise raised[0] return result[0] def yield_until(condition): while not condition(): time.sleep(0.1) wx.Yield()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 13, 29937, 4770, 2344, 26914, 2272, 448, 5190, 6987, 363, 26437, 8924, 2272, 13, 29937, 13, 29937, 13361, 29901, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 13, 13, 5215, 931, 13, 13, 5215, 26437, 13, 13, 13, 13, 1753, 1065, 29918, 2541, 29918, 23310, 29898, 9891, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 13, 1678, 13089, 403, 29934, 29874, 895, 353, 9049, 5085, 29889, 7323, 877, 7728, 351, 403, 29934, 29874, 895, 742, 5852, 29897, 13, 1678, 6257, 24996, 29871, 353, 9049, 5085, 29889, 7323, 877, 2962, 292, 24996, 742, 259, 29945, 29900, 29900, 29897, 13, 1678, 28321, 24996, 353, 9049, 5085, 29889, 7323, 877, 4951, 14424, 24996, 742, 29871, 29945, 29900, 29900, 29897, 13, 1678, 1246, 13555, 2052, 259, 353, 9049, 5085, 29889, 7323, 877, 4804, 13555, 2052, 742, 259, 6213, 29897, 13, 13, 1678, 1121, 353, 518, 8516, 29962, 13, 1678, 10425, 353, 518, 8516, 29962, 13, 13, 1678, 623, 1678, 353, 26437, 29889, 2052, 580, 13, 1678, 3515, 29871, 353, 26437, 29889, 4308, 29898, 8516, 29897, 13, 13, 1678, 565, 1246, 13555, 2052, 338, 451, 6213, 29901, 13, 4706, 1246, 13555, 2052, 580, 13, 13, 1678, 822, 12244, 7295, 13, 13, 4706, 1018, 29901, 13, 9651, 565, 3653, 338, 451, 6213, 29901, 13, 18884, 1121, 29961, 29900, 29962, 353, 3653, 10456, 5085, 29892, 3579, 19290, 29897, 13, 13, 4706, 5174, 8960, 408, 321, 29901, 13, 9651, 1596, 29898, 29872, 29897, 13, 9651, 10425, 29961, 29900, 29962, 353, 321, 13, 13, 4706, 7146, 29901, 13, 9651, 822, 8341, 7295, 13, 18884, 3515, 29889, 14994, 4727, 580, 13, 18884, 623, 29889, 24365, 6330, 18405, 580, 13, 9651, 26437, 29889, 5594, 29931, 1008, 29898, 4951, 14424, 24996, 29892, 8341, 29897, 13, 13, 1678, 3515, 29889, 8964, 580, 13, 13, 1678, 26437, 29889, 5594, 29931, 1008, 29898, 2962, 292, 24996, 29892, 12244, 29897, 13, 13, 1678, 623, 29889, 6330, 18405, 580, 13, 13, 1678, 565, 10425, 29961, 29900, 29962, 322, 13089, 403, 29934, 29874, 895, 29901, 13, 4706, 12020, 10425, 29961, 29900, 29962, 13, 13, 1678, 736, 1121, 29961, 29900, 29962, 13, 13, 13, 1753, 7709, 29918, 29305, 29898, 16122, 1125, 13, 1678, 1550, 451, 4195, 7295, 13, 4706, 931, 29889, 17059, 29898, 29900, 29889, 29896, 29897, 13, 4706, 26437, 29889, 29979, 969, 580, 13, 2 ]
examples/test_yield_8.py	MateuszG/django_auth	2	9014	import pytest @pytest.yield_fixture def passwd(): print ("\nsetup before yield") f = open("/etc/passwd") yield f.readlines() print ("teardown after yield") f.close() def test_has_lines(passwd): print ("test called") assert passwd	[ 1, 1053, 11451, 1688, 13, 13, 29992, 2272, 1688, 29889, 29891, 969, 29918, 7241, 15546, 13, 1753, 1209, 9970, 7295, 13, 1678, 1596, 4852, 29905, 29876, 14669, 1434, 7709, 1159, 13, 1678, 285, 353, 1722, 11974, 7070, 29914, 3364, 9970, 1159, 13, 1678, 7709, 285, 29889, 949, 9012, 580, 13, 1678, 1596, 4852, 371, 538, 776, 1156, 7709, 1159, 13, 1678, 285, 29889, 5358, 580, 13, 13, 1753, 1243, 29918, 5349, 29918, 9012, 29898, 3364, 9970, 1125, 13, 1678, 1596, 4852, 1688, 2000, 1159, 13, 1678, 4974, 1209, 9970, 13, 2 ]
examples/Oscillators/MallsOscillator/plotresults.py	doublefloyd/beluga	20	174838	from beluga.utils import load import matplotlib.pyplot as plt data = load('data.beluga') sol_set = data['solutions'] traj = sol_set[-1][-1] continuation = sol_set[-1] L = len(continuation) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.y[:, 0], sol.y[:, 1], linestyle='-', color=(1(ind/L), 0, 1(L-ind)/L)) plt.title('Phase Plot') plt.xlabel('$x_1$') plt.ylabel('$x_2$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.u[:, 0], linestyle='-', color=(1(ind/L), 0, 1(L-ind)/L)) plt.plot([traj.t[0], traj.t[-1]], [1, 1], color='k', linestyle='--') plt.plot([traj.t[0], traj.t[-1]], [-1, -1], color='k', linestyle='--') plt.title('Control History Plot') plt.xlabel('Time [s]') plt.ylabel('Control, $u$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 0], linestyle='-', color=(1(ind/L), 0, 1(L-ind)/L)) plt.title('$\\lambda_{x1}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x1}$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 1], linestyle='-', color=(1(ind/L), 0, 1(L-ind)/L)) plt.title('$\\lambda_{x2}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x2}$') plt.grid(True) plt.show()	[ 1, 515, 1339, 26281, 29889, 13239, 1053, 2254, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 13, 1272, 353, 2254, 877, 1272, 29889, 6596, 26281, 1495, 13, 13, 2929, 29918, 842, 353, 848, 1839, 2929, 17925, 2033, 13, 3018, 29926, 353, 899, 29918, 842, 14352, 29896, 3816, 29899, 29896, 29962, 13, 20621, 362, 353, 899, 29918, 842, 14352, 29896, 29962, 13, 29931, 353, 7431, 29898, 20621, 362, 29897, 13, 13, 572, 29873, 29889, 4532, 580, 13, 1454, 1399, 29892, 899, 297, 26985, 29898, 20621, 362, 1125, 13, 1678, 14770, 29889, 5317, 29898, 2929, 29889, 29891, 7503, 29892, 29871, 29900, 1402, 899, 29889, 29891, 7503, 29892, 29871, 29896, 1402, 6276, 342, 1508, 2433, 29899, 742, 2927, 7607, 29896, 16395, 513, 29914, 29931, 511, 29871, 29900, 29892, 29871, 29896, 16395, 29931, 29899, 513, 6802, 29931, 876, 13, 13, 572, 29873, 29889, 3257, 877, 4819, 559, 18399, 1495, 13, 572, 29873, 29889, 29916, 1643, 877, 29938, 29916, 29918, 29896, 29938, 1495, 13, 572, 29873, 29889, 29891, 1643, 877, 29938, 29916, 29918, 29906, 29938, 1495, 13, 572, 29873, 29889, 7720, 29898, 5574, 29897, 13, 13, 572, 29873, 29889, 4532, 580, 13, 1454, 1399, 29892, 899, 297, 26985, 29898, 20621, 362, 1125, 13, 1678, 14770, 29889, 5317, 29898, 2929, 29889, 29873, 29892, 899, 29889, 29884, 7503, 29892, 29871, 29900, 1402, 6276, 342, 1508, 2433, 29899, 742, 2927, 7607, 29896, 16395, 513, 29914, 29931, 511, 29871, 29900, 29892, 29871, 29896, 16395, 29931, 29899, 513, 6802, 29931, 876, 13, 13, 572, 29873, 29889, 5317, 4197, 3018, 29926, 29889, 29873, 29961, 29900, 1402, 1020, 29926, 29889, 29873, 14352, 29896, 20526, 518, 29896, 29892, 29871, 29896, 1402, 2927, 2433, 29895, 742, 6276, 342, 1508, 2433, 489, 1495, 13, 572, 29873, 29889, 5317, 4197, 3018, 29926, 29889, 29873, 29961, 29900, 1402, 1020, 29926, 29889, 29873, 14352, 29896, 20526, 21069, 29896, 29892, 448, 29896, 1402, 2927, 2433, 29895, 742, 6276, 342, 1508, 2433, 489, 1495, 13, 572, 29873, 29889, 3257, 877, 4809, 5298, 18399, 1495, 13, 572, 29873, 29889, 29916, 1643, 877, 2481, 518, 29879, 29962, 1495, 13, 572, 29873, 29889, 29891, 1643, 877, 4809, 29892, 395, 29884, 29938, 1495, 13, 572, 29873, 29889, 7720, 29898, 5574, 29897, 13, 13, 572, 29873, 29889, 4532, 580, 13, 1454, 1399, 29892, 899, 297, 26985, 29898, 20621, 362, 1125, 13, 1678, 14770, 29889, 5317, 29898, 2929, 29889, 29873, 29892, 899, 29889, 700, 284, 7503, 29892, 29871, 29900, 1402, 6276, 342, 1508, 2433, 29899, 742, 2927, 7607, 29896, 16395, 513, 29914, 29931, 511, 29871, 29900, 29892, 29871, 29896, 16395, 29931, 29899, 513, 6802, 29931, 876, 13, 13, 572, 29873, 29889, 3257, 877, 29938, 1966, 2892, 648, 29916, 29896, 1042, 5298, 18399, 1495, 13, 572, 29873, 29889, 29916, 1643, 877, 2481, 518, 29879, 29962, 1495, 13, 572, 29873, 29889, 29891, 1643, 877, 29938, 1966, 2892, 648, 29916, 29896, 1042, 1495, 13, 572, 29873, 29889, 7720, 29898, 5574, 29897, 13, 13, 572, 29873, 29889, 4532, 580, 13, 1454, 1399, 29892, 899, 297, 26985, 29898, 20621, 362, 1125, 13, 1678, 14770, 29889, 5317, 29898, 2929, 29889, 29873, 29892, 899, 29889, 700, 284, 7503, 29892, 29871, 29896, 1402, 6276, 342, 1508, 2433, 29899, 742, 2927, 7607, 29896, 16395, 513, 29914, 29931, 511, 29871, 29900, 29892, 29871, 29896, 16395, 29931, 29899, 513, 6802, 29931, 876, 13, 13, 572, 29873, 29889, 3257, 877, 29938, 1966, 2892, 648, 29916, 29906, 1042, 5298, 18399, 1495, 13, 572, 29873, 29889, 29916, 1643, 877, 2481, 518, 29879, 29962, 1495, 13, 572, 29873, 29889, 29891, 1643, 877, 29938, 1966, 2892, 648, 29916, 29906, 1042, 1495, 13, 572, 29873, 29889, 7720, 29898, 5574, 29897, 13, 572, 29873, 29889, 4294, 580, 13, 2 ]
src/lib/earlystopping.py	dreizehnutters/pcapae	0	34665	from os import path, makedirs, walk ,remove, scandir, unlink from numpy import inf from torch import save as t_save from lib.utils import sort_human, BOLD, CLR class EarlyStopping: def __init__(self, log_path, patience=7, model=None, verbose=False, exp_tag=""): """Early stops the training if validation loss doesn't improve after a given patience. Args: patience (int): How long to wait after last time validation loss improved. Default: 7 verbose (bool): If True, prints a message for each validation loss improvement. Default: False """ self.patience = patience self.verbose = verbose self.counter = 0 self.best_score = None self.early_stop = False self.val_loss_min = inf self.global_min_loss = inf save_dir = f"{log_path}/save_model/{exp_tag}" self.save_path = save_dir if not path.isdir(save_dir): makedirs(save_dir) save_dir = f"{self.save_path}/best/" if not path.isdir(save_dir): makedirs(save_dir) if model is not None: self.meta_info = {'meta':(model.encoder_params,\ model.decoder_params,\ model.n_frames_input,\ model.n_frames_output)} else: self.meta_info = {} def __str__(self): return '\n'.join(f"{k}={v}" for k, v in vars(self).items()) def __call__(self, val_loss, model, epoch, step=0): """Summary Args: val_loss (TYPE): Description model (TYPE): Description epoch (TYPE): Description """ score = -val_loss model.update(self.meta_info) if step != 0: self.save_checkpoint(val_loss, model, epoch, step) else: if self.best_score is None: self.best_score = score self.save_checkpoint(val_loss, model, epoch, step) elif score < self.best_score: self.counter += 1 if self.counter >= self.patience: self.early_stop = True print(f"{BOLD}[] early stopping at epoch {epoch} !{CLR}") else: print(f"[] early stopping counter: {BOLD}{self.counter}/{self.patience}{CLR}") # self.del_old_models() else: self.best_score = score self.save_checkpoint(val_loss, model, epoch, step) self.counter = 0 t_save(model, f"{self.save_path}/LAST_checkpoint_{epoch}_{step}_{val_loss:.6f}.pth.tar") def del_old_models(self, keep=10): _, _, files = next(walk(self.save_path)) file_count = len(files) if file_count > keep: for old_model in sort_human(files)[:keep//2]: remove(path.join(self.save_path, old_model)) def save_checkpoint(self, val_loss, model, epoch, step=0): """Saves model when validation loss decrease Args: val_loss (TYPE): Description model (TYPE): Description epoch (TYPE): Description """ # save best model if step != 0: save_flag = "IE" print(f"[$] saveing model at step: {step} in epoch {epoch}") self.del_old_models() t_save(model, f"{self.save_path}/{save_flag}checkpoint_{epoch}_{step}_{val_loss}.pth.tar") else: if val_loss < self.global_min_loss: if self.verbose: print(f"[*] validation loss record {BOLD}{val_loss}{CLR} in epoch: {BOLD}{epoch}{CLR}@{step}") self.global_min_loss = val_loss save_flag = "best/" for file in scandir(f"{self.save_path}/{save_flag}"): unlink(file.path) else: save_flag = "" #self.del_old_models() t_save(model, f"{self.save_path}/{save_flag}checkpoint_{epoch}_{step}_{val_loss}.pth.tar") self.val_loss_min = val_loss	[ 1, 515, 2897, 1053, 2224, 29892, 2136, 287, 12935, 29892, 6686, 1919, 5992, 29892, 885, 392, 381, 29892, 443, 2324, 13, 13, 3166, 12655, 1053, 3041, 13, 3166, 4842, 305, 1053, 4078, 408, 260, 29918, 7620, 13, 13, 3166, 4303, 29889, 13239, 1053, 2656, 29918, 26029, 29892, 350, 5607, 29928, 29892, 17332, 29934, 13, 13, 13, 1990, 11095, 20754, 3262, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1480, 29918, 2084, 29892, 282, 24701, 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ccn/graph_data.py	noahtren/Cooperative-Communication-Networks	13	102389	<reponame>noahtren/Cooperative-Communication-Networks """Representing trees as a subset of graph data structures, where all information is provided as TensorFlow tensors during training time. """ import os import random import code from typing import Dict import tensorflow as tf import numpy as np from graphviz import Digraph from tqdm import tqdm class NodeType: def __init__(self, token:str=None, is_value:bool=None, ordered_children:bool=None, min_children:int=None, max_children:int=None, parent_blacklist=[]): # input validation assert (token is None) ^ (is_value is None), 'node type must be value XOR token' if token: assert ordered_children is not None, 'order_children must be specified for token node type' assert max_children is not None, 'max_children must be specified for token node type' assert min_children is not None, 'min_children must be specified for token node type' else: assert ordered_children is None, 'order_children should not be specified for value node type' assert max_children is None, 'max_children should not be specified for token node type' assert min_children is None, 'min_children should not be specified for token node type' # assignment self.token = token self.is_value = is_value self.ordered_children = ordered_children self.min_children = min_children self.max_children = max_children self.parent_blacklist = parent_blacklist LanguageSpecs = { 'arithmetic': { 'data_structure': 'tree', 'node_types': [ NodeType(is_value=True), NodeType(token='+', ordered_children=False, min_children=2, max_children=3), NodeType(token='-', ordered_children=True, min_children=2, max_children=2), NodeType(token='', ordered_children=False, min_children=2, max_children=3), NodeType(token='/', ordered_children=True, min_children=2, max_children=2), NodeType(token='abs', ordered_children=False, min_children=1, max_children=1, parent_blacklist=['abs']), ], 'value_tokens': ['a', 'b', 'c', 'd'], 'max_children': 3 } } class TensorGraph: """Representation of a graph data structure as TensorFlow tensors. All graphs are defined by an adjacency matrix (adj) and node-wise features (nodes and order.) This implementation requires that edges are directed but not weighted. `adj` is a matrix of shape [N, N] where the i, j index is 1 if i has a directed edge to j, otherwise it is 0. `node_features` is a dictionary of named node features, each of shape [n, ?] where n is the maximum number of nodes and ? is the encoded feature space of that feature. `num_nodes` is an integer that is less than or equal to N. It is the number of nodes that are actually in the graph. """ def __init__(self, adj:tf.Tensor, node_features:Dict[str, tf.Tensor], num_nodes:int, attrs:dict, language_spec:str): N = adj.shape[0] assert adj.shape[0] == adj.shape[1] for nf_name, node_feature in node_features.items(): assert node_feature.shape[0] == N, f'{nf_name} first dimension should match N ({N}), but was {node_feature.shape[0]}' self.adj = adj self.node_features = node_features self.num_nodes = num_nodes self.attrs = attrs self.language_spec = language_spec def visualize(self, name='unnamed', view=True): dot = Digraph(name=name) # nodes node_names = [] for n_i in range(self.num_nodes): ord_ = np.argmax(self.node_features['order'][n_i]) - 1 node_name = f"{self.attrs['value_tokens_list'][n_i]} ord={ord_}, i={n_i}" dot.node(node_name) node_names.append(node_name) # edges for n_i in range(self.num_nodes): src_name = node_names[n_i] for a_i, adj_bool in enumerate(self.adj[n_i]): if adj_bool: targ_name = node_names[a_i] dot.edge(src_name, targ_name) os.makedirs('gviz', exist_ok=True) dot.render(os.path.join('gviz', f'{name}'), format='pdf', view=view) @classmethod def tree_to_instance(cls, adj_list:list, values_list:list, value_tokens_list:list, order_list:list, max_nodes:int, language_spec:str): """Convert Python data types to a TensorGraph instance with max_nodes, using padding where necessary. If the Python data has too many nodes (more than `max_nodes`), then this function returns none. """ if len(adj_list) > max_nodes: return None pad_amt = max_nodes - len(adj_list) # values and order lists are already in correct numpy format. they just need # to be padded. padding only needs to occur along the first axis values = np.stack(values_list) order = np.stack(order_list) values = np.pad(values, [[0, pad_amt], [0, 0]]) order = np.pad(order, [[0, pad_amt], [0, 0]]) adj_arrs = [] for adj_indices in adj_list: adj_arr = np.zeros((len(adj_list)), dtype=np.int32) for n_i in adj_indices: adj_arr[n_i] = 1 adj_arrs.append(adj_arr) adj = np.stack(adj_arrs) adj = np.pad(adj, [[0, pad_amt], [0, pad_amt]]) return TensorGraph(adj, node_features={ 'values': values, 'order': order }, num_nodes=len(adj_list), attrs={ 'value_tokens_list': value_tokens_list }, language_spec=language_spec ) @classmethod def random_tree(cls, language_spec:str, min_num_values:int, max_num_values:int, kwargs): """Generate a random, valid tree based on a language spec. Trees are generated as NumPy arrays and can be converted to TensorFlow tensors later. """ SPEC = LanguageSpecs[language_spec] assert SPEC['data_structure'] == 'tree', \ f'can not make tree batch with {language_spec} because it is not for trees.' adj_list = [] values_list = [] value_tokens_list = [] order_list = [] NUM_VALUE_TOKENS = len(SPEC['value_tokens']) NON_VALUE_NODE_TYPES = [nt for nt in SPEC['node_types'] if not nt.is_value] NUM_NODE_VALUES = len(NON_VALUE_NODE_TYPES) + NUM_VALUE_TOKENS NUM_ORDER_INDICES = 1 + SPEC['max_children'] # make initial values num_values = random.randint(min_num_values, max_num_values) values = [] for val_num in range(num_values): adj = [] val = np.zeros((NUM_NODE_VALUES), dtype=np.int32) val_idx = random.randint(0, NUM_VALUE_TOKENS - 1) val[val_idx] = 1 val_token = SPEC['value_tokens'][val_idx] order = np.zeros((NUM_ORDER_INDICES), dtype=np.int32) adj_list.append(adj) values_list.append(val) value_tokens_list.append(val_token) order_list.append(order) # build tree from node stack until it has a single value tree_stack = list(range(num_values)) next_idx = len(tree_stack) while len(tree_stack) != 1: node_type_i = random.randint(0, len(NON_VALUE_NODE_TYPES) - 1) new_node_type = NON_VALUE_NODE_TYPES[node_type_i] child_select_num = min([ len(tree_stack), random.randint(new_node_type.min_children, new_node_type.max_children) ]) valid_children = [] for child_i in tree_stack: child_token = value_tokens_list[child_i] if child_token not in new_node_type.parent_blacklist: valid_children.append(child_i) else: pass # print(f'IGNORE INDEX {child_i}') children = random.sample(tree_stack, k=child_select_num) # update child information for o_i, child_i in enumerate(children): if new_node_type.ordered_children: order_list[child_i][o_i + 1] = 1 else: order_list[child_i][0] = 1 # create parent information p_adj = children p_val = np.zeros((NUM_NODE_VALUES), dtype=np.int32) p_val[NUM_VALUE_TOKENS + node_type_i] = 1 p_val_token = new_node_type.token p_order = np.zeros((NUM_ORDER_INDICES), dtype=np.int32) adj_list.append(p_adj) values_list.append(p_val) value_tokens_list.append(p_val_token) order_list.append(p_order) # update stack for child_i in children: tree_stack.remove(child_i) tree_stack.append(next_idx) next_idx += 1 # set root node order to unordered order_list[-1][0] = 1 return adj_list, values_list, value_tokens_list, order_list @classmethod def parse_tree(cls, language_spec, adj_list, value_tokens_list, order_list, max_nodes): """Given an adjacency matrix, token names, and a list of token orders, validate that it is a tree and then produce an instance of the tree as a TensorGraph. """ SPEC = LanguageSpecs[language_spec] NON_VALUE_NODE_TYPES = [nt for nt in SPEC['node_types'] if not nt.is_value] NON_VALUE_NODE_TOKENS = [nt.token for nt in NON_VALUE_NODE_TYPES] values = [] for token in value_tokens_list: if token in NON_VALUE_NODE_TOKENS: token_i = NON_VALUE_NODE_TOKENS.index(token) + len(NON_VALUE_NODE_TOKENS) elif token in SPEC['value_tokens']: token_i = SPEC['value_tokens'].index(token) else: raise RuntimeError(f"token {token} doesn't exist in spec '{language_spec}'") value = np.zeros((len(NON_VALUE_NODE_TOKENS) + len(SPEC['value_tokens'])), dtype=np.int32) value[token_i] = 1 values.append(value) orders = [] for order_idx in order_list: order = np.zeros((1 + SPEC['max_children']), dtype=np.int32) order[order_idx + 1] = 1 orders.append(order) return adj_list, values, value_tokens_list, orders @classmethod def instances_to_tensors(cls, tensor_graphs:list): """Given a list of instances of this class, turn it into a training dataset stored in memory. Returns: batched adj tensor batched dictionary of node feature tensors node_feature_specs dictionary, mapping node feature names to dimensionality """ adj = [] node_features = {} node_feature_specs = {} num_nodes = [] for tg in tensor_graphs: adj.append(tg.adj) num_nodes.append(tg.num_nodes) for name, tensor in tg.node_features.items(): if name not in node_feature_specs: node_features[name] = [tensor] node_feature_specs[name] = tensor.shape[-1] else: node_features[name].append(tensor) # stack along batch axis adj = tf.stack(adj, axis=0) node_features = {name: tf.cast(tf.stack(nf_list, axis=0), tf.float32) for name, nf_list in node_features.items()} num_nodes = tf.stack(num_nodes, axis=0) return adj, node_features, node_feature_specs, num_nodes def label_data(node_features, adj, num_nodes): # postprocessing for data labels nf_labels = {} for name in node_features.keys(): empty_rows = (tf.math.reduce_max(node_features[name], axis=-1) == 0)[:, :, tf.newaxis] empty_rows = tf.cast(empty_rows, tf.float32) nf_labels[name] = tf.concat([node_features[name], empty_rows], axis=-1) identity = tf.eye(adj.shape[1], adj.shape[1], batch_shape=[adj.shape[0]], dtype=tf.int32) # only apply identity matrix to nodes that exist r = tf.range(adj.shape[1]) grid_max_idxs = tf.tile( tf.expand_dims( tf.math.maximum(r[tf.newaxis], r[:, tf.newaxis]), 0), [num_nodes.shape[0], 1, 1] ) identity = tf.where(grid_max_idxs < num_nodes[:, tf.newaxis, tf.newaxis], identity, tf.zeros_like(identity)) # NOTE: I ran 2 experiments to prove that this provides a significant # performance boost adj_labels = adj + identity return nf_labels, adj_labels def get_dataset(language_spec:str, min_num_values:int, max_num_values:int, max_nodes:int, num_samples:int, vis_first=False, test=False, dummy=False, kwargs): instances = [] gen_name = 'train' if test: gen_name = 'test' if dummy: gen_name = 'dummy' print(f"Generating {gen_name} dataset") num_samples = num_samples // 5 if test else num_samples pbar = tqdm(total=num_samples) while len(instances) < num_samples: # make tree adj_list, values_list, value_tokens_list, order_list = \ TensorGraph.random_tree(language_spec, min_num_values, max_num_values) # convert to instance instance = TensorGraph.tree_to_instance(adj_list, values_list, value_tokens_list, order_list, max_nodes, language_spec) if instance is not None: instances.append(instance) pbar.update(1) if vis_first and len(instances) == 1: instance.visualize() pbar.close() adj, node_features, node_feature_specs, num_nodes = TensorGraph.instances_to_tensors(instances) nf_labels, adj_labels = label_data(node_features, adj, num_nodes) ds = { 'adj': adj, 'node_features': node_features, 'num_nodes': num_nodes, 'adj_labels': adj_labels, 'nf_labels': nf_labels } return ds, node_feature_specs if __name__ == "__main__": for _ in range(10000): adj_list, values_list, value_tokens_list, order_list = TensorGraph.random_tree('arithmetic', 3, 5) tg = TensorGraph.tree_to_instance(adj_list, 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week_1/prime_num.py	Ashutosh781/QSTP-Kratos_electronics-Solutions	0	109823	<reponame>Ashutosh781/QSTP-Kratos_electronics-Solutions #! /usr/bin/env python #Asking for input until a list of 10 integers given while True: num = input("Enter a list of 10 integers separated by a ',' : ").split(',') l = len(num) try: for i in range(l): num[i] = int(num[i]) p = (l==10) if p: break except: continue #Finding prime numbers prime_num = [] for x in num: if x > 1: for i in range(2,((x//2)+1)): if x % i == 0: break else: prime_num.append(x) elif x == 1: #Number 1 is not considered as prime continue else: #For any negative numbers or 0 continue if len(prime_num) > 0: print(f'Prime numbers from the given list are : {prime_num}') else: print("There were no prime numbers in the given list") print("\nEnd")	[ 1, 529, 276, 1112, 420, 29958, 29909, 845, 329, 10578, 29955, 29947, 29896, 29914, 29984, 1254, 29925, 29899, 29968, 3605, 359, 29918, 15436, 1617, 1199, 29899, 13296, 17925, 13, 29937, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 29937, 29909, 808, 292, 363, 1881, 2745, 263, 1051, 310, 29871, 29896, 29900, 11920, 2183, 13, 8000, 5852, 29901, 13, 1678, 954, 353, 1881, 703, 10399, 263, 1051, 310, 29871, 29896, 29900, 11920, 13055, 491, 263, 525, 5501, 584, 376, 467, 5451, 29317, 1495, 13, 1678, 301, 353, 7431, 29898, 1949, 29897, 13, 1678, 1018, 29901, 13, 4706, 363, 474, 297, 3464, 29898, 29880, 1125, 13, 9651, 954, 29961, 29875, 29962, 353, 938, 29898, 1949, 29961, 29875, 2314, 13, 4706, 282, 353, 313, 29880, 1360, 29896, 29900, 29897, 13, 4706, 565, 282, 29901, 13, 9651, 2867, 13, 1678, 5174, 29901, 13, 4706, 6773, 13, 13, 13, 13, 29937, 29943, 4015, 6019, 3694, 13, 13, 10080, 29918, 1949, 353, 5159, 13, 13, 1454, 921, 297, 954, 29901, 13, 1678, 565, 921, 1405, 29871, 29896, 29901, 13, 4706, 363, 474, 297, 3464, 29898, 29906, 29892, 3552, 29916, 458, 29906, 7240, 29896, 22164, 13, 9651, 565, 921, 1273, 474, 1275, 29871, 29900, 29901, 13, 18884, 2867, 13, 4706, 1683, 29901, 13, 9651, 6019, 29918, 1949, 29889, 4397, 29898, 29916, 29897, 13, 268, 13, 1678, 25342, 921, 1275, 29871, 29896, 29901, 396, 4557, 29871, 29896, 338, 451, 5545, 408, 6019, 13, 4706, 6773, 13, 1678, 1683, 29901, 396, 2831, 738, 8178, 3694, 470, 29871, 29900, 13, 4706, 6773, 13, 13, 361, 7431, 29898, 10080, 29918, 1949, 29897, 1405, 29871, 29900, 29901, 13, 1678, 1596, 29898, 29888, 29915, 4040, 603, 3694, 515, 278, 2183, 1051, 526, 584, 426, 10080, 29918, 1949, 29913, 1495, 13, 2870, 29901, 13, 1678, 1596, 703, 8439, 892, 694, 6019, 3694, 297, 278, 2183, 1051, 1159, 13, 13, 2158, 14182, 29876, 5044, 1159, 2 ]
TWT/apps/challenges/views/__init__.py	avibn/twtcodejam.net	0	49488	from .home import HomeView from .logout import LogoutView from .new import NewChallengeView from .view import DetailView from .test import TestView from .end import EndView from .start import StartView from .stop_team import StopTeams from .start_submission import StartSubmission from .stop_submission import StopSubmission from .start_team import StartTeams from .unreleased import UnreleasedView from .delete_challenge import DeleteView from .start_voting import StartVoting from .stop_voting import StopVoting from .custom_page_view import CustomView start = StartView.as_view() end = EndView.as_view() test = TestView.as_view() logout = LogoutView.as_view() new = NewChallengeView.as_view() view = DetailView.as_view() start_team = StartTeams.as_view() stop_team = StopTeams.as_view() start_submission = StartSubmission.as_view() stop_submission = StopSubmission.as_view() unreleased_view = UnreleasedView.as_view() delete_view = DeleteView.as_view() start_voting = StartVoting.as_view() stop_voting = StopVoting.as_view() view_custom = CustomView.as_view()	[ 1, 515, 869, 5184, 1053, 8778, 1043, 13, 3166, 869, 1188, 449, 1053, 4522, 449, 1043, 13, 3166, 869, 1482, 1053, 1570, 1451, 11768, 1043, 13, 3166, 869, 1493, 1053, 5953, 737, 1043, 13, 3166, 869, 1688, 1053, 4321, 1043, 13, 3166, 869, 355, 1053, 2796, 1043, 13, 3166, 869, 2962, 1053, 7370, 1043, 13, 3166, 869, 9847, 29918, 14318, 1053, 22303, 7141, 2232, 13, 3166, 869, 2962, 29918, 1491, 6737, 1053, 7370, 4035, 6737, 13, 3166, 869, 9847, 29918, 1491, 6737, 1053, 22303, 4035, 6737, 13, 3166, 869, 2962, 29918, 14318, 1053, 7370, 7141, 2232, 13, 3166, 869, 348, 276, 4611, 1053, 853, 276, 4611, 1043, 13, 3166, 869, 8143, 29918, 305, 11768, 1053, 21267, 1043, 13, 3166, 869, 2962, 29918, 29894, 11427, 1053, 7370, 29963, 11427, 13, 3166, 869, 9847, 29918, 29894, 11427, 1053, 22303, 29963, 11427, 13, 3166, 869, 6341, 29918, 3488, 29918, 1493, 1053, 8701, 1043, 13, 2962, 353, 7370, 1043, 29889, 294, 29918, 1493, 580, 13, 355, 353, 2796, 1043, 29889, 294, 29918, 1493, 580, 13, 1688, 353, 4321, 1043, 29889, 294, 29918, 1493, 580, 13, 1188, 449, 353, 4522, 449, 1043, 29889, 294, 29918, 1493, 580, 13, 1482, 353, 1570, 1451, 11768, 1043, 29889, 294, 29918, 1493, 580, 13, 1493, 353, 5953, 737, 1043, 29889, 294, 29918, 1493, 580, 13, 2962, 29918, 14318, 353, 7370, 7141, 2232, 29889, 294, 29918, 1493, 580, 13, 9847, 29918, 14318, 353, 22303, 7141, 2232, 29889, 294, 29918, 1493, 580, 13, 2962, 29918, 1491, 6737, 353, 7370, 4035, 6737, 29889, 294, 29918, 1493, 580, 13, 9847, 29918, 1491, 6737, 353, 22303, 4035, 6737, 29889, 294, 29918, 1493, 580, 13, 348, 276, 4611, 29918, 1493, 353, 853, 276, 4611, 1043, 29889, 294, 29918, 1493, 580, 13, 8143, 29918, 1493, 353, 21267, 1043, 29889, 294, 29918, 1493, 580, 13, 2962, 29918, 29894, 11427, 353, 7370, 29963, 11427, 29889, 294, 29918, 1493, 580, 13, 9847, 29918, 29894, 11427, 353, 22303, 29963, 11427, 29889, 294, 29918, 1493, 580, 13, 1493, 29918, 6341, 353, 8701, 1043, 29889, 294, 29918, 1493, 580, 2 ]
active_reward_learning/common/constants.py	david-lindner/idrl	9	146690	<gh_stars>1-10 import os from collections import defaultdict, namedtuple PLOTS_FOLDER = "plots" ENV_FOLDER = "envs" MODELS_FOLDER = "models" BASE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..") PLOTS_PATH = os.path.join(BASE_PATH, PLOTS_FOLDER) ENV_PATH = os.path.join(BASE_PATH, ENV_FOLDER) MODELS_PATH = os.path.join(BASE_PATH, MODELS_FOLDER) TELEGRAM_TOKEN = "" TELEGRAM_MESSAGE_ID = 0 # source: https://gist.github.com/thriveth/8560036 color_cycle = [ "#377eb8", "#ff7f00", "#4daf4a", "#e41a1c", "#dede00", "#999999", "#f781bf", "#a65628", "#984ea3", ] hatch_cycle = ["/", "\\", "\|", "-", "+", "x", "o", "O", ".", ""] marker_cycle = ["", "^", "o", "x", "d", "+", "v", "."] # pygame keys keys_nt = namedtuple( "keys_nt", [ "RIGHT", "LEFT", "UP", "DOWN", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", "EIGTH", "NINE", "ZERO", ], ) KEYS = keys_nt( 275, 276, 273, 274, ord("1"), ord("2"), ord("3"), ord("4"), ord("5"), ord("6"), ord("7"), ord("8"), ord("9"), ord("0"), ) # LEGACY KEY_RIGHT, KEY_LEFT, KEY_UP, KEY_DOWN = 275, 276, 273, 274 # Acquisition functions LABELS = { "rand": "random", "rand_unobs": "random_unobserved", "var": "variance", "pi": "probability_of_improvement", "ei": "expected_improvement", "epd": "expected_policy_divergence", "idrl": "directed_information_gain", "evr": "expected_volume_removal", "evrb": "expected_volume_removal_bernoulli", "maxreg": "maximum_regret", } color_cycle = [ "#377eb8", "#ff7f00", "#4daf4a", "#e41a1c", "#dede00", "#999999", "#f781bf", "#a65628", "#984ea3", ] ## dig is deprecated but was still in some results AF_COLORS = { "rand": "#4daf4a", "var": "#984ea3", "ei": "#ff7f00", "epd": "#377eb8", "idrl": "#e41a1c", "maxreg": "#b3b300", } AF_COLORS = defaultdict(lambda: "blue", AF_COLORS) AF_MARKERS = { "rand": "s", "var": "^", "ei": "d", "epd": "x", "idrl": "o", "maxreg": "v", } AF_MARKERS = defaultdict(lambda: ".", AF_MARKERS) AF_ALPHA = { "idrl": 1.0, } AF_ALPHA = defaultdict(lambda: 0.6, AF_ALPHA) AF_ZORDER = { "idrl": 2, } AF_ZORDER = defaultdict(lambda: 1, AF_ZORDER)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 2897, 13, 3166, 16250, 1053, 2322, 8977, 29892, 4257, 23583, 13, 13, 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evaluation/lcnn/postprocess.py	mlpc-ucsd/LETR	90	24271	import numpy as np def pline(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py u = ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd)) dx = x1 + u * px - x dy = y1 + u * py - y return dx * dx + dy * dy def psegment(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py u = max(min(((x - x1) * px + (y - y1) * py) / float(dd), 1), 0) dx = x1 + u * px - x dy = y1 + u * py - y return dx * dx + dy * dy def plambda(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py return ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd)) def postprocess(lines, scores, threshold=0.01, tol=1e9, do_clip=False): nlines, nscores = [], [] for (p, q), score in zip(lines, scores): start, end = 0, 1 for a, b in nlines: # nlines: Selected lines. if ( min( max(pline(p, q, a), pline(p, q, b)), max(pline(a, b, p), pline(a, b, q)), ) > threshold ** 2 ): continue lambda_a = plambda(p, q, a) lambda_b = plambda(p, q, b) if lambda_a > lambda_b: lambda_a, lambda_b = lambda_b, lambda_a lambda_a -= tol lambda_b += tol # case 1: skip (if not do_clip) if start < lambda_a and lambda_b < end: continue # not intersect if lambda_b < start or lambda_a > end: continue # cover if lambda_a <= start and end <= lambda_b: start = 10 break # case 2 & 3: if lambda_a <= start and start <= lambda_b: start = lambda_b if lambda_a <= end and end <= lambda_b: end = lambda_a if start >= end: break if start >= end: continue nlines.append(np.array([p + (q - p) * start, p + (q - p) * end])) nscores.append(score) return np.array(nlines), np.array(nscores)	[ 1, 1053, 12655, 408, 7442, 13, 13, 13, 1753, 715, 457, 29898, 29916, 29896, 29892, 343, 29896, 29892, 921, 29906, 29892, 343, 29906, 29892, 921, 29892, 343, 1125, 13, 1678, 282, 29916, 353, 921, 29906, 448, 921, 29896, 13, 1678, 11451, 353, 343, 29906, 448, 343, 29896, 13, 1678, 24488, 353, 282, 29916, 334, 282, 29916, 718, 11451, 334, 11451, 13, 1678, 318, 353, 5135, 29916, 448, 921, 29896, 29897, 334, 282, 29916, 718, 313, 29891, 448, 343, 29896, 29897, 334, 11451, 29897, 847, 4236, 29898, 29896, 29872, 29899, 29929, 29892, 5785, 29898, 1289, 876, 13, 1678, 15414, 353, 921, 29896, 718, 318, 334, 282, 29916, 448, 921, 13, 1678, 13475, 353, 343, 29896, 718, 318, 334, 11451, 448, 343, 13, 1678, 736, 15414, 334, 15414, 718, 13475, 334, 13475, 13, 13, 13, 1753, 282, 28192, 29898, 29916, 29896, 29892, 343, 29896, 29892, 921, 29906, 29892, 343, 29906, 29892, 921, 29892, 343, 1125, 13, 1678, 282, 29916, 353, 921, 29906, 448, 921, 29896, 13, 1678, 11451, 353, 343, 29906, 448, 343, 29896, 13, 1678, 24488, 353, 282, 29916, 334, 282, 29916, 718, 11451, 334, 11451, 13, 1678, 318, 353, 4236, 29898, 1195, 3552, 29898, 29916, 448, 921, 29896, 29897, 334, 282, 29916, 718, 313, 29891, 448, 343, 29896, 29897, 334, 11451, 29897, 847, 5785, 29898, 1289, 511, 29871, 29896, 511, 29871, 29900, 29897, 13, 1678, 15414, 353, 921, 29896, 718, 318, 334, 282, 29916, 448, 921, 13, 1678, 13475, 353, 343, 29896, 718, 318, 334, 11451, 448, 343, 13, 1678, 736, 15414, 334, 15414, 718, 13475, 334, 13475, 13, 13, 13, 1753, 715, 2269, 29898, 29916, 29896, 29892, 343, 29896, 29892, 921, 29906, 29892, 343, 29906, 29892, 921, 29892, 343, 1125, 13, 1678, 282, 29916, 353, 921, 29906, 448, 921, 29896, 13, 1678, 11451, 353, 343, 29906, 448, 343, 29896, 13, 1678, 24488, 353, 282, 29916, 334, 282, 29916, 718, 11451, 334, 11451, 13, 1678, 736, 5135, 29916, 448, 921, 29896, 29897, 334, 282, 29916, 718, 313, 29891, 448, 343, 29896, 29897, 334, 11451, 29897, 847, 4236, 29898, 29896, 29872, 29899, 29929, 29892, 5785, 29898, 1289, 876, 13, 13, 13, 1753, 1400, 5014, 29898, 9012, 29892, 19435, 29892, 16897, 29922, 29900, 29889, 29900, 29896, 29892, 304, 29880, 29922, 29896, 29872, 29929, 29892, 437, 29918, 24049, 29922, 8824, 1125, 13, 1678, 302, 9012, 29892, 302, 1557, 2361, 353, 19997, 5159, 13, 1678, 363, 313, 29886, 29892, 3855, 511, 8158, 297, 14319, 29898, 9012, 29892, 19435, 1125, 13, 4706, 1369, 29892, 1095, 353, 29871, 29900, 29892, 29871, 29896, 13, 4706, 363, 263, 29892, 289, 297, 302, 9012, 29901, 29871, 396, 302, 9012, 29901, 22012, 3454, 29889, 13, 9651, 565, 313, 13, 18884, 1375, 29898, 13, 462, 1678, 4236, 29898, 572, 457, 10456, 29886, 29892, 334, 29939, 29892, 334, 29874, 511, 715, 457, 10456, 29886, 29892, 334, 29939, 29892, 334, 29890, 8243, 13, 462, 1678, 4236, 29898, 572, 457, 10456, 29874, 29892, 334, 29890, 29892, 334, 29886, 511, 715, 457, 10456, 29874, 29892, 334, 29890, 29892, 334, 29939, 8243, 13, 18884, 1723, 13, 18884, 1405, 16897, 3579, 29871, 29906, 13, 632, 1125, 13, 18884, 6773, 13, 9651, 14013, 29918, 29874, 353, 715, 2269, 10456, 29886, 29892, 334, 29939, 29892, 334, 29874, 29897, 13, 9651, 14013, 29918, 29890, 353, 715, 2269, 10456, 29886, 29892, 334, 29939, 29892, 334, 29890, 29897, 13, 9651, 565, 14013, 29918, 29874, 1405, 14013, 29918, 29890, 29901, 13, 18884, 14013, 29918, 29874, 29892, 14013, 29918, 29890, 353, 14013, 29918, 29890, 29892, 14013, 29918, 29874, 13, 9651, 14013, 29918, 29874, 22361, 304, 29880, 13, 9651, 14013, 29918, 29890, 4619, 304, 29880, 13, 13, 9651, 396, 1206, 29871, 29896, 29901, 14383, 313, 361, 451, 437, 29918, 24049, 29897, 13, 9651, 565, 1369, 529, 14013, 29918, 29874, 322, 14013, 29918, 29890, 529, 1095, 29901, 13, 18884, 6773, 13, 13, 9651, 396, 451, 25869, 13, 9651, 565, 14013, 29918, 29890, 529, 1369, 470, 14013, 29918, 29874, 1405, 1095, 29901, 13, 18884, 6773, 13, 13, 9651, 396, 4612, 13, 9651, 565, 14013, 29918, 29874, 5277, 1369, 322, 1095, 5277, 14013, 29918, 29890, 29901, 13, 18884, 1369, 353, 29871, 29896, 29900, 13, 18884, 2867, 13, 13, 9651, 396, 1206, 29871, 29906, 669, 29871, 29941, 29901, 13, 9651, 565, 14013, 29918, 29874, 5277, 1369, 322, 1369, 5277, 14013, 29918, 29890, 29901, 13, 18884, 1369, 353, 14013, 29918, 29890, 13, 9651, 565, 14013, 29918, 29874, 5277, 1095, 322, 1095, 5277, 14013, 29918, 29890, 29901, 13, 18884, 1095, 353, 14013, 29918, 29874, 13, 13, 9651, 565, 1369, 6736, 1095, 29901, 13, 18884, 2867, 13, 13, 4706, 565, 1369, 6736, 1095, 29901, 13, 9651, 6773, 13, 4706, 302, 9012, 29889, 4397, 29898, 9302, 29889, 2378, 4197, 29886, 718, 313, 29939, 448, 282, 29897, 334, 1369, 29892, 282, 718, 313, 29939, 448, 282, 29897, 334, 1095, 12622, 13, 4706, 302, 1557, 2361, 29889, 4397, 29898, 13628, 29897, 13, 1678, 736, 7442, 29889, 2378, 29898, 29876, 9012, 511, 7442, 29889, 2378, 29898, 29876, 1557, 2361, 29897, 13, 2 ]
config/users/views.py	sitepoint-editors/Django-photo-app	0	25890	from django.views.generic import CreateView from django.contrib.auth import authenticate, login from django.contrib.auth.views import LoginView from django.contrib.auth.forms import UserCreationForm from django.urls import reverse_lazy class SignUpView(CreateView): template_name = 'users/signup.html' form_class = UserCreationForm success_url = reverse_lazy('photo:list') def form_valid(self, form): to_return = super().form_valid(form) user = authenticate( username=form.cleaned_data["username"], password=form.cleaned_data["<PASSWORD>"], ) login(self.request, user) return to_return class CustomLoginView(LoginView): template_name = 'users/login.html'	[ 1, 515, 9557, 29889, 7406, 29889, 19206, 1053, 6204, 1043, 13, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 15585, 403, 29892, 6464, 13, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 7406, 1053, 19130, 1043, 13, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 9514, 1053, 4911, 9832, 362, 2500, 13, 13, 3166, 9557, 29889, 26045, 1053, 11837, 29918, 433, 1537, 13, 13, 1990, 9954, 3373, 1043, 29898, 4391, 1043, 1125, 13, 13, 1678, 4472, 29918, 978, 353, 525, 7193, 29914, 4530, 786, 29889, 1420, 29915, 13, 268, 13, 1678, 883, 29918, 1990, 353, 4911, 9832, 362, 2500, 13, 13, 1678, 2551, 29918, 2271, 353, 11837, 29918, 433, 1537, 877, 21596, 29901, 1761, 1495, 13, 13, 1678, 822, 883, 29918, 3084, 29898, 1311, 29892, 883, 1125, 13, 4706, 304, 29918, 2457, 353, 2428, 2141, 689, 29918, 3084, 29898, 689, 29897, 13, 13, 4706, 1404, 353, 15585, 403, 29898, 13, 9651, 8952, 29922, 689, 29889, 14941, 287, 29918, 1272, 3366, 6786, 12436, 13, 9651, 4800, 29922, 689, 29889, 14941, 287, 29918, 1272, 3366, 29966, 25711, 17013, 11903, 1402, 13, 4706, 1723, 13, 13, 4706, 6464, 29898, 1311, 29889, 3827, 29892, 1404, 29897, 13, 13, 4706, 736, 304, 29918, 2457, 13, 268, 13, 1990, 8701, 11049, 1043, 29898, 11049, 1043, 1125, 13, 268, 13, 1678, 4472, 29918, 978, 353, 525, 7193, 29914, 7507, 29889, 1420, 29915, 2 ]
test_project/products/example.py	SzatmariA/CharmFL-dev	0	140648	<reponame>SzatmariA/CharmFL-dev<gh_stars>0 # This is a simple example script for FL # It should represent a webshop, or at least part of it cart = {} def addToCart(product): if(product not in cart.keys()): cart[str(product)] = 1 else: cart[str(product)] = cart[(str(product))] + 2 # bug -> should be 1 def removeFromCart(product): if(product in cart.keys()): if(cart[str(product)] > 1): cart[str(product)] = cart[str(product)] - 1 elif(cart[str(product)] == 1): del cart[str(product)] else: print("Something's fishy") def printProductsInCart(): print("Your cart: ") for product_name in cart.keys(): product_count = cart[str(product_name)] #print("* " + str(product_name) + ": " + str(product_count)) def getProductCount(product): if(product not in cart.keys()): return 0 else: return cart[str(product)] """ # We need # 4 apples # 1 orange juice # cheeser mum # U for i in range(1,4): addToCart("apple") removeFromCart("apple") addToCart("OJ") addToCart("cheese") addToCart("Ur mum") printProductsInCart() """	[ 1, 529, 276, 1112, 420, 29958, 29903, 22046, 29885, 1306, 29909, 29914, 1451, 2817, 10536, 29899, 3359, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 910, 338, 263, 2560, 1342, 2471, 363, 383, 29931, 13, 29937, 739, 881, 2755, 263, 1856, 19032, 29892, 470, 472, 3203, 760, 310, 372, 13, 13, 13823, 353, 6571, 13, 13, 13, 1753, 788, 1762, 25233, 29898, 4704, 1125, 13, 1678, 565, 29898, 4704, 451, 297, 7774, 29889, 8149, 580, 1125, 13, 4706, 7774, 29961, 710, 29898, 4704, 4638, 353, 29871, 29896, 13, 1678, 1683, 29901, 13, 4706, 7774, 29961, 710, 29898, 4704, 4638, 353, 7774, 15625, 710, 29898, 4704, 28166, 718, 29871, 29906, 396, 6494, 1599, 881, 367, 29871, 29896, 13, 13, 13, 1753, 3349, 4591, 25233, 29898, 4704, 1125, 13, 1678, 565, 29898, 4704, 297, 7774, 29889, 8149, 580, 1125, 13, 4706, 565, 29898, 13823, 29961, 710, 29898, 4704, 4638, 1405, 29871, 29896, 1125, 13, 9651, 7774, 29961, 710, 29898, 4704, 4638, 353, 7774, 29961, 710, 29898, 4704, 4638, 448, 29871, 29896, 13, 4706, 25342, 29898, 13823, 29961, 710, 29898, 4704, 4638, 1275, 29871, 29896, 1125, 13, 9651, 628, 7774, 29961, 710, 29898, 4704, 4638, 13, 1678, 1683, 29901, 13, 4706, 1596, 703, 16804, 29915, 29879, 9427, 29891, 1159, 13, 13, 13, 1753, 1596, 25767, 797, 25233, 7295, 13, 1678, 1596, 703, 10858, 7774, 29901, 16521, 13, 1678, 363, 3234, 29918, 978, 297, 7774, 29889, 8149, 7295, 13, 4706, 3234, 29918, 2798, 353, 7774, 29961, 710, 29898, 4704, 29918, 978, 4638, 13, 4706, 396, 2158, 703, 29930, 376, 718, 851, 29898, 4704, 29918, 978, 29897, 718, 29242, 376, 718, 851, 29898, 4704, 29918, 2798, 876, 13, 13, 13, 1753, 679, 7566, 3981, 29898, 4704, 1125, 13, 1678, 565, 29898, 4704, 451, 297, 7774, 29889, 8149, 580, 1125, 13, 4706, 736, 29871, 29900, 13, 1678, 1683, 29901, 13, 4706, 736, 7774, 29961, 710, 29898, 4704, 4638, 13, 15945, 29908, 13, 29937, 1334, 817, 13, 29937, 308, 29946, 623, 793, 13, 29937, 308, 29896, 24841, 3623, 625, 13, 29937, 4706, 923, 267, 261, 286, 398, 13, 29937, 4706, 501, 13, 13, 1454, 474, 297, 3464, 29898, 29896, 29892, 29946, 1125, 13, 1678, 788, 1762, 25233, 703, 11548, 1159, 13, 13, 5992, 4591, 25233, 703, 11548, 1159, 13, 1202, 1762, 25233, 703, 29949, 29967, 1159, 13, 1202, 1762, 25233, 703, 1173, 968, 1159, 13, 1202, 1762, 25233, 703, 29965, 29878, 286, 398, 1159, 13, 13, 2158, 25767, 797, 25233, 580, 13, 15945, 29908, 2 ]
PopUp.py	Bogiasdroid/BackMeUp	0	173676	import sys from PyQt4 import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Dialog(QtGui.QDialog): def __init__(self): QtGui.QDialog.__init__(self) self.setupUi(self) def setupUi(self, Dialog): Dialog.setObjectName(_fromUtf8("Dialog")) Dialog.setFixedSize(293, 160) self.buttonBox = QtGui.QDialogButtonBox(Dialog) self.buttonBox.setEnabled(True) self.buttonBox.setGeometry(QtCore.QRect(120, 120, 161, 32)) self.buttonBox.setOrientation(QtCore.Qt.Horizontal) self.buttonBox.setStandardButtons(QtGui.QDialogButtonBox.Ok) self.buttonBox.setObjectName(_fromUtf8("buttonBox")) self.textBrowser = 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main.py	guysoft/kivy-external-storage-permission	1	22019	import kivy from kivy.app import App from kivy.uix.button import Button import android import os import time from android.permissions import Permission, request_permission, check_permission from kivy.clock import Clock class MyApp(App): def second_thread(self, data): print("starting second thread") permission_status = check_permission(Permission.WRITE_EXTERNAL_STORAGE) print(permission_status) if permission_status is not None and permission_status: print("got permission") path = os.environ["SECONDARY_STORAGE"] test_path = os.path.join(path, "test_yay") os.makedirs(test_path) else: Clock.schedule_once(self.second_thread, 1) def callback(self, data): print("Pushed button, running") print("request permission") print(request_permission(Permission.WRITE_EXTERNAL_STORAGE)) Clock.schedule_once(self.second_thread, 5) def build(self): return Button(text='Touch to test writing to ' + os.environ["SECONDARY_STORAGE"], on_press=self.callback) if __name__ == '__main__': MyApp().run()	[ 1, 1053, 413, 440, 29891, 13, 13, 3166, 413, 440, 29891, 29889, 932, 1053, 2401, 13, 3166, 413, 440, 29891, 29889, 29884, 861, 29889, 3092, 1053, 11025, 13, 5215, 1442, 13, 5215, 2897, 13, 5215, 931, 13, 3166, 1442, 29889, 17858, 6847, 1053, 20894, 2333, 29892, 2009, 29918, 16074, 29892, 1423, 29918, 16074, 13, 3166, 413, 440, 29891, 29889, 13058, 1053, 315, 908, 13, 13, 13, 13, 1990, 1619, 2052, 29898, 2052, 1125, 13, 1678, 822, 1473, 29918, 7097, 29898, 1311, 29892, 848, 1125, 13, 4706, 1596, 703, 2962, 292, 1473, 3244, 1159, 13, 4706, 10751, 29918, 4882, 353, 1423, 29918, 16074, 29898, 27293, 29889, 16365, 29918, 5746, 4945, 29940, 1964, 29918, 1254, 1955, 10461, 29897, 13, 4706, 1596, 29898, 16074, 29918, 4882, 29897, 13, 308, 13, 4706, 565, 10751, 29918, 4882, 338, 451, 6213, 322, 10751, 29918, 4882, 29901, 13, 9651, 1596, 703, 7085, 10751, 1159, 13, 9651, 2224, 353, 2897, 29889, 21813, 3366, 1660, 6007, 29928, 19926, 29918, 1254, 1955, 10461, 3108, 13, 9651, 1243, 29918, 2084, 353, 2897, 29889, 2084, 29889, 7122, 29898, 2084, 29892, 376, 1688, 29918, 29891, 388, 1159, 13, 9651, 2897, 29889, 29885, 12535, 12935, 29898, 1688, 29918, 2084, 29897, 13, 4706, 1683, 29901, 13, 9651, 315, 908, 29889, 816, 11272, 29918, 10646, 29898, 1311, 29889, 7496, 29918, 7097, 29892, 29871, 29896, 29897, 13, 13, 308, 13, 13, 632, 13, 13, 1678, 822, 6939, 29898, 1311, 29892, 848, 1125, 13, 4706, 1596, 703, 29925, 15392, 2826, 29892, 2734, 1159, 13, 4706, 1596, 703, 3827, 10751, 1159, 13, 4706, 1596, 29898, 3827, 29918, 16074, 29898, 27293, 29889, 16365, 29918, 5746, 4945, 29940, 1964, 29918, 1254, 1955, 10461, 876, 13, 4706, 315, 908, 29889, 816, 11272, 29918, 10646, 29898, 1311, 29889, 7496, 29918, 7097, 29892, 29871, 29945, 29897, 13, 13, 13, 13, 1678, 822, 2048, 29898, 1311, 1125, 13, 13, 4706, 736, 11025, 29898, 726, 2433, 15852, 304, 1243, 5007, 304, 525, 718, 2897, 29889, 21813, 3366, 1660, 6007, 29928, 19926, 29918, 1254, 1955, 10461, 12436, 373, 29918, 2139, 29922, 1311, 29889, 14035, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1619, 2052, 2141, 3389, 580, 13, 2 ]
concepttordf/betydningsbeskrivelse.py	jimjyang/concepttordf	0	1605641	<filename>concepttordf/betydningsbeskrivelse.py<gh_stars>0 from abc import ABC from enum import Enum class RelationToSource(Enum): sitatFraKilde = "quoteFromSource" basertPåKilde = "basedOnSource" egendefinert = "noSource" class Betydningsbeskrivelse(ABC): def __init__(self, b: dict = None): if b is not None: if 'text' in b: self.text = b['text'] if 'remark' in b: self.remark = b['remark'] if 'scope' in b: self.scope = b['scope'] if 'relationtosource' in b: self.relationtosource = b['relationtosource'] if 'source' in b: self.source = b['source'] if 'modified' in b: self.modified = b['modified'] @property def type(self): return self._type @type.setter def type(self, type): self._type = type @property def text(self) -> dict: return self._text @text.setter def text(self, text: dict): self._text = text @property def remark(self) -> dict: return self._remark @remark.setter def remark(self, remark: dict): self._remark = remark @property def scope(self) -> dict: return self._scope @scope.setter def scope(self, scope: dict): self._scope = scope @property def relationtosource(self) -> str: return self._relationtosource @relationtosource.setter def relationtosource(self, relationtosource: str): self._relationtosource = relationtosource @property def source(self) -> dict: return self._source @source.setter def source(self, source: dict): self._source = source @property def modified(self) -> dict: return self._modified @modified.setter def modified(self, modified: dict): self._modified = modified	[ 1, 529, 9507, 29958, 535, 1547, 29873, 536, 29888, 29914, 6878, 2941, 11753, 5707, 29895, 374, 955, 344, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 25638, 1053, 16417, 13, 3166, 14115, 1053, 1174, 398, 13, 13, 13, 1990, 6376, 362, 1762, 4435, 29898, 16854, 1125, 13, 1678, 269, 7366, 29943, 336, 29968, 7154, 353, 376, 1396, 4591, 4435, 29908, 13, 1678, 2362, 814, 29925, 30020, 29968, 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353, 9120, 13, 2 ]
ClassicFunction.py	gr0mph/SpringChallenge2020	0	93927	import sys, copy, heapq import math, random, time import numpy as np # Class from Challenge import Pacman def init_pacman_from_list(kanban_node, PACMAN_LIST): for d1 in PACMAN_LIST: pacman_new = Pacman(None) pacman_new.mine, pacman_new.x, pacman_new.y = d1[1], d1[2], d1[3] pacman_new.type = d1[4] if pacman_new.mine == 0 : pacman_new.id = -1 * (d1[0] + 1) kanban_node.opp[pacman_new.id] = pacman_new else : pacman_new.id = 1 * (d1[0] + 1) kanban_node.mine[pacman_new.id] = pacman_new return kanban_node def init_pellet_from_list(kanban_node, PELLET_LIST): for _, c1 in kanban_node.cases.items(): c1.pellet = 1 for c1 in PELLET_LIST: coord = c1[1], c1[0] if coord in kanban_node.nodes : kanban_node.nodes[coord].pellet = 10 if coord in kanban_node.cases : kanban_node.cases[coord].pellet = 10 return kanban_node	[ 1, 1053, 10876, 29892, 3509, 29892, 16947, 29939, 13, 5215, 5844, 29892, 4036, 29892, 931, 13, 13, 5215, 12655, 408, 7442, 13, 13, 29937, 4134, 13, 3166, 27211, 1053, 9790, 1171, 13, 13, 1753, 2069, 29918, 29886, 562, 1171, 29918, 3166, 29918, 1761, 29898, 11052, 2571, 29918, 3177, 29892, 349, 2477, 27616, 29918, 24360, 1125, 13, 1678, 363, 270, 29896, 297, 349, 2477, 27616, 29918, 24360, 29901, 13, 4706, 22906, 1171, 29918, 1482, 353, 9790, 1171, 29898, 8516, 29897, 13, 4706, 22906, 1171, 29918, 1482, 29889, 24669, 29892, 22906, 1171, 29918, 1482, 29889, 29916, 29892, 22906, 1171, 29918, 1482, 29889, 29891, 353, 270, 29896, 29961, 29896, 1402, 270, 29896, 29961, 29906, 1402, 270, 29896, 29961, 29941, 29962, 13, 4706, 22906, 1171, 29918, 1482, 29889, 1853, 353, 270, 29896, 29961, 29946, 29962, 13, 4706, 565, 22906, 1171, 29918, 1482, 29889, 24669, 1275, 29871, 29900, 584, 13, 9651, 22906, 1171, 29918, 1482, 29889, 333, 353, 448, 29896, 334, 313, 29881, 29896, 29961, 29900, 29962, 718, 29871, 29896, 29897, 13, 9651, 6841, 2571, 29918, 3177, 29889, 9354, 29961, 29886, 562, 1171, 29918, 1482, 29889, 333, 29962, 353, 22906, 1171, 29918, 1482, 13, 4706, 1683, 584, 13, 9651, 22906, 1171, 29918, 1482, 29889, 333, 353, 29871, 29896, 334, 313, 29881, 29896, 29961, 29900, 29962, 718, 29871, 29896, 29897, 13, 9651, 6841, 2571, 29918, 3177, 29889, 24669, 29961, 29886, 562, 1171, 29918, 1482, 29889, 333, 29962, 353, 22906, 1171, 29918, 1482, 13, 1678, 736, 6841, 2571, 29918, 3177, 13, 13, 1753, 2069, 29918, 13111, 1026, 29918, 3166, 29918, 1761, 29898, 11052, 2571, 29918, 3177, 29892, 349, 6670, 1307, 29911, 29918, 24360, 1125, 13, 1678, 363, 17117, 274, 29896, 297, 6841, 2571, 29918, 3177, 29889, 11436, 29889, 7076, 7295, 13, 4706, 274, 29896, 29889, 13111, 1026, 353, 29871, 29896, 13, 1678, 363, 274, 29896, 297, 349, 6670, 1307, 29911, 29918, 24360, 29901, 13, 4706, 29311, 353, 274, 29896, 29961, 29896, 1402, 274, 29896, 29961, 29900, 29962, 13, 4706, 565, 29311, 297, 6841, 2571, 29918, 3177, 29889, 18010, 584, 6841, 2571, 29918, 3177, 29889, 18010, 29961, 1111, 536, 1822, 13111, 1026, 353, 29871, 29896, 29900, 13, 4706, 565, 29311, 297, 6841, 2571, 29918, 3177, 29889, 11436, 584, 6841, 2571, 29918, 3177, 29889, 11436, 29961, 1111, 536, 1822, 13111, 1026, 353, 29871, 29896, 29900, 13, 1678, 736, 6841, 2571, 29918, 3177, 13, 2 ]

tools/prepare_macaque_data/split_trainval.py

MeowMeowLady/Towards-Neuro-Seg-Macaque-Brain

1

172353

<reponame>MeowMeowLady/Towards-Neuro-Seg-Macaque-Brain<gh_stars>1-10 # -*- coding: utf-8 -*- """ Created on 19-7-31 下午6:02 IDE PyCharm @author: <NAME> this script is used to split training and validation sets by generating list-txt. """ from os.path import join as opj import random from glob import glob import os txt_save_path = '' total_list = os.listdir('/') random.shuffle(total_list) random.shuffle(total_list) train_list = open(opj(txt_save_path, 'train.txt'), 'w+') valid_list = open(opj(txt_save_path, 'valid.txt'), 'w+') for i in range(3500): train_list.write('{}\n'.format(total_list[i][:-3])) for i in range(1000): valid_list.write('{}\n'.format(total_list[i+3500][:-3])) train_list.close() valid_list.close()

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productdb/testing/unittests/products.py

tspycher/python-productdb

0

38248

from . import BasicTestCase class ProductsTestCase(BasicTestCase): def test_basic(self): rv = self.client.get('/') assert rv.status_code == 200 data = self.parseJsonResponse(rv) assert '_links' in data def test_get_all_products(self): rv = self.client.get('/product') assert rv.status_code == 200 data = self.parseJsonResponse(rv) assert '_items' in data # add further data tests here, load basic data by fixtures pass

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core/cdag/node/sub.py

prorevizor/noc

84

16518

# ---------------------------------------------------------------------- # SubNode # ---------------------------------------------------------------------- # Copyright (C) 2007-2020 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # Python modules from typing import Optional # NOC modules from .base import BaseCDAGNode, ValueType, Category class SubNode(BaseCDAGNode): """ Subtract `y` from `y` """ name = "sub" categories = [Category.OPERATION] def get_value(self, x: ValueType, y: ValueType) -> Optional[ValueType]: return x - y

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kubeflow/utils/kaggle.py

GalaxyIT/kubeflow-utils

0

172939

<filename>kubeflow/utils/kaggle.py import json from pathlib import Path class Kaggle: def login(self, username, apikey): kaggle_json = Kaggle.__generate_kaggle_json_string(username, apikey) Kaggle.__save_kaggle_json(kaggle_json) @staticmethod def __generate_kaggle_json_string(username, apikey): data = { "username": username, "key": apikey } return json.dumps(data) @staticmethod def __save_kaggle_json(json_str): kaggle_path = Path.home().joinpath('.kaggle') if not kaggle_path.exists(): kaggle_path.mkdir(parents=True) with open(str(kaggle_path.joinpath('kaggle.json')), 'w+') as kaggle_json: print(json_str, file=kaggle_json)

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all_raspi_code_backup/DriveTesting.py

lord-pradhan/SnowBot

2

15482

<reponame>lord-pradhan/SnowBot<gh_stars>1-10 """ Program: DriveTesting.py Revised On: 12/01/2019 """ ### Library Imports from DriveArduino import DriveArduino import numpy as np from time import sleep from sys import exit from signal import signal, SIGINT ### ### CTRL + C Signal Handler & Resource Cleanup def signal_handler(sig, frame): """Handler for CTRL + C clean exit.""" print('Quitting program.') cleanup() def cleanup(): """Resource cleanup.""" drive.close() print('Resource cleanup completed.') exit(0) signal(SIGINT, signal_handler) ### ### Arduino Configuration addr = 0x08 drive = DriveArduino(addr) ### ### Main Program print('Press CTRL + C to exit.') while True: setpoints = np.array([25, 25, -25, -25]) drive.set_rpm(setpoints) sleep(1) drive.update() print(drive.rpm) print(drive.pwm) print() ###

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waterbutler/providers/dropbox/settings.py

laurenrevere/waterbutler

0

149895

<reponame>laurenrevere/waterbutler try: from waterbutler import settings except ImportError: settings = {} config = settings.get('DROPBOX_PROVIDER_CONFIG', {}) BASE_URL = config.get('BASE_URL', 'https://api.dropbox.com/1/') BASE_CONTENT_URL = config.get('BASE_CONTENT_URL', 'https://api-content.dropbox.com/1/')

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restrictive_growth_strings.py

oversj96/PyKabe

1

36889

"""restrictive_growth_strings.py: Constructs the lexicographic restrictive growth string representation of all the way to partition a set, given the length of the set.""" __author__ = "<NAME>" __copyright__ = "oversj96.github.io" def set_to_zero(working_set, index): """Given a set and an index, set all elements to 0 after the index.""" if index == len(working_set) - 1: return working_set else: for i in range(index + 1, len(working_set)): working_set[i] = 0 return working_set def update_b_row(a_row, b_row): """Update b row to reflect the max value of a sequence range in row a""" for i in range(1, len(a_row)): b_row[i - 1] = max(a_row[:i]) + 1 return b_row def restrictive_growth_strings(length): """Returns the set of all partitions of a set in a lexicographic integer format. The algorithm is based on Donald Knuth's volume 4A on combinatoric algorithms. Algorithm H.""" n = length - 1 a_string = [0 for i in range(0, length)] b_string = [1 for i in range(0, n)] lexico_string = [a_string.copy()] incrementable = True while incrementable: incrementable = False for index in range(n, 0, -1): if a_string[index] < n and a_string[index] < b_string[index - 1]: incrementable = True a_string[index] += 1 a_string = set_to_zero(a_string, index) b_string = update_b_row(a_string, b_string) lexico_string.append(a_string.copy()) break return lexico_string if __name__ == "__main__": """A quick way to check if the module works correctly is to compare the partition count to the bell number of the correct 'n' size. For a set of 7 elements, the partition count should be 877 by Bell's numbers.""" strings = restrictive_growth_strings(7) for string in strings: print(string) print(len(strings))

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tests/test_inchi.py

ginkgobioworks/pychemy

3

25988

<filename>tests/test_inchi.py from __future__ import print_function import unittest import six from pychemy.inchi import InChI class InchiTest(unittest.TestCase): def test_unicode_inchi(self): inchi_str = six.u('InChI=1S/C14H18O8/c1-20-9-4-7(5-15)2-3-8(9)21-14-13(19)12(18)11(17)10(6-16)22-14/h2-5,10-14,16-19H,6H2,1H3/t10-,11-,12+,13-,14-/m1/s1') inchi = InChI(inchi_str) formula = inchi.formula self.assertEqual(formula.formula, 'C14H18O8')

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aser/server/cli.py

HKUST-KnowComp/CSKB-Population

13

170992

<gh_stars>10-100 def main(): from aser.server import ASERServer from aser.utils.config import get_server_args_parser parser = get_server_args_parser() args = parser.parse_args() ASERServer(args)

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order/models/customers.py

fbsamples/cp_reference

2

26310

<reponame>fbsamples/cp_reference # Copyright 2004-present, Facebook. All Rights Reserved. from django.db import models from core.models import BaseModel class Customer(BaseModel): """Represent a single customer instance fields: store: the store this customer belongs to full_name: customer full name email: customer email addr: customer shipping addr """ store = models.ForeignKey("shop.Store", null=True, on_delete=models.SET_NULL) full_name = models.CharField(max_length=255) email = models.CharField(max_length=255) addr = models.TextField(blank=True, null=True) def __str__(self): return self.full_name

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pysoundcloud/client.py

omarcostahamido/PySoundCloud

4

14144

<filename>pysoundcloud/client.py import re import requests from typing import Union from pysoundcloud.soundcloudplaylists import SoundCloudPlaylists from pysoundcloud.soundcloudsearchresults import SoundCloudSearchResults from pysoundcloud.soundcloudlikedtracks import SoundCloudLikedTracks from pysoundcloud.soundcloudplaylist import SoundCloudPlaylist from pysoundcloud.soundcloudtrack import SoundCloudTrack from pysoundcloud.soundcloudrelatedtracks import SoundCloudRelatedTracks class Client: base_url: str = "https://api-v2.soundcloud.com/" client_id: str = "" """ :var base_url: The base url for all requests to the SoundCloud API :var client_id: The client ID to use with the SoundCloud API """ def __init__(self, client_id: str) -> None: """ Setup the SoundCloud client to interact with the API :param client_id: Your SoundCloud client ID :return: None """ self.client_id = client_id def search(self, query: str, limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudSearchResults]: """ Search SoundCloud for the specified query For some reason it doesn't always work and I have no clue why :param query: The query to search for :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudSearchResults, or False if response is not 200 """ parameters = {"q": query, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "search" response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudSearchResults(response, client_id=self.client_id, parent=self) def track(self, track_id: int) -> Union[bool, SoundCloudTrack]: """ Gets data about the track with the specified track_id :param track_id: The track id to search for :return: a SoundCloudTrack with data about the track, or False if response is not 200 """ parameters = {"client_id": self.client_id} url = self.base_url + "tracks/{}".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudTrack(response.json(), self.client_id, parent=self) def related(self, track_id: int, limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudRelatedTracks]: """ Gets tracks related to the specified track_id :param track_id: The track id to find related tracks for :param limit: The number of tracks to find :param offset: The number of tracks to search for from zero, so offset 10 and limit 10 means find tracks 10-20 :return: SoundCloudRelatedTracks with the tracks, or False if response is not 200 """ parameters = {"limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/related".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudRelatedTracks(response.json(), self.client_id) def playlists(self, track_id: int, representation: str = "mini", limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudPlaylists]: """ Gets playlists containing a specified track :param track_id: The track ID to find playlists containing :param representation: The type of representation (either full or mini) :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudPlaylists containing the track, or False if response is not 200 """ parameters = {"representation": representation, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/playlists_without_albums".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudPlaylists(response.json(), self.client_id, parent=self) def albums(self, track_id: int, representation: str = "mini", limit: int = 10, offset: int = 0) -> Union[bool, SoundCloudPlaylists]: """ Gets albums containing a specified track :param track_id: The track ID to find albums containing :param representation: The type of representation (either full or mini) :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudPlaylists containing the track, or False if response is not 200 """ parameters = {"representation": representation, "limit": limit, "offset": offset, "client_id": self.client_id} url = self.base_url + "tracks/{}/albums".format(track_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False if (len(response.json()["collection"]) == 0): return False return SoundCloudPlaylists(response.json(), self.client_id) def comments(self): """ .. todo:: Add a function to get comments on a specific track or by a specific user """ pass # Todo: add comments def web_profiles(self): """ .. todo:: Add a function to get the "web profiles" of a specific user """ pass # Todo: add web_profiles def liked_tracks(self, user_id: int, limit: int = 24, offset: int = 0) -> Union[bool, SoundCloudLikedTracks]: """ Gets the user's liked tracks :param user_id: The ID of the user to find liked tracks for :param limit: The number of results to return :param offset: The start position from 0 :return: SoundCloudLikedTracks containing all the tracks, or False if response is not 200 """ parameters = {"client_id": self.client_id, "limit": limit, "offset": offset} url = self.base_url + "users/{}/track_likes".format(user_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudLikedTracks(response, client_id=self.client_id) def playlist(self, playlist_id: int = None, playlist_url: str = None, representation: str = "full", secret_token: str = None) -> Union[bool, SoundCloudPlaylist]: """ Get a playlist based on a specified playlist_id or playlist_url :param playlist_id: The ID of the playlist :param playlist_url: The URL of the playlist :param representation: The playlist representation (either fill or mini) :param secret_token: An optional secret token :return: A SoundCloudPlaylist, or False if response is not 200 """ if (playlist_id is None): if (playlist_url is not None): response = requests.get(playlist_url) patterns = [ r'<meta property="twitter:app:url:(?:googleplay|iphone|ipad)' r'content="soundcloud:\/\/playlists:([0-9]+)">', r'<meta property="twitter:player" content="https:\/\/w\.soundcloud\.com\/player\/\?url=https' r'%3(?:a|A)%2(?:f|F)%2(?:f|F)api\.soundcloud\.com%2(?:f|F)playlists%2(?:f|F)([0-9]+)', r'<meta property="al:(?:ios|android):url" content="soundcloud:\/\/playlists:([0-9]+)">', r'<link rel="alternate" href="android-app:\/\/com\.soundcloud\.android\/soundcloud\/' r'playlists:([0-9]+)">', r'<link rel="alternate" href="ios-app:\/\/336353151\/soundcloud\/playlists:([0-9]+)">' ] for pattern in patterns: if (playlist_id is None): search_results = re.search(pattern, response.text) if (search_results is not None): playlist_id = search_results.group(1) if (playlist_id is None): print("Error: Could not find the playlist id from the url \"{}\"".format(playlist_url)) return False parameters = {"representation": representation, "client_id": self.client_id} if (secret_token is not None): parameters["secret_token"] = secret_token url = self.base_url + "playlists/{}".format(playlist_id) response = requests.get(url, params=parameters) if (response.status_code != 200): print("Error: Received code {}".format(response.status_code)) return False return SoundCloudPlaylist(response.json(), self.client_id, parent=self)

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app/api/handler/PersonSearch.py

jormanfernandez/crud

0

110207

import falcon import json from data.operators.PersonOperator import PersonOperator from utils.logger import Log from utils.form.fields import Fields fields = Fields() class PersonSearchHandler(object): """ It handles the request to no specific persons Args: object (object): No info """ def on_get(self, req, resp, mode, value): """ Handles the GET for search of persons and gives a list of people that match the search Args: req (Falcon HTTP Request): Request received by the server resp (Falcon HTTP Response): Response constructed by the server mode (str): The search method to be used (name, lastname, phone, email) value (str, int): The value to be search Returns: None """ Log(f"GET Request Received", req=req) resp.content_type = "application/json" if mode not in getattr(fields, "field"): resp.status = falcon.HTTP_400 resp.body = json.dumps({ "msg": "Search method not allowed" }) else: resp.status = falcon.HTTP_200 resp.body = json.dumps(PersonOperator.searchBy(key=mode, value=value), ensure_ascii=False) resp.body += "\n"

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taut_euler_class.py

henryseg/Veering

2

7614

<reponame>henryseg/Veering # # taut_euler_class.py # from file_io import parse_data_file, write_data_file from taut import liberal, isosig_to_tri_angle from transverse_taut import is_transverse_taut from sage.matrix.constructor import Matrix from sage.modules.free_module_element import vector from sage.arith.misc import gcd from sage.arith.functions import lcm # # Goal - given a transverse taut triangulation, decide if the # associated "absolute" euler class is torsion or not. If it is # torsion, determine its order. # # Contents and overview: # 1. References. # # 2. Background. # # 3. Helper functions. # # 4. Truncate. We build the correct "truncated" cell structure \calT' # from (M, \calT) and give generators for the cochain groups # C^k(\calT', \ZZ) (k = 1, 2). # # 5. Representative. We find a two-cocycle E \in Z^2(\calT', \ZZ) # that represents E(\calT) \in H^2(M, \ZZ). # # 6. Coboundary. We find the matrix for the coboundary operator # \delta^1. # # 7. Linear algebra. We solve the linear problem to decide if E is a # coboundary - that is, if E lies in B^2(\calT', \ZZ) - that is, if E # is in the image of \delta^1. # # 8. Remarks. # # 9. Calling code # # 1. References. # # <NAME> - Orderability and Dehn filling # Ghys - Groups acting on the circle # Thurston - A norm for the homology of three-manifolds # <NAME> - Foliations, chapter four # 2. Background: # Suppose that (M, \calT) is a transverse taut triangulation. Then # \calT^{2} is the "horizontal branched surface". This caries various # laminations, which extend to foliations on M. All of these have the # same Euler class, which we will denote E(\calT) \in H^2(M, \ZZ). # Suppose that \calF is a carried foliation and let UT\calF be the # unit tangent bundle over \calF. The Euler class E vanishes exactly # when UT\calF has a section; that is, when the unit tangent bundle is # trivialisable. # Recall: # Suppose that X is an F-bundle over B. We have # # i # F -------> X <--. # | | # | | # p| |s # | | # v | # B ---' # # So s \from B \to X is a \emph{section} if p \circ s = Id_B # 3. Helper functions def diagonal(D): return [D[i][i] for i in range(min(D.dimensions()))] # 4. Truncate. # Suppose that M is a connected, cusped, oriented three-manifold. Let # C = C(M) \geq 1 be the number of cusps of M. Suppose that \calT is a # transverse taut ideal triangulation of M. Let T = T(\calT) \geq 1 # be the number of tetrahedra of \calT. # We use Regina to number and orient the edges \{e_i\}_{i = 0}^{T-1}, # the faces \{f_i\}_{i = 0}^{2T-1}, and the tetrahedra \{t_i\}_{i = # 0}^{T-1} of \calT. We call all of these \emph{ideal} cells. Note # that the transverse structure also gives us co-orientations of the # e_i and the f_i, called "upwards" # We remove a small open neighbourbood of all ideal vertices of all # model tetrahedra. This gives the \emph{truncated} cell structure # \calT'. The remains of the ideal cells are called \emph{truncated} # cells; we abuse and reuse the notations e_i and f_i for these. The # truncated cells inherit orientations and co-orientations. The new # cells are called \emph{peripheral} cells. We number these as # follows: # e_{ij} is the peripheral edge cutting vertex v_j off of ideal face f_i # f_{ij} is the peripheral face cutting vertex v_j off of ideal tetrahedron t_i # Note that every truncated face is combinatorially a hexagon. The # boundary of this hexagon contains three truncated edges alternating # with three peripheral edges. We orient each peripheral edge e_{ij} # so that the orientation of e_{ij} agrees with the orientation # induced by \bdy f_i. We orient each peripheral face f_{ij} # anti-clockwise, as viewed from infinity (that is, from outside of # M). Also, we equip e_{ij} and f_{ij} with co-orientations pointing # out of M, called "outward". # e_{i0} # --- # / \ # e_2 / \ e_1 # / \ # / f_i \ # \ / # e_{i1} --------- e_{i2} # e_0 # For an edge e or a face f we use e^* and f^* to denote the dual in # C^1(\calT', \ZZ) or C^2(\calT', \ZZ). Thus \{e^*_i\} \cup # \{e^*_{ij}\} generates C^1(\calT', \ZZ) while \{f^*_i\} \cup # \{f^*_{ij}\} generates C^2(\calT', \ZZ). # For more pictures, see # /Veering_code/NotesPictures/euler_notes_from_nathan.jpg # 5. Representative # We now construct a two-cocycle E \in Z^2(\calT', \ZZ). For every # peripheral face f we take # E(f) = 0. # \begin{remark} # To see that this is correct, let \calF be any foliation of M, # transverse to the boundary. Suppose that f is the given peripheral # triangle. We have a section of the restriction of UT\calF to \bdy # f; namely the outward field. This extends over f to give a section # of UT\calF restricted to f. So there is no obstruction to the # extension. See below for a more precise discussion in terms of # "Poincar\'e-Hopf index". # \end{remark} # Now suppose that f is a truncated face. Suppose that e_0, e_1, e_2 # are its three truncated edges. Recall that these are all oriented. # Let AC(f) be the number of the edges e_0, e_1, e_2 that are # oriented anti-clockwise (that is, agree with their induced # orientation coming from f). We take # E(f) = AC(f) - 2 # If we flip the transverse direction: AC(f') = 3 - AC(f), # so E(f') = AC(f') - 2 = 1 - AC(f) = 2 - AC(f) - 1 = -E(f) - 1 # \begin{remark} # Here is one way to remember (and explain!) this rule. Suppose that # f is the given truncated face. Suppose that s is a section of UTf | # \bdy f. Then index(s) is the total rotation of s with respect to # the tangent field, _plus_ one. This can be rephrased in terms of # the index of tangent vector fields extending s over all of f. # Our choices of orientations of edges determine a section of UTf | # \bdy f. Since all of the boundary edges e_{ij} of f are oriented # the same way, we choose a standard framing there; Nathan tells us to # just use the outward pointing section on all of the e_{ij}. Our # choice of section on e_0 (say) has to (a) depend only on the # orientation of e_0 and (b) has to be outward at the endpoints of # e_0. The simplest choice is the section that rotates by +\pi with # respect to the tangent along \bdy f_i, as we move forward along e_0. # So s points _back_ at the beginning of e_0, points _right_ in the # middle of e_0, and points _forwards_ at the end of e_0. The total # rotation of the resulting field (with respect to the tangent field) # is AC(f) - 3. Thus E(f) = AC(f) - 2 is the index. You can check # this works by drawing the four possible pictures and computing the index # of any extension of s over f. # \end{remark} # Claim: \delta^2 E = 0. # That is, E is a cocycle. # Proof of claim: Fix a truncated tetrahedron t and fix some oriention # of its truncated edges. A direct calculation shows that # \delta E (t) = E \bdy t = 0. # Likewise, a direct computation shows that switching the orientation # of a single edge leaves E \bdy t unchanged. QED. ### It would be nice to have a less computational proof! def euler_cocycle(tri, angle): """ Given a regina triangulation "tri", with oriented edges, and a transverse taut angle structure "angle", returns the associated two-cocycle E representing the Euler class E(tri). """ assert is_transverse_taut(tri, angle) face_coorientations = is_transverse_taut(tri, angle, return_type = "face_coorientations") # E will be a _row_ vector, because it eats column vectors. E = [] # First deal with the truncated faces for face in tri.faces(2): # 2 = dimension # First we compute the number of Regina oriented edges that agree with the Regina orientation on face AC = 0 for i in range(3): perm = face.faceMapping(1, i) # print perm[0], perm[1] if perm[1] == ((perm[0] + 1) % 3): # the edge and face orientations agree so, AC = AC + 1 # print "AC", AC # Now we condition on whether or not Regina and angle agree on the (co-)orientation of the face. if face_coorientations[face.index()] == 1: E.append(AC - 2) else: E.append(1 - AC) # Now deal with the peripheral faces for tet in tri.tetrahedra(): for j in range(4): E.append(0) return E # 6. Coboundary # Suppose that e is a truncated edge. Let LF be the set of truncated # faces to the left of e and let RF be the set of faces to the right. Then # \delta e^* = \sum_{f \in LF} f^* - \sum_{f \in RF} f^*. # Suppose that e is a peripheral edge. So there is a unique truncated # face f meeting e. Note that f is to the left of e. There are # also a pair of boundary faces meeting e: say f' _above_ e and f'' # _below_ e. Then # \delta e^* = f^* + (f')^* - (f'')^*. def coboundary(tri, angle): """ Given a triangulation "tri" (T), with oriented edges, and a transverse taut angle structure "angle", returns the co-boundary operator delta^1 \from C^1(T', ZZ) \to C^2(T', ZZ), as a matrix, for the truncated triangulation T'. Note that, strictly speaking, we don't need to use "angle" for this, but we use it to determine orientation on faces for the Euler class, so we might as well use it again here. """ # \delta^1 takes row vectors (functions on edges) and spits out # row vectors (functions on faces). So, if c is a one-cochain # then c \cdot \delta is a two-cochain. delta = [] assert is_transverse_taut(tri, angle) tet_vert_coorientations = is_transverse_taut(tri, angle, return_type = "tet_vert_coorientations") face_coorientations = is_transverse_taut(tri, angle, return_type = "face_coorientations") for edge in tri.edges(): # A row for every truncated edge row = [] for face in tri.triangles(): # A row entry for every truncated face count = 0 for i in range(3): if face.edge(i) == edge: perm = face.faceMapping(1, i) if perm[1] == ((perm[0] + 1) % 3): # the edge and face orientations agree so, count += 1 else: count -= 1 row.append(count * face_coorientations[face.index()]) # +1 if face is to the left of the edge, -1 if face is to # the right of the edge, using Regina's edge orientation # when viewed from above (using the transverse taut notion # of up) # ,'| # ,' | # ,' | # ,' CCW | gets a +1 # `. ^ # `. | # `. | # `.| for tet in tri.simplices(): for i in range(4): row.append(0) delta.append(row) for face in tri.triangles(): face_embeddings = [] for j in range(2): face_embeddings.append( face.embedding(j) ) for i in range(3): # vertices of the face # A row for every peripheral edge row = [] for face2 in tri.triangles(): # A row entry for every truncated face if face2 == face: row.append(1) else: row.append(0) for tet in tri.simplices(): for k in range(4): # A row entry for every peripheral face count = 0 for j in range(2): if (tet == face_embeddings[j].simplex()) and (face_embeddings[j].vertices()[i] == k): # the tetrahedron is on the jth side of the # face and the ith vertex of face is the kth # vertex of tet face_num_in_tet = face_embeddings[j].vertices()[3] count -= tet_vert_coorientations[tet.index()][face_num_in_tet] # tet_vert_coorientations is +1 if # coorientation on face points out of the # tetrahedron, and we want count += 1 if # the peripheral face is above the # peripheral edge row.append(count) delta.append(row) return delta # 7. Linear algebra # We ask: is there a one-cocycle C \in C^1(\calT', \ZZ) so that # \delta C = E? If so, then [E] = E(\calT) is zero in H^2, as # desired. # This is a linear algebra problem, so can be solved by, say, sage. def order_of_euler_class(delta, E): """ Given the coboundary operator delta and an Euler two-cocycle E, returns k if [E] is k--torsion. By convention, returns zero if [E] is non-torsion. Note that the trivial element is 1--torsion. """ delta = Matrix(delta) E = vector(E) # Note that E is a coboundary if there is a one-cocycle C solving # # E = C*delta # # We can find C (if it exists at all) using Smith normal form. D, U, V = delta.smith_form() assert D == U*delta*V # So we are trying to solve # # C*delta = C*U.inverse()*D*V.inverse() = E # # for a one-cochain C. Multiply by V to get # # C*delta*V = C*U.inverse()*D = E*V # # Now set # # B = C*U.inverse(), and so B*U = C # # and rewrite to get # # B*U*delta*V = B*D = E*V # # So define E' by: Ep = E*V # Finally we attempt to solve B * D = Ep. Note that D is # diagonal: so if we can solve all of the equations # B[i] * D[i][i] == Ep[i] # with B[i] integers, then [E] = 0 in cohomology. diag = diagonal(D) if any( (diag[i] == 0 and Ep[i] != 0) for i in range(len(Ep)) ): return 0 # All zeros are at the end in Smith normal form. Since we've # passed the above we can now remove them. first_zero = diag.index(0) diag = diag[:first_zero] Ep = Ep[:first_zero] # Since diag[i] is (now) never zero we can divide to get the # fractions Ep[i]/diag[i] and then find the scaling that makes # them simultaneously integral. denoms = [ diag[i] / gcd(Ep[i], diag[i]) for i in range(len(Ep)) ] return lcm(denoms) # 8. Remarks # a) Here is a nice trick that proves [E] = 0 in some cases. Suppose # that \gamma is an oriented path in \bdy M. Suppose that \gamma is # transverse to the one-skeleton of \calT'. We form a one-cocycle # D_\gamma by adding up the boundary edges that \gamma crosses, with # sign. The sign is positive if \gamma crosses from below to above, # and negative otherwise. Note that \delta D_\gamma vanishes on all # boundary faces. # b) <NAME> says that we should take the paths that go up # through the centres of tetrahedra and take the Poincare dual. BUT I # think this is not what we want... Marc is thinking of the relative # Euler class as discussed on page 390 of his paper "Taut ideal # triangulations of three-manifolds". The relative Euler class lives # in H^2(M, \bdy M), so is Poincare dual to an element of H_1(M), # represented by a collection of loops. # c) [2019-03-31] It seems that, for transverse veering triangulations # in the 16 census, the Euler class is always zero or two-torsion. # Note that there are manifolds M in the census where H^2(M, \ZZ) has # positive rank... What about odd torsion? # Question: If the veering triangulation is edge-orientable, does the # Euler class vanish? # Answer: Yes. Here is a version of a discussion with Nathan # [2020-04-03] - he says the following: # Suppose that F is a foliation carried by the horizontal branched # surface. Let UTF be the unit tangent bundle to F. We think of # e(UTF) as being the obstruction to UTF having a section. Let G be # the foliation carried by the upper (aka green) branched surface. If # G is transversely orientable (aka edge-orientability of the veering # triangulation) then G \cap F gives the desired section, and e(UTF) = # 0. Note that G \cap F gives, for every point, a pair of points in # the unit tangent circle. So let PUTF be the projective unit tangent # bundle to F. This definitely has a section, so e(PUTF) = 0. Now, # the bundle UTF is a double cover of the bundle PUTF. # Claim: The euler class is multiplicative with respect to covers (in # both senses). # With the claim in hand, we have # 2 * e(UTF) = e(PUTF) = 0 # We deduce that e(UTF) is either zero or two-torsion. # 9. Calling code @liberal def order_of_euler_class_wrapper(tri, angle): """ Returns the order of the euler class. """ return order_of_euler_class(coboundary(tri, angle), euler_cocycle(tri, angle)) def compute_order_of_euler_classes(file_in, number=None, file_out=None): data_in = parse_data_file(file_in) data_in = [line.split(" ") for line in data_in] if number != None: data_in = data_in[:number] data_out = [] evil = [] for i, line in enumerate(data_in): if i % 50 == 0: print( ((1.0*i)/(1.0*len(data_in)), len(data_out)) ) sig = line[0] tri, angle = isosig_to_tri_angle(sig) # angle = [int(letter) for letter in angle_s] curr_euler = order_of_euler_class(coboundary(tri, angle), euler_cocycle(tri, angle)) if curr_euler == "non-torsion": evil.append(sig) print(sig + " has non-torsion Euler class!!!!") elif curr_euler == 1: # order is one so [E] = 0. Boring. pass else: line_out = [sig, str(curr_euler)] line_out.extend(line[1:]) data_out.append(line_out) if file_out != None: write_data_file(data_out, file_out) print( ("list of evil:", evil) ) return data_out

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tests/test_utils.py

nhoffman/static_html_demo

0

104010

<filename>tests/test_utils.py<gh_stars>0 """ Test utils module. """ import logging from static_html_demo.utils import opener from __init__ import TestBase, get_testfile log = logging.getLogger(__name__) class TestOpener(TestBase): def setUp(self): with open(get_testfile('lorem.txt')) as f: self.firstline = f.next() def test01(self): for suffix in ['txt', 'gz', 'bz2']: fn = get_testfile('lorem.'+suffix) fobj = opener(fn) self.assertEqual(fobj.next(), self.firstline) def test02(self): for suffix in ['txt', 'gz', 'bz2']: fn = get_testfile('lorem.'+suffix) fobj = opener(fn, 'r') self.assertEqual(fobj.next(), self.firstline)

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src/scout_apm/core/cli/core_agent_manager.py

tim-schilling/scout_apm_python

60

168454

<gh_stars>10-100 # coding=utf-8 from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging from scout_apm.core import CoreAgentManager logger = logging.getLogger(__name__) def main(argv=None): parser = argparse.ArgumentParser() parser.add_argument( "-v", "--verbose", help="increase output verbosity", action="count" ) subparsers = parser.add_subparsers( title="subcommands", description="valid subcommands", dest="subparser" ) subparsers.add_parser("download") subparsers.add_parser("launch") args = parser.parse_args(argv) if args.verbose is not None: if args.verbose >= 2: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) core_agent_manager = CoreAgentManager() getattr(core_agent_manager, args.subparser)()

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retry_requests.py

cube3x3x3/get_radio

0

197518

import logging import unittest import requests logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) def custom_session( retries=5, backoff_factor=0.5, status_forcelist=(500, 502, 504), session=None, ): session = session or requests.Session() retry = requests.packages.urllib3.util.retry.Retry( total=retries, read=retries, connect=retries, backoff_factor=backoff_factor, status_forcelist=status_forcelist, ) adapter = requests.adapters.HTTPAdapter(max_retries=retry) session.mount('http://', adapter) session.mount('https://', adapter) return session class TestRequestsMethods(unittest.TestCase): def test_retry(self): logger.info('start test_retry') with self.assertRaises(requests.exceptions.ConnectionError) as x: response = custom_session(2, 0.2).get( 'http://localhost:9999', ) logger.info('test_retry %s', x.exception) logger.info('end test_retry') def test_timeout(self): logger.info('start test_timeout2') with self.assertRaises(requests.exceptions.Timeout) as x: response = custom_session(2, 0.2).get( 'http://httpbin.org/delay/10', timeout=0.001 ) logger.info('test_timeout2 %s', x.exception) logger.info('end test_timeout2') def test_status500(self): logger.info('start test_status') with self.assertRaises(requests.exceptions.RetryError) as x: response = custom_session(2, 0.2).get( 'http://httpbin.org/status/500', ) logger.info('test_status500 %s', x.exception) if __name__ == '__main__': logging.basicConfig(level=logging.INFO) unittest.main(verbosity=2)

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janny/utils.py

icyphox/janny

5

117830

import json import re from datetime import timedelta from types import SimpleNamespace from janny.config import API_HOST from janny.auth import SESSION def get(path: str) -> SimpleNamespace: """Convert a JSON response into a Python object""" s = SESSION data = s.get(API_HOST + path).content obj = json.loads(data, object_hook=lambda d: SimpleNamespace(**d)) return obj timedelta_regex = ( r"((?P<days>-?\d+)d)?" r"((?P<hours>-?\d+)h)?" r"((?P<minutes>-?\d+)m)?" ) timedelta_pattern = re.compile(timedelta_regex, re.IGNORECASE) def parse_delta(delta: str) -> timedelta: """Parses a human readable timedelta (3d5h19m) into a datetime.timedelta. Delta includes: * Xd days * Xh hours * Xm minutes Values can be negative following timedelta's rules. Eg: -5h-30m """ match = timedelta_pattern.match(delta) if match: parts = {k: int(v) for k, v in match.groupdict().items() if v} return timedelta(**parts) return timedelta(0) # List of running threads. Storing it here to prevent circular imports. RUNNING = list()

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w11-functional-programming/teach-sort-lambda/pupils.py

carloswm85/2021-cs111-programming-with-functions

0

145744

import csv # Each row in the pupils.csv file contains three elements. # These are the indexes of the elements in each row. GIVEN_NAME_INDEX = 0 SURNAME_INDEX = 1 BIRTHDATE_INDEX = 2 def main(): path = "E:/GitHub/2021-cs111-programming-with-functions/w11-functional-programming/teach-sort/pupils.csv" # Call the read_compound_list function to read the pupils.csv file into a list named students_list. students_list = read_compound_list(path) # Write a lambda function that will extract the birthdate from a student. birthdate_func = lambda birthdate: birthdate[BIRTHDATE_INDEX] given_name_func = lambda given_name: given_name[GIVEN_NAME_INDEX] birthdate_func_2 = lambda birthdate: birthdate[BIRTHDATE_INDEX] # Write a call to the sorted function that will sort the students_list by birthdate from oldest to youngest. sorted_students_list_bd = sorted(students_list, key = birthdate_func) sorted_students_list_gn = sorted(students_list, key = given_name_func) sorted_students_list_bd_2 = sorted(students_list, key = birthdate_func_2) print("\nLISTA #1:") print_list(students_list) # Print the students_list by calling the print_list function. print("\nLISTA #2:") print_list(sorted_students_list_bd) # Within the main function, replace the code that sorts the students_list by birthdate, with code that sorts the students_list by given name. print("\nLISTA #3:") print_list(sorted_students_list_gn) # Within the main function, replace the code that sorts the students_list by birthdate, with code that sorts the students_list by birth month and day. In other words, the code should sort the students_list by birthdate but ignore the year when a student was born. print("\nLISTA #4:") print_list(sorted_students_list_bd_2) def read_compound_list(filename): """Read the text from a CSV file into a compound list. The compound list will contain small lists. Each small list will contain the data from one row of the CSV file. Parameter filename: the name of the CSV file to read. Return: the compound list """ # Create an empty list. compound_list = [] # Open the CSV file for reading. with open(filename, "rt") as csv_file: # Use the csv module to create a reader # object that will read from the opened file. reader = csv.reader(csv_file) # The first line of the CSV file contains column headings # and not a student's I-Number and name, so this statement # skips the first line of the CSV file. next(reader) # Process each row in the CSV file. for row in reader: # Append the current row at the end of the compound list. compound_list.append(row) return compound_list def print_list(lista): for element in lista: print(element) if __name__ == "__main__": main()

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chaban/bots/telegram_bot.py

ibrag8998/botchaban

1

92335

import typing as typ import httpx from chaban.config import global_settings, settings from chaban.core.exceptions import HttpMethodNotAllowed, TelegramAPIError from chaban.handlers.base import mh_registry from chaban.utils import MetaSingleton from .telegram_methods import TelegramMethodsMixin class TelegramBot(TelegramMethodsMixin, metaclass=MetaSingleton): """ Main telegram bot class. All methods related to telegram api are defined in ``TelegramMethodsMixin``. On init, get ``TELEGRAM_TOKEN`` from settings. Settings are getting that token from env. """ _allowed_http_methods = global_settings.TELEGRAM_ALLOWED_HTTP_METHODS def __init__(self): # bot don't need the token as a separate constant, token is only used as part of # the telegram api endpoint url self._endpoint = "https://api.telegram.org/bot{}/".format( settings.TELEGRAM_TOKEN ) def _build_url(self, method_name: str) -> str: return self._endpoint + method_name.lstrip("/") def request( self, method_name: str, http_method: str = "get", **kwargs ) -> typ.Dict[str, typ.Any]: """ Perform an HTTP :param http_method: request and pass the kwargs as params. Returns a JSON. """ http_method = http_method.lower() if http_method not in self._allowed_http_methods: raise HttpMethodNotAllowed return httpx.request( http_method, self._build_url(method_name), params=kwargs ).json() def start_polling(self): for message in self._poll_updates(): mh_registry.get_handler_and_handle(message) def _poll_updates(self) -> typ.Iterator[typ.Dict[str, typ.Any]]: """ Main loop. Getting updates from telegram, handling offset, yielding each update's message """ # set offset to 0 offset = 0 # start loop while True: # get json response from telegram resp = self.get_updates(offset=offset) # if not ok, raise the error if not resp["ok"]: raise TelegramAPIError("Response JSON: {}".format(resp)) # iterate through updates from resp json for update in resp["result"]: # update offset offset = max(offset, update["update_id"] + 1) # yield message yield update.get("message", update.get("edited_message"))

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tests/build_feedstock_test.py

jayfurmanek/open-ce-builder

0

1609967

<gh_stars>0 # ***************************************************************** # (C) Copyright IBM Corp. 2020, 2021. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ***************************************************************** import os import pathlib import logging import pytest from importlib.util import spec_from_loader, module_from_spec from importlib.machinery import SourceFileLoader test_dir = pathlib.Path(__file__).parent.absolute() spec = spec_from_loader("opence", SourceFileLoader("opence", os.path.join(test_dir, '..', 'open_ce', 'open-ce'))) opence = module_from_spec(spec) spec.loader.exec_module(opence) import helpers import open_ce.utils as utils from open_ce.errors import OpenCEError import open_ce.build_feedstock as build_feedstock def test_build_feedstock_default(mocker): """ Tests that the default arguments for 'build_feedstock' generate the correct 'conda_build.api.build' input args. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) expect_recipe = os.path.join(os.getcwd(),'recipe') expect_config = {'variant_config_files' : [], 'output_folder' : utils.DEFAULT_OUTPUT_FOLDER} mocker.patch( 'conda_build.api.build', side_effect=(lambda x, **kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, expect_config=expect_config, **kwargs)) ) opence._main(["build", build_feedstock.COMMAND]) def test_build_feedstock_failure(mocker): """ Tests that a 'conda_build.api.build' failure is handled correctly. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) mocker.patch( 'conda_build.api.build', side_effect=ValueError("invalid literal for int() with base 10: 'xy'") #using ValueError to simulate a failure. ) with pytest.raises(OpenCEError) as exc: opence._main(["build", build_feedstock.COMMAND]) assert "Unable to build recipe: test_recipe" in str(exc.value) def test_build_feedstock_working_dir(mocker): """ Tests that the 'working_dir' argument is correctly handled and the original working directory is restored after execution. """ dirTracker = helpers.DirTracker("/test/starting_dir") mocker.patch( 'os.getcwd', side_effect=dirTracker.mocked_getcwd ) mocker.patch( 'os.path.exists', return_value=False ) mocker.patch( 'conda_build.api.build', return_value=[] ) working_dir = "/test/my_work_dir" mocker.patch( 'os.chdir', side_effect=(lambda x: dirTracker.validate_chdir(x, expected_dirs=[working_dir, # First the working directory should be changed to the arg. "/test/starting_dir"])) # And then changed back to the starting directory. ) opence._main(["build", build_feedstock.COMMAND, "--working_directory", working_dir]) def test_build_feedstock_config_file(mocker): """ Tests that the 'recipe_config_file' argument is correctly handled.. """ expect_recipe = os.path.join(os.getcwd(),'cuda_recipe_path') #Checks that the value from the input config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, **kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, **kwargs)) ) opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", os.path.join(test_dir, "my_config.yaml"), "--build_type", "cuda"]) def test_build_feedstock_default_config_file(mocker): """ Tests that the default config file is loaded when no argument is specified. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=True #True for default config file. ) expect_recipe = os.path.join(os.getcwd(),'variants_from_default_config')#Checks that the value from the default config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, **kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, **kwargs)) ) test_recipe_config = {'recipes' : [{'name' : 'my_variant', 'path' : 'variants_from_default_config'}]} mocker.patch('open_ce.conda_utils.render_yaml', return_value=test_recipe_config) opence._main(["build", build_feedstock.COMMAND]) def test_build_feedstock_nonexist_config_file(mocker): """ Tests that execution fails and the correct error message is shown if the default config file doesn't exist. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=False ) with pytest.raises(OpenCEError) as exc: opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", "my_config.yml"]) assert "Unable to open provided config file: my_config.yml" in str(exc.value) def test_recipe_config_file_for_inapplicable_configuration(mocker, caplog): """ Tests the case when build is triggered for a configuration for which no recipes are applicable. """ expect_recipe = os.path.join(os.getcwd(),'cuda_recipe_path') #Checks that the value from the input config file is used. mocker.patch( 'conda_build.api.build', side_effect=(lambda x, **kwargs: helpers.validate_conda_build_args(x, expect_recipe=expect_recipe, **kwargs)) ) opence._main(["build", build_feedstock.COMMAND, "--recipe-config-file", os.path.join(test_dir, "my_config.yaml"), "--python_versions", "4.1"]) assert ("OPEN-CE", logging.INFO, "No recipe to build for given configuration.") in caplog.record_tuples def test_build_feedstock_local_src_dir_args(mocker): """ Tests that providing the local_src_dir argument sets the LOCAL_SRC_DIR environment variable correctly. """ mocker.patch( 'os.path.exists', return_value=True ) build_feedstock._set_local_src_dir("my_src_dir", None, None) assert os.environ["LOCAL_SRC_DIR"] == "my_src_dir" def test_build_feedstock_local_src_dir_args_fail(mocker): """ Tests that providing the local_src_dir argument to a non-existant file fails properly. """ mocker.patch( 'os.path.exists', return_value=False ) with pytest.raises(OpenCEError) as exc: build_feedstock._set_local_src_dir("my_src_dir", { 'local_src_dir' : "my_other_src_dir" }, None) assert "local_src_dir path \"my_src_dir\" specified doesn't exist" in str(exc.value) def test_build_feedstock_local_src_dir_recipe(mocker): """ Tests that providing the local_src_dir in a recipe sets the LOCAL_SRC_DIR environment variable correctly. """ mocker.patch( 'os.path.exists', return_value=True ) build_feedstock._set_local_src_dir(None, { 'local_src_dir' : "my_other_src_dir" } , "/test/location/recipe.yaml") assert os.environ["LOCAL_SRC_DIR"] == "/test/location/my_other_src_dir" def test_build_feedstock_extra_args(mocker): """ Tests that additional arguments add the expected values to the 'conda_build.api.build' arguments. """ mocker.patch( 'os.getcwd', return_value="/test/test_recipe" ) mocker.patch( 'os.path.exists', return_value=True ) expect_config = { 'channel_urls' : ['/test/test_recipe/condabuild', 'test_channel', 'test_channel_2', 'test_channel_from_config']} expect_variants = {'python': '3.6', 'build_type': 'cpu', 'mpi_type': 'openmpi'} reject_recipe = os.path.join(os.getcwd(),'test_recipe_extra') mocker.patch( 'conda_build.api.build', side_effect=(lambda x, **kwargs: helpers.validate_conda_build_args(x, expect_config=expect_config, expect_variants=expect_variants, reject_recipe=reject_recipe, **kwargs)) ) test_recipe_config = { 'recipes' : [{ 'name' : 'my_project', 'path' : 'recipe'}, { 'name' : 'my_variant', 'path': 'variants'}, { 'name' : 'test_recipe_extra', 'path' : 'extra'}], 'channels' : ['test_channel_from_config']} mocker.patch('open_ce.conda_utils.render_yaml', return_value=test_recipe_config) arg_input = ["build", build_feedstock.COMMAND, "--channels", "test_channel", "--channels", "test_channel_2", "--recipes", "my_project,my_variant", "--python_versions", "3.6", "--build_types", "cpu", "--mpi_types", "openmpi", "--cuda_versions", "10.2"] opence._main(arg_input) def test_build_feedstock_if_no_conda_build(mocker): ''' Test that build_feedstock should fail if conda_build isn't present ''' mocker.patch('pkg_resources.get_distribution', return_value=None) with pytest.raises(OpenCEError): assert opence._main(["build", build_feedstock.COMMAND]) == 1 def test_build_feedstock_conda_pkg_format(mocker): ''' Test that the --conda_pkg_format argument is correctly handled. ''' mocker.patch( 'conda_build.api.build', ) pkg_format = "conda" opence._main(["build", build_feedstock.COMMAND, "--conda_pkg_format", pkg_format])

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resources/migrations/0021_change_image_file_to_char.py

City-of-Helsinki/berth-reservations

3

65894

<reponame>City-of-Helsinki/berth-reservations # Generated by Django 3.1 on 2020-10-05 12:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("resources", "0020_change_berth_number_to_char"), ] operations = [ migrations.AlterField( model_name="harbor", name="image_file", field=models.CharField( blank=True, max_length=400, null=True, verbose_name="Image file" ), ), migrations.AlterField( model_name="winterstoragearea", name="image_file", field=models.CharField( blank=True, max_length=400, null=True, verbose_name="Image file" ), ), ]

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examples/DogsVsCats.py

YiweiLi4/tensorbay-python-sdk

0

130343

<filename>examples/DogsVsCats.py #!/usr/bin/env python3 # # Copyright 2021 Graviti. Licensed under MIT License. # # pylint: disable=wrong-import-position # pylint: disable=wrong-import-order # pylint: disable=not-callable # pylint: disable=ungrouped-imports # pylint: disable=import-error # pylint: disable=pointless-string-statement # pylint: disable=invalid-name # pylint: disable=unused-import # flake8: noqa: F401 """This files includes the python code example in dogsvscats.rst.""" """Authorize a Client Instance""" from tensorbay import GAS ACCESS_KEY = "Accesskey-*****" gas = GAS(ACCESS_KEY) """""" """Create Dataset""" gas.create_dataset("DogsVsCats") """""" """Organize Dataset / regular import""" from tensorbay.dataset import Data, Dataset from tensorbay.label import Classification """""" """Organize dataset / import dataloader""" from tensorbay.opendataset import DogsVsCats dataset = DogsVsCats("path/to/dataset/directory") """""" """Upload Dataset""" dataset_client = gas.upload_dataset(dataset) dataset_client.commit("initial commit") """""" """Read Dataset / get dataset""" dataset_client = gas.get_dataset("DogsVsCats") """""" """Read Dataset / list segment names""" dataset_client.list_segment_names() """""" """Read Dataset Class / get a segment""" train_segment = dataset.get_segment_by_name("train") first_segment = dataset[0] """""" """Read Dataset / get segment""" from tensorbay.dataset import Segment train_segment = Segment("train", dataset_client) """""" """Read Dataset / get data""" data = train_segment[0] """""" """Read Dataset / get label""" category = data.label.classification.category """""" """Delete Dataset""" gas.delete_dataset("DogsVsCats") """"""

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django_storage_url/base.py

hoopoe/django-storage-url

2

117242

<reponame>hoopoe/django-storage-url from django.conf import settings from django.utils.functional import LazyObject import furl from .backends import get_storage_class from .backends.not_implemented import NotImplementedStorage class _DSNConfiguredStorage(LazyObject): def _setup(self): dsn = getattr(settings, self._setting_name, None) if not dsn: args = [] storage_class = NotImplementedStorage else: url = furl.furl(dsn) storage_class = get_storage_class(url.scheme) args = [url] # Django >= 1.9 now knows about LazyObject and sets them up before # serializing them. To work around this behavior, the storage class # itself needs to be deconstructible. storage_class = type( storage_class.__name__, (storage_class,), {"deconstruct": self._deconstructor}, ) self._wrapped = storage_class(*args) def dsn_configured_storage_class(setting_name): path = "{}.{}".format( dsn_configured_storage.__module__, dsn_configured_storage.__name__ ) return type( "DSNConfiguredStorage", (_DSNConfiguredStorage,), { "_setting_name": setting_name, "_deconstructor": lambda self: (path, [setting_name], {}), }, ) def dsn_configured_storage(setting_name): return dsn_configured_storage_class(setting_name)() def get_storage(dsn): url = furl.furl(dsn) storage_class = get_storage_class(url.scheme) return storage_class(url)

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grpclib/server.py

panaetov/grpclib

0

12979

import abc import socket import logging import asyncio import warnings import h2.config import h2.exceptions from .utils import DeadlineWrapper from .const import Status from .stream import send_message, recv_message from .stream import StreamIterator from .metadata import Metadata, Deadline from .protocol import H2Protocol, AbstractHandler from .exceptions import GRPCError, ProtocolError from .encoding.base import GRPC_CONTENT_TYPE from .encoding.proto import ProtoCodec log = logging.getLogger(__name__) class Stream(StreamIterator): """ Represents gRPC method call – HTTP/2 request/stream, and everything you need to communicate with client in order to handle this request. As you can see, every method handler accepts single positional argument - stream: .. code-block:: python async def MakeLatte(self, stream: grpclib.server.Stream): task: cafe_pb2.LatteOrder = await stream.recv_message() ... await stream.send_message(empty_pb2.Empty()) This is true for every gRPC method type. """ # stream state _send_initial_metadata_done = False _send_message_count = 0 _send_trailing_metadata_done = False _cancel_done = False def __init__(self, stream, cardinality, codec, recv_type, send_type, *, metadata, deadline=None): self._stream = stream self._cardinality = cardinality self._codec = codec self._recv_type = recv_type self._send_type = send_type self.metadata = metadata self.deadline = deadline async def recv_message(self): """Coroutine to receive incoming message from the client. If client sends UNARY request, then you can call this coroutine only once. If client sends STREAM request, then you should call this coroutine several times, until it returns None. To simplify your code in this case, :py:class:`Stream` class implements async iteration protocol, so you can use it like this: .. code-block:: python async for massage in stream: do_smth_with(message) or even like this: .. code-block:: python messages = [msg async for msg in stream] HTTP/2 has flow control mechanism, so server will acknowledge received DATA frames as a message only after user consumes this coroutine. :returns: message """ return await recv_message(self._stream, self._codec, self._recv_type) async def send_initial_metadata(self): """Coroutine to send headers with initial metadata to the client. In gRPC you can send initial metadata as soon as possible, because gRPC doesn't use `:status` pseudo header to indicate success or failure of the current request. gRPC uses trailers for this purpose, and trailers are sent during :py:meth:`send_trailing_metadata` call, which should be called in the end. .. note:: This coroutine will be called implicitly during first :py:meth:`send_message` coroutine call, if not called before explicitly. """ if self._send_initial_metadata_done: raise ProtocolError('Initial metadata was already sent') await self._stream.send_headers([ (':status', '200'), ('content-type', (GRPC_CONTENT_TYPE + '+' + self._codec.__content_subtype__)), ]) self._send_initial_metadata_done = True async def send_message(self, message, **kwargs): """Coroutine to send message to the client. If server sends UNARY response, then you should call this coroutine only once. If server sends STREAM response, then you can call this coroutine as many times as you need. :param message: message object """ if 'end' in kwargs: warnings.warn('"end" argument is deprecated, use ' '"stream.send_trailing_metadata" explicitly', stacklevel=2) end = kwargs.pop('end', False) assert not kwargs, kwargs if not self._send_initial_metadata_done: await self.send_initial_metadata() if not self._cardinality.server_streaming: if self._send_message_count: raise ProtocolError('Server should send exactly one message ' 'in response') await send_message(self._stream, self._codec, message, self._send_type) self._send_message_count += 1 if end: await self.send_trailing_metadata() async def send_trailing_metadata(self, *, status=Status.OK, status_message=None): """Coroutine to send trailers with trailing metadata to the client. This coroutine allows sending trailers-only responses, in case of some failure conditions during handling current request, i.e. when ``status is not OK``. .. note:: This coroutine will be called implicitly at exit from request handler, with appropriate status code, if not called explicitly during handler execution. :param status: resulting status of this coroutine call :param status_message: description for a status """ if self._send_trailing_metadata_done: raise ProtocolError('Trailing metadata was already sent') if not self._send_message_count and status is Status.OK: raise ProtocolError('{!r} requires non-empty response' .format(status)) if self._send_initial_metadata_done: headers = [] else: # trailers-only response headers = [(':status', '200')] headers.append(('grpc-status', str(status.value))) if status_message is not None: headers.append(('grpc-message', status_message)) await self._stream.send_headers(headers, end_stream=True) self._send_trailing_metadata_done = True if status != Status.OK and self._stream.closable: self._stream.reset_nowait() async def cancel(self): """Coroutine to cancel this request/stream. Server will send RST_STREAM frame to the client, so it will be explicitly informed that there is nothing to expect from the server regarding this request/stream. """ if self._cancel_done: raise ProtocolError('Stream was already cancelled') await self._stream.reset() # TODO: specify error code self._cancel_done = True async def __aenter__(self): return self async def __aexit__(self, exc_type, exc_val, exc_tb): if ( self._send_trailing_metadata_done or self._cancel_done or self._stream._transport.is_closing() ): # to suppress exception propagation return True if exc_val is not None: if isinstance(exc_val, GRPCError): status = exc_val.status status_message = exc_val.message elif isinstance(exc_val, Exception): status = Status.UNKNOWN status_message = 'Internal Server Error' else: # propagate exception return elif not self._send_message_count: status = Status.UNKNOWN status_message = 'Empty response' else: status = Status.OK status_message = None try: await self.send_trailing_metadata(status=status, status_message=status_message) except h2.exceptions.StreamClosedError: pass # to suppress exception propagation return True async def request_handler(mapping, _stream, headers, codec, release_stream): try: headers_map = dict(headers) if headers_map[':method'] != 'POST': await _stream.send_headers([ (':status', '405'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return content_type = headers_map.get('content-type') if content_type is None: await _stream.send_headers([ (':status', '415'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Missing content-type header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return base_content_type, _, sub_type = content_type.partition('+') sub_type = sub_type or ProtoCodec.__content_subtype__ if ( base_content_type != GRPC_CONTENT_TYPE or sub_type != codec.__content_subtype__ ): await _stream.send_headers([ (':status', '415'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Unacceptable content-type header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return if headers_map.get('te') != 'trailers': await _stream.send_headers([ (':status', '400'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Required "te: trailers" header is missing'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return h2_path = headers_map[':path'] method = mapping.get(h2_path) if method is None: await _stream.send_headers([ (':status', '200'), ('grpc-status', str(Status.UNIMPLEMENTED.value)), ('grpc-message', 'Method not found'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return metadata = Metadata.from_headers(headers) try: deadline = Deadline.from_metadata(metadata) except ValueError: await _stream.send_headers([ (':status', '200'), ('grpc-status', str(Status.UNKNOWN.value)), ('grpc-message', 'Invalid grpc-timeout header'), ], end_stream=True) if _stream.closable: _stream.reset_nowait() return async with Stream(_stream, method.cardinality, codec, method.request_type, method.reply_type, metadata=metadata, deadline=deadline) as stream: deadline_wrapper = None try: if deadline: deadline_wrapper = DeadlineWrapper() with deadline_wrapper.start(deadline): with deadline_wrapper: await method.func(stream) else: await method.func(stream) except asyncio.TimeoutError: if deadline_wrapper and deadline_wrapper.cancelled: log.exception('Deadline exceeded') raise GRPCError(Status.DEADLINE_EXCEEDED) else: log.exception('Timeout occurred') raise except asyncio.CancelledError: log.exception('Request was cancelled') raise except Exception: log.exception('Application error') raise except Exception: log.exception('Server error') finally: release_stream() class _GC(abc.ABC): _gc_counter = 0 @property @abc.abstractmethod def __gc_interval__(self): raise NotImplementedError @abc.abstractmethod def __gc_collect__(self): pass def __gc_step__(self): self._gc_counter += 1 if not (self._gc_counter % self.__gc_interval__): self.__gc_collect__() class Handler(_GC, AbstractHandler): __gc_interval__ = 10 closing = False def __init__(self, mapping, codec, *, loop): self.mapping = mapping self.codec = codec self.loop = loop self._tasks = {} self._cancelled = set() def __gc_collect__(self): self._tasks = {s: t for s, t in self._tasks.items() if not t.done()} self._cancelled = {t for t in self._cancelled if not t.done()} def accept(self, stream, headers, release_stream): self.__gc_step__() self._tasks[stream] = self.loop.create_task( request_handler(self.mapping, stream, headers, self.codec, release_stream) ) def cancel(self, stream): task = self._tasks.pop(stream) task.cancel() self._cancelled.add(task) def close(self): for task in self._tasks.values(): task.cancel() self._cancelled.update(self._tasks.values()) self.closing = True async def wait_closed(self): if self._cancelled: await asyncio.wait(self._cancelled, loop=self.loop) def check_closed(self): self.__gc_collect__() return not self._tasks and not self._cancelled class Server(_GC, asyncio.AbstractServer): """ HTTP/2 server, which uses gRPC service handlers to handle requests. Handler is a subclass of the abstract base class, which was generated from .proto file: .. code-block:: python class CoffeeMachine(cafe_grpc.CoffeeMachineBase): async def MakeLatte(self, stream): task: cafe_pb2.LatteOrder = await stream.recv_message() ... await stream.send_message(empty_pb2.Empty()) server = Server([CoffeeMachine()], loop=loop) """ __gc_interval__ = 10 def __init__(self, handlers, *, loop, codec=None): """ :param handlers: list of handlers :param loop: asyncio-compatible event loop """ mapping = {} for handler in handlers: mapping.update(handler.__mapping__()) self._mapping = mapping self._loop = loop self._codec = codec or ProtoCodec() self._config = h2.config.H2Configuration( client_side=False, header_encoding='utf-8', ) self._tcp_server = None self._handlers = set() def __gc_collect__(self): self._handlers = {h for h in self._handlers if not (h.closing and h.check_closed())} def _protocol_factory(self): self.__gc_step__() handler = Handler(self._mapping, self._codec, loop=self._loop) self._handlers.add(handler) return H2Protocol(handler, self._config, loop=self._loop) async def start(self, host=None, port=None, *, family=socket.AF_UNSPEC, flags=socket.AI_PASSIVE, sock=None, backlog=100, ssl=None, reuse_address=None, reuse_port=None): """Coroutine to start the server. :param host: can be a string, containing IPv4/v6 address or domain name. If host is None, server will be bound to all available interfaces. :param port: port number. :param family: can be set to either :py:data:`python:socket.AF_INET` or :py:data:`python:socket.AF_INET6` to force the socket to use IPv4 or IPv6. If not set it will be determined from host. :param flags: is a bitmask for :py:meth:`~python:asyncio.AbstractEventLoop.getaddrinfo`. :param sock: sock can optionally be specified in order to use a preexisting socket object. If specified, host and port should be omitted (must be None). :param backlog: is the maximum number of queued connections passed to listen(). :param ssl: can be set to an :py:class:`~python:ssl.SSLContext` to enable SSL over the accepted connections. :param reuse_address: tells the kernel to reuse a local socket in TIME_WAIT state, without waiting for its natural timeout to expire. :param reuse_port: tells the kernel to allow this endpoint to be bound to the same port as other existing endpoints are bound to, so long as they all set this flag when being created. """ if self._tcp_server is not None: raise RuntimeError('Server is already started') self._tcp_server = await self._loop.create_server( self._protocol_factory, host, port, family=family, flags=flags, sock=sock, backlog=backlog, ssl=ssl, reuse_address=reuse_address, reuse_port=reuse_port ) def close(self): """Stops accepting new connections, cancels all currently running requests. Request handlers are able to handle `CancelledError` and exit properly. """ if self._tcp_server is None: raise RuntimeError('Server is not started') self._tcp_server.close() for handler in self._handlers: handler.close() async def wait_closed(self): """Coroutine to wait until all existing request handlers will exit properly. """ if self._tcp_server is None: raise RuntimeError('Server is not started') await self._tcp_server.wait_closed() if self._handlers: await asyncio.wait({h.wait_closed() for h in self._handlers}, loop=self._loop)

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pg_dicreate.py

zhuyeaini9/pytorch_test

0

23677

<reponame>zhuyeaini9/pytorch_test<filename>pg_dicreate.py import torch import torch.nn.functional as F import torch.nn as nn import gym from gym import spaces import torch.optim as optim from torch.distributions import Categorical import random import numpy as np class Net(nn.Module): def __init__(self, input_space, output_space): super(Net, self).__init__() self.fc1 = nn.Linear(input_space, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, output_space) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.softmax(self.fc3(x), dim=-1) return x class PGAgent(object): def __init__(self, env_name,batch_size = 3): self.m_batch_size = batch_size self.m_env = gym.make(env_name) self.m_env._max_episode_steps = 1000 self.m_action_type = self.get_action_type() self.m_action_size = self.get_action_size() self.m_net = Net(self.get_input_space(), self.get_output_space()) self.m_adam = optim.Adam(params=self.m_net.parameters(), lr=0.01) self.m_batch_reward = [] self.m_batch_state = [] self.m_batch_action = [] def get_input_space(self): return self.m_env.observation_space.shape[0] def get_action_size(self): if self.m_action_type == 1: return self.m_env.action_space.n def get_output_space(self): if self.m_action_type == 1: return self.m_env.action_space.n def get_action_type(self): if isinstance(self.m_env.action_space, spaces.Discrete): action_type = 1 else: action_type = 2 return action_type def get_action(self, police_action, step_index, step_all): if self.m_action_type == 1: action_distribution = Categorical(police_action) return action_distribution.sample().item() def calculate_discounted_rewards_normal(self, reward_list): re = self.calculate_discounted_rewards(reward_list) return self.normalise_rewards(re) def calculate_discounted_rewards(self, reward_list): cur_re = 0 re_discounted_reward = [] for re in reversed(reward_list): re = re + 0.99 * cur_re cur_re = re re_discounted_reward.append(re) re_discounted_reward.reverse() return re_discounted_reward def normalise_rewards(self, rewards): mean_reward = np.mean(rewards) std_reward = np.std(rewards) return (rewards - mean_reward) / (std_reward + 1e-8) def reset(self): self.m_batch_state = [] self.m_batch_action = [] self.m_batch_reward = [] def step(self, step_index, step_all): self.reset() state = self.m_env.reset() batch = 0 reward_record = [] reward_list = [] action_list = [] state_list = [] while True: #be careful of detach out_action = self.m_net(torch.tensor(state).float().unsqueeze(dim=0)).detach() out_action = out_action.squeeze(0) tar_action = self.get_action(out_action, step_index, step_all) new_state, reward, done, _ = self.m_env.step(tar_action) reward_list.append(reward) action_list.append(tar_action) state_list.append(state) state = new_state if done: batch += 1 self.m_batch_reward.extend(self.calculate_discounted_rewards_normal(reward_list)) self.m_batch_action.extend(action_list) self.m_batch_state.extend(state_list) reward_record.append(sum(reward_list)) reward_list = [] action_list = [] state_list = [] state = self.m_env.reset() if batch == self.m_batch_size: break print(step_index, reward_record) state_tensor = torch.FloatTensor(self.m_batch_state) reward_tensor = torch.FloatTensor(self.m_batch_reward) action_tensor = torch.LongTensor(self.m_batch_action) # Calculate loss log_prob = torch.log(self.m_net(state_tensor)) selected_logprobs = reward_tensor * log_prob[np.arange(len(action_tensor)), action_tensor] loss = -selected_logprobs.mean() self.m_adam.zero_grad() # Calculate gradients loss.backward() # Apply gradients self.m_adam.step() def run_n_step(self, n): for i in range(n): self.step(i, n) agent = PGAgent('CartPole-v0') agent.run_n_step(300)

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boo/dataframe/canonic.py

AirVetra/boo

1

47840

import numpy import pandas as pd from boo.columns import SHORT_COLUMNS from boo.errors import UnclassifiableCodeError QUOTE_CHAR = '"' EMPTY = int(0) NUMERIC_COLUMNS = SHORT_COLUMNS.numeric def adjust_rub(df, cols=NUMERIC_COLUMNS): rows = (df.unit == "385") df.loc[rows, cols] = df.loc[rows, cols].multiply(1000) df.loc[rows, "unit"] = "384" rows = (df.unit == "383") df.loc[rows, cols] = df.loc[rows, cols].divide(1000).round(0).astype(int) df.loc[rows, "unit"] = "384" return df def dequote(name: str): """Split company *name* to organisation and title.""" # Warning: will not work well on company names with more than 4 quotechars parts = name.split(QUOTE_CHAR) org = parts[0].strip() cnt = name.count(QUOTE_CHAR) if cnt == 2: title = parts[1].strip() elif cnt > 2: title = QUOTE_CHAR.join(parts[1:]) else: title = name return org, title.strip() def replace_names(title: str): return title .replace( "ПУБЛИЧНОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО", "ПАО") .replace( "ОТКРЫТОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО", "ОАО") .replace( "АКЦИОНЕРНОЕ ОБЩЕСТВО ЭНЕРГЕТИКИ И ЭЛЕКТРИФИКАЦИИ", "AO энерго") .replace( "НЕФТЕПЕРЕРАБАТЫВАЮЩИЙ ЗАВОД", "НПЗ") .replace( "ГЕНЕРИРУЮЩАЯ КОМПАНИЯ ОПТОВОГО РЫНКА ЭЛЕКТРОЭНЕРГИИ", "ОГК") .replace( "ГОРНО-ОБОГАТИТЕЛЬНЫЙ КОМБИНАТ", "ГОК") def add_title(df): s_ = df.name.apply(dequote) df['org'] = s_.apply(lambda x: x[0]) df['title'] = s_.apply(lambda x: replace_names(x[1])) return df def rename_rows(df): RENAME_DICT = { '2460066195': "РусГидро", '4716016979': "ФСК ЕЭС", '7702038150': "Московский метрополитен", '7721632827': "Концерн Росэнергоатом", '7706664260': "Атомэнергопром", '7703683145': "Холдинг ВТБ Капитал АЙ БИ", '9102048801': "Черноморнефтегаз", '7736036626': "РИТЭК" } keys = RENAME_DICT.keys() ix = df.index.isin(keys) if not ix.any(): return df sub = df.loc[ix, 'title'] for k, v in RENAME_DICT.items(): if k in sub.index: sub.loc[k] = v df.loc[ix, 'title'] = sub return df def split_okved(code_string: str): """Get 3 levels of OKVED codes from *code_string*.""" if code_string.count(".") > 2: raise UnclassifiableCodeError(code_string) try: codes = [int(x) for x in code_string.split(".")] except ValueError: raise UnclassifiableCodeError(code_string) return codes + [0] * (3 - len(codes)) def add_okved_subcode(df): df['ok1'], df['ok2'], df['ok3'] = zip(*df.okved.apply(split_okved)) return df def fst(x): try: return int(x[0:2]) except TypeError: return 0 def add_region(df): df['region'] = df.inn.apply(fst) return df def more_columns(df): return add_okved_subcode(add_region(add_title(df))) def canonic_df(df): """Преобразовать данные внтури датафрейма: - Привести все строки к одинаковым единицам измерения (тыс. руб.) - Убрать неиспользуемые колонки (date_revised, report_type) - Новые колонки: * короткое название компании * три уровня кода ОКВЭД * регион (по ИНН) """ df_ = add_okved_subcode(add_region(add_title(df))) df_ = adjust_rub(df_) df_ = df_.set_index('inn') df_ = rename_rows(df_) return df_[canonic_columns()] def canonic_columns(numeric=SHORT_COLUMNS.numeric): return (['title', 'org', 'okpo', 'okopf', 'okfs', 'okved'] + ['unit'] + ['ok1', 'ok2', 'ok3', 'region'] + numeric) def is_numeric_column(name, numeric=SHORT_COLUMNS.numeric): return name in numeric def columns_typed_as_integer(numeric=SHORT_COLUMNS.numeric): return numeric + ['ok1', 'ok2', 'ok3', 'region'] def canonic_dtypes(): def switch(col): int_columns = columns_typed_as_integer() return numpy.int64 if (col in int_columns) else str result = {col: switch(col) for col in canonic_columns()} result['inn'] = str return result

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src/obmak_debug.py

jeppeter/py-obcode

0

44426

#! /usr/bin/env python import sys import os import extargsparse import re import time ##importdebugstart sys.path.append(os.path.abspath(os.path.dirname(__file__))) from strparser import * from filehdl import * from fmthdl import * from extract_ob import * from obmaklib import * ##importdebugend REPLACE_IMPORT_LIB=1 REPLACE_STR_PARSER=1 REPLACE_FILE_HDL=1 REPLACE_FMT_HDL=1 REPLACE_EXTRACT_OB=1 REPLACE_OBMAK_LIB=1 def main(): commandline=''' { "verbose|v" : "+", "version|V" : false, "dump|D" : "obcode.json", "benchmark|B" : false, "makob<makob_handler>##srcfile to give the other code file ,this need environment variable MAKOB_FILE to get the default (makob.json)##" : { "namemin" : 5, "namemax" : 20, "$" : "+" }, "unmakob<unmakob_handler>##dstfile to give the origin ,this need environment variable MAKOB_FILE to get the default (makob.json)##" : { "short" : false, "$" : "+" }, "basename<basename_handler>##to make basename##" : { "$" : "+" }, "obtrans<obtrans_handler>##translate the srcdir to dstdir in makob file##" : { "srcdir" : "", "dstdir" : "", "$" : "+" }, "oblist<oblist_handler>##to list files ob files##" : { "$" : "*" }, "obuntrans<obuntrans_handler>##inputfile [outputfile] to trans file from MAKOB_FILE##" : { "$" : "+" }, "obunfunc<obunfunc_handler>##inputfile;outfile;funcs... to set obfuncs##" : { "$" : "+" } } ''' d = dict() d['version'] = "VERSION_RELACE_STRING" options = extargsparse.ExtArgsOptions(d) stime = time.time() parser = extargsparse.ExtArgsParse(options) parser.load_command_line_string(commandline) args = parser.parse_command_line(None,parser) if args.version: sys.stdout.write('%s\n'%(options.version)) sys.exit(0) if args.benchmark: etime = time.time() sys.stderr.write('run %s time %s second\n'%(sys.argv[1:],etime - stime)) return ##importdebugstart from obrelease import * import re def debug_release(): if '-v' in sys.argv[1:]: #sys.stderr.write('will make verbose\n') loglvl = logging.DEBUG if logging.root is not None and len(logging.root.handlers) > 0: logging.root.handlers = [] logging.basicConfig(level=loglvl,format='%(asctime)s:%(filename)s:%(funcName)s:%(lineno)d\t%(message)s') topdir = os.path.abspath(os.path.join(os.path.dirname(__file__),'..')) tofile= os.path.abspath(os.path.join(topdir,'obmak.py')) curdir = os.path.abspath(os.path.dirname(__file__)) rlfiles = ReleaseFiles(__file__) rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'strparser.py')),r'REPLACE_STR_PARSER=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'fmthdl.py')),r'REPLACE_FMT_HDL=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'filehdl.py')),r'REPLACE_FILE_HDL=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'obmaklib.py')),r'REPLACE_OBMAK_LIB=1') rlfiles.add_python_file(os.path.abspath(os.path.join(curdir,'extract_ob.py')),r'REPLACE_EXTRACT_OB=1') if len(sys.argv) > 2: for k in sys.argv[1:]: if not k.startswith('-'): tofile = k break versionfile = os.path.abspath(os.path.join(topdir,'VERSION')) if not os.path.exists(versionfile): raise Exception('can not find VERSION file') with open(versionfile,'r') as f: for l in f: l = l.rstrip('\r\n') vernum = l break #logging.info('str_c\n%s'%(strparser_c)) sarr = re.split('\.',vernum) if len(sarr) != 3: raise Exception('version (%s) not format x.x.x'%(vernum)) VERSIONNUMBER = vernum import_rets = fromat_ext_import_files(__file__,rlfiles.get_includes()) logging.info('import_rets\n%s'%(import_rets)) rlfiles.add_repls(r'VERSION_RELACE_STRING',VERSIONNUMBER) rlfiles.add_repls(r'debug_main','main') rlfiles.add_repls(r'REPLACE_IMPORT_LIB=1',make_string_slash_ok(import_rets)) #logging.info('repls %s'%(repls.keys())) disttools.release_file('__main__',tofile,[],[[r'##importdebugstart.*',r'##importdebugend.*']],[],rlfiles.get_repls()) return def debug_main(): if '--release' in sys.argv[1:]: debug_release() return main() return ##importdebugend if __name__ == '__main__': debug_main()

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snmn/nmn.py

jiangycTarheel/NMN-MultiHopQA

20

86347

<filename>snmn/nmn.py<gh_stars>10-100 import numpy as np import tensorflow as tf from tensorflow import convert_to_tensor as to_T, newaxis as ax from tensorflow.python.ops import tensor_array_ops, control_flow_ops from my.tensorflow.ops import bi_cudnn_rnn_encoder from snmn.config import cfg from snmn.util.cnn import fc_layer as fc, conv_layer as conv from basic.attention_modules import weighted_biattention from my.tensorflow.nn import dense from my.tensorflow.nn import linear_logits MODULE_INPUT_NUM = { '_NoOp': 2, '_Find': 0, '_Relocate': 1, '_Compare': 2, } MODULE_OUTPUT_NUM = { '_NoOp': 2, '_Find': 1, '_Relocate': 1, '_Compare': 1, } class NMN: def __init__(self, config, kb_batch, kb_mask_batch, kb_len_batch, c_list, cv_list, q, q_mask, module_names, module_prob_list, is_train, q_len, max_para_size=None, scope='NMN', reuse=None): """ NMN v4 with an attention stack """ with tf.variable_scope(scope, reuse=reuse): self.is_train = is_train self.main_config = config self.kb_batch = kb_batch self.kb_mask_batch = kb_mask_batch self.kb_len_batch = kb_len_batch self.c_list = c_list self.cv_list = cv_list self.q = q self.q_len = q_len self.q_mask = q_mask self.module_prob_list = module_prob_list self.kb_dim = cfg.MODEL.KB_DIM if self.main_config.nmn_relocate_move_ptr: MODULE_OUTPUT_NUM['_Relocate'] = MODULE_INPUT_NUM['_Relocate'] + 1 else: MODULE_OUTPUT_NUM['_Relocate'] = MODULE_INPUT_NUM['_Relocate'] self.T_ctrl = cfg.MODEL.T_CTRL self.mem_dim = cfg.MODEL.NMN.MEM_DIM self.N = tf.shape(kb_batch)[0] self.M = tf.shape(kb_batch)[1] self.JX = tf.shape(kb_batch)[2] self.att_shape = to_T([self.N, self.M, self.JX, 1]) self.max_para_size = max_para_size self.stack_len = cfg.MODEL.NMN.STACK.LENGTH # The initialial stack values are all zeros everywhere self.att_stack_init = tf.zeros( to_T([self.N, self.M, self.JX, self.kb_dim, self.stack_len])) # The initial stack pointer points to the stack bottom self.stack_ptr_init = tf.one_hot( tf.zeros(to_T([self.N]), tf.int32), self.stack_len) self.mem_init = tf.zeros(to_T([self.N, self.mem_dim])) # zero-outputs that can be easily used by the modules self.att_zero = tf.zeros(self.att_shape, tf.float32) if config.nmn_mem_init == 'zero': self.mem_zero = tf.zeros(to_T([self.N, self.mem_dim]), tf.float32) elif config.nmn_mem_init == 'random': self.mem_zero = tf.random.uniform(to_T([self.N, self.mem_dim]), minval=-100, maxval=100, dtype=tf.float32) else: raise NotImplementedError self.score_zero = tf.zeros(to_T([self.N, 1]), tf.float32) # the set of modules and functions (e.g. "_Find" -> Find) self.module_names = module_names self.module_funcs = [getattr(self, m[1:]) for m in module_names] self.num_module = len(module_names) self.module_validity_mat = _build_module_validity_mat(module_names) # unroll the modules with a fixed number of timestep T_ctrl self.att_list = [] self.att_stack_list = [] self.stack_ptr_list = [] self.mem_list = [] self.score_list = [] self.att_in_1, self.att_in_2 = None, None att_stack_prev = self.att_stack_init stack_ptr_prev = self.stack_ptr_init mem_prev = self.mem_init self.module_validity = [] for t in range(self.T_ctrl): c_i = self.c_list[t] cv_i = self.cv_list[t] module_prob = self.module_prob_list[t] # only keep the prob of valid modules (i.e. those won't cause # stack underflow or overflow. e.g. _Filter can't be run at # t = 0 since the stack is empty). if cfg.MODEL.NMN.VALIDATE_MODULES: module_validity = tf.matmul( stack_ptr_prev, self.module_validity_mat) if cfg.MODEL.NMN.HARD_MODULE_VALIDATION: module_validity = tf.round(module_validity) self.module_validity.append(module_validity) module_prob *= module_validity module_prob /= tf.reduce_sum( module_prob, axis=1, keepdims=True) self.module_prob_list[t] = module_prob # run all the modules, and average their results wrt module_w res = [f(att_stack_prev, stack_ptr_prev, mem_prev, c_i, cv_i, t, reuse=(t > 0)) for f in self.module_funcs] att_stack_avg = tf.reduce_sum( module_prob[:, ax, ax, ax, ax, :] * tf.stack([r[0] for r in res], axis=-1), axis=-1) stack_ptr_avg = _sharpen_ptr(tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[1] for r in res], axis=2), axis=-1)) mem_avg = tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[2] for r in res], axis=2), axis=-1) score_avg = tf.reduce_sum( module_prob[:, ax, :] * tf.stack([r[3] for r in res], axis=2), axis=-1) self.att_list.append(_read_from_stack(att_stack_avg, stack_ptr_avg)) self.att_stack_list.append(att_stack_avg) self.stack_ptr_list.append(stack_ptr_avg) self.mem_list.append(mem_avg) self.score_list.append(score_avg) att_stack_prev = att_stack_avg stack_ptr_prev = stack_ptr_avg mem_prev = mem_avg self.att_last = self.att_list[-1] self.att_first = self.att_list[0] self.att_second = self.att_list[1] self.mem_last = self.mem_list[-1] self.score_last = self.score_list[-1] def NoOp(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='NoOp', reuse=None): """ Does nothing. It leaves the stack pointer, the stack and mem vector as-is. """ return att_stack, stack_ptr, mem_in, self.score_zero def Find(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Find', reuse=None): """ Performs localization, and updates memory vector. """ with tf.variable_scope(scope, reuse=reuse): # Get attention # 1) linearly map the controller vectors to the KB dimension # 2) elementwise product with KB # 3) 1x1 convolution to get attention logits c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, \ is_train=self.is_train, dropout=self.main_config.nmn_dropout) kb_batch = self.kb_batch elt_prod = tf.nn.l2_normalize( kb_batch * c_mapped[:, ax, ax, :], axis=-1) att_out = self.apply_attention(elt_prod, cv_i, t) # Push to stack stack_ptr = _move_ptr_fw(stack_ptr) att_stack = _write_to_stack(att_stack, stack_ptr, att_out) # Find bridge entity if t == 0 and (self.main_config.supervise_bridge_entity): g1, _ = bi_cudnn_rnn_encoder('lstm', self.main_config.hidden_size, 1, 1-self.main_config.input_keep_prob, \ tf.squeeze(att_out, axis=1), tf.squeeze(self.kb_len_batch, axis=1), self.is_train) g1 = g1[:, ax, :, :] bridge_logits = linear_logits([g1, att_out], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train, scope='logits_bridge') self.bridge_logits = tf.squeeze(bridge_logits, axis=1) bridge_attn = tf.nn.softmax(self.bridge_logits) self.bridge_attn = bridge_attn new_mem = tf.einsum('ijk,ij->ik', tf.squeeze(kb_batch, axis=1), bridge_attn) if t == 0: self.inferred_bridge = new_mem return att_stack, stack_ptr, self.mem_zero, self.score_zero def Compare(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Compare', reuse=None): # Pop from stack att_in_2 = tf.squeeze(_read_from_stack(att_stack, stack_ptr), axis=-1) stack_ptr = _move_ptr_bw(stack_ptr) att_in_1 = tf.squeeze(_read_from_stack(att_stack, stack_ptr), axis=-1) #stack_ptr = _move_ptr_bw(stack_ptr) stack_ptr = _move_ptr_fw(stack_ptr) with tf.variable_scope(scope, reuse=reuse): att_prob_in_1 = linear_logits([att_in_1], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train) att_prob_in_2 = linear_logits([att_in_2], True, input_keep_prob=self.main_config.input_keep_prob, \ mask=self.kb_mask_batch, is_train=self.is_train, reuse=True) att_prob_in_1, att_prob_in_2 = tf.squeeze(att_prob_in_1, axis=1), tf.squeeze(att_prob_in_2, axis=1) self.att_in_1 = att_prob_in_1 self.att_in_2 = att_prob_in_2 c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, is_train=self.is_train, dropout=self.main_config.nmn_dropout) kb_att_in_1 = _extract_softmax_avg(self.kb_batch, att_prob_in_1[:, ax, :]) kb_att_in_2 = _extract_softmax_avg(self.kb_batch, att_prob_in_2[:, ax, :]) fc_in_1 = tf.concat([c_mapped, c_mapped * kb_att_in_1, c_mapped*kb_att_in_2, kb_att_in_1-kb_att_in_2], axis=1) mem_out = tf.nn.tanh(fc('fc_mem_out_1', fc_in_1, output_dim=self.mem_dim, is_train=self.is_train, dropout=self.main_config.nmn_dropout)) return att_stack, stack_ptr, mem_out, self.score_zero def Relocate(self, att_stack, stack_ptr, mem_in, c_i, cv_i, t, scope='Transform', reuse=None): """ Relocates the previous attention, and updates memory vector. """ kb_batch = self.kb_batch with tf.variable_scope(scope, reuse=reuse): c_mapped = fc('fc_c_mapped', c_i, output_dim=cfg.MODEL.KB_DIM, is_train=self.is_train, dropout=self.main_config.nmn_dropout) # Get attention # 1) linearly map the controller vectors to the KB dimension # 2) extract attended features from the input attention # 2) elementwise product with KB # 3) 1x1 convolution to get attention logits if t == 0: elt_prod = tf.nn.l2_normalize( kb_batch * c_mapped[:, ax, ax, :], axis=-1) else: elt_prod = tf.nn.l2_normalize( kb_batch * c_mapped[:, ax, ax, :] * self.inferred_bridge[:, ax, ax, :], axis=-1) att_out = self.apply_attention(elt_prod, cv_i, t, module='relocate') # Push to stack if self.main_config.nmn_relocate_move_ptr: stack_ptr = _move_ptr_fw(stack_ptr) # cancel-out above att_stack = _write_to_stack(att_stack, stack_ptr, att_out) return att_stack, stack_ptr, self.mem_zero, self.score_zero def apply_attention(self, h, u_weights, t, module='find'): if self.main_config.nmn_attention_type == 'conv': out = _1x1conv('conv_att_out', h, output_dim=1) logits = apply_mask(out, self.kb_mask_batch) elif self.main_config.nmn_attention_type == 'biattn': out = self.bi_attention(h, u_weights, t, module=module) else: raise NotImplementedError return out def bi_attention(self, h, u_weights, t, module='find'): u_weights = tf.transpose(tf.squeeze(u_weights, axis=-1), perm=[1, 0]) q, q_mask = self.q, self.q_mask p0, context_dim, weight_one = weighted_biattention( h, q, self.kb_dim, h_mask=self.kb_mask_batch, u_mask=tf.cast(q_mask, 'bool'), u_weights=u_weights, scope='biattn') if module == 'find' and t == 1: self.weight_one = weight_one p0 = dense(p0, self.main_config.hidden_size*2, scope='biattn_dense') return p0 def _move_ptr_fw(stack_ptr): """ Move the stack pointer forward (i.e. to push to stack). """ # Note: in TF, conv1d is implemented as auto-correlation (instead of # mathmatical convolution), so no flipping of the filter. filter_fw = to_T(np.array([1, 0, 0], np.float32).reshape((3, 1, 1))) new_stack_ptr = tf.squeeze( tf.nn.conv1d(stack_ptr[..., ax], filter_fw, 1, 'SAME'), axis=[2]) # when the stack pointer is already at the stack top, keep # the pointer in the same location (otherwise the pointer will be all zero) if cfg.MODEL.NMN.STACK.GUARD_STACK_PTR: stack_len = cfg.MODEL.NMN.STACK.LENGTH stack_top_mask = tf.one_hot(stack_len - 1, stack_len) new_stack_ptr += stack_top_mask * stack_ptr return new_stack_ptr def _move_ptr_bw(stack_ptr): """ Move the stack pointer backward (i.e. to pop from stack). """ # Note: in TF, conv1d is implemented as auto-correlation (instead of # mathmatical convolution), so no flipping of the filter. filter_fw = to_T(np.array([0, 0, 1], np.float32).reshape((3, 1, 1))) new_stack_ptr = tf.squeeze( tf.nn.conv1d(stack_ptr[..., ax], filter_fw, 1, 'SAME'), axis=[2]) # when the stack pointer is already at the stack bottom, keep # the pointer in the same location (otherwise the pointer will be all zero) if cfg.MODEL.NMN.STACK.GUARD_STACK_PTR: stack_len = cfg.MODEL.NMN.STACK.LENGTH stack_bottom_mask = tf.one_hot(0, stack_len) new_stack_ptr += stack_bottom_mask * stack_ptr return new_stack_ptr def _read_from_stack(att_stack, stack_ptr): """ Read the value at the given stack pointer. """ stack_ptr_expand = stack_ptr[:, ax, ax, ax, :] # The stack pointer is a one-hot vector, so just do dot product att = tf.reduce_sum(att_stack * stack_ptr_expand, axis=-1, keepdims=True) return att def _write_to_stack(att_stack, stack_ptr, att): """ Write value 'att' into the stack at the given stack pointer. Note that the result needs to be assigned back to att_stack """ stack_ptr_expand = stack_ptr[:, ax, ax, ax, :] att_stack = att[:, :, :, :, ax] * stack_ptr_expand + att_stack * (1 - stack_ptr_expand) return att_stack def _sharpen_ptr(stack_ptr): """ Sharpen the stack pointers into (nearly) one-hot vectors, using argmax or softmax. The stack values should always sum up to one for each instance. """ hard = cfg.MODEL.NMN.STACK.USE_HARD_SHARPEN if hard: # hard (non-differentiable) sharpening with argmax new_stack_ptr = tf.one_hot( tf.argmax(stack_ptr, axis=1), tf.shape(stack_ptr)[1]) else: # soft (differentiable) sharpening with softmax temperature = cfg.MODEL.NMN.STACK.SOFT_SHARPEN_TEMP new_stack_ptr = tf.nn.softmax(stack_ptr / temperature) return new_stack_ptr def _1x1conv(name, bottom, output_dim, reuse=None): return conv(name, bottom, kernel_size=1, stride=1, output_dim=output_dim, reuse=reuse) def apply_mask(x, mask): return x - 1e30 * (1. - tf.cast(mask[:, :, :, ax], 'float32')) def _spatial_softmax(att_raw): att_shape = tf.shape(att_raw) N = att_shape[0] att_softmax = tf.nn.softmax(tf.reshape(att_raw, to_T([N, -1])), axis=1) att_softmax = tf.reshape(att_softmax, att_shape) return att_softmax def _extract_softmax_avg(kb_batch, att_raw): att_softmax = _spatial_softmax(att_raw)[:, :, :, ax] return tf.reduce_sum(kb_batch * att_softmax, axis=[1, 2]) def _build_module_validity_mat(module_names): """ Build a module validity matrix, ensuring that only valid modules will have non-zero probabilities. A module is only valid to run if there are enough attentions to be popped from the stack, and have space to push into (e.g. _Find), so that stack will not underflow or overflow by design. module_validity_mat is a stack_len x num_module matrix, and is used to multiply with stack_ptr to get validity boolean vector for the modules. """ stack_len = cfg.MODEL.NMN.STACK.LENGTH module_validity_mat = np.zeros((stack_len, len(module_names)), np.float32) for n_m, m in enumerate(module_names): # a module can be run only when stack ptr position satisfies # (min_ptr_pos <= ptr <= max_ptr_pos), where max_ptr_pos is inclusive # 1) minimum position: # stack need to have MODULE_INPUT_NUM[m] things to pop from min_ptr_pos = MODULE_INPUT_NUM[m] # the stack ptr diff=(MODULE_OUTPUT_NUM[m] - MODULE_INPUT_NUM[m]) # ensure that ptr + diff <= stack_len - 1 (stack top) max_ptr_pos = ( stack_len - 1 + MODULE_INPUT_NUM[m] - MODULE_OUTPUT_NUM[m]) module_validity_mat[min_ptr_pos:max_ptr_pos+1, n_m] = 1. return to_T(module_validity_mat)

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29889, 13, 13, 29871, 736, 304, 29918, 29911, 29898, 5453, 29918, 3084, 537, 29918, 2922, 29897, 13, 2 ]

bhr_site/urls.py

amerck/bhr-site

23

172853

from django.conf.urls import include, url from django.contrib import admin from django.views.generic.base import RedirectView from django.contrib.auth import views as auth_views urlpatterns = [ # Examples: # url(r'^$', 'bhr_site.views.home', name='home'), # url(r'^blog/', include('blog.urls')), url(r'^admin/', admin.site.urls), url(r'^bhr/', include('bhr.urls')), url(r'^accounts/login/$', auth_views.LoginView.as_view(), {'template_name': 'login.html'}, name='accounts_login'), url(r'^accounts/logout/$', auth_views.LogoutView.as_view(), name='logout'), url(r'^$', RedirectView.as_view(url='/bhr', permanent=False), name='siteroot'), ]

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NVIDIA/benchmarks/ssd/implementations/pytorch/utils.py

goswamig/training_results_v0.7

48

24705

<gh_stars>10-100 # MIT License # # Copyright (c) 2018 kuangliu # # 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. # # ------------------------------------------------------------------------------ # # MIT License # # Copyright (c) 2017 <NAME>, <NAME> # # 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. # # ------------------------------------------------------------------------------ # # Copyright (c) 2018-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torchvision import torchvision.transforms as transforms import torch.utils.data as data from PIL import Image from xml.etree import ElementTree import os import glob from pathlib import Path import numpy as np import random import itertools import torch.nn.functional as F try: import ujson as json except ImportError: import json import gc import time import bz2 import pickle from math import sqrt, ceil, cos, sin, pi from mlperf_logging.mllog import constants from mlperf_logger import log_event from SSD import _C as C from fused_color_jitter import FusedColorJitter # This function is from https://github.com/kuangliu/pytorch-ssd def calc_iou_tensor(box1, box2): """ Calculation of IoU based on two boxes tensor, Reference to https://github.com/kuangliu/pytorch-ssd input: box1 (N, 4) box2 (M, 4) output: IoU (N, M) """ N = box1.size(0) M = box2.size(0) be1 = box1.unsqueeze(1).expand(-1, M, -1) be2 = box2.unsqueeze(0).expand(N, -1, -1) # Left Top & Right Bottom lt = torch.max(be1[:,:,:2], be2[:,:,:2]) #mask1 = (be1[:,:, 0] < be2[:,:, 0]) ^ (be1[:,:, 1] < be2[:,:, 1]) #mask1 = ~mask1 rb = torch.min(be1[:,:,2:], be2[:,:,2:]) #mask2 = (be1[:,:, 2] < be2[:,:, 2]) ^ (be1[:,:, 3] < be2[:,:, 3]) #mask2 = ~mask2 delta = rb - lt delta[delta < 0] = 0 intersect = delta[:,:,0]*delta[:,:,1] #*mask1.float()*mask2.float() delta1 = be1[:,:,2:] - be1[:,:,:2] area1 = delta1[:,:,0]*delta1[:,:,1] delta2 = be2[:,:,2:] - be2[:,:,:2] area2 = delta2[:,:,0]*delta2[:,:,1] iou = intersect/(area1 + area2 - intersect) return iou # This class is from https://github.com/chauhan-utk/ssd.DomainAdaptation class SSDCropping(object): """ Cropping for SSD, according to original paper Choose between following 3 conditions: 1. Preserve the original image 2. Random crop minimum IoU is among 0.1, 0.3, 0.5, 0.7, 0.9 3. Random crop Reference to https://github.com/chauhan-utk/ssd.DomainAdaptation """ def __init__(self): self.sample_options = ( # Do nothing None, # min IoU, max IoU (0.1, None), (0.3, None), (0.5, None), (0.7, None), (0.9, None), # no IoU requirements (None, None), ) # Implementation uses 1 iteration to find a possible candidate, this # was shown to produce the same mAP as using more iterations. self.num_cropping_iterations = 1 log_event(key=constants.MAX_SAMPLES, value=self.num_cropping_iterations) def __call__(self, img, img_size, bboxes, labels): # Ensure always return cropped image while True: mode = random.choice(self.sample_options) if mode is None: return img, img_size, bboxes, labels htot, wtot = img_size min_iou, max_iou = mode min_iou = float("-inf") if min_iou is None else min_iou max_iou = float("+inf") if max_iou is None else max_iou # Implementation use 50 iteration to find possible candidate for _ in range(self.num_cropping_iterations): # suze of each sampled path in [0.1, 1] 0.3*0.3 approx. 0.1 w = random.uniform(0.3 , 1.0) h = random.uniform(0.3 , 1.0) if w/h < 0.5 or w/h > 2: continue # left 0 ~ wtot - w, top 0 ~ htot - h left = random.uniform(0, 1.0 - w) top = random.uniform(0, 1.0 - h) right = left + w bottom = top + h ious = calc_iou_tensor(bboxes, torch.tensor([[left, top, right, bottom]])) # tailor all the bboxes and return if not ((ious > min_iou) & (ious < max_iou)).all(): continue # discard any bboxes whose center not in the cropped image xc = 0.5*(bboxes[:, 0] + bboxes[:, 2]) yc = 0.5*(bboxes[:, 1] + bboxes[:, 3]) masks = (xc > left) & (xc < right) & (yc > top) & (yc < bottom) # if no such boxes, continue searching again if not masks.any(): continue bboxes[bboxes[:, 0] < left, 0] = left bboxes[bboxes[:, 1] < top, 1] = top bboxes[bboxes[:, 2] > right, 2] = right bboxes[bboxes[:, 3] > bottom, 3] = bottom #print(left, top, right, bottom) #print(labels, bboxes, masks) bboxes = bboxes[masks, :] labels = labels[masks] left_idx = int(left*wtot) top_idx = int(top*htot) right_idx = int(right*wtot) bottom_idx = int(bottom*htot) #print(left_idx,top_idx,right_idx,bottom_idx) #img = img[:, top_idx:bottom_idx, left_idx:right_idx] img = img.crop((left_idx, top_idx, right_idx, bottom_idx)) bboxes[:, 0] = (bboxes[:, 0] - left)/w bboxes[:, 1] = (bboxes[:, 1] - top)/h bboxes[:, 2] = (bboxes[:, 2] - left)/w bboxes[:, 3] = (bboxes[:, 3] - top)/h htot = bottom_idx - top_idx wtot = right_idx - left_idx return img, (htot, wtot), bboxes, labels # Don't need to cast to float, already there (from FusedColorJitter) class ToTensor(object): def __init__(self): pass def __call__(self, img): img = torch.Tensor(np.array(img)) # Transform from HWC to CHW img = img.permute(2, 0 ,1).div(255) return img class RandomHorizontalFlip(object): def __init__(self, p=0.5): self.p = p def __call__(self, image, bboxes): if random.random() < self.p: bboxes[:, 0], bboxes[:, 2] = 1.0 - bboxes[:, 2], 1.0 - bboxes[:, 0] return image.transpose(Image.FLIP_LEFT_RIGHT), bboxes return image, bboxes # Do data augumentation class SSDTransformer(object): """ SSD Data Augumentation, according to original paper Composed by several steps: Cropping Resize Flipping Jittering """ def __init__(self, size = (300, 300), val=False): # define vgg16 mean self.size = size self.val = val self.crop = SSDCropping() self.img_trans = transforms.Compose([ transforms.Resize(self.size), #transforms.ColorJitter(brightness=0.125, contrast=0.5, # saturation=0.5, hue=0.05 #), #transforms.ToTensor(), FusedColorJitter(), ToTensor(), ]) self.hflip = RandomHorizontalFlip() # All Pytorch Tensor will be normalized # https://discuss.pytorch.org/t/how-to-preprocess-input-for-pre-trained-networks/683 normalization_mean = [0.485, 0.456, 0.406] normalization_std = [0.229, 0.224, 0.225] self.normalize = transforms.Normalize(mean=normalization_mean, std=normalization_std) self.trans_val = transforms.Compose([ transforms.Resize(self.size), transforms.ToTensor(), self.normalize,]) def __call__(self, img, img_size, bbox=None, label=None, max_num=200): #img = torch.tensor(img) if self.val: bbox_out = torch.zeros(max_num, 4) label_out = torch.zeros(max_num, dtype=torch.long) bbox_out[:bbox.size(0), :] = bbox label_out[:label.size(0)] = label return self.trans_val(img), img_size, bbox_out, label_out # random crop img, img_size, bbox, label = self.crop(img, img_size, bbox, label) # random horiz. flip img, bbox = self.hflip(img, bbox) # [Resize, ColorJitter, ToTensor] img = self.img_trans(img).contiguous() img = self.normalize(img) return img, img_size, bbox, label # Implement a datareader for COCO dataset class COCODetection(data.Dataset): def __init__(self, img_folder, annotate_file, transform=None, data=None): self.img_folder = img_folder self.annotate_file = annotate_file if data: self.data = data else: # Start processing annotation with open(annotate_file) as fin: # loading huge json files tends to cause the gc (cycle collector) to # waste a lot of time so: gc_old = gc.isenabled() gc.disable() self.data = json.load(fin) if gc_old: gc.enable() self.images = {} self.label_map = {} self.label_info = {} #print("Parsing COCO data...") start_time = time.time() # 0 stand for the background cnt = 0 self.label_info[cnt] = "background" for cat in self.data["categories"]: cnt += 1 self.label_map[cat["id"]] = cnt self.label_info[cnt] = cat["name"] # build inference for images for img in self.data["images"]: img_id = img["id"] img_name = img["file_name"] img_size = (img["height"],img["width"]) #print(img_name) if img_id in self.images: raise Exception("dulpicated image record") self.images[img_id] = (img_name, img_size, []) # read bboxes for bboxes in self.data["annotations"]: img_id = bboxes["image_id"] category_id = bboxes["category_id"] bbox = bboxes["bbox"] bbox_label = self.label_map[bboxes["category_id"]] self.images[img_id][2].append((bbox, bbox_label)) for k, v in list(self.images.items()): if len(v[2]) == 0: #print("empty image: {}".format(k)) self.images.pop(k) self.img_keys = list(self.images.keys()) self.transform = transform #print("End parsing COCO data, total time {}".format(time.time()-start_time)) @property def labelnum(self): return len(self.label_info) @staticmethod def load(pklfile): #print("Loading from {}".format(pklfile)) with bz2.open(pklfile, "rb") as fin: ret = pickle.load(fin) return ret def save(self, pklfile): #print("Saving to {}".format(pklfile)) with bz2.open(pklfile, "wb") as fout: pickle.dump(self, fout) def __len__(self): return len(self.images) def __getitem__(self, idx): img_id = self.img_keys[idx] img_data = self.images[img_id] fn = img_data[0] img_path = os.path.join(self.img_folder, fn) s = time.time() img = Image.open(img_path).convert("RGB") e = time.time() decode_time = e - s htot, wtot = img_data[1] bbox_sizes = [] bbox_labels = [] #for (xc, yc, w, h), bbox_label in img_data[2]: for (l,t,w,h), bbox_label in img_data[2]: r = l + w b = t + h #l, t, r, b = xc - 0.5*w, yc - 0.5*h, xc + 0.5*w, yc + 0.5*h bbox_size = (l/wtot, t/htot, r/wtot, b/htot) # filter out zero-size bboxes if l == r or t == b: continue bbox_sizes.append(bbox_size) bbox_labels.append(bbox_label) bbox_sizes = torch.tensor(bbox_sizes) bbox_labels = torch.tensor(bbox_labels) s = time.time() if self.transform != None: img, (htot, wtot), bbox_sizes, bbox_labels = \ self.transform(img, (htot, wtot), bbox_sizes, bbox_labels) else: pass # img = transforms.ToTensor()(img) return img, img_id, (htot, wtot), bbox_sizes, bbox_labels

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2 ]

world_creator/objects.py

lilSpeedwagon/zaWRka-project

1

48455

<gh_stars>1-10 #!/usr/bin/env python3 from data_structures import * import copy import logging as log import converter import math as m from enum import Enum import os APP_VERSION = 1.0 class SignsTypes(Enum): STOP = "stop sign" ONLY_FORWARD = "only forward sign" ONLY_RIGHT = "only right sign" ONLY_LEFT = "only left sign" FORWARD_OR_RIGHT = "forward or right sign" FORWARD_OR_LEFT = "forward or left sign" class ImagesPaths(): PATH_TO_IMAGE = 'models' STOP = os.path.join(PATH_TO_IMAGE, 'brick-sign/brick.png') ONLY_FORWARD = os.path.join(PATH_TO_IMAGE, 'forward-sign/forward.png') ONLY_LEFT = os.path.join(PATH_TO_IMAGE, 'left-sign/left.png') ONLY_RIGHT = os.path.join(PATH_TO_IMAGE, 'right-sign/right.png') FORWARD_OR_LEFT = os.path.join(PATH_TO_IMAGE, 'forward-left-sign/frwd_left.png') FORWARD_OR_RIGHT = os.path.join(PATH_TO_IMAGE, 'forward-right-sign/frwd_right.png') def sign_path_to_sign_type(img_path): if img_path is ImagesPaths.STOP: return SignsTypes.STOP.value elif img_path is ImagesPaths.ONLY_FORWARD: return SignsTypes.ONLY_FORWARD.value elif img_path is ImagesPaths.ONLY_LEFT: return SignsTypes.ONLY_LEFT.value elif img_path is ImagesPaths.ONLY_RIGHT: return SignsTypes.ONLY_RIGHT.value elif img_path is ImagesPaths.FORWARD_OR_LEFT: return SignsTypes.FORWARD_OR_LEFT.value elif img_path is ImagesPaths.FORWARD_OR_RIGHT: return SignsTypes.FORWARD_OR_RIGHT.value else: return " " def sign_type_to_sign_path(sign_type): if sign_type == SignsTypes.STOP.value: return ImagesPaths.STOP elif sign_type == SignsTypes.ONLY_FORWARD.value: return ImagesPaths.ONLY_FORWARD elif sign_type == SignsTypes.ONLY_LEFT.value: return ImagesPaths.ONLY_LEFT elif sign_type == SignsTypes.ONLY_RIGHT.value: return ImagesPaths.ONLY_RIGHT elif sign_type == SignsTypes.FORWARD_OR_LEFT.value: return ImagesPaths.FORWARD_OR_LEFT elif sign_type == SignsTypes.FORWARD_OR_RIGHT.value: return ImagesPaths.FORWARD_OR_RIGHT else: return " " class ObjectType(Enum): START = 10, WALL = 11, BOX = 12, SQUARE = 13, SIGN = 14, TRAFFIC_LIGHT = 15 class CellQuarter(Enum): RIGHT_TOP = 0 RIGHT_BOT = 1 LEFT_TOP = 2 LEFT_BOT = 3 class MapParams: def __init__(self, n_cells: Size2D, cell_sz: Size2D): self.n_cells = n_cells self.cell_sz = cell_sz self.phys_size = Size2D(self.n_cells.x * self.cell_sz.x, self.n_cells.y * self.cell_sz.y) print("World cells: count={0},size={1}".format(self.n_cells, self.cell_sz)) def serialize(self): data = { 'cell_cnt': self.n_cells.as_list(), 'cell_sz': self.cell_sz.as_list() } return data def __str__(self): return 'Map params: count({}) / size({})'.format(self.n_cells, self.cell_sz) @staticmethod def deserialize(data: dict): return MapParams(Point2D.from_list(data['cell_cnt']), Point2D.from_list(data['cell_sz'])) class Object: def render(self): pass def serialized(self): pass @staticmethod def deserialize(data: dict): if data['name'] not in SERIALIZATION_SUPPORT: log.error('Object type \'{}\' not found'.format(data['name'])) return None return SERIALIZATION_SUPPORT[data['name']].deserialize(data) class Wall(): TYPE = ObjectType.WALL def __init__(self, point1, point2): self.p1 = point1 self.p2 = point2 def __str__(self): return "[({}) p1 = {}, p2 = {}]".format(type(self), self.p1, self.p2) def distance_2_point(self, pnt): import numpy from numpy import arccos, array, dot, pi, cross from numpy.linalg import det, norm A = numpy.array(self.p1.as_list()) B = numpy.array(self.p2.as_list()) P = numpy.array(pnt.as_list()) if arccos(dot((P - A) / norm(P - A), (B - A) / norm(B - A))) > m.pi / 2: return norm(P - A) if arccos(dot((P - B) / norm(P - B), (A - B) / norm(A - B))) > m.pi / 2: return norm(P - B) return norm(cross(A-B, A-P))/norm(B-A) def render(self, qp): qp.drawWallLine(self.p1, self.p2, color=(0, 0, 0)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pnts': self.p1.as_list() + self.p2.as_list() } return data @staticmethod def deserialize(data: dict): return Wall(Point2D.from_list(data['pnts'][0:2]), Point2D.from_list(data['pnts'][2:4])) class Sign(Object): TYPE = ObjectType.SIGN def __init__(self, pos, orient, signType): self.pos = pos self.type = signType self.orient = orient def __str__(self): return "[({}) pose = {}, orient = {}, type = {}]".format(type(self), self.pos, self.orient, self.type) def render(self, qp): qp.drawQuarterImg(self.pos, self.orient, sign_type_to_sign_path(self.type)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pos': self.pos.as_list(), 'orient': self.orient.value, 'type': self.type } return data @staticmethod def deserialize(data: dict): return Sign(Point2D.from_list(data['pos']), CellQuarter(data['orient']), data['type']) class Box(Object): TYPE = ObjectType.BOX def __init__(self, pos: Point2D): self.pos = pos def render(self, qp): qp.fillCell(self.pos, color=(150, 150, 150)) def serialized(self): for name, _class in SERIALIZATION_SUPPORT.items(): if type(self) == _class: break data = { 'name': name, 'pos': self.pos.as_list() } return data @staticmethod def deserialize(data: dict): return Box(Point2D.from_list(data['pos'])) SERIALIZATION_SUPPORT = { 'wall': Wall, 'sign': Sign, 'box': Box }

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main project.py

Kshitijkrishnadas/haribol

0

139569

import mysql.connector as sql conn=sql.connect(host='localhost',user='root',passwd='<PASSWORD>',database='electronic_shop') cur=conn.cursor() if conn.is_connected: print(' ELECTRONIC SHOP SYSTEM') print(' 1.Admin') print(' 2.Customers') choice=int(input(' Enter the choice: ')) if choice == 1: pw = '<PASSWORD>' passwrd = input('Enter the password: ') if passwrd == pw: print(' 1.Enter data for Items') print(' 2.Enter data for Labour') print(' 3.Enter data for Customers') choose = int(input('Enter the choice for adding data: ')) if choose == 1: item = input('Enter the name of item: ') availability = int(input('Availability of the item: ')) price = int(input('Enter the price of item in rupees: ')) cur.execute("INSERT INTO ITEMS VALUES('" + item + "'," + str(availability) + "," + str(price) + ")") print('ENTRY SUCCESSFUL.') conn.commit() elif choose == 2: name = input('Enter the name of labour: ') age = int(input('Enter the age: ')) place = input('Enter the place of the labour: ') dept = input('Enter the name of the department: ') cur.execute("INSERT INTO LABOURS VALUES('" + name + "','" + str(age) + "','" + place + "','" + dept + "')") print('ENTRY SUCCESSFUL.') conn.commit() elif choose == 3: name = input('Enter the name of the customer: ') age = int(input('Enter the age: ')) place = input('Enter the place of the customer: ') item = input('Enter the item bought: ') cur.execute("INSERT INTO CUSTOMER VALUES('" + name + "','" + str(age) + "','" + place + "','" + item + "')") print('ENTRY SUCCESSFUL.') conn.commit() else: print('Invalid Login!') elif choice == 2: print(' 1.Display the list of Items.') print(' 2.Display the list of Labours.') print(' 3.Display the list of Customers.') ch = int(input('Enter the choice: ')) if ch == 1: print() cur.execute('SELECT * FROM ITEMS') dataa = cur.fetchall() print('List of items: ') for row in dataa: print() print('Item: ',row[0]) print('Availablity: ',row[1]) print('Price: ',row[2]) elif ch == 2: print() cur.execute('SELECT * FROM LABOURS') data = cur.fetchall() print('List of Labours:') for row in data: print() print('Name: ',row[0]) print('Age: ',row[1]) print('Place: ',row[2]) print('Department: ',row[3]) elif ch == 3: print() cur.execute('SELECT * FROM CUSTOMER') datas = cur.fetchall() print('List of customers:') for row in datas: print() print('Name: ',row[0]) print('Age: ',row[1]) print('Place: ',row[2]) print('Item bought: ',row[3])

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gooey/gui/processor.py

Jacke/Gooey

13,430

1610714

import os import re import subprocess import sys from functools import partial from threading import Thread from gooey.gui import events from gooey.gui.pubsub import pub from gooey.gui.util.casting import safe_float from gooey.gui.util.taskkill import taskkill from gooey.util.functional import unit, bind class ProcessController(object): def __init__(self, progress_regex, progress_expr, hide_progress_msg,encoding, shell=True): self._process = None self.progress_regex = progress_regex self.progress_expr = progress_expr self.hide_progress_msg = hide_progress_msg self.encoding = encoding self.wasForcefullyStopped = False self.shell_execution = shell def was_success(self): self._process.communicate() return self._process.returncode == 0 def poll(self): if not self._process: raise Exception('Not started!') return self._process.poll() def stop(self): if self.running(): self.wasForcefullyStopped = True taskkill(self._process.pid) def running(self): return self._process and self.poll() is None def run(self, command): self.wasForcefullyStopped = False env = os.environ.copy() env["GOOEY"] = "1" env["PYTHONIOENCODING"] = self.encoding try: self._process = subprocess.Popen( command.encode(sys.getfilesystemencoding()), stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.STDOUT, shell=self.shell_execution, env=env) except: self._process = subprocess.Popen( command, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr = subprocess.STDOUT, shell = self.shell_execution, env=env) t = Thread(target=self._forward_stdout, args=(self._process,)) t.start() def _forward_stdout(self, process): ''' Reads the stdout of `process` and forwards lines and progress to any interested subscribers ''' while True: line = process.stdout.readline() if not line: break _progress = self._extract_progress(line) pub.send_message(events.PROGRESS_UPDATE, progress=_progress) if _progress is None or self.hide_progress_msg is False: pub.send_message(events.CONSOLE_UPDATE, msg=line.decode(self.encoding)) pub.send_message(events.EXECUTION_COMPLETE) def _extract_progress(self, text): ''' Finds progress information in the text using the user-supplied regex and calculation instructions ''' # monad-ish dispatch to avoid the if/else soup find = partial(re.search, string=text.strip().decode(self.encoding)) regex = unit(self.progress_regex) match = bind(regex, find) result = bind(match, self._calculate_progress) return result def _calculate_progress(self, match): ''' Calculates the final progress value found by the regex ''' if not self.progress_expr: return safe_float(match.group(1)) else: return self._eval_progress(match) def _eval_progress(self, match): ''' Runs the user-supplied progress calculation rule ''' _locals = {k: safe_float(v) for k, v in match.groupdict().items()} if "x" not in _locals: _locals["x"] = [safe_float(x) for x in match.groups()] try: return int(eval(self.progress_expr, {}, _locals)) except: return None

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thermo/property_package_constants.py

RoryKurek/thermo

380

117506

<filename>thermo/property_package_constants.py # -*- coding: utf-8 -*- '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2018, 2019 <NAME> <<EMAIL>> 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. .. warning:: These classes were a first attempt at rigorous multiphase equilibrium. They may be useful in some special cases but they are not complete and further development will not happen. They were never documented as well. It is recommended to switch over to the :obj:`thermo.flash` interface which seeks to be more modular, easier to maintain and extend, higher-performance, and easier to modify. ''' from __future__ import division __all__ = ['PropertyPackageConstants', 'IDEAL_PKG', 'NRTL_PKG', 'UNIFAC_PKG', 'UNIFAC_DORTMUND_PKG', 'PR_PKG', 'SRK_PKG'] from fluids.numerics import brenth, ridder, derivative, numpy as np from chemicals.utils import log, exp from fluids.constants import R, pi, N_A from chemicals.identifiers import IDs_to_CASs from chemicals.rachford_rice import flash_inner_loop from thermo.eos_mix import PRMIX, SRKMIX from thermo.eos import PR, SRK from thermo.chemical import Chemical from thermo.mixture import Mixture from thermo.property_package import * IDEAL_PKG = 'Ideal' NRTL_PKG = 'NRTL' UNIFAC_PKG = 'Unifac' UNIFAC_DORTMUND_PKG = 'Unifac Dortmund' PR_PKG = 'PR' SRK_PKG = 'SRK' property_packages = [IDEAL_PKG, NRTL_PKG, UNIFAC_PKG, UNIFAC_DORTMUND_PKG, PR_PKG, SRK_PKG] property_packages_cubic = [PR_PKG, SRK_PKG] property_package_to_eos = {PR_PKG: PRMIX, SRK_PKG: SRKMIX} property_package_to_eos_pures = {PR_PKG: PR, SRK_PKG: SRK} property_package_names_to_objs = {IDEAL_PKG: IdealCaloric, NRTL_PKG: Nrtl, # Not complete - enthalpy missing UNIFAC_PKG: UnifacCaloric, UNIFAC_DORTMUND_PKG: UnifacDortmundCaloric, PR_PKG: GceosBase, SRK_PKG: GceosBase, } class PropertyPackageConstants(object): '''Class to store kijs, as well as allow properties to be edited; load them from the database and then be ready to store them. ''' def __init__(self, mixture, name=IDEAL_PKG, **kwargs): if isinstance(mixture, list): self.CASs = IDs_to_CASs(mixture) self.Chemicals = [Chemical(CAS) for CAS in self.CASs] elif isinstance(mixture, Mixture): self.Chemicals = mixture.Chemicals self.name = name if name not in property_packages_cubic: eos_mix = PRMIX eos = PR else: eos_mix = property_package_to_eos[name] eos = property_package_to_eos_pures[name] self.eos = eos self.eos_mix = eos_mix self.eos_in_a_box = [eos_mix] self.pkg_obj = property_package_names_to_objs[self.name] self.set_chemical_constants() self.set_Chemical_property_objects() self.set_TP_sources() self.kwargs = kwargs self.pkg = self.new_package() def new_package(self): pkg_args = {'VaporPressures': self.VaporPressures, 'Tms': self.Tms, 'Tbs': self.Tbs, 'Tcs': self.Tcs, 'Pcs': self.Pcs, 'omegas': self.omegas, 'VolumeLiquids': self.VolumeLiquids, 'HeatCapacityLiquids': self.HeatCapacityLiquids, 'HeatCapacityGases': self.HeatCapacityGases, 'EnthalpyVaporizations': self.EnthalpyVaporizations, 'VolumeLiquids': self.VolumeLiquids, 'VolumeGases': self.VolumeGases, 'eos': self.eos, 'eos_mix': self.eos_mix, 'MWs': self.MWs, 'atomss': self.atomss, 'Hfs': self.Hfgms, 'Gfs': self.Gfgms, } pkg_args.update(self.kwargs) if self.name == UNIFAC_PKG: pkg_args['UNIFAC_groups'] = self.UNIFAC_groups elif self.name == UNIFAC_DORTMUND_PKG: pkg_args['UNIFAC_groups'] = self.UNIFAC_Dortmund_groups return self.pkg_obj(**pkg_args) def from_json(self, json): self.__dict__.update(json) self.set_Chemical_property_objects() self.set_TP_sources() transfer_methods = ['set_chemical_constants', 'set_Chemical_property_objects', 'set_TP_sources', 'UNIFAC_Dortmund_groups', 'UNIFAC_groups', 'atomss'] try: for method in transfer_methods: attr = Mixture.__dict__[method] setattr(PropertyPackageConstants, method, attr) except: for method in transfer_methods: setattr(PropertyPackageConstants, method, getattr(Mixture, method))

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mlcomp/parallelm/common/buff_to_lines.py

lisapm/mlpiper

7

172185

class BufferToLines(object): def __init__(self): self._acc_buff = "" self._last_line = "" self._in_middle_of_line = False def add(self, buff): self._acc_buff += buff.decode() self._in_middle_of_line = False if self._acc_buff[-1] == '\n' else True def lines(self): lines = self._acc_buff.split('\n') up_to_index = len(lines) - 2 if self._in_middle_of_line else len(lines) - 1 self._acc_buff = lines[-1] if self._in_middle_of_line else "" for iii in range(up_to_index): yield lines[iii]

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motion_detection.py

udelblue/cv

0

165772

import cv2 import numpy as np def main(): #window_name="Cam feed" #cv2.namedWindow(window_name) cap=cv2.VideoCapture(0) #filename = 'F:\sample.avi' #codec=cv2.VideoWriter_fourcc('X','V','I','D') #framerate=30 #resolution = (500,500) # VideoFileOutput = cv2.VideoWriter(filename,codec,framerate,resolution) if cap.isOpened(): ret,frame = cap.read() else: ret =False ret,frame1 = cap.read() ret,frame2 = cap.read() while ret: ret,frame = cap.read() #VideoFileOutput.write(frame) d=cv2.absdiff(frame1,frame2) grey=cv2.cvtColor(d,cv2.COLOR_BGR2GRAY) blur =cv2.GaussianBlur(grey,(5,5),0) ret,th=cv2.threshold(blur,20,255,cv2.THRESH_BINARY) dilated=cv2.dilate(th,np.ones((3,3),np.uint8),iterations=3) img,c,h=cv2.findContours(dilated,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(frame1,c,-1,(0,255,0),2) #cv2.imshow("win1",frame2) cv2.imshow("inter",frame1) if cv2.waitKey(40) == 27: break frame1 = frame2 ret,frame2= cap.read() cv2.destroyAllWindows() #VideoFileOutput.release() cap.release() main()

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leetcode/389.py

GihwanKim/Baekjoon

0

187508

""" File: 389.py Title: Find the Difference Difficulty: Easy URL: https://leetcode.com/problems/find-the-difference/ """ import unittest class Solution: def findTheDifference(self, s: str, t: str) -> str: sorted_s = list(sorted(s)) sorted_t = list(sorted(t)) for i in range(len(s)): if sorted_s[i] != sorted_t[i]: return sorted_t[i] return sorted_t[-1] class SolutionTestCase(unittest.TestCase): def test_example1(self): # Input s = "abcd" t = "abcde" # Output output = "e" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example2(self): # Input s = "" t = "y" # Output output = "y" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example3(self): # Input s = "a" t = "aa" # Output output = "a" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) def test_example3(self): # Input s = "ae" t = "aea" # Output output = "a" solution = Solution() self.assertEqual(solution.findTheDifference(s, t), output) if __name__ == "__main__": unittest.main()

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parser/reader/__init__.py

cheery/better_web_language

136

56597

from stream import CStream from tokenizer import L2 from data import Expr, Literal, Position #import space #table = { # u'(': u'lp', u')': u'rp', # u'[': u'lb', u']': u'rb', # u'{': u'lc', u'}': u'rc', # u'and': u'and', u'or': u'or', u'not': u'not', # u'=': u'let', u':=': u'set', # u'<': u'chain', # u'>': u'chain', # u'<=': u'chain', # u'>=': u'chain', # u'==': u'chain', # u'!=': u'chain', # u'^': u'op', u'&': u'op', u'<<': u'op', # u'>>': u'op', u'!': u'op', u'*': u'op', # u'/': u'op', u'%': u'op', u'+': u'op', # u'-': u'op', u'|': u'op', u'++': u'op', # u':': u'symbol', # u'.': u'dot'} #binops = { # u'|': 10, # u'^': 10, # u'&': 20, # u'<<': 30, u'>>': 40, # u'++': 40, u'+': 40, u'-': 40, # u'*': 50, u'/': 50, u'%': 50, #} #right_binding = [] #prefixes = { # u'~': 90, # u'-': 90, # u'+': 90, #} #postfixes = { # u'!': 100, #} # #def read(source): # exps = [] # ts = L2(CStream(source), table) # while ts.filled: # if ts.position.col != 0: # raise space.Error(u"%s: layout error" % ts.first.start.repr()) # exps.append(toplevel(ts, 0)) # return exps # #def toplevel(ts, col): # head = expression(ts) # if head.dcf is not None and ts.filled: # if head.stop.lno == ts.position.lno: # head.dcf.capture = [toplevel(ts, col)] # elif ts.position.col > col: # head.dcf.capture = exps = [] # scol = ts.position.col # while ts.filled and ts.position.col == scol: # exp = toplevel(ts, scol) # exps.append(exp) # while ts.filled and ts.position.lno == exp.stop.lno and ts.position.col > scol: # exps.append(toplevel(ts, scol)) # return head # #def expressions(ts): # exps = [] # while ts.filled: # if match_some(ts.first, [u'rp', u'rb', u'rc']): # break # exps.append(expression(ts)) # return exps # #def expression(ts): # left = expression_and(ts) # if match(ts.first, u'or'): # op = ts.advance() # op.name = u'symbol' # right = expression(ts) # return Expr(left.start, right.stop, u'form', [op, left, right]) # return left # #def expression_and(ts): # left = expression_chain(ts) # if match(ts.first, u'and'): # op = ts.advance() # op.name = u'symbol' # right = expression_and(ts) # return Expr(left.start, right.stop, u'form', [op, left, right]) # return left # #def expression_chain(ts): # left = expression_bare(ts, 0) # if match(ts.first, u'chain'): # exps = [left] # while match(ts.first, u'chain'): # op = ts.advance() # op.name = u'symbol' # exps.append(op) # exps.append(expression_bare(ts, 0)) # left = Expr(exps[0].start, exps[len(exps)-1].stop, u'chain', exps) # return left # #def expression_bare(ts, rbp): # if on_prefix(ts): # op = ts.advance() # exp = expression_bare(ts, prefixes[op.value]) # op.name = u'symbol' # op.value = op.value+u'expr' # left = Expr(op.start, exp.stop, u'form', [op, exp]) # else: # left = terminal(ts) # while ts.filled: # if match(ts.first, u'dot'): # dot = ts.advance() # symbol = ts.advance() # if not match(symbol, u'symbol'): # raise space.Error(u"%s: expected symbol" % symbol.start.repr()) # left = Expr(left.start, symbol.stop, u'attr', [left, symbol]) # elif match(ts.first, u'lb') and left.stop.eq(ts.first.start): # lb = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rb'): # raise space.Error(u"%s: [] truncates at %s" % (lb.start.repr(), ts.position.repr())) # rb = ts.advance() # left = Expr(left.start, rb.stop, u'index', [left] + exps) # elif match_some(ts.first, [u'let', u'set']): # let = ts.advance() # exp = expression(ts) # left = Expr(left.start, exp.stop, let.name, [left, exp]) # elif match(ts.first, u'op') and match(ts.second, u'let') and ts.first.value in binops: # aug = ts.advance() # aug.name = u'symbol' # let = ts.advance() # exp = expression(ts) # left = Expr(left.start, exp.stop, u'aug', [aug, left, exp]) # else: # break # while ts.filled: # if on_binop(left, ts) and rbp < binops.get(ts.first.value, 0): # op = ts.advance() # op.name = u'symbol' # lbp = binops.get(op.value, 0) # right = expression_bare(ts, lbp - (ts.first.value in right_binding)) # left = Expr(left.start, right.stop, u'form', [op, left, right]) # elif on_postfix(left, ts) and rbp < postfixes.get(ts.first.value, 0): # op = ts.advance() # op.name = u'symbol' # lbp = postfixes.get(op.value, 0) # op.value = u'expr'+op.value # left = Expr(left.start, op.stop, u'form', [op, left]) # else: # break # return left # #def terminal(ts): # if match_some(ts.first, [u'symbol', u'string', u'int', u'hex', u'float']): # return ts.advance() # elif match(ts.first, u'lp'): # lp = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rp'): # raise space.Error(u"%s: form truncates at %s" % (lp.start.repr(), ts.position.repr())) # rp = ts.advance() # exp = Expr(lp.start, rp.stop, u'form', exps) # exp.dcf = exp # return exp # elif match(ts.first, u'lb'): # lb = ts.advance() # exps = expressions(ts) # if not match(ts.first, u'rb'): # raise space.Error(u"%s: list truncates at %s" % (lb.start.repr(), ts.position.repr())) # rb = ts.advance() # exp = Expr(lb.start, rb.stop, u'list', exps) # exp.dcf = exp # return exp # elif match(ts.first, u'lc'): # lc = ts.advance() # if match(ts.second, u'rc'): # exp = ts.advance() # exp.name = u'symbol' # else: # exp = expression(ts) # rc = ts.advance() # return exp # elif match(ts.first, u'not'): # op = ts.advance() # op.name = u'symbol' # exp = expression_chain(ts) # return Expr(op.start, exp.stop, u'form', [op, exp]) # if ts.filled: # raise space.Error(u"%s: expected term, got %s" % (ts.position.repr(), ts.first.value)) # raise space.Error(u"%s: expected term, got eof" % ts.position.repr()) # #def match_some(t, names): # return t is not None and t.name in names # #def match(t, name): # return t is not None and t.name == name # #def on_prefix(ts): # if match(ts.first, u'op') and ts.second is not None: # return ts.first.stop.eq(ts.second.start) # return False # #def on_binop(left, ts): # if match(ts.first, u'op') and ts.second is not None: # l = left.stop.eq(ts.first.start) # r = ts.first.stop.eq(ts.second.start) # return l == r # return False # #def on_postfix(left, ts): # if match(ts.first, u'op'): # l = left.stop.eq(ts.first.start) # r = ts.second is not None and ts.first.stop.eq(ts.second.start) # return l and not r # return False

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testfixtures/tests/test_roundcomparison.py

Alexhuszagh/XLDiscoverer

0

17953

# Copyright (c) 2014 Simplistix Ltd # See license.txt for license details. from decimal import Decimal from testfixtures import RoundComparison as R, compare, ShouldRaise from unittest import TestCase from ..compat import PY2, PY3 class Tests(TestCase): def test_equal_yes_rhs(self): self.assertTrue(0.123457 == R(0.123456, 5)) def test_equal_yes_lhs(self): self.assertTrue(R(0.123456, 5) == 0.123457) def test_equal_no_rhs(self): self.assertFalse(0.123453 == R(0.123456, 5)) def test_equal_no_lhs(self): self.assertFalse(R(0.123456, 5) == 0.123453) def test_not_equal_yes_rhs(self): self.assertFalse(0.123457 != R(0.123456, 5)) def test_not_equal_yes_lhs(self): self.assertFalse(R(0.123456, 5) != 0.123457) def test_not_equal_no_rhs(self): self.assertTrue(0.123453 != R(0.123456, 5)) def test_not_equal_no_lhs(self): self.assertTrue(R(0.123456, 5) != 0.123453) def test_equal_in_sequence_rhs(self): self.assertEqual((1, 2, 0.123457), (1, 2, R(0.123456, 5))) def test_equal_in_sequence_lhs(self): self.assertEqual((1, 2, R(0.123456, 5)), (1, 2, 0.123457)) def test_not_equal_in_sequence_rhs(self): self.assertNotEqual((1, 2, 0.1236), (1, 2, R(0.123456, 5))) def test_not_equal_in_sequence_lhs(self): self.assertNotEqual((1, 2, R(0.123456, 5)), (1, 2, 0.1236)) def test_not_numeric_rhs(self): with ShouldRaise(TypeError): 'abc' == R(0.123456, 5) def test_not_numeric_lhs(self): with ShouldRaise(TypeError): R(0.123456, 5) == 'abc' def test_repr(self): compare('<R:0.12346 to 5 digits>', repr(R(0.123456, 5))) def test_str(self): compare('<R:0.12346 to 5 digits>', repr(R(0.123456, 5))) def test_str_negative(self): if PY3: expected = '<R:123500 to -2 digits>' else: expected = '<R:123500.0 to -2 digits>' compare(expected, repr(R(123456, -2))) TYPE_ERROR_DECIMAL = TypeError( "Cannot compare <R:0.12346 to 5 digits> with <class 'decimal.Decimal'>" ) def test_equal_yes_decimal_to_float_rhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertTrue(Decimal("0.123457") == R(0.123456, 5)) def test_equal_yes_decimal_to_float_lhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertTrue(R(0.123456, 5) == Decimal("0.123457")) def test_equal_no_decimal_to_float_rhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertFalse(Decimal("0.123453") == R(0.123456, 5)) def test_equal_no_decimal_to_float_lhs(self): with ShouldRaise(self.TYPE_ERROR_DECIMAL, unless=PY2): self.assertFalse(R(0.123456, 5) == Decimal("0.123453")) TYPE_ERROR_FLOAT = TypeError( "Cannot compare <R:0.12346 to 5 digits> with <class 'float'>" ) def test_equal_yes_float_to_decimal_rhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertTrue(0.123457 == R(Decimal("0.123456"), 5)) def test_equal_yes_float_to_decimal_lhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertTrue(R(Decimal("0.123456"), 5) == 0.123457) def test_equal_no_float_to_decimal_rhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertFalse(0.123453 == R(Decimal("0.123456"), 5)) def test_equal_no_float_to_decimal_lhs(self): with ShouldRaise(self.TYPE_ERROR_FLOAT, unless=PY2): self.assertFalse(R(Decimal("0.123456"), 5) == 0.123453) def test_integer_float(self): with ShouldRaise(TypeError, unless=PY2): 1 == R(1.000001, 5) def test_float_integer(self): with ShouldRaise(TypeError, unless=PY2): R(1.000001, 5) == 1 def test_equal_yes_integer_other_rhs(self): self.assertTrue(10 == R(11, -1)) def test_equal_yes_integer_lhs(self): self.assertTrue(R(11, -1) == 10) def test_equal_no_integer_rhs(self): self.assertFalse(10 == R(16, -1)) def test_equal_no_integer_lhs(self): self.assertFalse(R(16, -1) == 10) def test_equal_integer_zero_precision(self): self.assertTrue(1 == R(1, 0)) def test_equal_yes_negative_precision(self): self.assertTrue(149.123 == R(101.123, -2)) def test_equal_no_negative_precision(self): self.assertFalse(149.123 == R(150.001, -2)) def test_decimal_yes_rhs(self): self.assertTrue(Decimal('0.123457') == R(Decimal('0.123456'), 5)) def test_decimal_yes_lhs(self): self.assertTrue(R(Decimal('0.123456'), 5) == Decimal('0.123457')) def test_decimal_no_rhs(self): self.assertFalse(Decimal('0.123453') == R(Decimal('0.123456'), 5)) def test_decimal_no_lhs(self): self.assertFalse(R(Decimal('0.123456'), 5) == Decimal('0.123453'))

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benchmarks/solve.py

Kludex/di

0

187816

"""Run this file like python benchmarks/solve.py It will print out results to the console and open web browser windows. """ import anyio from pyinstrument.profiler import Profiler from benchmarks.utils import GraphSize, SleepTimes, generate_dag from di import Container, Dependant, Depends from di.executors import ConcurrentAsyncExecutor, SimpleSyncExecutor INTERVAL = 10e-6 # 10 us async def async_bench(sleep: SleepTimes, graph: GraphSize, iters: int) -> None: container = Container(executor=ConcurrentAsyncExecutor()) solved = container.solve( Dependant(generate_dag(Depends, graph, sync=False, sleep=sleep)) ) p = Profiler() await container.execute_async(solved) p.start() for _ in range(iters): await container.execute_async(solved) p.stop() p.print() p.open_in_browser() def sync_bench(sleep: SleepTimes, graph: GraphSize, iters: int) -> None: container = Container(executor=SimpleSyncExecutor()) solved = container.solve( Dependant(generate_dag(Depends, graph, sync=True, sleep=sleep)) ) p = Profiler() container.execute_sync(solved) p.start() for _ in range(iters): container.execute_sync(solved) p.stop() p.print() p.open_in_browser() LARGE_GRAPH = GraphSize(25, 5, 5) SMALL_GRAPH = GraphSize(1, 1, 1) FAST_DEPS = SleepTimes(0, 0) SLOW_DEPS = SleepTimes(1e-3, 1e-3) if __name__ == "__main__": anyio.run(async_bench, FAST_DEPS, SMALL_GRAPH, 1_000) anyio.run(async_bench, FAST_DEPS, LARGE_GRAPH, 1_000) anyio.run(async_bench, SLOW_DEPS, SMALL_GRAPH, 1_000) anyio.run(async_bench, SLOW_DEPS, LARGE_GRAPH, 100) sync_bench(FAST_DEPS, SMALL_GRAPH, iters=1_000) sync_bench(FAST_DEPS, LARGE_GRAPH, iters=1_000) sync_bench(SLOW_DEPS, SMALL_GRAPH, iters=1_000) sync_bench(SLOW_DEPS, LARGE_GRAPH, iters=10)

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djl10n/views.py

ralkan/django-l10n

1

61672

<gh_stars>1-10 import json from django.conf import settings from django.template import Context, Template from django.http import HttpResponse from .helpers import get_translations js_catalog_template = r""" {% autoescape off %} (function (globals) { String.prototype.toTitleCase = function() { return this.replace( /([^\W_]+[^\s-_]*) */g, function(txt){ return txt.charAt(0).toUpperCase() + txt.substr(1).toLowerCase(); }); } String.prototype.toUpperCaseFirstChar = function() { return this.substr(0, 1).toUpperCase() + this.substr(1); }; String.prototype.isUpperCase = function() { return this == this.toUpperCase(); }; String.prototype.isTitleCase = function() { return this == this.toTitleCase(); }; String.prototype.isUpperCaseFirstChar = function() { return this == this.toUpperCaseFirstChar(); }; var django = globals.django || (globals.django = {}); /* gettext library */ django.translations = {{ catalog_str }}; django.localize = function (key, fallback, placeholders) { fallback = typeof(fallback) == "undefined" ? key : fallback; var value = django.translations[key.toLowerCase()]; if (typeof(value) == 'undefined') { {% if not debug %} value = fallback; {% else %} value = '\{\{' + key + '\}\}'; {% endif %} } if (placeholders && placeholders['plural']) { var plural = parseInt(placeholders['plural']); var values = value.split('|'); delete placeholders['plural']; if (plural == 1 || values.length < 2) { value = value.split('|')[0]; } else { value = value.split('|')[1]; } } for (var ph in placeholders) { value = value.replace(':' + ph, placeholders[ph]); } if (key.isUpperCase()) { value = value.toUpperCase(); } else if (key.isTitleCase()) { value = value.toTitleCase(); } else if (key.isUpperCaseFirstChar()) { value = value.toUpperCaseFirstChar(); } return value; }; /* formatting library */ globals.localize = django.localize; }(this)); {% endautoescape %} """ def render_javascript_catalog(translations=None): def _indent(s): return s.replace('\n', '\n ') template = Template(js_catalog_template) context = Context({ 'catalog_str': _indent(json.dumps( translations, sort_keys=True, indent=2)) if translations else {}, 'debug': settings.LOCALIZE_DEBUG }) return HttpResponse(template.render(context), 'text/javascript') def javascript_catalog(request, domain='globaljs', packages=None): locale = request.LANGUAGE_CODE translations = get_translations(locale, domain) return render_javascript_catalog(translations)

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tests/__init__.py

jdeepe/Dynamodb-ORM-Demo

0

121281

from api.dataset.models import DataSchema, Dataset def verify_settings(model, p_key, settings): details = eval(model).get(p_key) for key, setting in settings.items(): print(getattr(details, key), setting) setting = setting if setting else None assert getattr(details, key) == setting def load_data(): load_dataschema_data() load_dataset() def load_dataschema_data(): DataSchema.create_table(read_capacity_units=1, write_capacity_units=1, wait=True) for i in range(50): name = "WorkOrder_v%d" % i data_schema = DataSchema(name) data = { "dataset": name, "status": "RETIRED", "description": "Schema for %s" % name, "creation_timestamp": "2017-04-24T11:38:41.164Z", "last_updated_timestamp": "2017-12-24T22:38:47.346Z", } for key in data: setattr(data_schema, key, data[key]) data_schema.save() def load_dataset(): for i in range(50): data = { "name": "Forecast_v%d" % i, "description": "Providing a demand forecast", "status": "ACTIVE", "type": "TRANSACTIONAL", "frequency": "DAILY", "classification": "Orange", "owner": "Forecast team", "owner_contact": "forecast@", "service_arn": "arn:aws:s3:::org.ent-data-lake", "location_pointer": "my.org/Forecasting/Forecast_v1", "creation_timestamp": "2017-01-12T11:39:43.164Z", "derived_from": None, "replaced_by": None, "from_date": "2017-01-03", "to_date": None, "schema_name": "Forecast_schema_v1", "schema_location": None, "data_lineage": None, "compliance": None, "enforce_sla": None, "sla_cron": "0 6 * * *", "tags": [ {"key": "org", "value": "CRM"}, {"key": "cost", "value": "SupplyChain"}, ], "retention": None, "encryption": None, "encryption_type": None, "encryption_kms_id": None, "cross_region_replication": None, "config_crr": None, "password": None, "s3_logging_enabled": None, "config_s3_logging": None, "requestor_pays_enabled": None, } name = "Forecast_v%d" % i dataset = Dataset(name) for key in data: setattr(dataset, key, data[key]) dataset.save()

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lib/base_folder.py

collinahn/file_name_changer

1

1613963

# working dir 를 저장하는 싱글턴 클래스 from dataclasses import ( dataclass, field ) class MetaSingleton(type): _instances = {} def __call__(cls, *args, **kwargs): if cls not in cls._instances: cls._instances[cls] = super().__call__(*args, **kwargs) return cls._instances[cls] @dataclass(frozen=True) class WorkingDir(metaclass=MetaSingleton): abs_path: str = field(default_factory='') rel_path: str = field(default_factory='') if __name__ == '__main__': import utils p1 = WorkingDir(utils.extract_dir(), utils.get_relative_path('.')) print(p1) # Config(name='John', age='25') p2 = WorkingDir('', '') print(p2) # Config(name='John', age='25') print(p1 == p2) # True print(p1 is p2) # True

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changelogger/urls.py

zhelyabuzhsky/changelogger

1

45431

from django.conf.urls import include from django.contrib import admin from django.contrib.auth import views as auth_views from django.urls import path urlpatterns = [ path("", include("changelogs.urls")), path("admin/", admin.site.urls), path("accounts/", include("django.contrib.auth.urls")), path("login/", auth_views.LoginView.as_view()), path("logout/", auth_views.LogoutView.as_view()), path("oauth/", include("social_django.urls", namespace="social")), ]

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rnafbinv/mutator.py

matandro/RNAsfbinv

3

137545

<filename>rnafbinv/mutator.py #!/usr/bin/env python3 ''' enter description ''' import random from rnafbinv import IUPAC, tree_aligner from typing import Dict, Any import enum ''' 1) select motif? / select index -> identify motif? (single / double on stem) 2) identify sequence constrains ( / missing constraints) 3) select random possible mutation (addition, removal or modification) ''' class Action(enum.Enum): REPLACE = 1 ADD = 2 REMOVE = 3 def perturbate(current_sequence: str, match_tree: tree_aligner.Tree, options: Dict[str, Any]) -> str: min_length = len(options.get('target_structure')) - options.get('vlength') max_length = len(options.get('target_structure')) + options.get('vlength') actions = [Action.REPLACE] if len(current_sequence) < max_length: actions.append(Action.ADD) if len(current_sequence) > min_length: actions.append(Action.REMOVE) # mutated_sequence = simple_point_mutation(current_sequence, random.choice(actions)) mutated_sequence = multi_point_mutation(current_sequence, min_length, max_length, random.choice(actions)) return mutated_sequence def simple_point_mutation(old_sequence: str, action: Action=Action.REPLACE) -> str: index = random.randint(0, len(old_sequence) - 1) if action == Action.REPLACE: sequence = old_sequence[:index] + random.choice(IUPAC.IUPAC_RNA_BASE.replace(old_sequence[index], '')) + \ old_sequence[index + 1:] elif action == Action.ADD: sequence = old_sequence[:index] + random.choice(IUPAC.IUPAC_RNA_BASE.replace(old_sequence[index], '')) + \ old_sequence[index:] elif action == Action.REMOVE: sequence = old_sequence[:index] + old_sequence[index + 1:] return sequence def multi_point_mutation(old_sequence: str, min_length:int, max_length: int, action: Action=Action.REPLACE, max_size: int=5) -> str: def gen_sequence(gen_size: int, old_seq: str= None) -> str: res = '' # if we replace, select an index to be different for sure rand_loc = None if old_seq is not None: rand_loc = random.randint(0, len(old_seq) - 1) # generate new subseq for i in range(0, gen_size): selection = IUPAC.IUPAC_RNA_BASE if i == rand_loc: selection = selection.replace(old_seq[rand_loc], '') res += random.choice(selection) return res index = random.randint(0, len(old_sequence) - 1) dist = [] for i in range(1, max_size): dist += [i] * (max_size - i + 1) if action == Action.REPLACE: size = min(random.choice(dist), len(old_sequence) - index) new_part = gen_sequence(size, old_sequence[index : index + size]) sequence = old_sequence[:index] + new_part + old_sequence[index + size:] elif action == Action.ADD: size = min(random.choice(dist), max_length - len(old_sequence)) new_part = gen_sequence(size) sequence = old_sequence[:index] + new_part + old_sequence[index:] elif action == Action.REMOVE: size = min(random.choice(dist), len(old_sequence) - index, max(1, len(old_sequence) - min_length)) sequence = old_sequence[:index] + old_sequence[index + size:] return sequence

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skp_edu_docker/code/cluster/preprocess/pre_node_feed_text2fasttext.py

TensorMSA/hoyai_docker

8

104401

from cluster.preprocess.pre_node_feed import PreNodeFeed import os,h5py import numpy as np class PreNodeFeedText2FastText(PreNodeFeed): """ """ def run(self, conf_data): """ override init class """ super(PreNodeFeedText2FastText, self).run(conf_data) self._init_node_parm(conf_data['node_id']) def _convert_data_format(self, file_path, index): """ just pass hdf5 file chunk :param file_path: :param index: :return: """ try: h5file = h5py.File(file_path, mode='r') raw_data = h5file['rawdata'] data_set = raw_data[index.start: index.stop] filtered_data = [data_set[np.logical_not(data_set == '#')].tolist()] return filtered_data except Exception as e: raise Exception(e) finally: h5file.close() def data_size(self): try: h5file = h5py.File(self.input_paths[self.pointer], mode='r') return h5file['rawdata'].len() except Exception as e: raise Exception(e) finally: h5file.close()

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source/NVDAObjects/IAccessible/adobeFlash.py

SWEN-712/screen-reader-brandonp728

0

63706

<reponame>SWEN-712/screen-reader-brandonp728 #NVDAObjects/IAccessible/adobeFlash.py #A part of NonVisual Desktop Access (NVDA) #Copyright (C) 2009 NVDA Contributors <http://www.nvda-project.org/> #This file is covered by the GNU General Public License. #See the file COPYING for more details. import winUser import oleacc from . import IAccessible, getNVDAObjectFromEvent from NVDAObjects import NVDAObjectTextInfo from NVDAObjects.behaviors import EditableTextWithoutAutoSelectDetection from comtypes import COMError, IServiceProvider, hresult from comtypes.gen.FlashAccessibility import ISimpleTextSelection, IFlashAccessibility from logHandler import log class InputTextFieldTextInfo(NVDAObjectTextInfo): def _getStoryText(self): return self.obj.value or "" def _getRawSelectionOffsets(self): try: return self.obj.ISimpleTextSelectionObject.GetSelection() except COMError as e: if e.hresult == hresult.E_FAIL: # The documentation says that an empty field should return 0 for both values, but instead, we seem to get E_FAIL. # An empty field still has a valid caret. return 0, 0 else: raise RuntimeError except AttributeError: raise RuntimeError def _getCaretOffset(self): # We want the active (moving) end of the selection. return self._getRawSelectionOffsets()[1] def _getSelectionOffsets(self): # This might be a backwards selection, but for now, we should always return the values in ascending order. return sorted(self._getRawSelectionOffsets()) class InputTextField(EditableTextWithoutAutoSelectDetection, IAccessible): TextInfo = InputTextFieldTextInfo class Root(IAccessible): def _get_presentationType(self): return self.presType_content def _get_treeInterceptorClass(self): import virtualBuffers.adobeFlash return virtualBuffers.adobeFlash.AdobeFlash #Flash root client has broken accParent, force to return the flash root window root IAccessible def _get_parent(self): return getNVDAObjectFromEvent(self.windowHandle,0,0) class PluginClientWithBrokenFocus(IAccessible): """The client of a Flash plugin with broken focus behaviour. #2546: In Flash protected mode, the Flash content is in another window beneath the plugin window. Unfortunately, Flash doesn't bother to set focus to this window. To work around this, when focus hits this object, focus is forced to the child. """ def event_gainFocus(self): try: self.firstChild.firstChild.setFocus() except AttributeError: super(PluginClientWithBrokenFocus, self).event_gainFocus() def findExtraOverlayClasses(obj, clsList): """Determine the most appropriate class if this is a Flash object. This works similarly to L{NVDAObjects.NVDAObject.findOverlayClasses} except that it never calls any other findOverlayClasses method. """ iaRole = obj.IAccessibleRole if obj.windowClassName == "GeckoPluginWindow" and iaRole == oleacc.ROLE_SYSTEM_CLIENT and obj.childCount == 1 and obj.firstChild.windowClassName == "GeckoFPSandboxChildWindow": clsList.append(PluginClientWithBrokenFocus) return try: servProv = obj.IAccessibleObject.QueryInterface(IServiceProvider) except COMError: return # Check whether this is the Flash root accessible. if iaRole == oleacc.ROLE_SYSTEM_CLIENT: try: servProv.QueryService(IFlashAccessibility._iid_, IFlashAccessibility) clsList.append(Root) except COMError: pass # If this is a client and IFlashAccessibility wasn't present, this is not a Flash object. return # Check whether this is a Flash input text field. try: # We have to fetch ISimpleTextSelectionObject in order to check whether this is an input text field, so store it on the instance. obj.ISimpleTextSelectionObject = servProv.QueryService(ISimpleTextSelection._iid_, ISimpleTextSelection) clsList.append(InputTextField) except COMError: pass

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utils.py

nhh1501/E2E_MLT_VN

0

59579

#!/usr/bin/python # encoding: utf-8 import torch import albumentations as A import torch.nn as nn from torch.autograd import Variable from torch.utils.data import Dataset import collections from PIL import Image, ImageFilter import matplotlib.pyplot as plt import math import random import numpy as np import cv2 import os import PIL from data_gen import generate_rbox, generate_rbox2 from data_gen import load_gt_annoataion from data_gen import get_images import torchvision.transforms as transforms import tensorflow as tf from data_gen import draw_box_points class ImgAugTransform: def __init__(self): self.aug = A.Compose([ A.OneOf([ A.Blur(blur_limit=10,p=0.5), A.MedianBlur(blur_limit=5,p=0.5), ]), A.OneOf([ A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=255, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=170, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=85, p=0.5), A.CoarseDropout(max_holes=7, min_holes=3, min_height=3, min_width=1, max_height=16, max_width=4, fill_value=0, p=0.5), ]), # A.RandomContrast(limit=0.05, p=0.75), # A.RandomBrightness(limit=0.05, p=0.75), # A.RandomBrightnessContrast(contrast_limit=0.05, brightness_limit=0.05, p=0.75), ]) def __call__(self, img): img = np.array(img) transformed_img = self.aug(image=img)['image'] return Image.fromarray(transformed_img) # return transformed_img def to_array(img): img = np.asarray(img, dtype=np.float32) img /= 128 img -= 1 return img # return img def random_dilate(img): img = np.array(img) img = cv2.dilate(img, np.ones(shape=(random.randint(1, 3), random.randint(1, 3)), dtype=np.uint8)) return Image.fromarray(img) # return img def random_erode(img): img = np.array(img) img = cv2.erode(img, np.ones(shape=(random.randint(1, 3), random.randint(1, 3)), dtype=np.uint8)) return Image.fromarray(img) # return img def train_transforms(is_crnn = True): if is_crnn: transform = transforms.Compose([ transforms.RandomApply( [ random_dilate, ], p=0.15), transforms.RandomApply( [ random_erode, ], p=0.15), transforms.RandomAffine(degrees=3, scale=(0.95, 1.05), shear=3, resample=Image.NEAREST, fillcolor=(255,255,255)), # transforms.RandomApply( # [ # to_array, # ], # p=1), transforms.RandomApply( [ ImgAugTransform(), ], p=0.6), transforms.Grayscale(), transforms.ToTensor() ]) else: transform = transforms.Compose([ transforms.RandomApply( [ random_dilate, ], p=0.15), transforms.RandomApply( [ random_erode, ], p=0.15), transforms.RandomAffine(degrees=3, scale=(0.95, 1.05), shear=3, resample=Image.NEAREST, fillcolor=(255, 255, 255)), # transforms.RandomApply( # [ # to_array, # ], # p=1), transforms.RandomApply( [ ImgAugTransform(), ], p=0.6), transforms.ToTensor() ]) return transform def test_transforms(is_crnn = True): if is_crnn: transform = transforms.Compose([ transforms.Grayscale(), transforms.ToTensor() ]) else: transform = transforms.Compose([ transforms.ToTensor() ]) return transform def cut_image(img, new_size, word_gto): if len(word_gto) > 0: rep = True cnt = 0 while rep: if cnt > 30: return img text_poly = word_gto[random.randint(0, len(word_gto) - 1)] center = text_poly.sum(0) / 4 xs = int(center[0] - random.uniform(-100, 100) - new_size[1] / 2) xs = max(xs, 1) ys = int(center[1] - random.uniform(-100, 100) - new_size[0] / 2) ys = max(ys, 1) crop_rect = (xs, ys, xs + new_size[1], ys + new_size[0]) crop_img = img[crop_rect[1]:crop_rect[3], crop_rect[0]:crop_rect[2]] # cv2.imshow('dasd',crop_img) if crop_img.shape[0] == crop_img.shape[1]: rep = False else: cnt += 1 else: xs = int(random.uniform(0, img.shape[1])) ys = int(random.uniform(0, img.shape[0])) crop_rect = (xs, ys, xs + new_size[1], ys + new_size[0]) crop_img = img[crop_rect[1]:crop_rect[3], crop_rect[0]:crop_rect[2]] if len(word_gto) > 0: word_gto[:, :, 0] -= xs word_gto[:, :, 1] -= ys return crop_img class ocrDataset(Dataset): def __init__(self, root, norm_height = 48,in_train = True, target_transform=None,is_crnn = True): self.norm_height = norm_height self.path = self.get_path(root) self.root = root self.train_transform = train_transforms(is_crnn) self.test_transform = test_transforms(is_crnn) self.in_train = in_train self.target_transform = target_transform def get_path(self,data_path): base_dir = os.path.dirname(data_path) files_out = [] cnt = 0 with open(data_path) as f: while True: line = f.readline() if not line: break line = line.strip() if len(line) == 0: continue if not line[0] == '/': line = '{0}/{1}'.format(base_dir, line) files_out.append(line) cnt += 1 # if cnt > 100: # break return files_out def get_data(self,image_name): src_del = " " spl = image_name.split(" ") if len(spl) == 1: spl = image_name.split(",") src_del = "," image_name = spl[0].strip() # image_name = (spl[0] + ' '+ spl[1]).strip() gt_txt = '' if len(spl) > 1: gt_txt = "" delim = "" for k in range(1, len(spl)): gt_txt += delim + spl[k] delim = src_del if len(gt_txt) > 1 and gt_txt[0] == '"' and gt_txt[-1] == '"': gt_txt = gt_txt[1:-1] if image_name[len(image_name) - 1] == ',': image_name = image_name[0:-1] im = cv2.imread(image_name) im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) # im = im[2:im.shape[0]-2,:,:] # im_zero = np.zeros((self.norm_height,im.shape[1],3), np.uint8) if self.norm_height > im.shape[0]: im_samp = np.ones((self.norm_height,im.shape[1],3), np.uint8)*255 rand = np.random.randint(0,self.norm_height - im.shape[0]) im_samp[rand:rand + im.shape[0],:,:] = im else: scale = self.norm_height / float(im.shape[0]) width = int(im.shape[1] * scale) im_samp = cv2.resize(im, (int(width), self.norm_height)) image = PIL.Image.fromarray(np.uint8(im_samp)) # image = np.asarray(im, dtype=np.float32) # image /= 128 # image -= 1 #get labels # gt_labels = [] # for k in range(len(gt_txt)): # if gt_txt[k] in self.codec_rev: # gt_labels.append(self.codec_rev[gt_txt[k]]) # else: # gt_labels.append(3) # # return image,gt_labels return image,gt_txt def __len__(self): return len(self.path) def __getitem__(self, index): try: image,label = self.get_data(self.path[index]) except IOError: print('Corrupted image for %d' % index) return self[index + 1] if self.in_train : image = self.train_transform(image) else: image = self.test_transform(image) # image = np.array(image) if self.target_transform is not None: label = self.target_transform(label) return (image, label) class alignCollate(object): def __init__(self): pass def __call__(self, batch): images, labels = zip(*batch) c = images[0].size(0) h = max([p.size(1) for p in images]) w = max([p.size(2) for p in images]) batch_images = torch.zeros(len(images), c, h, w).fill_(1) for i, image in enumerate(images): started_h = max(0, random.randint(0, h - image.size(1))) started_w = max(0, random.randint(0, w - image.size(2))) batch_images[i, :, started_h:started_h + image.size(1), started_w:started_w + image.size(2)] = image return batch_images, labels # Transform class E2Edataset(Dataset): def __init__(self, train_list, input_size=512, normalize = True): super(E2Edataset, self).__init__() self.image_list = np.array(get_images(train_list)) self.input_size = input_size self.normalize = normalize print('{} training images in {}'.format(self.image_list.shape[0], train_list)) self.transform = transforms.Compose([ transforms.ColorJitter(.3,.3,.3,.3), transforms.RandomGrayscale(p=0.1) ]) def __len__(self): return len(self.image_list) def __getitem__(self, index): im_name = self.image_list[index] im = cv2.imread(im_name) # 图片 # cv2.imshow('sad',im) txt_fn = im_name.replace(os.path.basename(im_name).split('.')[1], 'txt') base_name = os.path.basename(txt_fn) txt_fn_gt = '{0}/gt_{1}'.format(os.path.dirname(im_name), base_name) text_polys, text_tags, labels_txt = load_gt_annoataion(txt_fn_gt, True) # 载入标注信息 resize_w = self.input_size resize_h = self.input_size scaled = cut_image(im, (self.input_size, self.input_size), text_polys) if scaled.shape[0] == 0 or scaled.shape[1] == 0: scaled = im if scaled.shape[1] != resize_w or scaled.shape[0] != resize_h: ratio_img= min(scaled.shape[1]/scaled.shape[0],scaled.shape[0]/scaled.shape[1]) if scaled.shape[0] > scaled.shape[1]: resize_w =resize_w * ratio_img scalex = scaled.shape[1] / resize_w scaley = scaled.shape[0] / resize_h scaled = cv2.resize(scaled, dsize=(int(resize_w), int(resize_h))) else : resize_h = resize_h * ratio_img scalex = scaled.shape[1] / resize_w scaley = scaled.shape[0] / resize_h scaled = cv2.resize(scaled, dsize=(int(resize_w), int(resize_h))) # continue if len(text_polys) > 0: text_polys[:, :, 0] /= scalex text_polys[:, :, 1] /= scaley scaled = cv2.copyMakeBorder(scaled, 0, self.input_size - scaled.shape[0], 0, self.input_size - scaled.shape[1], cv2.BORDER_CONSTANT,value=[255,255,255]) # scaled_show = scaled.copy() # for (i, c) in enumerate(text_polys): # draw_box_points(scaled_show, c, color=(0, 255, 0), thickness=1) # # instead of creating a new image, I simply modify the old one # # # # show the modified image with all the rectangles at the end. # cv2.imshow('img', scaled_show) pim = PIL.Image.fromarray(np.uint8(scaled)) if self.transform: pim = self.transform(pim) im = np.array(pim) new_h, new_w, _ = im.shape score_map, geo_map, training_mask, gt_idx, gt_out, labels_out = generate_rbox(im, (new_h, new_w), text_polys, text_tags, labels_txt, vis=False) im = np.asarray(im) ## if self.normalize: im = im.astype(np.float32) im /= 128 im -= 1 im = torch.from_numpy(im).permute(2,0,1) return im, score_map, geo_map, training_mask, gt_idx, gt_out, labels_out def E2Ecollate(batch): img = [] gt_boxes = [] texts = [] scores = [] training_masks = [] for per_batch in batch: img.append(per_batch[0]) scores.append(per_batch[1]) training_masks.append(per_batch[3]) gt_boxes.append(per_batch[5]) texts.append(per_batch[6]) return torch.stack(img, 0), gt_boxes, texts,scores,training_masks class BeamSearchDecoder(): def __init__(self, lib, corpus, chars, word_chars, beam_width=20, lm_type='Words', lm_smoothing=0.01, tfsess=None): word_beam_search_module = tf.load_op_library(lib) self.mat = tf.placeholder(tf.float32) corpus = open(corpus).read() chars = open(chars).read() word_chars = open(word_chars).read() self.beamsearch_decoder = word_beam_search_module.word_beam_search(self.mat, beam_width, lm_type, lm_smoothing, corpus, chars, word_chars) self.tfsess = tfsess or tf.Session() self.idx2char = dict(zip(range(0, len(chars)), chars)) def beamsearch(self, mat): mat = np.concatenate((mat[:, :, 1:], mat[:, :, :1]), axis=-1) results = self.tfsess.run(self.beamsearch_decoder, {self.mat: mat}) return results def decode(self, preds_idx): return [''.join([self.idx2char[idx] for idx in row if idx < len(self.idx2char)]) for row in preds_idx] if __name__ == '__main__': llist = './data/ICDAR2015.txt' data = E2Edataset(train_list=llist) E2Edataloader = torch.utils.data.DataLoader(data, batch_size=2, shuffle=False, collate_fn=E2Ecollate) for index, data in enumerate(E2Edataloader): im = data

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nnef/fold_os.py

lahplover/nnef

2

37534

import numpy as np import pandas as pd import torch from physics.protein_os import Protein import options from utils import write_pdb, write_pdb_sample, transform_profile, load_protein from physics.anneal import AnnealCoords, AnnealFrag # from physics.move import SampleICNext from physics.grad_minimizer import * from physics.dynamics import * import os import mdtraj as md from utils import test_setup import h5py ################################################# parser = options.get_fold_parser() args = options.parse_args_and_arch(parser) device, model, energy_fn, ProteinBase = test_setup(args) # position_weights = torch.zeros((1, args.seq_len + 1), device=device) # position_weights[:, 0:5] = 1 # energy_fn.energy_fn.position_weights = position_weights ################################################# data_path = 'data/fold/cullpdb_val_deep' protein_sample = pd.read_csv(f'{data_path}/sample.csv') pdb_selected = protein_sample['pdb'].values np.random.shuffle(pdb_selected) fold_engine = args.fold_engine mode = args.mode # sample_ic = SampleICNext(mode) exp_id = args.load_exp[-5:] save_dir = args.save_dir # if not os.path.exists(f'data/fold/{exp_id}'): # os.mkdir(f'data/fold/{exp_id}') if not os.path.exists(f'data/fold/{save_dir}'): os.mkdir(f'data/fold/{save_dir}') for pdb_id in pdb_selected: seq, coords_native, profile = load_protein(data_path, pdb_id, mode, device, args) protein_native = Protein(seq, coords_native, profile) energy_native = protein_native.get_energy(energy_fn).item() print('energy_native:', energy_native) rg2, collision = protein_native.get_rad_gyration(coords_native) print('native radius of gyration square:', rg2.item()) # residue_energy = protein_native.get_residue_energy(energy_fn) # print(residue_energy) # write_pdb(seq, coords_native, pdb_id, 'native', exp_id) protein = Protein(seq, coords_native.clone(), profile.clone()) if args.random_init: # random_coords_int = sample_ic.random_coords_int(len(seq)-3).to(device) # protein.update_coords_internal(random_coords_int) # extend_coords_int = torch.tensor([[5.367, 1.6, 0.0]], device=device).repeat((len(seq)-3, 1)) extend_coords_int = torch.tensor([[5.367, 0.1, 0.0]], device=device).repeat((len(seq)-3, 1)) protein.update_coords_internal(extend_coords_int) protein.update_cartesian_from_internal() coords_init = protein.coords energy_init = protein.get_energy(energy_fn).item() print('energy_init:', energy_init) # write_pdb(seq, coords_init, pdb_id, f'init_{mode}', exp_id) if fold_engine == 'anneal': # simulated annealing torch.set_grad_enabled(False) if args.anneal_type == 'int_one': annealer = AnnealCoords(energy_fn, protein, mode=mode, ic_move_std=args.ic_move_std, T_max=args.T_max, T_min=args.T_min, L=args.L) elif args.anneal_type == 'frag': frag_file = h5py.File(f'data/fragment/{pdb_id}/{pdb_id}_int.h5', 'r') query_pos = torch.tensor(frag_file['query_pos'][()], device=device) frag_int = torch.tensor(frag_file['coords_int'][()], device=device) annealer = AnnealFrag(energy_fn, protein, frag=(query_pos, frag_int), use_rg=args.use_rg, T_max=args.T_max, T_min=args.T_min, L=args.L) else: raise ValueError('anneal_type should be int_one / frag.') annealer.run() coords_best = annealer.x_best energy_best = annealer.energy_best sample = annealer.sample sample_energy = annealer.sample_energy elif fold_engine == 'grad': if args.x_type == 'cart': minimizer = GradMinimizerCartesian(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'internal': minimizer = GradMinimizerInternal(energy_fn, protein, lr=args.lr, num_steps=args.L, momentum=0.0) elif args.x_type == 'int_fast': minimizer = GradMinimizerIntFast(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'mixed': minimizer = GradMinimizerMixed(energy_fn, protein, lr=args.lr, num_steps=args.L) elif args.x_type == 'mix_fast': minimizer = GradMinimizerMixFast(energy_fn, protein, lr=args.lr, num_steps=args.L) else: raise ValueError('x_type should be cart / internal / mixed / int_fast / mix_fast.') minimizer.run() coords_best = minimizer.x_best energy_best = minimizer.energy_best sample = minimizer.sample sample_energy = minimizer.sample_energy elif fold_engine == 'dynamics': if args.x_type == 'cart': minimizer = Dynamics(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'internal': minimizer = DynamicsInternal(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'int_fast': minimizer = DynamicsIntFast(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'mixed': minimizer = DynamicsMixed(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) elif args.x_type == 'mix_fast': minimizer = DynamicsMixFast(energy_fn, protein, num_steps=args.L, lr=args.lr, t_noise=args.T_max) else: raise ValueError('x_type should be cart / internal / mixed / int_fast / mix_fast.') minimizer.run() coords_best = minimizer.x_best energy_best = minimizer.energy_best sample = minimizer.sample sample_energy = minimizer.sample_energy else: raise ValueError('fold_engine should be anneal / grad / dynamics') # protein.update_coords(coords_best) # residue_energy = protein.get_residue_energy(energy_fn) # print(residue_energy) # write_pdb(seq, coords_best, pdb_id, f'best_{mode}', exp_id) # save sampled structures sample = [coords_native.cpu(), coords_best.cpu(), coords_init.cpu()] + sample sample_energy = [energy_native, energy_best, energy_init] + sample_energy # write_pdb_sample(seq, sample, pdb_id, 'sample', exp_id) # pd.DataFrame({'sample_energy': sample_energy}).to_csv(f'data/fold/{exp_id}/{pdb_id}_energy.csv', index=False) write_pdb_sample(seq, sample, pdb_id, 'sample', save_dir) # compute RMSD, sample_xyz = torch.stack(sample, 0).cpu().detach().numpy() print(sample_xyz.shape) t = md.Trajectory(xyz=sample_xyz, topology=None) t = t.superpose(t, frame=0) write_pdb_sample(seq, t.xyz, pdb_id, 'sample2', save_dir) sample_rmsd = md.rmsd(t, t, frame=0) # computation will change sample_xyz; print(f'best RMSD: {sample_rmsd[1]}') df = pd.DataFrame({'sample_energy': sample_energy, 'sample_rmsd': sample_rmsd}) df.to_csv(f'data/fold/{save_dir}/{pdb_id}_energy.csv', index=False)

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seatsvotes/cvtools.py

ljwolf/seatsvotes

0

45282

<filename>seatsvotes/cvtools.py import numpy as np import statsmodels as __sm import pandas as pd sm = __sm.api def leverage(results): """ Compute the leverage matrix from a WLS model: H = W^.5 X(X'WX)^-1X'W^.5 where W^.5 is the scalar square root. """ if isinstance(results.model, sm.WLS): W = np.diag(results.model.weights) elif isinstance(results.model, sm.OLS): W = np.eye(results.model.exog.shape[0]) X = results.model.exog emp_cov = results.cov_params(scale=1) unW_H = X.dot(emp_cov).dot(X.T) return (W**.5).dot(unW_H).dot(W**.5) def jackknife(candidate, full=False, **kw): """ Estimate each leave-one-out model given the input data. Arguments --------- candidate: model statsmodels regression model or a pysal regression model. full : bool whether to return all reestimated models, or just the parameter estimates from the models. kw : keywords keyword arguments passed down to the resulting model estimation statement. If for a statsmodels model, should be separated into fit_kw and init_kw. """ if isinstance(candidate, __sm.base.wrapper.ResultsWrapper): out = _sm_jackknife(candidate, **kw) if not full: out = pd.DataFrame(np.vstack([mod.params.reshape(1,-1) for mod in out]), columns=candidate.params.index) return out else: _pysal_jackknife(candidate, **kw) def _sm_jackknife(results, init_kw=dict(), fit_kw=dict()): """ Jackknife the results object, meaning the model is re-estimated with a single observation left out each time. """ endog, exog = results.model.endog, results.model.exog if isinstance(results.model, sm.WLS): weights = results.model.weights elif isinstance(results.model, sm.OLS): weights = np.ones_like(endog) deletions = [sm.WLS(np.delete(endog, i), np.delete(exog, i, axis=0), weights=np.delete(weights, i), **init_kw).fit(**fit_kw) for i in range(len(endog))] return deletions def _pysal_jackknife(model, **kw): raise NotImplementedError X = model.X Y = model.Y yend = getattr(model, 'yend', None) z = getattr(model, 'z', None) W = getattr(model, 'W', None) fit = type(model)

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org.beerware.empty.app/assets/axp.py

pmp-p/projects

0

27484

from android import * print(widget.Button) from android.widget import TextView

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Lecture 12/Lecture12HWAssignment2.py

AtharvaJoshi21/PythonPOC

1

127065

# WAP to accept an unsorted list of integers from user and sort it using # a. Bubble Sort # b. Insertion Sort def BubbleSort(inputList): listCount = len(inputList) for x in range(listCount - 1): alreadySorted = True print('Iteration', x) for y in range(x+1, listCount - 1): if inputList[x] > inputList[y]: temp = inputList[x] inputList[x] = inputList[y] inputList[y] = temp alreadySorted = False else: alreadySorted = True if alreadySorted: continue return inputList def main(): unsortedList = eval(input('Please enter a list to be sorted: ')) print(BubbleSort(unsortedList)) if __name__ == '__main__': main()

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1-100/1-10/6-zigZagConversion/zigZagConversion.py

xuychen/Leetcode

0

44831

<reponame>xuychen/Leetcode<gh_stars>0 import operator class Solution(object): def convert(self, s, numRows): """ :type s: str :type numRows: int :rtype: str """ if numRows == 1: return s period = (numRows - 1) * 2 result = "" end = "" length = len(s) i = 0 while i * period < length: result += s[i*period] if i * period + numRows - 1< length: end += s[i * period + numRows - 1] i += 1 for i in range(1, numRows-1): j = 0 while j * period < length: lowerBase = j * period upperBase = lowerBase + period if (lowerBase + i < length): result += s[lowerBase+i] if (upperBase - i < length): result += s[upperBase-i] j += 1 return result + end def convert2(self, s, numRows): """ :type s: str :type numRows: int :rtype: str """ if numRows == 1: return s num_groups = 2 * (numRows - 1) strings = [""] * numRows for i in range(len(s)): mod = i % num_groups if mod > num_groups / 2: strings[num_groups-mod] += s[i] else: strings[mod] += s[i] return reduce(operator.__add__, strings)

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testtube/renderer.py

blaix/testtube

0

95743

<filename>testtube/renderer.py """Renderer that outputs formatted messaging for end users.""" import sys from termcolor import colored from testtube.conf import Settings # For python3 support, we must use print as a function rather than a statement class Renderer(object): """Utility that outputs formatted messages.""" def failure(self, message=''): """Print the passed message in red.""" print(colored(message, 'red')) def notice(self, message=''): """Print the passed message.""" print(message) def success(self, message=''): """Print the passed message in green.""" print(colored(message, 'green')) def divider(self): """Print a divider.""" print('=' * 71) def report(self, results): """Print a test report.""" self.notice("Test Report\n") for count, group in enumerate(results, 1): results = (self._format_test(test, res) for test, res in group) results = (', ').join(results) self.notice("Test group %s:\t%s" % (count, results)) self.divider() def audible_alert(self, count): """Beep the number of times specified.""" sys.stdout.write('\a' * count) def _format_test(self, test, result): color = 'green' if result else 'red' files = '' if not test.all_files: files = ' (%s)' % Settings.short_path(test.changed) return colored('%s%s' % (test.name, files), color)

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pipelinePhase.py

armbrustsamuel/PythonPipelineEmulator

0

166917

# PIPELINE PHASES import re import miscellaneous as misc import calculator as calc instructionMemory = [] # Search phase # @param program counter # @return a instruction such as "dadd","daddi","dsub" ... def search(pc): # Retrieve next instruction in memory instruction = misc.readInstructions("inputfile.txt") dummyReturn = [" "," "," "] if pc >= len(instruction): return dummyReturn return instruction[pc] #returnIntruction = ["lw", "$s1", "$s2", "$s3"] #return returnIntruction # Decode phase # @param # @return def decode(pc): # Decode instruction and call execute instructionMemory = misc.readInstructions("inputfile.txt") dummyDecode = [" "," "," "," "," "] # Adjust length if pc >= len(instructionMemory): return dummyDecode else: instructionMemory[pc].append(" ") # -- print(len(instructionMemory[pc])) if len(instructionMemory[pc]) > 3 : instructionMemory[pc][3] = instructionMemory[pc][2] if len(instructionMemory[pc]) > 2: instructionMemory[pc][2] = instructionMemory[pc][1] instructionMemory[pc][1] = instructionMemory[pc][0][5:].strip() instructionMemory[pc][0] = instructionMemory[pc][0][:5].strip() return instructionMemory[pc] # To be removed #misc.printInstruction(instructionMemory,pc) # Execute phase # @param # @return exit[code, message] # - 0 - correct execution , result of calculation # - 1 - wrong execution , "Wrong instruction" # - 2 - shift address , "ok" or "nok" def execute(opCode,reg1,reg2): # Read registers registers = misc.readRegisters('registers.txt') if reg1 != " ": op1 = registers[int(reg1)] if reg1 != " ": op2 = registers[int(reg2)] if opCode.find("dadd") != -1: return calc.sum(op1,op2) elif opCode.find("daddi") != -1: return calc.daddi(op1,op2) elif opCode.find("dsub") != -1: return calc.sub(op1,op2) elif opCode.find("lw") != -1: return "LW" elif opCode.find("dsubi") != -1: return calc.sub(int(op1),int(op2)) elif opCode.find("beqz") != -1: return calc.beqz(op1) elif opCode.find("bnez") != -1: return calc.bnez(op1) elif opCode.find("beq") != -1: return calc.beq(op1,op2) elif opCode.find("bne") != -1: return calc.bne(op1,op2) elif opCode.find("j") != -1: return "JUMP" elif opCode.find(" ") != -1: return 0 else: #print("Wrong instruction - review code") return 1 # MEMORY phase # Reuse already created function # Writeback # @param # @return def saveAtRegister(data, address, registers): registers[address] = data if ( misc.writeRegisters('registers.txt', registers) == 0): return 0 else: print("Error saving registers") return 1

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awstin/dynamodb/orm.py

k2bd/awstin

1

5047

<reponame>k2bd/awstin import uuid from abc import ABC, abstractmethod from collections import defaultdict from typing import Union from boto3.dynamodb.conditions import Attr as BotoAttr from boto3.dynamodb.conditions import Key as BotoKey from awstin.dynamodb.utils import from_decimal, to_decimal class NotSet: """ A value of an attribute on a data model is not present in a DynamoDB result """ def __str__(self): return "<<Attribute not set>>" def __repr__(self): return "<<Attribute not set>>" NOT_SET = NotSet() class BaseAttribute: def __init__(self, attribute_name: Union[str, None] = None): """ Parameters ---------- attribute_name : str, optional Name of the property in the DynamoDB table. Defaults to the name of the attribute on the DynamoModel class. """ # Set by user self._attribute_name = attribute_name # Set by Model self._name_on_model = None @property def _awstin_name(self): if self._attribute_name is not None: return self._attribute_name else: return self._name_on_model def __getattr__(self, name): """ Support for nested mapping queries """ try: return super().__getattr__(name) except AttributeError: return type(self)(attribute_name=f"{self._awstin_name}.{name}") def __getitem__(self, index): """ Support for nested container queries """ return type(self)(attribute_name=f"{self._awstin_name}[{index}]") # --- Query and scan filter expressions --- def begins_with(self, value): """ Filter results by a key or attribute beginning with a value Parameters ---------- value : str Starting string for returned results """ return self._query_type(self._awstin_name).begins_with(to_decimal(value)) def between(self, low, high): """ Filter results by range (inclusive) Parameters ---------- low : Any Low end of the range high : Any High end of the range """ return self._query_type(self._awstin_name).between( to_decimal(low), to_decimal(high), ) def __eq__(self, value): return self._query_type(self._awstin_name).eq(to_decimal(value)) def __gt__(self, value): return self._query_type(self._awstin_name).gt(to_decimal(value)) def __ge__(self, value): return self._query_type(self._awstin_name).gte(to_decimal(value)) def __lt__(self, value): return self._query_type(self._awstin_name).lt(to_decimal(value)) def __le__(self, value): return self._query_type(self._awstin_name).lte(to_decimal(value)) def attribute_type(self, value): """ Filter results by attribute type Parameters ---------- value : str Index for a DynamoDB attribute type (e.g. "N" for Number) """ return BotoAttr(self._awstin_name).attribute_type(to_decimal(value)) def contains(self, value): """ Filter results by attributes that are containers and contain the target value Parameters ---------- values : Any Result must contain this item """ return BotoAttr(self._awstin_name).contains(to_decimal(value)) def exists(self): """ Filter results by existence of an attribute """ return BotoAttr(self._awstin_name).exists() def in_(self, values): """ Filter results by existence in a set Parameters ---------- values : list of Any Allowed values of returned results """ in_values = [to_decimal(value) for value in values] return BotoAttr(self._awstin_name).is_in(in_values) def __ne__(self, value): return BotoAttr(self._awstin_name).ne(to_decimal(value)) def not_exists(self): """ Filter results by non-existence of an attribute """ return BotoAttr(self._awstin_name).not_exists() def size(self): """ Filter by size of a collection """ return Size(self._awstin_name) # --- Update expressions --- def set(self, expression): """ Set an attribute to a new value. Corresponds to SET as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand New value, or an expression defining a new value """ return SetOperator(self, UpdateOperand(expression)) def remove(self): """ Remove an attribute. Corresponds to REMOVE as part of the update expression in ``Table.update_item``. """ return RemoveOperator(self) def add(self, expression): """ Add to an attribute (numerical add or addition to a set). Corresponds to ADD as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand Value to add """ return AddOperator(self, UpdateOperand(expression)) def delete(self, expression): """ Delete part of a set attribute. Corresponds to DELETE as part of the update expression in ``Table.update_item``. Parameters ---------- expression : UpdateOperand Value to delete """ return DeleteOperator(self, UpdateOperand(expression)) def __add__(self, other): return CombineOperand(UpdateOperand(self), UpdateOperand(other), "+") def __sub__(self, other): return CombineOperand(UpdateOperand(self), UpdateOperand(other), "-") def __radd__(self, other): return CombineOperand(UpdateOperand(other), UpdateOperand(self), "+") def __rsub__(self, other): return CombineOperand(UpdateOperand(other), UpdateOperand(self), "-") def if_not_exists(self, value): """ Conditionally return a value if this attribute doesn't exist on the model """ return IfNotExistsOperand(UpdateOperand(self), UpdateOperand(value)) class Key(BaseAttribute): """ Used to define and query hash and sort key attributes on a dynamodb table data model """ _query_type = BotoKey class Attr(BaseAttribute): """ Used to define and query non-key attributes on a dynamodb table data model """ _query_type = BotoAttr def size_query(self, *args, **kwargs): return BotoAttr(self._awstin_name).size() class Size(BaseAttribute): _query_type = size_query class DynamoModelMeta(type): def __getattribute__(self, name): attr = super().__getattribute__(name) if isinstance(attr, BaseAttribute): attr._name_on_model = name return attr else: return attr def _dynamodb_attributes(self): result = { getattr(self, attr)._awstin_name: attr for attr in dir(self) if isinstance(getattr(self, attr), BaseAttribute) } return result def _get_kwargs(self): """ Kwargs that should be passed to query, scan, get_item """ return { **self._dynamo_projection(), **self._index_kwargs(), } def _dynamo_projection(self): """ Attributes to request when retrieving data from DynamoDB Returns ------- dict kwargs to be passed to DynamoDB get attribute calls to employ a projection expression and placeholders """ placeholders = { "#" + str(uuid.uuid4())[:8]: value for value in self._dynamodb_attributes().keys() } expression = ", ".join(placeholders.keys()) return dict( ProjectionExpression=expression, ExpressionAttributeNames=placeholders, ) def _index_kwargs(self): if hasattr(self, "_index_name_"): return dict( IndexName=self._index_name_, ) else: return {} class DynamoModel(metaclass=DynamoModelMeta): """ Class defining an ORM model for a DynamoDB table. Subclasses must have a ``_table_name_`` attribute. Attributes making up the data model should be Attr or Key instances. Subclasses representing indexes should also have an ``_index_name_`` attribute """ def __init__(self, **kwargs): """ Parameters ---------- **kwargs : dict of (str, Any) Initialization of Attr and Key attributes. """ model_attrs = type(self)._dynamodb_attributes().values() for name in model_attrs: setattr(self, name, NOT_SET) for name, value in kwargs.items(): if name not in model_attrs: msg = f"{type(self)!r} has no attribute {name!r}" raise AttributeError(msg) setattr(self, name, value) @classmethod def deserialize(cls, data): """ Deserialize JSON into a DynamoModel subclass. Internally converts Decimal to float in the deserialization. Parameters ---------- data : dict of (str, Any) Serialized model Returns ------- DynamoModel The deserialized data model """ model_attrs = cls._dynamodb_attributes() result = cls() for attr in model_attrs.values(): setattr(result, attr, NOT_SET) for db_attr, value in data.items(): if db_attr in model_attrs.keys(): if type(value) in [list, set, tuple]: value = type(value)(from_decimal(v) for v in value) elif type(value) is dict: value = {from_decimal(k): from_decimal(v) for k, v in value.items()} else: value = from_decimal(value) setattr(result, model_attrs[db_attr], value) return result def serialize(self): """ Serialize a DynamoModel subclass to JSON that can be inserted into DynamoDB. Internally converts float to Decimal. Returns ------- dict of (str, Any) The serialized JSON entry """ model_attrs = type(self)._dynamodb_attributes() result = {} for dynamo_name, model_name in model_attrs.items(): value = getattr(self, model_name) if value is not NOT_SET: if type(value) in [list, set, tuple]: value = type(value)(to_decimal(v) for v in value) elif type(value) is dict: value = {to_decimal(k): to_decimal(v) for k, v in value.items()} else: value = to_decimal(value) result[dynamo_name] = value return result # ---- Update Operators class UpdateOperator(ABC): """ A representation of an UpdateItem expression """ def __and__(self, other): """ Combine two update expressions """ return CombineOperator(self, other) @abstractmethod def update_dict(self): pass @staticmethod def update_expression(update_dict): expressions = [] for operation in "SET", "ADD", "DELETE", "REMOVE": if update_dict.get(operation): expressions.append(operation + " " + ", ".join(update_dict[operation])) return " ".join(expressions) def serialize(self): """ Produce kwargs to be passed to DynamoDB Table.update_item. Keys and values are: "UpdateExpression": string representing the update expression "ExpressionAttributeNames": Placeholder map for attribute names "ExpressionAttributeValues": Placeholder map for attribute values Returns ------- dict Kwargs for update_item """ update_dict = self.update_dict() result = { "UpdateExpression": self.update_expression(update_dict), } if update_dict["ExpressionAttributeNames"]: result["ExpressionAttributeNames"] = update_dict["ExpressionAttributeNames"] if update_dict["ExpressionAttributeValues"]: result["ExpressionAttributeValues"] = update_dict[ "ExpressionAttributeValues" ] return result class CombineOperator(UpdateOperator): """ Combine two update expressions """ def __init__(self, left, right): self.left = left self.right = right def update_dict(self): result = defaultdict(list) ser_left = self.left.update_dict() ser_right = self.right.update_dict() items = list(ser_left.items()) + list(ser_right.items()) for key, values in items: if key in ["SET", "ADD", "DELETE", "REMOVE"]: result[key].extend(values) result["ExpressionAttributeNames"] = dict( **ser_left["ExpressionAttributeNames"], **ser_right["ExpressionAttributeNames"], ) result["ExpressionAttributeValues"] = dict( **ser_left["ExpressionAttributeValues"], **ser_right["ExpressionAttributeValues"], ) return result class SetOperator(UpdateOperator): """ Support for SET """ def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( **serialized_operand["ExpressionAttributeNames"], **serialized_attr["ExpressionAttributeNames"], ) return { "SET": [ f"{serialized_attr['UpdateExpression']} = " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } class AddOperator(UpdateOperator): def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( **serialized_operand["ExpressionAttributeNames"], **serialized_attr["ExpressionAttributeNames"], ) return { "ADD": [ f"{serialized_attr['UpdateExpression']} " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } class RemoveOperator(UpdateOperator): def __init__(self, attr): self.attr = attr def update_dict(self): serialized_attr = itemize_attr(self.attr) return { "REMOVE": [serialized_attr["UpdateExpression"]], "ExpressionAttributeNames": serialized_attr["ExpressionAttributeNames"], "ExpressionAttributeValues": {}, } class DeleteOperator(UpdateOperator): def __init__(self, attr, operand): self.attr = attr self.operand = operand def update_dict(self): serialized_attr = itemize_attr(self.attr) serialized_operand = self.operand.serialize() attribute_names = dict( **serialized_operand["ExpressionAttributeNames"], **serialized_attr["ExpressionAttributeNames"], ) return { "DELETE": [ f"{serialized_attr['UpdateExpression']} " + serialized_operand["UpdateExpression"] ], "ExpressionAttributeNames": attribute_names, "ExpressionAttributeValues": serialized_operand[ "ExpressionAttributeValues" ], } # ---- Update Operands def serialize_operand(value): name = str(uuid.uuid4())[:8] if isinstance(value, UpdateOperand): return value.serialize() elif isinstance(value, BaseAttribute): return itemize_attr(value) elif type(value) in [list, set, tuple]: name = ":" + name value = type(value)([to_decimal(v) for v in value]) return { "UpdateExpression": name, "ExpressionAttributeNames": {}, "ExpressionAttributeValues": {name: value}, } else: name = ":" + name return { "UpdateExpression": name, "ExpressionAttributeNames": {}, "ExpressionAttributeValues": {name: to_decimal(value)}, } def itemize_attr(attr): # Separate indexes parts = [] current_section = "" for letter in attr._awstin_name: if letter == "[": parts.append(current_section) current_section = "[" elif letter == "]": parts.append(current_section + "]") current_section = "" else: current_section += letter if current_section: parts.append(current_section) serialized = "" name_map = {} # Separate attributes for part in parts: if "[" in part and "]" in part: serialized += part else: if part.startswith("."): serialized += "." part = part[1:] sections = part.split(".") serialized_sections = [] for section in sections: name = "#" + str(uuid.uuid4())[:8] name_map[name] = section serialized_sections.append(name) serialized += ".".join(serialized_sections) result = { "UpdateExpression": serialized, "ExpressionAttributeNames": name_map, "ExpressionAttributeValues": {}, } return result class UpdateOperand: """ Inner part of an update expression """ def __init__(self, value): self.value = value def serialize(self): return serialize_operand(self.value) class CombineOperand(UpdateOperand): """ Add or subtact two expressions """ def __init__(self, left, right, symbol): self.left = left self.right = right self.symbol = symbol def serialize(self): ser_left = serialize_operand(self.left) ser_right = serialize_operand(self.right) expression = ( f"{ser_left['UpdateExpression']} " f"{self.symbol} " f"{ser_right['UpdateExpression']}" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( **ser_left["ExpressionAttributeNames"], **ser_right["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( **ser_left["ExpressionAttributeValues"], **ser_right["ExpressionAttributeValues"], ), } class IfNotExistsOperand(UpdateOperand): """ Set a value if the given attribute does not exist """ def __init__(self, attr, value): self.attr = attr self.value = value def serialize(self): ser_attr = serialize_operand(self.attr) ser_value = serialize_operand(self.value) expression = ( f"if_not_exists({ser_attr['UpdateExpression']}, " f"{ser_value['UpdateExpression']})" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( **ser_attr["ExpressionAttributeNames"], **ser_value["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( **ser_attr["ExpressionAttributeValues"], **ser_value["ExpressionAttributeValues"], ), } class ListAppendOperand(UpdateOperand): """ Combine two lists """ def __init__(self, left, right): self.left = left self.right = right def serialize(self): ser_left = serialize_operand(self.left) ser_right = serialize_operand(self.right) expression = ( f"list_append({ser_left['UpdateExpression']}, " f"{ser_right['UpdateExpression']})" ) return { "UpdateExpression": expression, "ExpressionAttributeNames": dict( **ser_left["ExpressionAttributeNames"], **ser_right["ExpressionAttributeNames"], ), "ExpressionAttributeValues": dict( **ser_left["ExpressionAttributeValues"], **ser_right["ExpressionAttributeValues"], ), } def list_append(left, right): """ Set a value to the combination of two lists in an update expression """ return ListAppendOperand(UpdateOperand(left), UpdateOperand(right))

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tests/testapp/urls.py

matthiask/django-keyed-urls

0

105180

<reponame>matthiask/django-keyed-urls from django.conf.urls import include, patterns, url from django.conf.urls.i18n import i18n_patterns from django.contrib import admin admin.autodiscover() urlpatterns = patterns( '', url(r'^admin/', include(admin.site.urls)), ) urlpatterns += i18n_patterns( '', url(r'^ku/', include('keyed_urls.urls')), )

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LED2Net/Projection/Cube2Equirec.py

zhigangjiang/LED2-Net

57

96369

import os import sys import cv2 import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import scipy.misc as sic class Cube2Equirec(nn.Module): def __init__(self, cube_length, equ_h): super().__init__() self.cube_length = cube_length self.equ_h = equ_h equ_w = equ_h * 2 self.equ_w = equ_w theta = (np.arange(equ_w) / (equ_w-1) - 0.5) * 2 *np.pi phi = (np.arange(equ_h) / (equ_h-1) - 0.5) * np.pi theta, phi = np.meshgrid(theta, phi) x = np.sin(theta) * np.cos(phi) y = np.sin(phi) z = np.cos(theta) * np.cos(phi) xyz = np.concatenate([x[..., None], y[..., None], z[..., None]], axis=-1) planes = np.asarray([ [0, 0, 1, 1], # z = -1 [0, 1, 0, -1], # y = 1 [0, 0, 1, -1], # z = 1 [1, 0, 0, 1], # x = -1 [1, 0, 0, -1], # x = 1 [0, 1, 0, 1] # y = -1 ]) r_lst = np.array([ [0, 1, 0], [0.5, 0, 0], [0, 0, 0], [0, 0.5, 0], [0, -0.5, 0], [-0.5, 0, 0] ]) * np.pi f = cube_length / 2.0 self.K = np.array([ [f, 0, (cube_length-1)/2.0], [0, f, (cube_length-1)/2.0], [0, 0, 1] ]) self.R_lst = [cv2.Rodrigues(x)[0] for x in r_lst] self.mask, self.XY = self._intersection(xyz, planes) def forward(self, x, mode='bilinear'): assert mode in ['nearest', 'bilinear'] assert x.shape[0] % 6 == 0 equ_count = x.shape[0] // 6 equi = torch.zeros(equ_count, x.shape[1], self.equ_h, self.equ_w).to(x.device) for i in range(6): now = x[i::6, ...] mask = self.mask[i].to(x.device) mask = mask[None, ...].repeat(equ_count, x.shape[1], 1, 1) XY = (self.XY[i].to(x.device)[None, None, :, :].repeat(equ_count, 1, 1, 1) / (self.cube_length-1) - 0.5) * 2 sample = F.grid_sample(now, XY, mode=mode, align_corners=True)[..., 0, :] equi[mask] = sample.view(-1) return equi def _intersection(self, xyz, planes): abc = planes[:, :-1] depth = -planes[:, 3][None, None, ...] / np.dot(xyz, abc.T) depth[depth < 0] = np.inf arg = np.argmin(depth, axis=-1) depth = np.min(depth, axis=-1) pts = depth[..., None] * xyz mask_lst = [] mapping_XY = [] for i in range(6): mask = arg == i mask = np.tile(mask[..., None], [1, 1, 3]) XY = np.dot(np.dot(pts[mask].reshape([-1, 3]), self.R_lst[i].T), self.K.T) XY = np.clip(XY[..., :2].copy() / XY[..., 2:], 0, self.cube_length-1) mask_lst.append(mask[..., 0]) mapping_XY.append(XY) mask_lst = [torch.BoolTensor(x) for x in mask_lst] mapping_XY = [torch.FloatTensor(x) for x in mapping_XY] return mask_lst, mapping_XY if __name__ == '__main__': import matplotlib.pyplot as plt batch = torch.zeros(12, 3, 256, 256) + 20 c2e = Cube2Equirec(256, 512) equi = c2e(batch) plt.imshow(equi[0, ...].permute(1, 2, 0).cpu().numpy()) plt.show()

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favicons/_constants.py

checktheroads/favicons

7

197913

<filename>favicons/_constants.py<gh_stars>1-10 """Static values for one way import.""" SUPPORTED_FORMATS = (".svg", ".jpeg", ".jpg", ".png", ".tiff", ".tif") HTML_LINK = '<link rel="{rel}" type="{type}" href="{href}" />' ICON_TYPES = ( {"image_fmt": "ico", "rel": None, "dimensions": (64, 64), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (16, 16), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (32, 32), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (64, 64), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (96, 96), "prefix": "favicon"}, {"image_fmt": "png", "rel": "icon", "dimensions": (180, 180), "prefix": "favicon"}, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (57, 57), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (60, 60), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (72, 72), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (76, 76), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (114, 114), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (120, 120), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (144, 144), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (152, 152), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (167, 167), "prefix": "apple-touch-icon", }, { "image_fmt": "png", "rel": "apple-touch-icon", "dimensions": (180, 180), "prefix": "apple-touch-icon", }, {"image_fmt": "png", "rel": None, "dimensions": (70, 70), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (270, 270), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (310, 310), "prefix": "mstile"}, {"image_fmt": "png", "rel": None, "dimensions": (310, 150), "prefix": "mstile"}, {"image_fmt": "png", "rel": "shortcut icon", "dimensions": (196, 196), "prefix": "favicon"}, )

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unittest/buildtool/util_test.py

rantav/spinnaker

0

140841

<gh_stars>0 # Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=missing-docstring import datetime import logging import os import shutil import tempfile import unittest from buildtool import ( ensure_dir_exists, timedelta_string, write_to_path) class TestRunner(unittest.TestCase): @classmethod def setUpClass(cls): cls.base_temp_dir = tempfile.mkdtemp(prefix='buildtool.util_test') @classmethod def tearDownClass(cls): shutil.rmtree(cls.base_temp_dir) def test_ensure_dir(self): want = os.path.join(self.base_temp_dir, 'ensure', 'a', 'b', 'c') self.assertFalse(os.path.exists(want)) ensure_dir_exists(want) self.assertTrue(os.path.exists(want)) # Ok if already exists ensure_dir_exists(want) self.assertTrue(os.path.exists(want)) def test_write_to_path(self): path = os.path.join(self.base_temp_dir, 'test_write', 'file') content = 'First Line\nSecond Line' write_to_path(content, path) with open(path, 'r') as f: self.assertEquals(content, f.read()) def test_deltatime_string(self): timedelta = datetime.timedelta tests = [ (timedelta(1, 60 * 60 * 4 + 60 * 5 + 2, 123456), 'days=1 + 04:05:02'), (timedelta(1, 60 * 5 + 2, 123456), 'days=1 + 00:05:02'), (timedelta(1, 2, 123456), 'days=1 + 00:00:02'), (timedelta(0, 60 * 60 * 4 + 60 * 5 + 2, 123456), '04:05:02'), (timedelta(0, 60 * 5 + 2, 123456), '05:02'), (timedelta(0, 2, 123456), '2.123 secs') ] for test in tests: self.assertEquals(test[1], timedelta_string(test[0])) if __name__ == '__main__': logging.basicConfig( format='%(levelname).1s %(asctime)s.%(msecs)03d %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG) unittest.main(verbosity=2)

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notebooks/bqml/track_meta.py

roannav/learntools

359

32140

# See also examples/example_track/track_meta.py for a longer, commented example track = dict( author_username='dansbecker', course_name='Machine Learning', course_url='https://www.kaggle.com/learn/intro-to-machine-learning' ) lessons = [ dict( topic='Your First BiqQuery ML Model', ), ] notebooks = [ dict( filename='tut1.ipynb', lesson_idx=0, type='tutorial', scriptid=4076893, ), dict( filename='ex1.ipynb', lesson_idx=0, type='exercise', scriptid=4077160, ), ]

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Exercicios/068-ParouImparV2.py

LuisDjay/Python-Curso-em-Video

0

61669

<filename>Exercicios/068-ParouImparV2.py print('=====Desafio 068=====') from random import randint cont = 0 while True: jog = int(input('Diga Um valor: ')) resp = str(input('Par ou Ímpar? [P/I] ').upper()) comp = randint(0, 10) result = jog + comp result2 = result % 2 if result2 == 0 and resp == 'P': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu PAR') print(f'Você Venceu!') print(f'Vamos jogar novamente') elif result2 == 0 and resp == 'I': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu PAR') print(f'Você Perdeu!') break elif result2 != 0 and resp == 'I': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu IMPAR') print(f'Você Venceu!') print(f'Vamos jogar novamente') cont += 1 elif result2 != 0 and resp == 'P': print(f'Voce Jogou {jog} e o computador jogou {comp}. O total é {result} Deu IMPAR') print(f'Você Perdeu!') break cont += 1 print(f'Você venceu {cont} vezes.')

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page/playground_page.py

fnrcum/pytest_playwright_framework

0

1608143

from playwright.sync_api import Page class PlaygroundPage: def __init__(self, page: Page): self.page = page @property def bad_button(self): return self.page.wait_for_selector("#badButton")

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api/authentication/tests.py

app-generator/api-server-django-sample

2

76597

from django.urls import reverse from rest_framework.test import APITestCase from rest_framework import status class AuthenticationTest(APITestCase): base_url_register = reverse("api-users:register-list") base_url_login = reverse("api-users:login-list") base_url_logout = reverse("api-users:logout-list") base_url_check_session = reverse("api-users:check-session-list") data_register = {"username": "test", "password": "<PASSWORD>", "email": "<EMAIL>"} data_login = {"password": "<PASSWORD>", "email": "<EMAIL>"} def test_register(self): response = self.client.post( f"{self.base_url_register}", data=self.data_register ) self.assertEqual(response.status_code, status.HTTP_201_CREATED) response_data = response.json() self.assertEqual(response_data["success"], True) def test_login(self): response = self.client.post(f"{self.base_url_login}", data=self.data_login) self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["success"], True) def test_logout(self): # Login to retrieve token response = self.client.post(f"{self.base_url_login}", data=self.data_login) response_data = response.json() token = response_data["token"] self.client.credentials(HTTP_AUTHORIZATION=token) # Logout response = self.client.post(f"{self.base_url_logout}", data={"token": token}) self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["success"], True) def test_check_session(self): # Login to retrieve token response = self.client.post(f"{self.base_url_login}", data=self.data_login) response_data = response.json() token = response_data["token"] self.client.credentials(HTTP_AUTHORIZATION=token) # Check session response = self.client.post(f"{self.base_url_check_session}") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["success"], True)

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thuum/__main__.py

nickcoutsos/thuum

0

40802

import argparse import functools import sys from tornado import ( httpclient, ioloop, ) def add_package_to_path(): if not (__name__ == "__main__" and __package__ == ""): return import os sys.path.append(os.path.abspath(os.path.join( os.path.abspath(__file__), "..", ".."))) add_package_to_path() from thuum import ( reporters, runners, stats, ) class UsageError(Exception): def __init__(self, message, parser): super(UsageError, self).__init__(message) self.parser = parser class AddBody(argparse.Action): def __call__(self, parser, namespace, body, option_string=None): if namespace.body is not None: raise UsageError("Cannot specify -b/--body more than once.", parser) if namespace.method not in ("PATCH", "POST", "PUT"): raise UsageError( "Cannot specify -b/--body with %r." % namespace.method, parser) if body.startswith("py:"): pass elif body.startswith("@"): pass namespace.body = body class AddHeader(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): header = tuple(values.split(":")) namespace.headers.append(header) if len(header) != 2: raise UsageError("Headers must be of the form 'name:value'", parser) class StoreMappedChoice(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): choice = self.choices[values] setattr(namespace, self.dest, choice) def get_argument_parser(parser=None): parser = parser or argparse.ArgumentParser( description="Simple HTTP Load runner.") parser.add_argument( "-m", "--method", choices=("DELETE", "GET", "HEAD", "OPTIONS", "POST", "PUT"), help="HTTP Method to use for request", default="GET") parser.add_argument( "-b", "--body", action=AddBody, default=None, help=( "Request body. Prefix with 'py:' to indicate a fully-qualified " "python callable. Prefix with '@' to indicate a file path." )) parser.add_argument( "-c", "--concurrency", help="Number of requests to make concurrently.", dest="concurrency", default=1, type=int) parser.add_argument( "-H", "--header", dest="headers", help="Custom header. name:value", default=[], action=AddHeader) parser.add_argument( "--reporter", dest="reporter_class", help="Stats report format.", action=StoreMappedChoice, default=reporters.TerminalReporter, choices={ "csv": reporters.CSVReporter, "json": reporters.JSONReporter, "term": reporters.TerminalReporter, }) group = parser.add_mutually_exclusive_group(required=True) group.add_argument( "-n", "--requests", help="Number of requests", type=int) group.add_argument( "-d", "--duration", help="Run load test for specified length of time.", type=float) parser.add_argument("url", help="URL to hit") return parser def main(argv=sys.argv[1:], stdout=sys.stdout): parser = get_argument_parser() try: args = parser.parse_args(argv) except UsageError as exception: sys.exit("%s\n\n%s" % ( exception.message, exception.parser.format_usage() )) httpclient.AsyncHTTPClient.configure(None, max_clients=args.concurrency) client = httpclient.AsyncHTTPClient(io_loop=ioloop.IOLoop.current()) try: make_request = functools.partial( httpclient.HTTPRequest, args.url, args.method, args.headers, args.body) if args.duration: runner = runners.DurationRunner(client, make_request, args.duration) else: runner = runners.QuantityRunner(client, make_request, args.requests) reporter = args.reporter_class(stdout) progress = functools.partial(reporter.progress, runner) tracker = stats.Tracker(runner) tracker.events.on("request_finished", reporter.record) tracker.events.on("tests_finished", lambda t: progress()) tracker.events.on("tests_finished", reporter.summarize) client.io_loop.add_callback(progress) ioloop.PeriodicCallback(progress, 500, client.io_loop).start() runner.run() except KeyboardInterrupt: sys.exit("Tests interrupted.") if __name__ == "__main__": sys.exit(main(sys.argv[1:]))

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silica/transformations/replace_symbols.py

leonardt/fsm_dsl

0

195340

<reponame>leonardt/fsm_dsl import ast from copy import deepcopy class SymbolReplacer(ast.NodeTransformer): def __init__(self, symbol_table, ctx): self.symbol_table = symbol_table self.ctx = ctx def visit_Name(self, node): if node.id in self.symbol_table: if self.ctx is None: new_node = deepcopy(self.symbol_table[node.id]) new_node.ctx = node.ctx return new_node elif isinstance(node.ctx, self.ctx): return deepcopy(self.symbol_table[node.id]) return node def replace_symbols(tree, symbol_table, ctx=None): return SymbolReplacer(symbol_table, ctx).visit(tree)

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1701-1800/1711-1720/1711-countGoodMeals/countGoodMeals.py

xuychen/Leetcode

0

27963

<gh_stars>0 from collections import defaultdict class Solution(object): def countPairs(self, deliciousness): """ :type deliciousness: List[int] :rtype: int """ max_sum = max(deliciousness) * 2 count = 0 dictionary = defaultdict(int) for value in deliciousness: summation = 1 while summation <= max_sum: count = (count + dictionary[summation-value]) % 1000000007 summation <<= 1 dictionary[value] += 1 return count

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modin/pandas/datetimes.py

alclol/modin

0

68697

<reponame>alclol/modin # Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership. The Modin Development Team licenses this file to you 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 pandas from .dataframe import DataFrame from .series import Series def to_datetime( arg, errors="raise", dayfirst=False, yearfirst=False, utc=None, format=None, exact=True, unit=None, infer_datetime_format=False, origin="unix", cache=True, ): """Convert the arg to datetime format. If not Ray DataFrame, this falls back on pandas. Args: errors ('raise' or 'ignore'): If 'ignore', errors are silenced. Pandas blatantly ignores this argument so we will too. dayfirst (bool): Date format is passed in as day first. yearfirst (bool): Date format is passed in as year first. utc (bool): retuns a UTC DatetimeIndex if True. box (bool): If True, returns a DatetimeIndex. format (string): strftime to parse time, eg "%d/%m/%Y". exact (bool): If True, require an exact format match. unit (string, default 'ns'): unit of the arg. infer_datetime_format (bool): Whether or not to infer the format. origin (string): Define the reference date. Returns: Type depends on input: - list-like: DatetimeIndex - Series: Series of datetime64 dtype - scalar: Timestamp """ if not isinstance(arg, (DataFrame, Series)): return pandas.to_datetime( arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, utc=utc, format=format, exact=exact, unit=unit, infer_datetime_format=infer_datetime_format, origin=origin, cache=cache, ) return arg._default_to_pandas( pandas.to_datetime, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, utc=utc, format=format, exact=exact, unit=unit, infer_datetime_format=infer_datetime_format, origin=origin, cache=cache, )

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tests/test_gpreg.py

cdgreenidge/gdec

0

12283

<filename>tests/test_gpreg.py """Test gpreg.py.""" from typing import Tuple import numpy as np import pytest from gdec import gpreg, npgp @pytest.fixture(scope="module") def dataset() -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: np.random.seed(42) amplitude = 1.0 lengthscale = 12 sigma = 0.5 n = 128 def spectrum_fn(w: np.ndarray) -> np.ndarray: return npgp.rbf_spectrum(w, amplitude, lengthscale) basis, freqs = npgp.real_fourier_basis(n) coef_vars = npgp.rbf_spectrum(freqs, amplitude, lengthscale) z = np.arange(n) w = np.sqrt(coef_vars) * np.random.randn(n) f = basis @ w x = z.repeat(1) f_x = f.repeat(1) y = sigma * np.random.randn(*f_x.shape) + f_x return x[:, None], y, z[:, None], f def test_you_can_train_periodic_gp_regression_on_the_synthetic_dataset(dataset): X, y, z, f = dataset grid_size = np.unique(X).size model = gpreg.PeriodicGPRegression() model.fit(X, y, grid_size=grid_size) f_est = model.predict(z) error = np.max(np.abs(f - f_est)) assert error < 0.3 def test_training_pid_on_float_dataset_raises_value_error(dataset): X, y, _, _ = dataset X = X.astype(np.float32) grid_size = np.unique(X).size model = gpreg.PeriodicGPRegression() with pytest.raises(ValueError): model.fit(X, y, grid_size=grid_size)

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evaluate.py

OlinSTEP/silent_speech

0

144329

import sys import os import logging import torch from torch import nn from transduction_model import test, save_output, Model from read_emg import EMGDataset from asr import evaluate from data_utils import phoneme_inventory, print_confusion from absl import flags FLAGS = flags.FLAGS flags.DEFINE_list('models', [], 'identifiers of models to evaluate') class EnsembleModel(nn.Module): def __init__(self, models): super().__init__() self.models = nn.ModuleList(models) def forward(self, x, x_raw, sess): ys = [] ps = [] for model in self.models: y, p = model(x, x_raw, sess) ys.append(y) ps.append(p) return torch.stack(ys,0).mean(0), torch.stack(ps,0).mean(0) def main(): os.makedirs(FLAGS.output_directory, exist_ok=True) logging.basicConfig(handlers=[ logging.FileHandler(os.path.join(FLAGS.output_directory, 'eval_log.txt'), 'w'), logging.StreamHandler() ], level=logging.INFO, format="%(message)s") testset = EMGDataset(test=True) device = 'cuda' if torch.cuda.is_available() and not FLAGS.debug else 'cpu' models = [] for fname in FLAGS.models: state_dict = torch.load(fname) n_sess = 1 if FLAGS.no_session_embed else state_dict["session_emb.weight"].size(0) model = Model(testset.num_features, testset.num_speech_features, len(phoneme_inventory), n_sess).to(device) model.load_state_dict(state_dict) models.append(model) ensemble = EnsembleModel(models) _, _, confusion = test(ensemble, testset, device) print_confusion(confusion) for i, datapoint in enumerate(testset): save_output(ensemble, datapoint, os.path.join(FLAGS.output_directory, f'example_output_{i}.wav'), device) evaluate(testset, FLAGS.output_directory) if __name__ == "__main__": FLAGS(sys.argv) main()

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RNAPuzzles/RNAPuzzles/settings.py

whinyadventure/RNA-Puzzles

0

174781

""" Django settings for RNAPuzzles project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = os.getenv("SECRET_KEY", '<KEY>') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = os.getenv("DEBUG", "False") == "True" ALLOWED_HOSTS = ["127.0.0.1", "django-service"] LOGOUT_REDIRECT_URL = '/' DOMAIN_URL = "lepus.cs.put.poznan.pl" LOGIN_URL = "/accounts/signin/" # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'martor', 'crispy_forms', 'guardian', 'publications', 'captcha', 'tempus_dominus', 'rnapuzzles', ] CRISPY_TEMPLATE_PACK = 'bootstrap4' AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', # default 'guardian.backends.ObjectPermissionBackend', ) ANONYMOUS_USER_NAME = "<EMAIL>" MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'rnapuzzles.middleware.request.GuardianAuthenticationMiddleware', ] ROOT_URLCONF = 'RNAPuzzles.urls' TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', ) TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, "rnapuzzles/templates")], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'RNAPuzzles.wsgi.application' AUTH_USER_MODEL = 'rnapuzzles.CustomUser' if os.getenv("EMAIL_HOST_USER") and os.getenv("EMAIL_HOST_PASSWORD"): EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_USE_TLS = True EMAIL_PORT = 587 EMAIL_HOST_USER = os.getenv("EMAIL_HOST_USER",False) EMAIL_HOST_PASSWORD = os.getenv("EMAIL_HOST_PASSWORD",False) else: EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' from django.contrib.messages import constants as messages MESSAGE_TAGS = { messages.DEBUG: 'alert-info', messages.INFO: 'alert-info', messages.SUCCESS: 'alert-success', messages.WARNING: 'alert-warning', messages.ERROR: 'alert-danger', } # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.getenv("DB_NAME", 'rna'), 'USER': os.getenv("DB_USER",'postgres'), 'PASSWORD': os.getenv("DB_PASSWORD",'password'), 'HOST': os.getenv("DB_HOST", 'localhost'), 'PORT': os.getenv("DB_PORT", '5432'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ #LANGUAGE_CODE = 'en-GB' TIME_ZONE = 'CET' #USE_I18N = True USE_L10N = False USE_TZ = True DATETIME_FORMAT = 'd M Y, H:i ' DATETIME_INPUT_FORMATS = ['%d-%m-%Y %H:%M'] TEMPUS_DOMINUS_LOCALIZE = False TEMPUS_DOMINUS_INCLUDE_ASSETS = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) STATIC_URL = '/static/' STATIC_ROOT = "/var/www/html/"#os.path.join(PROJECT_ROOT, 'static') MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media') MEDIA_SUBMISSIONS = "submissions" STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) STATICFILES_DIRS = ( os.path.join(BASE_DIR, "rnapuzzles/static"), ) # Global martor settings # Input: string boolean, `true/false` MARTOR_ENABLE_CONFIGS = { 'imgur': 'false', # to enable/disable imgur/custom uploader. 'mention': 'false', # to enable/disable mention 'jquery': 'true', # to include/revoke jquery (require for admin default django) 'living': 'false', # to enable/disable live updates in preview 'spellcheck': 'true', } # To setup the martor editor with label or not (default is False) MARTOR_ENABLE_LABEL = True # Imgur API Keys MARTOR_IMGUR_CLIENT_ID = 'your-client-id' MARTOR_IMGUR_API_KEY = 'your-api-key' # Safe Mode MARTOR_MARKDOWN_SAFE_MODE = True # default # Markdownify MARTOR_MARKDOWNIFY_FUNCTION = 'martor.utils.markdownify' # default MARTOR_MARKDOWNIFY_URL = '/martor/markdownify/' # default # Markdown extensions (default) MARTOR_MARKDOWN_EXTENSIONS = [ 'markdown.extensions.extra', 'markdown.extensions.nl2br', 'markdown.extensions.smarty', 'markdown.extensions.fenced_code', # Custom markdown extensions. 'martor.extensions.urlize', 'martor.extensions.del_ins', # ~~strikethrough~~ and ++underscores++ 'martor.extensions.mention', # to parse markdown mention 'martor.extensions.emoji', # to parse markdown emoji 'martor.extensions.mdx_video', # to parse embed/iframe video ] # Markdown Extensions Configs MARTOR_MARKDOWN_EXTENSION_CONFIGS = {} # Markdown urls MARTOR_UPLOAD_URL = '/martor/uploader/' # default MARTOR_SEARCH_USERS_URL = '/martor/search-user/' # default # Markdown Extensions # MARTOR_MARKDOWN_BASE_EMOJI_URL = 'https://www.webfx.com/tools/emoji-cheat-sheet/graphics/emojis/' # from webfx MARTOR_MARKDOWN_BASE_EMOJI_URL = 'https://github.githubassets.com/images/icons/emoji/' # default from github MARTOR_MARKDOWN_BASE_MENTION_URL = 'https://python.web.id/author/' SILENCED_SYSTEM_CHECKS = ['captcha.recaptcha_test_key_error'] # RabbitMq CELERY_BROKER_URL = 'amqp://{user}:{password}@{host}:{port}'.format( host=os.getenv('RABBITMQ_HOST', 'localhost'), port=os.getenv('RABBITMQ_PORT', '5672'), user=os.getenv("RABBITMQ_USER", "user"), password=os.getenv("RABBITMQ_PASSWORD", "password") ) CELERY_ACCEPT_CONTENT = ['application/json'] CELERY_TASK_SERIALIZER = 'json' CELERY_RESULT_SERIALIZER = 'json'

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python/depthcharge/register/cp.py

youssefms/depthcharge

0

61757

# SPDX-License-Identifier: BSD-3-Clause # Depthcharge: <https://github.com/nccgroup/depthcharge> """ Implements FDTCrashRegisterReader """ from ..operation import Operation from .data_abort import DataAbortRegisterReader class CpCrashRegisterReader(DataAbortRegisterReader): """ This is a :py:class:`~.DataAbortRegisterReader` that uses the ``cp`` console command to trigger the Data Abort used to read registers. """ _required = { 'arch': 'ARM', 'commands': ['cp'], 'crash_or_reboot': True, } @classmethod def rank(cls, **_kwargs): return 10 # This is by intent; we are not using another memory reader. # pylint: disable=method-hidden def _trigger_data_abort(self): cmd = 'cp.l {addr:x} {addr:x} 1'.format(addr=self._crash_addr) return self._ctx.send_command(cmd) Operation.register(CpCrashRegisterReader)

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Day01-15/code/Day15/excel1.py

EngrSaad2/Python-100-Days

6

44704

<reponame>EngrSaad2/Python-100-Days<gh_stars>1-10 """ 创建Excel文件 Version: 0.1 Author: 骆昊 Date: 2018-03-26 """ from openpyxl import Workbook from openpyxl.worksheet.table import Table, TableStyleInfo workbook = Workbook() sheet = workbook.active data = [ [1001, '白元芳', '男', '13123456789'], [1002, '白洁', '女', '13233445566'] ] sheet.append(['学号', '姓名', '性别', '电话']) for row in data: sheet.append(row) tab = Table(displayName="Table1", ref="A1:E5") tab.tableStyleInfo = TableStyleInfo( name="TableStyleMedium9", showFirstColumn=False, showLastColumn=False, showRowStripes=True, showColumnStripes=True) sheet.add_table(tab) workbook.save('./res/全班学生数据.xlsx')

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playsound.py

Tony1527/playsound

2

89492

<reponame>Tony1527/playsound class PlaysoundException(Exception): pass def _playsoundWin(sound, block = True): ''' Utilizes windll.winmm. Tested and known to work with MP3 and WAVE on Windows 7 with Python 2.7. Probably works with more file formats. Probably works on Windows XP thru Windows 10. Probably works with all versions of Python. Inspired by (but not copied from) <NAME> <<EMAIL>>'s mp3play: https://github.com/michaelgundlach/mp3play I never would have tried using windll.winmm without seeing his code. ''' from ctypes import c_buffer, windll from random import random from time import sleep from sys import getfilesystemencoding def winCommand(*command): buf = c_buffer(255) command = ' '.join(command).encode(getfilesystemencoding()) errorCode = int(windll.winmm.mciSendStringA(command, buf, 254, 0)) if errorCode: errorBuffer = c_buffer(255) windll.winmm.mciGetErrorStringA(errorCode, errorBuffer, 254) exceptionMessage = ('\n Error ' + str(errorCode) + ' for command:' '\n ' + command.decode() + '\n ' + errorBuffer.value.decode()) raise PlaysoundException(exceptionMessage) return buf.value alias = 'playsound_' + str(random()) winCommand('open "' + sound + '" alias', alias) winCommand('set', alias, 'time format milliseconds') durationInMS = winCommand('status', alias, 'length') winCommand('play', alias, 'from 0 to', durationInMS.decode()) if block: sleep(float(durationInMS) / 1000.0) def _playsoundOSX(sound, block = True): ''' Utilizes AppKit.NSSound. Tested and known to work with MP3 and WAVE on OS X 10.11 with Python 2.7. Probably works with anything QuickTime supports. Probably works on OS X 10.5 and newer. Probably works with all versions of Python. Inspired by (but not copied from) Aaron's Stack Overflow answer here: http://stackoverflow.com/a/34568298/901641 I never would have tried using AppKit.NSSound without seeing his code. ''' from AppKit import NSSound from Foundation import NSURL from time import sleep if '://' not in sound: if not sound.startswith('/'): from os import getcwd sound = getcwd() + '/' + sound sound = 'file://' + sound url = NSURL.URLWithString_(sound) nssound = NSSound.alloc().initWithContentsOfURL_byReference_(url, True) if not nssound: raise IOError('Unable to load sound named: ' + sound) nssound.play() if block: sleep(nssound.duration()) def _playsoundNix(sound, block=True): """Play a sound using GStreamer. Inspired by this: https://gstreamer.freedesktop.org/documentation/tutorials/playback/playbin-usage.html """ if not block: raise NotImplementedError( "block=False cannot be used on this platform yet") # pathname2url escapes non-URL-safe characters import os try: from urllib.request import pathname2url except ImportError: # python 2 from urllib import pathname2url import gi gi.require_version('Gst', '1.0') from gi.repository import Gst Gst.init(None) playbin = Gst.ElementFactory.make('playbin', 'playbin') if sound.startswith(('http://', 'https://')): playbin.props.uri = sound else: playbin.props.uri = 'file://' + pathname2url(os.path.abspath(sound)) set_result = playbin.set_state(Gst.State.PLAYING) if set_result != Gst.StateChangeReturn.ASYNC: raise PlaysoundException( "playbin.set_state returned " + repr(set_result)) # FIXME: use some other bus method than poll() with block=False # https://lazka.github.io/pgi-docs/#Gst-1.0/classes/Bus.html bus = playbin.get_bus() bus.poll(Gst.MessageType.EOS, Gst.CLOCK_TIME_NONE) playbin.set_state(Gst.State.NULL) from platform import system system = system() if system == 'Windows': playsound = _playsoundWin elif system == 'Darwin': playsound = _playsoundOSX else: playsound = _playsoundNix del system from ctypes import c_buffer, windll from random import random from time import sleep from sys import getfilesystemencoding def winCommand(*command): buf = c_buffer(255) command = ' '.join(command).encode(getfilesystemencoding()) errorCode = int(windll.winmm.mciSendStringA(command, buf, 254, 0)) if errorCode: errorBuffer = c_buffer(255) windll.winmm.mciGetErrorStringA(errorCode, errorBuffer, 254) exceptionMessage = ('\n Error ' + str(errorCode) + ' for command:' '\n ' + command.decode() + '\n ' + errorBuffer.value.decode()) raise PlaysoundException(exceptionMessage) return buf.value from threading import Thread,Event,Lock from queue import Queue,Empty from collections import deque ''' music class which uses windows mci to play the music ''' class _music(object): __alias=None __running_idx=None __sound=None __start=None __end=None __is_repeat=False __id=-1 music_list=None ''' initialize the music object ''' def __init__(self,sound,id): self.__alias=['',''] self.__running_idx=0 self.__id=id self.preload(sound) def set_music_list(self,music_list): self.music_list = music_list def __eq__(self,value): return self.__id==value ''' clear the music object music will be closed ''' def close(self): self.stop() self.__clear() ''' get id of music music will not be affected ''' def get_id(self): return self.__id ''' return whether music plays repeatly music will not be affected ''' def is_repeat(self): return self.__is_repeat ''' return the range from start to end music will not be affected ''' def length(self): if self.__check_alias(): return self.__end-self.__start ''' return the mode of the music object music will not be affected ''' def mode(self): if self.__check_alias(): return winCommand('status',self.__get_alias(),'mode').decode() ''' pause the music music will be paused ''' def pause(self): if self.__check_alias(): winCommand('pause '+self.__get_alias()) ''' play the music from start to end music will be playing ''' def play(self,start=0,end=-1): self.__start,self.__end=self.__parse_start_end(start,end,self.total_length()) self.__play_implement(self.__start,self.__end) ''' return the position of the music music will not be affected ''' def position(self): if self.__check_alias(): return int(winCommand('status',self.__get_alias(),'position').decode()) ''' preload the music information ''' def preload(self,sound): self.__sound=sound for i in range(2): self.__alias[i]='playsound_'+str(random()) winCommand('open "'+self.__sound+'" alias',self.__alias[i]) winCommand('set',self.__alias[i],'time format milliseconds') length=self.total_length() self.__start=0 self.__end=length return length ''' resume playing music will be playing ''' def resume(self): if self.__check_alias(): if self.__is_repeat: self.__play_implement(self.position(),self.__end) else: winCommand('resume '+self.__get_alias()) ''' seek the music to pos. music will bee paused ''' def seek(self,pos): if self.__check_alias(): if pos>self.__end or pos<self.__start: raise PlaysoundException('position exceed range') winCommand('seek',self.__get_alias(),'to',str(pos)) winCommand('play',self.__get_alias(),'from '+ str(pos) +' to',str(self.__end)) self.pause() ''' set repeat flag of the music music will repeatly play ''' def set_repeat(self,repeat): self.__is_repeat=repeat ''' set id for music object music will not be affected ''' def set_id(self,id): self.__id=id ''' stop the music. music will be stopped ''' def stop(self): if self.__check_alias(): self.seek(self.__start) winCommand('stop '+self.__get_alias()) ''' total_length of the music object, the difference that total_length is the range is total music, but length is only range from start to end music will not be affected ''' def total_length(self): if self.__check_alias(): return int(winCommand('status',self.__get_alias(),'length').decode()) ''' update the record time of the music, ''' def update_mode(self,delay=0): mod = self.mode() if mod =='playing': #if self.__end-self.position()<delay then repeat the music if self.__is_repeat==True: if self.__end-self.position()<=delay: self.__running_idx=(self.__running_idx+1)%2 self.__play_implement(self.__start,self.__end) return mod def __get_alias(self): return self.__alias[self.__running_idx] def __check_alias(self): if self.__get_alias()!='': return True def __parse_start_end(self,start,end,length): if not (isinstance(start,int) and isinstance(end,int)): raise PlaysoundException('start and end must be int') _start=0 _end=0 if end==-1: _end = length elif end<=length: _end = end else: raise PlaysoundException('music range exceed limits') if start<0 or start>length: raise PlaysoundException('music range exceed limits') elif _end<start: raise PlaysoundException('end must be bigger than start') else: _start=start return _start,_end def __del__(self): self.__clear() def __clear(self): if self.__check_alias(): for i in range(2): winCommand('close '+self.__alias[i]) self.__alias=['',''] self.__start=None self.__end=None self.__is_repeat=False def __play_implement(self,start,end): winCommand('play',self.__get_alias(),'from '+ str(start) +' to',str(end)) def print(self): if self.__check_alias(): def format_miliseconds(t): return '%d:%d:%d.%d'%(t//3600000,(t%3600000)//60000,(t%60000)//1000,t%1000) print('music name:',self.__sound) print('mode：',self.mode()) print('total_length：',self.total_length()) print('position:',str(self.position())) print('start - end: {} - {}'.format(format_miliseconds(self.__start),format_miliseconds(self.__end))) ''' singleton ''' class _singleton(object): _mutex=Lock() def __init__(self): pass @classmethod def GetInstance(cls,*args,**kwargs): if not hasattr(cls,'_instance'): cls._mutex.acquire() if not hasattr(cls,'_instance'): cls._instance = cls() print('create instance',cls._instance) cls._mutex.release() return cls._instance ''' music tag is used to send message for music manager ''' class _music_tag(object): id=-1 #id is the connection between music player and _music object operator='' #operator of _music object args=None #parameters block_event=None block=False retval=None #return value for some methods of music player music_list=None #special deal with music list def __init__(self,id,operator,block=False,*args): self.id=id self.operator = operator self.args = args if block: self.block_event=Event() self.block=True def set_music_list(self,music_list): self.music_list = music_list ''' music player is the client who sends music tags to music manager which indeed plays music. music player controls music once you open the music. ''' class music_player(object): __id=-1 #identity of every _music object __music=None #sound static_id=0 #static variables mutex=Lock() #lock of static_id music_list=None #this music player belong to which music list def __init__(self,music_list=None): ''' if music player belongs to one of music list,then set music_list, otherwise you can ignore music_list parameter ''' self.music_list = music_list def get_music(self): ''' get name of sound ''' return self.__music def close(self): ''' close sound ''' self.__send('close',False) self.__id=-1 def length(self): ''' get the length of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('length',True) def mode(self): ''' get the mode of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('mode',True) def open(self,music): ''' open the music ''' self.__music=music self.mutex.acquire() self.__id=music_player.static_id music_player.static_id=music_player.static_id+1 self.mutex.release() self.__send('open',False,self.__music,self.__id) def pause(self): ''' pause the music ''' self.__send('pause',False) def play(self,start=0,end=-1): ''' play the music ''' self.__send('play',False,start,end) def position(self): ''' get the mode of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('position',True) def resume(self): ''' resume the music ''' self.__send('resume',False) def seek(self,pos): ''' seek the music to pos, which is defined in miliseconds ''' self.__send('seek',False,pos) def set_repeat(self,repeat): ''' play music repeatly ''' self.__send('set_repeat',False,repeat) def stop(self): ''' stop the music ''' self.__send('stop',False) def total_length(self): ''' get the total length of music. @warning: this method blocks current thread until music manager respond this functions ''' return self.__send('total_length',True) def __send(self,operator,block,*args): ''' send music tag to music manager ''' if self.__id==-1: raise PlaysoundException('No music has been opened') tag=_music_tag(self.__id,operator,block,*args) tag.music_list=self.music_list return music_manager.GetInstance().put_tag(tag) class music_list(object): __music_list=deque() def append_music(self,sound,repeat=False): music = music_player(self) music.open(sound) music.set_repeat(repeat) self.__music_list.append(music) if len(self.__music_list)==1: self.top().play() def play_next(self): if len(self.__music_list)>=2: self.__music_list[1].play() self.__music_list.popleft().close() def pause_music(self): if len(self.__music_list)>0 and self.mode()=='playing': self.top().pause() def resume_music(self): if len(self.__music_list)>0 and self.mode()=='paused': self.top().resume() def mode(self): return self.top().mode() def top(self): return self.__music_list[0] class music_manager(_singleton): __mutex=Lock() __sounds=[] # __music_list=[] __tag_queue=Queue() __running_event=Event() __end_running_event=Event() def __init__(self): self.reset_event() def reset_event(self): self.__running_event.set() self.__end_running_event.clear() def put_tag(self,tag): ''' push a music tag to music_manager @warning: if tag.block is True ,this method will block current thread until music manager respond this functions ''' if tag.block: tag.block_event.clear() self.__tag_queue.put(tag) if tag.block: tag.block_event.wait() return tag.retval def get_tag(self): try: #if there is no task for music player, then sleep the music manager, #otherwise get a tag immediately if len(self.__sounds)>0: tag=self.__tag_queue.get_nowait() else: tag=self.__tag_queue.get() retval=None if tag.operator == 'open': m=self.__add_music(*tag.args) m.set_music_list(tag.music_list) elif tag.operator == 'close': #remove the music from self.__sounds self.__rm_music(tag.id) else: (idx,item)=self.__get_music_idx_and_item(tag.id) #reflect retval=getattr(item,tag.operator)(*tag.args) #set return values in tag if tag.block==True: tag.retval=retval tag.block_event.set() except Empty: pass def __add_music(self,sound,id): m=_music(sound,id) self.__mutex.acquire() self.__sounds.append(m) self.__mutex.release() return m def __rm_music(self,id): idx,rm_item=self.__get_music_idx_and_item(id) rm_item.close() rm_item.set_id(-1) self.__mutex.acquire() self.__sounds.pop(idx) self.__mutex.release() def __get_music_idx_and_item(self,id): for i,x in enumerate(self.__sounds): if x.get_id()==id: return i,x raise PlaysoundException('Unknown music object found') @classmethod def start(cls): ''' start the music manager ''' Thread(target=music_manager._start_music_manager_impl).start() @classmethod def stop(cls): ''' stop the music manager ''' manager = cls.GetInstance() manager.__running_event.clear() manager.__end_running_event.wait() manager.reset_event() print('stop manager',manager) ''' main loop of music manager ''' @classmethod def _start_music_manager_impl(cls): manager = cls.GetInstance() print('start manager',manager) delay=100 while(manager.__running_event.isSet()): for m in manager.__sounds: mode = m.update_mode(delay) #callback the music_list if m.music_list!=None and mode=='playing' and not m.is_repeat(): pos = m.position() total_length=m.total_length() if total_length-pos<=delay: m.music_list.play_next() manager.get_tag() for x in manager.__sounds: x.close() manager.__end_running_event.set() music_manager.start()

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stage2/models/FuseNet.py

YU-Zhiyang/WEVI

14

193019

import torch import torch.nn as nn from models.baseModule import FuseBlock, BaseNet, Interp class FuseNet(BaseNet): def __init__(self): super(FuseNet, self).__init__() self.ConvIn = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1, stride=1, bias=False) self.AADBlk1 = FuseBlock(cin=512, cout=256, c_ef=512) self.AADBlk2 = FuseBlock(cin=256, cout=128, c_ef=256) self.AADBlk3 = FuseBlock(cin=128, cout=64, c_ef=128) self.AADBlk4 = FuseBlock(cin=64, cout=32, c_ef=64) self.Up2x = Interp(scale=2) self.ItStage1 = nn.Sequential(nn.ReplicationPad2d([1, 1, 1, 1]), nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(in_channels=32, out_channels=3, kernel_size=1), ) self.randomInitNet() def forward(self, z_e, z_f): ST16x = self.ConvIn(torch.cat([z_e[0], z_f[0]], dim=1)) # 64 ST8x = self.AADBlk1(self.Up2x(ST16x), z_e[1], z_f[1]) # 32 ST4x = self.AADBlk2(self.Up2x(ST8x), z_e[2], z_f[2]) # 16 ST2x = self.AADBlk3(self.Up2x(ST4x), z_e[3], z_f[3]) # 8 ST1x = self.AADBlk4(self.Up2x(ST2x), z_e[4], z_f[4]) # 4 ItStage1 = self.ItStage1(ST1x) return ItStage1, ST4x, ST2x, ST1x

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Tutorial 6 - Ptychography/ePIE_code/ptycho_recon/postProcess.py

paradimdata/Cornell_EM_SummerSchool_2021

8

112593

# -*- coding: utf-8 -*- """ Created on Tue Jun 1 23:29:40 2021 @author: <NAME> Converted from Yi Jiang's 'postProcess.m'' """ import numpy as np from skimage.transform import rotate, rescale def sind(x): return np.sin(x * np.pi / 180) def cosd(x): return np.cos(x * np.pi / 180) def removePhaseRamp(input, dx): Ny = input.shape[0] Nx = input.shape[1] y = np.linspace(-np.floor(Ny/2),np.ceil(Ny/2)-1,Ny) x = np.linspace(-np.floor(Nx/2),np.ceil(Nx/2)-1,Nx) [X,Y] = np.meshgrid(x,y) X = X*dx Y = Y*dx phase_image = np.angle(input) #fit ramp Xf = X.flatten() Yf = Y.flatten() A = np.array([Xf*0+1, Xf, Yf]).T B = phase_image.flatten() coeff, r, rank, s = np.linalg.lstsq(A, B) background = X*coeff[1]+Y*coeff[2] output = phase_image - background return output def postProcess(obj, rot_angle, px, py, dx): #Post process reconstructed object # Convert from Matlab postProcess.m Y<NAME> (<EMAIL>) #rotate object to 0 degree py_rot = px*-sind(-rot_angle) + py*cosd(-rot_angle) px_rot = px*cosd(-rot_angle) + py*sind(-rot_angle) obj_rot_r = rescale(obj.real,2) obj_rot_i = rescale(obj.imag,2)#upsample to reduce rotation artifacts obj_rot_r = rotate(obj_rot_r, -rot_angle) obj_rot_i = rotate(obj_rot_i, -rot_angle) obj_rot = obj_rot_r + 1j*obj_rot_i cen_rot = np.floor(np.size(obj_rot,1)/2)+1 dx = dx/2 y_lb = np.ceil(min(py_rot[0])/dx+cen_rot) y_ub = np.floor(max(py_rot[0])/dx+cen_rot) x_lb = np.ceil(min(px_rot[0])/dx+cen_rot) x_ub = np.floor(max(px_rot[0])/dx+cen_rot) obj_crop = obj_rot[int(y_lb):int(y_ub),int(x_lb):int(x_ub)] #remove phase ramp obj_crop_phase = removePhaseRamp(obj_crop, dx) obj_crop = abs(obj_crop) * np.exp(1j*obj_crop_phase) obj_crop_r = rescale(obj_crop.real,1/2) obj_crop_i = rescale(obj_crop.imag,1/2) #scale back to original size obj_crop = obj_crop_r + 1j*obj_crop_i return obj_crop

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data_sqlalchemy/word.py

natter1/estonian_learner

0

29159

import datetime from data_sqlalchemy.modelbase import SqlAlchemyBase import sqlalchemy as sa class Word(SqlAlchemyBase): __tablename__ = "words" # id = sa.Column(sa.Integer, primary_key=True, autoincrement=True) id = sa.Column(sa.String, primary_key=True) created_date = sa.Column(sa.DateTime, default=datetime.datetime.now, index=True) hint = sa.String() # not supported by sqlite: nullable=True def __repr__(self): # for more useful debug messages return f"<Package {self.id}>"

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src/features/junjie_features.py

davidjurgens/prosocial-conversation-forecasting

3

41252

import os import pandas as pd import datetime from genderperformr import GenderPerformr from agreementr import Agreementr from politenessr import Politenessr from supportr import Supportr import enchant import requests import json from googleapiclient import discovery from enchant.checker import SpellChecker from enchant.tokenize import get_tokenizer from nltk.tokenize import word_tokenize import nltk import time nltk.download('punkt') def clean_text(text): tokens = word_tokenize(text) words = [word for word in tokens if word.isalpha()] return ' '.join(words) def extract_features(tlc): """extract features from the text Args: tlc (dict[str]): all the attributes of a tlc Returns: [dict]: a dictionary of features extracted """ text = clean_text(tlc['body']) fields = dict() # add features here # fields['Top_comment_word_count'] = len(text.split(' ')) fields['Top_comment_text'] = text # Extract time-based features def get_day_of_week(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').weekday() + 1 def get_day_of_month(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').day def get_time_of_day(text): return datetime.datetime.strptime(text, '%Y-%m-%d %H:%M:%S').hour time_local = time.localtime(tlc['created_utc']) time_local = time.strftime("%Y-%m-%d %H:%M:%S", time_local) fields['Top_comment_day'] = get_day_of_month(time_local) fields['Top_comment_day_of_week'] = get_day_of_week(time_local) fields['Top_comment_hour'] = get_time_of_day(time_local) # Extract gender value gp = GenderPerformr() probs, _ = gp.predict(tlc['author']) # Rescale it from [0,1] to [-1,1] fields['Top_comment_author_gender_value'] = 2 * probs - 1 # Extract percentage of mispellings check = SpellChecker("en_US") tokenizer = get_tokenizer("en_US") # Prevent the denominator from 0 def weird_division(n, d): return n / d if d else 0 def get_mispellings_percentage(text): mispelling_count = 0 total_count = 0 if text == 'nan': return total_count else: check.set_text(text) for err in check: mispelling_count = mispelling_count + 1 for w in tokenizer(text): total_count = total_count + 1 value = weird_division(mispelling_count, total_count) return value fields['Top_comment_mispellings'] = get_mispellings_percentage(text) # Get politeness, agreement, support scores, and rescale them from [1,5] to [-1,1] ar = Agreementr() pr = Politenessr() sr = Supportr() fields['Top_comment_agreement_value'] = 0.5*float(ar.predict([text]))-1.5 fields['Top_comment_politeness_value'] = 0.5*float(pr.predict([text]))-1.5 fields['Top_comment_support_value'] = 0.5*float(sr.predict([text]))-1.5 # Get toxicity scores KEY = "yourkey.txt" # os.getenv("GOOGLE_API_KEY") service = discovery.build('commentanalyzer', 'v1alpha1', developerKey=KEY) def get_results(request_id, response, exception): toxicity_scores.append((request_id, response)) toxicity_scores = [] count = 0 batch = service.new_batch_http_request(callback=get_results) analyze_request = { 'comment': {'text': text}, "requestedAttributes": { "TOXICITY": {}, "SEVERE_TOXICITY": {}, "ATTACK_ON_COMMENTER": {} } } batch.add(service.comments().analyze(body=analyze_request), request_id=str(count)) batch.execute() toxic_score = toxicity_scores[0][1]['attributeScores']['TOXICITY']['summaryScore']['value'] attack_score = toxicity_scores[0][1]['attributeScores']['ATTACK_ON_COMMENTER']['summaryScore']['value'] if toxic_score > 0.5: fields['Top_comment_untuned_toxicity'] = 1 else: fields['Top_comment_untuned_toxicity'] = 0 if toxic_score > 0.8 and attack_score > 0.5: fields['Top_comment_tuned_toxicity'] = 1 else: fields['Top_comment_tuned_toxicity'] = 0 # end of feature extractions # return fields def close(istream): """call deconstructors if needed """ # e.g. close files or disconnect apis istream.close() def main(): istream, df = init() data = ["what's the date today?"] features = map(extract_features, data) close(istream) main()

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social_auth/fields.py

thesealion/django-social-auth

0

67994

<reponame>thesealion/django-social-auth import json from django.core.exceptions import ValidationError from django.db import models from django.utils.encoding import smart_unicode class SubfieldBase(type): """ A metaclass for custom Field subclasses. This ensures the model's attribute has the descriptor protocol attached to it. """ def __new__(cls, name, bases, attrs): new_class = super(SubfieldBase, cls).__new__(cls, name, bases, attrs) new_class.contribute_to_class = make_contrib( new_class, attrs.get('contribute_to_class') ) return new_class class Creator(object): """ A placeholder class that provides a way to set the attribute on the model. """ def __init__(self, field): self.field = field def __get__(self, obj, type=None): if obj is None: return self return obj.__dict__[self.field.name] def __set__(self, obj, value): obj.__dict__[self.field.name] = self.field.to_python(value) def make_contrib(superclass, func=None): """ Returns a suitable contribute_to_class() method for the Field subclass. If 'func' is passed in, it is the existing contribute_to_class() method on the subclass and it is called before anything else. It is assumed in this case that the existing contribute_to_class() calls all the necessary superclass methods. """ def contribute_to_class(self, cls, name, **kwargs): if func: func(self, cls, name, **kwargs) else: super(superclass, self).contribute_to_class(cls, name, **kwargs) setattr(cls, self.name, Creator(self)) return contribute_to_class class JSONField(models.TextField): """Simple JSON field that stores python structures as JSON strings on database. """ __metaclass__ = SubfieldBase def to_python(self, value): """ Convert the input JSON value into python structures, raises django.core.exceptions.ValidationError if the data can't be converted. """ if self.blank and not value: return None if isinstance(value, basestring): try: return json.loads(value) except Exception, e: raise ValidationError(str(e)) else: return value def validate(self, value, model_instance): """Check value is a valid JSON string, raise ValidationError on error.""" super(JSONField, self).validate(value, model_instance) try: return json.loads(value) except Exception, e: raise ValidationError(str(e)) def get_prep_value(self, value): """Convert value to JSON string before save""" try: return json.dumps(value) except Exception, e: raise ValidationError(str(e)) def value_to_string(self, obj): """Return value from object converted to string properly""" return smart_unicode(self.get_prep_value(self._get_val_from_obj(obj)))

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pages/admin.py

nnscr/nnscr.de

0

100200

<reponame>nnscr/nnscr.de from django.contrib import admin from django import forms from . import models from nnmarkdown.form import MarkdownWidget from nnscr.admin import site class PageAdminForm(forms.ModelForm): class Meta: model = models.Page exclude = ("slug",) widgets = { "text": MarkdownWidget } class PageAdmin(admin.ModelAdmin): form = PageAdminForm site.register(models.Page, PageAdmin)

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imageclassification/training/session.py

aisosalo/CIFAR-10

4

30637

<gh_stars>1-10 import sys import os import time import random import numpy as np from termcolor import colored from functools import partial from tensorboardX import SummaryWriter import torch from torch.utils.data import DataLoader from torchvision import transforms as tv_transforms import solt.transforms as slt import solt.core as slc import operator from imageclassification.training.arguments import parse_args from imageclassification.training.dataset import ImageClassificationDataset from imageclassification.training.dataset import apply_by_index, img_labels2solt, unpack_solt_data from imageclassification.training.transformations import init_train_augs from imageclassification.kvs import GlobalKVS import imageclassification.training.transformations as trnsfs PAD_TO = 34 CROP_SIZE = 32 DEBUG = sys.gettrace() is not None def init_session(): if not torch.cuda.is_available(): raise EnvironmentError('The code must be run on GPU.') kvs = GlobalKVS() args = parse_args() if DEBUG: args.n_threads = 0 torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) snapshot_name = time.strftime('%Y_%m_%d_%H_%M') os.makedirs(os.path.join(args.snapshots, args.dataset_name, snapshot_name), exist_ok=True) kvs.update('pytorch_version', torch.__version__) print('Pytorch version: ', torch.__version__) if torch.cuda.is_available(): kvs.update('cuda', torch.version.cuda) kvs.update('gpus', torch.cuda.device_count()) print('CUDA version: ', torch.version.cuda) else: kvs.update('cuda', None) kvs.update('gpus', None) kvs.update('snapshot_name', snapshot_name) kvs.update('args', args) kvs.save_pkl(os.path.join(args.snapshots, args.dataset_name, snapshot_name, 'session.pkl')) return args, snapshot_name def init_data_processing(ds): kvs = GlobalKVS() train_augs = init_train_augs(crop_mode='r', pad_mode='r') # random crop, reflective padding dataset = ImageClassificationDataset(ds, split=kvs['metadata'], color_space=kvs['args'].color_space, transformations=train_augs) mean_vector, std_vector = trnsfs.init_mean_std(dataset=dataset, batch_size=kvs['args'].bs, n_threads=kvs['args'].n_threads, save_mean_std=kvs['args'].snapshots + '/' + kvs['args'].dataset_name, color_space=kvs['args'].color_space) print('Color space: ', kvs['args'].color_space) print(colored('====> ', 'red') + 'Mean:', mean_vector) print(colored('====> ', 'red') + 'Std:', std_vector) norm_trf = tv_transforms.Normalize(torch.from_numpy(mean_vector).float(), torch.from_numpy(std_vector).float()) train_trf = tv_transforms.Compose([ train_augs, partial(apply_by_index, transform=norm_trf, idx=0) ]) val_trf = tv_transforms.Compose([ img_labels2solt, slc.Stream([ slt.PadTransform(pad_to=(PAD_TO, PAD_TO)), slt.CropTransform(crop_size=(CROP_SIZE, CROP_SIZE), crop_mode='c'), # center crop ]), unpack_solt_data, partial(apply_by_index, transform=tv_transforms.ToTensor(), idx=0), partial(apply_by_index, transform=norm_trf, idx=0) ]) kvs.update('train_trf', train_trf) kvs.update('val_trf', val_trf) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl')) def init_loaders(dataset, x_train, x_val): kvs = GlobalKVS() train_dataset = ImageClassificationDataset(dataset, split=x_train, color_space=kvs['args'].color_space, transformations=kvs['train_trf']) val_dataset = ImageClassificationDataset(dataset, split=x_val, color_space=kvs['args'].color_space, transformations=kvs['val_trf']) train_loader = DataLoader(train_dataset, batch_size=kvs['args'].bs, num_workers=kvs['args'].n_threads, drop_last=True, worker_init_fn=lambda wid: np.random.seed(np.uint32(torch.initial_seed() + wid))) val_loader = DataLoader(val_dataset, batch_size=kvs['args'].val_bs, num_workers=kvs['args'].n_threads) return train_loader, val_loader def init_folds(): kvs = GlobalKVS() writers = {} cv_split_train = {} for fold_id, split in enumerate(kvs['cv_split_all_folds']): if kvs['args'].fold != -1 and fold_id != kvs['args'].fold: continue kvs.update(f'losses_fold_[{fold_id}]', None, list) kvs.update(f'val_metrics_fold_[{fold_id}]', None, list) cv_split_train[fold_id] = split writers[fold_id] = SummaryWriter(os.path.join(kvs['args'].logs, kvs['args'].dataset_name, 'fold_{}'.format(fold_id), kvs['snapshot_name'])) kvs.update('cv_split_train', cv_split_train) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl')) return writers def save_checkpoint(model, val_metric_name, comparator='lt'): # lt, less than if isinstance(model, torch.nn.DataParallel): model = model.module kvs = GlobalKVS() fold_id = kvs['cur_fold'] epoch = kvs['cur_epoch'] val_metric = kvs[f'val_metrics_fold_[{fold_id}]'][-1][0][val_metric_name] comparator = getattr(operator, comparator) cur_snapshot_name = os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], f'fold_{fold_id}_epoch_{epoch+1}.pth') if kvs['prev_model'] is None: print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name) torch.save(model.state_dict(), cur_snapshot_name) kvs.update('prev_model', cur_snapshot_name) kvs.update('best_val_metric', val_metric) else: if comparator(val_metric, kvs['best_val_metric']): print(colored('====> ', 'red') + 'Snapshot was saved to', cur_snapshot_name) os.remove(kvs['prev_model']) torch.save(model.state_dict(), cur_snapshot_name) kvs.update('prev_model', cur_snapshot_name) kvs.update('best_val_metric', val_metric) kvs.save_pkl(os.path.join(kvs['args'].snapshots, kvs['args'].dataset_name, kvs['snapshot_name'], 'session.pkl'))

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OneTrack/MAIN/UI/Widget/__init__.py

aragubas/OneTrack

0

126759

<reponame>aragubas/OneTrack #!/usr/bin/python3.7 # Copyright 2020 Aragubas # # 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 pygame import Library.CoreUtils as Utils import Library.CorePrimitives as Shape from OneTrack import MAIN as Main from OneTrack.MAIN import UI class Widget_Controller: def __init__(self, Rectangle): self.Rectangle = Utils.Convert.List_PygameRect(Rectangle) self.WidgetCollection = list() self.LastInteractionID = -1 self.LastInteractionType = None self.Active = False self.ClickOffset = (0, 0) def Clear(self): self.WidgetCollection.clear() def Draw(self, DISPLAY): for widget in self.WidgetCollection: widget.Render(DISPLAY) if not self.LastInteractionID == -1: try: self.WidgetCollection[self.LastInteractionID].InteractionType = None except IndexError: print("Can't set property to unexistent widget.") def Append(self, Widget): self.WidgetCollection.append(Widget) for widget in self.WidgetCollection: widget.Update() def Update(self): self.LastInteractionID = -1 self.LastInteractionType = None self.Active = pygame.Rect(self.Rectangle[0] + self.ClickOffset[0], self.Rectangle[1] + self.ClickOffset[1], self.Rectangle[2], self.Rectangle[3]).collidepoint((pygame.mouse.get_pos()[0], pygame.mouse.get_pos()[1])) if not self.Active: for widget in self.WidgetCollection: widget.Active = False return for widget in self.WidgetCollection: widget.Update() if not widget.InteractionType is None: self.LastInteractionID = widget.ID self.LastInteractionType = widget.InteractionType def EventUpdate(self, event): for widget in self.WidgetCollection: if widget.AwaysUpdate: # -- If always update, update it no matter what if widget.EventUpdateable: widget.EventUpdate(event) continue else: # -- If not, only update when mouse is hovering it ColideRect = pygame.Rect(self.ClickOffset[0] + self.Rectangle[0] + widget.Rectangle[0], self.ClickOffset[1] + self.Rectangle[1] + widget.Rectangle[1], widget.Rectangle[2], widget.Rectangle[3]) if ColideRect.collidepoint(pygame.mouse.get_pos()): if widget.EventUpdateable: widget.CursorOffset = (ColideRect[0], ColideRect[1]) widget.EventUpdate(event) widget.Active = True else: widget.Active = False def GetWidget(self, WidgetID): for widget in self.WidgetCollection: if widget.ID == WidgetID: return widget class Widget_PictureBox: def __init__(self, Rectangle, ImageName, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.Rectangle = Utils.Convert.List_PygameRect(Rectangle) self.ImageName = ImageName self.ID = WidgetID self.InteractionType = None self.Active = False self.EventUpdateable = False self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): UI.ContentManager.ImageRender(DISPLAY, self.ImageName, self.Rectangle[0], self.Rectangle[1], self.Rectangle[2], self.Rectangle[3]) def Update(self): pass def EventUpdate(self, event): pass class Widget_ValueChanger: def __init__(self, Position, TitleName, ChangerInitialValue, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.Rectangle = Utils.Convert.List_PygameRect((Position[0], Position[1], 48, 34)) self.TitleName = TitleName self.ID = WidgetID self.Changer = UI.EditableNumberView(pygame.Rect(self.Rectangle[0], self.Rectangle[1] + 17, self.Rectangle[2], self.Rectangle[3] - 17), ChangerInitialValue) self.LastValue = ChangerInitialValue self.InteractionType = None self.Active = False self.EventUpdateable = True self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): # -- Render Background -- # BGColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") LineColor = UI.ThemesManager_GetProperty("Button_Active_IndicatorColor") if not self.Active: LineColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") Shape.Shape_Rectangle(DISPLAY, BGColor, self.Rectangle) Shape.Shape_Rectangle(DISPLAY, LineColor, self.Rectangle, 1) # -- Render Change Title -- # TitleX = self.Rectangle[0] + self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) / 2 UI.ContentManager.FontRender(DISPLAY, "/Ubuntu_Bold.ttf", 12, self.TitleName, (230, 230, 230), TitleX, self.Rectangle[1]) # -- Render EditableNumberView -- # self.Changer.Render(DISPLAY) def Update(self): self.Changer.Update() if not self.Changer.Value == self.LastValue: self.LastValue = self.Changer.Value self.InteractionType = self.Changer.Value if UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) > self.Rectangle[2]: self.Rectangle[2] = self.Rectangle[2] + UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) + 5 if UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) > self.Rectangle[2]: self.Rectangle[2] = self.Rectangle[2] + UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", 12, self.TitleName) self.Changer.Rectangle[0] = self.Rectangle[0] + self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, self.Changer.Value) / 2 def EventUpdate(self, event): self.Changer.EventUpdate(event) class Widget_Label: def __init__(self, FontName, Text, FontSize, Color, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = False self.EventUpdateable = False self.Text = Text self.FontSize = FontSize self.FontName = FontName self.Color = Color self.X = X self.Y = Y self.Rectangle = Utils.Convert.List_PygameRect((X, Y, UI.ContentManager.GetFont_width(self.FontName, FontSize, self.Text), UI.ContentManager.GetFont_height(self.FontName, FontSize, self.Text))) self.AwaysUpdate = False self.CursorOffset = (0, 0) def Render(self, DISPLAY): UI.ContentManager.FontRender(DISPLAY, self.FontName, self.FontSize, self.Text, self.Color, self.Rectangle[0], self.Rectangle[1]) def Update(self): self.Rectangle = Utils.Convert.List_PygameRect((self.X, self.Y, UI.ContentManager.GetFont_width(self.FontName, self.FontSize, self.Text), UI.ContentManager.GetFont_height(self.FontName, self.FontSize, self.Text))) def EventUpdate(self, event): pass class Widget_PianoKeys: def __init__(self, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = True self.EventUpdateable = True self.X = X self.Y = Y self.Rectangle = Utils.Convert.List_PygameRect((X, Y, 380, 45)) self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) self.LastRect = pygame.Rect(0, 0, 0, 0) self.LastNote = -1 self.AwaysUpdate = True self.CursorOffset = (0, 0) pygame.key.set_repeat(0, 0) def Render(self, DISPLAY): if not self.LastRect == self.Rectangle: self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) # -- Render Background -- # self.Surface.fill((190, 190, 190)) Shape.Shape_Rectangle(self.Surface, (100, 100, 100), (0, 0, self.Rectangle[2], self.Rectangle[3]), 5) for i in range(12): NoteLabel = self.GetNote_ByIndex(i) # -- Variables -- # Width = 30 Height = 25 X = i * (Width + 2) Y = self.Rectangle[3] - Height BackgroundColor = (100, 105, 155) TextColor = (0, 5, 100) IsHighNote = False if "#" in NoteLabel: Width = 30 X = i * (Width + 2) Width = 35 X -= 2 Y = 0 BackgroundColor = (10, 15, 25) TextColor = (200, 205, 255) IsHighNote = True TextX = X + (Width / 2 - UI.ContentManager.GetFont_width("/PressStart2P.ttf", 12, NoteLabel) / 2) if self.LastNote == i: BackgroundColor = (200, 205, 255) TextColor = (0, 0, 0) if not IsHighNote: Shape.Shape_Rectangle(self.Surface, BackgroundColor, (X, Y, Width, Height), 0, 0, 5, 5) else: Shape.Shape_Rectangle(self.Surface, BackgroundColor, (X, Y, Width, Height), 0, 0, 0, 0, 5, 5) UI.ContentManager.FontRender(self.Surface, "/PressStart2P.ttf", 12, NoteLabel, TextColor, TextX, Y + 5) DISPLAY.blit(self.Surface, (self.Rectangle[0], self.Rectangle[1])) def GetNote_ByIndex(self, i): if i == 0: return "C" elif i == 1: return "C#" elif i == 2: return "D" elif i == 3: return "D#" elif i == 4: return "E" elif i == 5: return "F" elif i == 6: return "F#" elif i == 7: return "G" elif i == 8: return "G#" elif i == 9: return "A" elif i == 10: return "A#" elif i == 11: return "B" def Update(self): pass def EventUpdate(self, event): if event.type == pygame.KEYUP: self.LastNote = -1 if event.type == pygame.KEYDOWN: # -- Note C -- # if event.key == pygame.K_z: self.LastNote = 0 # -- Note C# -- # if event.key == pygame.K_s: self.LastNote = 1 # -- Note D -- # if event.key == pygame.K_x: self.LastNote = 2 # -- Note D# -- # if event.key == pygame.K_d: self.LastNote = 3 # -- Note E -- # if event.key == pygame.K_c: self.LastNote = 4 # -- Note F -- # if event.key == pygame.K_v: self.LastNote = 5 # -- Note F# -- # if event.key == pygame.K_g: self.LastNote = 6 # -- Note G -- # if event.key == pygame.K_b: self.LastNote = 7 # -- Note G# -- # if event.key == pygame.K_h: self.LastNote = 8 # -- Note A -- # if event.key == pygame.K_n: self.LastNote = 9 # -- Note A# -- # if event.key == pygame.K_j: self.LastNote = 10 # -- Note B -- # if event.key == pygame.K_m: self.LastNote = 11 class Widget_Button: def __init__(self, Text, FontSize, X, Y, WidgetID): if WidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = WidgetID self.InteractionType = None self.Active = True self.EventUpdateable = True self.AwaysUpdate = False self.X = X self.Y = Y self.Text = Text self.FontSize = FontSize self.TextWidth = UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.TextHeight = UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.Rectangle = Utils.Convert.List_PygameRect((X - 2, Y - 2, self.TextWidth + 4, self.TextHeight + 4)) self.LastRect = self.Rectangle self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) self.Centred_X = self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.Centred_Y = self.Rectangle[3] / 2 - UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.ButtonState = 0 self.CursorOffset = (0, 0) self.BgColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") def Render(self, DISPLAY): # -- Render Background -- # Shape.Shape_Rectangle(self.Surface, self.BgColor, (0, 0, self.Rectangle[2], self.Rectangle[3])) # -- Render Indicator -- # Shape.Shape_Rectangle(self.Surface, self.IndicatorColor, (0, 0, self.Rectangle[2], self.Rectangle[3]), 1) # -- Render the Button Text -- # UI.ContentManager.FontRender(self.Surface, "/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text, (240, 240, 240), self.Centred_X, self.Centred_Y) DISPLAY.blit(self.Surface, (self.Rectangle[0], self.Rectangle[1])) if self.ButtonState == 2: self.ButtonState = 0 def Update(self): # -- Check if surface has the correct size -- # if not self.LastRect == self.Rectangle: self.Surface = pygame.Surface((self.Rectangle[2], self.Rectangle[3])) # -- Update all Size and Position Variables -- # self.TextWidth = UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.TextHeight = UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize, self.Text) self.Rectangle = Utils.Convert.List_PygameRect((self.Rectangle[0] - 2, self.Rectangle[1] - 2, self.TextWidth + 4, self.TextHeight + 4)) self.Centred_X = self.Rectangle[2] / 2 - UI.ContentManager.GetFont_width("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.Centred_Y = self.Rectangle[3] / 2 - UI.ContentManager.GetFont_height("/Ubuntu_Bold.ttf", self.FontSize - 2, self.Text) / 2 self.LastRect = self.Rectangle self.BgColor = UI.ThemesManager_GetProperty("Button_BackgroundColor") self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") if not self.Active: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") return if self.ButtonState == 0: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Inactive_IndicatorColor") elif self.ButtonState == 1: self.IndicatorColor = UI.ThemesManager_GetProperty("Button_Active_IndicatorColor") def EventUpdate(self, event): if event.type == pygame.MOUSEBUTTONDOWN: self.ButtonState = 1 if event.type == pygame.MOUSEBUTTONUP: self.ButtonState = 0 self.InteractionType = True class Widget_Textbox: def __init__(self, pFontName, pDefaultText, pFontSize, pColor, pX, pY, pWidgetID): if pWidgetID == -1: raise ValueError("WidgetID cannot be -1") self.ID = pWidgetID self.InteractionType = None self.Active = False self.EventUpdateable = True self.DefaultText = pDefaultText self.Text = self.DefaultText self.FontSize = pFontSize self.FontName = pFontName self.Color = pColor self.X = pX self.Y = pY self.Width = UI.ContentManager.GetFont_width(pFontName, pFontSize, pDefaultText) self.Height = UI.ContentManager.GetFont_height(pFontName, pFontSize, "ABC") self.AwaysUpdate = False self.CursorOffset = (0, 0) self.Rectangle = pygame.Rect(self.X, self.Y, self.Width, self.Height) self.LastHeight = 1 self.CustomWidth = False self.CharacterLimit = 0 def Set_X(self, Value): if not self.Rectangle[0] == Value: self.Rectangle = pygame.Rect(Value, self.Rectangle[1], self.Rectangle[2], self.Rectangle[3]) def Set_Y(self, Value): if not self.Rectangle[1] == Value: self.Rectangle = pygame.Rect(self.Rectangle[0], Value, self.Rectangle[2], self.Rectangle[3]) def Render(self, DISPLAY): # Blit the rect. Shape.Shape_Rectangle(DISPLAY, (15, 15, 15), self.Rectangle) if self.Text == self.DefaultText: UI.ContentManager.FontRender(DISPLAY, UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text, (140, 140, 140), self.Rectangle[0], self.Rectangle[1]) else: if not self.Text == "": UI.ContentManager.FontRender(DISPLAY, UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text, (240, 240, 240), self.Rectangle[0], self.Rectangle[1]) if not self.Active: Shape.Shape_Rectangle(DISPLAY, (255, 51, 102), (self.Rectangle[0], self.Rectangle[1] - 1, self.Rectangle[2], 1)) else: Shape.Shape_Rectangle(DISPLAY, (46, 196, 182), (self.Rectangle[0], self.Rectangle[1] - 1, self.Rectangle[2], 1)) def Update(self): # -- Resize the Textbox -- # try: if not self.CustomWidth: self.Width = max(100, UI.ContentManager.GetFont_width(UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text) + 10) self.Rectangle[2] = self.Width self.Rectangle[3] = UI.ContentManager.GetFont_height(UI.ThemesManager_GetProperty("InputBox_FontFile"), self.FontSize, self.Text) self.LastHeight = self.Rectangle[3] except: if not self.CustomWidth: self.Rectangle[2] = 100 self.Rectangle[3] = self.LastHeight if self.Text == "": self.Rectangle = pygame.Rect(self.Rectangle[0], self.Rectangle[1], self.Rectangle[2], self.Height) def EventUpdate(self, event): if event.type == pygame.KEYDOWN: if event.key == pygame.K_BACKSPACE: if len(self.Text) > 0: self.Text = self.Text[:-1] else: self.Text = self.DefaultText else: if not self.CharacterLimit == 0: if len(self.Text) < self.CharacterLimit: self.Text += event.unicode else: self.Text += event.unicode self.InteractionType = self.Text

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model/model.py

shubhMaheshwari/NeuralTracking

1

158319

<filename>model/model.py<gh_stars>1-10 import sys,os import torch import torch.nn as nn import numpy as np from timeit import default_timer as timer import math import kornia from timeit import default_timer as timer from decimal import Decimal import options as opt import utils.query as query from utils.nnutils import make_conv_2d, make_upscale_2d, make_downscale_2d, ResBlock2d, Identity from model import pwcnet from NeuralNRT._C import compute_pixel_anchors_geodesic as compute_pixel_anchors_geodesic_c from NeuralNRT._C import compute_pixel_anchors_euclidean as compute_pixel_anchors_euclidean_c from NeuralNRT._C import compute_mesh_from_depth as compute_mesh_from_depth_c from NeuralNRT._C import erode_mesh as erode_mesh_c from NeuralNRT._C import sample_nodes as sample_nodes_c from NeuralNRT._C import compute_edges_geodesic as compute_edges_geodesic_c from NeuralNRT._C import compute_edges_euclidean as compute_edges_euclidean_c class MaskNet(torch.nn.Module): def __init__(self): super().__init__() fn_0 = 16 self.input_fn = fn_0 + 6 * 2 fn_1 = 16 self.upconv1 = torch.nn.ConvTranspose2d(in_channels=565, out_channels=2*fn_0, kernel_size=4, stride=2, padding=1) self.upconv2 = torch.nn.ConvTranspose2d(in_channels=2*fn_0, out_channels=fn_0, kernel_size=4, stride=2, padding=1) if opt.use_batch_norm: custom_batch_norm = torch.nn.BatchNorm2d else: custom_batch_norm = Identity self.model = nn.Sequential( make_conv_2d(self.input_fn, fn_1, n_blocks=1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), ResBlock2d(fn_1, normalization=custom_batch_norm), nn.Conv2d(fn_1, 1, kernel_size=3, padding=1), torch.nn.Sigmoid() ) def forward(self, features, x): # Reduce number of channels and upscale to highest resolution features = self.upconv1(features) features = self.upconv2(features) x = torch.cat([features, x], 1) assert x.shape[1] == self.input_fn return self.model(x) class LinearSolverLU(torch.autograd.Function): """ LU linear solver. """ @staticmethod def forward(ctx, A, b): A_LU, pivots = torch.lu(A) x = torch.lu_solve(b, A_LU, pivots) ctx.save_for_backward(A_LU, pivots, x) return x @staticmethod def backward(ctx, grad_x): A_LU, pivots, x = ctx.saved_tensors # Math: # A * grad_b = grad_x # grad_A = -grad_b * x^T grad_b = torch.lu_solve(grad_x, A_LU, pivots) grad_A = -torch.matmul(grad_b, x.view(1, -1)) return grad_A, grad_b class DeformNet(torch.nn.Module): def __init__(self): super().__init__() self.gn_num_iter = 10 self.gn_data_flow = 0.001 self.gn_data_depth = 1.0 self.gn_arap = 1.0 self.gn_lm_factor = 0.1 # Optimizer fails for > 3 iterations. Current hack is to stop update is loss increases by 1 self.stop_loss_diff = 1 # Optical flow network self.flow_net = pwcnet.PWCNet() if opt.freeze_optical_flow_net: # Freeze for param in self.flow_net.parameters(): param.requires_grad = False # Weight prediction network self.mask_net = MaskNet() if opt.freeze_mask_net: # Freeze for param in self.mask_net.parameters(): param.requires_grad = False vec_to_skew_mat_np = np.array([ [0, 0, 0], [0, 0, -1], [0, 1, 0], [0, 0, 1], [0, 0, 0], [-1, 0, 0], [0, -1, 0], [1, 0, 0], [0, 0, 0] ], dtype=np.float32) self.vec_to_skew_mat = torch.from_numpy(vec_to_skew_mat_np).to('cuda') def forward( self, x1, x2, graph_nodes, graph_edges, graph_edges_weights, graph_clusters, pixel_anchors, pixel_weights, num_nodes_vec, intrinsics, evaluate=False, split="train", prev_rot=None,prev_trans=None ): batch_size = x1.shape[0] image_width = x1.shape[3] image_height = x1.shape[2] convergence_info = [] for i in range(batch_size): convergence_info.append({ "total": [], "arap": [], "data": [], "condition_numbers": [], "valid": 0, "errors": [] }) ######################################################################## # Compute dense flow from source to target. ######################################################################## flow2, flow3, flow4, flow5, flow6, features2 = self.flow_net.forward(x1[:,:3,:,:], x2[:,:3,:,:]) assert torch.isfinite(flow2).all() assert torch.isfinite(features2).all() flow = 20.0 * torch.nn.functional.interpolate(input=flow2, size=(image_height, image_width), mode='bilinear', align_corners=False) ######################################################################## # Initialize graph data. ######################################################################## num_nodes_total = graph_nodes.shape[1] num_neighbors = graph_edges.shape[2] # We assume we always use 4 nearest anchors. assert pixel_anchors.shape[3] == 4 ######################################################################## # Apply dense flow to warp the source points to target frame. ######################################################################## x_coords = torch.arange(image_width, dtype=torch.float32, device=x1.device).unsqueeze(0).expand(image_height, image_width).unsqueeze(0) y_coords = torch.arange(image_height, dtype=torch.float32, device=x1.device).unsqueeze(1).expand(image_height, image_width).unsqueeze(0) xy_coords = torch.cat([x_coords, y_coords], 0) xy_coords = xy_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1) # (bs, 2, 448, 640) # Apply the flow to pixel coordinates. xy_coords_warped = xy_coords + flow xy_pixels_warped = xy_coords_warped.clone() # Normalize to be between -1, and 1. # Since we use "align_corners=False", the boundaries of corner pixels # are -1 and 1, not their centers. xy_coords_warped[:,0,:,:] = (xy_coords_warped[:,0,:,:]) / (image_width - 1) xy_coords_warped[:,1,:,:] = (xy_coords_warped[:,1,:,:]) / (image_height - 1) xy_coords_warped = xy_coords_warped * 2 - 1 # Permute the warped coordinates to fit the grid_sample format. xy_coords_warped = xy_coords_warped.permute(0, 2, 3, 1) ######################################################################## # Construct point-to-point correspondences between source <-> target points. ######################################################################## # Mask out invalid source points. source_points = x1[:, 3:, :, :].clone() source_anchor_validity = torch.all(pixel_anchors >= 0.0, dim=3) # Sample target points at computed pixel locations. target_points = x2[:, 3:, :, :].clone() target_matches = torch.nn.functional.grid_sample( target_points, xy_coords_warped, mode=opt.gn_depth_sampling_mode, padding_mode='zeros', align_corners=False ) # We filter out any boundary matches, where any of the 4 pixels is invalid. target_validity = ((target_points > 0.0) & (target_points <= opt.gn_max_depth)).type(torch.float32) target_matches_validity = torch.nn.functional.grid_sample( target_validity, xy_coords_warped, mode="bilinear", padding_mode='zeros', align_corners=False ) target_matches_validity = target_matches_validity[:, 2, :, :] >= 0.999 # Prepare masks for both valid source points and target matches valid_source_points = (source_points[:, 2, :, :] > 0.0) & (source_points[:, 2, :, :] <= opt.gn_max_depth) & source_anchor_validity valid_target_matches = (target_matches[:, 2, :, :] > 0.0) & (target_matches[:, 2, :, :] <= opt.gn_max_depth) & target_matches_validity # Prepare the input of both the MaskNet and AttentionNet, if we actually use either of them if opt.use_mask: target_rgb = x2[:, :3, :, :].clone() target_rgb_warped = torch.nn.functional.grid_sample(target_rgb, xy_coords_warped, padding_mode='zeros', align_corners=False) mask_input = torch.cat([x1, target_rgb_warped, target_matches], 1) ######################################################################## # MaskNet ######################################################################## # We predict correspondence weights [0, 1], if we use mask network. mask_pred = None if opt.use_mask: mask_pred = self.mask_net(features2, mask_input).view(batch_size, image_height, image_width) # mask_pred[:,:,:] = 1.0 # print(mask_pred) # Compute mask of valid correspondences valid_correspondences = valid_source_points & valid_target_matches # Initialize correspondence weights with 1's. We might overwrite them with MaskNet-predicted weights next correspondence_weights = torch.ones(valid_target_matches.shape, dtype=torch.float32, device=valid_target_matches.device) # We invalidate target matches later, when we assign a weight to each match. if opt.use_mask: correspondence_weights = mask_pred if evaluate: # Hard threshold if opt.threshold_mask_predictions: correspondence_weights = torch.where(mask_pred < opt.threshold, torch.zeros_like(mask_pred), mask_pred) # Patch-wise threshold elif opt.patchwise_threshold_mask_predictions: pooled = torch.nn.functional.max_pool2d(input=mask_pred, kernel_size=opt.patch_size, stride=opt.patch_size) pooled = torch.nn.functional.interpolate(input=pooled.unsqueeze(1), size=(opt.image_height, opt.image_width), mode='nearest').squeeze(1) selected = (torch.abs(mask_pred - pooled) <= 1e-8).type(torch.float32) correspondence_weights = mask_pred * selected #* opt.patch_size**2 num_valid_correspondences = torch.sum(valid_correspondences, dim=(1, 2)) ######################################################################## # Initialize graph data. ######################################################################## num_nodes_total = graph_nodes.shape[1] num_neighbors = graph_edges.shape[2] # We assume we always use 4 nearest anchors. assert pixel_anchors.shape[3] == 4 node_rotations = torch.eye(3, dtype=x1.dtype, device=x1.device).view(1, 1, 3, 3).repeat(batch_size, num_nodes_total, 1, 1) node_translations = torch.zeros((batch_size, num_nodes_total, 3), dtype=x1.dtype, device=x1.device) deformations_validity = torch.zeros((batch_size, num_nodes_total), dtype=x1.dtype, device=x1.device) valid_solve = torch.zeros((batch_size), dtype=torch.uint8, device=x1.device) deformed_points_pred = torch.zeros((batch_size, opt.gn_max_warped_points, 3), dtype=x1.dtype, device=x1.device) deformed_points_idxs = torch.zeros((batch_size, opt.gn_max_warped_points), dtype=torch.int64, device=x1.device) deformed_points_subsampled = torch.zeros((batch_size), dtype=torch.uint8, device=x1.device) # Skip the solver if not evaluate and opt.skip_solver: return { "flow_data": [flow2, flow3, flow4, flow5, flow6], "node_rotations": node_rotations, "node_translations": node_translations, "deformations_validity": deformations_validity, "deformed_points_pred": deformed_points_pred, "valid_solve": valid_solve, "mask_pred": mask_pred, "correspondence_info": { "xy_coords_warped":xy_coords_warped, "source_points":source_points, "valid_source_points":valid_source_points, "target_matches":target_matches, "valid_target_matches":valid_target_matches, "valid_correspondences":None, "deformed_points_idxs":deformed_points_idxs, "deformed_points_subsampled": deformed_points_subsampled }, "convergence_info": convergence_info, "weight_info": { "total_corres_num": 0, "total_corres_weight": 0.0 } } ######################################################################## # Estimate node deformations using differentiable Gauss-Newton. ######################################################################## correspondences_exist = num_valid_correspondences > 0 total_num_matches_per_batch = 0 weight_info = { "total_corres_num": 0, "total_corres_weight": 0.0 } self.vec_to_skew_mat.to(x1.device) for i in range(batch_size): if opt.gn_debug: print() print("--Sample", i, "in batch--") num_nodes_i = num_nodes_vec[i] if num_nodes_i > opt.gn_max_nodes or num_nodes_i < opt.gn_min_nodes: print(f"\tSolver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") convergence_info[i]["errors"].append(f"Solver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") continue if not correspondences_exist[i]: print("\tSolver failed: No valid correspondences before filtering") convergence_info[i]["errors"].append("Solver failed: No valid correspondences before filtering") continue timer_start = timer() graph_nodes_i = graph_nodes[i, :num_nodes_i, :] # (num_nodes_i, 3) graph_edges_i = graph_edges[i, :num_nodes_i, :] # (num_nodes_i, 8) graph_edges_weights_i = graph_edges_weights[i, :num_nodes_i, :] # (num_nodes_i, 8) graph_clusters_i = graph_clusters[i, :num_nodes_i, :] # (num_nodes_i, 1) fx = intrinsics[i, 0] fy = intrinsics[i, 1] cx = intrinsics[i, 2] cy = intrinsics[i, 3] ############################################################################################################### # Filter invalid matches. ############################################################################################################### valid_correspondences_idxs = torch.where(valid_correspondences[i]) source_points_filtered = source_points[i].permute(1, 2, 0) source_points_filtered = source_points_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) target_matches_filtered = target_matches[i].permute(1, 2, 0) target_matches_filtered = target_matches_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) xy_pixels_warped_filtered = xy_pixels_warped[i].permute(1, 2, 0) # (height, width, 2) xy_pixels_warped_filtered = xy_pixels_warped_filtered[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 2, 1) correspondence_weights_filtered = correspondence_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1]].view(-1) # (num_matches) source_anchors = pixel_anchors[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) source_weights = pixel_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) num_matches = source_points_filtered.shape[0] ############################################################################################################### # Generate weight info to estimate average weight. ############################################################################################################### weight_info = { "total_corres_num": correspondence_weights_filtered.shape[0], "total_corres_weight": float(correspondence_weights_filtered.sum()) } ############################################################################################################### # Randomly subsample matches, if necessary. ############################################################################################################### if split == "val" or split == "test": max_num_matches = opt.gn_max_matches_eval elif split == "train": max_num_matches = opt.gn_max_matches_train else: raise Exception("Split {} is not defined".format(split)) if num_matches > max_num_matches: sampled_idxs = torch.randperm(num_matches)[:max_num_matches] source_points_filtered = source_points_filtered[sampled_idxs] target_matches_filtered = target_matches_filtered[sampled_idxs] xy_pixels_warped_filtered = xy_pixels_warped_filtered[sampled_idxs] correspondence_weights_filtered = correspondence_weights_filtered[sampled_idxs] source_anchors = source_anchors[sampled_idxs] source_weights = source_weights[sampled_idxs] num_matches = max_num_matches ############################################################################################################### # Remove nodes if their corresponding clusters don't have enough correspondences # (Correspondences that have "bad" nodes as anchors will also be removed) ############################################################################################################### map_opt_nodes_to_complete_nodes_i = list(range(0, num_nodes_i)) opt_num_nodes_i = num_nodes_i if opt.gn_remove_clusters_with_few_matches: source_anchors_numpy = source_anchors.clone().cpu().numpy() source_weights_numpy = source_weights.clone().cpu().numpy() # Compute number of correspondences (or matches) per node in the form of the # match weight sum match_weights_per_node = np.zeros(num_nodes_i) # This method adds weight contribution of each match to the corresponding node, # allowing duplicate node ids in the flattened array. np.add.at(match_weights_per_node, source_anchors_numpy.flatten(), source_weights_numpy.flatten()) total_match_weights = 0.0 match_weights_per_cluster = {} for node_id in range(num_nodes_i): # Get sum of weights for current node. match_weights = match_weights_per_node[node_id] # Get cluster id for current node cluster_id = graph_clusters_i[node_id].item() if cluster_id in match_weights_per_cluster: match_weights_per_cluster[cluster_id] += match_weights else: match_weights_per_cluster[cluster_id] = match_weights total_match_weights += match_weights # we'll build a mask that stores which nodes will survive valid_nodes_mask_i = torch.ones((num_nodes_i), dtype=torch.bool, device=x1.device) # if not enough matches in a cluster, mark all cluster's nodes for removal node_ids_for_removal = [] for cluster_id, cluster_match_weights in match_weights_per_cluster.items(): if opt.gn_debug: print('cluster_id', cluster_id, cluster_match_weights) if cluster_match_weights < opt.gn_min_num_correspondences_per_cluster: x = torch.where(graph_clusters_i == cluster_id)[0].tolist() node_ids_for_removal += x if opt.gn_debug: print("node_ids_for_removal", node_ids_for_removal) if len(node_ids_for_removal) > 0: # Mark invalid nodes valid_nodes_mask_i[node_ids_for_removal] = False # Kepp only nodes and edges for valid nodes graph_nodes_i = graph_nodes_i[valid_nodes_mask_i.squeeze()] graph_edges_i = graph_edges_i[valid_nodes_mask_i.squeeze()] graph_edges_weights_i = graph_edges_weights_i[valid_nodes_mask_i.squeeze()] # Update number of nodes opt_num_nodes_i = graph_nodes_i.shape[0] # Get mask of correspondences for which any one of their anchors is an invalid node valid_corresp_mask = torch.ones((num_matches), dtype=torch.bool, device=x1.device) for node_id_for_removal in node_ids_for_removal: valid_corresp_mask = valid_corresp_mask & torch.all(source_anchors != node_id_for_removal, axis=1) source_points_filtered = source_points_filtered[valid_corresp_mask] target_matches_filtered = target_matches_filtered[valid_corresp_mask] xy_pixels_warped_filtered = xy_pixels_warped_filtered[valid_corresp_mask] correspondence_weights_filtered = correspondence_weights_filtered[valid_corresp_mask] source_anchors = source_anchors[valid_corresp_mask] source_weights = source_weights[valid_corresp_mask] num_matches = source_points_filtered.shape[0] # Update node_ids in edges and anchors by mapping old indices to new indices map_opt_nodes_to_complete_nodes_i = [] node_count = 0 for node_id, is_node_valid in enumerate(valid_nodes_mask_i): if is_node_valid: graph_edges_i[graph_edges_i == node_id] = node_count source_anchors[source_anchors == node_id] = node_count map_opt_nodes_to_complete_nodes_i.append(node_id) node_count += 1 if num_matches == 0: if opt.gn_debug: print("\tSolver failed: No valid correspondences") convergence_info[i]["errors"].append("Solver failed: No valid correspondences after filtering") continue if opt_num_nodes_i > opt.gn_max_nodes or opt_num_nodes_i < opt.gn_min_nodes: if opt.gn_debug: print(f"\tSolver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") convergence_info[i]["errors"].append(f"Solver failed: Invalid number of nodes: {num_nodes_i}. Allowed Range=> {opt.gn_min_nodes}:{opt.gn_max_nodes}. Change node coverage parameter during graph creation.") continue # Since source anchor ids need to be long in order to be used as indices, # we need to convert them. assert torch.all(source_anchors >= 0) source_anchors = source_anchors.type(torch.int64) ############################################################################################################### # Filter invalid edges. ############################################################################################################### node_ids = torch.arange(opt_num_nodes_i, dtype=torch.int32, device=x1.device).view(-1, 1).repeat(1, num_neighbors) # (opt_num_nodes_i, num_neighbors) graph_edge_pairs = torch.cat([node_ids.view(-1, num_neighbors, 1), graph_edges_i.view(-1, num_neighbors, 1)], 2) # (opt_num_nodes_i, num_neighbors, 2) valid_edges = graph_edges_i >= 0 valid_edge_idxs = torch.where(valid_edges) graph_edge_pairs_filtered = graph_edge_pairs[valid_edge_idxs[0], valid_edge_idxs[1], :].type(torch.int64) graph_edge_weights_pairs = graph_edges_weights_i[valid_edge_idxs[0], valid_edge_idxs[1]] num_edges_i = graph_edge_pairs_filtered.shape[0] ############################################################################################################### # Execute Gauss-Newton solver. ############################################################################################################### num_gn_iter = self.gn_num_iter lambda_data_flow = math.sqrt(self.gn_data_flow) lambda_data_depth = math.sqrt(self.gn_data_depth) lambda_arap = math.sqrt(self.gn_arap) lm_factor = self.gn_lm_factor # The parameters in GN solver are 3 parameters for rotation and 3 parameters for # translation for every node. All node rotation parameters are listed first, and # then all node translation parameters are listed. # x = [w_current_all, t_current_all] if prev_rot is None and prev_trans is None: R_current = torch.eye(3, dtype=x1.dtype, device=x1.device).view(1, 3, 3).repeat(opt_num_nodes_i, 1, 1) t_current = torch.zeros((opt_num_nodes_i, 3, 1), dtype=x1.dtype, device=x1.device) else: R_current = torch.tensor(prev_rot[map_opt_nodes_to_complete_nodes_i, :, :],dtype=x1.dtype,device=x1.device).view(opt_num_nodes_i, 3, 3) t_current = torch.tensor(prev_trans[map_opt_nodes_to_complete_nodes_i, :],dtype=x1.dtype,device=x1.device).view(opt_num_nodes_i, 3, 1) if opt.gn_debug: print("\tNum. matches: {0} || Num. nodes: {1} || Num. edges: {2}".format(num_matches, opt_num_nodes_i, num_edges_i)) # Helper structures. data_increment_vec_0_3 = torch.arange(0, num_matches * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) data_increment_vec_1_3 = torch.arange(1, num_matches * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) data_increment_vec_2_3 = torch.arange(2, num_matches * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_matches) if num_edges_i > 0: arap_increment_vec_0_3 = torch.arange(0, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_increment_vec_1_3 = torch.arange(1, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_increment_vec_2_3 = torch.arange(2, num_edges_i * 3, 3, out=torch.cuda.LongTensor(), device=x1.device) # (num_edges_i) arap_one_vec = torch.ones((num_edges_i), dtype=x1.dtype, device=x1.device) ill_posed_system = False for gn_i in range(num_gn_iter): if gn_i % 3 == 2: lm_factor /= 2; timer_data_start = timer() ########################################## # Compute data residual and jacobian. ########################################## jacobian_data = torch.zeros((num_matches * 3, opt_num_nodes_i * 6), dtype=x1.dtype, device=x1.device) # (num_matches*3, opt_num_nodes_i*6) deformed_points = torch.zeros((num_matches, 3, 1), dtype=x1.dtype, device=x1.device) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors[:, k] # (num_matches) nodes_k = graph_nodes_i[node_idxs_k].view(num_matches, 3, 1) # (num_matches, 3, 1) # Compute deformed point contribution. rotated_points_k = torch.matmul(R_current[node_idxs_k], source_points_filtered - nodes_k) # (num_matches, 3, 1) = (num_matches, 3, 3) * (num_matches, 3, 1) deformed_points_k = rotated_points_k + nodes_k + t_current[node_idxs_k] deformed_points += source_weights[:, k].view(num_matches, 1, 1).repeat(1, 3, 1) * deformed_points_k # (num_matches, 3, 1) # Get necessary components of deformed points. eps = 1e-7 # Just as good practice, although matches should all have valid depth at this stage deformed_x = deformed_points[:, 0, :].view(num_matches) # (num_matches) deformed_y = deformed_points[:, 1, :].view(num_matches) # (num_matches) deformed_z_inverse = torch.div(1.0, deformed_points[:, 2, :].view(num_matches) + eps) # (num_matches) fx_mul_x = fx * deformed_x # (num_matches) fy_mul_y = fy * deformed_y # (num_matches) fx_div_z = fx * deformed_z_inverse # (num_matches) fy_div_z = fy * deformed_z_inverse # (num_matches) fx_mul_x_div_z = fx_mul_x * deformed_z_inverse # (num_matches) fy_mul_y_div_z = fy_mul_y * deformed_z_inverse # (num_matches) minus_fx_mul_x_div_z_2 = -fx_mul_x_div_z * deformed_z_inverse # (num_matches) minus_fy_mul_y_div_z_2 = -fy_mul_y_div_z * deformed_z_inverse # (num_matches) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors[:, k] # (num_matches) nodes_k = graph_nodes_i[node_idxs_k].view(num_matches, 3, 1) # (num_matches, 3, 1) weights_k = source_weights[:, k] * correspondence_weights_filtered # (num_matches) # Compute skew symetric part. rotated_points_k = torch.matmul(R_current[node_idxs_k], source_points_filtered - nodes_k) # (num_matches, 3, 1) = (num_matches, 3, 3) * (num_matches, 3, 1) weighted_rotated_points_k = weights_k.view(num_matches, 1, 1).repeat(1, 3, 1) * rotated_points_k # (num_matches, 3, 1) skew_symetric_mat_data = -torch.matmul(self.vec_to_skew_mat, weighted_rotated_points_k).view(num_matches, 3, 3) # (num_matches, 3, 3) # Compute jacobian wrt. TRANSLATION. # FLOW PART jacobian_data[data_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 0] += lambda_data_flow * weights_k * fx_div_z # (num_matches) jacobian_data[data_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_flow * weights_k * minus_fx_mul_x_div_z_2 # (num_matches) jacobian_data[data_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 1] += lambda_data_flow * weights_k * fy_div_z # (num_matches) jacobian_data[data_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_flow * weights_k * minus_fy_mul_y_div_z_2 # (num_matches) # DEPTH PART jacobian_data[data_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_k + 2] += lambda_data_depth * weights_k # (num_matches) # Compute jacobian wrt. ROTATION. # FLOW PART jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 0] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 0] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 1] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 1] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_0_3, 3 * node_idxs_k + 2] += lambda_data_flow * fx_div_z * skew_symetric_mat_data[:, 0, 2] + minus_fx_mul_x_div_z_2 * skew_symetric_mat_data[:, 2, 2] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 0] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 0] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 1] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 1] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_1_3, 3 * node_idxs_k + 2] += lambda_data_flow * fy_div_z * skew_symetric_mat_data[:, 1, 2] + minus_fy_mul_y_div_z_2 * skew_symetric_mat_data[:, 2, 2] # DEPTH PART jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 0] += lambda_data_depth * skew_symetric_mat_data[:, 2, 0] jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 1] += lambda_data_depth * skew_symetric_mat_data[:, 2, 1] jacobian_data[data_increment_vec_2_3, 3 * node_idxs_k + 2] += lambda_data_depth * skew_symetric_mat_data[:, 2, 2] assert torch.isfinite(jacobian_data).all(), jacobian_data res_data = torch.zeros((num_matches * 3, 1), dtype=x1.dtype, device=x1.device) # FLOW PART res_data[data_increment_vec_0_3, 0] = lambda_data_flow * correspondence_weights_filtered * (fx_mul_x_div_z + cx - xy_pixels_warped_filtered[:, 0, :].view(num_matches)) res_data[data_increment_vec_1_3, 0] = lambda_data_flow * correspondence_weights_filtered * (fy_mul_y_div_z + cy - xy_pixels_warped_filtered[:, 1, :].view(num_matches)) # DEPTH PART res_data[data_increment_vec_2_3, 0] = lambda_data_depth * correspondence_weights_filtered * (deformed_points[:, 2, :] - target_matches_filtered[:, 2, :]).view(num_matches) if opt.gn_print_timings: print("\t\tData term: {:.3f} s".format(timer() - timer_data_start)) timer_arap_start = timer() ########################################## # Compute arap residual and jacobian. ########################################## if num_edges_i > 0: jacobian_arap = torch.zeros((num_edges_i * 3, opt_num_nodes_i * 6), dtype=x1.dtype, device=x1.device) # (num_edges_i*3, opt_num_nodes_i*6) node_idxs_0 = graph_edge_pairs_filtered[:, 0] # i node node_idxs_1 = graph_edge_pairs_filtered[:, 1] # j node w = torch.ones_like(graph_edge_weights_pairs) if opt.gn_use_edge_weighting: # Since graph edge weights sum up to 1 for all neighbors, we multiply # it by the number of neighbors to make the setting in the same scale # as in the case of not using edge weights (they are all 1 then). w = float(num_neighbors) * graph_edge_weights_pairs w_repeat = w.unsqueeze(-1).repeat(1, 3).unsqueeze(-1) w_repeat_repeat = w_repeat.repeat(1, 1, 3) nodes_0 = graph_nodes_i[node_idxs_0].view(num_edges_i, 3, 1) nodes_1 = graph_nodes_i[node_idxs_1].view(num_edges_i, 3, 1) # Compute residual. rotated_node_delta = torch.matmul(R_current[node_idxs_0], nodes_1 - nodes_0) # (num_edges_i, 3) res_arap = lambda_arap * w_repeat * (rotated_node_delta + nodes_0 + t_current[node_idxs_0] - (nodes_1 + t_current[node_idxs_1])) res_arap = res_arap.view(num_edges_i * 3, 1) # Compute jacobian wrt. translations. jacobian_arap[arap_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 0] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 1] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_0 + 2] += lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_0_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 0] += -lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_1_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 1] += -lambda_arap * w * arap_one_vec # (num_edges_i) jacobian_arap[arap_increment_vec_2_3, 3 * opt_num_nodes_i + 3 * node_idxs_1 + 2] += -lambda_arap * w * arap_one_vec # (num_edges_i) # Compute jacobian wrt. rotations. # Derivative wrt. R_1 is equal to 0. skew_symetric_mat_arap = -lambda_arap * w_repeat_repeat * torch.matmul(self.vec_to_skew_mat, rotated_node_delta).view(num_edges_i, 3, 3) # (num_edges_i, 3, 3) jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 0, 0] jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 0, 1] jacobian_arap[arap_increment_vec_0_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 0, 2] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 1, 0] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 1, 1] jacobian_arap[arap_increment_vec_1_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 1, 2] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 0] += skew_symetric_mat_arap[:, 2, 0] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 1] += skew_symetric_mat_arap[:, 2, 1] jacobian_arap[arap_increment_vec_2_3, 3 * node_idxs_0 + 2] += skew_symetric_mat_arap[:, 2, 2] assert torch.isfinite(jacobian_arap).all(), jacobian_arap if opt.gn_print_timings: print("\t\tARAP term: {:.3f} s".format(timer() - timer_arap_start)) ########################################## # Solve linear system. ########################################## if num_edges_i > 0: res = torch.cat((res_data, res_arap), 0) jac = torch.cat((jacobian_data, jacobian_arap), 0) else: res = res_data jac = jacobian_data timer_system_start = timer() # Compute A = J^TJ and b = -J^Tr. jac_t = torch.transpose(jac, 0, 1) A = torch.matmul(jac_t, jac) b = torch.matmul(-jac_t, res) # Solve linear system Ax = b. A = A + torch.eye(A.shape[0], dtype=A.dtype, device=A.device) * lm_factor assert torch.isfinite(A).all(), A if opt.gn_print_timings: print("\t\tSystem computation: {:.3f} s".format(timer() - timer_system_start)) timer_cond_start = timer() # Check the determinant/condition number. # If unstable, we break optimization. if opt.gn_check_condition_num: with torch.no_grad(): # Condition number. values, _ = torch.eig(A) real_values = values[:, 0] assert torch.isfinite(real_values).all(), real_values max_eig_value = torch.max(torch.abs(real_values)) min_eig_value = torch.min(torch.abs(real_values)) condition_number = max_eig_value / min_eig_value condition_number = condition_number.item() convergence_info[i]["condition_numbers"].append(condition_number) if opt.gn_break_on_condition_num and (not math.isfinite(condition_number) or condition_number > opt.gn_max_condition_num): print("\t\tToo high condition number: {0:e} (max: {1:.3f}, min: {2:.3f}). Discarding sample".format(condition_number, max_eig_value.item(), min_eig_value.item())) convergence_info[i]["errors"].append("Too high condition number: {0:e} (max: {1:.3f}, min: {2:.3f}). Discarding sample".format(condition_number, max_eig_value.item(), min_eig_value.item())) ill_posed_system = True break elif opt.gn_debug: print("\t\tCondition number: {0:e} (max: {1:.3f}, min: {2:.3f})".format(condition_number, max_eig_value.item(), min_eig_value.item())) if opt.gn_print_timings: print("\t\tComputation of cond. num.: {:.3f} s".format(timer() - timer_cond_start)) timer_solve_start = timer() linear_solver = LinearSolverLU.apply try: x = linear_solver(A, b) except RuntimeError as e: ill_posed_system = True print("\t\tSolver failed: Ill-posed system!", e) convergence_info[i]["errors"].append("Solver failed: Ill-posed system!") break if not torch.isfinite(x).all(): ill_posed_system = True print("\t\tSolver failed: Non-finite solution x!") convergence_info[i]["errors"].append("Solver failed: Non-finite solution x!") break if opt.gn_print_timings: print("\t\tLinear solve: {:.3f} s".format(timer() - timer_solve_start)) loss_data = torch.norm(res_data).item() loss_total = torch.norm(res).item() if len(convergence_info[i]["total"]): if loss_total - convergence_info[i]["total"][-1] > self.stop_loss_diff: print("As loss is greater than before breaking optimization") break convergence_info[i]["data"].append(loss_data) convergence_info[i]["total"].append(loss_total) # Increment the current rotation and translation. R_inc = kornia.angle_axis_to_rotation_matrix(x[:opt_num_nodes_i*3].view(opt_num_nodes_i, 3)) t_inc = x[opt_num_nodes_i*3:].view(opt_num_nodes_i, 3, 1) R_current = torch.matmul(R_inc, R_current) t_current = t_current + t_inc if num_edges_i > 0: loss_arap = torch.norm(res_arap).item() convergence_info[i]["arap"].append(loss_arap) if opt.gn_debug: if num_edges_i > 0: print("\t\t-->Iteration: {0}. Lm:{1:.3f} Loss: \tdata = {2:.3f}, \tarap = {3:.3f}, \ttotal = {4:.3f}".format(gn_i, lm_factor,loss_data, loss_arap, loss_total)) else: print("\t\t-->Iteration: {0}. Loss: \tdata = {1:.3f}, \ttotal = {2:.3f}".format(gn_i, loss_data, loss_total)) ############################################################################################################### # Write the solutions. ############################################################################################################### if not ill_posed_system and torch.isfinite(res).all(): node_rotations[i, map_opt_nodes_to_complete_nodes_i, :, :] = R_current.view(opt_num_nodes_i, 3, 3) node_translations[i, map_opt_nodes_to_complete_nodes_i, :] = t_current.view(opt_num_nodes_i, 3) deformations_validity[i, map_opt_nodes_to_complete_nodes_i] = 1 valid_solve[i] = 1 ############################################################################################################### # Warp all valid source points using estimated deformations. ############################################################################################################### if valid_solve[i]: # Filter out any invalid pixel anchors, and invalid source points. source_points_i = source_points[i].permute(1, 2, 0) source_points_i = source_points_i[valid_correspondences_idxs[0], valid_correspondences_idxs[1], :].view(-1, 3, 1) source_anchors_i = pixel_anchors[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) source_weights_i = pixel_weights[i, valid_correspondences_idxs[0], valid_correspondences_idxs[1], :] # (num_matches, 4) num_points = source_points_i.shape[0] # Filter out points randomly, if too many are still left. if num_points > opt.gn_max_warped_points: sampled_idxs = torch.randperm(num_points)[:opt.gn_max_warped_points] source_points_i = source_points_i[sampled_idxs] source_anchors_i = source_anchors_i[sampled_idxs] source_weights_i = source_weights_i[sampled_idxs] num_points = opt.gn_max_warped_points deformed_points_idxs[i] = sampled_idxs deformed_points_subsampled[i] = 1 source_anchors_i = source_anchors_i.type(torch.int64) # Now we deform all source points. deformed_points_i = torch.zeros((num_points, 3, 1), dtype=x1.dtype, device=x1.device) graph_nodes_complete_i = graph_nodes[i, :num_nodes_i, :] R_final = node_rotations[i, :num_nodes_i, :, :].view(num_nodes_i, 3, 3) t_final = node_translations[i, :num_nodes_i, :].view(num_nodes_i, 3, 1) for k in range(4): # Our data uses 4 anchors for every point node_idxs_k = source_anchors_i[:, k] # (num_points) nodes_k = graph_nodes_complete_i[node_idxs_k].view(num_points, 3, 1) # (num_points, 3, 1) # Compute deformed point contribution. rotated_points_k = torch.matmul(R_final[node_idxs_k], source_points_i - nodes_k) # (num_points, 3, 1) = (num_points, 3, 3) * (num_points, 3, 1) deformed_points_k = rotated_points_k + nodes_k + t_final[node_idxs_k] deformed_points_i += source_weights_i[:, k].view(num_points, 1, 1).repeat(1, 3, 1) * deformed_points_k # (num_points, 3, 1) deformed_points_i = deformed_points_i.view(num_points, 3) # Store the results. deformed_points_pred[i, :num_points, :] = deformed_points_i.view(1, num_points, 3) if valid_solve[i]: total_num_matches_per_batch += num_matches if opt.gn_debug: if int(valid_solve[i].cpu().numpy()): print("\t\tValid solve ({:.3f} s)".format(timer() - timer_start)) else: print("\t\tInvalid solve ({:.3f} s)".format(timer() - timer_start)) convergence_info[i]["valid"] = int(valid_solve[i].item()) ############################################################################################################### # We invalidate complete batch if we have too many matches in total (otherwise backprop crashes) ############################################################################################################### if not evaluate and total_num_matches_per_batch > opt.gn_max_matches_train_per_batch: print("\t\tSolver failed: Too many matches per batch: {}".format(total_num_matches_per_batch)) for i in range(batch_size): convergence_info[i]["errors"].append("Solver failed: Too many matches per batch: {}".format(total_num_matches_per_batch)) valid_solve[i] = 0 return { "flow_data": [flow2, flow3, flow4, flow5, flow6], "node_rotations": node_rotations, "node_translations": node_translations, "deformations_validity": deformations_validity, "deformed_points_pred": deformed_points_pred, "valid_solve": valid_solve, "mask_pred": mask_pred, "correspondence_info": { "xy_coords_warped":xy_coords_warped, "source_points":source_points, "valid_source_points":valid_source_points, "target_matches":target_matches, "valid_target_matches":valid_target_matches, "valid_correspondences":valid_correspondences, "deformed_points_idxs":deformed_points_idxs, "deformed_points_subsampled":deformed_points_subsampled }, "convergence_info": convergence_info, "weight_info": weight_info }

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datasets/nhse_stats/topics/msa.py

nhsengland/publish-o-matic

0

198135

""" Scrapes the data at http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/ """ import calendar import datetime import re from lxml.html import fromstring, tostring import requests import slugify from publish.lib.helpers import to_markdown, anchor_to_resource TITLE_ROOT = "Mixed-Sex Accommodation Data" ROOT = "http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/" DATE_RE = re.compile(".*\s(.*)\s(\d{4})") MONTHS_LOOKUP = dict((v,k) for k,v in enumerate(calendar.month_name)) def process_block(p, title, description, current_year): if not current_year: return None dataset = { "title": "{} - {}".format(TITLE_ROOT, title), "notes": description, "tags": ["Statistics", current_year], "resources": [], "origin": "http://www.england.nhs.uk/statistics/statistical-work-areas/mixed-sex-accommodation/msa-data/", "groups": ["msa"] } for resource in p.cssselect('a'): r = anchor_to_resource(resource) if r['format'] == 'XLSM': r['format'] = 'XLS' dataset["resources"].append(r) dataset["name"] = slugify.slugify(dataset['title']).lower() return dataset def process_latest(datasets, latest): """ We process the latest data as a special case because it is all munged together in a separate block. We need to find the links, parse them, try and group them by name, and then decide how we're going to label the dataset. """ for anchor in latest: resource = anchor_to_resource(anchor) y = int(string_to_date(resource['description'])[:4]) finder = "{}-{}".format(y-1, str(y)[2:4]) finder = "{} - {}".format(TITLE_ROOT, finder) # We can find the first dataset in the list (datasets) whose # title starts with finder as the most recent years go at # the top of the list on the page. for dataset in datasets: if dataset['title'].startswith(finder): print "We think ", resource['description'], "goes in", dataset['title'] dataset['resources'].insert(0, resource) break def string_to_date(s, start=True): m = DATE_RE.match(s) if not m: return "" if not len(m.groups()) == 2: return "" mnth = MONTHS_LOOKUP.get(m.groups()[0].strip(), "") year = int(m.groups()[1].strip()) if not mnth: return "" day = 1 _, mend = calendar.monthrange(year, mnth) if not start: day = mend d = datetime.datetime(year=year, month=mnth, day=day) return d.strftime("%Y-%m-%d") def process_dates(datasets): for dataset in datasets: start_date = string_to_date(dataset["resources"][-1]['description']) end_date = string_to_date(dataset["resources"][0]['description'], start=False) dataset["coverage_start_date"] = start_date dataset["coverage_end_date"] = end_date dataset["frequency"] = "Monthly" qrtrs = [1 for r in dataset['resources'] if 'Quarter' in r['description']] if any(qrtrs): dataset["frequency"] = "Quarterly" def scrape(workspace): print "Scraping MSA with workspace {}".format(workspace) datasets = [] page = requests.get(ROOT) html = fromstring(page.content) center = html.cssselect('.column.center')[0] paras = list(center.cssselect('P')) current_year = None # Iterate through all of the Ps. From here until we find a <strong> # is the description description = [] num_p = 0 for p in paras: if len(p.cssselect('STRONG')) > 0: break num_p += 1 description.append(tostring(p)) description = to_markdown(''.join(description)) latest_data_links = [] # Process the individual datasets current_label = "" generator = (p for p in paras[num_p:]) for p in generator: strong = p.cssselect('STRONG') if len(strong) > 0: current_label = strong[0].text_content().strip() else: if current_label == 'Latest Data': latest_data_links = p.cssselect('a') continue datasets.append(process_block(p, current_label, description, current_label)) datasets = filter(lambda x: x is not None, datasets) process_dates(datasets) process_latest(datasets, latest_data_links) print len(datasets) return datasets

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test/test_ostool.py

JukeboxPipeline/jukebox-core

2

139610

from jukeboxcore import ostool def test_detect_sys(): """ Detect Platform :returns: None :rtype: None :raises: None """ ostool.detect_sys() def test_get_interface(): """ Get PlatformInterface for your Platform :returns: None :rtype: None :raises: None """ ostool.get_interface() def test_interface(): """ Run PlatformInterface methods :returns: None :rtype: None :raises: None """ inter = ostool.get_interface() loc = inter.get_maya_location() assert loc site = inter.get_maya_sitepackage_dir() assert site mayapy = inter.get_maya_python() assert mayapy exe = inter.get_maya_exe() assert exe

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wxnat/tests/__init__.py

pauldmccarthy/wxnatpy

1

56129

<gh_stars>1-10 #!/usr/bin/env python # # __init__.py - unit tests for wxnatpy # # Author: <NAME> <<EMAIL>> # import time import wx def run_with_wx(func, *args, **kwargs): propagateRaise = kwargs.pop('propagateRaise', True) startingDelay = kwargs.pop('startingDelay', 500) finishingDelay = kwargs.pop('finishingDelay', 500) callAfterApp = kwargs.pop('callAfterApp', None) result = [None] raised = [None] app = wx.App() frame = wx.Frame(None) if callAfterApp is not None: callAfterApp() def wrap(): try: if func is not None: result[0] = func(*args, **kwargs) except Exception as e: print(e) raised[0] = e finally: def finish(): frame.Destroy() app.ExitMainLoop() wx.CallLater(finishingDelay, finish) frame.Show() wx.CallLater(startingDelay, wrap) app.MainLoop() if raised[0] and propagateRaise: raise raised[0] return result[0] def yield_until(condition): while not condition(): time.sleep(0.1) wx.Yield()

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examples/test_yield_8.py

MateuszG/django_auth

2

9014

import pytest @pytest.yield_fixture def passwd(): print ("\nsetup before yield") f = open("/etc/passwd") yield f.readlines() print ("teardown after yield") f.close() def test_has_lines(passwd): print ("test called") assert passwd

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examples/Oscillators/MallsOscillator/plotresults.py

doublefloyd/beluga

20

174838

from beluga.utils import load import matplotlib.pyplot as plt data = load('data.beluga') sol_set = data['solutions'] traj = sol_set[-1][-1] continuation = sol_set[-1] L = len(continuation) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.y[:, 0], sol.y[:, 1], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('Phase Plot') plt.xlabel('$x_1$') plt.ylabel('$x_2$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.u[:, 0], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.plot([traj.t[0], traj.t[-1]], [1, 1], color='k', linestyle='--') plt.plot([traj.t[0], traj.t[-1]], [-1, -1], color='k', linestyle='--') plt.title('Control History Plot') plt.xlabel('Time [s]') plt.ylabel('Control, $u$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 0], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('$\\lambda_{x1}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x1}$') plt.grid(True) plt.figure() for ind, sol in enumerate(continuation): plt.plot(sol.t, sol.dual[:, 1], linestyle='-', color=(1*(ind/L), 0, 1*(L-ind)/L)) plt.title('$\\lambda_{x2}$ History Plot') plt.xlabel('Time [s]') plt.ylabel('$\\lambda_{x2}$') plt.grid(True) plt.show()

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src/lib/earlystopping.py

dreizehnutters/pcapae

0

34665

from os import path, makedirs, walk ,remove, scandir, unlink from numpy import inf from torch import save as t_save from lib.utils import sort_human, BOLD, CLR class EarlyStopping: def __init__(self, log_path, patience=7, model=None, verbose=False, exp_tag=""): """Early stops the training if validation loss doesn't improve after a given patience. Args: patience (int): How long to wait after last time validation loss improved. Default: 7 verbose (bool): If True, prints a message for each validation loss improvement. Default: False """ self.patience = patience self.verbose = verbose self.counter = 0 self.best_score = None self.early_stop = False self.val_loss_min = inf self.global_min_loss = inf save_dir = f"{log_path}/save_model/{exp_tag}" self.save_path = save_dir if not path.isdir(save_dir): makedirs(save_dir) save_dir = f"{self.save_path}/best/" if not path.isdir(save_dir): makedirs(save_dir) if model is not None: self.meta_info = {'meta':(model.encoder_params,\ model.decoder_params,\ model.n_frames_input,\ model.n_frames_output)} else: self.meta_info = {} def __str__(self): return '\n'.join(f"{k}={v}" for k, v in vars(self).items()) def __call__(self, val_loss, model, epoch, step=0): """Summary Args: val_loss (TYPE): Description model (TYPE): Description epoch (TYPE): Description """ score = -val_loss model.update(self.meta_info) if step != 0: self.save_checkpoint(val_loss, model, epoch, step) else: if self.best_score is None: self.best_score = score self.save_checkpoint(val_loss, model, epoch, step) elif score < self.best_score: self.counter += 1 if self.counter >= self.patience: self.early_stop = True print(f"{BOLD}[*] early stopping at epoch {epoch} !{CLR}") else: print(f"[*] early stopping counter: {BOLD}{self.counter}/{self.patience}{CLR}") # self.del_old_models() else: self.best_score = score self.save_checkpoint(val_loss, model, epoch, step) self.counter = 0 t_save(model, f"{self.save_path}/LAST_checkpoint_{epoch}_{step}_{val_loss:.6f}.pth.tar") def del_old_models(self, keep=10): _, _, files = next(walk(self.save_path)) file_count = len(files) if file_count > keep: for old_model in sort_human(files)[:keep//2]: remove(path.join(self.save_path, old_model)) def save_checkpoint(self, val_loss, model, epoch, step=0): """Saves model when validation loss decrease Args: val_loss (TYPE): Description model (TYPE): Description epoch (TYPE): Description """ # save best model if step != 0: save_flag = "IE" print(f"[$] saveing model at step: {step} in epoch {epoch}") self.del_old_models() t_save(model, f"{self.save_path}/{save_flag}checkpoint_{epoch}_{step}_{val_loss}.pth.tar") else: if val_loss < self.global_min_loss: if self.verbose: print(f"[*] validation loss record {BOLD}{val_loss}{CLR} in epoch: {BOLD}{epoch}{CLR}@{step}") self.global_min_loss = val_loss save_flag = "best/" for file in scandir(f"{self.save_path}/{save_flag}"): unlink(file.path) else: save_flag = "" #self.del_old_models() t_save(model, f"{self.save_path}/{save_flag}checkpoint_{epoch}_{step}_{val_loss}.pth.tar") self.val_loss_min = val_loss

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ccn/graph_data.py

noahtren/Cooperative-Communication-Networks

13

102389

<reponame>noahtren/Cooperative-Communication-Networks """Representing trees as a subset of graph data structures, where all information is provided as TensorFlow tensors during training time. """ import os import random import code from typing import Dict import tensorflow as tf import numpy as np from graphviz import Digraph from tqdm import tqdm class NodeType: def __init__(self, token:str=None, is_value:bool=None, ordered_children:bool=None, min_children:int=None, max_children:int=None, parent_blacklist=[]): # input validation assert (token is None) ^ (is_value is None), 'node type must be value XOR token' if token: assert ordered_children is not None, 'order_children must be specified for token node type' assert max_children is not None, 'max_children must be specified for token node type' assert min_children is not None, 'min_children must be specified for token node type' else: assert ordered_children is None, 'order_children should not be specified for value node type' assert max_children is None, 'max_children should not be specified for token node type' assert min_children is None, 'min_children should not be specified for token node type' # assignment self.token = token self.is_value = is_value self.ordered_children = ordered_children self.min_children = min_children self.max_children = max_children self.parent_blacklist = parent_blacklist LanguageSpecs = { 'arithmetic': { 'data_structure': 'tree', 'node_types': [ NodeType(is_value=True), NodeType(token='+', ordered_children=False, min_children=2, max_children=3), NodeType(token='-', ordered_children=True, min_children=2, max_children=2), NodeType(token='*', ordered_children=False, min_children=2, max_children=3), NodeType(token='/', ordered_children=True, min_children=2, max_children=2), NodeType(token='abs', ordered_children=False, min_children=1, max_children=1, parent_blacklist=['abs']), ], 'value_tokens': ['a', 'b', 'c', 'd'], 'max_children': 3 } } class TensorGraph: """Representation of a graph data structure as TensorFlow tensors. All graphs are defined by an adjacency matrix (adj) and node-wise features (nodes and order.) This implementation requires that edges are directed but not weighted. `adj` is a matrix of shape [N, N] where the i, j index is 1 if i has a directed edge to j, otherwise it is 0. `node_features` is a dictionary of named node features, each of shape [n, ?] where n is the maximum number of nodes and ? is the encoded feature space of that feature. `num_nodes` is an integer that is less than or equal to N. It is the number of nodes that are actually in the graph. """ def __init__(self, adj:tf.Tensor, node_features:Dict[str, tf.Tensor], num_nodes:int, attrs:dict, language_spec:str): N = adj.shape[0] assert adj.shape[0] == adj.shape[1] for nf_name, node_feature in node_features.items(): assert node_feature.shape[0] == N, f'{nf_name} first dimension should match N ({N}), but was {node_feature.shape[0]}' self.adj = adj self.node_features = node_features self.num_nodes = num_nodes self.attrs = attrs self.language_spec = language_spec def visualize(self, name='unnamed', view=True): dot = Digraph(name=name) # nodes node_names = [] for n_i in range(self.num_nodes): ord_ = np.argmax(self.node_features['order'][n_i]) - 1 node_name = f"{self.attrs['value_tokens_list'][n_i]} ord={ord_}, i={n_i}" dot.node(node_name) node_names.append(node_name) # edges for n_i in range(self.num_nodes): src_name = node_names[n_i] for a_i, adj_bool in enumerate(self.adj[n_i]): if adj_bool: targ_name = node_names[a_i] dot.edge(src_name, targ_name) os.makedirs('gviz', exist_ok=True) dot.render(os.path.join('gviz', f'{name}'), format='pdf', view=view) @classmethod def tree_to_instance(cls, adj_list:list, values_list:list, value_tokens_list:list, order_list:list, max_nodes:int, language_spec:str): """Convert Python data types to a TensorGraph instance with max_nodes, using padding where necessary. If the Python data has too many nodes (more than `max_nodes`), then this function returns none. """ if len(adj_list) > max_nodes: return None pad_amt = max_nodes - len(adj_list) # values and order lists are already in correct numpy format. they just need # to be padded. padding only needs to occur along the first axis values = np.stack(values_list) order = np.stack(order_list) values = np.pad(values, [[0, pad_amt], [0, 0]]) order = np.pad(order, [[0, pad_amt], [0, 0]]) adj_arrs = [] for adj_indices in adj_list: adj_arr = np.zeros((len(adj_list)), dtype=np.int32) for n_i in adj_indices: adj_arr[n_i] = 1 adj_arrs.append(adj_arr) adj = np.stack(adj_arrs) adj = np.pad(adj, [[0, pad_amt], [0, pad_amt]]) return TensorGraph(adj, node_features={ 'values': values, 'order': order }, num_nodes=len(adj_list), attrs={ 'value_tokens_list': value_tokens_list }, language_spec=language_spec ) @classmethod def random_tree(cls, language_spec:str, min_num_values:int, max_num_values:int, **kwargs): """Generate a random, valid tree based on a language spec. Trees are generated as NumPy arrays and can be converted to TensorFlow tensors later. """ SPEC = LanguageSpecs[language_spec] assert SPEC['data_structure'] == 'tree', \ f'can not make tree batch with {language_spec} because it is not for trees.' adj_list = [] values_list = [] value_tokens_list = [] order_list = [] NUM_VALUE_TOKENS = len(SPEC['value_tokens']) NON_VALUE_NODE_TYPES = [nt for nt in SPEC['node_types'] if not nt.is_value] NUM_NODE_VALUES = len(NON_VALUE_NODE_TYPES) + NUM_VALUE_TOKENS NUM_ORDER_INDICES = 1 + SPEC['max_children'] # make initial values num_values = random.randint(min_num_values, max_num_values) values = [] for val_num in range(num_values): adj = [] val = np.zeros((NUM_NODE_VALUES), dtype=np.int32) val_idx = random.randint(0, NUM_VALUE_TOKENS - 1) val[val_idx] = 1 val_token = SPEC['value_tokens'][val_idx] order = np.zeros((NUM_ORDER_INDICES), dtype=np.int32) adj_list.append(adj) values_list.append(val) value_tokens_list.append(val_token) order_list.append(order) # build tree from node stack until it has a single value tree_stack = list(range(num_values)) next_idx = len(tree_stack) while len(tree_stack) != 1: node_type_i = random.randint(0, len(NON_VALUE_NODE_TYPES) - 1) new_node_type = NON_VALUE_NODE_TYPES[node_type_i] child_select_num = min([ len(tree_stack), random.randint(new_node_type.min_children, new_node_type.max_children) ]) valid_children = [] for child_i in tree_stack: child_token = value_tokens_list[child_i] if child_token not in new_node_type.parent_blacklist: valid_children.append(child_i) else: pass # print(f'IGNORE INDEX {child_i}') children = random.sample(tree_stack, k=child_select_num) # update child information for o_i, child_i in enumerate(children): if new_node_type.ordered_children: order_list[child_i][o_i + 1] = 1 else: order_list[child_i][0] = 1 # create parent information p_adj = children p_val = np.zeros((NUM_NODE_VALUES), dtype=np.int32) p_val[NUM_VALUE_TOKENS + node_type_i] = 1 p_val_token = new_node_type.token p_order = np.zeros((NUM_ORDER_INDICES), dtype=np.int32) adj_list.append(p_adj) values_list.append(p_val) value_tokens_list.append(p_val_token) order_list.append(p_order) # update stack for child_i in children: tree_stack.remove(child_i) tree_stack.append(next_idx) next_idx += 1 # set root node order to unordered order_list[-1][0] = 1 return adj_list, values_list, value_tokens_list, order_list @classmethod def parse_tree(cls, language_spec, adj_list, value_tokens_list, order_list, max_nodes): """Given an adjacency matrix, token names, and a list of token orders, validate that it is a tree and then produce an instance of the tree as a TensorGraph. """ SPEC = LanguageSpecs[language_spec] NON_VALUE_NODE_TYPES = [nt for nt in SPEC['node_types'] if not nt.is_value] NON_VALUE_NODE_TOKENS = [nt.token for nt in NON_VALUE_NODE_TYPES] values = [] for token in value_tokens_list: if token in NON_VALUE_NODE_TOKENS: token_i = NON_VALUE_NODE_TOKENS.index(token) + len(NON_VALUE_NODE_TOKENS) elif token in SPEC['value_tokens']: token_i = SPEC['value_tokens'].index(token) else: raise RuntimeError(f"token {token} doesn't exist in spec '{language_spec}'") value = np.zeros((len(NON_VALUE_NODE_TOKENS) + len(SPEC['value_tokens'])), dtype=np.int32) value[token_i] = 1 values.append(value) orders = [] for order_idx in order_list: order = np.zeros((1 + SPEC['max_children']), dtype=np.int32) order[order_idx + 1] = 1 orders.append(order) return adj_list, values, value_tokens_list, orders @classmethod def instances_to_tensors(cls, tensor_graphs:list): """Given a list of instances of this class, turn it into a training dataset stored in memory. Returns: batched adj tensor batched dictionary of node feature tensors node_feature_specs dictionary, mapping node feature names to dimensionality """ adj = [] node_features = {} node_feature_specs = {} num_nodes = [] for tg in tensor_graphs: adj.append(tg.adj) num_nodes.append(tg.num_nodes) for name, tensor in tg.node_features.items(): if name not in node_feature_specs: node_features[name] = [tensor] node_feature_specs[name] = tensor.shape[-1] else: node_features[name].append(tensor) # stack along batch axis adj = tf.stack(adj, axis=0) node_features = {name: tf.cast(tf.stack(nf_list, axis=0), tf.float32) for name, nf_list in node_features.items()} num_nodes = tf.stack(num_nodes, axis=0) return adj, node_features, node_feature_specs, num_nodes def label_data(node_features, adj, num_nodes): # postprocessing for data labels nf_labels = {} for name in node_features.keys(): empty_rows = (tf.math.reduce_max(node_features[name], axis=-1) == 0)[:, :, tf.newaxis] empty_rows = tf.cast(empty_rows, tf.float32) nf_labels[name] = tf.concat([node_features[name], empty_rows], axis=-1) identity = tf.eye(adj.shape[1], adj.shape[1], batch_shape=[adj.shape[0]], dtype=tf.int32) # only apply identity matrix to nodes that exist r = tf.range(adj.shape[1]) grid_max_idxs = tf.tile( tf.expand_dims( tf.math.maximum(r[tf.newaxis], r[:, tf.newaxis]), 0), [num_nodes.shape[0], 1, 1] ) identity = tf.where(grid_max_idxs < num_nodes[:, tf.newaxis, tf.newaxis], identity, tf.zeros_like(identity)) # NOTE: I ran 2 experiments to prove that this provides a significant # performance boost adj_labels = adj + identity return nf_labels, adj_labels def get_dataset(language_spec:str, min_num_values:int, max_num_values:int, max_nodes:int, num_samples:int, vis_first=False, test=False, dummy=False, **kwargs): instances = [] gen_name = 'train' if test: gen_name = 'test' if dummy: gen_name = 'dummy' print(f"Generating {gen_name} dataset") num_samples = num_samples // 5 if test else num_samples pbar = tqdm(total=num_samples) while len(instances) < num_samples: # make tree adj_list, values_list, value_tokens_list, order_list = \ TensorGraph.random_tree(language_spec, min_num_values, max_num_values) # convert to instance instance = TensorGraph.tree_to_instance(adj_list, values_list, value_tokens_list, order_list, max_nodes, language_spec) if instance is not None: instances.append(instance) pbar.update(1) if vis_first and len(instances) == 1: instance.visualize() pbar.close() adj, node_features, node_feature_specs, num_nodes = TensorGraph.instances_to_tensors(instances) nf_labels, adj_labels = label_data(node_features, adj, num_nodes) ds = { 'adj': adj, 'node_features': node_features, 'num_nodes': num_nodes, 'adj_labels': adj_labels, 'nf_labels': nf_labels } return ds, node_feature_specs if __name__ == "__main__": for _ in range(10000): adj_list, values_list, value_tokens_list, order_list = TensorGraph.random_tree('arithmetic', 3, 5) tg = TensorGraph.tree_to_instance(adj_list, values_list, value_tokens_list, order_list, 6, 'arithmetic') if tg is not None: tg.visualize() code.interact(local={**locals(), **globals()})

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week_1/prime_num.py

Ashutosh781/QSTP-Kratos_electronics-Solutions

0

109823

<reponame>Ashutosh781/QSTP-Kratos_electronics-Solutions #! /usr/bin/env python #Asking for input until a list of 10 integers given while True: num = input("Enter a list of 10 integers separated by a ',' : ").split(',') l = len(num) try: for i in range(l): num[i] = int(num[i]) p = (l==10) if p: break except: continue #Finding prime numbers prime_num = [] for x in num: if x > 1: for i in range(2,((x//2)+1)): if x % i == 0: break else: prime_num.append(x) elif x == 1: #Number 1 is not considered as prime continue else: #For any negative numbers or 0 continue if len(prime_num) > 0: print(f'Prime numbers from the given list are : {prime_num}') else: print("There were no prime numbers in the given list") print("\nEnd")

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TWT/apps/challenges/views/__init__.py

avibn/twtcodejam.net

0

49488

from .home import HomeView from .logout import LogoutView from .new import NewChallengeView from .view import DetailView from .test import TestView from .end import EndView from .start import StartView from .stop_team import StopTeams from .start_submission import StartSubmission from .stop_submission import StopSubmission from .start_team import StartTeams from .unreleased import UnreleasedView from .delete_challenge import DeleteView from .start_voting import StartVoting from .stop_voting import StopVoting from .custom_page_view import CustomView start = StartView.as_view() end = EndView.as_view() test = TestView.as_view() logout = LogoutView.as_view() new = NewChallengeView.as_view() view = DetailView.as_view() start_team = StartTeams.as_view() stop_team = StopTeams.as_view() start_submission = StartSubmission.as_view() stop_submission = StopSubmission.as_view() unreleased_view = UnreleasedView.as_view() delete_view = DeleteView.as_view() start_voting = StartVoting.as_view() stop_voting = StopVoting.as_view() view_custom = CustomView.as_view()

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active_reward_learning/common/constants.py

david-lindner/idrl

9

146690

<gh_stars>1-10 import os from collections import defaultdict, namedtuple PLOTS_FOLDER = "plots" ENV_FOLDER = "envs" MODELS_FOLDER = "models" BASE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "..") PLOTS_PATH = os.path.join(BASE_PATH, PLOTS_FOLDER) ENV_PATH = os.path.join(BASE_PATH, ENV_FOLDER) MODELS_PATH = os.path.join(BASE_PATH, MODELS_FOLDER) TELEGRAM_TOKEN = "" TELEGRAM_MESSAGE_ID = 0 # source: https://gist.github.com/thriveth/8560036 color_cycle = [ "#377eb8", "#ff7f00", "#4daf4a", "#e41a1c", "#dede00", "#999999", "#f781bf", "#a65628", "#984ea3", ] hatch_cycle = ["/", "\\", "|", "-", "+", "x", "o", "O", ".", "*"] marker_cycle = ["*", "^", "o", "x", "d", "+", "v", "."] # pygame keys keys_nt = namedtuple( "keys_nt", [ "RIGHT", "LEFT", "UP", "DOWN", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", "EIGTH", "NINE", "ZERO", ], ) KEYS = keys_nt( 275, 276, 273, 274, ord("1"), ord("2"), ord("3"), ord("4"), ord("5"), ord("6"), ord("7"), ord("8"), ord("9"), ord("0"), ) # LEGACY KEY_RIGHT, KEY_LEFT, KEY_UP, KEY_DOWN = 275, 276, 273, 274 # Acquisition functions LABELS = { "rand": "random", "rand_unobs": "random_unobserved", "var": "variance", "pi": "probability_of_improvement", "ei": "expected_improvement", "epd": "expected_policy_divergence", "idrl": "directed_information_gain", "evr": "expected_volume_removal", "evrb": "expected_volume_removal_bernoulli", "maxreg": "maximum_regret", } color_cycle = [ "#377eb8", "#ff7f00", "#4daf4a", "#e41a1c", "#dede00", "#999999", "#f781bf", "#a65628", "#984ea3", ] ## dig is deprecated but was still in some results AF_COLORS = { "rand": "#4daf4a", "var": "#984ea3", "ei": "#ff7f00", "epd": "#377eb8", "idrl": "#e41a1c", "maxreg": "#b3b300", } AF_COLORS = defaultdict(lambda: "blue", AF_COLORS) AF_MARKERS = { "rand": "s", "var": "^", "ei": "d", "epd": "x", "idrl": "o", "maxreg": "v", } AF_MARKERS = defaultdict(lambda: ".", AF_MARKERS) AF_ALPHA = { "idrl": 1.0, } AF_ALPHA = defaultdict(lambda: 0.6, AF_ALPHA) AF_ZORDER = { "idrl": 2, } AF_ZORDER = defaultdict(lambda: 1, AF_ZORDER)

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evaluation/lcnn/postprocess.py

mlpc-ucsd/LETR

90

24271

import numpy as np def pline(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py u = ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd)) dx = x1 + u * px - x dy = y1 + u * py - y return dx * dx + dy * dy def psegment(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py u = max(min(((x - x1) * px + (y - y1) * py) / float(dd), 1), 0) dx = x1 + u * px - x dy = y1 + u * py - y return dx * dx + dy * dy def plambda(x1, y1, x2, y2, x, y): px = x2 - x1 py = y2 - y1 dd = px * px + py * py return ((x - x1) * px + (y - y1) * py) / max(1e-9, float(dd)) def postprocess(lines, scores, threshold=0.01, tol=1e9, do_clip=False): nlines, nscores = [], [] for (p, q), score in zip(lines, scores): start, end = 0, 1 for a, b in nlines: # nlines: Selected lines. if ( min( max(pline(*p, *q, *a), pline(*p, *q, *b)), max(pline(*a, *b, *p), pline(*a, *b, *q)), ) > threshold ** 2 ): continue lambda_a = plambda(*p, *q, *a) lambda_b = plambda(*p, *q, *b) if lambda_a > lambda_b: lambda_a, lambda_b = lambda_b, lambda_a lambda_a -= tol lambda_b += tol # case 1: skip (if not do_clip) if start < lambda_a and lambda_b < end: continue # not intersect if lambda_b < start or lambda_a > end: continue # cover if lambda_a <= start and end <= lambda_b: start = 10 break # case 2 & 3: if lambda_a <= start and start <= lambda_b: start = lambda_b if lambda_a <= end and end <= lambda_b: end = lambda_a if start >= end: break if start >= end: continue nlines.append(np.array([p + (q - p) * start, p + (q - p) * end])) nscores.append(score) return np.array(nlines), np.array(nscores)

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config/users/views.py

sitepoint-editors/Django-photo-app

0

25890

from django.views.generic import CreateView from django.contrib.auth import authenticate, login from django.contrib.auth.views import LoginView from django.contrib.auth.forms import UserCreationForm from django.urls import reverse_lazy class SignUpView(CreateView): template_name = 'users/signup.html' form_class = UserCreationForm success_url = reverse_lazy('photo:list') def form_valid(self, form): to_return = super().form_valid(form) user = authenticate( username=form.cleaned_data["username"], password=form.cleaned_data["<PASSWORD>"], ) login(self.request, user) return to_return class CustomLoginView(LoginView): template_name = 'users/login.html'

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test_project/products/example.py

SzatmariA/CharmFL-dev

0

140648

<reponame>SzatmariA/CharmFL-dev<gh_stars>0 # This is a simple example script for FL # It should represent a webshop, or at least part of it cart = {} def addToCart(product): if(product not in cart.keys()): cart[str(product)] = 1 else: cart[str(product)] = cart[(str(product))] + 2 # bug -> should be 1 def removeFromCart(product): if(product in cart.keys()): if(cart[str(product)] > 1): cart[str(product)] = cart[str(product)] - 1 elif(cart[str(product)] == 1): del cart[str(product)] else: print("Something's fishy") def printProductsInCart(): print("Your cart: ") for product_name in cart.keys(): product_count = cart[str(product_name)] #print("* " + str(product_name) + ": " + str(product_count)) def getProductCount(product): if(product not in cart.keys()): return 0 else: return cart[str(product)] """ # We need # 4 apples # 1 orange juice # cheeser mum # U for i in range(1,4): addToCart("apple") removeFromCart("apple") addToCart("OJ") addToCart("cheese") addToCart("Ur mum") printProductsInCart() """

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PopUp.py

Bogiasdroid/BackMeUp

0

173676

import sys from PyQt4 import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Dialog(QtGui.QDialog): def __init__(self): QtGui.QDialog.__init__(self) self.setupUi(self) def setupUi(self, Dialog): Dialog.setObjectName(_fromUtf8("Dialog")) Dialog.setFixedSize(293, 160) self.buttonBox = QtGui.QDialogButtonBox(Dialog) self.buttonBox.setEnabled(True) self.buttonBox.setGeometry(QtCore.QRect(120, 120, 161, 32)) self.buttonBox.setOrientation(QtCore.Qt.Horizontal) self.buttonBox.setStandardButtons(QtGui.QDialogButtonBox.Ok) self.buttonBox.setObjectName(_fromUtf8("buttonBox")) self.textBrowser = QtGui.QTextBrowser(Dialog) self.textBrowser.setEnabled(True) self.textBrowser.setGeometry(QtCore.QRect(20, 10, 256, 101)) font = QtGui.QFont() font.setFamily(_fromUtf8("Arial")) font.setBold(True) font.setWeight(75) self.textBrowser.setFont(font) self.textBrowser.setObjectName(_fromUtf8("textBrowser")) self.retranslateUi(Dialog) QtCore.QObject.connect(self.buttonBox, QtCore.SIGNAL(_fromUtf8("accepted()")), Dialog.accept) QtCore.QMetaObject.connectSlotsByName(Dialog) def retranslateUi(self, Dialog): Dialog.setWindowTitle(_translate("Dialog", "Dialog", None))

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main.py

guysoft/kivy-external-storage-permission

1

22019

import kivy from kivy.app import App from kivy.uix.button import Button import android import os import time from android.permissions import Permission, request_permission, check_permission from kivy.clock import Clock class MyApp(App): def second_thread(self, data): print("starting second thread") permission_status = check_permission(Permission.WRITE_EXTERNAL_STORAGE) print(permission_status) if permission_status is not None and permission_status: print("got permission") path = os.environ["SECONDARY_STORAGE"] test_path = os.path.join(path, "test_yay") os.makedirs(test_path) else: Clock.schedule_once(self.second_thread, 1) def callback(self, data): print("Pushed button, running") print("request permission") print(request_permission(Permission.WRITE_EXTERNAL_STORAGE)) Clock.schedule_once(self.second_thread, 5) def build(self): return Button(text='Touch to test writing to ' + os.environ["SECONDARY_STORAGE"], on_press=self.callback) if __name__ == '__main__': MyApp().run()

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concepttordf/betydningsbeskrivelse.py

jimjyang/concepttordf

0

1605641

<filename>concepttordf/betydningsbeskrivelse.py<gh_stars>0 from abc import ABC from enum import Enum class RelationToSource(Enum): sitatFraKilde = "quoteFromSource" basertPåKilde = "basedOnSource" egendefinert = "noSource" class Betydningsbeskrivelse(ABC): def __init__(self, b: dict = None): if b is not None: if 'text' in b: self.text = b['text'] if 'remark' in b: self.remark = b['remark'] if 'scope' in b: self.scope = b['scope'] if 'relationtosource' in b: self.relationtosource = b['relationtosource'] if 'source' in b: self.source = b['source'] if 'modified' in b: self.modified = b['modified'] @property def type(self): return self._type @type.setter def type(self, type): self._type = type @property def text(self) -> dict: return self._text @text.setter def text(self, text: dict): self._text = text @property def remark(self) -> dict: return self._remark @remark.setter def remark(self, remark: dict): self._remark = remark @property def scope(self) -> dict: return self._scope @scope.setter def scope(self, scope: dict): self._scope = scope @property def relationtosource(self) -> str: return self._relationtosource @relationtosource.setter def relationtosource(self, relationtosource: str): self._relationtosource = relationtosource @property def source(self) -> dict: return self._source @source.setter def source(self, source: dict): self._source = source @property def modified(self) -> dict: return self._modified @modified.setter def modified(self, modified: dict): self._modified = modified

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ClassicFunction.py

gr0mph/SpringChallenge2020

0

93927

import sys, copy, heapq import math, random, time import numpy as np # Class from Challenge import Pacman def init_pacman_from_list(kanban_node, PACMAN_LIST): for d1 in PACMAN_LIST: pacman_new = Pacman(None) pacman_new.mine, pacman_new.x, pacman_new.y = d1[1], d1[2], d1[3] pacman_new.type = d1[4] if pacman_new.mine == 0 : pacman_new.id = -1 * (d1[0] + 1) kanban_node.opp[pacman_new.id] = pacman_new else : pacman_new.id = 1 * (d1[0] + 1) kanban_node.mine[pacman_new.id] = pacman_new return kanban_node def init_pellet_from_list(kanban_node, PELLET_LIST): for _, c1 in kanban_node.cases.items(): c1.pellet = 1 for c1 in PELLET_LIST: coord = c1[1], c1[0] if coord in kanban_node.nodes : kanban_node.nodes[coord].pellet = 10 if coord in kanban_node.cases : kanban_node.cases[coord].pellet = 10 return kanban_node

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